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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c5af1b1c066c1a49573ef543d9894567d5909df2 | 9e482a31dd8c1661ad1953d7fbd24a532306f58c | /Plays/Play10/medium_batch_normalization.py | 2d0166d6dce71a2e1ec99d39a56e366341b49c99 | []
| no_license | margaridav27/feup-fpro | 49a66f6643c83adb948ff110f522948f43508519 | e805e08d0fdd273db272300e3e9676c585030f23 | refs/heads/master | 2023-01-23T01:16:11.534704 | 2020-11-25T10:48:00 | 2020-11-25T10:48:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,115 | py | # -*- coding: utf-8 -*-
"""
Created on Wed Dec 4 10:14:35 2019
@author: Margarida Viera
"""
#solution using generator
def batch_norm(alist, batch_size):
while len(alist) > 0:
batch = []
l = alist.copy()
if len(alist) < batch_size:
batch_size = len(alist)
for i in range(batch_size):
batch.append(l[i])
alist.remove(l[i])
from statistics import median
med = median(batch)
for n in range(len(batch)):
batch[n] -= med
yield batch
#solution using normal function return
def batch_norm(alist, batch_size):
batches = []
while len(alist) > batch_size:
batches.append(alist[:batch_size])
alist = alist[batch_size:]
if len(alist) != 0:
batches.append(alist)
from statistics import median
for batch in batches:
med = median(batch)
for n in range(len(batch)):
batch[n] -= med
return batches
print(batch_norm([10, 1, -12, 5, 1, 3, 7, 3, 3], 5)) | [
"[email protected]"
]
| |
79800f0401cbb5ee3dfdafd55b2f0532cd565719 | ab37cdd76b8d4da54ff1ce30b0fa2e3dfadd207f | /1001-1099/1008/1008.py | e2d869f2abde08e4f8eb119987332f9815576b5f | []
| no_license | hay86/timus | b163d94052d3dedd51c82f5c10874402f805c6e1 | 0d06073228c23538ca785938c862d2b5e08bda63 | refs/heads/master | 2023-03-08T06:34:28.707612 | 2021-02-20T14:38:48 | 2021-02-20T14:38:48 | 100,444,783 | 0 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,553 | py | import sys
img = [[False for i in range(12)] for j in range(12)]
def one():
for i in range(n):
x, y = [int(j) for j in sys.stdin.readline().split()]
img[x][y] = True
if i == 0:
x0, y0 = x, y
v = [[False for i in range(12)] for j in range(12)]
q = [(x0, y0)]
v[x0][y0] = True
print '%d %d' % (x0, y0)
while len(q) > 0:
x, y = q.pop(0)
o = ''
if img[x+1][y] and not v[x+1][y]:
o += 'R'
q.append((x+1, y))
v[x+1][y] = True
if img[x][y+1] and not v[x][y+1]:
o += 'T'
q.append((x, y+1))
v[x][y+1] = True
if img[x-1][y] and not v[x-1][y]:
o += 'L'
q.append((x-1, y))
v[x-1][y] = True
if img[x][y-1] and not v[x][y-1]:
o += 'B'
q.append((x, y-1))
v[x][y-1] = True
if len(q) == 0:
print '.'
else:
print o+','
def two():
xn, yn = x0, y0
xm, ym = x0, y0
count = 1
q=[(x0, y0)]
img[x0][y0] = True
for line in sys.stdin:
if '.' in line:
break
x, y = q.pop(0)
if 'R' in line:
q.append((x+1, y))
count += 1
img[x+1][y] = True
xm = max(xm, x+1)
if 'T' in line:
q.append((x, y+1))
count += 1
img[x][y+1] = True
ym = max(ym, y+1)
if 'L' in line:
q.append((x-1, y))
count += 1
img[x-1][y] = True
xn = min(xn, x-1)
if 'B' in line:
q.append((x, y-1))
count += 1
img[x][y-1] = True
yn = min(yn, y-1)
print count
for x in range(xn, xm+1):
for y in range(yn, ym+1):
if img[x][y]:
print x, y
line = sys.stdin.readline()
if not ' ' in line:
n = int(line)
one()
else:
x0, y0 = [int(y) for y in line.split()]
two()
| [
"[email protected]"
]
| |
81a081122381d85928f0ad8fb3aab7f699135e78 | aca971629c16f16a4b0360669579d0751fd5da67 | /src/indelPlot.py | f896ef722c39a732f5391609743e33d9627abe56 | [
"MIT"
]
| permissive | ngannguyen/referenceViz | 43769ded8cb3c77445391e26233352a61ed72744 | 6990a00739a712ccd1371e996229882252fa8f91 | refs/heads/master | 2021-01-01T06:26:33.975514 | 2012-03-22T21:34:54 | 2012-03-22T21:34:54 | 1,750,339 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,150 | py | #!/usr/bin/env python
"""
Create Snp plots
nknguyen at soe dot ucsc dot edu
Jun 15 2011
"""
import os, sys
from optparse import OptionParser
import xml.etree.ElementTree as ET
#from numpy import *
import libPlotting as libplot
import matplotlib.pyplot as pyplot
import matplotlib.pylab as pylab
from matplotlib.ticker import *
from matplotlib.font_manager import FontProperties
class Sample():
def __init__( self, xmlnode ):
self.name = xmlnode.attrib[ 'sampleName' ]
self.refname = xmlnode.attrib[ 'referenceName' ]
self.dels = float(xmlnode.attrib['totalDeletionPerAlignedBase'])
self.ins = float(xmlnode.attrib['totalInsertionPerAlignedBase'])
self.indel = float(xmlnode.attrib['totalInsertionAndDeletionPerAlignedBase'])
#add cases where it's both insertion & deletion (indel) to insertion & deletion:
self.dels += self.indel
self.ins += self.indel
def readfiles( options ):
statsList = []
for f in options.files:
samples = []
xmltree = ET.parse( f )
root = xmltree.getroot()
for s in root.findall( 'statsForSample' ):
name = s.attrib[ 'sampleName' ]
if name != 'aggregate' and name != 'ROOT' and name != '' and name not in options.filteredSamples:
samples.append( Sample( s ) )
statsList.append( samples )
return statsList
def initOptions( parser ):
parser.add_option( '--outdir', dest='outdir', default='.', help='Output directory' )
#parser.add_option( '--numOutliners', dest='numOutliners', type='int', default=0, help='Number of outliners' )
parser.add_option('--filteredSamples', dest='filteredSamples', help='Hyphen separated list of samples that were filtered out (not to include in the plot)')
def checkOptions( args, options, parser ):
if len( args ) < 2:
parser.error( 'Please provide two snpStats xml files.\n' )
options.files = []
for f in args:
if not os.path.exists(f):
parser.error( 'File %s does not exist.\n' % f )
options.files.append( f )
if options.filteredSamples:
options.filteredSamples = options.filteredSamples.split('-')
else:
options.filteredSamples = []
def setAxes(fig, range1, range2):
axleft = 0.12
axright = 0.95
axwidth = axright - axleft
axbottom = 0.15
axtop = 0.95
axheight = axtop - axbottom
margin = 0.015
h1 = (axheight - margin)*(range1/(range1 + range2))
h2 = axheight - margin - h1
ax2 = fig.add_axes( [axleft, axbottom, axwidth, h2] )
ax = fig.add_axes( [axleft, axbottom + h2 + margin, axwidth, h1] )
return ax, ax2
def drawPlot( options, samples1, samples2, type ):
#Sorted in decreasing order of errorPerSite in samples1
if type == 'insertion':
samples1 = sorted( samples1, key=lambda s:s.ins, reverse=True )
else:
samples1 = sorted( samples1, key=lambda s:s.dels, reverse=True )
if len( samples1 ) < 1:
return
#remove chimpSample:
chimpSample = None
for i, s in enumerate(samples1):
if s.name == 'panTro3':
chimpSample = samples1.pop(i)
break
refname1 = samples1[0].refname
refname2 = samples2[0].refname
y1data = [ s.ins for s in samples1 ]
if type == 'deletion':
y1data = [ s.dels for s in samples1 ]
xticklabels = [ s.name for s in samples1 ]
#indel of refname1 w.r.t itself (0)
y1data.append(0)
xticklabels.append(refname1)
y2data = []
for name in xticklabels:
if name == refname2:#indel of refname2 w.r.t itself (0)
y2data.append(0)
for s in samples2:
if s.name == name:
if type == 'insertion':
y2data.append(s.ins)
else:
y2data.append(s.dels)
break
if len(xticklabels) != len(y2data):
sys.stderr.write("Input file 1 and 2 do not have the same set of samples\n")
sys.exit( 1 )
#add the average column:
num = 1
y1avr = sum(y1data)/float(len(y1data) - 1)
y1data.append(y1avr)
xticklabels.append('average')
y2avr = sum(y2data)/float(len(y2data) - 1)
y2data.append(y2avr)
print "%s Average: %s %f, %s %f" %(type, refname1, y1avr, refname2, y2avr)
#Add chimp:
samples1.append(chimpSample)
if type == 'insertion':
y1data.append( chimpSample.ins )
else:
y1data.append( chimpSample.dels )
for s in samples2:
if s.name == 'panTro3':
if type == 'insertion':
y2data.append(s.ins)
else:
y2data.append(s.dels)
xticklabels.append("panTro3")
minMajority = min( [min(y2data), min(y1data)] ) - 0.0001
maxMajority = max( [max(y2data), max(y1data)] ) + 0.0001
basename = os.path.basename(options.files[0])
options.out = os.path.join( options.outdir, '%s_%s' %( type, basename.lstrip('pathStats').lstrip('_').rstrip('.xml') ) )
fig, pdf = libplot.initImage( 11.2, 10.0, options )
#ax, ax2 = setAxes(fig, maxOutlier - minOutlier, maxMajority - minMajority)
ax2 = fig.add_axes( [0.15, 0.15, 0.8, 0.8] )
l2 = ax2.plot( y2data, marker='.', markersize=14.0, linestyle='none', color="#E31A1C" )
l1 = ax2.plot( y1data, marker='.', markersize=14.0, linestyle='none', color="#1F78B4" )
#Legend
fontP = FontProperties()
fontP.set_size("x-small")
legend = ax2.legend([l1, l2], [libplot.properName(refname1), libplot.properName(refname2)], 'upper right', numpoints=1, prop=fontP)
legend._drawFrame = False
ax2.set_ylim( minMajority, maxMajority )
ax2.set_xlim( -0.5, len(xticklabels) -0.5 )
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)
ax2.xaxis.tick_bottom()
ax2.yaxis.set_ticks_position( 'left' )
ax2.set_xticks( range( 0, len(xticklabels) ) )
properxticklabels = [ libplot.properName(l) for l in xticklabels ]
ax2.set_xticklabels( properxticklabels )
for label in ax2.xaxis.get_ticklabels():
label.set_rotation( 90 )
ax2.yaxis.grid(b=True, color="#CCCCCC", linestyle='-', linewidth=0.005)
ax2.xaxis.grid(b=True, color="#CCCCCC", linestyle='-', linewidth=0.005)
ax2.set_xlabel( 'Samples' )
title = 'Deletions'
#if type == 'insertion':
if type == 'insertion':
ax2.set_ylabel( 'Insertions per site' )
title = 'Insertions'
else:
ax2.set_ylabel( 'Deletions per site' )
ax2.set_title( title )
libplot.writeImage( fig, pdf, options )
def main():
usage = ('Usage: %prog [options] file1.xml file2.xml\n\n')
parser = OptionParser( usage = usage )
initOptions( parser )
libplot.initOptions( parser )
options, args = parser.parse_args()
checkOptions( args, options, parser )
libplot.checkOptions( options, parser )
statsList = readfiles( options )
drawPlot( options, statsList[0], statsList[1], 'insertion' )
drawPlot( options, statsList[0], statsList[1], 'deletion' )
if __name__ == "__main__":
main()
| [
"[email protected]"
]
| |
d82ed1cfeb3e9cf9432826e65574bb198fceddb4 | 01dad4d1d2ffaf2fa070e99fe828d42f59a9f9d1 | /src/pycrop2ml_ui/packages/SQ_Energy_Balance/src/openalea/Penman.py | ab023da150792ac8a482b0e9d2ed39cd94ea4ed8 | [
"MIT",
"BSD-3-Clause"
]
| permissive | AgriculturalModelExchangeInitiative/Pycrop2ml_ui | 5e210facf9689348bb57c16060967118b7c5f49a | 3d5d2b87a74f0be306056b71808286922fef2945 | refs/heads/master | 2023-06-24T13:52:39.933728 | 2023-06-17T00:17:26 | 2023-06-17T00:17:26 | 193,912,881 | 0 | 4 | MIT | 2023-02-25T13:26:57 | 2019-06-26T13:44:34 | Jupyter Notebook | UTF-8 | Python | false | false | 6,614 | py | # coding: utf8
import numpy
from math import *
def model_penman(evapoTranspirationPriestlyTaylor = 449.367,
hslope = 0.584,
VPDair = 2.19,
psychrometricConstant = 0.66,
Alpha = 1.5,
lambdaV = 2.454,
rhoDensityAir = 1.225,
specificHeatCapacityAir = 0.00101,
conductance = 598.685):
"""
- Description:
* Title: Penman Model
* Author: Pierre Martre
* Reference: Modelling energy balance in the wheat crop model SiriusQuality2:
Evapotranspiration and canopy and soil temperature calculations
* Institution: INRA/LEPSE Montpellier
* Abstract: This method is used when wind and vapor pressure daily data are available
- inputs:
* name: evapoTranspirationPriestlyTaylor
** min : 0
** default : 449.367
** max : 10000
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** variablecategory : rate
** datatype : DOUBLE
** inputtype : variable
** unit : g m-2 d-1
** description : evapoTranspiration of Priestly Taylor
* name: hslope
** min : 0
** default : 0.584
** max : 1000
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** variablecategory : auxiliary
** datatype : DOUBLE
** inputtype : variable
** unit : hPa °C-1
** description : the slope of saturated vapor pressure temperature curve at a given temperature
* name: VPDair
** min : 0
** default : 2.19
** max : 1000
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** variablecategory : auxiliary
** datatype : DOUBLE
** inputtype : variable
** unit : hPa
** description : vapour pressure density
* name: psychrometricConstant
** parametercategory : constant
** min : 0
** datatype : DOUBLE
** max : 1
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** default : 0.66
** inputtype : parameter
** unit :
** description : psychrometric constant
* name: Alpha
** parametercategory : constant
** min : 0
** datatype : DOUBLE
** max : 100
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** default : 1.5
** inputtype : parameter
** unit :
** description : Priestley-Taylor evapotranspiration proportionality constant
* name: lambdaV
** parametercategory : constant
** min : 0
** datatype : DOUBLE
** max : 10
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** default : 2.454
** inputtype : parameter
** unit :
** description : latent heat of vaporization of water
* name: rhoDensityAir
** parametercategory : constant
** datatype : DOUBLE
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** default : 1.225
** inputtype : parameter
** unit :
** description : Density of air
* name: specificHeatCapacityAir
** parametercategory : constant
** min : 0
** datatype : DOUBLE
** max : 1
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** default : 0.00101
** inputtype : parameter
** unit :
** description : Specific heat capacity of dry air
* name: conductance
** min : 0
** default : 598.685
** max : 10000
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** variablecategory : state
** datatype : DOUBLE
** inputtype : variable
** unit : m d-1
** description : conductance
- outputs:
* name: evapoTranspirationPenman
** min : 0
** variablecategory : rate
** max : 5000
** uri : http://www1.clermont.inra.fr/siriusquality/?page_id=547
** datatype : DOUBLE
** unit : g m-2 d-1
** description : evapoTranspiration of Penman Monteith
"""
evapoTranspirationPenman = evapoTranspirationPriestlyTaylor / Alpha + (1000.0 * (rhoDensityAir * specificHeatCapacityAir * VPDair * conductance / (lambdaV * (hslope + psychrometricConstant))))
return evapoTranspirationPenman | [
"[email protected]"
]
| |
07fd478a0c99e3470575c12f1bb74ad945580d0c | e9a083fb04bf9061a2c49871cfbec9b37ff8f71b | /docs/source/conf.py | c609679b4ac4c8100904a33ce92c16726bc46c12 | []
| no_license | olaurino/rama | 7f86223d66f42c639672da6b8979eacaf56b28ed | 2c88ca2263ccbf6d0737fea0ac5dc0341d71c53a | refs/heads/master | 2021-01-25T14:49:32.330753 | 2018-06-04T14:25:27 | 2018-06-04T14:25:27 | 123,731,355 | 0 | 2 | null | 2018-05-08T13:25:28 | 2018-03-03T21:05:53 | Python | UTF-8 | Python | false | false | 5,608 | py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
from rama.utils import Singleton
from sphinx.ext.autodoc import ClassDocumenter
import sphinx_rtd_theme
# -- Project information -----------------------------------------------------
project = 'Rama'
copyright = '2018, Omar Laurino'
author = 'Omar Laurino'
# The short X.Y version
version = ''
# The full version, including alpha/beta/rc tags
release = '0.1'
# -- General configuration ---------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.doctest',
'sphinx.ext.todo',
'sphinx.ext.coverage',
'sphinx.ext.viewcode',
'sphinx.ext.githubpages',
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
# The master toctree document.
master_doc = 'index'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path .
exclude_patterns = []
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
# html_theme = 'alabaster'
html_theme = 'sphinx_rtd_theme'
# html_theme_options = {}
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# The default sidebars (for documents that don't match any pattern) are
# defined by theme itself. Builtin themes are using these templates by
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
# 'searchbox.html']``.
#
# html_sidebars = {}
# -- Options for HTMLHelp output ---------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'Ramadoc'
# -- Options for LaTeX output ------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'Rama.tex', 'Rama Documentation',
'Omar Laurino', 'manual'),
]
# -- Options for manual page output ------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'rama', 'Rama Documentation',
[author], 1)
]
# -- Options for Texinfo output ----------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'Rama', 'Rama Documentation',
author, 'Rama', 'One line description of project.',
'Miscellaneous'),
]
# -- Extension configuration -------------------------------------------------
# -- Options for todo extension ----------------------------------------------
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
class SingletonDocumenter(ClassDocumenter):
objtype = 'Singleton'
directivetype = 'class'
@classmethod
def can_document_member(cls, member, membername, isattr, parent):
return isinstance(member, Singleton)
def setup(app):
app.add_autodocumenter(SingletonDocumenter)
| [
"[email protected]"
]
| |
2b64d7c87cb71a336307dcbab4db4d5d324b4e43 | 95f1541cac9e356b108fd4a7698dccd469f3996e | /backend/app/misc/tasks.py | 1b2ff7d1a667b0fef02eb0b51a72ae62a22e53c2 | []
| no_license | ramonsaraiva/multiplicae | f98c29e57cf53e358540fd590d55472311d9c382 | 29054abc83bad2309ab66698d7f3f3bbd683d570 | refs/heads/master | 2023-08-15T00:07:51.203417 | 2020-02-19T02:57:24 | 2020-02-19T02:57:24 | 238,992,039 | 2 | 1 | null | 2021-09-22T18:35:52 | 2020-02-07T18:00:24 | JavaScript | UTF-8 | Python | false | false | 114 | py | import dramatiq
@dramatiq.actor(
queue_name='default',
max_retries=1,
)
def healthcheck():
return 0
| [
"[email protected]"
]
| |
8ed992846cecdd828e575dfa6c66da38336b9797 | acf7457d3a799cb9bff12686d2d616688bcd4b5b | /packages/python/plotly/plotly/validators/scatter/legendgrouptitle/font/_family.py | 630d0965793bea6d3cb6b80383b246586092f423 | [
"MIT"
]
| permissive | plotly/plotly.py | f4f61639f08160f16195efc95b5901dc5a937346 | 975a704074f01c078e0fdfa32bdf17130bf89e69 | refs/heads/master | 2023-09-06T06:15:08.340035 | 2023-08-24T12:28:14 | 2023-08-24T12:28:14 | 14,579,099 | 14,751 | 2,989 | MIT | 2023-09-08T19:55:32 | 2013-11-21T05:53:08 | Python | UTF-8 | Python | false | false | 554 | py | import _plotly_utils.basevalidators
class FamilyValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self,
plotly_name="family",
parent_name="scatter.legendgrouptitle.font",
**kwargs,
):
super(FamilyValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
no_blank=kwargs.pop("no_blank", True),
strict=kwargs.pop("strict", True),
**kwargs,
)
| [
"[email protected]"
]
| |
12c48d5ff16675e2a8f6a2d902504de1ea724719 | 3540272eb4522c637fb293a924a6ad8d7b365718 | /tribune/news/models.py | 33b78f25b5e2b10758918424db9589c053a886ce | []
| no_license | nevooronni/tribune | b0e80a4613758690702fa88eb99f44f4e8e66a30 | 7218d3514277ce408128b4e8c66da5639cf7dec4 | refs/heads/master | 2021-08-14T16:55:46.280863 | 2017-11-16T08:41:18 | 2017-11-16T08:41:18 | 110,561,868 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,167 | py | from django.db import models#import models class configured to allow us to communicate with the db
import datetime as dt
class Editor(models.Model):#created editor class inherits from model class
first_name = models.CharField(max_length = 30)#charfield is the sql equivalent to varchar a string field for small to large size strings
last_name = models.CharField(max_length = 30)
email = models.EmailField()
phone_number = models.CharField(max_length = 10,blank = True)
def save_editor(self):
self.save()
def delete_editor(self):
self.delete()
#def display_all(self):
#self.objects.all()
#this updates our models so we can easily read it in the shell
def __str__(self):#string representation of our model
return self.first_name
class Meta:
ordering = ['first_name']
class tags(models.Model):
name = models.CharField(max_length = 30)
def __str__(self):
return self.name
class Article(models.Model):
title = models.CharField(max_length = 60)
post = models.TextField()#textarea tag in html
editor = models.ForeignKey(Editor)#foreign key column defines one to many relationship to editor
tags = models.ManyToManyField(tags)#many to many relationship with the tags class
pub_date = models.DateTimeField(auto_now_add=True)#timestamp to establish when the articles were published
article_image = models.ImageField(upload_to = 'articles/')#image field takes upload_to argument defines where the image will be stored in the file system.
#def save_article(self):
#self.save()
def __str__(self):
return self.title
@classmethod
def todays_news(cls):
today = dt.date.today()#module to get todays date
news = cls.objects.filter(pub_date__date = today)#qeury db to filter articles by current date
return news
@classmethod
def day_news(cls,date):#takes date object as an argument
news = cls.objects.filter(pub_date__date = date)
return news
@classmethod
def search_by_title(cls,search_term):
news = cls.objects.filter(title__icontains=search_term)#will filter our model data using the __icontains filter will check if any word in the title field of our articles matches the search_term
return news
| [
"[email protected]"
]
| |
902d047c818eadc130281316b90cfde772634bd0 | 6b1aaded6a6d7ad8133eb93f5570d087b9ecefc0 | /57.py | a778a4be509d89c1f7027528296bead44d2f43f7 | []
| no_license | huangyingw/Leetcode-Python | 53a772e1ecf298c829c0f058faa54c420420b002 | 9513e215d40145a5f2f40095b459693c79c4b560 | refs/heads/master | 2021-07-16T10:10:02.457765 | 2020-07-01T05:35:21 | 2020-07-01T05:35:21 | 192,150,219 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 813 | py | # Definition for an interval.
# class Interval:
# def __init__(self, s=0, e=0):
# self.start = s
# self.end = e
class Solution:
def insert(self, intervals, newInterval):
"""
:type intervals: List[Interval]
:type newInterval: Interval
:rtype: List[Interval]
"""
res = []
i = 0
while i < len(intervals) and intervals[i].end < newInterval.start:
res.append(intervals[i])
i += 1
while i < len(intervals) and intervals[i].start <= newInterval.end:
newInterval.start = min(intervals[i].start, newInterval.start)
newInterval.end = max(intervals[i].end, newInterval.end)
i += 1
res.append(newInterval)
res.extend(intervals[i:])
return res | [
"[email protected]"
]
| |
50d9115c118e9ba2d5ebc6b7d58c98818ecd010c | bd4734d50501e145bc850426c8ed595d1be862fb | /7Kyu - Growth of a Populationdef nb_year-p0- percent- aug- p- count - 0 while-p0-p- p0 - p0 - p0-percent/7Kyu - Growth of a Population.py | 1259b444757fdd602a08df17a598257ae2dcc7d2 | []
| no_license | OrdinaryCoder00/CODE-WARS-PROBLEMS-SOLUTIONS | f61ff9e5268305519ffeed4964589289f4148cfd | 5711114ddcc6a5f22f143d431b2b2e4e4e8ac9fb | refs/heads/master | 2021-10-23T09:09:45.670850 | 2019-03-16T13:24:17 | 2019-03-16T13:24:17 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 154 | py | def nb_year(p0, percent, aug, p):
count = 0
while(p0<p):
p0 = p0 + p0*(percent/100) + aug
count = count + 1
return count
| [
"[email protected]"
]
| |
bfe91353f94b7324769a5908cb44a049673dd6e2 | de24f83a5e3768a2638ebcf13cbe717e75740168 | /moodledata/vpl_data/303/usersdata/287/85098/submittedfiles/testes.py | 890ae53b050f276e8465ffbca31d1de6d83407c7 | []
| no_license | rafaelperazzo/programacao-web | 95643423a35c44613b0f64bed05bd34780fe2436 | 170dd5440afb9ee68a973f3de13a99aa4c735d79 | refs/heads/master | 2021-01-12T14:06:25.773146 | 2017-12-22T16:05:45 | 2017-12-22T16:05:45 | 69,566,344 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 88 | py | # -*- coding: utf-8 -*-
from minha_bib import*
print(multiplicacao(2,3))
| [
"[email protected]"
]
| |
57b906c83a2d61619b54bb2afe90bb43616f21ce | 4111ca5a73a22174f189361bef654c3f91c3b7ed | /Lintcode/Ladder_47_BigData/128. Hash Function.py | 6d7607ff24020011e66b6d9c8c4b6774644f2261 | [
"MIT"
]
| permissive | ctc316/algorithm-python | 58b541b654509ecf4e9eb8deebfcbdf785699cc4 | ac4580d55e05e93e407c6156c9bb801808027d60 | refs/heads/master | 2020-03-16T06:09:50.130146 | 2019-08-02T02:50:49 | 2019-08-02T02:50:49 | 132,548,222 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 283 | py | class Solution:
"""
@param key: A string you should hash
@param HASH_SIZE: An integer
@return: An integer
"""
def hashCode(self, key, HASH_SIZE):
code = 0
for ch in key:
code = (code * 33 + ord(ch)) % HASH_SIZE
return code | [
"[email protected]"
]
| |
3f7c1ecec5f580016a5068bd48f1de2040b2bf6b | 0a801544da5ad2f1969348512f7def8fa9176c7d | /backend/simplicite_23801/urls.py | 25d91cd59bdf8a1ad01544b6a94abcbe42abf6ab | []
| no_license | crowdbotics-apps/simplicite-23801 | 75477b76531c2c53992f74183e9e2e80aefd22e4 | e6a6f569c61449e50988201cf58cc5203d23e039 | refs/heads/master | 2023-02-11T12:27:04.807579 | 2021-01-13T03:01:57 | 2021-01-13T03:01:57 | 329,176,579 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,668 | py | """simplicite_23801 URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/2.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path, include
from allauth.account.views import confirm_email
from rest_framework import permissions
from drf_yasg.views import get_schema_view
from drf_yasg import openapi
urlpatterns = [
path("", include("home.urls")),
path("accounts/", include("allauth.urls")),
path("modules/", include("modules.urls")),
path("api/v1/", include("home.api.v1.urls")),
path("admin/", admin.site.urls),
path("users/", include("users.urls", namespace="users")),
path("rest-auth/", include("rest_auth.urls")),
# Override email confirm to use allauth's HTML view instead of rest_auth's API view
path("rest-auth/registration/account-confirm-email/<str:key>/", confirm_email),
path("rest-auth/registration/", include("rest_auth.registration.urls")),
path("api/v1/", include("task.api.v1.urls")),
path("task/", include("task.urls")),
path("api/v1/", include("task_profile.api.v1.urls")),
path("task_profile/", include("task_profile.urls")),
path("api/v1/", include("tasker_business.api.v1.urls")),
path("tasker_business/", include("tasker_business.urls")),
path("api/v1/", include("location.api.v1.urls")),
path("location/", include("location.urls")),
path("api/v1/", include("wallet.api.v1.urls")),
path("wallet/", include("wallet.urls")),
path("api/v1/", include("task_category.api.v1.urls")),
path("task_category/", include("task_category.urls")),
path("home/", include("home.urls")),
]
admin.site.site_header = "Simplicite"
admin.site.site_title = "Simplicite Admin Portal"
admin.site.index_title = "Simplicite Admin"
# swagger
api_info = openapi.Info(
title="Simplicite API",
default_version="v1",
description="API documentation for Simplicite App",
)
schema_view = get_schema_view(
api_info,
public=True,
permission_classes=(permissions.IsAuthenticated,),
)
urlpatterns += [
path("api-docs/", schema_view.with_ui("swagger", cache_timeout=0), name="api_docs")
]
| [
"[email protected]"
]
| |
c826ef5b94146713cbfb40ea8d2456a72ea50850 | 11137bde91389c04a95df6f6fdaf64f7f49f5f80 | /introduction_MIT/16_2图表会骗人.py | 582608abe22748eee617a7fb4bb7c90df1af46fb | []
| no_license | starschen/learning | cf3c5a76c867567bce73e9cacb2cf0979ba053d9 | 34decb8f9990117a5f40b8db6dba076a7f115671 | refs/heads/master | 2020-04-06T07:02:56.444233 | 2016-08-24T08:11:49 | 2016-08-24T08:11:49 | 39,417,895 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,191 | py | #encoding:utf8
#16_2图表会骗人.py
#绘制房价
import pylab
def plotHousing(impression):
'''假设impression是一个字符串，必须是‘flat’， ‘volatile’或者是‘fair’
生成房价随时间变化的图表'''
f=open('midWestHousingPrices.txt','r')
#文件的每一行是年季度价格
#数据来自美国中部区域
labels,prices=([],[])
for line in f:
year,quarter,price=line.split(' ')
label=year[2:4]+'\n Q'+quarter[1]
labels.append(label)
prices.append(float(price)/1000)
quarters=pylab.arange(len(labels))
width=0.8
if impression=='flat':
pylab.semilogy()
pylab.bar(quarters,prices,width)
pylab.xticks(quarters+width/2.0,labels)
pylab.title('Housing Prices in U.S. Midwest')
pylab.xlabel('Quarter')
pylab.ylabel('Average Price($1,000\'s)')
if impression=='flat':
pylab.ylim(10,10**3)
elif impression =='volatile':
pylab.ylim(180,220)
elif impression=='fair':
pylab.ylim(150,250)
else:
raise ValueError
plotHousing('flat')
pylab.figure()
plotHousing('volatile')
pylab.figure()
plotHousing('fair')
pylab.show()
| [
"[email protected]"
]
| |
1b2a6c3181548e466eacfa3b040cbc883242e73b | 35f1a21affd266e0069bfc5a1c83218847f13802 | /pastie-5073437.py | 9cc5c027ce00562a66e8461c02cfa8768964c92c | []
| no_license | KarenWest/pythonClassProjects | ff1e1116788174a2affaa96bfcb0e97df3ee92da | 5aa496a71d36ffb9892ee6e377bd9f5d0d8e03a0 | refs/heads/master | 2016-09-16T15:20:26.882688 | 2014-02-21T20:07:57 | 2014-02-21T20:07:57 | 17,055,355 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 772 | py | #balance = 5000
#annualInterestRate = 0.18
#monthlyPaymentRate = 0.02
months = range(1, 13) # 1, 2, ... , 11, 12
owe = balance # It made sense to me to call this total amount oweing
totalPaid = 0
for month in months:
minPay = owe * monthlyPaymentRate # calculate our minimum payment
interest = (owe - minPay) * (annualInterestRate / 12) # same for interest
owe = owe - minPay + interest # calculate our new balance
totalPaid += minPay # Sum up how much we've paid so far
print('Month: %d' % month) # %d will be replaced by month
print('Minimum monthly payment: %.2f' % minPay) # %.2f replaced by minPay, with 2 decimal places
print('Remaining balance: %.2f' % owe)
print('Total paid %.2f' % totalPaid)
print('Remaining balance: %.2f' % owe)
| [
"[email protected]"
]
| |
4d909ba4892e6c9c564466ba0ea7fe903b3857ab | 8bc7ba8eb10e30b38f2bcf00971bfe540c9d26b7 | /paxes_cinder/k2aclient/v1/virtualswitch_manager.py | 6d956eb5bdf9ebcdab6aba2f1378e4c6151dbbdc | [
"Apache-2.0"
]
| permissive | windskyer/k_cinder | f8f003b2d1f9ca55c423ea0356f35a97b5294f69 | 000ee539ee4842a158071d26ee99d12c7c0a87da | refs/heads/master | 2021-01-10T02:19:51.072078 | 2015-12-08T15:24:33 | 2015-12-08T15:24:33 | 47,629,931 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,938 | py | #
#
# =================================================================
# =================================================================
"""VirtualSwitch interface."""
from paxes_cinder.k2aclient import base
from paxes_cinder.k2aclient.v1 import k2uom
class VirtualSwitchManager(base.ManagerWithFind):
"""Manage :class:`ClientNetworkAdapter` resources."""
resource_class = k2uom.VirtualSwitch
def new(self):
return self.resource_class(self, None)
def list(self, managedsystem, xa=None):
"""Get a list of all VirtualSwitch for a particular
ManagedSystem accessed through a particular hmc.
:rtype: list of :class:`ClientNetworkAdapter`.
"""
return self._list("/rest/api/uom/ManagedSystem/%s/VirtualSwitch"
% managedsystem, xa=xa)
def get(self, managedsystem, virtualswitch, xa=None):
"""Given managedsystem, get a specific VirtualSwitch.
:param virtualswitch: The ID of the :class:`VirtualSwitch`.
:rtype: :class:`VirtualSwitch`
"""
return self._get("/rest/api/uom/ManagedSystem/%s/VirtualSwitch/%s"
% (managedsystem, virtualswitch,),
xa=xa)
def delete(self, managedsystem, virtualswitch, xa=None):
"""Delete the specified instance
"""
return self._delete("uom",
managedsystem,
child=virtualswitch,
xa=xa)
def deletebyid(self, managedsystem_id, virtualswitch_id, xa=None):
"""Delete the specified instance
"""
return self._deletebyid("uom",
"ManagedSystem",
managedsystem_id,
child_type=k2uom.VirtualSwitch,
child_id=virtualswitch_id,
xa=xa)
| [
"leidong@localhost"
]
| leidong@localhost |
e463337960661352eec76273356b1323176686ca | 04ae1836b9bc9d73d244f91b8f7fbf1bbc58ff29 | /170/Solution.py | 1ece9965f93617191e3d6e484aeefd64c54f0c67 | []
| no_license | zhangruochi/leetcode | 6f739fde222c298bae1c68236d980bd29c33b1c6 | cefa2f08667de4d2973274de3ff29a31a7d25eda | refs/heads/master | 2022-07-16T23:40:20.458105 | 2022-06-02T18:25:35 | 2022-06-02T18:25:35 | 78,989,941 | 14 | 6 | null | null | null | null | UTF-8 | Python | false | false | 2,272 | py | """
Design and implement a TwoSum class. It should support the following operations: add and find.
add - Add the number to an internal data structure.
find - Find if there exists any pair of numbers which sum is equal to the value.
Example 1:
add(1); add(3); add(5);
find(4) -> true
find(7) -> false
Example 2:
add(3); add(1); add(2);
find(3) -> true
find(6) -> false
"""
from collections import defaultdict
class TwoSum:
def __init__(self):
"""
Initialize your data structure here.
"""
self.count = 0
self.numbers = defaultdict(list)
def add(self, number):
"""
Add the number to an internal data structure..
:type number: int
:rtype: void
"""
self.numbers[number].append(self.count)
self.count += 1
def find(self, value):
"""
Find if there exists any pair of numbers which sum is equal to the value.
:type value: int
:rtype: bool
"""
for num, indexs in self.numbers.items():
tmp = value - num
if tmp in self.numbers:
if tmp == num and len(indexs) > 1:
return True
if tmp != num:
return True
return False
class TwoSum(object):
def __init__(self):
"""
Initialize your data structure here.
"""
self.nums = {}
def add(self, number):
"""
Add the number to an internal data structure..
:type number: int
:rtype: None
"""
if number not in self.nums:
self.nums[number] = 1
else:
self.nums[number] += 1
def find(self, value):
"""
Find if there exists any pair of numbers which sum is equal to the value.
:type value: int
:rtype: bool
"""
for key in self.nums:
second = value - key
if ((key != second) and second in self.nums) or (key == second and self.nums[key] > 1):
return True
return False
# Your TwoSum object will be instantiated and called as such:
# obj = TwoSum()
# obj.add(number)
# param_2 = obj.find(value) | [
"[email protected]"
]
| |
51bacc265849e99d5638fe2aa84fb25204c57781 | a0dda8be5892a390836e19bf04ea1d098e92cf58 | /叶常春视频例题/chap05/5-2-9-生词清单.py | 6c9697103cde9015e7e96499929c29627c18643d | []
| no_license | wmm98/homework1 | d9eb67c7491affd8c7e77458ceadaf0357ea5e6b | cd1f7f78e8dbd03ad72c7a0fdc4a8dc8404f5fe2 | refs/heads/master | 2020-04-14T19:22:21.733111 | 2019-01-08T14:09:58 | 2019-01-08T14:09:58 | 164,055,018 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 307 | py | # 例5-2-9 生词清单
new_words = []
for i in range(1, 101):
word = input("输入生词：")
if word == "*":
break # break语句的作用是跳出循环，执行循环后面的语句。
if word not in new_words:
new_words.append(word)
print("生词清单：", new_words) | [
"[email protected]"
]
| |
d4e630393d97b23b24b61c540310af9eced66716 | 4d4fcde3efaa334f7aa56beabd2aa26fbcc43650 | /server/src/uds/core/managers/userservice/comms.py | e2b06381d46b8a78ab640c8bf0c609b7fff00dda | []
| no_license | xezpeleta/openuds | a8b11cb34eb0ef7bb2da80f67586a81b2de229ef | 840a7a02bd7c9894e8863a8a50874cdfdbf30fcd | refs/heads/master | 2023-08-21T17:55:48.914631 | 2021-10-06T10:39:06 | 2021-10-06T10:39:06 | 414,489,331 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,264 | py | # -*- coding: utf-8 -*-
#
# Copyright (c) 2019-2021 Virtual Cable S.L.U.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of Virtual Cable S.L.U. nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
.. moduleauthor:: Adolfo Gómez, dkmaster at dkmon dot com
"""
import os
import json
import base64
import tempfile
import logging
import typing
import requests
if typing.TYPE_CHECKING:
from uds.models import UserService
logger = logging.getLogger(__name__)
TIMEOUT = 2
class NoActorComms(Exception):
pass
class OldActorVersion(NoActorComms):
pass
def _requestActor(
userService: 'UserService',
method: str,
data: typing.Optional[typing.MutableMapping[str, typing.Any]] = None,
minVersion: typing.Optional[str] = None,
) -> typing.Any:
"""
Makes a request to actor using "method"
if data is None, request is done using GET, else POST
if no communications url is provided or no min version, raises a "NoActorComms" exception (or OldActorVersion, derived from NoActorComms)
Returns request response value interpreted as json
"""
url = userService.getCommsUrl()
if not url:
# logger.warning('No notification is made because agent does not supports notifications: %s', userService.friendly_name)
raise NoActorComms(
'No notification urls for {}'.format(userService.friendly_name)
)
minVersion = minVersion or '2.0.0'
version = userService.getProperty('actor_version') or '0.0.0'
if '-' in version or version < minVersion:
logger.warning(
'Pool %s has old actors (%s)', userService.deployed_service.name, version
)
raise OldActorVersion(
'Old actor version {} for {}'.format(version, userService.friendly_name)
)
url += '/' + method
proxy = userService.deployed_service.proxy
try:
if proxy:
r = proxy.doProxyRequest(url=url, data=data, timeout=TIMEOUT)
else:
verify: typing.Union[bool, str]
cert = userService.getProperty('cert')
# cert = '' # Untils more tests, keep as previous.... TODO: Fix this when fully tested
if cert:
# Generate temp file, and delete it after
verify = tempfile.mktemp('udscrt')
with open(verify, 'wb') as f:
f.write(cert.encode()) # Save cert
else:
verify = False
if data is None:
r = requests.get(url, verify=verify, timeout=TIMEOUT)
else:
r = requests.post(
url,
data=json.dumps(data),
headers={'content-type': 'application/json'},
verify=verify,
timeout=TIMEOUT,
)
if verify:
try:
os.remove(typing.cast(str, verify))
except Exception:
logger.exception('removing verify')
js = r.json()
if version >= '3.0.0':
js = js['result']
logger.debug('Requested %s to actor. Url=%s', method, url)
except Exception as e:
logger.warning(
'Request %s failed: %s. Check connection on destination machine: %s',
method,
e,
url,
)
js = None
return js
def notifyPreconnect(userService: 'UserService', userName: str, protocol: str) -> None:
"""
Notifies a preconnect to an user service
"""
ip, hostname = userService.getConnectionSource()
try:
_requestActor(
userService,
'preConnect',
{'user': userName, 'protocol': protocol, 'ip': ip, 'hostname': hostname},
)
except NoActorComms:
pass # If no preconnect, warning will appear on UDS log
def checkUuid(userService: 'UserService') -> bool:
"""
Checks if the uuid of the service is the same of our known uuid on DB
"""
try:
uuid = _requestActor(userService, 'uuid')
if (
uuid and uuid != userService.uuid
): # Empty UUID means "no check this, fixed pool machine"
logger.info(
'Machine %s do not have expected uuid %s, instead has %s',
userService.friendly_name,
userService.uuid,
uuid,
)
return False
except NoActorComms:
pass
return True # Actor does not supports checking
def requestScreenshot(userService: 'UserService') -> bytes:
"""
Returns an screenshot in PNG format (bytes) or empty png if not supported
"""
emptyPng = 'iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg=='
try:
png = _requestActor(
userService, 'screenshot', minVersion='3.0.0'
) # First valid version with screenshot is 3.0
except NoActorComms:
png = None
return base64.b64decode(png or emptyPng)
def sendScript(userService: 'UserService', script: str, forUser: bool = False) -> None:
"""
If allowed, send script to user service
"""
try:
data: typing.MutableMapping[str, typing.Any] = {'script': script}
if forUser:
data['user'] = forUser
_requestActor(userService, 'script', data=data)
except NoActorComms:
pass
def requestLogoff(userService: 'UserService') -> None:
"""
Ask client to logoff user
"""
try:
_requestActor(userService, 'logout', data={})
except NoActorComms:
pass
def sendMessage(userService: 'UserService', message: str) -> None:
"""
Sends an screen message to client
"""
try:
_requestActor(userService, 'message', data={'message': message})
except NoActorComms:
pass
| [
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]
| |
c886c20cf7de550debdfb0a88d9edcbafb45a992 | 0726db2d56b29f02a884885718deeddbf86df628 | /lienp/visualize.py | af217e55f8ec1524e381eb022bcd47bbb2f01101 | []
| no_license | makora9143/EquivCNP | 515dfd95557d8d3a21d3fc0f295ce885a9deb913 | a78dea12ab672e796c86427823c9f1b2fdd8df8d | refs/heads/master | 2023-03-17T04:34:26.320055 | 2021-03-05T18:02:18 | 2021-03-05T18:02:18 | 254,292,834 | 5 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,239 | py | import io
import PIL.Image
import matplotlib.pyplot as plt
import torch
from torchvision.transforms import ToTensor, ToPILImage
from torchvision.utils import make_grid
def mnist_plot_function(target_x, target_y, context_x, context_y):
img = torch.zeros((28, 28, 3))
img[:, :, 2] = torch.ones((28, 28))
idx = (context_x + 14).clamp(0, 27).long()
img[idx[:, 0], idx[:, 1]] = context_y
print(f'num context:{context_x.shape[0]}')
plt.figure(figsize=(8, 4))
plt.subplot(121)
plt.imshow(img.numpy())
plt.gray()
plt.subplot(122)
plt.imshow(target_y.reshape(28, 28).numpy())
plt.show()
def plot_and_save_image(ctxs, tgts, preds, epoch=None):
ctx_img = []
tgt_img = []
pred_img = []
for ctx, tgt, tgt_y_dist in zip(ctxs, tgts, preds):
ctx_coords, ctx_values = ctx
tgt_coords, tgt_values = tgt
img = torch.zeros((28, 28, 3))
img[:, :, 2] = torch.ones((28, 28))
idx = (ctx_coords[0] + 14).clamp(0, 27).long()
img[idx[:, 0], idx[:, 1]] = ctx_values[0]
ctx_img.append(img.unsqueeze(0))
tgt_img.append(tgt_values.reshape(1, 1, 28, 28).repeat(1, 3, 1, 1))
pred_img.append(tgt_y_dist.mean.reshape(1, 1, 28, 28).repeat(1, 3, 1, 1))
ctx_img = torch.cat(ctx_img, 0).permute(0, 3, 1, 2).unsqueeze(1).to(torch.device('cpu'))
tgt_img = torch.cat(tgt_img, 0).unsqueeze(1).to(torch.device('cpu'))
pred_img = torch.cat(pred_img, 0).unsqueeze(1).to(torch.device('cpu'))
img = torch.cat([ctx_img, tgt_img, pred_img], 1).reshape(-1, 3, 28, 28)
img = make_grid(img, nrow=6).permute(1, 2, 0).clamp(0, 1)
plt.imsave("epoch_{}.png".format(epoch if epoch is not None else "test"), img.numpy())
def plot_and_save_image2(ctxs, tgts, preds, img_shape, epoch=None):
ctx_img = []
tgt_img = []
pred_img = []
C, W, H = img_shape
for ctx_mask, tgt, tgt_y_dist in zip(ctxs, tgts, preds):
img = torch.zeros((W, H, 3))
img[:, :, 2] = torch.ones((W, H))
img[ctx_mask[0, 0] == 1] = tgt[0, 0][ctx_mask[0, 0] == 1].unsqueeze(-1)
ctx_img.append(img)
tgt_img.append(tgt.repeat(1, 3//C, 1, 1))
pred_img.append(tgt_y_dist.mean.reshape(1, W, H, C).repeat(1, 1, 1, 3//C))
ctx_img = torch.stack(ctx_img, 0).permute(0, 3, 1, 2).unsqueeze(1).to(torch.device('cpu'))
tgt_img = torch.cat(tgt_img, 0).unsqueeze(1).to(torch.device('cpu'))
pred_img = torch.cat(pred_img, 0).unsqueeze(1).to(torch.device('cpu')).permute(0, 1, 4, 2, 3)
img = torch.cat([ctx_img, tgt_img, pred_img], 1).reshape(-1, 3, W, H)
img = make_grid(img, nrow=6).permute(1, 2, 0).clamp(0, 1)
plt.imsave("epoch_{}.png".format(epoch if epoch is not None else "test"), img.numpy())
def plot_and_save_graph(ctxs, tgts, preds, gp_preds, epoch=None):
graphs = []
for ctx, tgt, tgt_y_dist, gp_dist in zip(ctxs, tgts, preds, gp_preds):
ctx_coords, ctx_values = ctx
tgt_coords, tgt_values = tgt
mean = tgt_y_dist.mean.cpu()
lower, upper = tgt_y_dist.confidence_region()
gp_mean = gp_dist.mean.cpu()
gp_lower, gp_upper = gp_dist.confidence_region()
plt.plot(tgt_coords.reshape(-1).cpu(), gp_mean.detach().cpu().reshape(-1), color='green')
plt.fill_between(tgt_coords.cpu().reshape(-1), gp_lower.detach().cpu().reshape(-1), gp_upper.detach().cpu().reshape(-1), alpha=0.2, color='green')
plt.plot(tgt_coords.reshape(-1).cpu(), mean.detach().cpu().reshape(-1), color='blue')
plt.fill_between(tgt_coords.cpu().reshape(-1), lower.detach().cpu().reshape(-1), upper.detach().cpu().reshape(-1), alpha=0.2, color='blue')
plt.plot(tgt_coords.reshape(-1).cpu(), tgt_values.reshape(-1), '--', color='gray')
plt.plot(ctx_coords.reshape(-1).cpu(), ctx_values.reshape(-1).cpu(), 'o', color='black')
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.clf()
plt.close()
img = PIL.Image.open(buf)
img = ToTensor()(img)
buf.close()
graphs.append(img)
img = ToPILImage()(make_grid(torch.stack(graphs, 0), nrow=2))
img.save("epoch_{}.png".format(epoch if epoch is not None else "test"))
| [
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]
| |
d019acbd04f6f92c44b1c6b5ef4f6c1d988e6d74 | c50c22c8f814c8d9b697337891904aa0be0edf56 | /shortest_string.py | b26e0a4d78a888e7c2d4a6252bf9b0d4e510728a | []
| no_license | mhiloca/Codewars | a6dc6e8ea5e5c1e97fb4a3d01a059b3120b556b7 | 3155e4b20fbd96c8e7fbe6564014a136d095c079 | refs/heads/master | 2020-07-11T12:41:19.997254 | 2019-11-01T12:44:38 | 2019-11-01T12:44:38 | 204,541,593 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 222 | py | """
Simple, given a string of words, return the length of the shortest word(s).
String will never be empty and you do not need to account for different data types.
"""
def find_short(s):
return min(len(x) for x in s) | [
"[email protected]"
]
| |
c5d4d2f46c24a51bf6824c2c8735e80bc1f67f80 | 3f4464c932403615c1fbbaf82eaec096426b1ef5 | /StartOutPy4/CH6 Files and Exceptions/write_sales.py | ecb7c7a8611babac6e9db4c42a7bbdc92ed31f8e | []
| no_license | arcstarusa/prime | 99af6e3fed275982bf11ada7bf1297294d527e91 | 5f1102aa7b6eaba18f97eb388525d48ab4cac563 | refs/heads/master | 2020-03-22T14:07:08.079963 | 2019-05-09T11:45:21 | 2019-05-09T11:45:21 | 140,154,408 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 740 | py | # This program prompts the user for sales amounts
# and writes those amounts to the sales.txt file. 6-8
def main():
# Get the numbers of days.
num_days = int(input('For how many days do ' + 'you have sales? '))
# Open a nuew file named sales.txt.
sales_file = open('sales.txt', 'w')
# Get the amount of sales for each day and write
# it to the file.
for count in range (1, num_days + 1):
# Get the sales for a day.
sales = float(input('Enter the sales for day #' + str(count) + ': '))
# Write the sales amount to the file.
sales_file.write(str(sales) + '\n')
# Close the file.
sales_file.close()
print('Data written to sales.txt')
# Call the main function.
main() | [
"[email protected]"
]
| |
ee3ca22e9c0f5e05bbf59b552966f070d8a674d9 | 8613ec7f381a6683ae24b54fb2fb2ac24556ad0b | /20~29/ABC021/honest.py | 9c7f3d43c9ab13670129a435bb52b6b7b42038ab | []
| no_license | Forest-Y/AtCoder | 787aa3c7dc4d999a71661465349428ba60eb2f16 | f97209da3743026920fb4a89fc0e4d42b3d5e277 | refs/heads/master | 2023-08-25T13:31:46.062197 | 2021-10-29T12:54:24 | 2021-10-29T12:54:24 | 301,642,072 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 296 | py | n = int(input())
a, b = map(int, input().split())
m = int(input())
x, y = [0] * m, [0] * m
data = [[] for _ in range(n)]
for i in range(m):
x, y = map(int, input().split())
data[x - 1].append(y - 1)
data[y - 1].append(x - 1)
dist = [[-1] * n for i in range(n)]
dist[a - 1][b - 1] = 0 | [
"[email protected]"
]
| |
4bbad83e050e46e0bd882f7147d3faa597ef6614 | 26d6c34df00a229dc85ad7326de6cb5672be7acc | /msgraph-cli-extensions/beta/files_beta/setup.py | 917c1b376b39507335d14d388323f62f011a8a2c | [
"MIT"
]
| permissive | BrianTJackett/msgraph-cli | 87f92471f68f85e44872939d876b9ff5f0ae6b2c | 78a4b1c73a23b85c070fed2fbca93758733f620e | refs/heads/main | 2023-06-23T21:31:53.306655 | 2021-07-09T07:58:56 | 2021-07-09T07:58:56 | 386,993,555 | 0 | 0 | NOASSERTION | 2021-07-17T16:56:05 | 2021-07-17T16:56:05 | null | UTF-8 | Python | false | false | 1,858 | py | #!/usr/bin/env python
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
from codecs import open
from setuptools import setup, find_packages
# HISTORY.rst entry.
VERSION = '0.1.0'
try:
from azext_files_beta.manual.version import VERSION
except ImportError:
pass
# The full list of classifiers is available at
# https://pypi.python.org/pypi?%3Aaction=list_classifiers
CLASSIFIERS = [
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Intended Audience :: System Administrators',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'License :: OSI Approved :: MIT License',
]
DEPENDENCIES = []
try:
from azext_files_beta.manual.dependency import DEPENDENCIES
except ImportError:
pass
with open('README.md', 'r', encoding='utf-8') as f:
README = f.read()
with open('HISTORY.rst', 'r', encoding='utf-8') as f:
HISTORY = f.read()
setup(
name='files_beta',
version=VERSION,
description='Microsoft Azure Command-Line Tools Files Extension',
author='Microsoft Corporation',
author_email='[email protected]',
url='https://github.com/Azure/azure-cli-extensions/tree/master/files_beta',
long_description=README + '\n\n' + HISTORY,
license='MIT',
classifiers=CLASSIFIERS,
packages=find_packages(),
install_requires=DEPENDENCIES,
package_data={'azext_files_beta': ['azext_metadata.json']},
)
| [
"[email protected]"
]
| |
d005e74749c88692012dd32e899d20852ffbc130 | c223e858c9ebf1b734221e4db4b3d594993a5536 | /thespian/system/timing.py | ec5f22afa79083a0f4d2f9c23f08117403194959 | [
"MIT"
]
| permissive | jfasenfest/Thespian | 17f9738aff648328a40f94d3225427d82fe27e39 | 5979a2c9791b774fb620253bb62253c95cf7d4b5 | refs/heads/master | 2020-12-26T00:27:09.446001 | 2016-11-28T21:48:20 | 2016-11-28T21:48:20 | 48,067,029 | 0 | 0 | null | 2016-11-21T23:17:52 | 2015-12-15T20:24:12 | Python | UTF-8 | Python | false | false | 6,566 | py | from datetime import datetime, timedelta
###
### Time Management
###
def timePeriodSeconds(basis, other=None):
if isinstance(basis, datetime):
if isinstance(other, datetime):
return timePeriodSeconds(other - basis)
if isinstance(basis, timedelta):
try:
return basis.total_seconds()
except AttributeError:
# Must be Python 2.6... which doesn't have total_seconds yet
return (basis.days * 24.0 * 60 * 60) + basis.seconds + (basis.microseconds / 1000.0 / 1000)
raise TypeError('Cannot determine time from a %s argument'%str(type(basis)))
def toTimeDeltaOrNone(timespec):
if timespec is None: return None
if isinstance(timespec, timedelta): return timespec
if isinstance(timespec, int): return timedelta(seconds=timespec)
if isinstance(timespec, float):
return timedelta(seconds=int(timespec),
microseconds = int((timespec - int(timespec)) * 1000 * 1000))
raise TypeError('Unknown type for timespec: %s'%type(timespec))
class ExpiryTime(object):
def __init__(self, duration):
self._time_to_quit = None if duration is None else (datetime.now() + duration)
def expired(self):
return False if self._time_to_quit is None else (datetime.now() >= self._time_to_quit)
def remaining(self, forever=None):
return forever if self._time_to_quit is None else \
(timedelta(seconds=0) if datetime.now() > self._time_to_quit else \
(self._time_to_quit - datetime.now()))
def remainingSeconds(self, forever=None):
return forever if self._time_to_quit is None else \
(0 if datetime.now() > self._time_to_quit else \
timePeriodSeconds(self._time_to_quit - datetime.now()))
def __str__(self):
if self._time_to_quit is None: return 'Forever'
if self.expired():
return 'Expired_for_%s'%(datetime.now() - self._time_to_quit)
return 'Expires_in_' + str(self.remaining())
def __eq__(self, o):
if isinstance(o, timedelta):
o = ExpiryTime(o)
if self._time_to_quit == o._time_to_quit: return True
if self._time_to_quit == None or o._time_to_quit == None: return False
if self.expired() and o.expired(): return True
return abs(self._time_to_quit - o._time_to_quit) < timedelta(microseconds=1)
def __lt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
if self._time_to_quit is None: return False
if isinstance(o, timedelta):
o = ExpiryTime(o)
if o._time_to_quit is None: return True
return self._time_to_quit < o._time_to_quit
def __gt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
return not self.__lt__(o)
def __le__(self, o): return self.__eq__(o) or self.__lt__(o)
def __ge__(self, o): return self.__eq__(o) or self.__gt__(o)
def __ne__(self, o): return not self.__eq__(o)
def __bool__(self): return self.expired()
def __nonzero__(self): return self.expired()
class ExpirationTimer(object):
"""Keeps track of a duration relative to an original time and
indicates whether that duration has expired or how much time is
left before it expires. As an optimization, this object will
not call datetime.now() itself and must be updated via the
`update_time_now()` method to accurately measure elapsed
time.
May also be initialized with a duration of None, indicating
that it should never timeout and that `remaining()` should
return the forever value (defaulting to None).
"""
def __init__(self, duration, timenow=None):
self._time_now = timenow or datetime.now()
self._time_to_quit = None if duration is None else (self._time_now + duration)
def update_time_now(self, timenow):
"Call this to update the elapsed time."
self._time_now = timenow
def expired(self):
"Returns true if the indicated duration has passed since this was created."
return False if self._time_to_quit is None else (self._time_now >= self._time_to_quit)
def remaining(self, forever=None):
"""Returns a timedelta of remaining time until expiration, or 0 if the
duration has already expired. Returns forever if no timeout."""
return forever if self._time_to_quit is None else \
(timedelta(seconds=0) if self._time_now > self._time_to_quit else \
(self._time_to_quit - self._time_now))
def remainingSeconds(self, forever=None):
"""Similar to `remaining()`, but returns an floating point value of the
number of remaining seconds instead of returning a
timedelta object.
"""
return forever if self._time_to_quit is None else \
(0 if self._time_now > self._time_to_quit else \
timePeriodSeconds(self._time_to_quit - self._time_now))
def __str__(self):
if self._time_to_quit is None: return 'Forever'
if self.expired():
return 'Expired_for_%s'%(self._time_now - self._time_to_quit)
return 'Expires_in_' + str(self.remaining())
def __eq__(self, o):
if isinstance(o, timedelta):
o = ExpiryTime(o)
if self._time_to_quit == o._time_to_quit: return True
if self._time_to_quit == None or o._time_to_quit == None: return False
if self.expired() and o.expired(): return True
return abs(self._time_to_quit - o._time_to_quit) < timedelta(microseconds=1)
def __lt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
if self._time_to_quit is None: return False
if isinstance(o, timedelta):
o = ExpiryTime(o)
if o._time_to_quit is None: return True
return self._time_to_quit < o._time_to_quit
def __gt__(self, o):
try:
if self._time_to_quit is None and o._time_to_quit is None: return False
except Exception: pass
return not self.__lt__(o)
def __le__(self, o): return self.__eq__(o) or self.__lt__(o)
def __ge__(self, o): return self.__eq__(o) or self.__gt__(o)
def __ne__(self, o): return not self.__eq__(o)
def __bool__(self): return self.expired()
def __nonzero__(self): return self.expired()
| [
"[email protected]"
]
| |
62604aec9fec8d853af4fd5bdc81a077ae397e7c | 4e567fc53288f53cdcfa37c0f5490a29cf4bc0cd | /projects-inventory/inventory/inventorya.py | dc89e6b234d8d28aaf3a41b5b98a5039f3ac59d3 | []
| no_license | sidarmawan/ansible_training | 3e4bba3bde4557a23c92735314f09328319463c9 | 77d3acb3430143458de07ca2b8257f3a2637f59b | refs/heads/master | 2022-04-08T06:01:31.043044 | 2020-01-16T09:56:41 | 2020-01-16T09:56:41 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 103 | py | htttps://materials.example.com/labs/projects-inventory/inventorya.py: Unsupported scheme ‘htttps’.
| [
"[email protected]"
]
| |
65a4c9be5fe8054649cd3ece5dbe367bc18a3e9e | 2dc17d12ff6ea9794177c81aa4f385e4e09a4aa5 | /archive/55JumpGame.py | f503b433be7298c6acd75a571e1e0b2c43dd3322 | []
| no_license | doraemon1293/Leetcode | 924b19f840085a80a9e8c0092d340b69aba7a764 | 48ba21799f63225c104f649c3871444a29ab978a | refs/heads/master | 2022-10-01T16:20:07.588092 | 2022-09-08T02:44:56 | 2022-09-08T02:44:56 | 122,086,222 | 0 | 0 | null | null | null | null | WINDOWS-1252 | Python | false | false | 398 | py | # coding=utf-8
'''
Created on 2017�3�7�
@author: Administrator
'''
class Solution(object):
def canJump(self, nums):
"""
:type nums: List[int]
:rtype: bool
"""
mini_true = len(nums) - 1
for i in xrange(len(nums) - 1, -1, -1):
if nums[i] >= (mini_true - i):
mini_true = i
return mini_true == 0
| [
"[email protected]"
]
| |
db7d00585385b6589b6b11d0e3b16814d349cc17 | a61ebd1507eeaa334aff44800b022ef0a258752a | /Code/CodeChef/remainder.py | 37c511c002d48cc5390d47f937a2ec559c35e257 | [
"MIT"
]
| permissive | Jimut123/competitive_programming | 14ce0ab65414e6086763519f95487cddc91205a9 | b4cdebaceee719c1a256921829ebafda11c515f5 | refs/heads/master | 2023-03-05T15:42:57.194176 | 2022-04-08T08:53:26 | 2022-04-08T08:53:26 | 156,541,142 | 1 | 0 | null | 2019-05-29T17:10:28 | 2018-11-07T12:09:55 | C++ | UTF-8 | Python | false | false | 152 | py | #[email protected]
t = int(input())
l = []
for i in range(t):
m,n = map(int,input().split())
l.append(m%n)
for item in l:
print(item)
| [
"[email protected]"
]
| |
aa9c2bf1b305cc6403a880948c9ce34f01af5268 | 2d19317ab9af09be9e6c8f0a25d4a43d4632b680 | /first_project/urls.py | 44eee9ee4a82b0a41d31cece1c0325b3b2218316 | []
| no_license | rudiq4/first_project | 73837d297b21ccd7c706fc08373473e9e4cd8b29 | ba0e987f863f599da9700c355875af76158b76f0 | refs/heads/master | 2021-01-25T13:41:56.102967 | 2018-03-27T14:15:26 | 2018-03-27T14:15:26 | 123,607,387 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,132 | py | """first_project URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.11/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url, include
from django.contrib import admin
from django.conf.urls.static import static
from django.conf import settings
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^', include('Auto.urls')),
url(r'^', include('Post.urls')),
url(r'user/', include('User.urls')),
] \
+ static(settings.STATIC_URL, document_root=settings.STATIC_ROOT) \
+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) | [
"[email protected]"
]
| |
6093d2129fcc9b86264e32f2199313f4ee2360fc | bdf86d69efc1c5b21950c316ddd078ad8a2f2ec0 | /venv/Lib/site-packages/twisted/web/_http2.py | 1a425a7729ffd88e8e0c2c4b4f98294cbdc6f975 | [
"LicenseRef-scancode-unknown-license-reference",
"MIT"
]
| permissive | DuaNoDo/PythonProject | 543e153553c58e7174031b910fd6451399afcc81 | 2c5c8aa89dda4dec2ff4ca7171189788bf8b5f2c | refs/heads/master | 2020-05-07T22:22:29.878944 | 2019-06-14T07:44:35 | 2019-06-14T07:44:35 | 180,941,166 | 1 | 1 | null | 2019-06-04T06:27:29 | 2019-04-12T06:05:42 | Python | UTF-8 | Python | false | false | 45,541 | py | # -*- test-case-name: twisted.web.test.test_http2 -*-
# Copyright (c) Twisted Matrix Laboratories.
# See LICENSE for details.
"""
HTTP2 Implementation
This is the basic server-side protocol implementation used by the Twisted
Web server for HTTP2. This functionality is intended to be combined with the
HTTP/1.1 and HTTP/1.0 functionality in twisted.web.http to provide complete
protocol support for HTTP-type protocols.
This API is currently considered private because it's in early draft form. When
it has stabilised, it'll be made public.
"""
from __future__ import absolute_import, division
import io
import sys
import warnings
from collections import deque
import h2.config
import h2.connection
import h2.errors
import h2.events
import h2.exceptions
import priority
from twisted.internet._producer_helpers import _PullToPush
from twisted.internet.defer import Deferred
from twisted.internet.error import ConnectionLost
from twisted.internet.interfaces import (
IProtocol, ITransport, IConsumer, IPushProducer, ISSLTransport
)
from twisted.internet.protocol import Protocol
from twisted.logger import Logger
from twisted.protocols.policies import TimeoutMixin
from twisted.python.failure import Failure
from zope.interface import implementer
# This API is currently considered private.
__all__ = []
_END_STREAM_SENTINEL = object()
# Python versions 2.7.3 and older don't have a memoryview object that plays
# well with the struct module, which h2 needs. On those versions, just refuse
# to import.
if sys.version_info < (2, 7, 4):
warnings.warn(
"HTTP/2 cannot be enabled because this version of Python is too "
"old, and does not fully support memoryview objects.",
UserWarning,
stacklevel=2,
)
raise ImportError("HTTP/2 not supported on this Python version.")
@implementer(IProtocol, IPushProducer)
class H2Connection(Protocol, TimeoutMixin):
"""
A class representing a single HTTP/2 connection.
This implementation of L{IProtocol} works hand in hand with L{H2Stream}.
This is because we have the requirement to register multiple producers for
a single HTTP/2 connection, one for each stream. The standard Twisted
interfaces don't really allow for this, so instead there's a custom
interface between the two objects that allows them to work hand-in-hand here.
@ivar conn: The HTTP/2 connection state machine.
@type conn: L{h2.connection.H2Connection}
@ivar streams: A mapping of stream IDs to L{H2Stream} objects, used to call
specific methods on streams when events occur.
@type streams: L{dict}, mapping L{int} stream IDs to L{H2Stream} objects.
@ivar priority: A HTTP/2 priority tree used to ensure that responses are
prioritised appropriately.
@type priority: L{priority.PriorityTree}
@ivar _consumerBlocked: A flag tracking whether or not the L{IConsumer}
that is consuming this data has asked us to stop producing.
@type _consumerBlocked: L{bool}
@ivar _sendingDeferred: A L{Deferred} used to restart the data-sending loop
when more response data has been produced. Will not be present if there
is outstanding data still to send.
@type _consumerBlocked: A L{twisted.internet.defer.Deferred}, or L{None}
@ivar _outboundStreamQueues: A map of stream IDs to queues, used to store
data blocks that are yet to be sent on the connection. These are used
both to handle producers that do not respect L{IConsumer} but also to
allow priority to multiplex data appropriately.
@type _outboundStreamQueues: A L{dict} mapping L{int} stream IDs to
L{collections.deque} queues, which contain either L{bytes} objects or
C{_END_STREAM_SENTINEL}.
@ivar _sender: A handle to the data-sending loop, allowing it to be
terminated if needed.
@type _sender: L{twisted.internet.task.LoopingCall}
@ivar abortTimeout: The number of seconds to wait after we attempt to shut
the transport down cleanly to give up and forcibly terminate it. This
is only used when we time a connection out, to prevent errors causing
the FD to get leaked. If this is L{None}, we will wait forever.
@type abortTimeout: L{int}
@ivar _abortingCall: The L{twisted.internet.base.DelayedCall} that will be
used to forcibly close the transport if it doesn't close cleanly.
@type _abortingCall: L{twisted.internet.base.DelayedCall}
"""
factory = None
site = None
abortTimeout = 15
_log = Logger()
_abortingCall = None
def __init__(self, reactor=None):
config = h2.config.H2Configuration(
client_side=False, header_encoding=None
)
self.conn = h2.connection.H2Connection(config=config)
self.streams = {}
self.priority = priority.PriorityTree()
self._consumerBlocked = None
self._sendingDeferred = None
self._outboundStreamQueues = {}
self._streamCleanupCallbacks = {}
self._stillProducing = True
if reactor is None:
from twisted.internet import reactor
self._reactor = reactor
# Start the data sending function.
self._reactor.callLater(0, self._sendPrioritisedData)
# Implementation of IProtocol
def connectionMade(self):
"""
Called by the reactor when a connection is received. May also be called
by the L{twisted.web.http._GenericHTTPChannelProtocol} during upgrade
to HTTP/2.
"""
self.setTimeout(self.timeOut)
self.conn.initiate_connection()
self.transport.write(self.conn.data_to_send())
def dataReceived(self, data):
"""
Called whenever a chunk of data is received from the transport.
@param data: The data received from the transport.
@type data: L{bytes}
"""
self.resetTimeout()
try:
events = self.conn.receive_data(data)
except h2.exceptions.ProtocolError:
# A remote protocol error terminates the connection.
dataToSend = self.conn.data_to_send()
self.transport.write(dataToSend)
self.transport.loseConnection()
self.connectionLost(Failure())
return
for event in events:
if isinstance(event, h2.events.RequestReceived):
self._requestReceived(event)
elif isinstance(event, h2.events.DataReceived):
self._requestDataReceived(event)
elif isinstance(event, h2.events.StreamEnded):
self._requestEnded(event)
elif isinstance(event, h2.events.StreamReset):
self._requestAborted(event)
elif isinstance(event, h2.events.WindowUpdated):
self._handleWindowUpdate(event)
elif isinstance(event, h2.events.PriorityUpdated):
self._handlePriorityUpdate(event)
elif isinstance(event, h2.events.ConnectionTerminated):
self.transport.loseConnection()
self.connectionLost(ConnectionLost("Remote peer sent GOAWAY"))
dataToSend = self.conn.data_to_send()
if dataToSend:
self.transport.write(dataToSend)
def timeoutConnection(self):
"""
Called when the connection has been inactive for
L{self.timeOut<twisted.protocols.policies.TimeoutMixin.timeOut>}
seconds. Cleanly tears the connection down, attempting to notify the
peer if needed.
We override this method to add two extra bits of functionality:
- We want to log the timeout.
- We want to send a GOAWAY frame indicating that the connection is
being terminated, and whether it was clean or not. We have to do this
before the connection is torn down.
"""
self._log.info(
"Timing out client {client}", client=self.transport.getPeer()
)
# Check whether there are open streams. If there are, we're going to
# want to use the error code PROTOCOL_ERROR. If there aren't, use
# NO_ERROR.
if (self.conn.open_outbound_streams > 0 or
self.conn.open_inbound_streams > 0):
error_code = h2.errors.ErrorCodes.PROTOCOL_ERROR
else:
error_code = h2.errors.ErrorCodes.NO_ERROR
self.conn.close_connection(error_code=error_code)
self.transport.write(self.conn.data_to_send())
# Don't let the client hold this connection open too long.
if self.abortTimeout is not None:
# We use self.callLater because that's what TimeoutMixin does, even
# though we have a perfectly good reactor sitting around. See
# https://twistedmatrix.com/trac/ticket/8488.
self._abortingCall = self.callLater(
self.abortTimeout, self.forceAbortClient
)
# We're done, throw the connection away.
self.transport.loseConnection()
def forceAbortClient(self):
"""
Called if C{abortTimeout} seconds have passed since the timeout fired,
and the connection still hasn't gone away. This can really only happen
on extremely bad connections or when clients are maliciously attempting
to keep connections open.
"""
self._log.info(
"Forcibly timing out client: {client}",
client=self.transport.getPeer()
)
# We want to lose track of the _abortingCall so that no-one tries to
# cancel it.
self._abortingCall = None
self.transport.abortConnection()
def connectionLost(self, reason):
"""
Called when the transport connection is lost.
Informs all outstanding response handlers that the connection has been
lost, and cleans up all internal state.
"""
self._stillProducing = False
self.setTimeout(None)
for stream in self.streams.values():
stream.connectionLost(reason)
for streamID in list(self.streams.keys()):
self._requestDone(streamID)
# If we were going to force-close the transport, we don't have to now.
if self._abortingCall is not None:
self._abortingCall.cancel()
self._abortingCall = None
# Implementation of IPushProducer
#
# Here's how we handle IPushProducer. We have multiple outstanding
# H2Streams. Each of these exposes an IConsumer interface to the response
# handler that allows it to push data into the H2Stream. The H2Stream then
# writes the data into the H2Connection object.
#
# The H2Connection needs to manage these writes to account for:
#
# - flow control
# - priority
#
# We manage each of these in different ways.
#
# For flow control, we simply use the equivalent of the IPushProducer
# interface. We simply tell the H2Stream: "Hey, you can't send any data
# right now, sorry!". When that stream becomes unblocked, we free it up
# again. This allows the H2Stream to propagate this backpressure up the
# chain.
#
# For priority, we need to keep a backlog of data frames that we can send,
# and interleave them appropriately. This backlog is most sensibly kept in
# the H2Connection object itself. We keep one queue per stream, which is
# where the writes go, and then we have a loop that manages popping these
# streams off in priority order.
#
# Logically then, we go as follows:
#
# 1. Stream calls writeDataToStream(). This causes a DataFrame to be placed
# on the queue for that stream. It also informs the priority
# implementation that this stream is unblocked.
# 2. The _sendPrioritisedData() function spins in a tight loop. Each
# iteration it asks the priority implementation which stream should send
# next, and pops a data frame off that stream's queue. If, after sending
# that frame, there is no data left on that stream's queue, the function
# informs the priority implementation that the stream is blocked.
#
# If all streams are blocked, or if there are no outstanding streams, the
# _sendPrioritisedData function waits to be awoken when more data is ready
# to send.
#
# Note that all of this only applies to *data*. Headers and other control
# frames deliberately skip this processing as they are not subject to flow
# control or priority constraints.
def stopProducing(self):
"""
Stop producing data.
This tells the L{H2Connection} that its consumer has died, so it must
stop producing data for good.
"""
self.connectionLost(ConnectionLost("Producing stopped"))
def pauseProducing(self):
"""
Pause producing data.
Tells the L{H2Connection} that it has produced too much data to process
for the time being, and to stop until resumeProducing() is called.
"""
self._consumerBlocked = Deferred()
def resumeProducing(self):
"""
Resume producing data.
This tells the L{H2Connection} to re-add itself to the main loop and
produce more data for the consumer.
"""
if self._consumerBlocked is not None:
d = self._consumerBlocked
self._consumerBlocked = None
d.callback(None)
def _sendPrioritisedData(self, *args):
"""
The data sending loop. This function repeatedly calls itself, either
from L{Deferred}s or from
L{reactor.callLater<twisted.internet.interfaces.IReactorTime.callLater>}
This function sends data on streams according to the rules of HTTP/2
priority. It ensures that the data from each stream is interleved
according to the priority signalled by the client, making sure that the
connection is used with maximal efficiency.
This function will execute if data is available: if all data is
exhausted, the function will place a deferred onto the L{H2Connection}
object and wait until it is called to resume executing.
"""
# If producing has stopped, we're done. Don't reschedule ourselves
if not self._stillProducing:
return
stream = None
while stream is None:
try:
stream = next(self.priority)
except priority.DeadlockError:
# All streams are currently blocked or not progressing. Wait
# until a new one becomes available.
assert self._sendingDeferred is None
self._sendingDeferred = Deferred()
self._sendingDeferred.addCallback(self._sendPrioritisedData)
return
# Wait behind the transport.
if self._consumerBlocked is not None:
self._consumerBlocked.addCallback(self._sendPrioritisedData)
return
self.resetTimeout()
remainingWindow = self.conn.local_flow_control_window(stream)
frameData = self._outboundStreamQueues[stream].popleft()
maxFrameSize = min(self.conn.max_outbound_frame_size, remainingWindow)
if frameData is _END_STREAM_SENTINEL:
# There's no error handling here even though this can throw
# ProtocolError because we really shouldn't encounter this problem.
# If we do, that's a nasty bug.
self.conn.end_stream(stream)
self.transport.write(self.conn.data_to_send())
# Clean up the stream
self._requestDone(stream)
else:
# Respect the max frame size.
if len(frameData) > maxFrameSize:
excessData = frameData[maxFrameSize:]
frameData = frameData[:maxFrameSize]
self._outboundStreamQueues[stream].appendleft(excessData)
# There's deliberately no error handling here, because this just
# absolutely should not happen.
# If for whatever reason the max frame length is zero and so we
# have no frame data to send, don't send any.
if frameData:
self.conn.send_data(stream, frameData)
self.transport.write(self.conn.data_to_send())
# If there's no data left, this stream is now blocked.
if not self._outboundStreamQueues[stream]:
self.priority.block(stream)
# Also, if the stream's flow control window is exhausted, tell it
# to stop.
if self.remainingOutboundWindow(stream) <= 0:
self.streams[stream].flowControlBlocked()
self._reactor.callLater(0, self._sendPrioritisedData)
# Internal functions.
def _requestReceived(self, event):
"""
Internal handler for when a request has been received.
@param event: The Hyper-h2 event that encodes information about the
received request.
@type event: L{h2.events.RequestReceived}
"""
stream = H2Stream(
event.stream_id,
self, event.headers,
self.requestFactory,
self.site,
self.factory
)
self.streams[event.stream_id] = stream
self._streamCleanupCallbacks[event.stream_id] = Deferred()
self._outboundStreamQueues[event.stream_id] = deque()
# Add the stream to the priority tree but immediately block it.
try:
self.priority.insert_stream(event.stream_id)
except priority.DuplicateStreamError:
# Stream already in the tree. This can happen if we received a
# PRIORITY frame before a HEADERS frame. Just move on: we set the
# stream up properly in _handlePriorityUpdate.
pass
else:
self.priority.block(event.stream_id)
def _requestDataReceived(self, event):
"""
Internal handler for when a chunk of data is received for a given
request.
@param event: The Hyper-h2 event that encodes information about the
received data.
@type event: L{h2.events.DataReceived}
"""
stream = self.streams[event.stream_id]
stream.receiveDataChunk(event.data, event.flow_controlled_length)
def _requestEnded(self, event):
"""
Internal handler for when a request is complete, and we expect no
further data for that request.
@param event: The Hyper-h2 event that encodes information about the
completed stream.
@type event: L{h2.events.StreamEnded}
"""
stream = self.streams[event.stream_id]
stream.requestComplete()
def _requestAborted(self, event):
"""
Internal handler for when a request is aborted by a remote peer.
@param event: The Hyper-h2 event that encodes information about the
reset stream.
@type event: L{h2.events.StreamReset}
"""
stream = self.streams[event.stream_id]
stream.connectionLost(
ConnectionLost("Stream reset with code %s" % event.error_code)
)
self._requestDone(event.stream_id)
def _handlePriorityUpdate(self, event):
"""
Internal handler for when a stream priority is updated.
@param event: The Hyper-h2 event that encodes information about the
stream reprioritization.
@type event: L{h2.events.PriorityUpdated}
"""
try:
self.priority.reprioritize(
stream_id=event.stream_id,
depends_on=event.depends_on or None,
weight=event.weight,
exclusive=event.exclusive,
)
except priority.MissingStreamError:
# A PRIORITY frame arrived before the HEADERS frame that would
# trigger us to insert the stream into the tree. That's fine: we
# can create the stream here and mark it as blocked.
self.priority.insert_stream(
stream_id=event.stream_id,
depends_on=event.depends_on or None,
weight=event.weight,
exclusive=event.exclusive,
)
self.priority.block(event.stream_id)
def writeHeaders(self, version, code, reason, headers, streamID):
"""
Called by L{twisted.web.http.Request} objects to write a complete set
of HTTP headers to a stream.
@param version: The HTTP version in use. Unused in HTTP/2.
@type version: L{bytes}
@param code: The HTTP status code to write.
@type code: L{bytes}
@param reason: The HTTP reason phrase to write. Unused in HTTP/2.
@type reason: L{bytes}
@param headers: The headers to write to the stream.
@type headers: L{twisted.web.http_headers.Headers}
@param streamID: The ID of the stream to write the headers to.
@type streamID: L{int}
"""
headers.insert(0, (b':status', code))
try:
self.conn.send_headers(streamID, headers)
except h2.exceptions.StreamClosedError:
# Stream was closed by the client at some point. We need to not
# explode here: just swallow the error. That's what write() does
# when a connection is lost, so that's what we do too.
return
else:
self.transport.write(self.conn.data_to_send())
def writeDataToStream(self, streamID, data):
"""
May be called by L{H2Stream} objects to write response data to a given
stream. Writes a single data frame.
@param streamID: The ID of the stream to write the data to.
@type streamID: L{int}
@param data: The data chunk to write to the stream.
@type data: L{bytes}
"""
self._outboundStreamQueues[streamID].append(data)
# There's obviously no point unblocking this stream and the sending
# loop if the data can't actually be sent, so confirm that there's
# some room to send data.
if self.conn.local_flow_control_window(streamID) > 0:
self.priority.unblock(streamID)
if self._sendingDeferred is not None:
d = self._sendingDeferred
self._sendingDeferred = None
d.callback(streamID)
if self.remainingOutboundWindow(streamID) <= 0:
self.streams[streamID].flowControlBlocked()
def endRequest(self, streamID):
"""
Called by L{H2Stream} objects to signal completion of a response.
@param streamID: The ID of the stream to write the data to.
@type streamID: L{int}
"""
self._outboundStreamQueues[streamID].append(_END_STREAM_SENTINEL)
self.priority.unblock(streamID)
if self._sendingDeferred is not None:
d = self._sendingDeferred
self._sendingDeferred = None
d.callback(streamID)
def abortRequest(self, streamID):
"""
Called by L{H2Stream} objects to request early termination of a stream.
This emits a RstStream frame and then removes all stream state.
@param streamID: The ID of the stream to write the data to.
@type streamID: L{int}
"""
self.conn.reset_stream(streamID)
self.transport.write(self.conn.data_to_send())
self._requestDone(streamID)
def _requestDone(self, streamID):
"""
Called internally by the data sending loop to clean up state that was
being used for the stream. Called when the stream is complete.
@param streamID: The ID of the stream to clean up state for.
@type streamID: L{int}
"""
del self._outboundStreamQueues[streamID]
self.priority.remove_stream(streamID)
del self.streams[streamID]
cleanupCallback = self._streamCleanupCallbacks.pop(streamID)
cleanupCallback.callback(streamID)
def remainingOutboundWindow(self, streamID):
"""
Called to determine how much room is left in the send window for a
given stream. Allows us to handle blocking and unblocking producers.
@param streamID: The ID of the stream whose flow control window we'll
check.
@type streamID: L{int}
@return: The amount of room remaining in the send window for the given
stream, including the data queued to be sent.
@rtype: L{int}
"""
# TODO: This involves a fair bit of looping and computation for
# something that is called a lot. Consider caching values somewhere.
windowSize = self.conn.local_flow_control_window(streamID)
sendQueue = self._outboundStreamQueues[streamID]
alreadyConsumed = sum(
len(chunk) for chunk in sendQueue
if chunk is not _END_STREAM_SENTINEL
)
return windowSize - alreadyConsumed
def _handleWindowUpdate(self, event):
"""
Manage flow control windows.
Streams that are blocked on flow control will register themselves with
the connection. This will fire deferreds that wake those streams up and
allow them to continue processing.
@param event: The Hyper-h2 event that encodes information about the
flow control window change.
@type event: L{h2.events.WindowUpdated}
"""
streamID = event.stream_id
if streamID:
if not self._streamIsActive(streamID):
# We may have already cleaned up our stream state, making this
# a late WINDOW_UPDATE frame. That's fine: the update is
# unnecessary but benign. We'll ignore it.
return
# If we haven't got any data to send, don't unblock the stream. If
# we do, we'll eventually get an exception inside the
# _sendPrioritisedData loop some time later.
if self._outboundStreamQueues.get(streamID):
self.priority.unblock(streamID)
self.streams[streamID].windowUpdated()
else:
# Update strictly applies to all streams.
for stream in self.streams.values():
stream.windowUpdated()
# If we still have data to send for this stream, unblock it.
if self._outboundStreamQueues.get(stream.streamID):
self.priority.unblock(stream.streamID)
def getPeer(self):
"""
Get the remote address of this connection.
Treat this method with caution. It is the unfortunate result of the
CGI and Jabber standards, but should not be considered reliable for
the usual host of reasons; port forwarding, proxying, firewalls, IP
masquerading, etc.
@return: An L{IAddress} provider.
"""
return self.transport.getPeer()
def getHost(self):
"""
Similar to getPeer, but returns an address describing this side of the
connection.
@return: An L{IAddress} provider.
"""
return self.transport.getHost()
def openStreamWindow(self, streamID, increment):
"""
Open the stream window by a given increment.
@param streamID: The ID of the stream whose window needs to be opened.
@type streamID: L{int}
@param increment: The amount by which the stream window must be
incremented.
@type increment: L{int}
"""
self.conn.acknowledge_received_data(increment, streamID)
data = self.conn.data_to_send()
if data:
self.transport.write(data)
def _isSecure(self):
"""
Returns L{True} if this channel is using a secure transport.
@returns: L{True} if this channel is secure.
@rtype: L{bool}
"""
# A channel is secure if its transport is ISSLTransport.
return ISSLTransport(self.transport, None) is not None
def _send100Continue(self, streamID):
"""
Sends a 100 Continue response, used to signal to clients that further
processing will be performed.
@param streamID: The ID of the stream that needs the 100 Continue
response
@type streamID: L{int}
"""
headers = [(b':status', b'100')]
self.conn.send_headers(headers=headers, stream_id=streamID)
self.transport.write(self.conn.data_to_send())
def _respondToBadRequestAndDisconnect(self, streamID):
"""
This is a quick and dirty way of responding to bad requests.
As described by HTTP standard we should be patient and accept the
whole request from the client before sending a polite bad request
response, even in the case when clients send tons of data.
Unlike in the HTTP/1.1 case, this does not actually disconnect the
underlying transport: there's no need. This instead just sends a 400
response and terminates the stream.
@param streamID: The ID of the stream that needs the 100 Continue
response
@type streamID: L{int}
"""
headers = [(b':status', b'400')]
self.conn.send_headers(
headers=headers,
stream_id=streamID,
end_stream=True
)
self.transport.write(self.conn.data_to_send())
stream = self.streams[streamID]
stream.connectionLost(ConnectionLost("Invalid request"))
self._requestDone(streamID)
def _streamIsActive(self, streamID):
"""
Checks whether Twisted has still got state for a given stream and so
can process events for that stream.
@param streamID: The ID of the stream that needs processing.
@type streamID: L{int}
@return: Whether the stream still has state allocated.
@rtype: L{bool}
"""
return streamID in self.streams
@implementer(ITransport, IConsumer, IPushProducer)
class H2Stream(object):
"""
A class representing a single HTTP/2 stream.
This class works hand-in-hand with L{H2Connection}. It acts to provide an
implementation of L{ITransport}, L{IConsumer}, and L{IProducer} that work
for a single HTTP/2 connection, while tightly cleaving to the interface
provided by those interfaces. It does this by having a tight coupling to
L{H2Connection}, which allows associating many of the functions of
L{ITransport}, L{IConsumer}, and L{IProducer} to objects on a
stream-specific level.
@ivar streamID: The numerical stream ID that this object corresponds to.
@type streamID: L{int}
@ivar producing: Whether this stream is currently allowed to produce data
to its consumer.
@type producing: L{bool}
@ivar command: The HTTP verb used on the request.
@type command: L{unicode}
@ivar path: The HTTP path used on the request.
@type path: L{unicode}
@ivar producer: The object producing the response, if any.
@type producer: L{IProducer}
@ivar site: The L{twisted.web.server.Site} object this stream belongs to,
if any.
@type site: L{twisted.web.server.Site}
@ivar factory: The L{twisted.web.http.HTTPFactory} object that constructed
this stream's parent connection.
@type factory: L{twisted.web.http.HTTPFactory}
@ivar _producerProducing: Whether the producer stored in producer is
currently producing data.
@type _producerProducing: L{bool}
@ivar _inboundDataBuffer: Any data that has been received from the network
but has not yet been received by the consumer.
@type _inboundDataBuffer: A L{collections.deque} containing L{bytes}
@ivar _conn: A reference to the connection this stream belongs to.
@type _conn: L{H2Connection}
@ivar _request: A request object that this stream corresponds to.
@type _request: L{twisted.web.iweb.IRequest}
@ivar _buffer: A buffer containing data produced by the producer that could
not be sent on the network at this time.
@type _buffer: L{io.BytesIO}
"""
# We need a transport property for t.w.h.Request, but HTTP/2 doesn't want
# to expose it. So we just set it to None.
transport = None
def __init__(self, streamID, connection, headers,
requestFactory, site, factory):
"""
Initialize this HTTP/2 stream.
@param streamID: The numerical stream ID that this object corresponds
to.
@type streamID: L{int}
@param connection: The HTTP/2 connection this stream belongs to.
@type connection: L{H2Connection}
@param headers: The HTTP/2 request headers.
@type headers: A L{list} of L{tuple}s of header name and header value,
both as L{bytes}.
@param requestFactory: A function that builds appropriate request
request objects.
@type requestFactory: A callable that returns a
L{twisted.web.iweb.IRequest}.
@param site: The L{twisted.web.server.Site} object this stream belongs
to, if any.
@type site: L{twisted.web.server.Site}
@param factory: The L{twisted.web.http.HTTPFactory} object that
constructed this stream's parent connection.
@type factory: L{twisted.web.http.HTTPFactory}
"""
self.streamID = streamID
self.site = site
self.factory = factory
self.producing = True
self.command = None
self.path = None
self.producer = None
self._producerProducing = False
self._hasStreamingProducer = None
self._inboundDataBuffer = deque()
self._conn = connection
self._request = requestFactory(self, queued=False)
self._buffer = io.BytesIO()
self._convertHeaders(headers)
def _convertHeaders(self, headers):
"""
This method converts the HTTP/2 header set into something that looks
like HTTP/1.1. In particular, it strips the 'special' headers and adds
a Host: header.
@param headers: The HTTP/2 header set.
@type headers: A L{list} of L{tuple}s of header name and header value,
both as L{bytes}.
"""
gotLength = False
for header in headers:
if not header[0].startswith(b':'):
gotLength = (
_addHeaderToRequest(self._request, header) or gotLength
)
elif header[0] == b':method':
self.command = header[1]
elif header[0] == b':path':
self.path = header[1]
elif header[0] == b':authority':
# This is essentially the Host: header from HTTP/1.1
_addHeaderToRequest(self._request, (b'host', header[1]))
if not gotLength:
if self.command in (b'GET', b'HEAD'):
self._request.gotLength(0)
else:
self._request.gotLength(None)
self._request.parseCookies()
expectContinue = self._request.requestHeaders.getRawHeaders(b'expect')
if expectContinue and expectContinue[0].lower() == b'100-continue':
self._send100Continue()
# Methods called by the H2Connection
def receiveDataChunk(self, data, flowControlledLength):
"""
Called when the connection has received a chunk of data from the
underlying transport. If the stream has been registered with a
consumer, and is currently able to push data, immediately passes it
through. Otherwise, buffers the chunk until we can start producing.
@param data: The chunk of data that was received.
@type data: L{bytes}
@param flowControlledLength: The total flow controlled length of this
chunk, which is used when we want to re-open the window. May be
different to C{len(data)}.
@type flowControlledLength: L{int}
"""
if not self.producing:
# Buffer data.
self._inboundDataBuffer.append((data, flowControlledLength))
else:
self._request.handleContentChunk(data)
self._conn.openStreamWindow(self.streamID, flowControlledLength)
def requestComplete(self):
"""
Called by the L{H2Connection} when the all data for a request has been
received. Currently, with the legacy L{twisted.web.http.Request}
object, just calls requestReceived unless the producer wants us to be
quiet.
"""
if self.producing:
self._request.requestReceived(self.command, self.path, b'HTTP/2')
else:
self._inboundDataBuffer.append((_END_STREAM_SENTINEL, None))
def connectionLost(self, reason):
"""
Called by the L{H2Connection} when a connection is lost or a stream is
reset.
@param reason: The reason the connection was lost.
@type reason: L{str}
"""
self._request.connectionLost(reason)
def windowUpdated(self):
"""
Called by the L{H2Connection} when this stream's flow control window
has been opened.
"""
# If we don't have a producer, we have no-one to tell.
if not self.producer:
return
# If we're not blocked on flow control, we don't care.
if self._producerProducing:
return
# We check whether the stream's flow control window is actually above
# 0, and then, if a producer is registered and we still have space in
# the window, we unblock it.
remainingWindow = self._conn.remainingOutboundWindow(self.streamID)
if not remainingWindow > 0:
return
# We have a producer and space in the window, so that producer can
# start producing again!
self._producerProducing = True
self.producer.resumeProducing()
def flowControlBlocked(self):
"""
Called by the L{H2Connection} when this stream's flow control window
has been exhausted.
"""
if not self.producer:
return
if self._producerProducing:
self.producer.pauseProducing()
self._producerProducing = False
# Methods called by the consumer (usually an IRequest).
def writeHeaders(self, version, code, reason, headers):
"""
Called by the consumer to write headers to the stream.
@param version: The HTTP version.
@type version: L{bytes}
@param code: The status code.
@type code: L{int}
@param reason: The reason phrase. Ignored in HTTP/2.
@type reason: L{bytes}
@param headers: The HTTP response headers.
@type: Any iterable of two-tuples of L{bytes}, representing header
names and header values.
"""
self._conn.writeHeaders(version, code, reason, headers, self.streamID)
def requestDone(self, request):
"""
Called by a consumer to clean up whatever permanent state is in use.
@param request: The request calling the method.
@type request: L{twisted.web.iweb.IRequest}
"""
self._conn.endRequest(self.streamID)
def _send100Continue(self):
"""
Sends a 100 Continue response, used to signal to clients that further
processing will be performed.
"""
self._conn._send100Continue(self.streamID)
def _respondToBadRequestAndDisconnect(self):
"""
This is a quick and dirty way of responding to bad requests.
As described by HTTP standard we should be patient and accept the
whole request from the client before sending a polite bad request
response, even in the case when clients send tons of data.
Unlike in the HTTP/1.1 case, this does not actually disconnect the
underlying transport: there's no need. This instead just sends a 400
response and terminates the stream.
"""
self._conn._respondToBadRequestAndDisconnect(self.streamID)
# Implementation: ITransport
def write(self, data):
"""
Write a single chunk of data into a data frame.
@param data: The data chunk to send.
@type data: L{bytes}
"""
self._conn.writeDataToStream(self.streamID, data)
return
def writeSequence(self, iovec):
"""
Write a sequence of chunks of data into data frames.
@param iovec: A sequence of chunks to send.
@type iovec: An iterable of L{bytes} chunks.
"""
for chunk in iovec:
self.write(chunk)
def loseConnection(self):
"""
Close the connection after writing all pending data.
"""
self._conn.endRequest(self.streamID)
def abortConnection(self):
"""
Forcefully abort the connection by sending a RstStream frame.
"""
self._conn.abortRequest(self.streamID)
def getPeer(self):
"""
Get information about the peer.
"""
return self._conn.getPeer()
def getHost(self):
"""
Similar to getPeer, but for this side of the connection.
"""
return self._conn.getHost()
def isSecure(self):
"""
Returns L{True} if this channel is using a secure transport.
@returns: L{True} if this channel is secure.
@rtype: L{bool}
"""
return self._conn._isSecure()
# Implementation: IConsumer
def registerProducer(self, producer, streaming):
"""
Register to receive data from a producer.
This sets self to be a consumer for a producer. When this object runs
out of data (as when a send(2) call on a socket succeeds in moving the
last data from a userspace buffer into a kernelspace buffer), it will
ask the producer to resumeProducing().
For L{IPullProducer} providers, C{resumeProducing} will be called once
each time data is required.
For L{IPushProducer} providers, C{pauseProducing} will be called
whenever the write buffer fills up and C{resumeProducing} will only be
called when it empties.
@param producer: The producer to register.
@type producer: L{IProducer} provider
@param streaming: L{True} if C{producer} provides L{IPushProducer},
L{False} if C{producer} provides L{IPullProducer}.
@type streaming: L{bool}
@raise RuntimeError: If a producer is already registered.
@return: L{None}
"""
if self.producer:
raise ValueError(
"registering producer %s before previous one (%s) was "
"unregistered" % (producer, self.producer))
if not streaming:
self.hasStreamingProducer = False
producer = _PullToPush(producer, self)
producer.startStreaming()
else:
self.hasStreamingProducer = True
self.producer = producer
self._producerProducing = True
def unregisterProducer(self):
"""
@see: L{IConsumer.unregisterProducer}
"""
# When the producer is unregistered, we're done.
if self.producer is not None and not self.hasStreamingProducer:
self.producer.stopStreaming()
self._producerProducing = False
self.producer = None
self.hasStreamingProducer = None
# Implementation: IPushProducer
def stopProducing(self):
"""
@see: L{IProducer.stopProducing}
"""
self.producing = False
self.abortConnection()
def pauseProducing(self):
"""
@see: L{IPushProducer.pauseProducing}
"""
self.producing = False
def resumeProducing(self):
"""
@see: L{IPushProducer.resumeProducing}
"""
self.producing = True
consumedLength = 0
while self.producing and self._inboundDataBuffer:
# Allow for pauseProducing to be called in response to a call to
# resumeProducing.
chunk, flowControlledLength = self._inboundDataBuffer.popleft()
if chunk is _END_STREAM_SENTINEL:
self.requestComplete()
else:
consumedLength += flowControlledLength
self._request.handleContentChunk(chunk)
self._conn.openStreamWindow(self.streamID, consumedLength)
def _addHeaderToRequest(request, header):
"""
Add a header tuple to a request header object.
@param request: The request to add the header tuple to.
@type request: L{twisted.web.http.Request}
@param header: The header tuple to add to the request.
@type header: A L{tuple} with two elements, the header name and header
value, both as L{bytes}.
@return: If the header being added was the C{Content-Length} header.
@rtype: L{bool}
"""
requestHeaders = request.requestHeaders
name, value = header
values = requestHeaders.getRawHeaders(name)
if values is not None:
values.append(value)
else:
requestHeaders.setRawHeaders(name, [value])
if name == b'content-length':
request.gotLength(int(value))
return True
return False
| [
"[email protected]"
]
| |
8d4588530f69c619168a4cc1e6f9fb07ba1e6326 | d2c4934325f5ddd567963e7bd2bdc0673f92bc40 | /tests/artificial/transf_RelativeDifference/trend_Lag1Trend/cycle_12/ar_12/test_artificial_128_RelativeDifference_Lag1Trend_12_12_20.py | b92931e266c853cfe294b5ace5bc7d11ca7edc8c | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference"
]
| permissive | jmabry/pyaf | 797acdd585842474ff4ae1d9db5606877252d9b8 | afbc15a851a2445a7824bf255af612dc429265af | refs/heads/master | 2020-03-20T02:14:12.597970 | 2018-12-17T22:08:11 | 2018-12-17T22:08:11 | 137,104,552 | 0 | 0 | BSD-3-Clause | 2018-12-17T22:08:12 | 2018-06-12T17:15:43 | Python | UTF-8 | Python | false | false | 280 | py | import pyaf.Bench.TS_datasets as tsds
import pyaf.tests.artificial.process_artificial_dataset as art
art.process_dataset(N = 128 , FREQ = 'D', seed = 0, trendtype = "Lag1Trend", cycle_length = 12, transform = "RelativeDifference", sigma = 0.0, exog_count = 20, ar_order = 12); | [
"[email protected]"
]
| |
89474e153defaaa9938f24de4429e92defcd0542 | d723b9c2dcfc9e3366928fd0ea18ee5ee19c2b3c | /backend/apps/detections/upload_sets.py | 140c2d9bcfbee0781d15cc18a2aab00c879d9188 | []
| no_license | skarzi/yb_hackathon_2019 | ff8266e89ae6fa74d57c61e4117d6fc176dba825 | 83c3d96795f6b14f97683ad5c998579adb3faaf4 | refs/heads/master | 2020-09-11T01:34:55.206979 | 2020-07-19T07:50:16 | 2020-07-19T07:50:16 | 221,895,345 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 219 | py | from flask import current_app
from flask_uploads import (
IMAGES,
UploadSet,
configure_uploads,
)
detections = UploadSet(name='detections', extensions=IMAGES)
configure_uploads(current_app, (detections,))
| [
"[email protected]"
]
| |
71402662a43efd9f3ece9bfc6b5fb824add27987 | c676bf5e77ba43639faa6f17646245f9d55d8687 | /tests/ut/python/ops/test_tuple_slice.py | ea5112995c06203210d7c6ca569e2949187c6f26 | [
"Apache-2.0",
"BSD-3-Clause-Open-MPI",
"MPL-2.0-no-copyleft-exception",
"LGPL-2.1-only",
"BSD-3-Clause",
"MPL-2.0",
"MPL-1.0",
"Libpng",
"AGPL-3.0-only",
"MPL-1.1",
"LicenseRef-scancode-proprietary-license",
"MIT",
"IJG",
"LicenseRef-scancode-unknown-license-reference",
"Unlicense",
"Zlib",
"GPL-2.0-only",
"BSL-1.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause"
]
| permissive | zhengnengjin/mindspore | 1e2644e311f54a8bd17010180198a46499e9c88f | 544b859bb5f46611882749088b44c5aebae0fba1 | refs/heads/master | 2022-05-13T05:34:21.658335 | 2020-04-28T06:39:53 | 2020-04-28T06:39:53 | 259,522,589 | 2 | 0 | Apache-2.0 | 2020-04-28T03:35:33 | 2020-04-28T03:35:33 | null | UTF-8 | Python | false | false | 4,665 | py | # Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
""" test_tuple_slice """
import numpy as np
import pytest
from mindspore import Tensor
from mindspore.nn import Cell
import mindspore.ops.operations as P
from ....mindspore_test_framework.mindspore_test import mindspore_test
from ....mindspore_test_framework.pipeline.forward.compile_forward \
import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
from ....mindspore_test_framework.pipeline.forward.verify_exception \
import pipeline_for_verify_exception_for_case_by_case_config
class NetWork_1(Cell):
""" NetWork_1 definition """
def __init__(self):
super(NetWork_1, self).__init__()
self.addN = P.AddN()
def construct(self, tensor_tuple):
tensor_tuple_slice0 = tensor_tuple[:]
tensor_tuple_slice1 = tensor_tuple[:3]
tensor_tuple_slice2 = tensor_tuple[1:]
tensor_tuple_slice3 = tensor_tuple[2:5:1]
sum0 = self.addN(tensor_tuple_slice0)
sum1 = self.addN(tensor_tuple_slice1)
sum2 = self.addN(tensor_tuple_slice2)
sum3 = self.addN(tensor_tuple_slice3)
ret = sum0 + sum1 + sum2 + sum3
return ret
class NetWork_2(Cell):
""" NetWork_2 definition """
def __init__(self):
super(NetWork_2, self).__init__()
self.addN = P.AddN()
def construct(self, tensor_tuple):
tensor_tuple_slice0 = tensor_tuple[::-1]
tensor_tuple_slice1 = tensor_tuple[-1::-1]
tensor_tuple_slice2 = tensor_tuple[:-4:-1]
tensor_tuple_slice3 = tensor_tuple[-6:3]
tensor_tuple_slice4 = tensor_tuple[-1:-6:-2]
sum0 = self.addN(tensor_tuple_slice0)
sum1 = self.addN(tensor_tuple_slice1)
sum2 = self.addN(tensor_tuple_slice2)
sum3 = self.addN(tensor_tuple_slice3)
sum4 = self.addN(tensor_tuple_slice4)
ret = sum0 + sum1 + sum2 + sum3 + sum4
return ret
class NetWork_3(Cell):
""" NetWork_3 definition """
def __init__(self):
super(NetWork_3, self).__init__()
self.addN = P.AddN()
def construct(self, tensor_tuple, start, stop, step=1):
tensor_tuple_slice0 = tensor_tuple[start:stop:step]
res = self.addN(tensor_tuple_slice0)
return res
test_cases = [
('SlicePositive', {
'block': NetWork_1(),
'desc_inputs': [(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
('SliceNegative', {
'block': NetWork_2(),
'desc_inputs': [(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
]
test_cases_for_verify_exception = [
('SliceStartCross', {
'block': (NetWork_3(), {'exception': RuntimeError}),
'desc_inputs': [*(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
('SliceStepZero', {
'block': (NetWork_3(), {'exception': RuntimeError}),
'desc_inputs': [*(Tensor(np.ones([2, 3, 4], np.int32)),
Tensor(np.zeros([2, 3, 4], np.int32)),
Tensor(np.ones([2, 3, 4], np.int32)))],
}),
]
@mindspore_test(pipeline_for_compile_forward_ge_graph_for_case_by_case_config)
def test_compile():
return test_cases
@mindspore_test(pipeline_for_verify_exception_for_case_by_case_config)
def test_check_exception():
return test_cases_for_verify_exception
| [
"[email protected]"
]
| |
4b4a097a95f1da6da6dfa3927d7c83d66941ecdf | d8f0761acc94f9f1c0365e5a1716c9e17c6e4e16 | /scrapers/bs4_selectors/selector.py | cabcd3ea0bed6889945755aac7fe5cf0cdf9cd8c | []
| no_license | lesleyfon/one-time-scrapers | 75ca851107d59b4f2b7cd816b2ae46ecd11d6bc0 | 6ee5443497c9e05924abf5704c16112beb740064 | refs/heads/master | 2023-05-02T12:58:21.693133 | 2021-05-21T13:09:57 | 2021-05-21T13:09:57 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,990 | py | ##############################
#
# Beautiful Soup cheat sheet
#
# by
#
# Code Monkey King
#
##############################
# Step 1: import packages
import requests
from bs4 import BeautifulSoup
# Step 2: define target URL
url = 'https://podsearch.com/listing/car-talk.html'
# Step 3: make HTTP request to the target URL
response = requests.get(url)
# Step 4: parse entire HTML document
content = BeautifulSoup(response.text, 'lxml')
# Step 5: parse PARENT element conteining needed data
parent = content.find('div', {'class': 'col-md-8 col-sm-12 col-xs-12 pdl0'})
# Step 6: parse CHILD element containing the exact data we need
child = parent.find('span').text
# Step 7: split the target string if needed
data = child.split(': ')[-1]
# Step 8: print data to console
print(data)
#####################################
#
# Useful data extraction techniques
#
#####################################
# extract FIRST data occurence by unique class
description = content.find('p', {'class': 'pre-line'}).text
print('\n', description)
# extract ALL data occurences by unique class
text = [
item.text
for item in
content.find_all('p', {'class': 'pre-line'})
]
print('\n', text)
# reference similar data occurences by index
print('\n', text[0])
print('\n', text[1])
# join list elements into one single string by whatever character
print('\n', '\n joined by new line \n'.join(text))
# reference element by whatever attribute (ID in this case)
button = content.find('button', {'id': 'headerSearchButton'}).text
print(button)
# extract FIRST other but textual node data element, e.g. HREF attribute or whatever
link = content.find('a')['href']
print(link)
# extract ALL other but textual node data elements, e.g. HREF attribute or whatever
links = [
link['href']
for link in
content.find_all('a')
# filter on condition if needed
#if link['href'] == 'https://podsearch.com/listing/rethinking-weight-loss.html'
]
print(links)
| [
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]
| |
3f710f824a9ba3fc05f946ea786168e280edb9f3 | 05040f0dce123be0d88e760808fdf6b1bbf1ac43 | /backend/manage.py | 38f19f1341ecef8ebd95be44a20975de9823d12e | []
| no_license | crowdbotics-apps/mobile-8-dec-dev-16453 | 8026421c163ab200a0106faaf3567faf469b348f | 3cc2feeba0d1a753a98db7167491e5a28d7ae6fe | refs/heads/master | 2023-01-23T10:02:48.817260 | 2020-12-08T15:42:47 | 2020-12-08T15:42:47 | 319,665,608 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 642 | py | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mobile_8_dec_dev_16453.settings")
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == "__main__":
main()
| [
"[email protected]"
]
| |
ec63ed048f6211cd69e8bc2bc40d3e6f418eaf0d | 336cd9225281befde93e01858ede15f70d3e5b47 | /params/cartpole_obs/shm_default copy.py | 2ab5c02726c7c6586d11fff9f5736ea8bffe8c5f | []
| no_license | GuancongLuo/mpc-mpnet-py | 7d6ba9f0c954185a724421091b1b098ec6d148e6 | 3d8d8ef743fd467fd2ffe177021edc6e852fd094 | refs/heads/master | 2023-02-06T03:49:06.072105 | 2020-12-07T11:01:08 | 2020-12-07T11:01:08 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,333 | py | import numpy as np
def get_params():
params = {
'solver_type': "cem",
'n_problem': 1,
'n_sample': 32,
'n_elite': 8,
'n_t': 1,
'max_it': 5,
'converge_r': 0.1,
'dt': 2e-3,
'mu_u': [0],
'sigma_u': [400],
'mu_t': 0.5,
'sigma_t': 0.5,
't_max': 1,
'verbose': False, # True,#
'step_size': 1,
"goal_radius": 1.5,
"sst_delta_near": .3,
"sst_delta_drain": 0.1,
"goal_bias": 0.05,
"width": 4,
"hybrid": False,
"hybrid_p": 0.0,
"cost_samples": 1,
"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_small_model.pt",
#"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_v2_deep.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_nonorm.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_subsample0.5_10k.pt",
# "cost_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_10k.pt",
"cost_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_10k.pt",
"cost_to_go_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_to_go_obs.pt",
"refine": False,
"using_one_step_cost": False,
"refine_lr": 0,
"refine_threshold": 0,
"device_id": "cuda:3",
"cost_reselection": False,
"number_of_iterations": 100000,
"weights_array": [1, 1, 1, 0.5],
'max_planning_time': 50,
'shm_max_steps': 40
}
cuda_batch_params = {
'solver_type' : "cem",
'n_problem' : 1,
'n_sample': 32,
'n_elite': 2,
'n_t': 1,
'max_it': 5,
'converge_r': 1e-1,
'dt': 2e-3,
'mu_u': [0],
'sigma_u': [400],
'mu_t': 0.4,
'sigma_t': 0.5,
't_max': 1,
'verbose': False,#True,#
'step_size': 1,
"goal_radius": 1.5,
"sst_delta_near": .6,
"sst_delta_drain": .3,
"goal_bias": 0.05,
"width": 4,
"hybrid": False,
"hybrid_p": 0.0,
"cost_samples": 5,
"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_small_model.pt",
#"mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_external_v2_deep.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_10k_nonorm.pt",
# "mpnet_weight_path":"mpnet/exported/output/cartpole_obs/mpnet_subsample0.5_10k.pt",
"cost_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_10k.pt",
"cost_to_go_predictor_weight_path": "mpnet/exported/output/cartpole_obs/cost_to_go_obs.pt",
"refine": False,
"using_one_step_cost": False,
"refine_lr": 0.0,
"refine_threshold": 0.0,
"device_id": "cuda:0",
"cost_reselection": False,
"number_of_iterations": 40000,
"weights_array": [1, 1, 1, .5],
'max_planning_time': 50,
'shm_max_steps': 40
}
return cuda_batch_params
| [
"[email protected]"
]
| |
5d8565f123ea80979f9cd6a4454521fd2ddff15c | b0de612c2f7d03399c0d02c5aaf858a72c9ad818 | /armi/nuclearDataIO/cccc/tests/test_rzflux.py | 93771c4e863ba214363f58516bdda65027c1eb5c | [
"GPL-1.0-or-later",
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown",
"Apache-2.0"
]
| permissive | wangcj05/armi | 2007e7abf4b422caca0157fc4405b7f45fc6c118 | 8919afdfce75451b291e45ca1bc2e03c044c2090 | refs/heads/master | 2022-12-22T00:05:47.561722 | 2022-12-13T16:46:57 | 2022-12-13T16:46:57 | 277,868,987 | 0 | 0 | Apache-2.0 | 2020-07-07T16:32:40 | 2020-07-07T16:32:39 | null | UTF-8 | Python | false | false | 2,673 | py | # Copyright 2019 TerraPower, LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Test rzflux reading and writing.
"""
# pylint: disable=missing-function-docstring,missing-class-docstring,protected-access,invalid-name,no-self-use,no-method-argument,import-outside-toplevel
import os
import unittest
from armi.nuclearDataIO.cccc import rzflux
from armi.utils.directoryChangers import TemporaryDirectoryChanger
THIS_DIR = os.path.dirname(__file__)
# This RZFLUX was made by DIF3D 11 in a Cartesian test case.
SIMPLE_RZFLUX = os.path.join(THIS_DIR, "fixtures", "simple_cartesian.rzflux")
class TestRzflux(unittest.TestCase):
"""Tests the rzflux class"""
def test_readRzflux(self):
"""Ensure we can read a RZFLUX file."""
flux = rzflux.readBinary(SIMPLE_RZFLUX)
self.assertEqual(
flux.groupFluxes.shape, (flux.metadata["NGROUP"], flux.metadata["NZONE"])
)
def test_writeRzflux(self):
"""Ensure that we can write a modified RZFLUX file."""
with TemporaryDirectoryChanger():
flux = rzflux.readBinary(SIMPLE_RZFLUX)
rzflux.writeBinary(flux, "RZFLUX2")
self.assertTrue(binaryFilesEqual(SIMPLE_RZFLUX, "RZFLUX2"))
# perturb off-diag item to check row/col ordering
flux.groupFluxes[2, 10] *= 1.1
flux.groupFluxes[12, 1] *= 1.2
rzflux.writeBinary(flux, "RZFLUX3")
flux2 = rzflux.readBinary("RZFLUX3")
self.assertAlmostEqual(flux2.groupFluxes[12, 1], flux.groupFluxes[12, 1])
def test_rwAscii(self):
"""Ensure that we can read/write in ascii format."""
with TemporaryDirectoryChanger():
flux = rzflux.readBinary(SIMPLE_RZFLUX)
rzflux.writeAscii(flux, "RZFLUX.ascii")
flux2 = rzflux.readAscii("RZFLUX.ascii")
self.assertTrue((flux2.groupFluxes == flux.groupFluxes).all())
def binaryFilesEqual(fn1, fn2):
"""True if two files are bytewise identical."""
with open(fn1, "rb") as f1, open(fn2, "rb") as f2:
for byte1, byte2 in zip(f1, f2):
if byte1 != byte2:
return False
return True
| [
"[email protected]"
]
| |
70dd6b6891e4793418f9b327dcf8ddb1de563ef7 | 52b5773617a1b972a905de4d692540d26ff74926 | /.history/clouds_20200703183549.py | deecd69a4f52fe13e3dd7c9a278d545d91b636a2 | []
| no_license | MaryanneNjeri/pythonModules | 56f54bf098ae58ea069bf33f11ae94fa8eedcabc | f4e56b1e4dda2349267af634a46f6b9df6686020 | refs/heads/master | 2022-12-16T02:59:19.896129 | 2020-09-11T12:05:22 | 2020-09-11T12:05:22 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 439 | py | def jumpingClouds(c):
i = 0
jumps = 0
while i < len(c)-2:
if c[i] == 0 and c[i+2] == 0:
print('here')
print('c---->',c[i],'i-->',i)
jumps +=1
i +=2
elif c[i] == 0 and c[i+1] == 0:
print('here2')
print('c---->',c[i],'i-->',i)
jumps +=1
i +=1
print(jumps)
jumpingClouds([0 ,0, 1, 0, 0, 1, 0]) | [
"[email protected]"
]
| |
2dda85a9ba04d01eb6f79efbf26e1aa3f5fe73a8 | b23f9b54f622032e71a80a497ca2d7dbd48469ad | /setup.py | d7560750ca56a0500f4ae92ea7dba81932711c29 | []
| no_license | h4ck3rm1k3/pycparserext | 15cf0a02429f3fd6bad977cd612e74ca7b20b891 | 489fd9c4804e7b3f17760b0800cf81a930a2ec7e | refs/heads/master | 2021-01-21T08:32:39.114178 | 2016-04-03T05:22:38 | 2016-04-03T05:22:38 | 55,293,358 | 0 | 0 | null | 2016-04-02T12:29:40 | 2016-04-02T12:29:40 | null | UTF-8 | Python | false | false | 893 | py | #!/usr/bin/env python
# -*- coding: latin1 -*-
from setuptools import setup
setup(name="pycparserext",
version="2016.1",
description="Extensions for pycparser",
long_description=open("README.rst", "r").read(),
classifiers=[
'Development Status :: 4 - Beta',
'Intended Audience :: Developers',
'Intended Audience :: Other Audience',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: MIT License',
'Natural Language :: English',
'Programming Language :: Python',
'Topic :: Utilities',
],
install_requires=[
"ply>=3.4",
"pycparser>=2.14",
],
author="Andreas Kloeckner",
url="http://pypi.python.org/pypi/pycparserext",
author_email="[email protected]",
license="MIT",
packages=["pycparserext"])
| [
"[email protected]"
]
| |
4bbf1ff6013d7a48ce63d817454fb8940a26487f | 7d23fff61314842d6d7d8ca106382d163a04f139 | /watch/models.py | 3a423333ca2230cec766903c543e4a2e444032de | [
"MIT"
]
| permissive | GeGe-K/Neighbourhood | 8b71bc789a72d34769436a5a912ffde87b3c014b | 366667dff147141558732e5c6f5004fe4cff221e | refs/heads/master | 2022-12-09T18:26:27.536704 | 2019-01-16T14:13:37 | 2019-01-16T14:13:37 | 165,236,886 | 0 | 0 | MIT | 2022-12-08T01:32:31 | 2019-01-11T12:00:41 | Python | UTF-8 | Python | false | false | 3,462 | py | from django.db import models
from django.contrib.auth.models import User
import datetime as dt
# Create your models here.
class Location(models.Model):
name = models.CharField(max_length=40)
def __str__(self):
return self.name
class Neighbourhood(models.Model):
'''
Neighbourhood class has the following properties
'''
neighbourhood_name = models.CharField(max_length = 30)
neighborhood_location = models.ForeignKey('Location', on_delete = models.CASCADE, null = True, blank =True)
occupants = models.IntegerField(null = True)
admin = models.ForeignKey(User, on_delete = models.CASCADE)
def create_neighbourhood(self):
self.save
def delete_neighbourhood(self):
self.delete()
def __str__(self):
return self.neighbourhood_name
@classmethod
def find_neighbourhood(cls, neighbourhood_id):
neighbourhood = cls.objects.get(id = neighbourhood_id)
return neighbourhood
def update_nighbourhood(self):
self.save()
def update_occupants(self):
self.occupants +=1
self.save()
class UserProfile(models.Model):
'''
UserProfile class has the following properties
'''
first_name = models.CharField(max_length=20, blank=True)
last_name = models.CharField(max_length=20,blank=True)
email = models.EmailField()
user = models.ForeignKey(User,on_delete=models.CASCADE)
neighborhood = models.ForeignKey('Neighbourhood', on_delete=models.CASCADE, null=True, blank=True)
def assign_neighbourhood(self, neighbourhood):
self.neighbourhood = neighborhood
self.save()
def save_profile(self):
self.save()
def delete_profile(self):
self.delete()
def __str__(self):
return f'{self.user.username}'
class Business(models.Model):
'''
Business class has the following properties
'''
business_name = models.CharField(max_length = 50)
owner = models.ForeignKey(User, on_delete = models.CASCADE)
business_neighbourhood = models.ForeignKey(
'Neighbourhood', on_delete = models.CASCADE)
email = models.EmailField()
def create_business(self):
self.save()
def delete_business(self):
self.delete()
@classmethod
def find_business(cls, business_id):
business = cls.objects.get(id = business_id)
return business
def update_business(self, business_name):
self.name = business_name
self.save()
def __str__(self):
return self.business_name
class EmergencyContacts(models.Model):
'''
Emergency contact class has the following properties
'''
name = models.CharField(max_length = 30)
contacts = models.CharField(max_length = 20)
email = models.EmailField()
neighbourhood_contact = models.ForeignKey(
'Neighbourhood', on_delete = models.CASCADE)
def __str__(self):
return f'{self.name},{self.email}'
class Post(models.Model):
'''
Post class has the following properties
'''
title = models.CharField(max_length=40)
post_description = models.TextField(blank = True)
posted_by = models.ForeignKey(User, on_delete = models.CASCADE)
post_hood = models.ForeignKey('Neighbourhood', on_delete = models.CASCADE)
posted_on = models.DateTimeField(auto_now_add = True)
def __str__(self):
return f'{self.title},{self.post_hood.neighbourhood_name}'
| [
"[email protected]"
]
| |
3ef2026eb83017aa5c24665674b8d15767fb2008 | 51d8f003828d6ee6e6611f0e133b1e35cf400601 | /ipaxi/ixbr_api/core/tests/use_cases_tests/test_service_use_case.py | 830992ce3f08fe190f0264ea26fd19099f6e8a39 | [
"Apache-2.0"
]
| permissive | tatubola/xpto | 23b5f7a42c13c7d39eb321e52b9b4b2d1ef76c4c | 6ed8cec23b06bccb1edf57e6b67af017f9a162d3 | refs/heads/master | 2020-04-02T11:05:24.560009 | 2018-10-23T17:41:10 | 2018-10-23T17:41:10 | 154,370,519 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,817 | py | from unittest.mock import patch
from django.core.exceptions import ValidationError
from django.test import TestCase
from model_mommy import mommy
from ...models import ContactsMap, MLPAv4, Tag
from ...use_cases.service_use_case import delete_service_use_case
from ..login import DefaultLogin
class ServiceUseCaseTest(TestCase):
def setUp(self):
DefaultLogin.__init__(self)
p = patch('ixbr_api.core.models.HistoricalTimeStampedModel.full_clean')
p.start()
self.addCleanup(p.stop)
p = patch('ixbr_api.core.models.create_all_ips')
p.start()
self.addCleanup(p.stop)
p = patch('ixbr_api.core.models.create_tag_by_channel_port')
p.start()
self.addCleanup(p.stop)
def test_delete_service_use_case(self):
tag = mommy.make(Tag, status='PRODUCTION')
contacts_map = mommy.make(ContactsMap)
service_mlpav4 = mommy.make(MLPAv4, tag=tag, make_m2m=True)
service_mlpav4.asn.contactsmap_set.add(contacts_map)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 1)
delete_service_use_case(pk=service_mlpav4.pk)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 0)
def test_fail_delete_service_use_case(self):
tag = mommy.make(Tag, status='PRODUCTION')
contacts_map = mommy.make(ContactsMap)
service_mlpav4 = mommy.make(MLPAv4, tag=tag, make_m2m=True)
service_mlpav4.asn.contactsmap_set.add(contacts_map)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 1)
with self.assertRaisesMessage(ValidationError, "Invalid service primary key"):
delete_service_use_case(pk=tag.pk)
self.assertEqual(MLPAv4.objects.filter(pk=service_mlpav4.pk).count(), 1)
| [
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]
| |
dca84f844680918ece78d15a17c804d7d4f4dc67 | b7683c108e68ee2d28573edf55923eb34cc2f5ee | /3_Image_Processing/9_Contours/1_Intro/1_Contours_on_binary.py | 9be454d9d82d0531c5524d093ed11ff8b9fa6b0f | []
| no_license | aCuissot/openVC_win_py_tutorial | cc42ab1a1fb6eaefe5a91c7e1bb1926a776b0e01 | 7186b629747cb16f2bf42a03d2339d3dc3ea77bd | refs/heads/master | 2020-05-18T12:17:04.619047 | 2019-07-10T13:45:00 | 2019-07-10T13:45:00 | 184,403,715 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 525 | py | import numpy as np
import cv2 as cv
im = cv.imread('../../../Data/in/a.jpg')
imgray = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
_, thresh = cv.threshold(imgray, 127, 255, 0)
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
cv.drawContours(im, contours, -1, (0, 255, 0), 3)
"""
pour ne dessiner qu'un contour, le 3e par ex:
cv.drawContours(im, contours, 2, (0, 255, 0), 3)
ou
cnt = contours[3]
cv.drawContours(img, [cnt], 0, (0,255,0), 3)
"""
cv.imshow('', im)
cv.waitKey(0)
cv.destroyAllWindows() | [
"[email protected]"
]
| |
5656efc34e8254aae61d10bea0f54846789da243 | 338062cc2bb422f1364fd18ad5e721f6f713907a | /30. Библиотеки Python. Встроенные модули/Классная работа/Дни рождения друзей.py | 901cba0c8631ab75c84e2788ce36d59850346786 | []
| no_license | rady1337/FirstYandexLyceumCourse | f3421d5eac7e7fbea4f5e266ebeb6479b89941cf | 0d27e452eda046ddd487d6471eeb7d9eb475bd39 | refs/heads/master | 2022-06-17T03:07:51.017888 | 2020-05-12T22:17:34 | 2020-05-12T22:17:34 | 263,459,364 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 119 | py | import datetime as dtdin = dt.datetime.now()dn = dt.timedelta(days=int(input()))print((din + dn).day, (din + dn).month) | [
"[email protected]"
]
| |
3da46091f694239b44ce3c59e315748ab9fcae39 | 09efb7c148e82c22ce6cc7a17b5140aa03aa6e55 | /env/lib/python3.6/site-packages/pandas/tests/groupby/test_filters.py | 2ce04fc77408301e12a3a44d30366dafee4d3aad | [
"MIT"
]
| permissive | harryturr/harryturr_garmin_dashboard | 53071a23b267116e1945ae93d36e2a978c411261 | 734e04f8257f9f84f2553efeb7e73920e35aadc9 | refs/heads/master | 2023-01-19T22:10:57.374029 | 2020-01-29T10:47:56 | 2020-01-29T10:47:56 | 235,609,069 | 4 | 0 | MIT | 2023-01-05T05:51:27 | 2020-01-22T16:00:13 | Python | UTF-8 | Python | false | false | 20,388 | py | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Series, Timestamp
import pandas.util.testing as tm
def test_filter_series():
s = pd.Series([1, 3, 20, 5, 22, 24, 7])
expected_odd = pd.Series([1, 3, 5, 7], index=[0, 1, 3, 6])
expected_even = pd.Series([20, 22, 24], index=[2, 4, 5])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
tm.assert_series_equal(grouped.filter(lambda x: x.mean() < 10), expected_odd)
tm.assert_series_equal(grouped.filter(lambda x: x.mean() > 10), expected_even)
# Test dropna=False.
tm.assert_series_equal(
grouped.filter(lambda x: x.mean() < 10, dropna=False),
expected_odd.reindex(s.index),
)
tm.assert_series_equal(
grouped.filter(lambda x: x.mean() > 10, dropna=False),
expected_even.reindex(s.index),
)
def test_filter_single_column_df():
df = pd.DataFrame([1, 3, 20, 5, 22, 24, 7])
expected_odd = pd.DataFrame([1, 3, 5, 7], index=[0, 1, 3, 6])
expected_even = pd.DataFrame([20, 22, 24], index=[2, 4, 5])
grouper = df[0].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
tm.assert_frame_equal(grouped.filter(lambda x: x.mean() < 10), expected_odd)
tm.assert_frame_equal(grouped.filter(lambda x: x.mean() > 10), expected_even)
# Test dropna=False.
tm.assert_frame_equal(
grouped.filter(lambda x: x.mean() < 10, dropna=False),
expected_odd.reindex(df.index),
)
tm.assert_frame_equal(
grouped.filter(lambda x: x.mean() > 10, dropna=False),
expected_even.reindex(df.index),
)
def test_filter_multi_column_df():
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": [1, 1, 1, 1]})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
expected = pd.DataFrame({"A": [12, 12], "B": [1, 1]}, index=[1, 2])
tm.assert_frame_equal(
grouped.filter(lambda x: x["A"].sum() - x["B"].sum() > 10), expected
)
def test_filter_mixed_df():
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
expected = pd.DataFrame({"A": [12, 12], "B": ["b", "c"]}, index=[1, 2])
tm.assert_frame_equal(grouped.filter(lambda x: x["A"].sum() > 10), expected)
def test_filter_out_all_groups():
s = pd.Series([1, 3, 20, 5, 22, 24, 7])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
tm.assert_series_equal(grouped.filter(lambda x: x.mean() > 1000), s[[]])
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
tm.assert_frame_equal(grouped.filter(lambda x: x["A"].sum() > 1000), df.loc[[]])
def test_filter_out_no_groups():
s = pd.Series([1, 3, 20, 5, 22, 24, 7])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
filtered = grouped.filter(lambda x: x.mean() > 0)
tm.assert_series_equal(filtered, s)
df = pd.DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
grouper = df["A"].apply(lambda x: x % 2)
grouped = df.groupby(grouper)
filtered = grouped.filter(lambda x: x["A"].mean() > 0)
tm.assert_frame_equal(filtered, df)
def test_filter_out_all_groups_in_df():
# GH12768
df = pd.DataFrame({"a": [1, 1, 2], "b": [1, 2, 0]})
res = df.groupby("a")
res = res.filter(lambda x: x["b"].sum() > 5, dropna=False)
expected = pd.DataFrame({"a": [np.nan] * 3, "b": [np.nan] * 3})
tm.assert_frame_equal(expected, res)
df = pd.DataFrame({"a": [1, 1, 2], "b": [1, 2, 0]})
res = df.groupby("a")
res = res.filter(lambda x: x["b"].sum() > 5, dropna=True)
expected = pd.DataFrame({"a": [], "b": []}, dtype="int64")
tm.assert_frame_equal(expected, res)
def test_filter_condition_raises():
def raise_if_sum_is_zero(x):
if x.sum() == 0:
raise ValueError
else:
return x.sum() > 0
s = pd.Series([-1, 0, 1, 2])
grouper = s.apply(lambda x: x % 2)
grouped = s.groupby(grouper)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
grouped.filter(raise_if_sum_is_zero)
def test_filter_with_axis_in_groupby():
# issue 11041
index = pd.MultiIndex.from_product([range(10), [0, 1]])
data = pd.DataFrame(np.arange(100).reshape(-1, 20), columns=index, dtype="int64")
result = data.groupby(level=0, axis=1).filter(lambda x: x.iloc[0, 0] > 10)
expected = data.iloc[:, 12:20]
tm.assert_frame_equal(result, expected)
def test_filter_bad_shapes():
df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
s = df["B"]
g_df = df.groupby("B")
g_s = s.groupby(s)
f = lambda x: x
msg = "filter function returned a DataFrame, but expected a scalar bool"
with pytest.raises(TypeError, match=msg):
g_df.filter(f)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
g_s.filter(f)
f = lambda x: x == 1
msg = "filter function returned a DataFrame, but expected a scalar bool"
with pytest.raises(TypeError, match=msg):
g_df.filter(f)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
g_s.filter(f)
f = lambda x: np.outer(x, x)
msg = "can't multiply sequence by non-int of type 'str'"
with pytest.raises(TypeError, match=msg):
g_df.filter(f)
msg = "the filter must return a boolean result"
with pytest.raises(TypeError, match=msg):
g_s.filter(f)
def test_filter_nan_is_false():
df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
s = df["B"]
g_df = df.groupby(df["B"])
g_s = s.groupby(s)
f = lambda x: np.nan
tm.assert_frame_equal(g_df.filter(f), df.loc[[]])
tm.assert_series_equal(g_s.filter(f), s[[]])
def test_filter_against_workaround():
np.random.seed(0)
# Series of ints
s = Series(np.random.randint(0, 100, 1000))
grouper = s.apply(lambda x: np.round(x, -1))
grouped = s.groupby(grouper)
f = lambda x: x.mean() > 10
old_way = s[grouped.transform(f).astype("bool")]
new_way = grouped.filter(f)
tm.assert_series_equal(new_way.sort_values(), old_way.sort_values())
# Series of floats
s = 100 * Series(np.random.random(1000))
grouper = s.apply(lambda x: np.round(x, -1))
grouped = s.groupby(grouper)
f = lambda x: x.mean() > 10
old_way = s[grouped.transform(f).astype("bool")]
new_way = grouped.filter(f)
tm.assert_series_equal(new_way.sort_values(), old_way.sort_values())
# Set up DataFrame of ints, floats, strings.
from string import ascii_lowercase
letters = np.array(list(ascii_lowercase))
N = 1000
random_letters = letters.take(np.random.randint(0, 26, N))
df = DataFrame(
{
"ints": Series(np.random.randint(0, 100, N)),
"floats": N / 10 * Series(np.random.random(N)),
"letters": Series(random_letters),
}
)
# Group by ints; filter on floats.
grouped = df.groupby("ints")
old_way = df[grouped.floats.transform(lambda x: x.mean() > N / 20).astype("bool")]
new_way = grouped.filter(lambda x: x["floats"].mean() > N / 20)
tm.assert_frame_equal(new_way, old_way)
# Group by floats (rounded); filter on strings.
grouper = df.floats.apply(lambda x: np.round(x, -1))
grouped = df.groupby(grouper)
old_way = df[grouped.letters.transform(lambda x: len(x) < N / 10).astype("bool")]
new_way = grouped.filter(lambda x: len(x.letters) < N / 10)
tm.assert_frame_equal(new_way, old_way)
# Group by strings; filter on ints.
grouped = df.groupby("letters")
old_way = df[grouped.ints.transform(lambda x: x.mean() > N / 20).astype("bool")]
new_way = grouped.filter(lambda x: x["ints"].mean() > N / 20)
tm.assert_frame_equal(new_way, old_way)
def test_filter_using_len():
# BUG GH4447
df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
grouped = df.groupby("B")
actual = grouped.filter(lambda x: len(x) > 2)
expected = DataFrame(
{"A": np.arange(2, 6), "B": list("bbbb"), "C": np.arange(2, 6)},
index=np.arange(2, 6),
)
tm.assert_frame_equal(actual, expected)
actual = grouped.filter(lambda x: len(x) > 4)
expected = df.loc[[]]
tm.assert_frame_equal(actual, expected)
# Series have always worked properly, but we'll test anyway.
s = df["B"]
grouped = s.groupby(s)
actual = grouped.filter(lambda x: len(x) > 2)
expected = Series(4 * ["b"], index=np.arange(2, 6), name="B")
tm.assert_series_equal(actual, expected)
actual = grouped.filter(lambda x: len(x) > 4)
expected = s[[]]
tm.assert_series_equal(actual, expected)
def test_filter_maintains_ordering():
# Simple case: index is sequential. #4621
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]}
)
s = df["pid"]
grouped = df.groupby("tag")
actual = grouped.filter(lambda x: len(x) > 1)
expected = df.iloc[[1, 2, 4, 7]]
tm.assert_frame_equal(actual, expected)
grouped = s.groupby(df["tag"])
actual = grouped.filter(lambda x: len(x) > 1)
expected = s.iloc[[1, 2, 4, 7]]
tm.assert_series_equal(actual, expected)
# Now index is sequentially decreasing.
df.index = np.arange(len(df) - 1, -1, -1)
s = df["pid"]
grouped = df.groupby("tag")
actual = grouped.filter(lambda x: len(x) > 1)
expected = df.iloc[[1, 2, 4, 7]]
tm.assert_frame_equal(actual, expected)
grouped = s.groupby(df["tag"])
actual = grouped.filter(lambda x: len(x) > 1)
expected = s.iloc[[1, 2, 4, 7]]
tm.assert_series_equal(actual, expected)
# Index is shuffled.
SHUFFLED = [4, 6, 7, 2, 1, 0, 5, 3]
df.index = df.index[SHUFFLED]
s = df["pid"]
grouped = df.groupby("tag")
actual = grouped.filter(lambda x: len(x) > 1)
expected = df.iloc[[1, 2, 4, 7]]
tm.assert_frame_equal(actual, expected)
grouped = s.groupby(df["tag"])
actual = grouped.filter(lambda x: len(x) > 1)
expected = s.iloc[[1, 2, 4, 7]]
tm.assert_series_equal(actual, expected)
def test_filter_multiple_timestamp():
# GH 10114
df = DataFrame(
{
"A": np.arange(5, dtype="int64"),
"B": ["foo", "bar", "foo", "bar", "bar"],
"C": Timestamp("20130101"),
}
)
grouped = df.groupby(["B", "C"])
result = grouped["A"].filter(lambda x: True)
tm.assert_series_equal(df["A"], result)
result = grouped["A"].transform(len)
expected = Series([2, 3, 2, 3, 3], name="A")
tm.assert_series_equal(result, expected)
result = grouped.filter(lambda x: True)
tm.assert_frame_equal(df, result)
result = grouped.transform("sum")
expected = DataFrame({"A": [2, 8, 2, 8, 8]})
tm.assert_frame_equal(result, expected)
result = grouped.transform(len)
expected = DataFrame({"A": [2, 3, 2, 3, 3]})
tm.assert_frame_equal(result, expected)
def test_filter_and_transform_with_non_unique_int_index():
# GH4620
index = [1, 1, 1, 2, 1, 1, 0, 1]
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_multiple_non_unique_int_index():
# GH4620
index = [1, 1, 1, 2, 0, 0, 0, 1]
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_non_unique_float_index():
# GH4620
index = np.array([1, 1, 1, 2, 1, 1, 0, 1], dtype=float)
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_non_unique_timestamp_index():
# GH4620
t0 = Timestamp("2013-09-30 00:05:00")
t1 = Timestamp("2013-10-30 00:05:00")
t2 = Timestamp("2013-11-30 00:05:00")
index = [t1, t1, t1, t2, t1, t1, t0, t1]
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_and_transform_with_non_unique_string_index():
# GH4620
index = list("bbbcbbab")
df = DataFrame(
{"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
index=index,
)
grouped_df = df.groupby("tag")
ser = df["pid"]
grouped_ser = ser.groupby(df["tag"])
expected_indexes = [1, 2, 4, 7]
# Filter DataFrame
actual = grouped_df.filter(lambda x: len(x) > 1)
expected = df.iloc[expected_indexes]
tm.assert_frame_equal(actual, expected)
actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
expected = df.copy()
expected.iloc[[0, 3, 5, 6]] = np.nan
tm.assert_frame_equal(actual, expected)
# Filter Series
actual = grouped_ser.filter(lambda x: len(x) > 1)
expected = ser.take(expected_indexes)
tm.assert_series_equal(actual, expected)
actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
NA = np.nan
expected = Series([NA, 1, 1, NA, 2, NA, NA, 3], index, name="pid")
# ^ made manually because this can get confusing!
tm.assert_series_equal(actual, expected)
# Transform Series
actual = grouped_ser.transform(len)
expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
tm.assert_series_equal(actual, expected)
# Transform (a column from) DataFrameGroupBy
actual = grouped_df.pid.transform(len)
tm.assert_series_equal(actual, expected)
def test_filter_has_access_to_grouped_cols():
df = DataFrame([[1, 2], [1, 3], [5, 6]], columns=["A", "B"])
g = df.groupby("A")
# previously didn't have access to col A #????
filt = g.filter(lambda x: x["A"].sum() == 2)
tm.assert_frame_equal(filt, df.iloc[[0, 1]])
def test_filter_enforces_scalarness():
df = pd.DataFrame(
[
["best", "a", "x"],
["worst", "b", "y"],
["best", "c", "x"],
["best", "d", "y"],
["worst", "d", "y"],
["worst", "d", "y"],
["best", "d", "z"],
],
columns=["a", "b", "c"],
)
with pytest.raises(TypeError, match="filter function returned a.*"):
df.groupby("c").filter(lambda g: g["a"] == "best")
def test_filter_non_bool_raises():
df = pd.DataFrame(
[
["best", "a", 1],
["worst", "b", 1],
["best", "c", 1],
["best", "d", 1],
["worst", "d", 1],
["worst", "d", 1],
["best", "d", 1],
],
columns=["a", "b", "c"],
)
with pytest.raises(TypeError, match="filter function returned a.*"):
df.groupby("a").filter(lambda g: g.c.mean())
def test_filter_dropna_with_empty_groups():
# GH 10780
data = pd.Series(np.random.rand(9), index=np.repeat([1, 2, 3], 3))
groupped = data.groupby(level=0)
result_false = groupped.filter(lambda x: x.mean() > 1, dropna=False)
expected_false = pd.Series([np.nan] * 9, index=np.repeat([1, 2, 3], 3))
tm.assert_series_equal(result_false, expected_false)
result_true = groupped.filter(lambda x: x.mean() > 1, dropna=True)
expected_true = pd.Series(index=pd.Index([], dtype=int))
tm.assert_series_equal(result_true, expected_true)
| [
"[email protected]"
]
| |
a2c28551321a031321b269385b8a40dee9d39c56 | f4434c85e3814b6347f8f8099c081ed4af5678a5 | /sdk/tables/azure-data-tables/azure/data/tables/_generated/aio/operations/_service_operations.py | 4ef1391d9b929f9750111cbb76a4882ccc33b059 | [
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later"
]
| permissive | yunhaoling/azure-sdk-for-python | 5da12a174a37672ac6ed8e3c1f863cb77010a506 | c4eb0ca1aadb76ad892114230473034830116362 | refs/heads/master | 2022-06-11T01:17:39.636461 | 2020-12-08T17:42:08 | 2020-12-08T17:42:08 | 177,675,796 | 1 | 0 | MIT | 2020-03-31T20:35:17 | 2019-03-25T22:43:40 | Python | UTF-8 | Python | false | false | 13,225 | py | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from typing import Any, Callable, Dict, Generic, Optional, TypeVar
import warnings
from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest
from ... import models
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class ServiceOperations:
"""ServiceOperations async operations.
You should not instantiate this class directly. Instead, you should create a Client instance that
instantiates it for you and attaches it as an attribute.
:ivar models: Alias to model classes used in this operation group.
:type models: ~azure.data.tables.models
:param client: Client for service requests.
:param config: Configuration of service client.
:param serializer: An object model serializer.
:param deserializer: An object model deserializer.
"""
models = models
def __init__(self, client, config, serializer, deserializer) -> None:
self._client = client
self._serialize = serializer
self._deserialize = deserializer
self._config = config
async def set_properties(
self,
table_service_properties: "models.TableServiceProperties",
timeout: Optional[int] = None,
request_id_parameter: Optional[str] = None,
**kwargs
) -> None:
"""Sets properties for an account's Table service endpoint, including properties for Analytics and
CORS (Cross-Origin Resource Sharing) rules.
:param table_service_properties: The Table Service properties.
:type table_service_properties: ~azure.data.tables.models.TableServiceProperties
:param timeout: The timeout parameter is expressed in seconds.
:type timeout: int
:param request_id_parameter: Provides a client-generated, opaque value with a 1 KB character
limit that is recorded in the analytics logs when analytics logging is enabled.
:type request_id_parameter: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: None, or the result of cls(response)
:rtype: None
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[None]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
restype = "service"
comp = "properties"
content_type = kwargs.pop("content_type", "application/xml")
accept = "application/xml"
# Construct URL
url = self.set_properties.metadata['url'] # type: ignore
path_format_arguments = {
'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['restype'] = self._serialize.query("restype", restype, 'str')
query_parameters['comp'] = self._serialize.query("comp", comp, 'str')
if timeout is not None:
query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0)
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str')
if request_id_parameter is not None:
header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id_parameter", request_id_parameter, 'str')
header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
body_content_kwargs = {} # type: Dict[str, Any]
body_content = self._serialize.body(table_service_properties, 'TableServiceProperties', is_xml=True)
body_content_kwargs['content'] = body_content
request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize(models.TableServiceError, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['x-ms-client-request-id']=self._deserialize('str', response.headers.get('x-ms-client-request-id'))
response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))
response_headers['x-ms-version']=self._deserialize('str', response.headers.get('x-ms-version'))
if cls:
return cls(pipeline_response, None, response_headers)
set_properties.metadata = {'url': '/'} # type: ignore
async def get_properties(
self,
timeout: Optional[int] = None,
request_id_parameter: Optional[str] = None,
**kwargs
) -> "models.TableServiceProperties":
"""Gets the properties of an account's Table service, including properties for Analytics and CORS
(Cross-Origin Resource Sharing) rules.
:param timeout: The timeout parameter is expressed in seconds.
:type timeout: int
:param request_id_parameter: Provides a client-generated, opaque value with a 1 KB character
limit that is recorded in the analytics logs when analytics logging is enabled.
:type request_id_parameter: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: TableServiceProperties, or the result of cls(response)
:rtype: ~azure.data.tables.models.TableServiceProperties
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.TableServiceProperties"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
restype = "service"
comp = "properties"
accept = "application/xml"
# Construct URL
url = self.get_properties.metadata['url'] # type: ignore
path_format_arguments = {
'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['restype'] = self._serialize.query("restype", restype, 'str')
query_parameters['comp'] = self._serialize.query("comp", comp, 'str')
if timeout is not None:
query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0)
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str')
if request_id_parameter is not None:
header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id_parameter", request_id_parameter, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize(models.TableServiceError, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['x-ms-client-request-id']=self._deserialize('str', response.headers.get('x-ms-client-request-id'))
response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))
response_headers['x-ms-version']=self._deserialize('str', response.headers.get('x-ms-version'))
deserialized = self._deserialize('TableServiceProperties', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
get_properties.metadata = {'url': '/'} # type: ignore
async def get_statistics(
self,
timeout: Optional[int] = None,
request_id_parameter: Optional[str] = None,
**kwargs
) -> "models.TableServiceStats":
"""Retrieves statistics related to replication for the Table service. It is only available on the
secondary location endpoint when read-access geo-redundant replication is enabled for the
account.
:param timeout: The timeout parameter is expressed in seconds.
:type timeout: int
:param request_id_parameter: Provides a client-generated, opaque value with a 1 KB character
limit that is recorded in the analytics logs when analytics logging is enabled.
:type request_id_parameter: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: TableServiceStats, or the result of cls(response)
:rtype: ~azure.data.tables.models.TableServiceStats
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["models.TableServiceStats"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
restype = "service"
comp = "stats"
accept = "application/xml"
# Construct URL
url = self.get_statistics.metadata['url'] # type: ignore
path_format_arguments = {
'url': self._serialize.url("self._config.url", self._config.url, 'str', skip_quote=True),
}
url = self._client.format_url(url, **path_format_arguments)
# Construct parameters
query_parameters = {} # type: Dict[str, Any]
query_parameters['restype'] = self._serialize.query("restype", restype, 'str')
query_parameters['comp'] = self._serialize.query("comp", comp, 'str')
if timeout is not None:
query_parameters['timeout'] = self._serialize.query("timeout", timeout, 'int', minimum=0)
# Construct headers
header_parameters = {} # type: Dict[str, Any]
header_parameters['x-ms-version'] = self._serialize.header("self._config.version", self._config.version, 'str')
if request_id_parameter is not None:
header_parameters['x-ms-client-request-id'] = self._serialize.header("request_id_parameter", request_id_parameter, 'str')
header_parameters['Accept'] = self._serialize.header("accept", accept, 'str')
request = self._client.get(url, query_parameters, header_parameters)
pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize(models.TableServiceError, response)
raise HttpResponseError(response=response, model=error)
response_headers = {}
response_headers['x-ms-client-request-id']=self._deserialize('str', response.headers.get('x-ms-client-request-id'))
response_headers['x-ms-request-id']=self._deserialize('str', response.headers.get('x-ms-request-id'))
response_headers['x-ms-version']=self._deserialize('str', response.headers.get('x-ms-version'))
response_headers['Date']=self._deserialize('rfc-1123', response.headers.get('Date'))
deserialized = self._deserialize('TableServiceStats', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
get_statistics.metadata = {'url': '/'} # type: ignore
| [
"[email protected]"
]
| |
56c2dc305f24ba5731f349d4284d1ede0e056579 | 91d1a6968b90d9d461e9a2ece12b465486e3ccc2 | /ec2_write_1/ebs-default-kms-key-id_modify.py | 9590bdb3b63ab3aa9cd39ae2bf409a3fec3eef4f | []
| no_license | lxtxl/aws_cli | c31fc994c9a4296d6bac851e680d5adbf7e93481 | aaf35df1b7509abf5601d3f09ff1fece482facda | refs/heads/master | 2023-02-06T09:00:33.088379 | 2020-12-27T13:38:45 | 2020-12-27T13:38:45 | 318,686,394 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,870 | py | #!/usr/bin/python
# -*- codding: utf-8 -*-
import os
import sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
from common.execute_command import write_one_parameter
# url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/modify-ebs-default-kms-key-id.html
if __name__ == '__main__':
"""
get-ebs-default-kms-key-id : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/get-ebs-default-kms-key-id.html
reset-ebs-default-kms-key-id : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/ec2/reset-ebs-default-kms-key-id.html
"""
parameter_display_string = """
# kms-key-id : The identifier of the AWS Key Management Service (AWS KMS) customer master key (CMK) to use for Amazon EBS encryption. If this parameter is not specified, your AWS managed CMK for EBS is used. If KmsKeyId is specified, the encrypted state must be true .
You can specify the CMK using any of the following:
Key ID. For example, 1234abcd-12ab-34cd-56ef-1234567890ab.
Key alias. For example, alias/ExampleAlias.
Key ARN. For example, arn:aws:kms:us-east-1:012345678910:key/1234abcd-12ab-34cd-56ef-1234567890ab.
Alias ARN. For example, arn:aws:kms:us-east-1:012345678910:alias/ExampleAlias.
AWS authenticates the CMK asynchronously. Therefore, if you specify an ID, alias, or ARN that is not valid, the action can appear to complete, but eventually fails.
Amazon EBS does not support asymmetric CMKs.
"""
add_option_dict = {}
#######################################################################
# parameter display string
add_option_dict["parameter_display_string"] = parameter_display_string
# ex: add_option_dict["no_value_parameter_list"] = "--single-parameter"
write_one_parameter("ec2", "modify-ebs-default-kms-key-id", "kms-key-id", add_option_dict)
| [
"[email protected]"
]
| |
e30eac1ded6ffcfd4458f5a272fdbbeb01c07f3a | f3a7eae3031bb9afe75116a9b86278490ac4a7e6 | /text/symbols.py | c329f2df647246d4d8e564a02e78e26b68ac2691 | [
"BSD-3-Clause",
"MIT"
]
| permissive | LOCS-AI/Multilanguage_Tacotron_2 | ea34c4fb41e8112537529945b5a31cf2e78d0610 | 82c788fb26d93c6735c54c2fe4ae7bcbd0eec69f | refs/heads/master | 2022-12-31T01:39:57.432804 | 2020-10-08T00:04:58 | 2020-10-08T00:04:58 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 989 | py | """ from https://github.com/keithito/tacotron """
'''
Defines the set of symbols used in text input to the model.
The default is a set of ASCII characters that works well for English or text that has been run through Unidecode. For other data, you can modify _characters. See TRAINING_DATA.md for details. '''
from text import cmudict
_pad = '_'
_punctuation = '!\'(),.:;? '
_special = '-'
_letters = 'abcdefghijklmnopqrstuvwxyz'
# Prepend "@" to ARPAbet symbols to ensure uniqueness (some are the same as uppercase letters):
_arpabet = ['@' + s for s in cmudict.valid_symbols]
hangul_symbol = u'''␀␃%"ᄀᄁᄂᄃᄄᄅᄆᄇᄈᄉᄊᄋᄌᄍᄎᄏᄐᄑᄒᅌᅡᅢᅣᅤᅥᅦᅧᅨᅩᅪᅫᅬᅭᅮᅯᅰᅱᅲᅳᅴᅵᆞᆢᆨᆩᆫᆬᆭᆮᆯᆰᆱᆲᆴᆶᆪᆷᆸᆹᆺᆻᆼᆽᆾᆿᇀᇁᇂ'''
# Export all symbols:
symbols = [_pad] + list(_special) + list(_punctuation) + _arpabet + list(_letters)
symbols = list(hangul_symbol) + symbols | [
"[email protected]"
]
| |
03608d220d4d293c64e7d19d2c5178953574c174 | 0e1e643e864bcb96cf06f14f4cb559b034e114d0 | /Exps_7_v3/doc3d/Ablation4_ch016_ep003_7/W_w_M_to_C_pyr/pyr_6s/L7/step10_a.py | 6c005ce2d39cf14a6d40bc0a6f470140b0365a40 | []
| no_license | KongBOy/kong_model2 | 33a94a9d2be5b0f28f9d479b3744e1d0e0ebd307 | 1af20b168ffccf0d5293a393a40a9fa9519410b2 | refs/heads/master | 2022-10-14T03:09:22.543998 | 2022-10-06T11:33:42 | 2022-10-06T11:33:42 | 242,080,692 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 942,122 | py | #############################################################################################################################################################################################################
#############################################################################################################################################################################################################
### 把 kong_model2 加入 sys.path
import os
code_exe_path = os.path.realpath(__file__) ### 目前執行 step10_b.py 的 path
code_exe_path_element = code_exe_path.split("\\") ### 把 path 切分 等等 要找出 kong_model 在第幾層
code_dir = "\\".join(code_exe_path_element[:-1])
kong_layer = code_exe_path_element.index("kong_model2") ### 找出 kong_model2 在第幾層
kong_model2_dir = "\\".join(code_exe_path_element[:kong_layer + 1]) ### 定位出 kong_model2 的 dir
import sys ### 把 kong_model2 加入 sys.path
sys.path.append(kong_model2_dir)
sys.path.append(code_dir)
# print(__file__.split("\\")[-1])
# print(" code_exe_path:", code_exe_path)
# print(" code_exe_path_element:", code_exe_path_element)
# print(" code_dir:", code_dir)
# print(" kong_layer:", kong_layer)
# print(" kong_model2_dir:", kong_model2_dir)
#############################################################################################################################################################################################################
kong_to_py_layer = len(code_exe_path_element) - 1 - kong_layer ### 中間 -1 是為了長度轉index
# print(" kong_to_py_layer:", kong_to_py_layer)
if (kong_to_py_layer == 0): template_dir = ""
elif(kong_to_py_layer == 2): template_dir = code_exe_path_element[kong_layer + 1][0:] ### [7:] 是為了去掉 step1x_， 後來覺得好像改有意義的名字不去掉也行所以 改 0
elif(kong_to_py_layer == 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] ### [5:] 是為了去掉 mask_ ，前面的 mask_ 是為了python 的 module 不能 數字開頭， 隨便加的這樣子， 後來覺得 自動排的順序也可以接受， 所以 改0
elif(kong_to_py_layer > 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] + "/" + "/".join(code_exe_path_element[kong_layer + 3: -1])
# print(" template_dir:", template_dir) ### 舉例： template_dir: 7_mask_unet/5_os_book_and_paper_have_dtd_hdr_mix_bg_tv_s04_mae
#############################################################################################################################################################################################################
exp_dir = template_dir
#############################################################################################################################################################################################################
from step06_a_datas_obj import *
from step09_6side_L7 import *
from step10_a2_loss_info_obj import *
from step10_b2_exp_builder import Exp_builder
rm_paths = [path for path in sys.path if code_dir in path]
for rm_path in rm_paths: sys.path.remove(rm_path)
rm_moduless = [module for module in sys.modules if "step09" in module]
for rm_module in rm_moduless: del sys.modules[rm_module]
#############################################################################################################################################################################################################
'''
exp_dir 是 決定 result_dir 的 "上一層"資料夾 名字喔！ exp_dir要巢狀也沒問題～
比如：exp_dir = "6_mask_unet/自己命的名字"，那 result_dir 就都在：
6_mask_unet/自己命的名字/result_a
6_mask_unet/自己命的名字/result_b
6_mask_unet/自己命的名字/...
'''
use_db_obj = type8_blender_kong_doc3d_in_W_and_I_gt_F
use_loss_obj = [mae_s001_sobel_k9_s001_loss_info_builder.set_loss_target("UNet_W").copy()] ### z, y, x 順序是看 step07_b_0b_Multi_UNet 來對應的喔
#############################################################
### 為了resul_analyze畫空白的圖，建一個empty的 Exp_builder
empty = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="為了resul_analyze畫空白的圖，建一個empty的 Exp_builder")
#############################################################
###################
############# 1s1
######### 2s1
##### 3s1
### 4s1
ch032_1side_1__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s2
######### 2s1
##### 3s1
### 4s1
ch032_1side_2__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s1
##### 3s1
### 4s1
ch032_1side_2__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_2__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_2__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_2__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_2__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s3
######### 2s1
##### 3s1
### 4s1
ch032_1side_3__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_3__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_3__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_3__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_3__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_3__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_3__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_3__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_3__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_3__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_3__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s4
######### 2s1
##### 3s1
### 4s1
ch032_1side_4__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_4__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_4__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_4__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_4__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_4__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_4__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_4__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_4__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_4__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_4__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_4__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_4__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_4__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_4__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_4__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s5
######### 2s1
##### 3s1
### 4s1
ch032_1side_5__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_5__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_5__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_5__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_5__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_5__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_5__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_5__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_5__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_5__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_5__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_5__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_5__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_5__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_5__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_5__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_5__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_5__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_5__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s6
######### 2s1
##### 3s1
### 4s1
ch032_1side_6__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_6__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_6__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_6__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_6__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_6__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_6__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_6__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_6__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s6
##### 3s1
### 4s1
ch032_1side_6__2side_6__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_6__2side_6__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_6__2side_6__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_6__2side_6__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_6__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_6__2side_6__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_6__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_6__2side_6__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_6__2side_6__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_6__2side_6__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_6__2side_6__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_6__2side_6__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_6__2side_6__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_6__2side_6__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_6__2side_6__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s7
######### 2s1
##### 3s1
### 4s1
ch032_1side_7__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_7__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_7__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_7__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_7__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_7__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s6
##### 3s1
### 4s1
ch032_1side_7__2side_6__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_6__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_6__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_6__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_6__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_7__2side_6__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_6__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_6__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_7__2side_6__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_6__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_6__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_6__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_6__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_7__2side_6__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_6__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s7
##### 3s1
### 4s1
ch032_1side_7__2side_7__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_7__2side_7__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_7__2side_7__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_7__2side_7__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_7__2side_7__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_7__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_7__2side_7__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_7__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_7__2side_7__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s7
### 4s1
ch032_1side_7__2side_7__3side_7_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_7__2side_7__3side_7_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_7__2side_7__3side_7_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_7__2side_7__3side_7_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_7__2side_7__3side_7_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_7__2side_7__3side_7_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_7__2side_7__3side_7_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_7__2side_7__3side_7_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
###################
############# 1s8
######### 2s1
##### 3s1
### 4s1
ch032_1side_8__2side_1__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_1__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_1__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s2
##### 3s1
### 4s1
ch032_1side_8__2side_2__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_2__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_2__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_2__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_2__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s3
##### 3s1
### 4s1
ch032_1side_8__2side_3__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_3__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_3__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_3__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_3__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_3__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_3__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s4
##### 3s1
### 4s1
ch032_1side_8__2side_4__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_4__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_4__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_4__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_4__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_4__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_4__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_4__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_4__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_4__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_4__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s5
##### 3s1
### 4s1
ch032_1side_8__2side_5__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_5__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_5__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_5__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_5__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_5__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_5__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_5__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_5__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_5__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_5__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_5__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_5__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s6
##### 3s1
### 4s1
ch032_1side_8__2side_6__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_6__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_6__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_6__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_6__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_6__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_6__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_6__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_8__2side_6__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_6__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_6__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_6__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_6__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_6__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_6__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s7
##### 3s1
### 4s1
ch032_1side_8__2side_7__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_7__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_7__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_7__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_7__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_7__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_8__2side_7__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_7__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_7__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s7
### 4s1
ch032_1side_8__2side_7__3side_7_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_7__3side_7_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_7__3side_7_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_7__3side_7_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_7__3side_7_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_7__3side_7_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_8__2side_7__3side_7_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_7__3side_7_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
######### 2s8
##### 3s1
### 4s1
ch032_1side_8__2side_8__3side_1_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_1_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_1_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s2
### 4s1
ch032_1side_8__2side_8__3side_2_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_2_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_2_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_2_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s3
### 4s1
ch032_1side_8__2side_8__3side_3_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_3_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_3_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_3_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s4
### 4s1
ch032_1side_8__2side_8__3side_4_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_4_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_4_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_4_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_4_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s5
### 4s1
ch032_1side_8__2side_8__3side_5_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_5_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_5_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_5_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_5_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_5_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s6
### 4s1
ch032_1side_8__2side_8__3side_6_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_6_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_6_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_6_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_6_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_8__3side_6_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_6_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s7
### 4s1
ch032_1side_8__2side_8__3side_7_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_7_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_7_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_7_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_7_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_8__3side_7_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_8__2side_8__3side_7_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_7_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
##### 3s8
### 4s1
ch032_1side_8__2side_8__3side_8_4side_1_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_1_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_1_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s2
ch032_1side_8__2side_8__3side_8_4side_2_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_2_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_2_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s3
ch032_1side_8__2side_8__3side_8_4side_3_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_3_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s4
ch032_1side_8__2side_8__3side_8_4side_4_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_4_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s5
ch032_1side_8__2side_8__3side_8_4side_5_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_5_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s6
ch032_1side_8__2side_8__3side_8_4side_6_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_6_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s7
ch032_1side_8__2side_8__3side_8_4side_7_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_7_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
### 4s8
ch032_1side_8__2side_8__3side_8_4side_8_5s1_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s1_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s1_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s2_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s2_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s3_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s3_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s3_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s4_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s5_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s6_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s7_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s1.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s2.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s3.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s4.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s5.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s6.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s7 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s7, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s7.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
ch032_1side_8__2side_8__3side_8_4side_8_5s8_6s8 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s8, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_6s8.kong_model.model_describe) .set_train_args(epochs= 3) .set_train_iter_args(it_see_fq=900 * 5, it_save_fq=900 * 5, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="")
#############################################################
if(__name__ == "__main__"):
print("build exps cost time:", time.time() - start_time)
if len(sys.argv) < 2:
############################################################################################################
### 直接按 F5 或打 python step10_b1_exp_obj_load_and_train_and_test.py，後面沒有接東西喔！才不會跑到下面給 step10_b_subprocss.py 用的程式碼~~~
ch032_1side_1__2side_1__3side_1_4side_1_5s1_6s1.build().run()
# print('no argument')
sys.exit()
### 以下是給 step10_b_subprocess.py 用的，相當於cmd打 python step10_b1_exp_obj_load_and_train_and_test.py 某個exp.build().run()
eval(sys.argv[1])
| [
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1ac1bf0d486a318d12379426563fee9a8f6f22d6 | fe85138c949c6198184c591780831fd2e183a24a | /Address Book.py | 251c32fc6f328cd1f9352bc08e897b68bbe90efc | []
| no_license | valeri1383/Personal-Python-Projects | e98f6b7171298def019db4e28f6d176a709615cc | b7db81cb44668f549a7fd15de84c0cb23654ac3d | refs/heads/main | 2023-05-26T09:02:24.260700 | 2023-05-22T14:40:28 | 2023-05-22T14:40:28 | 337,518,678 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,253 | py | from tkinter import *
root = Tk()
root.geometry('400x400')
root.configure(bg='cyan')
root.resizable(1, 1)
root.title('Address Book')
contact_list = [
['John Smith', '07567374343'],
['Terry Adams', '07569984343'],
['Allen Gibson', '07564474743'],
['Grant Foster', '07567396843'],
['Hall Grey', '07567746343']
]
Name = StringVar()
Number = StringVar()
frame = Frame(root)
frame.pack(side=RIGHT)
scroll = Scrollbar(frame, orient=VERTICAL)
select = Listbox(frame,bg='light goldenrod', yscrollcommand=scroll.set, width=30, height=33)
scroll.configure(command=select.yview)
scroll.pack(side=RIGHT, fill=Y)
select.pack(side=LEFT, fill=BOTH, expand=1)
def Selected():
return int(select.curselection()[0])
def AddContact():
contact_list.append([Name.get(), Number.get()])
Select_set()
def EDIT():
contact_list[Selected()] = [Name.get(), Number.get()]
Select_set()
def DELETE():
del contact_list[Selected()]
Select_set()
def VIEW():
NAME, PHONE = contact_list[Selected()]
Name.set(NAME)
Number.set(PHONE)
def EXIT():
root.destroy()
def RESET():
Name.set('')
Number.set('')
def Select_set():
contact_list.sort()
select.delete(0, END)
for name, phone in contact_list:
select.insert(END, name)
Select_set()
Label(root, text='NAME', font='arial 15 bold', bg='cyan').pack()
Entry(root, font=20, bg='light yellow', textvariable=Name).pack()
Label(root, text='PHONE NO.', font='arial 15 bold', bg='cyan').pack()
Entry(root, font=20,bg='light yellow', textvariable=Number).pack()
Button(root, text='ADD', width=7, font='arial 15 bold', bg='SlateGray4', command=AddContact).pack()
Button(root, text='EDIT', width=7, font='arial 15 bold', bg='SlateGray4', command=EDIT).pack()
Button(root, text="DELETE", width=7, font='arial 15 bold', bg='SlateGray4', command=DELETE).pack()
Button(root, text="VIEW", width=7, font='arial 15 bold', bg='SlateGray4', command=VIEW).pack()
Button(root, text="EXIT", width=7, font='arial 15 bold', bg='tomato', command=EXIT).pack()
Button(root, text="RESET", width=7, font='arial 15 bold', bg='SlateGray4', command=RESET).pack()
mainloop()
| [
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]
| |
df8348437cb3f52a36143204a8098092a7baae05 | cdd2003610c4c451dc38781d5ece2cf4e8138c27 | /src/convert_rviz.py | cd66d10b1a8cd9aecf17d38b1ef969533384d9a9 | []
| no_license | DLu/rwt_config_generator | 7efb29d773dddae0868be14606ba91893fae806c | 873b1aa0d4c94cdba3b15ef85d46f70c26f6dc86 | refs/heads/master | 2020-12-24T16:24:02.304617 | 2016-03-03T19:04:52 | 2016-03-03T19:04:52 | 39,230,985 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 3,622 | py | #!/usr/bin/python
from __future__ import print_function
import sys
import yaml
from rwt_config_generator import *
import argparse
import rospy
def warning(*objs):
print("WARNING: ", *objs, file=sys.stderr)
parser = argparse.ArgumentParser()
parser.add_argument('rviz_config')
parser.add_argument('output_html_file', nargs='?')
parser.add_argument('-b', '--bson', action='store_true')
parser.add_argument('-u', '--host', type=str, nargs='?')
args = parser.parse_args(rospy.myargv()[1:])
rviz = yaml.load( open(args.rviz_config) )['Visualization Manager']
def to_hex(s):
if s is None:
return None
ns = tuple(map(int, s.split(';')))
s = '0x%02x%02x%02x'%ns
return s
def get(key, d=None):
if d is None:
d = rviz
for s in key.split('/'):
d = d.get(s, None)
if d==None:
return None
return d
def parse_displays(c, displays):
for display in displays:
if not display.get('Enabled', True):
continue
cls = display['Class']
if cls == 'rviz/Grid':
c.add_grid()
elif cls == 'rviz/RobotModel':
c.add_model(param=display.get('Robot Description'), tfPrefix=display.get('TF Prefix'))
elif cls == 'rviz/Marker':
c.add_markers(topic=display.get('Marker Topic'))
elif cls == 'rviz/MarkerArray':
c.add_marker_array(topic=display.get('Marker Topic'))
elif cls == 'rviz/InteractiveMarkers':
topic = display.get('Update Topic')
topic = topic.replace('/update', '')
c.add_imarkers(topic=topic)
elif cls == 'rviz/PointCloud2':
c.add_pointcloud(topic=display.get('Topic'), size=display.get('Size (m)'))
elif cls == 'rviz/LaserScan':
c.add_laserscan(topic=display.get('Topic'), color=to_hex(display.get('Color')), size=display.get('Size (m)'))
elif cls == 'rviz/Path':
c.add_path(topic=display.get('Topic'), color=to_hex(display.get('Color')))
elif cls == 'rviz/Polygon':
c.add_polygon(topic=display.get('Topic'), color=to_hex(display.get('Color')))
elif cls == 'rviz/Pose':
c.add_pose(topic=display.get('Topic'), color=to_hex(display.get('Color')),
shaft_radius=display.get('Shaft Radius'),
head_radius=display.get('Head Radius'),
shaft_length=display.get('Shaft Length'),
head_length=display.get('Head Length'))
elif cls == 'rviz/Odometry':
c.add_odometry(topic=display.get('Topic'), color=to_hex(display.get('Color')),
shaft_length=display.get('Length'), keep=display.get('Keep'))
elif cls == 'rviz/PoseArray':
c.add_posearray(topic=display.get('Topic'), color=to_hex(display.get('Color')), length=display.get('Arrow Length'))
elif cls == 'rviz/PointStamped':
c.add_point(topic=display.get('Topic'), color=to_hex(display.get('Color')), radius=display.get('Radius'))
elif cls == 'rviz/Group':
parse_displays( c, display['Displays'] )
elif cls == 'rviz/Map':
c.add_map(topic=display.get('Topic'), alpha=display.get('Alpha'), tf=True)
else:
warning("Class %s not supported yet!"%cls)
frame = get('Global Options/Fixed Frame')
c = RWTConfig(host=args.host, fixed_frame=frame)
if args.bson:
c.add_bson_header()
parse_displays(c, get('Displays'))
if args.output_html_file:
with open(args.output_html_file, 'w') as f:
f.write(str(c))
else:
print(c)
| [
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]
| |
3c36c0d10742f9c25af173e2077d9c835a3e3ff8 | 1d928c3f90d4a0a9a3919a804597aa0a4aab19a3 | /python/celery/2015/12/graph.py | d441a54ca1edf2545aaaa16e0d18be8ec8d7318d | []
| no_license | rosoareslv/SED99 | d8b2ff5811e7f0ffc59be066a5a0349a92cbb845 | a062c118f12b93172e31e8ca115ce3f871b64461 | refs/heads/main | 2023-02-22T21:59:02.703005 | 2021-01-28T19:40:51 | 2021-01-28T19:40:51 | 306,497,459 | 1 | 1 | null | 2020-11-24T20:56:18 | 2020-10-23T01:18:07 | null | UTF-8 | Python | false | false | 6,432 | py | # -*- coding: utf-8 -*-
"""
The :program:`celery graph` command.
.. program:: celery graph
"""
from __future__ import absolute_import, unicode_literals
from operator import itemgetter
from celery.datastructures import DependencyGraph, GraphFormatter
from celery.five import items
from .base import Command
__all__ = ['graph']
class graph(Command):
args = """<TYPE> [arguments]
..... bootsteps [worker] [consumer]
..... workers [enumerate]
"""
def run(self, what=None, *args, **kwargs):
map = {'bootsteps': self.bootsteps, 'workers': self.workers}
if not what:
raise self.UsageError('missing type')
elif what not in map:
raise self.Error('no graph {0} in {1}'.format(what, '|'.join(map)))
return map[what](*args, **kwargs)
def bootsteps(self, *args, **kwargs):
worker = self.app.WorkController()
include = {arg.lower() for arg in args or ['worker', 'consumer']}
if 'worker' in include:
graph = worker.blueprint.graph
if 'consumer' in include:
worker.blueprint.connect_with(worker.consumer.blueprint)
else:
graph = worker.consumer.blueprint.graph
graph.to_dot(self.stdout)
def workers(self, *args, **kwargs):
def simplearg(arg):
return maybe_list(itemgetter(0, 2)(arg.partition(':')))
def maybe_list(l, sep=','):
return (l[0], l[1].split(sep) if sep in l[1] else l[1])
args = dict(simplearg(arg) for arg in args)
generic = 'generic' in args
def generic_label(node):
return '{0} ({1}://)'.format(type(node).__name__,
node._label.split('://')[0])
class Node(object):
force_label = None
scheme = {}
def __init__(self, label, pos=None):
self._label = label
self.pos = pos
def label(self):
return self._label
def __str__(self):
return self.label()
class Thread(Node):
scheme = {'fillcolor': 'lightcyan4', 'fontcolor': 'yellow',
'shape': 'oval', 'fontsize': 10, 'width': 0.3,
'color': 'black'}
def __init__(self, label, **kwargs):
self._label = 'thr-{0}'.format(next(tids))
self.real_label = label
self.pos = 0
class Formatter(GraphFormatter):
def label(self, obj):
return obj and obj.label()
def node(self, obj):
scheme = dict(obj.scheme) if obj.pos else obj.scheme
if isinstance(obj, Thread):
scheme['label'] = obj.real_label
return self.draw_node(
obj, dict(self.node_scheme, **scheme),
)
def terminal_node(self, obj):
return self.draw_node(
obj, dict(self.term_scheme, **obj.scheme),
)
def edge(self, a, b, **attrs):
if isinstance(a, Thread):
attrs.update(arrowhead='none', arrowtail='tee')
return self.draw_edge(a, b, self.edge_scheme, attrs)
def subscript(n):
S = {'0': '₀', '1': '₁', '2': '₂', '3': '₃', '4': '₄',
'5': '₅', '6': '₆', '7': '₇', '8': '₈', '9': '₉'}
return ''.join([S[i] for i in str(n)])
class Worker(Node):
pass
class Backend(Node):
scheme = {'shape': 'folder', 'width': 2,
'height': 1, 'color': 'black',
'fillcolor': 'peachpuff3', 'color': 'peachpuff4'}
def label(self):
return generic_label(self) if generic else self._label
class Broker(Node):
scheme = {'shape': 'circle', 'fillcolor': 'cadetblue3',
'color': 'cadetblue4', 'height': 1}
def label(self):
return generic_label(self) if generic else self._label
from itertools import count
tids = count(1)
Wmax = int(args.get('wmax', 4) or 0)
Tmax = int(args.get('tmax', 3) or 0)
def maybe_abbr(l, name, max=Wmax):
size = len(l)
abbr = max and size > max
if 'enumerate' in args:
l = ['{0}{1}'.format(name, subscript(i + 1))
for i, obj in enumerate(l)]
if abbr:
l = l[0:max - 1] + [l[size - 1]]
l[max - 2] = '{0}⎨…{1}⎬'.format(
name[0], subscript(size - (max - 1)))
return l
try:
workers = args['nodes']
threads = args.get('threads') or []
except KeyError:
replies = self.app.control.inspect().stats()
workers, threads = [], []
for worker, reply in items(replies):
workers.append(worker)
threads.append(reply['pool']['max-concurrency'])
wlen = len(workers)
backend = args.get('backend', self.app.conf.result_backend)
threads_for = {}
workers = maybe_abbr(workers, 'Worker')
if Wmax and wlen > Wmax:
threads = threads[0:3] + [threads[-1]]
for i, threads in enumerate(threads):
threads_for[workers[i]] = maybe_abbr(
list(range(int(threads))), 'P', Tmax,
)
broker = Broker(args.get(
'broker', self.app.connection_for_read().as_uri()))
backend = Backend(backend) if backend else None
graph = DependencyGraph(formatter=Formatter())
graph.add_arc(broker)
if backend:
graph.add_arc(backend)
curworker = [0]
for i, worker in enumerate(workers):
worker = Worker(worker, pos=i)
graph.add_arc(worker)
graph.add_edge(worker, broker)
if backend:
graph.add_edge(worker, backend)
threads = threads_for.get(worker._label)
if threads:
for thread in threads:
thread = Thread(thread)
graph.add_arc(thread)
graph.add_edge(thread, worker)
curworker[0] += 1
graph.to_dot(self.stdout)
| [
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]
| |
4eb09dfed6ad25c8eddd6132f2dc73dff3fcc6a3 | 1933ef2c5b3ec58feeb50dd092d670f58a3ec2bb | /kospeech/models/modules.py | 352b6a0bd0bf59f8861fa3d7e573569560a2ad30 | [
"Apache-2.0"
]
| permissive | hephaex/KoSpeech | 68275af311ae5c53548f7c7bc27fe9dd5b1e441b | bf3fa0dc6d50089164fd0b47e02620062718d407 | refs/heads/master | 2022-12-02T02:00:01.164265 | 2020-08-05T08:47:55 | 2020-08-05T08:47:55 | 285,344,731 | 0 | 0 | Apache-2.0 | 2020-08-12T14:53:11 | 2020-08-05T16:22:59 | null | UTF-8 | Python | false | false | 1,579 | py | import torch
import torch.nn as nn
import torch.nn.init as init
from torch import Tensor
class Linear(nn.Module):
"""
Wrapper class of torch.nn.Linear
Weight initialize by xavier initialization and bias initialize to zeros.
"""
def __init__(self, in_features: int, out_features: int, bias: bool = True) -> None:
super(Linear, self).__init__()
self.linear = nn.Linear(in_features, out_features, bias=bias)
init.xavier_uniform_(self.linear.weight)
if bias:
init.zeros_(self.linear.bias)
def forward(self, x: Tensor) -> Tensor:
return self.linear(x)
class LayerNorm(nn.Module):
""" Wrapper class of torch.nn.LayerNorm """
def __init__(self, dim: int, eps: float = 1e-6) -> None:
super(LayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(dim))
self.beta = nn.Parameter(torch.zeros(dim))
self.eps = eps
def forward(self, z: Tensor) -> Tensor:
mean = z.mean(dim=-1, keepdim=True)
std = z.std(dim=-1, keepdim=True)
output = (z - mean) / (std + self.eps)
output = self.gamma * output + self.beta
return output
class View(nn.Module):
""" Wrapper class of torch.view() for Sequential module. """
def __init__(self, shape: tuple, contiguous: bool = False):
super(View, self).__init__()
self.shape = shape
self.contiguous = contiguous
def forward(self, inputs):
if self.contiguous:
inputs = inputs.contiguous()
return inputs.view(*self.shape)
| [
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525051e2943540875900fe0b6db434ee527c30ba | 80d50ea48e10674b1b7d3f583a1c4b7d0b01200f | /examples/v1/usage-metering/GetUsageNetworkFlows_1239422069.py | 60afb66b6f88d5918aba22ca4b3b72c0ab5be76d | [
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"MPL-2.0"
]
| permissive | DataDog/datadog-api-client-python | 3e01fa630278ad0b5c7005f08b7f61d07aa87345 | 392de360e7de659ee25e4a6753706820ca7c6a92 | refs/heads/master | 2023-09-01T20:32:37.718187 | 2023-09-01T14:42:04 | 2023-09-01T14:42:04 | 193,793,657 | 82 | 36 | Apache-2.0 | 2023-09-14T18:22:39 | 2019-06-25T22:52:04 | Python | UTF-8 | Python | false | false | 599 | py | """
Get hourly usage for Network Flows returns "OK" response
"""
from datetime import datetime
from dateutil.relativedelta import relativedelta
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v1.api.usage_metering_api import UsageMeteringApi
configuration = Configuration()
with ApiClient(configuration) as api_client:
api_instance = UsageMeteringApi(api_client)
response = api_instance.get_usage_network_flows(
start_hr=(datetime.now() + relativedelta(days=-5)),
end_hr=(datetime.now() + relativedelta(days=-3)),
)
print(response)
| [
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]
| |
16ffe2ce0b7d1d05344cc7814fd04b63e4a84196 | 98c6ea9c884152e8340605a706efefbea6170be5 | /examples/data/Assignment_4/hrbmax002/piglatin.py | 32eb09647dd7f6c75cec56edc0b28a10e8811327 | []
| no_license | MrHamdulay/csc3-capstone | 479d659e1dcd28040e83ebd9e3374d0ccc0c6817 | 6f0fa0fa1555ceb1b0fb33f25e9694e68b6a53d2 | refs/heads/master | 2021-03-12T21:55:57.781339 | 2014-09-22T02:22:22 | 2014-09-22T02:22:22 | 22,372,174 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 998 | py | def toPigLatin(s):
if s[len(s)-1] != " ":
s = s + " "
answer = ""
while len(s)>0:
temp = s[0:s.index(" ")]
s = s[s.index(" ")+1:]
if temp[0].upper() in ["A","E","I","O","U"]:
temp = temp + "way "
else:
temp = temp + "a"
while temp[0].upper() not in ["A","E","I","O","U"]:
temp = temp[1:] + temp[0]
temp = temp + "ay "
answer = answer + temp
answer = answer[0:len(answer)-1]
return answer
def toEnglish(s):
if s[len(s)-1] != " ":
s = s + " "
answer = ""
while len(s)>0:
temp = s[0:s.index(" ")]
s = s[s.index(" ")+1:]
if temp[-3:]=="way":
answer = answer + " " + temp[0:-3]
else:
temp = temp[0:-2]
while temp[-1] != "a":
temp = temp[-1] + temp[0:-1]
answer = answer + " " + temp[0:-1]
return answer[1:] | [
"[email protected]"
]
| |
eb085418aab782c970d7166273fd9b9262c46f5b | c858d9511cdb6a6ca723cd2dd05827d281fa764d | /MFTU/lesson 7/Test work/test_F.py | b6885f38ec053864866d442146f62a2ba115c3a5 | []
| no_license | DontTouchMyMind/education | 0c904aa929cb5349d7af7e06d9b1bbaab972ef95 | 32a53eb4086b730cc116e633f68cf01f3d4ec1d1 | refs/heads/master | 2021-03-12T11:15:02.479779 | 2020-09-17T08:19:50 | 2020-09-17T08:19:50 | 246,616,542 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 577 | py | # Необходимо найти НОД двух чисел, используя алгоритм Евклида.
#
# Формат входных данных
# На вход подаются два натуральных числа, по числу в новой строке.
#
# Формат выходных данных
# Одно число - НОД входных чисел.
def gcd(a, b):
if a == b:
return a
elif a > b:
return gcd(a - b, b)
else:
return gcd(a, b - a)
n1 = int(input())
n2 = int(input())
print(gcd(n1, n2))
| [
"[email protected]"
]
| |
51705550782e5a0f8c41b524d7d0cf60b7edc565 | fcbf3ddca275606830d455a69df73e20ced6546a | /doc/conf.py | 9ca4ca664b3f765a31dd264254f24c060e447023 | [
"Apache-2.0"
]
| permissive | KarchinLab/probabilistic2020 | 5f56e30e0c8484ac524081dd022c0159f24508ce | 8e0b1b9578bd8189b1690dd2f17476c3305b98dc | refs/heads/master | 2023-07-26T12:06:28.647117 | 2019-07-28T12:37:50 | 2019-07-28T12:37:50 | 57,408,263 | 8 | 7 | Apache-2.0 | 2023-07-06T21:02:44 | 2016-04-29T19:32:49 | Python | UTF-8 | Python | false | false | 8,727 | py | # -*- coding: utf-8 -*-
#
# 20/20 Permutation Test documentation build configuration file, created by
# sphinx-quickstart on Mon Jul 28 13:53:42 2014.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath('..'))
sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.abspath('./img'))
# on_rtd is whether we are on readthedocs.org, this line of code grabbed from docs.readthedocs.org
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if not on_rtd: # only import and set the theme if we're building docs locally
import sphinx_rtd_theme
html_theme = 'sphinx_rtd_theme'
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.autodoc',
'sphinx.ext.doctest',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode',
#'numpydoc',
#'IPython.sphinxext.ipython_console_highlighting',
#'IPython.sphinxext.ipython_directive',
#'matplotlib.sphinxext.plot_directive'
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'Probabilistic 20/20'
copyright = u'2014-19, Collin Tokheim'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '1.2'
# The full version, including alpha/beta/rc tags.
release = '1.2.3'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
# html_theme = 'sphinx_rtd_theme'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
#html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'Probabilistic2020doc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'Probabilistic2020.tex', u'Probabilistic 20/20 Documentation',
u'Collin Tokheim', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'Probabilistic 20/20 Documentation', u'Probabilistic 20/20 Documentation',
[u'Collin Tokheim'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'Probabilistic2020', u'Probabilistic 20/20 Documentation',
u'Collin Tokheim', 'Probabilistic2020', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
| [
"[email protected]"
]
| |
6450073c33cb50db18dc4b145b95d18e75ee47b0 | e2d22f12f8e540a80d31de9debe775d35c3c5c22 | /blousebrothers/confs/migrations/0037_auto_20170117_1535.py | 6841343b2a40c2fbb431ff15ae9ddfd4cd5a80ee | [
"MIT"
]
| permissive | sladinji/blousebrothers | 360c3b78ec43379977dbf470e5721e6a695b2354 | 461de3ba011c0aaed3f0014136c4497b6890d086 | refs/heads/master | 2022-12-20T10:24:07.631454 | 2019-06-13T13:17:35 | 2019-06-13T13:17:35 | 66,867,705 | 1 | 0 | NOASSERTION | 2022-12-19T18:15:44 | 2016-08-29T18:04:33 | Python | UTF-8 | Python | false | false | 813 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.9.7 on 2017-01-17 15:35
from __future__ import unicode_literals
from decimal import Decimal
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('confs', '0036_auto_20170110_1100'),
]
operations = [
migrations.AlterField(
model_name='conference',
name='price',
field=models.DecimalField(decimal_places=2, default=Decimal('0.5'), help_text='', max_digits=6, verbose_name='Prix de vente'),
),
migrations.AlterField(
model_name='conference',
name='type',
field=models.CharField(choices=[('DCP', 'DCP'), ('QI', 'QI'), ('LCA', 'LCA')], default='DP', max_length=10, verbose_name='Type'),
),
]
| [
"[email protected]"
]
| |
c8e2155ef68a3eba87ea0e8c4cab9b582c3f5355 | 8bc3e7bd0fa1714b3d0466e940ed801cf9a4c5d4 | /pyvisual/node/io/system_var.py | 2e6dfeaf5a70761d5951b4abff26e7ec2a04eaae | []
| no_license | m0r13/pyvisual | d99b3512fefaf4a2164362a0b7aabd1df9ecee03 | f6b3e2217e647b80f1379716c00e8adb53975bca | refs/heads/master | 2022-02-21T22:24:22.467475 | 2019-06-17T20:38:48 | 2019-06-17T20:38:48 | 140,211,941 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,802 | py | import json
import os
import time
from collections import defaultdict, OrderedDict
import imgui
from pyvisual.node import dtype, value
from pyvisual.node.base import Node
from pyvisual.editor import widget
SERIALIZATION_WRITE_INTERVAL = 5.0
SERIALIZATION_FILE = "system_vars.json"
# if you add another variable with another dtype than here, add the name of the dtype below!
VARIABLES = OrderedDict([
("gain", {"dtype" : dtype.float, "dtype_args" : {"default" : 4.0, "range" : [0.0, float("inf")]}}),
("threshold", {"dtype" : dtype.float, "dtype_args" : {"default" : 0.4, "range" : [0.0, float("inf")]}}),
("ref_aspect", {"dtype" : dtype.str, "dtype_args" : {"default" : "16:9"}}),
("ref_highres_height", {"dtype" : dtype.int, "dtype_args" : {"default" : 1080, "range" : [0, float("inf")]}}),
("ref_lowres_height", {"dtype" : dtype.int, "dtype_args" : {"default" : 720, "range" : [0, float("inf")]}}),
("ref_noiseres_height", {"dtype" : dtype.int, "dtype_args" : {"default" : 512, "range" : [0, float("inf")]}}),
])
# name -> value for each variable
values = OrderedDict()
# name -> widget for each variable
widgets = OrderedDict()
# dtype -> list of (name, value)
values_by_dtype = defaultdict(lambda: [])
# initialize values and widgets that are associated with variables
for name, spec in VARIABLES.items():
assert "dtype" in spec
dt = spec["dtype"]
dt_args = spec.get("dtype_args", {})
default_value = dt.default
if "default" in dt_args:
default_value = dt_args["default"]
v = value.SettableValue(default_value)
w = widget.create_widget(dt, dt_args)
w.width = widget.WIDGET_WIDTH * 1.5
values[name] = v
values_by_dtype[dt].append((name, v))
widgets[name] = w
_variables_dirty = False
_variables_last_written = 0
_node_instances = set()
# Important: Call this when changed a value! (Is done by editor for example)
def notify_change():
global _variables_dirty
_variables_dirty = True
for instance in _node_instances:
instance.force_evaluate()
# if the nodes would take over the values if they are changed only,
# then this would need to be changed probably
for value in values.values():
value.reset_changed()
def read_variables():
serialized_values = {}
if not os.path.isfile(SERIALIZATION_FILE):
return
serialized_values = json.load(open(SERIALIZATION_FILE))
for name, serialized_value in serialized_values.items():
if name not in VARIABLES:
continue
value = values[name]
dt = VARIABLES[name]["dtype"]
value.value = dt.base_type.unserialize(serialized_values[name])
notify_change()
read_variables()
def write_variables(force=False):
global _variables_dirty, _variables_last_written
if force or time.time() - _variables_last_written > SERIALIZATION_WRITE_INTERVAL:
_variables_dirty = False
_variables_last_written = time.time()
data = {}
for name, spec in VARIABLES.items():
value = values[name].value
data[name] = spec["dtype"].base_type.serialize(value)
with open("system_vars.json", "w") as f:
json.dump(data, f)
class GetSystemVar(Node):
DTYPE = None
class Meta:
inputs = [
{"name" : "name", "dtype" : dtype.str, "hide" : True}
]
options = {
"virtual" : True
}
def __init__(self):
super().__init__()
self._value = None
@property
def collapsed_node_title(self):
return "get system var: %s" % self.get("name")
def start(self, graph):
_node_instances.add(self)
name = self.get("name")
if name:
self._value = values.get(name, None)
if self._value is None:
self.get_input("name").value = ""
def _evaluate(self):
output = self.get_output("output")
if self._value != None:
output.value = self._value.value
def stop(self):
_node_instances.remove(self)
def _show_custom_ui(self):
selected_name = self.get("name")
preview = selected_name if selected_name else "<none>"
if imgui.begin_combo("", preview):
is_selected = not selected_name
opened, selected = imgui.selectable("<none>", is_selected)
if opened:
self.get_input("name").value = ""
self._value = None
if is_selected:
imgui.set_item_default_focus()
imgui.separator()
for name, value in values_by_dtype.get(self.DTYPE, []):
is_selected = name == selected_name
opened, selected = imgui.selectable(name, is_selected)
if opened:
self.get_input("name").value = name
self._value = value
if is_selected:
imgui.set_item_default_focus()
imgui.end_combo()
@classmethod
def get_presets(cls, graph):
presets = []
for name, value in values_by_dtype.get(cls.DTYPE, []):
presets.append((name, {"i_name" : name}))
return presets
dtype_capital_names = {
dtype.float : "Float",
dtype.str : "Str",
dtype.int : "Int",
}
# create a GetXXXSystemVar class for each dtype
node_classes = []
for dt in values_by_dtype.keys():
name = "Get%sSystemVar" % dtype_capital_names[dt]
class Meta:
outputs = [
{"name" : "output", "dtype" : dt, "manual_input": True},
]
options = {
"virtual" : False,
"show_title" : False
}
cls = type(name, (GetSystemVar,), {"DTYPE" : dt, "Meta" : Meta, "__module__" : __name__})
node_classes.append(cls)
| [
"[email protected]"
]
| |
1465bbad98fe6c51d22d31a82efaa6fba3362f45 | e8a285cb1dcdae6f1b6d8506b8d25a1d031d6cd7 | /cpptools/tests/test_write_pythia_hepmc3.py | d4e73a3185bc0137d2756b3b3f25a6b491647b97 | []
| no_license | matplo/heppy | f30558e4ff3c1720c63b4d82f739b3f8acadc53e | 88c931e3e7dcf57a3a476ef0a92f0204491cafb9 | refs/heads/master | 2023-07-07T18:17:04.486149 | 2023-06-29T20:45:32 | 2023-06-29T20:45:32 | 201,352,733 | 5 | 8 | null | 2023-07-04T21:57:31 | 2019-08-08T23:33:39 | C | UTF-8 | Python | false | false | 782 | py | #!/usr/bin/env python
import pythia8
import pythiahepmc3
def create_and_init_pythia(config_strings=[]):
pythia = pythia8.Pythia()
for s in config_strings:
pythia.readString(s)
for extra_s in ["Next:numberShowEvent = 0", "Next:numberShowInfo = 0", "Next:numberShowProcess = 0", "Next:numberCount = 0"]:
pythia.readString(extra_s)
if pythia.init():
return pythia
return None
def main():
pythia = create_and_init_pythia(["PhaseSpace:pTHatMin = 2", "HardQCD:all = on"])
sfoutname = "test_write_pythia_hepmc3.dat"
pyhepmcwriter = pythiahepmc3.Pythia8HepMCWrapper(sfoutname)
for iEvent in range(100):
if not pythia.next(): continue
pyhepmcwriter.fillEvent(pythia)
pythia.stat()
print("[i] done writing to {}".format(sfoutname))
if __name__ == '__main__':
main()
| [
"[email protected]"
]
| |
b1dc9e505c919a677e4ad516ba5eb32f5820c244 | 610dedfb6e21d297e8cdbcba599a4e564bd785cb | /EstruturaDeRepeticao/estruturaderepeticao-09.py | 8b4c1153a41989cbf2047c8067840d6a96441880 | []
| no_license | zumbipy/PythonExercicios | f7b2ddf2376b9ecb2aedc77531e3571dc746a12b | 7a17b78cf927a2889b93238542e90e00810c43e6 | refs/heads/master | 2021-01-23T10:43:47.997462 | 2018-07-22T14:58:44 | 2018-07-22T14:58:44 | 93,086,120 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 682 | py | # Telegram: @ZumbiPy __ _ ___
# /_ / __ ____ _ / / (_) _ \__ __
# / /_/ // / ' \/ _ \/ / ___/ // /
# /___/\_,_/_/_/_/_.__/_/_/ \_, /
# E-mail: [email protected] /___/
"""
09 - Faça um programa que imprima na tela apenas os números
ímpares entre 1 e 50.
"""
# ================================================================================
# Logica do Programa.
# ================================================================================
for i in range(1, 50):
# Quando resto de uma divisao por 2 for 0 ele e par se nao e ímpar.
if i % 2 != 0:
print(i)
print("=" * 72)
# ou
for i in range(1, 50, 2):
print(i)
| [
"[email protected]"
]
| |
dc6940ccab54fe26f6cdd8418152ac93e3a870f6 | 080c13cd91a073457bd9eddc2a3d13fc2e0e56ae | /MY_REPOS/awesome-4-new-developers/tensorflow-master/tensorflow/python/tpu/feature_column_v2.py | 1a5bddb173a599ee196c98ef4cd8bf3483151377 | [
"Apache-2.0"
]
| permissive | Portfolio-Projects42/UsefulResourceRepo2.0 | 1dccc8961a09347f124d3ed7c27c6d73b9806189 | 75b1e23c757845b5f1894ebe53551a1cf759c6a3 | refs/heads/master | 2023-08-04T12:23:48.862451 | 2021-09-15T12:51:35 | 2021-09-15T12:51:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 50,102 | py | # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===================================================================
"""TPU Feature Column Library."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import enum
from tensorflow.python.feature_column import feature_column as fc
from tensorflow.python.feature_column import feature_column_lib as fc_lib
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.tpu import tpu
from tensorflow.python.tpu.feature_column import _is_running_on_cpu
from tensorflow.python.tpu.feature_column import _record_variable_scope_and_name
from tensorflow.python.tpu.feature_column import _SUPPORTED_CATEGORICAL_COLUMNS_V2
from tensorflow.python.tpu.feature_column import _SUPPORTED_SEQUENCE_COLUMNS
from tensorflow.python.tpu.feature_column import _TPUBaseEmbeddingColumn
from tensorflow.python.util.tf_export import tf_export
# pylint: disable=protected-access
_ALLOWED_DEVICES = ["cpu", "tpu_tensor_core", "tpu_embedding_core"]
_TENSOR_CORE_MASK_KEY_SUFFIX = "__TENSOR_CORE_MASK"
class EmbeddingDevice(enum.Enum):
CPU = 1
TPU_TENSOR_CORE = 2
TPU_EMBEDDING_CORE = 3
@tf_export(v1=["tpu.experimental.embedding_column"])
def embedding_column_v2(
categorical_column,
dimension,
combiner="mean",
initializer=None,
max_sequence_length=0,
learning_rate_fn=None,
embedding_lookup_device=None,
tensor_core_shape=None,
use_safe_embedding_lookup=True,
):
"""TPU version of `tf.compat.v1.feature_column.embedding_column`.
Note that the interface for `tf.tpu.experimental.embedding_column` is
different from that of `tf.compat.v1.feature_column.embedding_column`: The
following arguments are NOT supported: `ckpt_to_load_from`,
`tensor_name_in_ckpt`, `max_norm` and `trainable`.
Use this function in place of `tf.compat.v1.feature_column.embedding_column`
when you want to use the TPU to accelerate your embedding lookups via TPU
embeddings.
```
column = tf.feature_column.categorical_column_with_identity(...)
tpu_column = tf.tpu.experimental.embedding_column(column, 10)
...
def model_fn(features):
dense_feature = tf.keras.layers.DenseFeature(tpu_column)
embedded_feature = dense_feature(features)
...
estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn,
...
embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
column=[tpu_column],
...))
```
Args:
categorical_column: A categorical column returned from
`categorical_column_with_identity`, `weighted_categorical_column`,
`categorical_column_with_vocabulary_file`,
`categorical_column_with_vocabulary_list`,
`sequence_categorical_column_with_identity`,
`sequence_categorical_column_with_vocabulary_file`,
`sequence_categorical_column_with_vocabulary_list`
dimension: An integer specifying dimension of the embedding, must be > 0.
combiner: A string specifying how to reduce if there are multiple entries
in a single row for a non-sequence column. For more information, see
`tf.feature_column.embedding_column`.
initializer: A variable initializer function to be used in embedding
variable initialization. If not specified, defaults to
`tf.compat.v1.truncated_normal_initializer` with mean `0.0` and
standard deviation `1/sqrt(dimension)`.
max_sequence_length: An non-negative integer specifying the max sequence
length. Any sequence shorter then this will be padded with 0 embeddings
and any sequence longer will be truncated. This must be positive for
sequence features and 0 for non-sequence features.
learning_rate_fn: A function that takes global step and returns learning
rate for the embedding table. If you intend to use the same learning rate
for multiple embedding tables, please ensure that you pass the exact same
python function to all calls of embedding_column, otherwise performence
may suffer.
embedding_lookup_device: The device on which to run the embedding lookup.
Valid options are "cpu", "tpu_tensor_core", and "tpu_embedding_core".
If specifying "tpu_tensor_core", a tensor_core_shape must be supplied.
If not specified, the default behavior is embedding lookup on
"tpu_embedding_core" for training and "cpu" for inference.
Valid options for training : ["tpu_embedding_core", "tpu_tensor_core"]
Valid options for serving : ["cpu", "tpu_tensor_core"]
For training, tpu_embedding_core is good for large embedding vocab (>1M),
otherwise, tpu_tensor_core is often sufficient.
For serving, doing embedding lookup on tpu_tensor_core during serving is
a way to reduce host cpu usage in cases where that is a bottleneck.
tensor_core_shape: If supplied, a list of integers which specifies
the intended dense shape to run embedding lookup for this feature on
TensorCore. The batch dimension can be left None or -1 to indicate
a dynamic shape. Only rank 2 shapes currently supported.
use_safe_embedding_lookup: If true, uses safe_embedding_lookup_sparse
instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures
there are no empty rows and all weights and ids are positive at the
expense of extra compute cost. This only applies to rank 2 (NxM) shaped
input tensors. Defaults to true, consider turning off if the above checks
are not needed. Note that having empty rows will not trigger any error
though the output result might be 0 or omitted.
Returns:
A `_TPUEmbeddingColumnV2`.
Raises:
ValueError: if `dimension` not > 0.
ValueError: if `initializer` is specified but not callable.
"""
if not isinstance(categorical_column, _SUPPORTED_CATEGORICAL_COLUMNS_V2):
raise TypeError(
"categorical_column for tpu "
" embedding_column must be type %s, got %s."
% (
" or ".join([cc.__name__ for cc in _SUPPORTED_CATEGORICAL_COLUMNS_V2]),
type(categorical_column),
)
)
if (dimension is None) or (dimension < 1):
raise ValueError("Invalid dimension {}.".format(dimension))
if tensor_core_shape and len(tensor_core_shape) != 2:
raise ValueError(
"tensor_core_shape must be size 2. Got {}.".format(tensor_core_shape)
)
if (initializer is not None) and (not callable(initializer)):
raise ValueError(
"initializer must be callable if specified. "
"Embedding of column_name: {}".format(categorical_column.name)
)
if initializer is None:
initializer = init_ops.truncated_normal_initializer(
mean=0.0, stddev=1 / math.sqrt(dimension)
)
if embedding_lookup_device and embedding_lookup_device not in _ALLOWED_DEVICES:
raise ValueError(
"If set, embedding_lookup_device must be in ", _ALLOWED_DEVICES
)
if embedding_lookup_device == "cpu":
embedding_lookup_device = EmbeddingDevice.CPU
elif embedding_lookup_device == "tpu_tensor_core":
embedding_lookup_device = EmbeddingDevice.TPU_TENSOR_CORE
elif embedding_lookup_device == "tpu_embedding_core":
embedding_lookup_device = EmbeddingDevice.TPU_EMBEDDING_CORE
if embedding_lookup_device == EmbeddingDevice.TPU_TENSOR_CORE:
if not tensor_core_shape:
raise ValueError(
"Using embedding_lookup_device=tpu_tensor_core requires "
"tensor_core_shape to be set."
)
if isinstance(categorical_column, _SUPPORTED_SEQUENCE_COLUMNS):
raise ValueError(
"embedding_lookup_device=tpu_tensor_core currently does "
"not support sequence columns."
)
if not embedding_lookup_device:
return _TPUEmbeddingColumnV2(
categorical_column=categorical_column,
dimension=dimension,
combiner=combiner,
initializer=initializer,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
else:
return _TPUDeviceSpecificEmbeddingColumnV2(
categorical_column=categorical_column,
dimension=dimension,
combiner=combiner,
initializer=initializer,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
embedding_lookup_device=embedding_lookup_device,
tensor_core_shape=tensor_core_shape,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
@tf_export(v1=["tpu.experimental.shared_embedding_columns"])
def shared_embedding_columns_v2(
categorical_columns,
dimension,
combiner="mean",
initializer=None,
shared_embedding_collection_name=None,
max_sequence_lengths=None,
learning_rate_fn=None,
embedding_lookup_device=None,
tensor_core_shape=None,
use_safe_embedding_lookup=True,
):
"""TPU version of `tf.compat.v1.feature_column.shared_embedding_columns`.
Note that the interface for `tf.tpu.experimental.shared_embedding_columns` is
different from that of `tf.compat.v1.feature_column.shared_embedding_columns`:
The following arguments are NOT supported: `ckpt_to_load_from`,
`tensor_name_in_ckpt`, `max_norm` and `trainable`.
Use this function in place of
tf.compat.v1.feature_column.shared_embedding_columns` when you want to use the
TPU to accelerate your embedding lookups via TPU embeddings.
```
column_a = tf.feature_column.categorical_column_with_identity(...)
column_b = tf.feature_column.categorical_column_with_identity(...)
tpu_columns = tf.tpu.experimental.shared_embedding_columns(
[column_a, column_b], 10)
...
def model_fn(features):
dense_feature = tf.keras.layers.DenseFeature(tpu_columns)
embedded_feature = dense_feature(features)
...
estimator = tf.estimator.tpu.TPUEstimator(
model_fn=model_fn,
...
embedding_config_spec=tf.estimator.tpu.experimental.EmbeddingConfigSpec(
column=tpu_columns,
...))
```
Args:
categorical_columns: A list of categorical columns returned from
`categorical_column_with_identity`, `weighted_categorical_column`,
`categorical_column_with_vocabulary_file`,
`categorical_column_with_vocabulary_list`,
`sequence_categorical_column_with_identity`,
`sequence_categorical_column_with_vocabulary_file`,
`sequence_categorical_column_with_vocabulary_list`
dimension: An integer specifying dimension of the embedding, must be > 0.
combiner: A string specifying how to reduce if there are multiple entries in
a single row for a non-sequence column. For more information, see
`tf.feature_column.embedding_column`.
initializer: A variable initializer function to be used in embedding
variable initialization. If not specified, defaults to
`tf.truncated_normal_initializer` with mean `0.0` and standard deviation
`1/sqrt(dimension)`.
shared_embedding_collection_name: Optional name of the collection where
shared embedding weights are added. If not given, a reasonable name will
be chosen based on the names of `categorical_columns`. This is also used
in `variable_scope` when creating shared embedding weights.
max_sequence_lengths: An list of non-negative integers, either None or empty
or the same length as the argument categorical_columns. Entries
corresponding to non-sequence columns must be 0 and entries corresponding
to sequence columns specify the max sequence length for the column. Any
sequence shorter then this will be padded with 0 embeddings and any
sequence longer will be truncated.
learning_rate_fn: A function that takes global step and returns learning
rate for the embedding table. If you intend to use the same learning rate
for multiple embedding tables, please ensure that you pass the exact same
python function to all calls of shared_embedding_columns, otherwise
performence may suffer.
embedding_lookup_device: The device on which to run the embedding lookup.
Valid options are "cpu", "tpu_tensor_core", and "tpu_embedding_core". If
specifying "tpu_tensor_core", a tensor_core_shape must be supplied.
Defaults to "cpu". If not specified, the default behavior is embedding
lookup on "tpu_embedding_core" for training and "cpu" for inference.
Valid options for training : ["tpu_embedding_core", "tpu_tensor_core"]
Valid options for serving : ["cpu", "tpu_tensor_core"]
For training, tpu_embedding_core is good for large embedding vocab (>1M),
otherwise, tpu_tensor_core is often sufficient.
For serving, doing embedding lookup on tpu_tensor_core during serving is
a way to reduce host cpu usage in cases where that is a bottleneck.
tensor_core_shape: If supplied, a list of integers which specifies the
intended dense shape to run embedding lookup for this feature on
TensorCore. The batch dimension can be left None or -1 to indicate a
dynamic shape. Only rank 2 shapes currently supported.
use_safe_embedding_lookup: If true, uses safe_embedding_lookup_sparse
instead of embedding_lookup_sparse. safe_embedding_lookup_sparse ensures
there are no empty rows and all weights and ids are positive at the
expense of extra compute cost. This only applies to rank 2 (NxM) shaped
input tensors. Defaults to true, consider turning off if the above checks
are not needed. Note that having empty rows will not trigger any error
though the output result might be 0 or omitted.
Returns:
A list of `_TPUSharedEmbeddingColumnV2`.
Raises:
ValueError: if `dimension` not > 0.
ValueError: if `initializer` is specified but not callable.
ValueError: if `max_sequence_lengths` is specified and not the same length
as `categorical_columns`.
ValueError: if `max_sequence_lengths` is positive for a non sequence column
or 0 for a sequence column.
"""
for categorical_column in categorical_columns:
if not isinstance(categorical_column, _SUPPORTED_CATEGORICAL_COLUMNS_V2):
raise TypeError(
"categorical_column for tpu "
" shared_embedding_columns must be type %s, got %s."
% (
" or ".join(
[cc.__name__ for cc in _SUPPORTED_CATEGORICAL_COLUMNS_V2]
),
type(categorical_column),
)
)
if not max_sequence_lengths:
max_sequence_lengths = [0] * len(categorical_columns)
if len(max_sequence_lengths) != len(categorical_columns):
raise ValueError(
"max_sequence_lengths and categorical_columns must be of "
"the same length. len(max_sequence_lengths)={} "
"len(categorical_columns)={}.".format(
len(max_sequence_lengths), len(categorical_columns)
)
)
if (dimension is None) or (dimension < 1):
raise ValueError("Invalid dimension {}.".format(dimension))
if tensor_core_shape and len(tensor_core_shape) != 2:
raise ValueError(
"tensor_core_shape must be size 2. Got {}.".format(tensor_core_shape)
)
if (initializer is not None) and (not callable(initializer)):
raise ValueError("initializer must be callable if specified. ")
if initializer is None:
initializer = init_ops.truncated_normal_initializer(
mean=0.0, stddev=1 / math.sqrt(dimension)
)
# Sort the columns so the default collection name is deterministic even if the
# user passes columns from an unsorted collection, such as dict.values().
sorted_columns = sorted(categorical_columns, key=lambda x: x.name)
num_buckets = sorted_columns[0]._num_buckets # pylint: disable=protected-access
for c in sorted_columns[1:]:
if num_buckets != c._num_buckets: # pylint: disable=protected-access
raise ValueError(
"To use shared_embedding_column, all categorical_columns must have "
"the same number of buckets. Given column: {} with buckets: {} does "
"not match column: {} with buckets: {}".format(
sorted_columns[0], num_buckets, c, c._num_buckets
)
) # pylint: disable=protected-access
if not shared_embedding_collection_name:
shared_embedding_collection_name = "_".join(c.name for c in sorted_columns)
shared_embedding_collection_name += "_shared_embedding"
tpu_columns = []
column_creator = fc_lib.SharedEmbeddingColumnCreator(
dimension=dimension,
initializer=initializer,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
num_buckets=num_buckets,
trainable=None,
name=shared_embedding_collection_name,
)
if embedding_lookup_device and embedding_lookup_device not in _ALLOWED_DEVICES:
raise ValueError(
"If set, embedding_lookup_device must be in ", _ALLOWED_DEVICES
)
if embedding_lookup_device == "cpu":
embedding_lookup_device = EmbeddingDevice.CPU
elif embedding_lookup_device == "tpu_tensor_core":
embedding_lookup_device = EmbeddingDevice.TPU_TENSOR_CORE
elif embedding_lookup_device == "tpu_embedding_core":
embedding_lookup_device = EmbeddingDevice.TPU_EMBEDDING_CORE
if embedding_lookup_device == EmbeddingDevice.TPU_TENSOR_CORE:
if not tensor_core_shape:
raise ValueError(
"Using embedding_lookup_device=tpu_tensor_core requires "
"tensor_core_shape to be set."
)
for c in sorted_columns:
if isinstance(c, _SUPPORTED_SEQUENCE_COLUMNS):
raise ValueError(
"embedding_lookup_device=tpu_tensor_core currently "
"does not support sequence columns."
)
# Create the state (_SharedEmbeddingColumnLayer) here.
for categorical_column, max_sequence_length in zip(
categorical_columns, max_sequence_lengths
):
if not embedding_lookup_device:
column = _TPUSharedEmbeddingColumnV2(
categorical_column=categorical_column,
shared_embedding_column_creator=column_creator,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
else:
column = _TPUSharedDeviceSpecificEmbeddingColumnV2(
categorical_column=categorical_column,
shared_embedding_column_creator=column_creator,
combiner=combiner,
initializer=initializer,
shared_embedding_collection_name=shared_embedding_collection_name,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
embedding_lookup_device=embedding_lookup_device,
tensor_core_shape=tensor_core_shape,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
tpu_columns.append(column)
return tpu_columns
class _TPUEmbeddingColumnV2(_TPUBaseEmbeddingColumn, fc_lib.EmbeddingColumn):
"""Core Embedding Column."""
def __new__(
cls,
categorical_column,
dimension,
combiner="mean",
initializer=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
bypass_scope_validation=False,
):
del bypass_scope_validation
# pylint: disable=redundant-keyword-arg
return fc_lib.EmbeddingColumn.__new__(
cls,
categorical_column,
dimension,
combiner=combiner,
initializer=initializer,
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
max_norm=None,
trainable=True,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
def __getnewargs__(self):
return (
self._tpu_categorical_column,
self.dimension,
self.combiner,
self.initializer,
self._max_sequence_length,
self._learning_rate_fn,
self.use_safe_embedding_lookup,
self._bypass_scope_validation,
)
def __deepcopy__(self, memo):
return _TPUEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__())
)
def __init__(
self,
categorical_column,
dimension,
combiner="mean",
initializer=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
bypass_scope_validation=False,
):
_TPUBaseEmbeddingColumn.__init__(
self,
categorical_column,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
)
self._key = None
# If true, scope validation is skipped to allow the same column to be used
# in multiple variable scopes. By default, this is False, and we expect a
# 1:1 mapping between feature columns and scopes.
self._bypass_scope_validation = bypass_scope_validation
def get_combiner(self):
return self.combiner
def get_embedding_table_size(self):
"""Returns num_ids and width."""
return (self.categorical_column._num_buckets, self.dimension)
def get_feature_key_name(self):
"""get_feature_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.categorical_column.name
return self.categorical_column.name
def get_weight_key_name(self):
"""get_weight_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.weight_feature_key
return None
def get_embedding_var_name(self):
"""get_embedding_var_name."""
return self.categorical_column.name
def get_initializer(self):
return self.initializer
def is_categorical_column_weighted(self):
"""Check if the categorical column of the embedding column is weighted."""
if isinstance(
self.categorical_column,
(
fc._WeightedCategoricalColumn, # pylint: disable=protected-access
fc_lib.WeightedCategoricalColumn,
),
):
return True
return False
def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn._get_dense_tensor(
self, inputs, weight_collections, trainable
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn._get_dense_tensor(
self, inputs, weight_collections, trainable
)
# TPU mode
# Get the embeddings from the LazyBuilder.
tensor = inputs.get(self.get_feature_key_name())
# Add to collection for _create_tpu_embedding_variables_and_ops
_record_variable_scope_and_name(
self.get_embedding_var_name(),
"embedding_weights",
bypass_scope_validation=self._bypass_scope_validation,
)
return tensor
def create_state(self, state_manager):
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn.create_state(self, state_manager)
# Create state is called for the EmbeddingColumn to create its embedding
# variables under feature column V2, if we are on TPU so record the scope
# here.
_record_variable_scope_and_name(
self.get_embedding_var_name(),
"embedding_weights",
bypass_scope_validation=self._bypass_scope_validation,
)
def get_dense_tensor(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn.get_dense_tensor(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn.get_dense_tensor(
self, transformation_cache, state_manager
)
# TPU mode
# Get the embeddings from the FeatureTransformationCache.
tensor = transformation_cache.get(self.get_feature_key_name(), state_manager)
return tensor
def _get_sequence_dense_tensor(
self, inputs, weight_collections=None, trainable=None
):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn._get_sequence_dense_tensor(
self, inputs, weight_collections, trainable
)
tensor = inputs.get(self.get_feature_key_name())
tensor_lengths = inputs.get(self.get_sequence_length_feature_key_name())
# inputs is a _LazyBuilder and for rank 1 tensors, it calls expand_dims(-1).
# We need to undo this to match the standard CPU sequence embedding.
tensor_lengths = array_ops.squeeze(tensor_lengths, -1)
# Add to collection for _create_tpu_embedding_variables_and_ops
_record_variable_scope_and_name(
self.get_embedding_var_name(),
"embedding_weights",
bypass_scope_validation=self._bypass_scope_validation,
)
return fc_lib.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=tensor, sequence_length=tensor_lengths
)
def get_sequence_dense_tensor(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.EmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.EmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
tensor = transformation_cache.get(self.get_feature_key_name(), state_manager)
tensor_lengths = transformation_cache.get(
self.get_sequence_length_feature_key_name(), state_manager
)
# FeatureTransformationCache expands rank 1 tensors (like sequence length)
# to rank 2. We need to undo this to match the standard CPU sequence
# embedding.
tensor_lengths = array_ops.squeeze(tensor_lengths, -1)
return fc_lib.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=tensor, sequence_length=tensor_lengths
)
class _TPUSharedEmbeddingColumnV2(
_TPUBaseEmbeddingColumn, fc_lib.SharedEmbeddingColumn
):
"""Core Shared Embedding Column."""
def __new__(
cls,
categorical_column,
shared_embedding_column_creator,
combiner="mean",
initializer=None,
shared_embedding_collection_name=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
):
# pylint: disable=redundant-keyword-arg
return fc_lib.SharedEmbeddingColumn.__new__(
cls,
categorical_column,
combiner=combiner,
shared_embedding_column_creator=shared_embedding_column_creator,
max_norm=None,
use_safe_embedding_lookup=use_safe_embedding_lookup,
)
def __getnewargs__(self):
return (
self._tpu_categorical_column,
self.shared_embedding_column_creator,
self.combiner,
self._initializer,
self._shared_embedding_collection_name,
self._max_sequence_length,
self._learning_rate_fn,
)
def __deepcopy__(self, memo):
return _TPUSharedEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__())
)
def __init__(
self,
categorical_column,
shared_embedding_column_creator,
combiner="mean",
initializer=None,
shared_embedding_collection_name=None,
max_sequence_length=0,
learning_rate_fn=None,
use_safe_embedding_lookup=True,
):
_TPUBaseEmbeddingColumn.__init__(
self,
categorical_column,
max_sequence_length=max_sequence_length,
learning_rate_fn=learning_rate_fn,
)
self._initializer = initializer
self._shared_embedding_collection_name = shared_embedding_collection_name
def get_combiner(self):
return self.combiner
def get_embedding_table_size(self):
"""Returns num_ids and width."""
return (
self.categorical_column._num_buckets,
self.shared_embedding_column_creator.dimension,
)
def get_feature_key_name(self):
"""get_feature_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.categorical_column.name
return self.categorical_column.name
def get_weight_key_name(self):
"""get_weight_key_name."""
if self.is_categorical_column_weighted():
return self.categorical_column.weight_feature_key
return None
def get_embedding_var_name(self):
"""get_embedding_var_name."""
return self._shared_embedding_collection_name
def get_initializer(self):
return self._initializer
def is_categorical_column_weighted(self):
"""Check if the categorical column of the embedding column is weighted."""
if isinstance(
self.categorical_column,
(
fc._WeightedCategoricalColumn, # pylint: disable=protected-access
fc_lib.WeightedCategoricalColumn,
),
):
return True
return False
def _get_dense_tensor_internal(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.SharedEmbeddingColumn._get_dense_tensor_internal(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.SharedEmbeddingColumn._get_dense_tensor_internal(
self, transformation_cache, state_manager
)
# TPU mode
# Get the embeddings from the FeatureTransformationCache.
tensor = transformation_cache.get(self.get_feature_key_name(), state_manager)
# Add to collection for _create_tpu_embedding_variables_and_ops
# Note that in Feature Column V2, shared embeddings have no scope.
_record_variable_scope_and_name(
self.get_embedding_var_name(),
self.shared_embedding_column_creator._name,
is_shared_embedding=True,
)
return tensor
def get_sequence_dense_tensor(self, transformation_cache, state_manager):
if tpu.under_tpu_inference_context():
def host_computation():
return fc_lib.SharedEmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
return tpu.outside_compilation(host_computation)
if _is_running_on_cpu():
return fc_lib.SharedEmbeddingColumn.get_sequence_dense_tensor(
self, transformation_cache, state_manager
)
tensor = self._get_dense_tensor_internal(transformation_cache, state_manager)
tensor_lengths = transformation_cache.get(
self.get_sequence_length_feature_key_name(), state_manager
)
# FeatureTransformationCache expands rank 1 tensors (like sequence length)
# to rank 2. We need to undo this to match the standard CPU sequence
# embedding.
tensor_lengths = array_ops.squeeze(tensor_lengths, -1)
return fc_lib.SequenceDenseColumn.TensorSequenceLengthPair(
dense_tensor=tensor, sequence_length=tensor_lengths
)
def split_sequence_columns_v2(feature_columns):
"""Split a list of _TPUEmbeddingColumn into sequence and non-sequence columns.
For use in a TPUEstimator model_fn function. E.g.
def model_fn(features):
sequence_columns, feature_columns = (
tf.tpu.feature_column.split_sequence_columns(feature_columns))
input = tf.feature_column.input_layer(
features=features, feature_columns=feature_columns)
sequence_features, sequence_lengths = (
tf.contrib.feature_column.sequence_input_layer(
features=features, feature_columns=sequence_columns))
Args:
feature_columns: A list of _TPUEmbeddingColumns to split.
Returns:
Two lists of _TPUEmbeddingColumns, the first is the sequence columns and the
second is the non-sequence columns.
"""
sequence_columns = []
non_sequence_columns = []
for column in feature_columns:
if not isinstance(column, (_TPUEmbeddingColumnV2, _TPUSharedEmbeddingColumnV2)):
raise TypeError(
"column must be a _TPUEmbeddingColumnV2 or "
"_TPUSharedEmbeddingColumnV2 but got %s instead." % (type(column))
)
if column.is_sequence_column():
sequence_columns.append(column)
else:
non_sequence_columns.append(column)
return sequence_columns, non_sequence_columns
def sparse_embedding_aggregate_slice(
params,
values_and_values_mask,
combiner="mean",
name="sparse_embedding_aggregate_slice",
):
"""Uses XLA's dynamic slice operations to perform embedding lookups.
From third_party/cloud_tpu/models/movielens/tpu_embedding.py
Args:
params: Tensor of embedding table. Rank 2 (table_size x embedding dim)
values_and_values_mask: is a two-tuple that contains: values - Tensor of
embedding indices. Rank 2 (batch x n_indices) values_mask - Tensor of mask
/ weights. Rank 2 (batch x n_indices)
combiner: The combiner to use for the embedding lookup. Currently supports
'sum' and 'mean'.
name: Optional name scope for created ops
Returns:
Rank 2 tensor of aggregated (per batch element) embedding vectors.
Raises:
ValueError: Combiner is not supported.
"""
values, values_mask = values_and_values_mask # unpack the two-tuple
with ops.name_scope(name):
_, embedding_dimension = params.get_shape().as_list()
n_batch, n_indices_padded = values.get_shape().as_list()
if not n_batch:
n_batch = -1
emb_lookup = array_ops.reshape(
embedding_ops.embedding_lookup(
params, array_ops.reshape(values, [n_batch, n_indices_padded])
),
[n_batch, n_indices_padded, embedding_dimension],
)
values_mask_broadcast = array_ops.reshape(
values_mask, [n_batch, n_indices_padded, 1]
)
aggregate_emb = math_ops.reduce_sum(emb_lookup * values_mask_broadcast, axis=1)
if combiner == "sum":
return aggregate_emb
elif combiner == "mean":
# In the case we have an empty row, both aggregate_emb and
# math_ops.reduce_sum(values_mask_broadcast, axis=1) will be 0. Thus,
# we can take max it with a non-zero value to prevent NaNs. Note that
# math_ops.reduce_sum(values_mask_broadcast, axis=1) will have integer
# values so 1.0 is the smallest value.
return aggregate_emb / math_ops.maximum(
math_ops.reduce_sum(values_mask_broadcast, axis=1), 1.0
)
else:
raise ValueError(
"Dense TPU Embedding does not support combiner "
"other than sum and mean."
)
def pad_sparse_embedding_lookup_indices(sparse_indices, padded_size):
"""Creates statically-sized Tensors containing indices and weights.
From third_party/cloud_tpu/models/movielens/tpu_embedding.py
Also computes sparse_indices.values % embedding_table_size, for equivalent
functionality to sparse_column_with_integerized_feature. The returned
padded weight Tensor also doubles as a mask indicating which values in
the returned padded indices Tensor are indices versus padded zeros.
Args:
sparse_indices: SparseTensor of embedding lookup indices.
padded_size: Number of columns of the returned Tensors. Indices which fall
out of bounds will be truncated to the padded size.
Returns:
(sparse_indices.values padded to the specified size,
a mask the same size as the returned padded values in which 0s
indicate padded locations and 1s (or values from sparse_weights)
indicate actual values)
"""
batch_size = sparse_indices.dense_shape[0]
sparse_indices = sparse_ops.sparse_slice(
sparse_indices, [0, 0], [batch_size, padded_size]
)
indices, values = sparse_indices.indices, sparse_indices.values
padded_values = array_ops.scatter_nd(
indices, math_ops.cast(values, dtypes.int32), shape=(batch_size, padded_size)
)
weights = array_ops.ones_like(values, dtype=dtypes.float32)
padded_mask = array_ops.scatter_nd(
indices, weights, shape=(batch_size, padded_size)
)
return padded_values, padded_mask
def _check_invalid_cases(embedding_lookup_device):
"""Checks for invalid embedding_lookup_device configurations."""
if (
tpu.under_tpu_inference_context()
and embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE
):
raise ValueError(
"Using embedding_lookup_device=tpu_embedding_core during inference "
"is not supported."
)
if embedding_lookup_device == EmbeddingDevice.CPU:
if not tpu.under_tpu_inference_context():
raise ValueError(
'Using TPUEmbeddingColumn with embedding_lookup_device="cpu" '
"during training is not supported."
)
class _TPUDeviceSpecificEmbeddingColumnV2(_TPUEmbeddingColumnV2):
"""TPUEmbeddingColumn which allows serving on TensorCore."""
def __new__(cls, *args, **kwargs):
# For __new__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
cls._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
cls._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
return _TPUEmbeddingColumnV2.__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# For __init__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
self._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
self._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
_TPUEmbeddingColumnV2.__init__(self, *args, **kwargs)
def __deepcopy__(self, memo):
return _TPUDeviceSpecificEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__()),
tensor_core_shape=self._tensor_core_shape,
embedding_lookup_device=self._embedding_lookup_device
)
def create_state(self, state_manager):
_check_invalid_cases(self._embedding_lookup_device)
# CPU case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return fc_lib.EmbeddingColumn.create_state(self, state_manager)
# TPU_EMBEDDING_CORE case.
elif self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self).create_state(
state_manager
)
# TPU_EMBEDDING_CORE case.
return fc_lib.EmbeddingColumn.create_state(self, state_manager)
def get_dense_tensor(self, transformation_cache, state_manager):
"""Private method that follows get_dense_tensor."""
_check_invalid_cases(self._embedding_lookup_device)
# CPU Case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self).get_dense_tensor(
transformation_cache, state_manager
)
# TPU_EMBEDDING_CORE case.
elif self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self).get_dense_tensor(
transformation_cache, state_manager
)
# TPU_EMBEDDING_CORE cases.
if tpu.under_tpu_inference_context():
# For inference, use outside compile to densify and pad the input tensors.
sparse_tensor = transformation_cache.get(
self.categorical_column.name, state_manager
)
def host_computation():
return pad_sparse_embedding_lookup_indices(
sparse_tensor, self._tensor_core_shape[1]
)
values, mask = tpu.outside_compilation(host_computation)
else:
# For training, the inputs should already have been densified and padded.
values = transformation_cache.get(
self.categorical_column.name, state_manager
)
mask = transformation_cache.get(
self.categorical_column.name + _TENSOR_CORE_MASK_KEY_SUFFIX,
state_manager,
)
embedding_weights = state_manager.get_variable(self, name="embedding_weights")
return sparse_embedding_aggregate_slice(
embedding_weights, (values, mask), self.get_combiner()
)
def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
_check_invalid_cases(self._embedding_lookup_device)
# CPU Case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self)._get_dense_tensor(
inputs, weight_collections, trainable
)
# TPU_EMBEDDING_CORE case.
elif self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(_TPUDeviceSpecificEmbeddingColumnV2, self)._get_dense_tensor(
inputs, weight_collections, trainable
)
# TPU_EMBEDDING_CORE cases.
if tpu.under_tpu_inference_context():
# For inference, use outside compile to densify and pad the input tensors.
sparse_tensor = inputs.get(self.get_feature_key_name())
def host_computation():
return pad_sparse_embedding_lookup_indices(
sparse_tensor, self._tensor_core_shape[1]
)
values, mask = tpu.outside_compilation(host_computation)
else:
# For training, the inputs should already have been densified and padded.
values = inputs.get(self.get_feature_key_name())
mask = inputs.get(
self.get_feature_key_name() + _TENSOR_CORE_MASK_KEY_SUFFIX
)
embedding_shape = (
self.categorical_column._num_buckets,
self.dimension,
) # pylint: disable=protected-access
if (
weight_collections
and ops.GraphKeys.GLOBAL_VARIABLES not in weight_collections
):
weight_collections.append(ops.GraphKeys.GLOBAL_VARIABLES)
embedding_weights = variable_scope.get_variable(
name="embedding_weights",
shape=embedding_shape,
dtype=dtypes.float32,
initializer=self.initializer,
trainable=self.trainable and trainable,
collections=weight_collections,
)
return sparse_embedding_aggregate_slice(
embedding_weights, (values, mask), self.get_combiner()
)
class _TPUSharedDeviceSpecificEmbeddingColumnV2(_TPUSharedEmbeddingColumnV2):
"""TPUSharedEmbeddingColumnV2 which allows serving on TensorCore."""
def __new__(cls, *args, **kwargs):
# For __new__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
cls._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
cls._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
return _TPUSharedEmbeddingColumnV2.__new__(cls, *args, **kwargs)
def __init__(self, *args, **kwargs):
# For __init__, just capture the inference dense shape and call parent.
if "tensor_core_shape" in kwargs:
self._tensor_core_shape = kwargs["tensor_core_shape"]
del kwargs["tensor_core_shape"]
if "embedding_lookup_device" in kwargs:
self._embedding_lookup_device = kwargs["embedding_lookup_device"]
del kwargs["embedding_lookup_device"]
_TPUSharedEmbeddingColumnV2.__init__(self, *args, **kwargs)
def __deepcopy__(self, memo):
return _TPUSharedDeviceSpecificEmbeddingColumnV2(
*(copy.deepcopy(a, memo) for a in self.__getnewargs__()),
tensor_core_shape=self._tensor_core_shape,
embedding_lookup_device=self._embedding_lookup_device
)
def _get_dense_tensor_internal(self, transformation_cache, state_manager):
"""Private method that follows _get_dense_tensor_internal."""
_check_invalid_cases(self._embedding_lookup_device)
# CPU Case.
is_cpu = self._embedding_lookup_device == EmbeddingDevice.CPU
is_cpu = is_cpu or _is_running_on_cpu()
if is_cpu:
return super(
_TPUSharedDeviceSpecificEmbeddingColumnV2, self
)._get_dense_tensor_internal(transformation_cache, state_manager)
# TPU_EMBEDDING_CORE case.
if self._embedding_lookup_device == EmbeddingDevice.TPU_EMBEDDING_CORE:
return super(
_TPUSharedDeviceSpecificEmbeddingColumnV2, self
)._get_dense_tensor_internal(transformation_cache, state_manager)
# TPU_EMBEDDING_CORE cases.
if tpu.under_tpu_inference_context():
# For inference, use outside compile to densify and pad the input tensors.
sparse_tensor = transformation_cache.get(
self.categorical_column.name, state_manager
)
def host_computation():
return pad_sparse_embedding_lookup_indices(
sparse_tensor, self._tensor_core_shape[1]
)
values, mask = tpu.outside_compilation(host_computation)
else:
# For training, the inputs should already have been densified and padded.
values = transformation_cache.get(
self.categorical_column.name, state_manager
)
mask = transformation_cache.get(
self.categorical_column.name + _TENSOR_CORE_MASK_KEY_SUFFIX,
state_manager,
)
# Do a dense embedding lookup on TensorCore.
embedding_weights = self.shared_embedding_column_creator.embedding_weights
return sparse_embedding_aggregate_slice(
embedding_weights, (values, mask), self.get_combiner()
)
| [
"[email protected]"
]
| |
66f6e4500621285bbbbaf51d4c572120cb3598e7 | 3b1229c458aa232bfcf11cd6da5f1275e9bb3a8f | /python/Python基础/截图和代码/if、while、for/PaxHeader/01-if比较运算符.py | 147895a71cc09ac233a82e6fae85d44e6ae21569 | []
| no_license | sunjianbo/learning | 4fee3ddc5e3d4040a49f2ef3e6f239fd6a67b393 | 384cb4e73cc67e390ee2f4be0da9fe0319d93644 | refs/heads/master | 2021-02-17T16:32:22.557614 | 2020-03-09T05:29:51 | 2020-03-09T05:29:51 | 245,111,571 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 105 | py | 78 path=Python基础/截图和代码/if、while、for/01-if比较运算符.py
27 mtime=1491131771.711676
| [
"sunjianbo"
]
| sunjianbo |
c5b435586383bec7e14c2017d6182ce5f217272e | 449147399b91db8ca3192e9960834a73967cd01d | /pandas-ml-utils/pandas_ml_utils/ml/data/reconstruction/__init__.py | 52b22fdb3e0d667496a779c3c98ac6e25c9b2549 | [
"MIT"
]
| permissive | brunoreisportela/pandas-ml-quant | 04b81568b900d226bb7028ccbe81ea97d0c00587 | a80b06aab28f38c3c6cb298e96f497e4fcdb95a5 | refs/heads/master | 2022-12-18T23:51:38.297857 | 2020-09-08T06:14:16 | 2020-09-08T06:14:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 49 | py | from .prediction import assemble_prediction_frame | [
"[email protected]"
]
| |
e3bb0a08160c3b5afbb1561fc67f5e5b2b320380 | 43a676d507c9f3e007d46b9335c82f77e35350f6 | /config/wsgi.py | df17ccb416ed061cc0afd7cf24b277bc198a94b4 | []
| no_license | Zoxon470/nekidaem-blog | 79136fd9f4747afd01beb02bfd9d0c524493a6f6 | c2539963d149841397e9eb2d4153a73abea15da2 | refs/heads/master | 2022-05-02T20:14:05.805564 | 2019-06-27T21:50:57 | 2019-06-27T21:50:57 | 194,165,211 | 0 | 2 | null | 2022-04-22T21:53:15 | 2019-06-27T21:25:07 | JavaScript | UTF-8 | Python | false | false | 340 | py | import os
import sys
from django.core.wsgi import get_wsgi_application
app_path = os.path.abspath(os.path.join(
os.path.dirname(os.path.abspath(__file__)), os.pardir))
sys.path.append(os.path.join(app_path, 'nekidaem-blog'))
os.environ.setdefault("DJANGO_SETTINGS_MODULE", 'config.settings.dev')
application = get_wsgi_application()
| [
"[email protected]"
]
| |
8001d2f7a9d565237552aea7cbf4fd1650d437b9 | 912196d86c93c29b3b031792e3cf886420a0fbde | /core/rnn/rnn_minibatch_test.py | c8e233c8e50eb5ebf1c20a8a41d63c0c12daa8c2 | [
"Apache-2.0"
]
| permissive | brian-lau/guac | fce363745c9a778733f1df765fd9c3b832fdeef4 | c3db6cdbe56a1cb04486650ea5473287ba159ad4 | refs/heads/master | 2020-05-29T11:55:34.494957 | 2015-10-28T02:17:34 | 2015-10-28T02:17:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 48,065 | py | # adapted from https://github.com/lisa-lab/DeepLearningTutorials
from collections import OrderedDict
import copy
import os
import re
import random
import timeit
from hyperopt import STATUS_OK
import numpy as np
import pandas as pd
from scipy import stats
import theano
from theano import tensor as T
import common
from ..util import defines
from ..util import file_handling as fh
from ..experiment import reusable_holdout
from ..experiment import evaluation
# Otherwise the deepcopy fails
import sys
sys.setrecursionlimit(5000)
THEANO_FLAGS='floatX=float32'
# utils functions
def shuffle(lol, seed=None):
'''
lol :: list of list as input
seed :: seed the shuffling
shuffle inplace each list in the same order
'''
for l in lol:
random.seed(seed)
random.shuffle(l)
class RNN(object):
''' elman neural net model '''
def __init__(self, nh, nc, ne, de, cs, init_scale=0.2, initial_embeddings=None,
rnn_type='basic', # 'basic', 'GRU', or 'LSTM'
pooling_method='max', #'max', 'mean', 'attention1' or 'attention2',
extra_input_dims=0, train_embeddings=True, clip_gradients=False,
bidirectional=True, bi_combine='concat' # 'concat', 'sum', or 'mean'
):
'''
nh :: dimension of the hidden layer
nc :: number of classes
ne :: number of word embeddings in the vocabulary
de :: dimension of the word embeddings
cs :: word window context size
'''
# initialize parameters
dx = de * cs
if extra_input_dims > 0:
dx += extra_input_dims
bi = 1
if bidirectional and bi_combine == 'concat':
bi = 2
if initial_embeddings is None:
self.emb = theano.shared(name='embeddings',
value=init_scale * np.random.uniform(-1.0, 1.0,
(ne, de)).astype(theano.config.floatX))
#(ne+1, de)) # add one for padding at the end
else:
self.emb = theano.shared(name='embeddings', value=initial_embeddings.astype(theano.config.floatX))
if extra_input_dims > 0:
self.W_drld = theano.shared(name='W_drld', value=init_scale * np.random.uniform(-1.0, 1.0, (1, nh))
.astype(theano.config.floatX))
# common paramters (feeding into hidden node)
self.W_xh = theano.shared(name='W_xh', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hh = theano.shared(name='W_hh', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_h = theano.shared(name='b_h', value=np.array(np.random.uniform(0.0, 1.0, nh),
dtype=theano.config.floatX))
# output layer parameters
self.W_s = theano.shared(name='W_s', value=init_scale * np.random.uniform(-1.0, 1.0, (nh * bi, nc))
.astype(theano.config.floatX))
self.b_s = theano.shared(name='b_s', value=np.zeros(nc, dtype=theano.config.floatX))
# temporary parameters
#self.h_i_f = theano.shared(name='h_i_f', value=np.zeros((2, nh), dtype=theano.config.floatX))
#if bidirectional:
# self.h_i_r = theano.shared(name='h_i_r', value=np.zeros(nh, dtype=theano.config.floatX))
# Attention parameters
if pooling_method == 'attention1' or pooling_method == 'attention2':
self.W_a = theano.shared(name='W_a', value=init_scale * np.random.uniform(-1.0, 1.0, (bi*nh, 1))
.astype(theano.config.floatX))
self.b_a = theano.shared(name='b_a', value=0.0)
# GRU parameters
if rnn_type == 'GRU':
self.W_xr = theano.shared(name='W_xr', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hr = theano.shared(name='W_hr', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_r = theano.shared(name='b_r', value=np.zeros(nh, dtype=theano.config.floatX))
self.W_xz = theano.shared(name='W_xz', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hz = theano.shared(name='W_hz', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_z = theano.shared(name='b_z', value=np.zeros(nh, dtype=theano.config.floatX))
# LSTM paramters
if rnn_type == 'LSTM':
# forget gate (needs special initialization)
self.W_xf = theano.shared(name='W_xf', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hf = theano.shared(name='W_hf', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.W_cf = theano.shared(name='W_cf', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_f = theano.shared(name='b_f', value=np.array(np.random.uniform(0.0, 1.0, nh),
dtype=theano.config.floatX))
# input gate
self.W_xi = theano.shared(name='W_xi', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_hi = theano.shared(name='W_hi', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.W_ci = theano.shared(name='W_ci', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_i = theano.shared(name='b_i', value=np.zeros(nh, dtype=theano.config.floatX))
# output gate
self.W_xo = theano.shared(name='W_xo', value=init_scale * np.random.uniform(-1.0, 1.0, (dx, nh))
.astype(theano.config.floatX))
self.W_ho = theano.shared(name='W_ho', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.W_co = theano.shared(name='W_co', value=init_scale * np.random.uniform(-1.0, 1.0, (nh, nh))
.astype(theano.config.floatX))
self.b_o = theano.shared(name='b_o', value=np.zeros(nh, dtype=theano.config.floatX))
# use normal ->hidden weights for memory cell
# temp
#self.c_i_f = theano.shared(name='c_i_f', value=np.zeros(nh, dtype=theano.config.floatX))
#if bidirectional:
# self.c_i_r = theano.shared(name='c_i_r', value=np.zeros(nh, dtype=theano.config.floatX))
self.params = [self.W_xh, self.W_hh, self.b_h,
self.W_s, self.b_s]
#self.params += [self.h_i_f]
if train_embeddings:
self.params += [self.emb]
if pooling_method == 'attention':
self.params += [self.W_a, self.b_a]
if rnn_type == 'GRU':
self.params += [self.W_xr, self.W_hr, self.b_r,
self.W_xz, self.W_hz, self.b_z]
if rnn_type == 'LSTM':
self.params += [self.W_xf, self.W_hf, self.W_cf, self.b_f,
self.W_xi, self.W_hi, self.W_ci, self.b_i,
self.W_xo, self.W_ho, self.W_co, self.b_o]
#self.c_i_f]
#if bidirectional:
# self.params += [self.c_i_r]
#if bidirectional:
# self.params += [self.h_i_r]
# create an X object based on the size of the object at the index [elements, emb_dim * window]
idxs = T.tensor3('idxs', dtype='int32')
if extra_input_dims:
extra = T.tensor3('extra')
extra_3d = extra.repeat(idxs.shape[0], axis=0)
#x = T.concatenate([self.emb[idxs].reshape((idxs.shape[0], de*cs)),
# T.repeat(extra, idxs.shape[0], axis=0)], axis=1)
#temp = T.printing.Print('temp')(self.emb[idxs].reshape((idxs.shape[0], idxs.shape[1], de*cs)))
temp = self.emb[idxs].reshape((idxs.shape[0], idxs.shape[1], de*cs))
x = T.concatenate([temp, extra_3d], axis=2)
else:
#x = T.printing.Print('x')(self.emb[idxs])
x = self.emb[idxs].reshape((idxs.shape[0], idxs.shape[1], de*cs)) # [n_elements, minibatch_size, emb_dim]
#x = self.emb[idxs]
y = T.imatrix('y')
mask = T.tensor3('mask')
mask_3d = mask.repeat(nh, axis=2)
minibatch_size = T.iscalar()
def recurrence_basic(x_t, mask_t, h_tm1):
#h_t = theano.printing.Print('h_t')(T.nnet.sigmoid(T.dot(x_t, self.W_xh) + T.dot(h_tm1, self.W_hh) + self.b_h))
h_t = T.nnet.sigmoid(T.dot(x_t, self.W_xh) + T.dot(h_tm1, self.W_hh) + self.b_h)
#masked_h_t = T.printing.Print('masked_h_t')(mask_t * h_t + (1 - mask_t) * h_tm1)
# apply the mask to propogate the last (unmaksed) element in sequence to the end
return mask_t * h_t + (1 - mask_t) * h_tm1
#return h_t
def recurrence_basic_reverse(x_t, mask_t, h_tp1):
h_t = T.nnet.sigmoid(T.dot(x_t, self.W_xh) + T.dot(h_tp1, self.W_hh) + self.b_h)
return mask_t * h_t + (1 - mask_t) * h_tp1
def recurrence_gru(x_t, mask_t, h_tm1):
r_t = T.nnet.sigmoid(T.dot(x_t, self.W_xr) + T.dot(h_tm1, self.W_hr) + self.b_r)
z_t = T.nnet.sigmoid(T.dot(x_t, self.W_xz) + T.dot(h_tm1, self.W_hz) + self.b_z)
g_t = T.tanh(T.dot(x_t, self.W_xh) + r_t * T.dot(h_tm1, self.W_hh) + self.b_h)
h_t = (1 - z_t) * h_tm1 + z_t * g_t
return mask_t * h_t + (1 - mask_t) * h_tm1
def recurrence_gru_reverse(x_t, mask_t, h_tp1):
r_t = T.nnet.sigmoid(T.dot(x_t, self.W_xr) + T.dot(h_tp1, self.W_hr) + self.b_r)
z_t = T.nnet.sigmoid(T.dot(x_t, self.W_xz) + T.dot(h_tp1, self.W_hz) + self.b_z)
g_t = T.tanh(T.dot(x_t, self.W_xh) + r_t * T.dot(h_tp1, self.W_hh) + self.b_h)
h_t = (1 - z_t) * h_tp1 + z_t * g_t
return mask_t * h_t + (1 - mask_t) * h_tp1
def recurrence_lstm(x_t, mask_t, h_tm1, c_tm1):
i_t = T.nnet.sigmoid(T.dot(x_t, self.W_xi) + T.dot(h_tm1, self.W_hi) + T.dot(c_tm1, self.W_ci) + self.b_i)
f_t = T.nnet.sigmoid(T.dot(x_t, self.W_xf) + T.dot(h_tm1, self.W_hf) + T.dot(c_tm1, self.W_cf) + self.b_f)
d_t = T.tanh(T.dot(x_t, self.W_xh) + T.dot(h_tm1, self.W_hh) + self.b_h)
c_t = f_t * c_tm1 + i_t * d_t
o_t = T.nnet.sigmoid(T.dot(x_t, self.W_xo) + T.dot(h_tm1, self.W_ho) + T.dot(c_t, self.W_co) + self.b_o)
h_t = o_t * c_t
return [mask_t * h_t + (1 - mask_t) * h_tm1, mask_t * c_t + (1 - mask_t) * c_tm1]
def recurrence_lstm_reverse(x_t, mask_t, h_tp1, c_tp1):
i_t = T.nnet.sigmoid(T.dot(x_t, self.W_xi) + T.dot(h_tp1, self.W_hi) + T.dot(c_tp1, self.W_ci) + self.b_i)
f_t = T.nnet.sigmoid(T.dot(x_t, self.W_xf) + T.dot(h_tp1, self.W_hf) + T.dot(c_tp1, self.W_cf) + self.b_f)
d_t = T.tanh(T.dot(x_t, self.W_xh) + T.dot(h_tp1, self.W_hh) + self.b_h)
c_t = f_t * c_tp1 + i_t * d_t
o_t = T.nnet.sigmoid(T.dot(x_t, self.W_xo) + T.dot(h_tp1, self.W_ho) + T.dot(c_t, self.W_co) + self.b_o)
h_t = o_t * c_t
return [mask_t * h_t + (1 - mask_t) * h_tp1, mask_t * c_t + (1 - mask_t) * c_tp1]
h_r = None
if rnn_type == 'GRU':
h_f, _ = theano.scan(fn=recurrence_gru, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
n_steps=x.shape[0])
if bidirectional:
h_r, _ = theano.scan(fn=recurrence_gru_reverse, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
go_backwards=True)
elif rnn_type == 'LSTM':
[h_f, c_f], _ = theano.scan(fn=recurrence_lstm, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh),
T.alloc(np.array(0.), minibatch_size, nh)],
n_steps=x.shape[0])
if bidirectional:
[h_r, c_r], _ = theano.scan(fn=recurrence_lstm_reverse, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh),
T.alloc(np.array(0.), minibatch_size, nh)],
go_backwards=True)
#[h_r, c_r], _ = theano.scan(fn=recurrence_lstm_reverse, sequences=x,
# outputs_info=[self.h_i_r, self.c_i_r], go_backwards=True)
else:
#h_f, _ = theano.scan(fn=recurrence_basic, sequences=x, outputs_info=[self.h_i_f], n_steps=x.shape[0])
temp, _ = theano.scan(fn=recurrence_basic, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
n_steps=x.shape[0])
#h_f = theano.printing.Print('h_f')(temp)
h_f = temp
if bidirectional:
h_r, _ = theano.scan(fn=recurrence_basic_reverse, sequences=[x, mask_3d],
outputs_info=[T.alloc(np.array(0.), minibatch_size, nh)],
go_backwards=True)
if bidirectional:
# reverse the second hidden layer so it lines up with the first
h_r = h_r[::-1, :, :]
if bi_combine == 'max':
h = T.maximum(h_f, h_r)
elif bi_combine == 'mean':
h = (h_f + h_r) / 2.0
else: # concatenate
#h = theano.printing.Print('h:')(T.concatenate([h_fp, h_rp], axis=1))
h = T.concatenate([h_f, h_r], axis=2)
else:
#temp = T.printing.Print('isnan')(T.max(T.isnan(h_f)))
#h = h_f * (1-temp)
h = h_f #[n_elements, minibatch_size, n_hidden] (?)
a_sum = T.sum([1])
if pooling_method == 'attention1': # combine hidden nodes, then transform and sigmoid
# THIS IS NOT WORKIGN...
# SOFTMAX normalizes across the row (axis=1)
#a = T.nnet.softmax((T.dot(h, self.W_a) + self.b_a).T)
temp = T.dot(h, self.W_a) + self.b_a
# softmax?
a = T.exp(temp)/T.exp(temp).sum(axis=0, keepdims=True)
a_sum = T.sum(a, ) # to check a is normalized
a_rep = T.repeat(a, nh*bi, axis=2)
weighted_sum = T.sum(h * a_rep, axis=0)
p_y_given_x_sentence = T.nnet.sigmoid(T.dot(weighted_sum, self.W_s) + self.b_s) # [1, nc] in R(0,1)
y_pred = p_y_given_x_sentence > 0.5 # note, max is just to coerce into proper shape
#element_weights = T.outer(a, p_y_given_x_sentence) # [ne, nc]
#p_y_given_x_sentence = T.nnet.sigmoid(T.dot(T.dot(a, h), self.W_s) + self.b_s) # [1, nc] in R(0,1)
#y_pred = T.max(p_y_given_x_sentence, axis=0) > 0.5 # note, max is just to coerce into proper shape
#element_weights = T.outer(a, p_y_given_x_sentence) # [ne, nc]
elif pooling_method == 'attention2': # transform hidden nodes, sigmoid, then combine
temp = T.dot(h, self.W_a) + self.b_a
# softmax?
a = T.exp(temp)/T.exp(temp).sum(axis=0, keepdims=True) # [ne, minibatch_size, 1]: normalized over ne
#a = T.nnet.softmax((T.dot(h, self.W_a) + self.b_a))
a_sum = T.sum(a, axis=0)
temp = T.nnet.sigmoid(T.dot(h, self.W_s) + self.b_s) # [ne, minibatch_size, nc]
p_y_given_x_sentence = T.sum(temp * T.repeat(a, nc, axis=2), axis=0) # [minibatch_size, nc] in R(0,1)
y_pred = p_y_given_x_sentence > 0.5
#element_weights = T.repeat(a.T, nc, axis=1) * temp # [ne, nc]
elif pooling_method == 'mean':
s = T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)) # [n_elements, nc] in R(0,1)
p_y_given_x_sentence = T.mean(s, axis=0)
y_pred = p_y_given_x_sentence > 0.5
element_weights = s
elif pooling_method == 'max':
#s = T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)) # [n_elements, minibatch_size, nc] in R(0,1)
s = T.printing.Print('s')(T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)))
#s_shape = T.printing.Print('s_shape')(s.shape)
#p_y_given_x_sentence = T.max(s_shape[0] * s, axis=0)
p_y_given_x_sentence = T.max(s, axis=0)
#p_y_given_x_sentence = T.printing.Print('p_y')(T.max(s, axis=0))
#temp = T.printing.Print('p_y')(p_y_given_x_sentence)
#y_pred = T.printing.Print('y_pred')(p_y_given_x_sentence > 0.5)
y_pred = p_y_given_x_sentence > 0.5
element_weights = s
elif pooling_method == 'last':
s = T.nnet.sigmoid((T.dot(h, self.W_s) + self.b_s)) # [n_elements, minibatch_size, nc] in R(0,1)
p_y_given_x_sentence = s[-1, :, :]
y_pred = p_y_given_x_sentence > 0.5
element_weights = s
else:
sys.exit("Pooling method not recognized")
# cost and gradients and learning rate
lr = T.scalar('lr_main')
lr_emb_fac = T.scalar('lr_emb')
#sentence_nll = T.mean(T.sum(-T.log(y*p_y_given_x_sentence + (1-y)*(1-p_y_given_x_sentence)), axis=1))
sentence_nll = T.sum(-T.log(y*p_y_given_x_sentence + (1-y)*(1-p_y_given_x_sentence)))
sentence_gradients = T.grad(sentence_nll, self.params)
if clip_gradients:
sentence_gradients= [T.clip(g, -1, 1) for g in sentence_gradients]
sentence_updates = OrderedDict((p, p - lr * g) for p, g in zip(self.params, [lr_emb_fac *
sentence_gradients[0]]
+ sentence_gradients[1:]))
# theano functions to compile
if extra_input_dims > 0:
self.sentence_classify = theano.function(inputs=[idxs, mask, extra, minibatch_size], outputs=y_pred)
self.sentence_train = theano.function(inputs=[idxs, mask, extra, y, lr, lr_emb_fac, minibatch_size],
outputs=[sentence_nll, a_sum],
updates=sentence_updates)
#if pooling_method == 'attention1' or pooling_method == 'attention2':
# self.a_sum_check = theano.function(inputs=[idxs, extra], outputs=a_sum)
self.sentence_step_through = theano.function(inputs=[idxs, mask, extra, minibatch_size],
outputs=[h, self.W_s, self.b_s, p_y_given_x_sentence])
else:
self.sentence_classify = theano.function(inputs=[idxs, mask, minibatch_size], outputs=y_pred)
self.sentence_train = theano.function(inputs=[idxs, mask, y, lr, lr_emb_fac, minibatch_size],
outputs=[sentence_nll, a_sum],
updates=sentence_updates)
#if pooling_method == 'attention1' or pooling_method == 'attention2':
# self.a_sum_check = theano.function(inputs=[idxs, mask, minibatch_size], outputs=a_sum)
self.sentence_step_through = theano.function(inputs=[idxs, mask, minibatch_size],
outputs=[h, self.W_s, self.b_s, p_y_given_x_sentence])
self.normalize = theano.function(inputs=[],
updates={self.emb: self.emb / T.sqrt((self.emb**2).sum(axis=1))
.dimshuffle(0, 'x')})
def step_through(self, x, mask, window_size, extra_input_dims=0, extra=None):
seq_len, minibatch_size, window_size = x.shape
words = x
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
if extra_input_dims > 0:
extra = np.array(extra).astype('int32').reshape((1, minibatch_size, extra_input_dims))
return self.sentence_step_through(words, mask, extra, minibatch_size)
else:
return self.sentence_step_through(words, mask, minibatch_size)
def classify(self, x, mask, window_size, extra_input_dims=0, extra=None):
#assert window_size == 1
#assert extra_input_dims == 0
#cwords = contextwin(x, window_size)
## make an array of these windows
#words = map(lambda x: np.asarray(x).astype('int32'), cwords)
"""
for i in range(x.shape[0]):
cwords = contextwin(list(x[i, :]), window_size)
words = map(lambda q: np.asarray(q).astype('int32'), cwords)
x[i, :] = words
if len(x.shape) == 2:
minibatch_size, seq_len = x.shape
words = np.array(x.T).astype('int32')
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
else:
minibatch_size = 1
seq_len = x.shape[0]
words = np.array(x).astype('int32').reshape((seq_len, minibatch_size))
mask = np.array(mask).astype('int32').reshape((seq_len, minibatch_size, 1))
"""
"""
if len(x.shape) == 2:
minibatch_size, seq_len = x.shape
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
if window_size > 1:
for i in range(minibatch_size):
cwords = contextwin(list(x[i, :]), window_size)
words_i = np.array(cwords, dtype='int32')
#[words_i.extend(j) for j in cwords]
words[:, i, :] = words_i
x = words.T
words = np.array(x.T).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
else:
minibatch_size = 1
seq_len = x.shape[0]
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
cwords = contextwin(x, window_size)
words[:, 0, :] = np.array(cwords, dtype='int32')
#words = np.array(words).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask).astype('int32').reshape((seq_len, 1, 1))
`
"""
seq_len, minibatch_size, window_size = x.shape
words = x
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
if extra_input_dims > 0:
extra = np.array(extra).astype('int32').reshape((1, minibatch_size, extra_input_dims))
return self.sentence_classify(words, mask, extra, minibatch_size)
else:
return self.sentence_classify(words, mask, minibatch_size)
def train(self, x, mask, y, window_size, learning_rate, emb_lr_factor, extra_input_dims=0, extra=None):
#assert window_size == 1
#assert extra_input_dims == 0
# concatenate words in a window
#cwords = contextwin(x, window_size)
# make an array of these windows
#words = map(lambda x: np.asarray(x).astype('int32'), cwords)
# if minibatch_size is 1, X = 1D list of indices, i.e. X.shape[0] = seq_len
# if minibatch_size > 0, X = np.array([minibatch_size, seq_len])
"""
if len(x.shape) == 2:
minibatch_size, seq_len = x.shape
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
if window_size > 1:
for i in range(minibatch_size):
cwords = contextwin(list(x[i, :]), window_size)
words_i = np.array(cwords, dtype='int32')
#[words_i.extend(j) for j in cwords]
words[:, i, :] = words_i
x = words.T
words = np.array(x.T).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
y = np.array(y).astype('int32')
else:
minibatch_size = 1
seq_len = x.shape[0]
words = np.zeros([seq_len, minibatch_size, window_size], dtype='int32')
cwords = contextwin(x, window_size)
words[:, 0, :] = np.array(cwords, dtype='int32')
#words = np.array(words).astype('int32').reshape((seq_len, minibatch_size, window_size))
mask = np.array(mask).astype('int32').reshape((seq_len, 1, 1))
y = np.array(y).astype('int32').reshape((1, len(y)))
"""
seq_len, minibatch_size, window_size = x.shape
words = x
mask = np.array(mask.T).astype('int32').reshape((seq_len, minibatch_size, 1))
y = np.array(y).astype('int32')
# train on these sentences and normalize
if extra_input_dims > 0:
extra = np.array(extra).astype('int32').reshape((1, minibatch_size, extra_input_dims))
nll = self.sentence_train(words, mask, extra, y, learning_rate, emb_lr_factor, minibatch_size)
else:
nll = self.sentence_train(words, mask, y, learning_rate, emb_lr_factor, minibatch_size)
self.normalize()
return nll
def save(self, output_dir):
for param in self.params:
np.save(os.path.join(output_dir, param.name + '.npy'), param.get_value())
def load(self, input_dir):
for param in self.params:
param.set_value(np.load(os.path.join(input_dir, param.name + '.npy')))
def print_embeddings(self):
for param in self.params:
print param.name, param.get_value()
def save_embeddings(self, filename):
np.save(filename, self.emb)
def contextwin(l, win):
'''
win :: int corresponding to the size of the window
given a list of indexes composing a sentence
l :: array containing the word indexes
it will return a list of list of indexes corresponding
to context windows surrounding each word in the sentence
'''
assert (win % 2) == 1
assert win >= 1
l = list(l)
lpadded = win // 2 * [-1] + l + win // 2 * [-1]
out = [lpadded[i:(i + win)] for i in range(len(l))]
assert len(out) == len(l)
return out
def main(params=None):
if params is None:
params = {
'dataset': 'DRLD',
'exp_name': 'char_test',
'test_fold': 0,
'n_dev_folds': 1,
'min_doc_thresh': 1,
'initialize_word_vectors': True,
'vectors': 'chars_word2vec_25', # default_word2vec_300, anes_word2vec_300, chars_word2vec_25, eye_1 ...
'init_scale': 0.2,
'add_OOV_dim': True,
'win': 1, # size of context window
'add_DRLD': True,
'rnn_type': 'basic', # basic, GRU, or LSTM
'n_hidden': 50, # size of hidden units
'pooling_method': 'max', # max, mean, or attention1/2
'bidirectional': True,
'bi_combine': 'concat', # concat, max, or mean
'train_embeddings': True,
'lr': 0.1, # learning rate
'lr_emb_fac': 1, # factor to modify learning rate for embeddings
'decay_delay': 10, # number of epochs with no improvement before decreasing learning rate
'decay_factor': 0.5, # factor by which to multiply learning rate in case of delay
'n_epochs': 300,
'add_OOV_noise': True,
'OOV_noise_prob': 0.01,
'minibatch_size': 16,
'classify_minibatch_size': 64,
'ensemble': False,
'save_model': True,
'seed': 42,
'verbose': 1,
'reuse': False,
'orig_T': 0.04,
'tau': 0.01,
'clip_gradients': False
}
#params = fh.read_json('/Users/dcard/Projects/CMU/ARK/guac/experiments/best_mod.json')
#params['exp_name'] += '_best'
#params['n_hidden'] = int(params['n_hidden'])
rnn_base_dir = '/Users/dcard/Projects/CMU/ARK/guac/experiments/rnn/car_test/'
params_filename = fh.make_filename(rnn_base_dir, 'params', 'txt')
params = fh.read_json(params_filename)
fold = params['test_fold']
rnn_input_dir = fh.makedirs(rnn_base_dir, 'fold' + str(fold))
keys = params.keys()
keys.sort()
for key in keys:
print key, ':', params[key]
# seed the random number generators
np.random.seed(params['seed'])
random.seed(params['seed'])
vector_type = params['vectors'].split('_')[0]
params['word2vec_dim'] = int(params['vectors'].split('_')[-1])
reuser = None
if params['reuse']:
reuser = reusable_holdout.ReuseableHoldout(T=params['orig_T'], tau=params['tau'])
if params['dataset'] == 'DRLD':
datasets = ['Democrat-Likes', 'Democrat-Dislikes', 'Republican-Likes', 'Republican-Dislikes']
elif params['dataset'] == 'MIP':
datasets = ['MIP-Personal-1', 'MIP-Personal-2', 'MIP-Political-1', 'MIP-Political-2']
elif params['dataset'] == 'MOLD':
datasets = ['McCain-Likes', 'McCain-Dislikes', 'Obama-Likes', 'Obama-Dislikes']
elif params['dataset'] == 'Primary':
datasets = ['Obama-Primary', 'Clinton-Primary']
elif params['dataset'] == 'General':
datasets = ['Obama-General', 'McCain-General']
else:
datasets = [params['dataset']]
np.random.seed(params['seed'])
random.seed(params['seed'])
best_valid_f1s = []
best_true_valid_f1s = []
best_test_f1s = []
best_train_f1s = []
test_prediction_arrays = []
output_dir = fh.makedirs(defines.exp_dir, 'rnn', params['exp_name'])
output_filename = fh.make_filename(output_dir, 'params', 'txt')
fh.write_to_json(params, output_filename)
for dev_fold in range(params['n_dev_folds']):
print "dev fold =", dev_fold
output_dir = fh.makedirs(defines.exp_dir, 'rnn', params['exp_name'], 'fold' + str(dev_fold))
if vector_type == 'chars':
all_data, words2idx, items, all_labels = common.load_char_data(datasets, params['test_fold'], dev_fold)
else:
all_data, words2idx, items, all_labels = common.load_data(datasets, params['test_fold'], dev_fold,
params['min_doc_thresh'])
train_xy, valid_xy, test_xy = all_data
train_lex, train_y = train_xy
valid_lex, valid_y = valid_xy
test_lex, test_y = test_xy
#if params['minibatch_size'] > 1 or params['classify_minibatch_size'] > 1:
print "padding input with zeros"
all_data, all_masks = common.prepare_data(train_lex, valid_lex, test_lex)
train_lex, valid_lex, test_lex = all_data
train_masks, valid_masks, test_masks = all_masks
#else:
# train_masks = [np.ones(len(x)).astype('int32') for x in train_lex]
# valid_masks = [np.ones(len(x)).astype('int32') for x in valid_lex]
# test_masks = [np.ones(len(x)).astype('int32') for x in test_lex]
print "expanding x with context win dows"
# Rejigger to convert x to contex win in advance
train_x_win = expand_x_with_context_win(train_lex, params['win'])
valid_x_win = expand_x_with_context_win(valid_lex, params['win'])
test_x_win = expand_x_with_context_win(test_lex, params['win'])
order = range(len(train_lex))
print "done"
train_items, dev_items, test_items = items
vocsize = len(words2idx.keys())
idx2words = dict((k, v) for v, k in words2idx.iteritems())
best_test_predictions = None
n_sentences = len(train_lex)
print "vocsize = ", vocsize, 'n_train', n_sentences
codes = all_labels.columns
n_items, n_codes = all_labels.shape
# get the words in the sentences for the test and validation sets
words_valid = [map(lambda x: idx2words[x], w) for w in valid_lex]
groundtruth_test = test_y[:]
words_test = [map(lambda x: idx2words[x], w) for w in test_lex]
#if vector_type == 'eye':
# initial_embeddings = np.eye(vocsize)
# emb_dim = initial_embeddings.shape[1]
if params['initialize_word_vectors']:
initial_embeddings = common.load_embeddings(params, words2idx)
emb_dim = initial_embeddings.shape[1]
else:
initial_embeddings = None
emb_dim = params['word2vec_dim']
print "embedding dim =", emb_dim
temp_output = fh.make_filename(output_dir, 'embedding_labels', 'json')
fh.write_to_json(idx2words, temp_output)
extra_input_dims = 0
if params['add_DRLD']:
extra_input_dims = 2
print "Building RNN"
rnn = RNN(nh=params['n_hidden'],
nc=n_codes,
ne=vocsize,
de=emb_dim,
cs=params['win'],
extra_input_dims=extra_input_dims,
initial_embeddings=initial_embeddings,
init_scale=params['init_scale'],
rnn_type=params['rnn_type'],
train_embeddings=params['train_embeddings'],
pooling_method=params['pooling_method'],
bidirectional=params['bidirectional'],
bi_combine=params['bi_combine'],
clip_gradients=params['clip_gradients']
)
rnn.load(rnn_input_dir)
#temp_filename = fh.make_filename(output_dir, 'initial_embeddings', 'npy')
#rnn.save_embeddings(temp_filename)
train_likes = [1 if re.search('Likes', i) else 0 for i in train_items]
dev_likes = [1 if re.search('Likes', i) else 0 for i in dev_items]
test_likes = [1 if re.search('Likes', i) else 0 for i in test_items]
train_dem = [1 if re.search('Democrat', i) else 0 for i in train_items]
dev_dem = [1 if re.search('Democrat', i) else 0 for i in dev_items]
test_dem = [1 if re.search('Democrat', i) else 0 for i in test_items]
train_extra = [[train_likes[i], train_dem[i]] for i, t in enumerate(train_items)]
dev_extra = [[dev_likes[i], dev_dem[i]] for i, t in enumerate(dev_items)]
test_extra = [[test_likes[i], test_dem[i]] for i, t in enumerate(test_items)]
ms = 1
mb_x, mb_masks, mb_extra, mb_y = select_minibatch(train_x_win, train_masks, train_extra, train_y,
params['win'], 0, 1, order=np.arange(n_sentences))
print '\n'.join([' '.join([idx2words[idx] for idx in mb_x[:, k, 0].tolist()]) for k in range(ms)])
prediction = rnn.classify(mb_x, mb_masks, params['win'], extra_input_dims, mb_extra)
print prediction
h, W, b, p_y = rnn.step_through(mb_x, mb_masks, params['win'], extra_input_dims, mb_extra)
print p_y
print W
print b
temp = np.dot(h, W) + b
s = 1.0/(1.0 + np.exp(-temp))
print s
p_y_calc = np.max(s, axis=0)
print p_y_calc
print np.array(p_y_calc > 0.5, dtype='int')
sys.exit()
# train with early stopping on validation set
best_f1 = -np.inf
params['clr'] = params['lr']
for e in xrange(params['n_epochs']):
# shuffle
#shuffle([train_lex, train_y, train_extra, train_masks], params['seed']) # shuffle the input data
shuffle([order, train_lex, train_y, train_extra, train_masks], params['seed']) # shuffle the input data
params['ce'] = e # store the current epoch
tic = timeit.default_timer()
ms = params['minibatch_size']
n_train = len(train_lex)
nll = 0
#for i, orig_x in enumerate(train_lex):
for iteration, i in enumerate(range(0, n_train, ms)):
#orig_x = train_lex[i]
#n_words = len(orig_x)
#if params['add_OOV_noise']:
# draws = np.random.rand(n_words)
# x = [OOV_index if draws[i] < params['OOV_noise_prob'] else orig_x[i] for i in range(n_words)]
#else:
# x = orig_x
#y = train_y[i]
extra = train_extra[i]
#mask = train_masks[i]
minibatch_x, minibatch_mask,\
minibatch_extra, minibatch_y= select_minibatch(train_x_win, train_masks, train_extra, train_y,
params['win'], i, ms, order,
params['add_OOV_noise'], params['OOV_noise_prob'])
#if i == 0:
# print '\n'.join([' '.join([idx2words[idx] for idx in minibatch_x[:, k, 0].tolist()]) for
# k in range(ms)])
nll_i, a_sum = rnn.train(minibatch_x, minibatch_mask, minibatch_y, params['win'],
params['clr'],
params['lr_emb_fac'], extra_input_dims, minibatch_extra)
nll += nll_i
#rnn.train(x, mask, y, params['win'], params['clr'], params['lr_emb_fac'],
# extra_input_dims, extra)
print '[learning] epoch %i >> %2.2f%%' % (
e, (i + 1) * 100. / float(n_sentences)),
print 'completed in %.2f (sec), nll = %.2f, a_sum = %.1f <<\r' % (timeit.default_timer() - tic,
nll, np.max(a_sum)),
sys.stdout.flush()
if np.isnan(nll) or np.isinf(nll):
if best_f1 > 0:
break
else:
return {'loss': 1.0,
'final_test_f1': 0,
'valid_f1s': 0,
'true_valid_f1s': 0,
'train_f1s': 0,
'test_f1s': 0,
'status': STATUS_OK
}
# evaluation // back into the real world : idx -> words
print ""
#print "true y", train_y[-1]
#y_pred = rnn.classify(np.array(train_x_win[-1]).reshape((1, len(train_x_win[-1]))),
# train_masks[-1], params['win'], extra_input_dims, train_extra[-1])[0]
#print "pred y", y_pred
#if params['pooling_method'] == 'attention1' or params['pooling_method'] == 'attention2':
# if extra_input_dims == 0:
# r = np.random.randint(0, len(train_lex))
# print r, rnn.a_sum_check(np.asarray(contextwin(train_lex[r], params['win'])).astype('int32'))
predictions_train = predict(n_train, params['classify_minibatch_size'], train_x_win, train_masks,
train_y, params['win'], extra_input_dims, train_extra, rnn, order)
n_valid = len(valid_lex)
n_test = len(test_lex)
predictions_valid = predict(n_valid, params['classify_minibatch_size'], valid_x_win, valid_masks,
valid_y, params['win'], extra_input_dims, dev_extra, rnn)
predictions_test = predict(n_test, params['classify_minibatch_size'], test_x_win, test_masks,
test_y, params['win'], extra_input_dims, test_extra, rnn)
"""
predictions_train = [rnn.classify(x, train_masks[i], params['win'],
extra_input_dims, train_extra[i])[0] for i, x in enumerate(train_lex)]
predictions_valid = [rnn.classify(x, valid_masks[i], params['win'],
extra_input_dims, dev_extra[i])[0] for i, x in enumerate(valid_lex)]
predictions_test = [rnn.classify(x, test_masks[i], params['win'],
extra_input_dims, test_extra[i])[0] for i, x in enumerate(test_lex)]
"""
train_f1 = common.calc_mean_f1(predictions_train, train_y)
test_f1 = common.calc_mean_f1(predictions_test, test_y)
valid_f1 = common.calc_mean_f1(predictions_valid, valid_y)
question_f1s = []
question_pps = []
print "train_f1 =", train_f1, "valid_f1 =", valid_f1, "test_f1 =", test_f1
if valid_f1 > best_f1:
best_rnn = copy.deepcopy(rnn)
best_f1 = valid_f1
best_test_predictions = predictions_test
if params['verbose']:
print('NEW BEST: epoch', e,
'valid f1', valid_f1,
'best test f1', test_f1)
params['tr_f1'] = train_f1
params['te_f1'] = test_f1
params['v_f1'] = valid_f1
params['be'] = e # store the current epoch as a new best
# learning rate decay if no improvement in a given number of epochs
if abs(params['be']-params['ce']) >= params['decay_delay']:
params['clr'] *= params['decay_factor']
params['be'] = params['ce']
print "Reverting to current best; new learning rate = ", params['clr']
# also reset to the previous best
rnn = best_rnn
if params['clr'] < 1e-5:
break
if best_f1 == 1.0:
break
if best_f1 == 0 and e > 7:
break
if params['save_model']:
predictions_valid = predict(len(valid_y), params['classify_minibatch_size'], valid_x_win, valid_masks,
valid_y, params['win'], extra_input_dims, dev_extra, rnn)
#predictions_valid = [best_rnn.classify(np.asarray(contextwin(x, params['win'])).astype('int32')) for x in valid_lex]
best_rnn.save(output_dir)
common.write_predictions(datasets, params['test_fold'], dev_fold, predictions_valid, dev_items, output_dir)
print('BEST RESULT: epoch', params['be'],
'train F1 ', params['tr_f1'],
'valid F1', params['v_f1'],
'best test F1', params['te_f1'],
'with the model', output_dir)
best_true_valid_f1s.append(params['v_f1'])
best_test_f1s.append(params['te_f1'])
best_train_f1s.append(params['tr_f1'])
if reuser is not None:
best_valid_f1 = reuser.mask_value(params['v_f1'], params['tr_f1'])
else:
best_valid_f1 = params['v_f1']
best_valid_f1s.append(best_valid_f1)
test_prediction_arrays.append(np.array(best_test_predictions, dtype=int))
params['ensemble'] = False
if params['ensemble']:
test_predictions_stack = np.dstack(test_prediction_arrays)
final_predictions = stats.mode(test_predictions_stack, axis=2)[0][:, :, 0]
predicted_df = pd.DataFrame(final_predictions, index=test_items, columns=codes)
true_df = pd.DataFrame(np.array(test_y), index=test_items, columns=codes)
final_test_f1, final_test_pp = evaluation.calc_macro_mean_f1_pp(true_df, predicted_df)
else:
final_test_f1 = np.median(best_test_f1s)
return {'loss': -np.median(best_valid_f1s),
'final_test_f1': final_test_f1,
'valid_f1s': best_valid_f1s,
'train_f1s': best_train_f1s,
'true_valid_f1s': best_true_valid_f1s,
'test_f1s': best_test_f1s,
'status': STATUS_OK
}
def expand_x_with_context_win(lex, window_size):
x = np.vstack(lex)
n_items, seq_len = x.shape
x_win = np.zeros([seq_len, n_items, window_size], dtype='int32')
if window_size > 1:
for i in range(n_items):
x_win[:, i, :] = np.array(contextwin(list(x[i, :]), window_size), dtype='int32')
#x_i =
#x_win = [[np.array(w).astype('int32') for w in contextwin(list(x), window_size)] for x in lex]
else:
x_win[:, :, 0] = x.T
print "x_win.shape", x_win.shape
return x_win
def select_minibatch(x_win, masks, extra, y, window_size, i, minibatch_size, order=None, add_oov_noise=False, oov_noise_prob=0.0):
n = len(masks)
if order is None:
order = range(n)
ms = min(minibatch_size, n-i)
if ms > 1:
minibatch_mask = np.vstack([masks[j] for j in range(i, min(i+ms, n))])
max_len = np.max(np.argmin(minibatch_mask, axis=1))
if max_len == 0:
max_len = len(masks[i])
try:
minibatch_mask = minibatch_mask[:, 0: max_len].reshape((ms, max_len))
except:
e = sys.exc_info()[0]
print e
print max_len
print minibatch_mask
minibatch_x = x_win[0: max_len, order[i: min(i+ms, n)], :]
minibatch_extra = np.vstack([extra[j] for j in range(i, min(i+ms, n))])
minibatch_y = np.vstack([y[j] for j in range(i, min(i+ms, n))])
else:
max_len = np.argmin(masks[i])
if max_len == 0:
max_len = len(masks[i])
minibatch_mask = np.array(masks[i][0: max_len]).reshape((1, max_len))
minibatch_x = x_win[0: max_len, order[i], :].reshape((max_len, 1, window_size))
minibatch_extra = np.array(extra[i]).reshape((1, len(extra[i])))
minibatch_y = np.array(y[i]).reshape((1, len(y[i])))
if add_oov_noise:
draws = np.random.rand(max_len, ms, window_size)
minibatch_x = np.array(minibatch_x * np.array(draws > oov_noise_prob, dtype='int32'), dtype='int32')
return minibatch_x, minibatch_mask, minibatch_extra, minibatch_y
def predict(n, ms, x_win, masks, y, window_size, extra_input_dims, extra, rnn, order=None):
predictions = []
for i in range(0, n, ms):
mb_x, mb_masks, mb_extra, mb_y = select_minibatch(x_win, masks, extra, y, window_size, i, ms, order=order)
if ms > 1:
prediction = rnn.classify(mb_x, mb_masks, window_size, extra_input_dims, mb_extra)
for p in prediction:
predictions.append(p)
else:
prediction = rnn.classify(mb_x, mb_masks, window_size, extra_input_dims, mb_extra)
predictions.append(prediction)
return predictions
if __name__ == '__main__':
report = main()
print report | [
"[email protected]"
]
| |
b2cd196a4e77d83e542be25199838e0b8ec80ff9 | ad357cfbec64afb8f4cc4043b212996768f9755c | /api/assessment/automate/formatters.py | dac02f8f9749219cec476cf1e0392f3c9036f96a | [
"MIT"
]
| permissive | uktrade/market-access-api | 6b4680e6455eb5c25480ccd3e3d9445654269f36 | 4da26d1be53843d22411577409d9489010bdda09 | refs/heads/master | 2023-08-30T14:47:10.373148 | 2023-08-29T13:58:08 | 2023-08-29T13:58:08 | 131,856,014 | 2 | 3 | MIT | 2023-09-14T08:04:42 | 2018-05-02T13:38:37 | Python | UTF-8 | Python | false | false | 2,065 | py | def rca(import_value, export_value):
if import_value is None or export_value is None:
return "NA"
elif import_value > 0 and export_value > 0:
return "Specialised"
elif import_value < 0 and export_value < 0:
return "Unspecialised"
return "Inconclusive"
def rca_diff(import_value, export_value, country1, country2):
if import_value is None or export_value is None:
return "NA"
elif import_value > 0 and export_value > 0:
return f"{country2} more specialised globally than in {country1}"
elif import_value < 0 and export_value < 0:
return f"{country2} more specialised in {country1} than globally"
return "Inconclusive"
def rca_diff_glob(import_value, export_value, country1, country2):
if import_value is None or export_value is None:
return "NA"
elif import_value > 0 and export_value > 0:
return f"{country2} more specialised globally than {country1}"
elif import_value < 0 and export_value < 0:
return f"{country1} more specialised globally than {country2}"
return "Inconclusive"
def format_value(value):
if value < 1000:
return f"£{round(value, 0)}"
elif value > 1000000000:
return f"£{round(value, -8) / 1000000000}bn"
elif value > 1000000:
return f"£{round(value, -5) / 1000000}m"
return f"£{round(value, -2) / 1000}k"
def value_range(import_value, export_value):
if import_value < export_value:
return f"{format_value(import_value)} - {format_value(export_value)}"
return f"{format_value(export_value)} - {format_value(import_value)}"
def percent_range(import_value, export_value, decimal_places):
import_value *= 100
export_value *= 100
if import_value == export_value:
return f"{round(import_value, decimal_places)}%"
elif import_value < export_value:
return f"{round(import_value, decimal_places)}% - {round(export_value, decimal_places)}%"
return f"{round(export_value, decimal_places)}% - {round(import_value, decimal_places)}%"
| [
"[email protected]"
]
| |
e706179c11effcfa8f133d63d2655724fca4d1e9 | 0005e05b9d8b8ad0d3c3c0539b2ded9db6e9f1dd | /codechef_client/models/tag.py | 4cdd6e64295823ef02e369ae6ce1a056970ea646 | []
| no_license | termicoder/codechef-client-lib | a3e3de2b300355c5daa5ed3fad03a9859af13d86 | 74d6b21787c75a987e3451751f5554e4cc6cf469 | refs/heads/master | 2020-03-27T17:58:45.298121 | 2018-09-30T18:03:14 | 2018-09-30T18:03:14 | 146,889,644 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,094 | py | # coding: utf-8
"""
CodeChef API
CodeChef API to support different applications. # noqa: E501
OpenAPI spec version: 1.0.0
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class Tag(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'tag': 'str',
'type': 'str',
'count': 'int'
}
attribute_map = {
'tag': 'tag',
'type': 'type',
'count': 'count'
}
def __init__(self, tag=None, type=None, count=None): # noqa: E501
"""Tag - a model defined in Swagger""" # noqa: E501
self._tag = None
self._type = None
self._count = None
self.discriminator = None
if tag is not None:
self.tag = tag
if type is not None:
self.type = type
if count is not None:
self.count = count
@property
def tag(self):
"""Gets the tag of this Tag. # noqa: E501
Value # noqa: E501
:return: The tag of this Tag. # noqa: E501
:rtype: str
"""
return self._tag
@tag.setter
def tag(self, tag):
"""Sets the tag of this Tag.
Value # noqa: E501
:param tag: The tag of this Tag. # noqa: E501
:type: str
"""
self._tag = tag
@property
def type(self):
"""Gets the type of this Tag. # noqa: E501
author/tag # noqa: E501
:return: The type of this Tag. # noqa: E501
:rtype: str
"""
return self._type
@type.setter
def type(self, type):
"""Sets the type of this Tag.
author/tag # noqa: E501
:param type: The type of this Tag. # noqa: E501
:type: str
"""
self._type = type
@property
def count(self):
"""Gets the count of this Tag. # noqa: E501
Count of problems with this tag # noqa: E501
:return: The count of this Tag. # noqa: E501
:rtype: int
"""
return self._count
@count.setter
def count(self, count):
"""Sets the count of this Tag.
Count of problems with this tag # noqa: E501
:param count: The count of this Tag. # noqa: E501
:type: int
"""
self._count = count
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, Tag):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
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0ee27c2b6c2029409b39052286ba40d81a836616 | d3efc82dfa61fb82e47c82d52c838b38b076084c | /Autocase_Result/SjShHBJJMM/YW_HBJJMM_SHSJ_067.py | 4cb90cd9223c79893514c907a5e29a58cc20a03f | []
| no_license | nantongzyg/xtp_test | 58ce9f328f62a3ea5904e6ed907a169ef2df9258 | ca9ab5cee03d7a2f457a95fb0f4762013caa5f9f | refs/heads/master | 2022-11-30T08:57:45.345460 | 2020-07-30T01:43:30 | 2020-07-30T01:43:30 | 280,388,441 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,142 | py | #!/usr/bin/python
# -*- encoding: utf-8 -*-
import sys
sys.path.append("/home/yhl2/workspace/xtp_test/xtp/api")
from xtp_test_case import *
sys.path.append("/home/yhl2/workspace/xtp_test/service")
from ServiceConfig import *
from log import *
sys.path.append("/home/yhl2/workspace/xtp_test/MoneyFund/moneyfundservice")
from mfmainService import *
from mfQueryStkPriceQty import *
sys.path.append("/home/yhl2/workspace/xtp_test/MoneyFund/moneyfundmysql")
from mfCaseParmInsertMysql import *
sys.path.append("/home/yhl2/workspace/xtp_test/utils")
from QueryOrderErrorMsg import queryOrderErrorMsg
class YW_HBJJMM_SHSJ_067(xtp_test_case):
# YW_HBJJMM_SHSJ_067
def test_YW_HBJJMM_SHSJ_067(self):
title = '上海Ａ股股票交易日五档即成转限价卖——错误的价格(价格10亿)'
# 定义当前测试用例的期待值
# 期望状态：初始、未成交、部成、全成、部撤已报、部撤、已报待撤、已撤、废单、撤废、内部撤单
# xtp_ID和cancel_xtpID默认为0，不需要变动
case_goal = {
'期望状态': '全成',
'errorID': 0,
'errorMSG': '',
'是否生成报单': '是',
'是否是撤废': '否',
'xtp_ID': 0,
'cancel_xtpID': 0,
}
logger.warning(title)
# 定义委托参数信息------------------------------------------
# 参数：证券代码、市场、证券类型、证券状态、交易状态、买卖方向（Ｂ买Ｓ卖）、期望状态、Api
stkparm = QueryStkPriceQty('999999', '1', '111', '2', '0', 'S', case_goal['期望状态'], Api)
# 如果下单参数获取失败，则用例失败
if stkparm['返回结果'] is False:
rs = {
'用例测试结果': stkparm['返回结果'],
'测试错误原因': '获取下单参数失败,' + stkparm['错误原因'],
}
self.assertEqual(rs['用例测试结果'], True)
else:
wt_reqs = {
'business_type': Api.const.XTP_BUSINESS_TYPE['XTP_BUSINESS_TYPE_CASH'],
'order_client_id':2,
'market': Api.const.XTP_MARKET_TYPE['XTP_MKT_SH_A'],
'ticker': stkparm['证券代码'],
'side': Api.const.XTP_SIDE_TYPE['XTP_SIDE_SELL'],
'price_type': Api.const.XTP_PRICE_TYPE['XTP_PRICE_BEST5_OR_LIMIT'],
'price': 1000000000,
'quantity': 200,
'position_effect': Api.const.XTP_POSITION_EFFECT_TYPE['XTP_POSITION_EFFECT_INIT']
}
ParmIni(Api, case_goal['期望状态'], wt_reqs['price_type'])
CaseParmInsertMysql(case_goal, wt_reqs)
rs = serviceTest(Api, case_goal, wt_reqs)
logger.warning('执行结果为' + str(rs['用例测试结果']) + ','
+ str(rs['用例错误源']) + ',' + str(rs['用例错误原因']))
self.assertEqual(rs['用例测试结果'], True) # 0
if __name__ == '__main__':
unittest.main()
| [
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3e0492db360ce01a76f540ff3bf14d2133ae8153 | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/nouns/_bogies.py | e575bb083362fdfd4e25d0bf21f424dc5070f88d | [
"MIT"
]
| permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 226 | py |
from xai.brain.wordbase.nouns._bogy import _BOGY
#calss header
class _BOGIES(_BOGY, ):
def __init__(self,):
_BOGY.__init__(self)
self.name = "BOGIES"
self.specie = 'nouns'
self.basic = "bogy"
self.jsondata = {}
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| |
dffede7cbbfa98929853b8241f6a1e945007f560 | e5fb2d912415c302221604126afa7cbbb0a039c0 | /keras_gym/policies/test_special.py | d19afe8e363fc4399127c8f76a179ab42414bef4 | [
"MIT"
]
| permissive | KristianHolsheimer/keras-gym | fc034025a1180b1124fe1a25886b54088d2f3552 | 0296ddcc8685e1ce732c3173caaa0fd25af9ef58 | refs/heads/master | 2021-06-28T21:57:50.122753 | 2020-09-30T04:29:15 | 2020-09-30T04:29:15 | 174,637,157 | 17 | 5 | MIT | 2019-08-02T22:48:41 | 2019-03-09T02:09:03 | Python | UTF-8 | Python | false | false | 1,012 | py | from gym.envs.toy_text.frozen_lake import FrozenLakeEnv, RIGHT, DOWN
from .special import UserInputPolicy
class MockInputFunction:
def __init__(self, return_value=None):
self.return_value = return_value
self._orig_input_fn = __builtins__['input']
def _mock_input_fn(self, prompt):
print(prompt + str(self.return_value))
return self.return_value
def __enter__(self):
__builtins__['input'] = self._mock_input_fn
def __exit__(self, type, value, traceback):
__builtins__['input'] = self._orig_input_fn
class TestUserInputPolicy:
def test_expected(self):
env = FrozenLakeEnv(is_slippery=False)
policy = UserInputPolicy(env)
s = env.reset()
env.render()
for i in [RIGHT, RIGHT, DOWN, DOWN, DOWN, RIGHT]:
with MockInputFunction(return_value=i):
a = policy(s)
s, r, done, info = env.step(a)
env.render()
if done:
break
| [
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| |
097439d4e5e15a04cbe777f77fd0434256fd16d1 | a61ca7b89ef5817b2027239ece9dd175f776c8f3 | /rcsb/app/chem/LogFilterUtils.py | 86c6b9113eaef1e38f51a767d80d66d89057586c | [
"Apache-2.0"
]
| permissive | rcsb/py-rcsb_app_chem | 7da2941f6e0d0f8ff0f5a802a3edb689d283659b | 64ca10e6ccf8b604fa3d16ab72406408b22c0aca | refs/heads/master | 2023-08-17T21:33:51.660687 | 2023-01-09T17:30:07 | 2023-01-09T17:30:07 | 245,858,180 | 0 | 0 | Apache-2.0 | 2023-01-09T17:30:08 | 2020-03-08T17:31:37 | Python | UTF-8 | Python | false | false | 866 | py | ##
# File: LogFilterUtils.py
# Date: 29-Jun-2020 jdw
#
# Pre-filter for Gunicorn/Uvicorn health check requests -
##
# pylint: disable=E1101
import logging
logger = logging.getLogger(__name__)
class HealthCheckFilter(logging.Filter):
def filter(self, record):
return record.getMessage().find("/healthcheck") == -1
class LogFilterUtils(object):
def __init__(self):
pass
def addFilters(self):
logger.debug("Current loggers are: %r", [name for name in logging.root.manager.loggerDict]) # pylint: disable=no-member
for name in logging.root.manager.loggerDict: # pylint: disable=no-member
if any(x in name for x in ["uvicorn", "gunicorn"]):
logger.debug("Add filter to logger %r", name)
loggerT = logging.getLogger(name)
loggerT.addFilter(HealthCheckFilter())
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| |
937ca15e0fcc69c211f17b69de785760dbc1afb7 | 9e87897c988af634c3fddc42113992a65ec006f4 | /sims/repfam_fs/test/metrics_v2.py | e6aeb826dec6e7fd91e6f424c75731b25e4345ea | [
"MIT"
]
| permissive | luiarthur/cytof5 | 152eb06030785fdff90220f0d0a244a02204c2e9 | 6b4df5e9fd94bfd586e96579b8c618fdf6f913ed | refs/heads/master | 2021-07-20T13:39:45.821597 | 2021-03-02T23:27:35 | 2021-03-02T23:27:35 | 145,253,611 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 8,927 | py | import collections
import os
import sys
import re
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import rcparams
KTRUE = [4, 5]
def parse_Rs(path_to_Rs_csv):
Rs = pd.read_csv(path_to_Rs_csv).rename(columns=dict(mean='Mean'))
return Rs
def get_ks(results_dir):
# Get dir names for each K
kdirs = sorted(os.listdir(results_dir))
# Get all values of K
ks = [d.replace('KMCMC', '') for d in kdirs]
return ks, kdirs
def parse_log(path_to_log):
# Read log file
with open(path_to_log, "r") as f:
contents = f.read()
# Keep only the metrics
metrics = contents.split('metrics:')[1]
# Separate into lines
metrics = metrics.split('\n')
# keep only lines with '=>'
metrics = list(filter(lambda line: '=>' in line, metrics))
# Create empty dict to return
out = dict()
# store metrics in a dict
for metric in metrics:
key, val = metric.split('=>')
out[key.strip()] = float(val.strip())
return out
def count_num_small_phenotypes(path, thresh=.01):
rgx = lambda f: re.match('W\d+_hat', f)
w_hat_paths = list(filter(rgx, os.listdir(path)))
num_small_phenotypes = 0
for wpath in w_hat_paths:
wi = np.genfromtxt('{}/{}'.format(path, wpath))
# num_small_phenotypes += ((0 < wi) * (wi < thresh)).sum()
num_small_phenotypes += (wi < thresh).sum()
return num_small_phenotypes
def compute_num_selected_features(path):
rgx = lambda f: re.match('W\d+_hat', f)
w_hat_paths = list(filter(rgx, os.listdir(path)))
di = []
for wpath in sorted(w_hat_paths):
wi = np.genfromtxt('{}/{}'.format(path, wpath))
di.append((wi > 0).sum())
return di
def get_metrics_for_each_dir(results_dir, thresh=.01):
# Create dict to store results
out = dict()
# Traverse results
for root, dirs, files in os.walk(results_dir):
for f in files:
if f == 'log.txt':
path_to_log = '{}/{}'.format(root, f)
# Parse LPML / DIC
metrics = parse_log(path_to_log)
# Parse W
path_to_W = '{}/img/yz/txt/'.format(root)
num_small_phenotypes = count_num_small_phenotypes(path_to_W,
thresh)
metrics['num_selected_features'] = compute_num_selected_features(path_to_W)
metrics['num_small_phenotypes'] = num_small_phenotypes
# Parse R
path_to_R = '{}/img/txt/'.format(root)
R_df = parse_Rs('{}/Rs.csv'.format(path_to_R))
metrics['I'] = R_df.shape[0]
metrics['R_mean'] = R_df.Mean.to_numpy()
metrics['R_lower'] = R_df.p_02_5.to_numpy()
metrics['R_upper'] = R_df.p_97_5.to_numpy()
# metrics['R_mean'] = R_df.p_50_0.to_numpy()
# metrics['R_lower'] = R_df.p_25_0.to_numpy()
# metrics['R_upper'] = R_df.p_75_0.to_numpy()
# Parse Rprob
path_to_Rprob = path_to_R
R_prob = np.loadtxt('{}/prob_R_equals_K.txt'.format(path_to_R))
metrics['Rprob'] = R_prob.T
# Append to metrics
out[path_to_log] = metrics
return out
def get_exp_dict(results_dir):
# Get Ks and KMCMC dirname
ks, kdirs = get_ks(results_dir)
# For each directory,
all_metrics = get_metrics_for_each_dir(results_dir)
# Experiments dictionary, indexed by (z, scale)
exp_dict = dict()
# Split all the keys
for key in all_metrics:
path = key.replace(results_dir + '/', '')
kmcmc, z, scale, _, seed, _ = path.split('/')
kmcmc_int = int(kmcmc.replace('KMCMC', ''))
scale_float = float(scale.replace('scale', ''))
new_key = (z, scale_float, seed)
if new_key not in exp_dict:
exp_dict[new_key] = dict()
exp_dict[new_key][kmcmc_int] = all_metrics[key]
return exp_dict
def graph_for_setting(setting, exp_dict, metric, label, labels=None):
d = exp_dict[setting]
if metric == 'num_small_phenotypes':
lpml = []
num_small = []
ks = []
for kmcmc in sorted(d.keys()):
ks.append(kmcmc)
lpml.append(d[kmcmc]['LPML'])
num_small.append(d[kmcmc]['num_small_phenotypes'])
plt.plot(num_small, lpml, marker='o', label=label)
plt.xlabel('number of obscure phenotypes')
plt.ylabel('LPML')
elif metric == 'Rprob':
K_min = list(d.keys())[0]
I = d[K_min]['I']
if labels is not None:
if len(labels) == 2:
c = {labels[0]: 'blue', labels[1]: 'red'}
else:
print('NotImplemented!')
for i in range(I):
plt.subplot(I, 1, i + 1)
ks = []
Ri_prob_equals_K_TRUE = []
Ks = sorted(d.keys())
for kmcmc in Ks:
ks.append(kmcmc)
if kmcmc < KTRUE[i]:
Ri_prob_equals_K_TRUE.append(0)
else:
Ri_prob_equals_K_TRUE.append(d[kmcmc]['Rprob'][i, KTRUE[i] - 1])
plt.plot(ks, Ri_prob_equals_K_TRUE,
color=c[label], marker='o', label=label)
plt.xlabel('KMCMC')
plt.ylabel('Prob(Ri = K_TRUE)')
plt.ylim([-0.1, 1.1])
elif metric == 'R':
K_min = list(d.keys())[0]
I = d[K_min]['I']
if labels is not None:
if len(labels) == 2:
c = {labels[0]: 'blue', labels[1]: 'red'}
else:
print('NotImplemented!')
for i in range(I):
plt.subplot(I, 1, i + 1)
ks = []
Ri_mean = []
Ri_lower = []
Ri_upper = []
Ks = sorted(d.keys())
for kmcmc in Ks:
ks.append(kmcmc)
Ri_mean.append(d[kmcmc]['R_mean'][i])
Ri_lower.append(d[kmcmc]['R_lower'][i])
Ri_upper.append(d[kmcmc]['R_upper'][i])
plt.plot(ks, Ri_mean, color=c[label], marker='o', label=label)
plt.fill_between(ks, Ri_lower, Ri_upper,
color=c[label], alpha=.3)
plt.xlabel('KMCMC')
plt.ylabel('R_{}'.format(i + 1))
plt.yticks(range(min(Ks) - 2, int(max(Ri_upper) + .5), 2),
range(min(Ks) - 2, int(max(Ri_upper) + .5), 2))
else:
ks = []
ms = []
for kmcmc in sorted(d.keys()):
ks.append(kmcmc)
ms.append(d[kmcmc][metric])
plt.plot(ks, ms, marker='o', label=label)
plt.xlabel('K')
plt.ylabel(metric)
plt.xticks(ks)
if __name__ == '__main__':
if len(sys.argv) > 1:
results_dir = sys.argv[1]
else:
results_dir = 'results/test-sims-5-5'
print('Results dir: {}'.format(results_dir))
# Get a dictionary indexed by experiment setting (z, scale, seed)
exp_dict = get_exp_dict(results_dir)
# Metrics to plot
# metrics = ['LPML', 'DIC', 'num_small_phenotypes', 'R']
# metrics = ['LPML', 'DIC', 'num_small_phenotypes']
metrics = ['LPML', 'DIC', 'R']
# Name of metrics dir
metrics_dir = '{}/metrics'.format(results_dir)
# Get unique zs
zs = set([key[0] for key in exp_dict.keys()])
print('zs: {}'.format(zs))
# Get unique seeds
seeds = set([key[2] for key in exp_dict.keys()])
print('seeds: {}'.format(seeds))
# Get unique scales
scales = set([key[1] for key in exp_dict.keys()])
num_scales = len(scales)
print('scales: {}'.format(scales))
# sorted exp_dict keys
exp_dict_keys_sorted = sorted(exp_dict.keys())
# TODO:
# graph Rs
labels = ['scale={}'.format(scale)for scale in scales]
for z in zs:
for seed in seeds:
for metric in metrics:
for setting in exp_dict_keys_sorted:
zidx, scale, sd = setting
if z == zidx and sd == seed:
label = 'scale={}'.format(scale)
graph_for_setting(setting, exp_dict, metric, label,
labels=labels)
dest_dir = '{}/{}/{}'.format(metrics_dir, z, seed)
if metric == 'R':
plt.legend(loc='lower right')
elif metric == 'Rprob':
plt.legend(loc='lower center')
else:
plt.legend()
plt.tight_layout()
# Make destination dir if needed
os.makedirs(dest_dir, exist_ok=True)
plt.savefig('{}/{}.pdf'.format(dest_dir, metric))
plt.close()
| [
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| |
8ef9af340d5e228e081e4752208ca6f0fc86e61c | 45284836ae85685226b1f1e3b83e207e184aee0e | /05_ProbabilityAndStatistics/01_ProbAndStatsInPython_Beginner/01_IntroductionToStatistics/11_MeasuresOfCentralTendency.py | f822187cd0dd31bf9867f58f1fd34ff63b9187d8 | []
| no_license | gaurab123/DataQuest | 5060efc3d3449e6e098cb77d7fed913516aabdbd | a9da9a90fab639d239340edfc7d0b2010edf2b35 | refs/heads/master | 2021-09-14T15:10:13.047034 | 2018-05-02T19:11:23 | 2018-05-02T19:11:23 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,400 | py | print("this mission cannot be run locally as the data used is loaded \"behind the scenes\" and I really don't have access to it")
import matplotlib.pyplot as plt
# Let's put a line over our plot that shows the mean.
# This is the same histogram we plotted for skew a few screens ago.
plt.hist(test_scores_normal)
# We can use the .mean() method of a numpy array to compute the mean.
mean_test_score = test_scores_normal.mean()
# The axvline function will plot a vertical line over an existing plot.
plt.axvline(mean_test_score)
# Now we can show the plot and clear the figure.
plt.show()
# When we plot test_scores_negative, which is a very negatively skewed distribution, we see that the small values on the left pull the mean in that direction.
# Very large and very small values can easily skew the mean.
# Very skewed distributions can make the mean misleading.
plt.hist(test_scores_negative)
plt.axvline(test_scores_negative.mean())
plt.show()
# We can do the same with the positive side.
# Notice how the very high values pull the mean to the right more than we would expect.
plt.hist(test_scores_positive)
plt.axvline(test_scores_positive.mean())
plt.show()
mean_normal = test_scores_normal.mean()
mean_negative = test_scores_negative.mean()
mean_positive = test_scores_positive.mean()
print(mean_normal)
print(mean_negative)
print(mean_positive) | [
"[email protected]"
]
| |
c4c6e0af2a87a16415a3f0575945f66d748ea0f4 | 2ed1cccb49ee1549f09747061a2513fb053c707d | /20181004/DProposed_gpu3.py | 91281bf826beb09c8480f4a1812fba4e8869a002 | []
| no_license | hhjung1202/Prob_network | 1c766ef5191727a63a38654622e21f0d986b923e | dedd4e525c9393f15452709dda377ceee9849c15 | refs/heads/master | 2020-03-22T11:42:27.705442 | 2018-11-11T14:29:39 | 2018-11-11T14:29:39 | 139,990,155 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 7,940 | py | import torch
from torch.autograd import Variable
import torch.optim as optim
from torchvision import datasets, transforms
from DPmodel import *
import os
import torch.backends.cudnn as cudnn
import time
import utils
os.environ["CUDA_VISIBLE_DEVICES"] = '3'
def main(model_dir, model, dataset, batch_size=128):
utils.default_model_dir = model_dir
utils.c = None
utils.str_w = ''
# model = model
lr = 0.1
start_time = time.time()
if dataset == 'cifar10':
if batch_size is 128:
train_loader, test_loader = utils.cifar10_loader()
elif batch_size is 64:
train_loader, test_loader = utils.cifar10_loader_64()
elif dataset == 'cifar100':
train_loader, test_loader = utils.cifar100_loader()
if torch.cuda.is_available():
# os.environ["CUDA_VISIBLE_DEVICES"] = '0'
print("USE", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model).cuda()
cudnn.benchmark = True
else:
print("NO GPU -_-;")
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=1e-4, nesterov=True)
criterion = nn.CrossEntropyLoss().cuda()
start_epoch = 0
checkpoint = utils.load_checkpoint(model_dir)
if not checkpoint:
pass
else:
start_epoch = checkpoint['epoch'] + 1
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
utils.init_learning(model.module)
for epoch in range(start_epoch, 300):
if epoch < 150:
learning_rate = lr
elif epoch < 225:
learning_rate = lr * 0.1
else:
learning_rate = lr * 0.01
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate
train(model, optimizer, criterion, train_loader, epoch, True)
test(model, criterion, test_loader, epoch, True)
utils.switching_learning(model.module)
print('switching_learning to Gate')
train(model, optimizer, criterion, train_loader, epoch, False)
test(model, criterion, test_loader, epoch, False)
utils.switching_learning(model.module)
print('switching_learning to Gate')
if epoch % 5 == 0:
model_filename = 'checkpoint_%03d.pth.tar' % epoch
utils.save_checkpoint({
'epoch': epoch,
'model': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, model_filename, model_dir)
now = time.gmtime(time.time() - start_time)
weight_extract(model, optimizer, criterion, train_loader, epoch)
utils.conv_weight_L1_printing(model.module)
print('{} hours {} mins {} secs for training'.format(now.tm_hour, now.tm_min, now.tm_sec))
def train(model, optimizer, criterion, train_loader, epoch, is_main):
model.train()
train_loss = 0
total = 0
correct = 0
for batch_idx, (data, target) in enumerate(train_loader):
if torch.cuda.is_available():
data, target = Variable(data.cuda()), Variable(target.cuda())
else:
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.data[0]
_, predicted = torch.max(output.data, 1)
total += target.size(0)
correct += predicted.eq(target.data).cpu().sum()
if batch_idx % 10 == 0 and is_main is True:
utils.print_log('Epoch: {} | Batch: {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{})'
.format(epoch, batch_idx, train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
print('Epoch: {} | Batch: {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{})'
.format(epoch, batch_idx, train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
elif batch_idx % 10 == 0 and is_main is False:
utils.print_log('SWICH: {} | Batch: {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{})'
.format(epoch, batch_idx, train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
print('SWICH: {} | Batch: {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{})'
.format(epoch, batch_idx, train_loss / (batch_idx + 1), 100. * correct / total, correct, total))
def weight_extract(model, optimizer, criterion, train_loader, epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
if torch.cuda.is_available():
data, target = Variable(data.cuda()), Variable(target.cuda())
else:
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
utils.c = target.view(-1,1) # batch array torch.tensor[128]
utils.c = utils.c.type(torch.cuda.FloatTensor)
utils.weight_extract_densenet(model.module)
for i in utils.c:
for j in i:
utils.str_w = utils.str_w + str(j.tolist()) + ','
utils.str_w += '\n'
utils.save_to_csv()
utils.str_w = ''
if batch_idx % 100 == 0:
print('Epoch: {}'.format(epoch))
def test(model, criterion, test_loader, epoch, is_main):
model.eval()
test_loss = 0
correct = 0
total = 0
for batch_idx, (data, target) in enumerate(test_loader):
if torch.cuda.is_available():
data, target = Variable(data.cuda()), Variable(target.cuda())
else:
data, target = Variable(data), Variable(target)
outputs = model(data)
loss = criterion(outputs, target)
test_loss += loss.data[0]
_, predicted = torch.max(outputs.data, 1)
total += target.size(0)
correct += predicted.eq(target.data).cpu().sum()
max_result.append(correct)
if is_main is True:
utils.print_log('# TEST : Epoch : {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{}) | Err: ({:.2f}%) | Max: ({})'
.format(epoch, test_loss/(batch_idx+1), 100.*correct/total, correct, total, 100-100.*correct/total, max(max_result)))
print('# TEST : Epoch : {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{}) | Err: ({:.2f}% | Max: ({}))'
.format(epoch, test_loss/(batch_idx+1), 100.*correct/total, correct, total, 100-100.*correct/total, max(max_result)))
elif is_main is False:
utils.print_log('$ TEST_S : Epoch : {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{}) | Err: ({:.2f}%) | Max: ({})'
.format(epoch, test_loss/(batch_idx+1), 100.*correct/total, correct, total, 100-100.*correct/total, max(max_result)))
print('$ TEST_S : Epoch : {} | Loss: ({:.4f}) | Acc: ({:.2f}%) ({}/{}) | Err: ({:.2f}% | Max: ({}))'
.format(epoch, test_loss/(batch_idx+1), 100.*correct/total, correct, total, 100-100.*correct/total, max(max_result)))
layer_set = [14, 20, 32, 44, 56, 110]
def do_learning(model_dir, db, layer, num_gate=0, batch_s=128, block_config=(6,6,6), is_bottleneck=True):
global max_result
max_result = []
model_selection = DenseNet(num_classes=10, num_gate=num_gate
, block_config=block_config, is_bottleneck=is_bottleneck)
dataset = 'cifar' + str(db)
main(model_dir, model_selection, dataset, batch_s)
if __name__=='__main__':
for i in range(10):
if i % 2 == 0:
block_config = (12, 12, 12)
is_bottleneck = False
else:
block_config = (6,6,6)
is_bottleneck = True
model_dir = '../hhjung/Dense_Prop/cifar10/DenseNet40/' + str(i)
do_learning(model_dir, 10, layer_set[5], num_gate=0
, batch_s=64, block_config=block_config, is_bottleneck=is_bottleneck) | [
"[email protected]"
]
| |
e69f0f7583c1022af9442415e61c2769e37c4122 | dbf770eef8233f7da1850309cc4b7145bd8d67f1 | /PYTHON-ADVANCED-SEPT-2020/PYTHON ADVANCED/03_MULTYDIMENSINAL LISTS/EXERCISE/06_chess.py | 654a9eb872ce75844a3566d42fa88934b8ec214a | []
| no_license | vasil-panoff/PYTHON-ADVANCED-SEPT-2020_repo | 610a37d1681ce9d0aa86628523620e1571b438dd | c63434f91de42d2f1241b6d76a96c7c63711c1d0 | refs/heads/master | 2023-03-22T07:44:53.620221 | 2021-03-15T20:42:14 | 2021-03-15T20:42:14 | 309,829,800 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,217 | py | possible_moves = (
(-1, -2),
(-1, 2),
(1, -2),
(1, 2),
(2, -1),
(2, 1),
(-2, 1),
(-2, -1),
)
board_size = int(input())
matrix = [['0'] * board_size for i in range(board_size)]
def is_valid(i, j):
if i < 0 or j < 0 or i >= board_size or j >= board_size:
return False
return matrix[i][j] == "K"
knights_dict = {}
def update_knights(i1, j1, i2, j2):
if not is_valid(i2, j2):
return
if (i2, j2) not in knights_dict:
knights_dict[(i2, j2)] = []
knights_dict[(i2, j2)].append((i1, j1))
if (i1, j1) not in knights_dict:
knights_dict[i1, j1] = []
knights_dict[(i1, j1)].append((i2, j2))
for i in range(board_size):
row = list(input())
for j in range(board_size):
if row[j] == "K":
matrix[i][j] = "K"
for move_i, move_j in possible_moves:
i1 = i
j1 = j
i2 = i + move_i
j2 = j + move_j
update_knights(i1, j1, i2, j2)
num_removed = 0
max_knight = get_max_knight(knights_dict)
while len(max_knight) > 0:
remove_knight(matrix, max_knight)
knights_dict
num_removed += 1
print(num_removed)
| [
"[email protected]"
]
| |
fdb64336cf73e67d472705398ef382a50c520887 | 9b9a02657812ea0cb47db0ae411196f0e81c5152 | /repoData/kushaldas-retask/allPythonContent.py | d19c699f3ba9cd5827f25619f2fb80dfe0dc8340 | []
| no_license | aCoffeeYin/pyreco | cb42db94a3a5fc134356c9a2a738a063d0898572 | 0ac6653219c2701c13c508c5c4fc9bc3437eea06 | refs/heads/master | 2020-12-14T14:10:05.763693 | 2016-06-27T05:15:15 | 2016-06-27T05:15:15 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 29,401 | py | __FILENAME__ = conf
# -*- coding: utf-8 -*-
#
# retask documentation build configuration file, created by
# sphinx-quickstart on Tue Jul 3 14:56:38 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(os.path.abspath('_themes'))
html_theme_path = ['_themes']
html_theme = 'kr'
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# -- General configuration -----------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.intersphinx', 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.doctest', 'sphinx.ext.coverage']
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'retask'
copyright = u'2012, Kushal Das'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.4'
# The full version, including alpha/beta/rc tags.
release = '0.4'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
#pygments_style = 'flask_theme_support.FlaskyStyle'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# -- Options for HTML output ---------------------------------------------------
html_sidebars = {
'index': ['sidebarintro.html', 'sourcelink.html', 'searchbox.html'],
'**': ['sidebarlogo.html', 'localtoc.html', 'relations.html',
'sourcelink.html', 'searchbox.html']
}
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#html_theme = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'retaskdoc'
# -- Options for LaTeX output --------------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
('index', 'retask.tex', u'retask Documentation',
u'Kushal Das', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output --------------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'retask', u'retask Documentation',
[u'Kushal Das'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output ------------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'retask', u'retask Documentation',
u'Kushal Das', 'retask', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
########NEW FILE########
__FILENAME__ = flask_theme_support
# flasky extensions. flasky pygments style based on tango style
from pygments.style import Style
from pygments.token import Keyword, Name, Comment, String, Error, \
Number, Operator, Generic, Whitespace, Punctuation, Other, Literal
class FlaskyStyle(Style):
background_color = "#f8f8f8"
default_style = ""
styles = {
# No corresponding class for the following:
#Text: "", # class: ''
Whitespace: "underline #f8f8f8", # class: 'w'
Error: "#a40000 border:#ef2929", # class: 'err'
Other: "#000000", # class 'x'
Comment: "italic #8f5902", # class: 'c'
Comment.Preproc: "noitalic", # class: 'cp'
Keyword: "bold #004461", # class: 'k'
Keyword.Constant: "bold #004461", # class: 'kc'
Keyword.Declaration: "bold #004461", # class: 'kd'
Keyword.Namespace: "bold #004461", # class: 'kn'
Keyword.Pseudo: "bold #004461", # class: 'kp'
Keyword.Reserved: "bold #004461", # class: 'kr'
Keyword.Type: "bold #004461", # class: 'kt'
Operator: "#582800", # class: 'o'
Operator.Word: "bold #004461", # class: 'ow' - like keywords
Punctuation: "bold #000000", # class: 'p'
# because special names such as Name.Class, Name.Function, etc.
# are not recognized as such later in the parsing, we choose them
# to look the same as ordinary variables.
Name: "#000000", # class: 'n'
Name.Attribute: "#c4a000", # class: 'na' - to be revised
Name.Builtin: "#004461", # class: 'nb'
Name.Builtin.Pseudo: "#3465a4", # class: 'bp'
Name.Class: "#000000", # class: 'nc' - to be revised
Name.Constant: "#000000", # class: 'no' - to be revised
Name.Decorator: "#888", # class: 'nd' - to be revised
Name.Entity: "#ce5c00", # class: 'ni'
Name.Exception: "bold #cc0000", # class: 'ne'
Name.Function: "#000000", # class: 'nf'
Name.Property: "#000000", # class: 'py'
Name.Label: "#f57900", # class: 'nl'
Name.Namespace: "#000000", # class: 'nn' - to be revised
Name.Other: "#000000", # class: 'nx'
Name.Tag: "bold #004461", # class: 'nt' - like a keyword
Name.Variable: "#000000", # class: 'nv' - to be revised
Name.Variable.Class: "#000000", # class: 'vc' - to be revised
Name.Variable.Global: "#000000", # class: 'vg' - to be revised
Name.Variable.Instance: "#000000", # class: 'vi' - to be revised
Number: "#990000", # class: 'm'
Literal: "#000000", # class: 'l'
Literal.Date: "#000000", # class: 'ld'
String: "#4e9a06", # class: 's'
String.Backtick: "#4e9a06", # class: 'sb'
String.Char: "#4e9a06", # class: 'sc'
String.Doc: "italic #8f5902", # class: 'sd' - like a comment
String.Double: "#4e9a06", # class: 's2'
String.Escape: "#4e9a06", # class: 'se'
String.Heredoc: "#4e9a06", # class: 'sh'
String.Interpol: "#4e9a06", # class: 'si'
String.Other: "#4e9a06", # class: 'sx'
String.Regex: "#4e9a06", # class: 'sr'
String.Single: "#4e9a06", # class: 's1'
String.Symbol: "#4e9a06", # class: 'ss'
Generic: "#000000", # class: 'g'
Generic.Deleted: "#a40000", # class: 'gd'
Generic.Emph: "italic #000000", # class: 'ge'
Generic.Error: "#ef2929", # class: 'gr'
Generic.Heading: "bold #000080", # class: 'gh'
Generic.Inserted: "#00A000", # class: 'gi'
Generic.Output: "#888", # class: 'go'
Generic.Prompt: "#745334", # class: 'gp'
Generic.Strong: "bold #000000", # class: 'gs'
Generic.Subheading: "bold #800080", # class: 'gu'
Generic.Traceback: "bold #a40000", # class: 'gt'
}
########NEW FILE########
__FILENAME__ = async_producer
from retask import Task
from retask import Queue
import time
queue = Queue('example')
info1 = {'user': 'Fedora planet', 'url': 'http://planet.fedoraproject.org'}
task1 = Task(info1)
queue.connect()
job = queue.enqueue(task1)
print job.result
time.sleep(30)
print job.result
########NEW FILE########
__FILENAME__ = async_worker
from retask import Queue
import time
queue = Queue('example')
queue.connect()
task = queue.wait()
print task.data
time.sleep(15)
queue.send(task, "We received your information dear %s" % task.data['user'])
########NEW FILE########
__FILENAME__ = consumer
from retask import Queue
queue = Queue('example')
queue.connect()
while queue.length != 0:
task = queue.dequeue()
print task.data
########NEW FILE########
__FILENAME__ = producer
from retask import Task
from retask import Queue
queue = Queue('example')
info1 = {'user':'kushal', 'url':'http://kushaldas.in'}
info2 = {'user':'fedora planet', 'url':'http://planet.fedoraproject.org'}
task1 = Task(info1)
task2 = Task(info2)
queue.connect()
queue.enqueue(task1)
queue.enqueue(task2)
########NEW FILE########
__FILENAME__ = sync_producer
from retask import Task
from retask import Queue
queue = Queue('example')
info1 = {'user': 'Fedora planet', 'url': 'http://planet.fedoraproject.org'}
task1 = Task(info1)
queue.connect()
job = queue.enqueue(task1)
job.wait()
print job.result
########NEW FILE########
__FILENAME__ = exceptions
# -*- coding: utf-8 -*-
"""
retask.exceptions
~~~~~~~~~~~~~~~~~~~
This module contains the set of Retask's exceptions.
"""
class RetaskException(RuntimeError):
"""Some ambiguous exception occurred"""
class ConnectionError(RetaskException):
"""A Connection error occurred."""
########NEW FILE########
__FILENAME__ = queue
#Copyright (C) 2012, Kushal Das <[email protected]>
#Permission is hereby granted, free of charge, to any person obtaining a copy of
#this software and associated documentation files (the "Software"), to deal in
#the Software without restriction, including without limitation the rights to
#use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
#of the Software, and to permit persons to whom the Software is furnished to do
#so, subject to the following conditions:
#The above copyright notice and this permission notice shall be included in all
#copies or substantial portions of the Software.
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#SOFTWARE.
__author__ = 'Kushal Das <[email protected]>'
__copyright__ = 'Copyright (c) 2012-2013 Kushal Das'
__license__ = 'MIT'
__status__ = 'Development'
__version__ = '0.3'
"""
retask Queue implementation
"""
import json
import redis
import uuid
import six
from .task import Task
from .exceptions import ConnectionError
class Queue(object):
"""
Returns the ``Queue`` object with the given name. If the user
passes optional config dictionary with details for Redis
server, it will connect to that instance. By default it connects
to the localhost.
"""
def __init__(self, name, config=None):
specified_config = config or {}
self.name = name
self._name = 'retaskqueue-' + name
self.config = {
'host': 'localhost',
'port': 6379,
'db': 0,
'password': None,
}
self.config.update(specified_config)
self.rdb = None
self.connected = False
@property
def length(self):
"""
Gives the length of the queue. Returns ``None`` if the queue is not
connected.
If the queue is not connected then it will raise
:class:`retask.ConnectionError`.
"""
if not self.connected:
raise ConnectionError('Queue is not connected')
try:
length = self.rdb.llen(self._name)
except redis.exceptions.ConnectionError as err:
raise ConnectionError(str(err))
return length
def connect(self):
"""
Creates the connection with the redis server.
Return ``True`` if the connection works, else returns
``False``. It does not take any arguments.
:return: ``Boolean`` value
.. note::
After creating the ``Queue`` object the user should call
the ``connect`` method to create the connection.
.. doctest::
>>> from retask import Queue
>>> q = Queue('test')
>>> q.connect()
True
"""
config = self.config
self.rdb = redis.Redis(config['host'], config['port'], config['db'],\
config['password'])
try:
info = self.rdb.info()
self.connected = True
except redis.ConnectionError:
return False
return True
def wait(self, wait_time=0):
"""
Returns a :class:`~retask.task.Task` object from the queue. Returns ``False`` if it timeouts.
:arg wait_time: Time in seconds to wait, default is infinite.
:return: :class:`~retask.task.Task` object from the queue or False if it timeouts.
.. doctest::
>>> from retask import Queue
>>> q = Queue('test')
>>> q.connect()
True
>>> task = q.wait()
>>> print task.data
{u'name': u'kushal'}
.. note::
This is a blocking call, you can specity wait_time argument for timeout.
"""
if not self.connected:
raise ConnectionError('Queue is not connected')
data = self.rdb.brpop(self._name, wait_time)
if data:
task = Task()
task.__dict__ = json.loads(data[1])
return task
else:
return False
def dequeue(self):
"""
Returns a :class:`~retask.task.Task` object from the queue. Returns ``None`` if the
queue is empty.
:return: :class:`~retask.task.Task` object from the queue
If the queue is not connected then it will raise
:class:`retask.ConnectionError`
.. doctest::
>>> from retask import Queue
>>> q = Queue('test')
>>> q.connect()
True
>>> t = q.dequeue()
>>> print t.data
{u'name': u'kushal'}
"""
if not self.connected:
raise ConnectionError('Queue is not connected')
if self.rdb.llen(self._name) == 0:
return None
data = self.rdb.rpop(self._name)
if not data:
return None
if isinstance(data, six.binary_type):
data = six.text_type(data, 'utf-8', errors = 'replace')
task = Task()
task.__dict__ = json.loads(data)
return task
def enqueue(self, task):
"""
Enqueues the given :class:`~retask.task.Task` object to the queue and returns
a :class:`~retask.queue.Job` object.
:arg task: ::class:`~retask.task.Task` object
:return: :class:`~retask.queue.Job` object
If the queue is not connected then it will raise
:class:`retask.ConnectionError`.
.. doctest::
>>> from retask import Queue
>>> q = Queue('test')
>>> q.connect()
True
>>> from retask.task import Task
>>> task = Task({'name':'kushal'})
>>> job = q.enqueue(task)
"""
if not self.connected:
raise ConnectionError('Queue is not connected')
try:
#We can set the value to the queue
job = Job(self.rdb)
task.urn = job.urn
text = json.dumps(task.__dict__)
self.rdb.lpush(self._name, text)
except Exception as err:
return False
return job
def send(self, task, result, expire=60):
"""
Sends the result back to the producer. This should be called if only you
want to return the result in async manner.
:arg task: ::class:`~retask.task.Task` object
:arg result: Result data to be send back. Should be in JSON serializable.
:arg expire: Time in seconds after the key expires. Default is 60 seconds.
"""
self.rdb.lpush(task.urn, json.dumps(result))
self.rdb.expire(task.urn, expire)
def __repr__(self):
if not self:
return '%s()' % (self.__class__.__name__,)
return '%s(%r)' % (self.__class__.__name__, self.name)
def find(self, obj):
"""Returns the index of the given object in the queue, it might be string
which will be searched inside each task.
:arg obj: object we are looking
:return: -1 if the object is not found or else the location of the task
"""
if not self.connected:
raise ConnectionError('Queue is not connected')
data = self.rdb.lrange(self._name, 0, -1)
for i, datum in enumerate(data):
if datum.find(str(obj)) != -1:
return i
return -1
class Job(object):
"""
Job object containing the result from the workers.
:arg rdb: The underlying redis connection.
"""
def __init__(self, rdb):
self.rdb = rdb
self.urn = uuid.uuid4().urn
self.__result = None
@property
def result(self):
"""
Returns the result from the worker for this job. This is used to pass
result in async way.
"""
if self.__result:
return self.__result
data = self.rdb.rpop(self.urn)
if data:
self.rdb.delete(self.urn)
data = json.loads(data)
self.__result = data
return data
else:
return None
def wait(self, wait_time=0):
"""
Blocking call to check if the worker returns the result. One can use
job.result after this call returns ``True``.
:arg wait_time: Time in seconds to wait, default is infinite.
:return: `True` or `False`.
.. note::
This is a blocking call, you can specity wait_time argument for timeout.
"""
if self.__result:
return True
data = self.rdb.brpop(self.urn, wait_time)
if data:
self.rdb.delete(self.urn)
data = json.loads(data[1])
self.__result = data
return True
else:
return False
########NEW FILE########
__FILENAME__ = release
NAME = 'retask'
VERSION = '0.4'
DESCRIPTION = 'Task Queue implementation in python'
LONG_DESCRIPTION = '''Retask is a simple task queue implementation written for human beings. It provides generic solution to create and manage task queues.'''
AUTHOR = 'Kushal Das'
EMAIL = '[email protected]'
COPYRIGHT = '2012-13 Kushal Das'
URL = 'https://github.com/kushaldas/retask'
LICENSE = 'MIT'
__all__ = ('NAME', 'VERSION', 'DESCRIPTION', 'LONG_DESCRIPTION', 'AUTHOR', 'EMAIL', 'COPYRIGHT', 'URL', 'LICENSE')
########NEW FILE########
__FILENAME__ = task
#Copyright (C) 2012, Kushal Das <[email protected]>
#Permission is hereby granted, free of charge, to any person obtaining a copy of
#this software and associated documentation files (the "Software"), to deal in
#the Software without restriction, including without limitation the rights to
#use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
#of the Software, and to permit persons to whom the Software is furnished to do
#so, subject to the following conditions:
#The above copyright notice and this permission notice shall be included in all
#copies or substantial portions of the Software.
#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#SOFTWARE.
__author__ = 'Kushal Das <[email protected]>'
__copyright__ = 'Copyright (c) 2012-2013 Kushal Das'
__license__ = 'MIT'
__status__ = 'Development'
__version__ = '0.3'
"""
Task Class
"""
import json
class Task(object):
"""
Returns a new Task object, the information for the task is passed through
argument ``data``
:kwarg data: Python object which contains information for the task. Should be serializable through ``JSON``.
"""
def __init__(self, data=None, raw=False, urn=None):
if not raw:
self._data = json.dumps(data)
else:
self._data = data
self.urn = urn
@property
def data(self):
"""
The python object containing information for the current task
"""
return json.loads(self._data)
@property
def rawdata(self):
"""
The string representation of the actual python objects for the task
.. note::
This should not be used directly by the users. This is for internal use
only.
"""
return self._data
def __repr__(self):
return '%s(%s)' % (self.__class__.__name__, repr(self.data))
########NEW FILE########
__FILENAME__ = tests
import unittest
import redis
from mock import patch
from retask import Task
from retask import Queue
class ConnectTest(unittest.TestCase):
"""
Test the connect method
"""
def runTest(self):
queue = Queue('testqueue')
self.assertTrue(queue.connect())
class LengthTest(unittest.TestCase):
"""
Tests the length method of the Queue
"""
@patch('redis.Redis')
def runTest(self, mock_redis):
m = mock_redis.return_value
m.llen.return_value = 2
queue = Queue('testqueue')
queue.connect()
self.assertEqual(queue.length, 2)
class SetTest(unittest.TestCase):
"""
Sets a task in the Queue
"""
def runTest(self):
queue = Queue('testqueue')
queue.connect()
t = Task({'name':'kushal'})
self.assertTrue(queue.enqueue(t))
def tearDown(self):
rdb = redis.Redis()
rdb.delete('retaskqueue-testqueue')
class GetTest(unittest.TestCase):
"""
Gets a task in the Queue
"""
def setUp(self):
queue = Queue('testqueue')
queue.connect()
t = Task({'name':'kushal'})
queue.enqueue(t)
def runTest(self):
queue = Queue('testqueue')
queue.connect()
task = queue.dequeue()
i = task.data
self.assertEqual(task.data['name'], 'kushal')
if __name__ == '__main__':
unittest.main()
########NEW FILE########
| [
"[email protected]"
]
| |
00784a17b99b4077db9e72d37bf5cb26749d3043 | 64bf39b96a014b5d3f69b3311430185c64a7ff0e | /intro-ansible/venv3/lib/python3.8/site-packages/ansible_test/_data/sanity/code-smell/changelog.py | 710b10f6c08ec6f6580b2837b46f9a06e6302fd6 | [
"MIT"
]
| permissive | SimonFangCisco/dne-dna-code | 7072eba7da0389e37507b7a2aa5f7d0c0735a220 | 2ea7d4f00212f502bc684ac257371ada73da1ca9 | refs/heads/master | 2023-03-10T23:10:31.392558 | 2021-02-25T15:04:36 | 2021-02-25T15:04:36 | 342,274,373 | 0 | 0 | MIT | 2021-02-25T14:39:22 | 2021-02-25T14:39:22 | null | UTF-8 | Python | false | false | 1,420 | py | #!/usr/bin/env python
from __future__ import (absolute_import, division, print_function)
__metaclass__ = type
import os
import sys
import subprocess
def main():
paths = sys.argv[1:] or sys.stdin.read().splitlines()
allowed_extensions = ('.yml', '.yaml')
config_path = 'changelogs/config.yaml'
# config must be detected independent of the file list since the file list only contains files under test (changed)
has_config = os.path.exists(config_path)
paths_to_check = []
for path in paths:
if path == config_path:
continue
if path.startswith('changelogs/fragments/.'):
if path in ('changelogs/fragments/.keep', 'changelogs/fragments/.gitkeep'):
continue
print('%s:%d:%d: file must not be a dotfile' % (path, 0, 0))
continue
ext = os.path.splitext(path)[1]
if ext not in allowed_extensions:
print('%s:%d:%d: extension must be one of: %s' % (path, 0, 0, ', '.join(allowed_extensions)))
paths_to_check.append(path)
if not has_config:
print('changelogs/config.yaml:0:0: config file does not exist')
return
if not paths_to_check:
return
cmd = [sys.executable, '-m', 'antsibull_changelog', 'lint'] + paths_to_check
subprocess.call(cmd) # ignore the return code, rely on the output instead
if __name__ == '__main__':
main()
| [
"[email protected]"
]
| |
9c7efaaa782f42236b3ee163464ef9d613bc033c | 0a5c103662e2ccea7698480bca28fb5c285aeafb | /info/dicom.py | 033a6a5fb0ed810ef20d7d4d98a1b7d9b7f8d109 | []
| no_license | joanshen0508/image_preprocessing | a8b9dc90e92552ca11af8b220a2ce235a558aef1 | 478e63593884d572a049590588df158c59447bab | refs/heads/master | 2022-04-02T17:08:56.559871 | 2019-10-29T15:24:48 | 2019-10-29T15:24:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,477 | py | from __future__ import division, print_function
import os
from os.path import join
from pandas import DataFrame
import re
import dicom
from inout.io_common import get_dicom_files_in_folder
class DicomDataSummary():
"""
This function allows the generation of information stored on nrrd files.
"""
def __init__(self, **kwargs):
self.input_folder = 'input'
self.output_folder = 'output'
# All the arguments that are passed to the constructor of the class MUST have its name on it.
for arg_name, arg_value in kwargs.items():
self.__dict__["_" + arg_name] = arg_value
def __getattr__(self, attr):
'''Generic getter for all the properties of the class'''
return self.__dict__["_" + attr]
def __setattr__(self, attr, value):
'''Generic setter for all the properties of the class'''
self.__dict__["_" + attr] = value
def generate_data_summary(self, folder_name_regex, file_name='data_summary'):
"""It generates a small summary from the data_sum as a CSV file (shape and voxel size)
:param folder_name_regex:
:return:
"""
cases = [x for x in os.listdir(self._input_folder) if os.path.isdir(join(self._input_folder, x))]
cases.sort()
colums_dic = {'Date':'AcquisitionDate',
'EchoTime':'EchoTime',
'EchoTrainLength':'EchoTrainLength',
'Manufacturer':'Manufacturer',
'Model':'ManufacturerModelName',
'Modality':'Modality',
'RepetitionTime': 'RepetitionTime',
'Orientation': 'ImageOrientationPatient'}
extra_columns = ['Size', 'Spacing', 'PixelSize']
all_columns = extra_columns + list(colums_dic.keys())
data_sum = DataFrame(index=cases, columns=all_columns)
# In this case we look for folders inside each case
for c_case in cases:
print(F"---------- {c_case}----------")
try:
matched_folders = [x for x in os.listdir(join(self._input_folder, c_case)) if not (re.search(folder_name_regex, x) is None)]
if len(matched_folders) > 1:
print(F'Warning: more than one folder matched: {matched_folders}')
if len(matched_folders) == 0:
print(F'Warning: folder not matched for {c_case}')
continue
else:
final_folder_name = join(self._input_folder, c_case, matched_folders[0])
all_dicom_files = get_dicom_files_in_folder(final_folder_name)
ds = dicom.read_file(all_dicom_files[0]) # Reads dataset
for c_name, c_key in colums_dic.items():
data_sum.loc[c_case][c_name] = eval(F'ds.{c_key}')
data_sum.loc[c_case]['Size'] = F'{ds.Rows} x {ds.Columns} x {len(all_dicom_files)}'
spacing = ds.PixelSpacing
data_sum.loc[c_case]['Spacing'] = F'{spacing[0]} x {spacing[1]} x {ds.SliceThickness}'
data_sum.loc[c_case]['PixelSize'] = F'{spacing[0]*spacing[1]*ds.SliceThickness:.2f}'
except Exception as e:
print(F'Failed for folder {c_case}: {e}')
continue
data_sum.to_csv(join(self._output_folder, file_name))
| [
"[email protected]"
]
| |
1d272705faf2bbdc1fdbd6b49ad2bb71b1a70d85 | 810a8ed334a29b81ddd5a4364c06d0272c3aae39 | /clash-of-code/shortest/hexagon.py | 4fd47cf99b5eb0c26472bb476aeafcc46280b64a | [
"MIT"
]
| permissive | charlesfranciscodev/codingame | d7bdfc244cb58fa356dec73fb2f2ec5470755dbc | 37b8e269e40f1b4e9807c305874f3b97f7c03a03 | refs/heads/master | 2023-08-16T23:26:20.818882 | 2023-08-16T17:12:06 | 2023-08-16T17:12:06 | 179,561,845 | 53 | 26 | MIT | 2021-05-19T19:36:46 | 2019-04-04T19:16:12 | Python | UTF-8 | Python | false | false | 24 | py | print(6*int(input())**2) | [
"[email protected]"
]
| |
9d1e8ffeefbf7cee1e32d4c38a282759cf4dd220 | 577ba42cbf0a3230966ac66ef60fd401486e4c06 | /website/apps/core/migrations/0021_transfer_year.py | 96778623b064dec78ae6724511bdcd803f81ac46 | [
"Apache-2.0"
]
| permissive | shh-dlce/pulotu | 984ca86de3ffe03e83bbb15b0d497f1ebf190ecd | 82acbb8a3b7f3ec3acc76baffd4047265a77f7d3 | refs/heads/master | 2021-01-10T03:51:13.337840 | 2015-12-09T09:46:55 | 2015-12-09T09:46:55 | 46,917,922 | 2 | 0 | Apache-2.0 | 2021-11-16T11:51:48 | 2015-11-26T09:48:42 | Python | UTF-8 | Python | false | false | 8,072 | py | # -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import DataMigration
from django.db import models
class Migration(DataMigration):
def forwards(self, orm):
"Write your forwards methods here."
# Note: Don't use "from appname.models import ModelName".
# Use orm.ModelName to refer to models in this application,
# and orm['appname.ModelName'] for models in other applications.
for source in orm.Source.objects.all():
source.year_new = source.year
source.save()
def backwards(self, orm):
"Write your backwards methods here."
raise RuntimeError("Cannot reverse this migration!")
models = {
u'auth.group': {
'Meta': {'object_name': 'Group'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
u'auth.permission': {
'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Group']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Permission']"}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
u'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
u'core.culture': {
'Meta': {'ordering': "['culture']", 'object_name': 'Culture', 'db_table': "'cultures'"},
'added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'coder': ('django.db.models.fields.CharField', [], {'max_length': '256', 'null': 'True', 'blank': 'True'}),
'culture': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '128', 'db_index': 'True'}),
'editor': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['auth.User']"}),
'fact': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'languages': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['core.Language']", 'symmetrical': 'False', 'blank': 'True'}),
'notes': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '128'})
},
u'core.language': {
'Meta': {'ordering': "['language']", 'unique_together': "(('isocode', 'language'),)", 'object_name': 'Language', 'db_table': "'languages'"},
'abvdcode': ('django.db.models.fields.IntegerField', [], {'db_index': 'True', 'unique': 'True', 'null': 'True', 'blank': 'True'}),
'added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'classification': ('django.db.models.fields.TextField', [], {}),
'editor': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['auth.User']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'isocode': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '3', 'db_index': 'True'}),
'language': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'})
},
u'core.section': {
'Meta': {'ordering': "['id']", 'object_name': 'Section', 'db_table': "'sections'"},
'added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'editor': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['auth.User']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'notes': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'section': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '128'})
},
u'core.source': {
'Meta': {'ordering': "['author', 'year']", 'unique_together': "(['author', 'year'],)", 'object_name': 'Source', 'db_table': "'sources'", 'index_together': "[['author', 'year']]"},
'added': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'author': ('django.db.models.fields.CharField', [], {'max_length': '255', 'db_index': 'True'}),
'bibtex': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'comment': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'editor': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['auth.User']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'reference': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '1000'}),
'year': ('django.db.models.fields.IntegerField', [], {'db_index': 'True', 'null': 'True', 'blank': 'True'}),
'year_new': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '255', 'null': 'True', 'blank': 'True'})
}
}
complete_apps = ['core']
symmetrical = True
| [
"[email protected]"
]
| |
0cb269eb77b00fc282b0b7a98450a744901f9bee | af4abf0a22db1cebae466c56b45da2f36f02f323 | /parser/fase2/team08/Tytus_SQLPARSER_G8/optimizacion/Instrucciones/C3D/LlamadaC3D.py | dc3d1a8494a7f8ac134381a1aa4f9d6d7c4e705b | [
"MIT",
"BSD-3-Clause"
]
| permissive | joorgej/tytus | 0c29408c09a021781bd3087f419420a62194d726 | 004efe1d73b58b4b8168f32e01b17d7d8a333a69 | refs/heads/main | 2023-02-17T14:00:00.571200 | 2021-01-09T00:48:47 | 2021-01-09T00:48:47 | 322,429,634 | 3 | 0 | MIT | 2021-01-09T00:40:50 | 2020-12-17T22:40:05 | Python | UTF-8 | Python | false | false | 594 | py | from optimizacion.Instrucciones.TablaSimbolos.InstruccionC3D import InstruccionC3D
class LlamadaC3D(InstruccionC3D):
def __init__(self, id,linea, columna):
InstruccionC3D.__init__(self,linea,columna)
self.id = id
print("ENTRO A expresiones")
def ejecutar(self, tabla, arbol):
super().ejecutar(tabla,arbol)
print(" linea: " + str(self.linea) + " columna: " + str(self.columna))
if self.id != None :
if(self.id == "main"):
return self.id + "()"
else:
return self.id +"()"
| [
"[email protected]"
]
| |
b93260df15ec3b7ec598572a2cee1d41b1db0c22 | 41a672c9505b5b53c58a01d5455acc410949aa24 | /tests/aoutgoing/negative/group/C_39.py | 2f601a227919b3259ac2e7c4da1ce6d2ad77009c | []
| no_license | Alexsorgo/mobile_iOS | b045a0ea058726841c88158be8407b7ae45e893e | 7e298f890b408cedad9db9d0aefeccd9c10d6002 | refs/heads/master | 2022-12-12T17:26:14.039876 | 2020-03-18T06:34:56 | 2020-03-18T06:34:56 | 248,154,882 | 0 | 0 | null | 2021-06-02T01:13:05 | 2020-03-18T06:25:17 | Python | UTF-8 | Python | false | false | 956 | py | from configs import config
from enums import error_enums
from screens.group.group_screen import GroupScreen
from controls.menu import Menu
from tests.aoutgoing.base_test import BaseTest
from utils.logs import log
from utils.verify import Verify
class TestC39(BaseTest):
"""
User has the ability to create group chat with 1 more user
"""
EMPTY_NAME = ''
FRIEND = config.AMERICA_FIRSTNAME + ' ' + config.AMERICA_LASTNAME
def test_c39(self):
log.info("Create group with empty group name")
menu = Menu(self.driver)
group = GroupScreen(self.driver)
menu.go_to(menu.wenums.GROUPS, [menu.wenums.NEW_GROUP])
group.add_user(self.FRIEND)
group.tap_done()
group.tap_group_name()
group.set_group_name(self.EMPTY_NAME)
group.tap_save()
log.info("Verify group doesn't create")
Verify.true(group.error_verify(error_enums.GROUP_NAME_MIN), "Group created")
| [
"[email protected]"
]
| |
255564aec5fbd2217b5838adab632433649e48fb | 4a5f3b26fca176a80ca8eca796bc646bb225b017 | /LSTM/lstm.py | fd4fa6f6352b46b189efd111d6f07ccf1a6d69d2 | []
| no_license | musyoku/NLP | 9a63dc882b07b017f7cfc72d863c4d9e5cbeff5e | 9b040bb960b65fb2a1c330adafa6c52e3284a0c1 | refs/heads/master | 2021-01-21T04:53:57.029200 | 2016-07-10T17:08:03 | 2016-07-10T17:08:03 | 55,848,677 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 8,963 | py | # -*- coding: utf-8 -*-
import os, time
import numpy as np
import chainer
from chainer import cuda, Variable, optimizers, serializers, function, link
from chainer.utils import type_check
from chainer import functions as F
from chainer import links as L
from bnlstm import BNLSTM
from embed_id import EmbedID
activations = {
"sigmoid": F.sigmoid,
"tanh": F.tanh,
"softplus": F.softplus,
"relu": F.relu,
"leaky_relu": F.leaky_relu,
"elu": F.elu
}
class Conf:
def __init__(self):
self.use_gpu = True
self.n_vocab = -1
# 文字埋め込みベクトルの次元数
self.embed_size = 200
# 各隠れ層のユニット数を入力側から出力側に向かって並べる
# Unit sizes of each hidden layers
# e.g 500(input vector)->250->100(output vector)
# q_fc_units = [250]
# We don't contain input and output unit size here.
self.lstm_hidden_units = [1000]
# if true, it uses BNLSTM
self.lstm_apply_batchnorm = False
self.lstm_apply_dropout = False
# Fully-connected network that converts an output of the LSTM to a label distribution or an embed vector
# We don't contain input and output unit size here.
self.fc_hidden_units = [500]
self.fc_apply_batchnorm = False
self.fc_apply_dropout = False
self.fc_activation_function = "tanh"
# "embed_vector": outputs an embed vector. Instead of softmax layer, We use EmbedID.reverse() to convert vector to label id.
# "softmax": outputs a probability distribution of label ids using softmax layer
self.fc_output_type = LSTM.OUTPUT_TYPE_SOFTMAX
self.learning_rate = 0.0025
self.gradient_momentum = 0.95
def check(self):
if len(self.lstm_hidden_units) < 1:
raise Exception("You need to add one or more hidden layers to LSTM network.")
if len(self.fc_hidden_units) < 1:
raise Exception("You need to add one or more hidden layers to fully-connected network.")
class LSTMNetwork(chainer.Chain):
def __init__(self, **layers):
super(LSTMNetwork, self).__init__(**layers)
self.n_layers = 0
self.apply_dropout = False
def forward_one_step(self, x, test):
chain = [x]
# Hidden layers
for i in range(self.n_layers):
u = getattr(self, "layer_%i" % i)(chain[-1])
output = u
if self.apply_dropout:
output = F.dropout(output, train=not test)
chain.append(output)
return chain[-1]
def reset_state(self):
for i in range(self.n_layers):
getattr(self, "layer_%i" % i).reset_state()
def __call__(self, x, test=False):
return self.forward_one_step(x, test=test)
class FullyConnectedNetwork(chainer.Chain):
def __init__(self, **layers):
super(FullyConnectedNetwork, self).__init__(**layers)
self.n_layers = 0
self.activation_function = "tanh"
self.apply_dropout = False
self.apply_batchnorm = False
def forward_one_step(self, x, test):
f = activations[self.activation_function]
chain = [x]
# Hidden layers
for i in range(self.n_layers):
u = chain[-1]
if self.apply_batchnorm:
u = getattr(self, "batchnorm_%i" % i)(u, test=test)
u = getattr(self, "layer_%i" % i)(u)
output = f(u)
if self.apply_dropout and i != self.n_layers - 1:
output = F.dropout(output, train=not test)
chain.append(output)
return chain[-1]
def __call__(self, x, test=False):
return self.forward_one_step(x, test=test)
class LSTM:
OUTPUT_TYPE_SOFTMAX = 1
OUTPUT_TYPE_EMBED_VECTOR = 2
def __init__(self, conf, name="lstm"):
self.output_type = conf.fc_output_type
self.embed_id, self.lstm, self.fc = self.build(conf)
self.name = name
self.optimizer_lstm = optimizers.Adam(alpha=conf.learning_rate, beta1=conf.gradient_momentum)
self.optimizer_lstm.setup(self.lstm)
self.optimizer_lstm.add_hook(chainer.optimizer.GradientClipping(10.0))
self.optimizer_fc = optimizers.Adam(alpha=conf.learning_rate, beta1=conf.gradient_momentum)
self.optimizer_fc.setup(self.fc)
self.optimizer_fc.add_hook(chainer.optimizer.GradientClipping(10.0))
self.optimizer_embed_id = optimizers.Adam(alpha=conf.learning_rate, beta1=conf.gradient_momentum)
self.optimizer_embed_id.setup(self.embed_id)
self.optimizer_embed_id.add_hook(chainer.optimizer.GradientClipping(10.0))
def build(self, conf):
conf.check()
wscale = 1.0
embed_id = EmbedID(conf.n_vocab, conf.embed_size, ignore_label=-1)
if conf.use_gpu:
embed_id.to_gpu()
lstm_attributes = {}
lstm_units = [(conf.embed_size, conf.lstm_hidden_units[0])]
lstm_units += zip(conf.lstm_hidden_units[:-1], conf.lstm_hidden_units[1:])
for i, (n_in, n_out) in enumerate(lstm_units):
if conf.lstm_apply_batchnorm:
lstm_attributes["layer_%i" % i] = BNLSTM(n_in, n_out)
else:
lstm_attributes["layer_%i" % i] = L.LSTM(n_in, n_out)
lstm = LSTMNetwork(**lstm_attributes)
lstm.n_layers = len(lstm_units)
lstm.apply_dropout = conf.lstm_apply_dropout
if conf.use_gpu:
lstm.to_gpu()
fc_attributes = {}
fc_units = [(conf.lstm_hidden_units[-1], conf.fc_hidden_units[0])]
fc_units += zip(conf.fc_hidden_units[:-1], conf.fc_hidden_units[1:])
if conf.fc_output_type == self.OUTPUT_TYPE_EMBED_VECTOR:
fc_units += [(conf.fc_hidden_units[-1], conf.embed_size)]
elif conf.fc_output_type == self.OUTPUT_TYPE_SOFTMAX:
fc_units += [(conf.fc_hidden_units[-1], conf.n_vocab)]
else:
raise Exception()
for i, (n_in, n_out) in enumerate(fc_units):
fc_attributes["layer_%i" % i] = L.Linear(n_in, n_out, wscale=wscale)
fc_attributes["batchnorm_%i" % i] = L.BatchNormalization(n_in)
fc = FullyConnectedNetwork(**fc_attributes)
fc.n_layers = len(fc_units)
fc.activation_function = conf.fc_activation_function
fc.apply_batchnorm = conf.fc_apply_batchnorm
fc.apply_dropout = conf.fc_apply_dropout
if conf.use_gpu:
fc.to_gpu()
return embed_id, lstm, fc
def __call__(self, x, test=False, softmax=True):
output = self.embed_id(x)
output = self.lstm(output, test=test)
output = self.fc(output, test=test)
if softmax and self.output_type == self.OUTPUT_TYPE_SOFTMAX:
output = F.softmax(output)
return output
@property
def xp(self):
return np if self.lstm.layer_0._cpu else cuda.cupy
@property
def gpu(self):
return True if self.xp is cuda.cupy else False
def reset_state(self):
self.lstm.reset_state()
def predict(self, word, test=True, argmax=False):
xp = self.xp
c0 = Variable(xp.asarray([word], dtype=np.int32))
if self.output_type == self.OUTPUT_TYPE_SOFTMAX:
output = self(c0, test=test, softmax=True)
if xp is cuda.cupy:
output.to_cpu()
if argmax:
ids = np.argmax(output.data, axis=1)
else:
ids = [np.random.choice(np.arange(output.data.shape[1]), p=output.data[0])]
elif self.output_type == self.OUTPUT_TYPE_EMBED_VECTOR:
output = self(c0, test=test, softmax=False)
if argmax:
ids = self.embed_id.reverse(output.data, to_cpu=True, sample=False)
else:
ids = self.embed_id.reverse(output.data, to_cpu=True, sample=True)
return ids[0]
def distribution(self, word, test=True):
xp = self.xp
c0 = Variable(xp.asarray([word], dtype=np.int32))
output = self(c0, test=test, softmax=True)
if xp is cuda.cupy:
output.to_cpu()
return output.data
def train(self, seq_batch, test=False):
self.reset_state()
xp = self.xp
sum_loss = 0
seq_batch = seq_batch.T
for c0, c1 in zip(seq_batch[:-1], seq_batch[1:]):
c0 = Variable(xp.asanyarray(c0, dtype=np.int32))
c1 = Variable(xp.asanyarray(c1, dtype=np.int32))
output = self(c0, test=test, softmax=False)
if self.output_type == self.OUTPUT_TYPE_SOFTMAX:
loss = F.softmax_cross_entropy(output, c1)
elif self.output_type == self.OUTPUT_TYPE_EMBED_VECTOR:
target = Variable(self.embed_id(c1).data)
loss = F.mean_squared_error(output, target)
else:
raise Exception()
sum_loss += loss
self.zero_grads()
sum_loss.backward()
self.update()
if self.gpu:
sum_loss.to_cpu()
return sum_loss.data
def zero_grads(self):
self.optimizer_lstm.zero_grads()
self.optimizer_fc.zero_grads()
self.optimizer_embed_id.zero_grads()
def update(self):
self.optimizer_lstm.update()
self.optimizer_fc.update()
self.optimizer_embed_id.update()
def should_save(self, prop):
if isinstance(prop, chainer.Chain) or isinstance(prop, chainer.optimizer.GradientMethod) or isinstance(prop, EmbedID):
return True
return False
def load(self, dir=None):
if dir is None:
raise Exception()
for attr in vars(self):
prop = getattr(self, attr)
if self.should_save(prop):
filename = dir + "/%s_%s.hdf5" % (self.name, attr)
if os.path.isfile(filename):
print "loading", filename
serializers.load_hdf5(filename, prop)
else:
print filename, "missing."
print "model loaded."
def save(self, dir=None):
if dir is None:
raise Exception()
try:
os.mkdir(dir)
except:
pass
for attr in vars(self):
prop = getattr(self, attr)
if self.should_save(prop):
serializers.save_hdf5(dir + "/%s_%s.hdf5" % (self.name, attr), prop)
print "model saved."
| [
"[email protected]"
]
| |
9544265ec76e9e6bce081dee8ea03c2ea278a212 | c7e0c86a24521a13c3b06c73244e9f5854f47284 | /smarts/env/tests/test_metrics.py | a9d43943678df687bf3dc894e97b9aeab7c2cbb1 | [
"LGPL-3.0-only",
"LicenseRef-scancode-warranty-disclaimer",
"CC-BY-NC-4.0",
"GPL-1.0-or-later",
"LicenseRef-scancode-generic-exception",
"LicenseRef-scancode-other-copyleft",
"LicenseRef-scancode-unknown-license-reference",
"LGPL-2.0-or-later",
"GPL-3.0-or-later",
"BSD-3-Clause",
"MIT",
"LGPL-3.0-or-later",
"BSD-3-Clause-Modification",
"LicenseRef-scancode-free-unknown",
"Zlib",
"LicenseRef-scancode-proprietary-license",
"LGPL-2.1-or-later",
"GPL-2.0-or-later",
"LicenseRef-scancode-protobuf",
"LGPL-2.1-only",
"HPND",
"GPL-2.0-only",
"GPL-3.0-only",
"Apache-2.0",
"LicenseRef-scancode-other-permissive",
"Python-2.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause",
"CDDL-1.0"
]
| permissive | huawei-noah/SMARTS | 243d1f1fa4d3afe52a1dd8f7c6c500054d4a1a97 | 2ae8bd76a0b6e4da5699629cec0fefa5aa47ce67 | refs/heads/master | 2023-08-31T05:06:29.064270 | 2023-08-28T23:11:31 | 2023-08-28T23:11:31 | 301,903,883 | 824 | 212 | MIT | 2023-08-08T14:52:00 | 2020-10-07T02:11:23 | Python | UTF-8 | Python | false | false | 10,884 | py | # MIT License
#
# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
import dataclasses
from unittest import mock
import gymnasium as gym
import numpy as np
import pytest
from smarts.core.agent_interface import AgentInterface, DoneCriteria
from smarts.core.controllers import ActionSpaceType
from smarts.core.coordinates import Heading, Point
from smarts.core.plan import EndlessGoal, Goal, Mission, PositionalGoal, Start
from smarts.env.gymnasium.wrappers.metric.metrics import Metrics, MetricsError
from smarts.zoo.agent_spec import AgentSpec
def _intrfc_improper():
return [
{"accelerometer": False},
{"max_episode_steps": None},
{"neighborhood_vehicle_states": False},
{"waypoint_paths": False},
{
"done_criteria": DoneCriteria(
collision=False,
off_road=True,
)
},
{
"done_criteria": DoneCriteria(
collision=True,
off_road=False,
)
},
]
@pytest.fixture
def get_agent_spec(request):
base_intrfc = AgentInterface(
action=ActionSpaceType.TargetPose,
accelerometer=True,
done_criteria=DoneCriteria(
collision=True,
off_road=True,
off_route=False,
on_shoulder=False,
wrong_way=False,
not_moving=False,
agents_alive=None,
),
max_episode_steps=5,
neighborhood_vehicle_states=True,
waypoint_paths=True,
)
return AgentSpec(interface=dataclasses.replace(base_intrfc, **request.param))
@pytest.fixture(scope="module")
def get_scenario(request):
from pathlib import Path
from smarts.sstudio.scenario_construction import build_scenario
if request.param == "single_agent_intersection":
scenario = str(
Path(__file__).resolve().parents[3]
/ "scenarios"
/ "sumo"
/ "intersections"
/ "1_to_1lane_left_turn_c_agents_1"
)
num_agents = 1
elif request.param == "multi_agent_merge":
scenario = str(
Path(__file__).resolve().parents[3]
/ "scenarios"
/ "sumo"
/ "merge"
/ "3lane_agents_2"
)
num_agents = 2
build_scenario(scenario=scenario)
return (scenario, num_agents)
@pytest.fixture
def make_env(get_agent_spec, get_scenario):
env = gym.make(
"smarts.env:hiway-v1",
scenarios=[get_scenario[0]],
agent_interfaces={
f"AGENT_{agent_id}": get_agent_spec.interface
for agent_id in range(get_scenario[1])
},
headless=True,
)
yield env
env.close()
@pytest.mark.parametrize("get_agent_spec", _intrfc_improper(), indirect=True)
@pytest.mark.parametrize("get_scenario", ["single_agent_intersection"], indirect=True)
def test_improper_interface(make_env):
# Verify proper agent interface enabled.
with pytest.raises(AttributeError):
env = Metrics(env=make_env)
@pytest.mark.parametrize("get_agent_spec", [{}], indirect=True)
@pytest.mark.parametrize("get_scenario", ["single_agent_intersection"], indirect=True)
def test_init(make_env):
# Verify instantiation of Metrics wrapper.
env = Metrics(env=make_env)
# Verify blocked access to underlying private variables.
for elem in ["_scen", "_road_map", "_records", "smarts"]:
with pytest.raises(AttributeError):
getattr(env, elem)
def _mock_mission(start: Start, goal: Goal):
def func(scenario_root, agents_to_be_briefed):
return [Mission(start=start, goal=goal)]
return func
@pytest.mark.parametrize("get_agent_spec", [{}], indirect=True)
@pytest.mark.parametrize("get_scenario", ["single_agent_intersection"], indirect=True)
def test_reset(make_env):
# Verify a scenario without PositionalGoal fails suitability check.
with mock.patch(
"smarts.core.scenario.Scenario.discover_agent_missions",
side_effect=_mock_mission(
start=Start(position=np.array([0, 0, 0]), heading=Heading(0)),
goal=EndlessGoal(),
),
):
with pytest.raises(MetricsError):
env = Metrics(env=make_env)
env.reset()
return
@pytest.mark.parametrize("get_agent_spec", [{}], indirect=True)
@pytest.mark.parametrize("get_scenario", ["single_agent_intersection"], indirect=True)
def test_end_in_off_road(make_env):
# Verify that env.score() can be computed when vehicle goes off road.
env = Metrics(env=make_env)
obs, _ = env.reset()
agent_name = next(iter(env.agent_interfaces.keys()))
dones = {"__all__": False}
while not dones["__all__"]:
actions = {
agent_name: np.append(
obs[agent_name]["ego_vehicle_state"]["position"][:2]
+ np.array([0.5, -0.8]),
[obs[agent_name]["ego_vehicle_state"]["heading"], 0.1],
)
}
obs, _, dones, _, _ = env.step(actions)
assert obs[agent_name]["events"]["off_road"], (
"Expected vehicle to go off road, but it did not. "
f"Events: {obs[agent_name]['events']}."
)
env.score()
# Verify that Count values increase with episode.
records = env.records()
scen_name = next(iter(records.keys()))
counts = records[scen_name][agent_name].counts
assert counts.goals == 0
assert counts.episodes == 1
assert counts.steps == 3
@pytest.mark.parametrize(
"get_agent_spec",
[{"max_episode_steps": 27, "done_criteria": DoneCriteria(off_route=True)}],
indirect=True,
)
@pytest.mark.parametrize("get_scenario", ["single_agent_intersection"], indirect=True)
def test_end_in_off_route(make_env):
# Verify that env.score() can be computed when vehicle ends in off route.
# Note:
# Point(-12, -1.6, 0) lies on edge-west-WE_0, i.e., to the left of the junction.
# Point( 12, -1.6, 0) lies on edge-east-WE_0, i.e., to the right of the junction.
# Point(1.5, 30.5, 0) lies on edge-north-SN_0, i.e., to the top of the junction.
with mock.patch(
"smarts.core.scenario.Scenario.discover_agent_missions",
side_effect=_mock_mission(
start=Start(position=np.array([-12, -1.6, 0]), heading=Heading(-1.57)),
goal=PositionalGoal(position=Point(x=1.5, y=30.5, z=0), radius=3),
),
):
env = Metrics(env=make_env)
obs, _ = env.reset()
agent_name = next(iter(env.agent_interfaces.keys()))
dones = {"__all__": False}
while not dones["__all__"]:
actions = {
agent_name: np.append(
obs[agent_name]["ego_vehicle_state"]["position"][:2]
+ np.array([1, 0]),
[obs[agent_name]["ego_vehicle_state"]["heading"], 0.1],
)
}
obs, _, dones, _, _ = env.step(actions)
assert (
obs[agent_name]["ego_vehicle_state"]["lane_id"].rstrip() == "edge-east-WE_0"
), (
"Expected vehicle to drive off route, but it is at lane: "
f"{obs[agent_name]['ego_vehicle_state']['lane_id']}."
)
assert obs[agent_name]["events"]["off_route"], (
"Expected vehicle to go off route, but it did not. "
f"Events: {obs[agent_name]['events']}."
)
env.score()
@pytest.mark.parametrize("get_agent_spec", [{"max_episode_steps": 1}], indirect=True)
@pytest.mark.parametrize("get_scenario", ["single_agent_intersection"], indirect=True)
def test_end_in_junction(make_env):
# Verify that env.score() can be computed when vehicle ends in a junction.
# Note:
# Point(-1.76, 2.05, 0) lies on :junction-intersection_1_0, i.e., inside the junction.
with mock.patch(
"smarts.core.scenario.Scenario.discover_agent_missions",
side_effect=_mock_mission(
start=Start(position=np.array([-1.86, 1.95, 0]), heading=Heading(-1.00)),
goal=PositionalGoal(position=Point(x=1.5, y=30.5, z=0), radius=3),
),
):
env = Metrics(env=make_env)
obs, _ = env.reset()
agent_name = next(iter(obs.keys()))
actions = {
agent_id: np.array([-1.76, 2.05, -0.91, 0.1]) for agent_id in obs.keys()
}
obs, _, dones, _, _ = env.step(actions)
assert (
obs[agent_name]["ego_vehicle_state"]["lane_id"].rstrip()
== ":junction-intersection_1_0"
), (
"Expected vehicle to be inside junction, but it is at lane: "
f"{obs[agent_name]['ego_vehicle_state']['lane_id']}."
)
assert (
obs[agent_name]["events"]["reached_max_episode_steps"] and dones["__all__"]
), (
"Expected vehicle to reach max episode steps and become done, but "
f"it did not. Dones: {dones}. Events: {obs[agent_name]['events']}."
)
env.score()
@pytest.mark.parametrize("get_agent_spec", [{}], indirect=True)
@pytest.mark.parametrize("get_scenario", ["multi_agent_merge"], indirect=True)
def test_records_and_scores(make_env):
# Verify that records and scores are functional in multi-agent environment.
# Note:
# env.score() is only callable after >=1 episode. Hence step through 1 episode.
env = Metrics(env=make_env)
obs, _ = env.reset()
terminated = {"__all__": False}
while not terminated["__all__"]:
actions = {
agent_name: np.append(
agent_obs["ego_vehicle_state"]["position"][:2], [0, 0.1]
)
for agent_name, agent_obs in obs.items()
}
obs, _, terminated, _, _ = env.step(actions)
env.records()
env.score()
| [
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]
| |
0afb5da3c5bf377521020e90704fbd297b46c016 | e5b778a273e3888ad0575a9dada39d458158127a | /students/migrations/0009_lesson_icon.py | a4f54c33999a2c365ea2cd47c5cf66dca551542c | []
| no_license | SevenLines/django-tealeaf | 896784baead7b9514e83edad8c3c2defdcdd060b | 959dbcbdd37a4e8f45de400e71710c5e746a97da | refs/heads/master | 2021-01-23T00:01:43.793383 | 2015-05-15T15:58:52 | 2015-05-15T15:58:52 | 17,891,988 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 550 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
import filer.fields.image
class Migration(migrations.Migration):
dependencies = [
('filer', '__first__'),
('students', '0008_auto_20141128_1807'),
]
operations = [
migrations.AddField(
model_name='lesson',
name='icon',
field=filer.fields.image.FilerImageField(default=None, blank=True, to='filer.Image', null=True),
preserve_default=True,
),
]
| [
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]
| |
e7a9408e49112ddd9f5aafdb874c3377f4ad2d1c | 767745e9c6207db9f6a9cf4f0be1af4732e7a111 | /raiden/tests/integration/transfer/test_directransfer_invalid.py | d88e7c9f8a32e7f40677ba8b9fc4c75d7c1a3340 | [
"MIT"
]
| permissive | gcarq/raiden | ecc91860b99447028baea7fd171c19996644a5ef | 82241c6da9188c4e029aef3bb42f0ab9f055c0e4 | refs/heads/master | 2020-03-10T03:31:55.174762 | 2018-04-11T19:18:21 | 2018-04-11T19:18:21 | 129,167,527 | 0 | 0 | MIT | 2018-04-11T23:52:12 | 2018-04-11T23:52:12 | null | UTF-8 | Python | false | false | 6,738 | py | # -*- coding: utf-8 -*-
import pytest
from raiden.api.python import RaidenAPI
from raiden.messages import DirectTransfer
from raiden.transfer import channel
from raiden.transfer.state import EMPTY_MERKLE_ROOT
from raiden.tests.utils.blockchain import wait_until_block
from raiden.tests.utils.factories import (
UNIT_HASHLOCK,
make_address,
make_privkey_address,
)
from raiden.tests.utils.transfer import (
assert_synched_channel_state,
get_channelstate,
sign_and_inject,
)
@pytest.mark.skip(reason='direct_transfer_async doesnt return AsyncResult anymore')
@pytest.mark.parametrize('channels_per_node', [1])
@pytest.mark.parametrize('number_of_nodes', [2])
def test_failsfast_directtransfer_exceeding_distributable(
raiden_network,
token_addresses,
deposit
):
alice_app, bob_app = raiden_network
token_address = token_addresses[0]
async_result = alice_app.raiden.direct_transfer_async(
token_address,
deposit * 2,
bob_app.raiden.address,
identifier=1,
)
assert not async_result.get_nowait()
@pytest.mark.parametrize('number_of_nodes', [2])
@pytest.mark.parametrize('channels_per_node', [1])
def test_receive_directtransfer_invalidtoken(raiden_network, deposit, token_addresses):
app0, app1 = raiden_network
token_address = token_addresses[0]
channel0 = get_channelstate(app0, app1, token_address)
identifier = 1
invalid_token_address = make_address()
channel_identifier = channel0.identifier
direct_transfer_message = DirectTransfer(
identifier=identifier,
nonce=1,
token=invalid_token_address,
channel=channel_identifier,
transferred_amount=0,
recipient=app1.raiden.address,
locksroot=EMPTY_MERKLE_ROOT,
)
sign_and_inject(
direct_transfer_message,
app0.raiden.private_key,
app0.raiden.address,
app1,
)
assert_synched_channel_state(
token_address,
app0, deposit, [],
app1, deposit, [],
)
@pytest.mark.parametrize('number_of_nodes', [2])
@pytest.mark.parametrize('channels_per_node', [1])
def test_receive_directtransfer_invalidlocksroot(raiden_network, token_addresses):
app0, app1 = raiden_network
token_address = token_addresses[0]
channel0 = get_channelstate(app0, app1, token_address)
balance0 = channel.get_balance(channel0.our_state, channel0.partner_state)
balance1 = channel.get_balance(channel0.partner_state, channel0.our_state)
identifier = 1
invalid_locksroot = UNIT_HASHLOCK
channel_identifier = channel0.identifier
direct_transfer_message = DirectTransfer(
identifier=identifier,
nonce=1,
token=token_address,
channel=channel_identifier,
transferred_amount=0,
recipient=app1.raiden.address,
locksroot=invalid_locksroot,
)
sign_and_inject(
direct_transfer_message,
app0.raiden.private_key,
app0.raiden.address,
app1,
)
assert_synched_channel_state(
token_address,
app0, balance0, [],
app1, balance1, []
)
@pytest.mark.parametrize('number_of_nodes', [2])
@pytest.mark.parametrize('channels_per_node', [1])
def test_receive_directtransfer_invalidsender(raiden_network, deposit, token_addresses):
app0, app1 = raiden_network
token_address = token_addresses[0]
other_key, other_address = make_privkey_address()
channel0 = get_channelstate(app0, app1, token_address)
channel_identifier = channel0.identifier
direct_transfer_message = DirectTransfer(
identifier=1,
nonce=1,
token=token_address,
channel=channel_identifier,
transferred_amount=10,
recipient=app0.raiden.address,
locksroot=EMPTY_MERKLE_ROOT,
)
sign_and_inject(
direct_transfer_message,
other_key,
other_address,
app0,
)
assert_synched_channel_state(
token_address,
app0, deposit, [],
app1, deposit, []
)
@pytest.mark.parametrize('number_of_nodes', [2])
@pytest.mark.parametrize('channels_per_node', [1])
def test_receive_directtransfer_invalidnonce(raiden_network, deposit, token_addresses):
app0, app1 = raiden_network
token_address = token_addresses[0]
channel0 = get_channelstate(app0, app1, token_address)
transferred_amount = 10
same_identifier = 1
event = channel.send_directtransfer(
channel0,
transferred_amount,
same_identifier,
)
direct_transfer_message = DirectTransfer.from_event(event)
sign_and_inject(
direct_transfer_message,
app0.raiden.private_key,
app0.raiden.address,
app1,
)
# Send a *different* direct transfer with the *same nonce*
invalid_transferred_amount = transferred_amount // 2
invalid_direct_transfer_message = DirectTransfer(
identifier=same_identifier,
nonce=1,
token=token_address,
channel=channel0.identifier,
transferred_amount=invalid_transferred_amount,
recipient=app1.raiden.address,
locksroot=EMPTY_MERKLE_ROOT,
)
sign_and_inject(
invalid_direct_transfer_message,
app0.raiden.private_key,
app0.raiden.address,
app1,
)
assert_synched_channel_state(
token_address,
app0, deposit - transferred_amount, [],
app1, deposit + transferred_amount, [],
)
@pytest.mark.parametrize('number_of_nodes', [2])
@pytest.mark.parametrize('channels_per_node', [1])
@pytest.mark.parametrize('settle_timeout', [30])
def test_received_directtransfer_closedchannel(raiden_network, token_addresses, deposit):
app0, app1 = raiden_network
token_address = token_addresses[0]
channel0 = get_channelstate(app0, app1, token_address)
RaidenAPI(app1.raiden).channel_close(
token_address,
app0.raiden.address,
)
wait_until_block(
app0.raiden.chain,
app0.raiden.chain.block_number() + 1,
)
# Now receive one direct transfer for the closed channel
direct_transfer_message = DirectTransfer(
identifier=1,
nonce=1,
token=token_address,
channel=channel0.identifier,
transferred_amount=10,
recipient=app0.raiden.address,
locksroot=EMPTY_MERKLE_ROOT,
)
sign_and_inject(
direct_transfer_message,
app0.raiden.private_key,
app0.raiden.address,
app1,
)
# The local state must not change since the channel is already closed
assert_synched_channel_state(
token_address,
app0, deposit, [],
app1, deposit, [],
)
| [
"[email protected]"
]
| |
b020ce1d7374b7195c3545ce178c7b9387f9ddd1 | 72b8e2d69cca8b5ecd28e61ef61fef85f9dd0489 | /q190.py | 3bf0f6319123cdb7f2dd25ae44e6f074a9eafef1 | []
| no_license | maples1993/LeetCode | f975bc8570729d998481b097ee04effe5a7c5977 | 032016724564d0bee85f9e1b9d9d6c769d0eb667 | refs/heads/master | 2020-03-27T22:05:07.397746 | 2018-11-07T06:13:56 | 2018-11-07T06:13:56 | 147,203,152 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 418 | py | """
Date: 2018/9/6
"""
class Solution:
# @param n, an integer
# @return an integer
def reverseBits(self, n):
n &= 0xFFFFFFFF
print(bin(n))
res = 0 & 0xFFFFFFFF
count = 0
while count < 32:
count += 1
res <<= 1
if n & 1 == 1:
res += 1
n >>= 1
return res
print(Solution().reverseBits(43261596)) | [
"[email protected]"
]
| |
e1fc43f35600eb1ab30bcb687acd093d5345c74f | 9743d5fd24822f79c156ad112229e25adb9ed6f6 | /xai/brain/wordbase/adjectives/_veritable.py | c4260f18bbed72556a78374e7679857fe6dc69a3 | [
"MIT"
]
| permissive | cash2one/xai | de7adad1758f50dd6786bf0111e71a903f039b64 | e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6 | refs/heads/master | 2021-01-19T12:33:54.964379 | 2017-01-28T02:00:50 | 2017-01-28T02:00:50 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 488 | py |
#calss header
class _VERITABLE():
def __init__(self,):
self.name = "VERITABLE"
self.definitions = [u'used to describe something as another, more exciting, interesting, or unusual thing, as a way of emphasizing its character: ']
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.specie = 'adjectives'
def run(self, obj1, obj2):
self.jsondata[obj2] = {}
self.jsondata[obj2]['properties'] = self.name.lower()
return self.jsondata
| [
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]
| |
55eb160926cb77920b63568d4be18c54eeebdb2d | 41b59a9c8381fa3a92f5d2c37c91261afb9c82c4 | /QCDEventShape/2017/MC/test/crab_bin_py8_3200_inf.py | ad911d60a95de92ad286c8ea8f0a46bafbafeab1 | []
| no_license | Sumankkundu/ChargedParticle | c6d4f90b55df49321df2ecd758bb1f39db896f8c | eb5bada24b37a58ded186d6e5d2d7bd00898fefe | refs/heads/master | 2023-07-15T03:34:33.377203 | 2021-08-31T05:01:32 | 2021-08-31T05:01:32 | 231,091,587 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,495 | py | #from CRABClient.UserUtilities import config, getUsernameFromSiteDB
from CRABClient.UserUtilities import config
config = config()
config.General.requestName ='ESVQCD_UL_Ptbinned_3200toinf_tuneCP5_bin'
#config.General.workArea = 'crab_projects_1'
config.General.workArea = 'crab_projects'
config.General.transferOutputs = True
config.General.transferLogs = True
config.JobType.pluginName = 'Analysis'
config.JobType.psetName = 'Run_QCD_test_miaod_v2_106x_mc_cfg.py'
#config.JobType.maxMemoryMB = 9000 # Default is 2500 : Max I have used is 13000
#config.JobType.maxJobRuntimeMin = 2750 #Default is 1315; 2750 minutes guaranteed to be available; Max I have used is 9000
#config.JobType.numCores = 4
config.JobType.inputFiles= [
"/afs/cern.ch/work/s/sukundu/private/ESV_charge_CMSSW/Uncertainty2017/AK4PFCHS_Summer19UL/Summer19UL17_JRV2_MC_PtResolution_AK4PFchs.txt",
"/afs/cern.ch/work/s/sukundu/private/ESV_charge_CMSSW/Uncertainty2017/AK4PFCHS_Summer19UL/Summer19UL17_JRV2_MC_SF_AK4PFchs.txt",
"/afs/cern.ch/work/s/sukundu/private/ESV_charge_CMSSW/Uncertainty2017/AK4PFCHS_Summer19UL/Summer19UL17_RunB_V5_DATA_UncertaintySources_AK4PFchs.txt",
"/afs/cern.ch/work/s/sukundu/private/ESV_charge_CMSSW/Uncertainty2017/AK4PFCHS_Summer19UL/Summer19UL17_RunC_V5_DATA_UncertaintySources_AK4PFchs.txt",
"/afs/cern.ch/work/s/sukundu/private/ESV_charge_CMSSW/Uncertainty2017/AK4PFCHS_Summer19UL/Summer19UL17_RunD_V5_DATA_UncertaintySources_AK4PFchs.txt",
"/afs/cern.ch/work/s/sukundu/private/ESV_charge_CMSSW/Uncertainty2017/AK4PFCHS_Summer19UL/Summer19UL17_RunE_V5_DATA_UncertaintySources_AK4PFchs.txt",
"/afs/cern.ch/work/s/sukundu/private/ESV_charge_CMSSW/Uncertainty2017/AK4PFCHS_Summer19UL/Summer19UL17_RunF_V5_DATA_UncertaintySources_AK4PFchs.txt"
]
config.Data.inputDataset ='/QCD_Pt_3200toInf_TuneCP5_13TeV_pythia8/RunIISummer19UL17MiniAOD-106X_mc2017_realistic_v6-v2/MINIAODSIM'
config.Data.inputDBS = 'global'
#config.Data.splitting = 'EventBased'
#config.Data.splitting = 'LumiBased'
config.Data.splitting = 'FileBased'
#config.Data.splitting = 'Automatic'
#config.Data.unitsPerJob = 10 # for Automatic must be 180-2700 range
config.Data.unitsPerJob = 1 #For Filebased or Lumibased
#config.Data.outLFNDirBase = '/store/user/%s/' % (getUsernameFromSiteDB())
#config.Data.outLFNDirBase = '/store/user/%s/' % (sukundu)
config.Data.publication = True
config.Data.outputDatasetTag = 'MC_PY82017UL_Bin'
config.JobType.allowUndistributedCMSSW = True
config.Site.storageSite ='T2_IN_TIFR'
| [
"[email protected]"
]
| |
64592d3ee4f2219d3ea1f98f687bdb1984f866da | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p02780/s702903623.py | ef5c9f02ab956fe90728da489ecd4bc87f90841f | []
| no_license | Aasthaengg/IBMdataset | 7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901 | f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8 | refs/heads/main | 2023-04-22T10:22:44.763102 | 2021-05-13T17:27:22 | 2021-05-13T17:27:22 | 367,112,348 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 326 | py | n,k = map(int,input().split())
P = list(map(int,input().split()))
P[0] = (P[0]+1.)/2
for i in range(1,len(P)):
P[i] = (P[i]+1.)/2
P[i] = P[i-1]+P[i]
ans = 0.
if n==1:
ans = P[0]
elif len(P)-k==0:
ans = P[k-1]
else:
for i in range(len(P)-k):
ans = max(ans,(P[i+k]-P[i]))
print(ans) | [
"[email protected]"
]
| |
5208906c09939f76f644bef4f999ef65b8a1cfae | 37438771565238194ea997fa65619bd32c823706 | /catkin_ws/17-11-16/LPH/build/catkin_generated/order_packages.py | 24ce42469160b8cc3411cbaef6a5190b3592e0f2 | []
| no_license | Aaron9477/restore | b040b8be695c513946c0243c4acb735f427d8bba | 8dc13ed7cf0c4e5cde911169d11e330d826f40bd | refs/heads/master | 2021-09-15T10:50:59.969952 | 2018-05-31T03:11:55 | 2018-05-31T03:11:55 | 110,834,815 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 310 | py | # generated from catkin/cmake/template/order_packages.context.py.in
source_root_dir = "/home/zq610/LPH/src"
whitelisted_packages = "".split(';') if "" != "" else []
blacklisted_packages = "".split(';') if "" != "" else []
underlay_workspaces = "/opt/ros/kinetic".split(';') if "/opt/ros/kinetic" != "" else []
| [
"[email protected]"
]
| |
310ba0cb9368a175620ca3cbcbd62104bf3f9f8b | edc1f1369794a4a1c499c6e9d5fe49a712657611 | /algorithms/leetcode_all/560.subarray-sum-equals-k/subarray-sum-equals-k.py | 74c28e9996672f15fe435da46bf9edd7cf5ffdc2 | []
| no_license | williamsyb/mycookbook | 93d4aca1a539b506c8ed2797863de6da8a0ed70f | dd917b6eba48eef42f1086a54880bab6cd1fbf07 | refs/heads/master | 2023-03-07T04:16:18.384481 | 2020-11-11T14:36:54 | 2020-11-11T14:36:54 | 280,005,004 | 2 | 0 | null | 2023-03-07T02:07:46 | 2020-07-15T23:34:24 | Python | UTF-8 | Python | false | false | 379 | py | class Solution(object):
def subarraySum(self, nums, k):
"""
:type nums: List[int]
:type k: int
:rtype: int
"""
preSum = ans = 0
visit = {0: 1}
for i, n in enumerate(nums):
preSum += n
ans += visit.get(preSum - k, 0)
visit[preSum] = visit.get(preSum, 0) + 1
return ans | [
"[email protected]"
]
| |
835f35f32d97ac1b55d4dda8b712add353ad0796 | 66052f5ba08ddac0a56ee140af17cf78b1ff1174 | /PLURALSIGHT_BEGINNERS/lib/python3.9/site-packages/holoviews/tests/plotting/matplotlib/testpointplot.py | ad2dbfe315b9cd12e93a78996c55f6e2f0f001f8 | []
| no_license | enriquefariasrdz/Python | 34704ceed001bbe8a23471eebefbe536b00031a5 | b9191f7ad87b709a1b83c5cb3797a866b56aaa0d | refs/heads/master | 2022-12-26T03:06:26.481456 | 2022-04-20T14:09:57 | 2022-04-20T14:09:57 | 27,020,899 | 1 | 1 | null | 2022-12-18T21:02:43 | 2014-11-23T03:33:52 | Python | UTF-8 | Python | false | false | 14,958 | py | import numpy as np
from holoviews.core.overlay import NdOverlay
from holoviews.core.spaces import HoloMap
from holoviews.element import Points
from .testplot import TestMPLPlot, mpl_renderer
from ..utils import ParamLogStream
try:
from matplotlib import pyplot
except:
pass
class TestPointPlot(TestMPLPlot):
def test_points_non_numeric_size_warning(self):
data = (np.arange(10), np.arange(10), list(map(chr, range(94,104))))
points = Points(data, vdims=['z']).opts(plot=dict(size_index=2))
with ParamLogStream() as log:
mpl_renderer.get_plot(points)
log_msg = log.stream.read()
warning = ('z dimension is not numeric, '
'cannot use to scale Points size.\n')
self.assertEqual(log_msg, warning)
def test_points_cbar_extend_both(self):
img = Points(([0, 1], [0, 3])).redim(y=dict(range=(1,2)))
plot = mpl_renderer.get_plot(img.opts(colorbar=True, color_index=1))
self.assertEqual(plot.handles['cbar'].extend, 'both')
def test_points_cbar_extend_min(self):
img = Points(([0, 1], [0, 3])).redim(y=dict(range=(1, None)))
plot = mpl_renderer.get_plot(img.opts(colorbar=True, color_index=1))
self.assertEqual(plot.handles['cbar'].extend, 'min')
def test_points_cbar_extend_max(self):
img = Points(([0, 1], [0, 3])).redim(y=dict(range=(None, 2)))
plot = mpl_renderer.get_plot(img.opts(colorbar=True, color_index=1))
self.assertEqual(plot.handles['cbar'].extend, 'max')
def test_points_cbar_extend_clime(self):
img = Points(([0, 1], [0, 3])).opts(style=dict(clim=(None, None)))
plot = mpl_renderer.get_plot(img.opts(colorbar=True, color_index=1))
self.assertEqual(plot.handles['cbar'].extend, 'neither')
def test_points_rcparams_do_not_persist(self):
opts = dict(fig_rcparams={'text.usetex': True})
points = Points(([0, 1], [0, 3])).opts(plot=opts)
mpl_renderer.get_plot(points)
self.assertFalse(pyplot.rcParams['text.usetex'])
def test_points_rcparams_used(self):
opts = dict(fig_rcparams={'grid.color': 'red'})
points = Points(([0, 1], [0, 3])).opts(plot=opts)
plot = mpl_renderer.get_plot(points)
ax = plot.state.axes[0]
lines = ax.get_xgridlines()
self.assertEqual(lines[0].get_color(), 'red')
def test_points_padding_square(self):
points = Points([1, 2, 3]).options(padding=0.1)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], -0.2)
self.assertEqual(x_range[1], 2.2)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_curve_padding_square_per_axis(self):
curve = Points([1, 2, 3]).options(padding=((0, 0.1), (0.1, 0.2)))
plot = mpl_renderer.get_plot(curve)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0)
self.assertEqual(x_range[1], 2.2)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.4)
def test_points_padding_hard_xrange(self):
points = Points([1, 2, 3]).redim.range(x=(0, 3)).options(padding=0.1)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0)
self.assertEqual(x_range[1], 3)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_points_padding_soft_xrange(self):
points = Points([1, 2, 3]).redim.soft_range(x=(0, 3)).options(padding=0.1)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0)
self.assertEqual(x_range[1], 3)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_points_padding_unequal(self):
points = Points([1, 2, 3]).options(padding=(0.05, 0.1))
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], -0.1)
self.assertEqual(x_range[1], 2.1)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_points_padding_nonsquare(self):
points = Points([1, 2, 3]).options(padding=0.1, aspect=2)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], -0.1)
self.assertEqual(x_range[1], 2.1)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_points_padding_logx(self):
points = Points([(1, 1), (2, 2), (3,3)]).options(padding=0.1, logx=True)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 0.89595845984076228)
self.assertEqual(x_range[1], 3.3483695221017129)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_points_padding_logy(self):
points = Points([1, 2, 3]).options(padding=0.1, logy=True)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], -0.2)
self.assertEqual(x_range[1], 2.2)
self.assertEqual(y_range[0], 0.89595845984076228)
self.assertEqual(y_range[1], 3.3483695221017129)
def test_points_padding_datetime_square(self):
points = Points([(np.datetime64('2016-04-0%d' % i), i) for i in range(1, 4)]).options(
padding=0.1
)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 16891.8)
self.assertEqual(x_range[1], 16894.2)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_points_padding_datetime_nonsquare(self):
points = Points([(np.datetime64('2016-04-0%d' % i), i) for i in range(1, 4)]).options(
padding=0.1, aspect=2
)
plot = mpl_renderer.get_plot(points)
x_range, y_range = plot.handles['axis'].get_xlim(), plot.handles['axis'].get_ylim()
self.assertEqual(x_range[0], 16891.9)
self.assertEqual(x_range[1], 16894.1)
self.assertEqual(y_range[0], 0.8)
self.assertEqual(y_range[1], 3.2)
def test_points_sizes_scalar_update(self):
hmap = HoloMap({i: Points([1, 2, 3]).opts(s=i*10) for i in range(1, 3)})
plot = mpl_renderer.get_plot(hmap)
artist = plot.handles['artist']
plot.update((1,))
self.assertEqual(artist.get_sizes(), np.array([10]))
plot.update((2,))
self.assertEqual(artist.get_sizes(), np.array([20]))
###########################
# Styling mapping #
###########################
def test_point_color_op(self):
points = Points([(0, 0, '#000000'), (0, 1, '#FF0000'), (0, 2, '#00FF00')],
vdims='color').options(color='color')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_facecolors(),
np.array([[0, 0, 0, 1], [1, 0, 0, 1], [0, 1, 0, 1]]))
def test_point_color_op_update(self):
points = HoloMap({0: Points([(0, 0, '#000000'), (0, 1, '#FF0000'), (0, 2, '#00FF00')],
vdims='color'),
1: Points([(0, 0, '#0000FF'), (0, 1, '#00FF00'), (0, 2, '#FF0000')],
vdims='color')}).options(color='color')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
plot.update((1,))
self.assertEqual(artist.get_facecolors(),
np.array([[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 0, 1]]))
def test_point_line_color_op(self):
points = Points([(0, 0, '#000000'), (0, 1, '#FF0000'), (0, 2, '#00FF00')],
vdims='color').options(edgecolors='color')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_edgecolors(),
np.array([[0, 0, 0, 1], [1, 0, 0, 1], [0, 1, 0, 1]]))
def test_point_line_color_op_update(self):
points = HoloMap({0: Points([(0, 0, '#000000'), (0, 1, '#FF0000'), (0, 2, '#00FF00')],
vdims='color'),
1: Points([(0, 0, '#0000FF'), (0, 1, '#00FF00'), (0, 2, '#FF0000')],
vdims='color')}).options(edgecolors='color')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
plot.update((1,))
self.assertEqual(artist.get_edgecolors(),
np.array([[0, 0, 1, 1], [0, 1, 0, 1], [1, 0, 0, 1]]))
def test_point_fill_color_op(self):
points = Points([(0, 0, '#000000'), (0, 1, '#FF0000'), (0, 2, '#00FF00')],
vdims='color').options(facecolors='color')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_facecolors(),
np.array([[0, 0, 0, 1], [1, 0, 0, 1], [0, 1, 0, 1]]))
def test_point_linear_color_op(self):
points = Points([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
vdims='color').options(color='color')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(np.asarray(artist.get_array()), np.array([0, 1, 2]))
self.assertEqual(artist.get_clim(), (0, 2))
def test_point_linear_color_op_update(self):
points = HoloMap({0: Points([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
vdims='color'),
1: Points([(0, 0, 2.5), (0, 1, 3), (0, 2, 1.2)],
vdims='color')}).options(color='color', framewise=True)
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_clim(), (0, 2))
plot.update((1,))
self.assertEqual(np.asarray(artist.get_array()), np.array([2.5, 3, 1.2]))
self.assertEqual(artist.get_clim(), (1.2, 3))
def test_point_categorical_color_op(self):
points = Points([(0, 0, 'A'), (0, 1, 'B'), (0, 2, 'A')],
vdims='color').options(color='color')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(np.asarray(artist.get_array()), np.array([0, 1, 0]))
self.assertEqual(artist.get_clim(), (0, 1))
def test_point_size_op(self):
points = Points([(0, 0, 1), (0, 1, 4), (0, 2, 8)],
vdims='size').options(s='size')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_sizes(), np.array([1, 4, 8]))
def test_point_size_op_update(self):
points = HoloMap({0: Points([(0, 0, 3), (0, 1, 1), (0, 2, 2)],
vdims='size'),
1: Points([(0, 0, 2.5), (0, 1, 3), (0, 2, 1.2)],
vdims='size')}).options(s='size')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_sizes(), np.array([3, 1, 2]))
plot.update((1,))
self.assertEqual(artist.get_sizes(), np.array([2.5, 3, 1.2]))
def test_point_line_width_op(self):
points = Points([(0, 0, 1), (0, 1, 4), (0, 2, 8)],
vdims='line_width').options(linewidth='line_width')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_linewidths(), [1, 4, 8])
def test_point_line_width_op_update(self):
points = HoloMap({0: Points([(0, 0, 3), (0, 1, 1), (0, 2, 2)],
vdims='line_width'),
1: Points([(0, 0, 2.5), (0, 1, 3), (0, 2, 1.2)],
vdims='line_width')}).options(linewidth='line_width')
plot = mpl_renderer.get_plot(points)
artist = plot.handles['artist']
self.assertEqual(artist.get_linewidths(), [3, 1, 2])
plot.update((1,))
self.assertEqual(artist.get_linewidths(), [2.5, 3, 1.2])
def test_point_marker_op(self):
points = Points([(0, 0, 'circle'), (0, 1, 'triangle'), (0, 2, 'square')],
vdims='marker').options(marker='marker')
with self.assertRaises(Exception):
mpl_renderer.get_plot(points)
def test_point_alpha_op(self):
points = Points([(0, 0, 0), (0, 1, 0.2), (0, 2, 0.7)],
vdims='alpha').options(alpha='alpha')
with self.assertRaises(Exception):
mpl_renderer.get_plot(points)
def test_op_ndoverlay_value(self):
markers = ['d', 's']
overlay = NdOverlay({marker: Points(np.arange(i))
for i, marker in enumerate(markers)},
'Marker').options('Points', marker='Marker')
plot = mpl_renderer.get_plot(overlay)
for subplot, marker in zip(plot.subplots.values(), markers):
style = dict(subplot.style[subplot.cyclic_index])
style = subplot._apply_transforms(subplot.current_frame, {}, style)
self.assertEqual(style['marker'], marker)
def test_point_color_index_color_clash(self):
points = Points([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
vdims='color').options(color='color', color_index='color')
with ParamLogStream() as log:
mpl_renderer.get_plot(points)
log_msg = log.stream.read()
warning = ("Cannot declare style mapping for 'color' option "
"and declare a color_index; ignoring the color_index.\n")
self.assertEqual(log_msg, warning)
def test_point_size_index_size_clash(self):
points = Points([(0, 0, 0), (0, 1, 1), (0, 2, 2)],
vdims='size').options(s='size', size_index='size')
with ParamLogStream() as log:
mpl_renderer.get_plot(points)
log_msg = log.stream.read()
warning = ("Cannot declare style mapping for 's' option "
"and declare a size_index; ignoring the size_index.\n")
self.assertEqual(log_msg, warning)
| [
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]
| |
d6b0539a2cd34a3318a634029493799c8d1029ff | 2aec9c5e8c72b731d3abf22f2a407fe09c1cde09 | /ZQZ510/ZQZ510/spiders/zqz.py | 3a4ae2a8fc42615dd7eaaf1a56965897c452c5d3 | []
| no_license | jiangyg/ZWFproject | 8b24cc34970ae0a9c2a2b0039dc527c83a5862b5 | aa35bc59566d92721f23d2dd00b0febd268ac2dd | refs/heads/master | 2020-09-26T17:01:00.229380 | 2019-11-15T13:16:21 | 2019-11-15T13:16:21 | 226,297,631 | 0 | 1 | null | 2019-12-06T09:55:37 | 2019-12-06T09:55:36 | null | UTF-8 | Python | false | false | 5,422 | py | # -*- coding: utf-8 -*-
import scrapy
import time
import json
from ZQZ510.items import Zqz510Item
empty_word = 'null'
class ZqzSpider(scrapy.Spider):
name = 'zqz'
allowed_domains = ['zqz510.com']
start_urls = ['http://login.zqz510.com/judgmentDoc']
def parse(self, response):
url = 'http://api.zqz510.com//tmof/query?ftxt=&ti=&apS=&pdStart=&pdEnd=&ty=&psty=&law=&litem=&pageNum=1' \
'&apS=&apD=&ag=&judgd=&tid=&cid=&callback=_jqjsp&_{}='.format(str(int(time.time() * 1000)))
self.cookie = {
'uid': '213facea-5ac7-4069-ae4a-97168d559ebc',
'oid': 'UAGAP00003919',
'JSESSIONID': '9867C3C37D24634CB9D44D1AA5C6188F',
'c': '82f5dd5f-f8ae-459b-9907-fd0bb01d97cb',
}
yield scrapy.Request(url=url, callback=self.parse_first, cookies=self.cookie)
def parse_first(self, response):
json_text = json.loads(response.text[7:-1], encoding='utf-8')
total = int(json_text['total'])
all_page = int(total / 10) + 1
for page in range(all_page):
url = 'http://api.zqz510.com//tmof/query?ftxt=&ti=&apS=&pdStart=&pdEnd=&ty=&psty=&law=&litem=&pageNum={}' \
'&apS=&apD=&ag=&judgd=&tid=&cid=&callback=_jqjsp&_{}='.format(str(page + 1), str(int(time.time() * 1000)))
yield scrapy.Request(url=url, callback=self.parse_list, cookies=self.cookie)
def parse_list(self, response):
json_text = json.loads(response.text[7:-1], encoding='utf-8')
for data in json_text['data']:
item = Zqz510Item()
if 'agS' in data:
item['agS'] = data['agS']
else:
item['agS'] = empty_word
if 'agidS' in data:
item['agidS'] = data['agidS']
else:
item['agidS'] = empty_word
if 'an' in data:
item['an'] = data['an']
else:
item['an'] = empty_word
if 'anDest' in data:
item['anDest'] = data['anDest']
else:
item['anDest'] = empty_word
if 'anList' in data:
item['anList'] = str(data['anList'])
else:
item['anList'] = empty_word
if 'apS' in data:
item['apS'] = data['apS']
else:
item['apS'] = empty_word
if 'apidS' in data:
item['apidS'] = data['apidS']
else:
item['apidS'] = empty_word
if 'cid' in data:
item['cid'] = data['cid']
else:
item['cid'] = empty_word
if 'docid' in data:
item['docid'] = data['docid']
else:
item['docid'] = empty_word
if 'law' in data:
item['law'] = data['law']
else:
item['law'] = empty_word
if 'link' in data:
item['link'] = data['link']
else:
item['link'] = empty_word
if 'litem' in data:
item['litem'] = data['litem']
else:
item['litem'] = empty_word
if 'ltid' in data:
item['ltid'] = data['ltid']
else:
item['ltid'] = empty_word
if 'pd' in data:
item['pd'] = data['pd']
else:
item['pd'] = empty_word
if 'psty' in data:
item['psty'] = data['psty']
else:
item['psty'] = empty_word
if 'rid' in data:
item['rid'] = data['rid']
else:
item['rid'] = empty_word
if 'ti' in data:
item['ti'] = data['ti']
else:
item['ti'] = empty_word
if 'ty' in data:
item['ty'] = data['ty']
else:
item['ty'] = empty_word
detail_url = 'http://api.zqz510.com/tmof/detail?docid={}&callback=_jqjsp&_{}='.format(item['docid'], str(int(time.time() * 1000)))
yield scrapy.Request(url=detail_url, callback=self.parse_detail, meta={'item': item}, cookies=self.cookie)
def parse_detail(self, response):
json_text = json.loads(response.text[7:-1], encoding='utf-8')
item = response.meta['item']
if 'dtls' in json_text:
item['dtls'] = str(json_text['dtls'])
else:
item['dtls'] = empty_word
if 'ftxt' in json_text:
item['ftxt'] = json_text['ftxt']
else:
item['ftxt'] = empty_word
if 'judg' in json_text:
item['judg'] = str(json_text['judg'])
else:
item['judg'] = empty_word
if 'judgList' in json_text:
item['judgList'] = str(json_text['judgList'])
else:
item['judgList'] = empty_word
if 'links' in json_text:
item['links'] = str(json_text['links'])
else:
item['links'] = empty_word
if 'ltidAll' in json_text:
item['ltidAll'] = str(json_text['ltidAll'])
else:
item['ltidAll'] = empty_word
if 'pdCn' in json_text:
item['pdCn'] = str(json_text['pdCn'])
else:
item['pdCn'] = empty_word
yield item | [
"[email protected]"
]
| |
d72f0e6e1d8aaabc1a02b10a8fbc864b8f6d0b65 | 29345337bf86edc938f3b5652702d551bfc3f11a | /python/src/main/python/pyalink/alink/tests/examples/from_docs/test_totensorstreamop.py | 78c1de91112c783148b8652120fe7425e975fcf9 | [
"Apache-2.0"
]
| permissive | vacaly/Alink | 32b71ac4572ae3509d343e3d1ff31a4da2321b6d | edb543ee05260a1dd314b11384d918fa1622d9c1 | refs/heads/master | 2023-07-21T03:29:07.612507 | 2023-07-12T12:41:31 | 2023-07-12T12:41:31 | 283,079,072 | 0 | 0 | Apache-2.0 | 2020-07-28T02:46:14 | 2020-07-28T02:46:13 | null | UTF-8 | Python | false | false | 553 | py | import unittest
from pyalink.alink import *
import numpy as np
import pandas as pd
class TestToTensorStreamOp(unittest.TestCase):
def test_totensorstreamop(self):
df = pd.DataFrame(["FLOAT#6#0.0 0.1 1.0 1.1 2.0 2.1 "])
source = StreamOperator.fromDataframe(df, schemaStr='vec string')
source.link(
ToTensorStreamOp()
.setSelectedCol("vec")
.setTensorShape([2, 3])
.setTensorDataType("float")
).print()
StreamOperator.execute()
pass | [
"[email protected]"
]
| |
9ab8c1cfef72c9b54df1a43e0a919da8d13a725c | 9c81c170f03ba925bf3d0682526245c202e384a7 | /superset/cli/test.py | f175acec470cd59f06f6d1ad8de07765a2520901 | [
"Apache-2.0",
"OFL-1.1"
]
| permissive | zcong1993/incubator-superset | 2a08177641eff178dee9db852887ad2d19d70d54 | 269c99293f42089958dc98b5d6e5899509fc3111 | refs/heads/master | 2023-08-17T12:24:59.438120 | 2023-08-17T10:50:24 | 2023-08-17T10:50:24 | 209,522,299 | 0 | 0 | Apache-2.0 | 2023-03-06T08:10:31 | 2019-09-19T10:09:21 | TypeScript | UTF-8 | Python | false | false | 2,860 | py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import logging
import click
from colorama import Fore
from flask.cli import with_appcontext
import superset.utils.database as database_utils
from superset import app, security_manager
logger = logging.getLogger(__name__)
@click.command()
@with_appcontext
def load_test_users() -> None:
"""
Loads admin, alpha, and gamma user for testing purposes
Syncs permissions for those users/roles
"""
print(Fore.GREEN + "Loading a set of users for unit tests")
load_test_users_run()
def load_test_users_run() -> None:
"""
Loads admin, alpha, and gamma user for testing purposes
Syncs permissions for those users/roles
"""
if app.config["TESTING"]:
sm = security_manager
examples_db = database_utils.get_example_database()
examples_pv = sm.add_permission_view_menu("database_access", examples_db.perm)
sm.sync_role_definitions()
gamma_sqllab_role = sm.add_role("gamma_sqllab")
sm.add_permission_role(gamma_sqllab_role, examples_pv)
gamma_no_csv_role = sm.add_role("gamma_no_csv")
sm.add_permission_role(gamma_no_csv_role, examples_pv)
for role in ["Gamma", "sql_lab"]:
for perm in sm.find_role(role).permissions:
sm.add_permission_role(gamma_sqllab_role, perm)
if str(perm) != "can csv on Superset":
sm.add_permission_role(gamma_no_csv_role, perm)
users = (
("admin", "Admin"),
("gamma", "Gamma"),
("gamma2", "Gamma"),
("gamma_sqllab", "gamma_sqllab"),
("alpha", "Alpha"),
("gamma_no_csv", "gamma_no_csv"),
)
for username, role in users:
user = sm.find_user(username)
if not user:
sm.add_user(
username,
username,
"user",
username + "@fab.org",
sm.find_role(role),
password="general",
)
sm.get_session.commit()
| [
"[email protected]"
]
| |
f4771bd090478972d022ce9b450d530bb2408052 | 6c3ab38e350734f1bc4f0c746ea55a12838ce5ee | /pcserver/mainapp/handlers.py | 93a7d32aa090f9a76b8f6ab1bca16d7d2eda3868 | []
| no_license | joelsemar/Programming-Challenge | 1dd4fb487d02e05ed494e66da99a627970832988 | b8bf8e115dc3c242d62bf696d3268a4b31019592 | refs/heads/master | 2020-05-17T15:16:45.892328 | 2011-08-31T19:17:15 | 2011-08-31T19:17:15 | 2,298,598 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,662 | py | from webservice_tools.utils import BaseHandler, AutoListHandler
from webservice_tools.decorators import login_required
from mainapp.models import * #@UnusedWildImport
#Create your handlers here
class PhotosHandler(AutoListHandler):
model = Photo
allowed_methods = ('GET',)
extra_fields = ('image_url',)
exclude = ('image', )
@login_required
def read(self, request, response):
"""
Returns a list of Photo objects.
API Handler: GET /photos
Params:
@key [string] your api key
Returns:
@photos [Photo] list of photos, see Photo docs for details
"""
return super(PhotosHandler, self).read(request, response)
class PhotoHandler(BaseHandler):
model = Photo
allowed_methods = ('GET',)
extra_fields = ('image_url',)
exclude = ('image', )
@login_required
def read(self, request, id, response):
"""
Fetch the details of a photo by id
API Handler: GET /photo/{id}
Params:
@id [id] id of the photo (in the url)
@key [string] your api key
Returns:
@title [string] title
@description [string] a short description
@image_url [url] a url to the corresponding image
"""
return super(PhotoHandler, self).read(request, id, response)
#ALL DEFINITION EOF
module_name = globals().get('__name__')
handlers = sys.modules[module_name]
handlers._all_ = []
for handler_name in dir():
m = getattr(handlers, handler_name)
if type(m) == type(BaseHandler):
handlers._all_.append(handler_name)
| [
"[email protected]"
]
| |
43e3f69a4d43e8fd97a6995fa95b1197d002dc0e | 0315255c749b12216a7c8ac26378d8921466284a | /tests/integration/client/standard.py | 969611b4d0a0800f10b1c10258875138538f5b08 | [
"Apache-2.0"
]
| permissive | jhutchins/salt | a32de1362c6787ec96df7ce57bf9b98f20eaf30a | 22ec0cee6a8a842ec426b7a3e634723ea7ce7256 | refs/heads/master | 2021-01-21T00:05:05.782149 | 2012-04-06T22:03:19 | 2012-04-06T22:03:19 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,254 | py | # Import python libs
import subprocess
# Import salt libs
import integration
class StdTest(integration.ModuleCase):
'''
Test standard client calls
'''
def test_cli(self):
'''
Test cli function
'''
cmd_iter = self.client.cmd_cli(
'minion',
'test.ping',
)
for ret in cmd_iter:
self.assertTrue(ret['minion'])
def test_iter(self):
'''
test cmd_iter
'''
cmd_iter = self.client.cmd_iter(
'minion',
'test.ping',
)
for ret in cmd_iter:
self.assertTrue(ret['minion'])
def test_iter_no_block(self):
'''
test cmd_iter_no_block
'''
cmd_iter = self.client.cmd_iter_no_block(
'minion',
'test.ping',
)
for ret in cmd_iter:
if ret is None:
continue
self.assertTrue(ret['minion'])
def test_full_returns(self):
'''
test cmd_iter
'''
ret = self.client.cmd_full_return(
'minion',
'test.ping',
)
self.assertTrue(ret['minion'])
| [
"[email protected]"
]
| |
0aa4fad95e735af119da27c643164c508715fe23 | 58c122786263edf8aec4a6b6b4986b2f3d4ff1d5 | /modules/s3/pyvttbl/qsturng.py | 49e432d4a21df43c96c06038b370da50df36ded8 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
]
| permissive | andygimma/eden | c9d0819e87ef05ff607cac6120dbddc86e55bc31 | 716d5e11ec0030493b582fa67d6f1c35de0af50d | refs/heads/master | 2021-01-15T21:54:03.240072 | 2012-11-16T05:13:11 | 2012-11-16T05:13:11 | 6,726,106 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 47,225 | py | # Copyright (c) 2011, Roger Lew [see LICENSE.txt]
# This software is funded in part by NIH Grant P20 RR016454.
"""
Implementation of Gleason's (1999) non-iterative upper quantile
studentized range approximation.
According to Gleason this method should be more accurate than the
AS190 FORTRAN algorithm of Lund and Lund (1983) and works from .5
<= p <= .999 (The AS190 only works from .9 <= p <= .99).
It is more efficient then the Copenhaver & Holland (1988) algorithm
(used by the _qtukey_ R function) although it requires storing the A
table in memory. (q distribution) approximations in Python.
see:
Gleason, J. R. (1999). An accurate, non-iterative approximation
for studentized range quantiles. Computational Statistics &
Data Analysis, (31), 147-158.
Gleason, J. R. (1998). A table of quantile points of the
Studentized range distribution.
http://www.stata.com/stb/stb46/dm64/sturng.pdf
"""
import math
import scipy.stats
import numpy as np
inf = float('inf')
__version__ = '0.2.1'
# changelog
# 0.1 - initial release
# 0.1.1 - vectorized
# 0.2 - psturng added
# 0.2.1 - T, R generation script relegated to make_tbls.py
# Gleason's table was derived using least square estimation on the tabled
# r values for combinations of p and v. In total there are 206
# estimates over p-values of .5, .75, .9, .95, .975, .99, .995,
# and .999, and over v (degrees of freedom) of (1) - 20, 24, 30, 40,
# 60, 120, and inf. combinations with p < .95 don't have coefficients
# for v = 1. Hence the parentheses. These coefficients allow us to
# form f-hat. f-hat with the inverse t transform of tinv(p,v) yields
# a fairly accurate estimate of the studentized range distribution
# across a wide range of values. According to Gleason this method
# should be more accurate than algorithm AS190 of Lund and Lund (1983)
# and work across a wider range of values (The AS190 only works
# from .9 <= p <= .99). R's qtukey algorithm was used to add tables
# at .675, .8, and .85. These aid approximations when p < .9.
#
# The code that generated this table is called make_tbls.py and is
# located in version control.
A = {(0.1, 2.0): [-2.2485085243379075, -1.5641014278923464, 0.55942294426816752, -0.060006608853883377],
(0.1, 3.0): [-2.2061105943901564, -1.8415406600571855, 0.61880788039834955, -0.062217093661209831],
(0.1, 4.0): [-2.1686691786678178, -2.008196172372553, 0.65010084431947401, -0.06289005500114471],
(0.1, 5.0): [-2.145077200277393, -2.112454843879346, 0.66701240582821342, -0.062993502233654797],
(0.1, 6.0): [-2.0896098049743155, -2.2400004934286497, 0.70088523391700142, -0.065907568563272748],
(0.1, 7.0): [-2.0689296655661584, -2.3078445479584873, 0.71577374609418909, -0.067081034249350552],
(0.1, 8.0): [-2.0064956480711262, -2.437400413087452, 0.76297532367415266, -0.072805518121505458],
(0.1, 9.0): [-2.3269477513436061, -2.0469494712773089, 0.60662518717720593, -0.054887108437009016],
(0.1, 10.0): [-2.514024350177229, -1.8261187841127482, 0.51674358077906746, -0.044590425150963633],
(0.1, 11.0): [-2.5130181309130828, -1.8371718595995694, 0.51336701694862252, -0.043761825829092445],
(0.1, 12.0): [-2.5203508109278823, -1.8355687130611862, 0.5063486549107169, -0.042646205063108261],
(0.1, 13.0): [-2.5142536438310477, -1.8496969402776282, 0.50616991367764153, -0.042378379905665363],
(0.1, 14.0): [-2.3924634153781352, -2.013859173066078, 0.56421893251638688, -0.048716888109540266],
(0.1, 15.0): [-2.3573552940582574, -2.0576676976224362, 0.57424068771143233, -0.049367487649225841],
(0.1, 16.0): [-2.3046427483044871, -2.1295959138627993, 0.59778272657680553, -0.051864829216301617],
(0.1, 17.0): [-2.2230551072316125, -2.2472837435427127, 0.64255758243215211, -0.057186665209197643],
(0.1, 18.0): [-2.3912859179716897, -2.0350604070641269, 0.55924788749333332, -0.047729331835226464],
(0.1, 19.0): [-2.4169773092220623, -2.0048217969339146, 0.54493039319748915, -0.045991241346224065],
(0.1, 20.0): [-2.4264087194660751, -1.9916614057049267, 0.53583555139648154, -0.04463049934517662],
(0.1, 24.0): [-2.3969903132061869, -2.0252941869225345, 0.53428382141200137, -0.043116495567779786],
(0.1, 30.0): [-2.2509922780354623, -2.2309248956124894, 0.60748041324937263, -0.051427415888817322],
(0.1, 40.0): [-2.1310090183854946, -2.3908466074610564, 0.65844375382323217, -0.05676653804036895],
(0.1, 60.0): [-1.9240060179027036, -2.6685751031012233, 0.75678826647453024, -0.067938584352398995],
(0.1, 120.0): [-1.9814895487030182, -2.5962051736978373, 0.71793969041292693, -0.063126863201511618],
(0.1, inf): [-1.913410267066703, -2.6947367328724732, 0.74742335122750592, -0.06660897234304515],
(0.5, 2.0): [-0.88295935738770648, -0.1083576698911433, 0.035214966839394388, -0.0028576288978276461],
(0.5, 3.0): [-0.89085829205846834, -0.10255696422201063, 0.033613638666631696, -0.0027101699918520737],
(0.5, 4.0): [-0.89627345339338116, -0.099072524607668286, 0.032657774808907684, -0.0026219007698204916],
(0.5, 5.0): [-0.89959145511941052, -0.097272836582026817, 0.032236187675182958, -0.0025911555217019663],
(0.5, 6.0): [-0.89959428735702474, -0.098176292411106647, 0.032590766960226995, -0.0026319890073613164],
(0.5, 7.0): [-0.90131491102863937, -0.097135907620296544, 0.032304124993269533, -0.0026057965808244125],
(0.5, 8.0): [-0.90292500599432901, -0.096047500971337962, 0.032030946615574568, -0.0025848748659053891],
(0.5, 9.0): [-0.90385598607803697, -0.095390771554571888, 0.031832651111105899, -0.0025656060219315991],
(0.5, 10.0): [-0.90562524936125388, -0.093954488089771915, 0.031414451048323286, -0.0025257834705432031],
(0.5, 11.0): [-0.90420347371173826, -0.095851656370277288, 0.0321150356209743, -0.0026055056400093451],
(0.5, 12.0): [-0.90585973471757664, -0.094449306296728028, 0.031705945923210958, -0.0025673330195780191],
(0.5, 13.0): [-0.90555437067293054, -0.094792991050780248, 0.031826594964571089, -0.0025807109129488545],
(0.5, 14.0): [-0.90652756604388762, -0.093792156994564738, 0.031468966328889042, -0.0025395175361083741],
(0.5, 15.0): [-0.90642323700400085, -0.094173017520487984, 0.031657517378893905, -0.0025659271829033877],
(0.5, 16.0): [-0.90716338636685234, -0.093785178083820434, 0.031630091949657997, -0.0025701459247416637],
(0.5, 17.0): [-0.90790133816769714, -0.093001147638638884, 0.031376863944487084, -0.002545143621663892],
(0.5, 18.0): [-0.9077432927051563, -0.093343516378180599, 0.031518139662395313, -0.0025613906133277178],
(0.5, 19.0): [-0.90789499456490286, -0.09316964789456067, 0.031440782366342901, -0.0025498353345867453],
(0.5, 20.0): [-0.90842707861030725, -0.092696016476608592, 0.031296040311388329, -0.0025346963982742186],
(0.5, 24.0): [-0.9083281347135469, -0.092959308144970776, 0.031464063190077093, -0.0025611384271086285],
(0.5, 30.0): [-0.90857624050016828, -0.093043139391980514, 0.031578791729341332, -0.0025766595412777147],
(0.5, 40.0): [-0.91034085045438684, -0.091978035738914568, 0.031451631000052639, -0.0025791418103733297],
(0.5, 60.0): [-0.91084356681030032, -0.091452675572423425, 0.031333147984820044, -0.0025669786958144843],
(0.5, 120.0): [-0.90963649561463833, -0.093414563261352349, 0.032215602703677425, -0.0026704024780441257],
(0.5, inf): [-0.91077157500981665, -0.092899220350334571, 0.032230422399363315, -0.0026696941964372916],
(0.675, 2.0): [-0.67231521026565144, -0.097083624030663451, 0.027991378901661649, -0.0021425184069845558],
(0.675, 3.0): [-0.65661724764645824, -0.08147195494632696, 0.02345732427073333, -0.0017448570400999351],
(0.675, 4.0): [-0.65045677697461124, -0.071419073399450431, 0.020741962576852499, -0.0015171262565892491],
(0.675, 5.0): [-0.64718875357808325, -0.064720611425218344, 0.019053450246546449, -0.0013836232986228711],
(0.675, 6.0): [-0.64523003702018655, -0.059926313672731824, 0.017918997181483924, -0.0012992250285556828],
(0.675, 7.0): [-0.64403313148478836, -0.056248191513784476, 0.017091446791293721, -0.0012406558789511822],
(0.675, 8.0): [-0.64325095865764359, -0.053352543126426684, 0.016471879286491072, -0.0011991839050964099],
(0.675, 9.0): [-0.64271152754911653, -0.051023769620449078, 0.01599799600547195, -0.0011693637984597086],
(0.675, 10.0): [-0.64232244408502626, -0.049118327462884373, 0.015629704966568955, -0.0011477775513952285],
(0.675, 11.0): [-0.64203897854353564, -0.047524627960277892, 0.015334801262767227, -0.0011315057284007177],
(0.675, 12.0): [-0.64180344973512771, -0.046205907576003291, 0.015108290595438166, -0.0011207364514518488],
(0.675, 13.0): [-0.64162086456823342, -0.045076099336874231, 0.0149226565346125, -0.0011126140690497352],
(0.675, 14.0): [-0.64146906480198984, -0.044108523550512715, 0.014772954218646743, -0.0011069708562369386],
(0.675, 15.0): [-0.64133915151966603, -0.043273370927039825, 0.014651691599222836, -0.0011032216539514398],
(0.675, 16.0): [-0.64123237842752079, -0.042538925012463868, 0.014549992487506169, -0.0011005633864334021],
(0.675, 17.0): [-0.64113034037536609, -0.041905699463005854, 0.014470805560767184, -0.0010995286436738471],
(0.675, 18.0): [-0.64104137391561256, -0.041343885546229336, 0.014404563657113593, -0.0010991304223377683],
(0.675, 19.0): [-0.64096064882827297, -0.04084569291139839, 0.014350159655133801, -0.0010993656711121901],
(0.675, 20.0): [-0.64088647405089572, -0.040402175957178085, 0.014305769823654429, -0.0011001304776712105],
(0.675, 24.0): [-0.64063763965937837, -0.039034716348048545, 0.014196703837251648, -0.0011061961945598175],
(0.675, 30.0): [-0.64034987716294889, -0.037749651156941719, 0.014147040999127263, -0.0011188251352919833],
(0.675, 40.0): [-0.6399990514713938, -0.036583307574857803, 0.014172070700846548, -0.0011391004138624943],
(0.675, 60.0): [-0.63955586202430248, -0.035576938958184395, 0.014287299153378865, -0.0011675811805794236],
(0.675, 120.0): [-0.63899242674778622, -0.034763757512388853, 0.014500726912982405, -0.0012028491454427466],
(0.675, inf): [-0.63832682579247613, -0.034101476695520404, 0.014780921043580184, -0.0012366204114216408],
(0.75, 2.0): [-0.60684073638504454, -0.096375192078057031, 0.026567529471304554, -0.0019963228971914488],
(0.75, 3.0): [-0.57986144519102656, -0.078570292718034881, 0.021280637925009449, -0.0015329306898533772],
(0.75, 4.0): [-0.56820771686193594, -0.0668113563896649, 0.018065284051059189, -0.0012641485481533648],
(0.75, 5.0): [-0.56175292435740221, -0.058864526929603825, 0.016046735025708799, -0.0011052560286524044],
(0.75, 6.0): [-0.55773449282066356, -0.053136923269827351, 0.014684258167069347, -0.0010042826823561605],
(0.75, 7.0): [-0.55509524598867332, -0.048752649191139405, 0.013696566605823626, -0.00093482210003133898],
(0.75, 8.0): [-0.55324993686191515, -0.045305558708724644, 0.012959681992062138, -0.00088583541601696021],
(0.75, 9.0): [-0.55189259054026196, -0.042539819902381634, 0.012398791106424769, -0.00085083962241435827],
(0.75, 10.0): [-0.55085384656956893, -0.040281425755686585, 0.01196442242722482, -0.00082560322161492677],
(0.75, 11.0): [-0.55003198103541273, -0.038410176100193948, 0.011623294239447784, -0.00080732975034320073],
(0.75, 12.0): [-0.54936541596319177, -0.036838543267887103, 0.011351822637895701, -0.0007940703654926442],
(0.75, 13.0): [-0.54881015972753833, -0.035506710625568455, 0.011134691307865171, -0.0007846360016355809],
(0.75, 14.0): [-0.54834094346071949, -0.034364790609906569, 0.010958873929274728, -0.00077796645357008291],
(0.75, 15.0): [-0.54793602418304255, -0.033379237455748029, 0.010816140998057593, -0.00077344175064785099],
(0.75, 16.0): [-0.54758347689728037, -0.032520569145898917, 0.010699240399358219, -0.00077050847328596678],
(0.75, 17.0): [-0.54727115963795303, -0.031769277192927527, 0.010603749751170481, -0.0007688642392748113],
(0.75, 18.0): [-0.54699351808826535, -0.031105476267880995, 0.010524669113016114, -0.00076810656837464093],
(0.75, 19.0): [-0.54674357626419079, -0.030516967201954001, 0.010459478822937069, -0.00076808652582440037],
(0.75, 20.0): [-0.54651728378950126, -0.029992319199769232, 0.010405694998386575, -0.0007686417223966138],
(0.75, 24.0): [-0.54578309546828363, -0.028372628574010936, 0.010269939602271542, -0.00077427370647261838],
(0.75, 30.0): [-0.54501246434397554, -0.026834887880579802, 0.010195603314317611, -0.00078648615954105515],
(0.75, 40.0): [-0.54418127442022624, -0.025413224488871379, 0.010196455193836855, -0.00080610785749523739],
(0.75, 60.0): [-0.543265189207915, -0.024141961069146383, 0.010285001019536088, -0.00083332193364294587],
(0.75, 120.0): [-0.54224757817994806, -0.023039071833948214, 0.010463365295636302, -0.00086612828539477918],
(0.75, inf): [-0.54114579815367159, -0.02206592527426093, 0.01070374099737127, -0.00089726564005122183],
(0.8, 2.0): [-0.56895274046831146, -0.096326255190541957, 0.025815915364208686, -0.0019136561019354845],
(0.8, 3.0): [-0.5336038380862278, -0.077585191014876181, 0.020184759265389905, -0.0014242746007323785],
(0.8, 4.0): [-0.51780274285934258, -0.064987738443608709, 0.016713309796866204, -0.001135379856633562],
(0.8, 5.0): [-0.50894361222268403, -0.056379186603362705, 0.014511270339773345, -0.00096225604117493205],
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(0.99, 17.0): [-0.21548926805467686, -0.017447822179412719, 0.0028994805120482812, -0.00012001887015183794],
(0.99, 18.0): [-0.21365014687077843, -0.01688869353338961, 0.0028778031289216546, -0.00012591199104792711],
(0.99, 19.0): [-0.21236653761262406, -0.016057151563612645, 0.0027571468998022017, -0.00012049196593780046],
(0.99, 20.0): [-0.21092693178421842, -0.015641706950956638, 0.0027765989877361293, -0.00013084915163086915],
(0.99, 24.0): [-0.20681960327410207, -0.013804298040271909, 0.0026308276736585674, -0.0001355061502101814],
(0.99, 30.0): [-0.20271691131071576, -0.01206095288359876, 0.0025426138004198909, -0.00014589047959047533],
(0.99, 40.0): [-0.19833098054449289, -0.010714533963740719, 0.0025985992420317597, -0.0001688279944262007],
(0.99, 60.0): [-0.19406768821236584, -0.0093297106482013985, 0.0026521518387539584, -0.00018884874193665104],
(0.99, 120.0): [-0.19010213174677365, -0.0075958207221300924, 0.0025660823297025633, -0.00018906475172834352],
(0.99, inf): [-0.18602070255787137, -0.0062121155165363188, 0.0026328293420766593, -0.00020453366529867131],
(0.995, 1.0): [-0.65135583544951825, -0.1266868999507193, 0.036067522182457165, -0.0028654516958844922],
(0.995, 2.0): [-0.45229774013072793, -0.09869462954369547, 0.024381858599368908, -0.0017594734553033394],
(0.995, 3.0): [-0.35935765236429706, -0.076650408326671915, 0.016823026893528978, -0.0010835134496404637],
(0.995, 4.0): [-0.30704474720931169, -0.063093047731613019, 0.012771683306774929, -0.00075852491621809955],
(0.995, 5.0): [-0.27582551740863454, -0.052533353137885791, 0.0097776009845174372, -0.00051338031756399129],
(0.995, 6.0): [-0.25657971464398704, -0.043424914996692286, 0.0074324147435969991, -0.00034105188850494067],
(0.995, 7.0): [-0.24090407819707738, -0.039591604712200287, 0.0068848429451020387, -0.00034737131709273414],
(0.995, 8.0): [-0.23089540800827862, -0.034353305816361958, 0.0056009527629820111, -0.00024389336976992433],
(0.995, 9.0): [-0.22322694848310584, -0.030294770709722547, 0.0046751239747245543, -0.00017437479314218922],
(0.995, 10.0): [-0.21722684126671632, -0.026993563560163809, 0.0039811592710905491, -0.00013135281785826703],
(0.995, 11.0): [-0.21171635822852911, -0.025156193618212551, 0.0037507759652964205, -0.00012959836685175671],
(0.995, 12.0): [-0.20745332165849167, -0.023318819535607219, 0.0034935020002058903, -0.00012642826898405916],
(0.995, 13.0): [-0.20426054591612508, -0.021189796175249527, 0.003031472176128759, -9.0497733877531618e-05],
(0.995, 14.0): [-0.20113536905578902, -0.020011536696623061, 0.0029215880889956729, -9.571527213951222e-05],
(0.995, 15.0): [-0.19855601561006403, -0.018808533734002542, 0.0027608859956002344, -9.2472995256929217e-05],
(0.995, 16.0): [-0.19619157579534008, -0.017970461530551096, 0.0027113719105000371, -9.9864874982890861e-05],
(0.995, 17.0): [-0.19428015140726104, -0.017009762497670704, 0.0025833389598201345, -9.6137545738061124e-05],
(0.995, 18.0): [-0.19243180236773033, -0.01631617252107519, 0.0025227443561618621, -9.8067580523432881e-05],
(0.995, 19.0): [-0.19061294393069844, -0.01586226613672222, 0.0025207005902641781, -0.00010466151274918466],
(0.995, 20.0): [-0.18946302696580328, -0.014975796567260896, 0.0023700506576419867, -9.5507779057884629e-05],
(0.995, 24.0): [-0.18444251428695257, -0.013770955893918012, 0.0024579445553339903, -0.00012688402863358003],
(0.995, 30.0): [-0.18009742499570078, -0.011831341846559026, 0.0022801125189390046, -0.00012536249967254906],
(0.995, 40.0): [-0.17562721880943261, -0.010157142650455463, 0.0022121943861923474, -0.000134542652873434],
(0.995, 60.0): [-0.17084630673594547, -0.0090224965852754805, 0.0023435529965815565, -0.00016240306777440115],
(0.995, 120.0): [-0.16648414081054147, -0.0074792163241677225, 0.0023284585524533607, -0.00017116464012147041],
(0.995, inf): [-0.16213921875452461, -0.0058985998630496144, 0.0022605819363689093, -0.00016896211491119114],
(0.999, 1.0): [-0.65233994072089363, -0.12579427445444219, 0.035830577995679271, -0.0028470555202945564],
(0.999, 2.0): [-0.45050164311326341, -0.098294804380698292, 0.024134463919493736, -0.0017269603956852841],
(0.999, 3.0): [-0.35161741499307819, -0.076801152272374273, 0.016695693063138672, -0.0010661121974071864],
(0.999, 4.0): [-0.29398448788574133, -0.06277319725219685, 0.012454220010543127, -0.00072644165723402445],
(0.999, 5.0): [-0.25725364564365477, -0.053463787584337355, 0.0099664236557431545, -0.00054866039388980659],
(0.999, 6.0): [-0.23674225795168574, -0.040973155890031254, 0.0062599481191736696, -0.00021565734226586692],
(0.999, 7.0): [-0.21840108878983297, -0.037037020271877719, 0.0055908063671900703, -0.00020238790479809623],
(0.999, 8.0): [-0.2057964743918449, -0.032500885103194356, 0.0046441644585661756, -0.00014769592268680274],
(0.999, 9.0): [-0.19604592954882674, -0.029166922919677936, 0.0040644333111949814, -0.00012854052861297006],
(0.999, 10.0): [-0.18857328935948367, -0.026316705703161091, 0.0035897350868809275, -0.00011572282691335702],
(0.999, 11.0): [-0.18207431428535406, -0.024201081944369412, 0.0031647372098056077, -8.1145935982296439e-05],
(0.999, 12.0): [-0.17796358148991101, -0.021054306118620879, 0.0023968085939602055, -1.5907156771296993e-05],
(0.999, 13.0): [-0.17371965962745489, -0.019577162950177709, 0.0022391783473999739, -2.0613023472812558e-05],
(0.999, 14.0): [-0.16905298116759873, -0.01967115985443986, 0.0026495208325889269, -9.1074275220634073e-05],
(0.999, 15.0): [-0.16635662558214312, -0.017903767183469876, 0.0022301322677100496, -5.1956773935885426e-05],
(0.999, 16.0): [-0.16388776549525449, -0.016671918839902419, 0.0020365289602744382, -4.3592447599724942e-05],
(0.999, 17.0): [-0.16131934177990759, -0.015998918405126326, 0.0019990454743285904, -4.8176277491327653e-05],
(0.999, 18.0): [-0.15880633110376571, -0.015830715141055916, 0.0021688405343832091, -8.061825248932771e-05],
(0.999, 19.0): [-0.15644841913314136, -0.015729364721105681, 0.0022981443610378136, -0.00010093672643417343],
(0.999, 20.0): [-0.15516596606222705, -0.014725095968258637, 0.0021117117014292155, -8.8806880297328484e-05],
(0.999, 24.0): [-0.14997437768645827, -0.012755323295476786, 0.0018871651510496939, -8.0896370662414938e-05],
(0.999, 30.0): [-0.14459974882323703, -0.011247323832877647, 0.0018637400643826279, -9.6415323191606741e-05],
(0.999, 40.0): [-0.13933285919392555, -0.0097151769692496587, 0.0018131251876208683, -0.00010452598991994023],
(0.999, 60.0): [-0.13424555343804143, -0.0082163027951669444, 0.0017883427892173382, -0.00011415865110808405],
(0.999, 120.0): [-0.12896119523040372, -0.0070426701112581112, 0.0018472364154226955, -0.00012862202979478294],
(0.999, inf): [-0.12397213562666673, -0.0056901201604149998, 0.0018260689406957129, -0.00013263452567995485]}
# p values that are defined in the A table
p_keys = [.1,.5,.675,.75,.8,.85,.9,.95,.975,.99,.995,.999]
# v values that are defined in the A table
v_keys = range(2, 21) + [24, 30, 40, 60, 120, inf]
def _isfloat(x):
"""
returns True if x is a float,
returns False otherwise
"""
try:
float(x)
except:
return False
return True
def _phi(p):
"""returns the pth quantile inverse norm"""
return scipy.stats.norm.isf(p)
def _ptransform(p):
"""function for p-value abcissa transformation"""
return -1. / (1. + 1.5 * _phi((1. + p)/2.))
def _select_points(a, list_like):
"""
returns one above a, one below a, and the third
closest point to a sorted in ascending order
for quadratic interpolation. Assumes that points
above and below a exist.
"""
foo = [x for x in list(list_like) if x-a <= 0]
z = [min(foo, key=lambda x : abs(x-a))]
foo = [x for x in list(list_like) if x-a > 0]
z.append(min(foo, key=lambda x : abs(x-a)))
foo = [x for x in list(list_like) if x not in z]
z.append(min(foo, key=lambda x : abs(x-a)))
return sorted(z)
def _func(a, p, r, v):
"""
calculates f-hat for the coefficients in a, probability p,
sample mean difference r, and degrees of freedom v.
"""
# eq. 2.3
f = a[0]*math.log(r-1.) + \
a[1]*math.log(r-1.)**2 + \
a[2]*math.log(r-1.)**3 + \
a[3]*math.log(r-1.)**4
# eq. 2.7 and 2.8 corrections
if r == 3:
f += -0.002 / (1. + 12. * _phi(p)**2)
if v <= 4.364:
f += 1./517. - 1./(312.*(v,1e38)[v==inf])
else:
f += 1./(191.*(v,1e38)[v==inf])
return -f
def _interpolate_p(p, r, v):
"""
interpolates p based on the values in the A table for the
scalar value of r and the scalar value of v
"""
# interpolate p (v should be in table)
# if .5 < p < .75 use linear interpolation in q
# if p > .75 use quadratic interpolation in log(y + r/v)
# by -1. / (1. + 1.5 * _phi((1. + p)/2.))
# find the 3 closest v values
p0, p1, p2 = _select_points(p, p_keys)
y0 = _func(A[(p0, v)], p0, r, v) + 1.
y1 = _func(A[(p1, v)], p1, r, v) + 1.
y2 = _func(A[(p2, v)], p2, r, v) + 1.
y_log0 = math.log(y0 + float(r)/float(v))
y_log1 = math.log(y1 + float(r)/float(v))
y_log2 = math.log(y2 + float(r)/float(v))
# If p < .85 apply only the ordinate transformation
# if p > .85 apply the ordinate and the abcissa transformation
# In both cases apply quadratic interpolation
if p > .85:
p_t = _ptransform(p)
p0_t = _ptransform(p0)
p1_t = _ptransform(p1)
p2_t = _ptransform(p2)
# calculate derivatives for quadratic interpolation
d2 = 2*((y_log2-y_log1)/(p2_t-p1_t) - \
(y_log1-y_log0)/(p1_t-p0_t))/(p2_t-p0_t)
if (p2+p0)>=(p1+p1):
d1 = (y_log2-y_log1)/(p2_t-p1_t) - 0.5*d2*(p2_t-p1_t)
else:
d1 = (y_log1-y_log0)/(p1_t-p0_t) + 0.5*d2*(p1_t-p0_t)
d0 = y_log1
# interpolate value
y_log = (d2/2.) * (p_t-p1_t)**2. + d1 * (p_t-p1_t) + d0
# transform back to y
y = math.exp(y_log) - float(r)/float(v)
elif p > .5:
# calculate derivatives for quadratic interpolation
d2 = 2*((y_log2-y_log1)/(p2-p1) - \
(y_log1-y_log0)/(p1-p0))/(p2-p0)
if (p2+p0)>=(p1+p1):
d1 = (y_log2-y_log1)/(p2-p1) - 0.5*d2*(p2-p1)
else:
d1 = (y_log1-y_log0)/(p1-p0) + 0.5*d2*(p1-p0)
d0 = y_log1
# interpolate values
y_log = (d2/2.) * (p-p1)**2. + d1 * (p-p1) + d0
# transform back to y
y = math.exp(y_log) - float(r)/float(v)
else:
# linear interpolation in q and p
q0 = math.sqrt(2) * -y0 * \
scipy.stats.t.isf((1.+p0)/2., (v,1e38)[v>1e38])
q1 = math.sqrt(2) * -y1 * \
scipy.stats.t.isf((1.+p1)/2., (v,1e38)[v>1e38])
d1 = (q1-q0)/(p1-p0)
d0 = q0
# interpolate values
q = d1 * (p-p0) + d0
# transform back to y
y = -q / (math.sqrt(2) * \
scipy.stats.t.isf((1.+p)/2., (v,1e38)[v>1e38]))
return y
def _interpolate_v(p, r, v):
"""
interpolates v based on the values in the A table for the
scalar value of r and th
"""
# interpolate v (p should be in table)
# ordinate: y**2
# abcissa: 1./v
# find the 3 closest v values
v0, v1, v2 = _select_points(v, v_keys+([],[1])[p>=.90])
# y = f - 1.
y0 = _func(A[(p,v0)], p, r, v0) + 1.
y1 = _func(A[(p,v1)], p, r, v1) + 1.
y2 = _func(A[(p,v2)], p, r, v2) + 1.
# if v2 is inf set to a big number so interpolation
# calculations will work
if v2 > 1e38: v2 = 1e38
# calculate derivatives for quadratic interpolation
d2 = 2.*((y2**2-y1**2)/(1./v2-1./v1) - \
(y0**2-y1**2)/(1./v0-1./v1)) / (1./v2-1./v0)
if (1./v2 + 1./v0) >= (1./v1+1./v1):
d1 = (y2**2-y1**2) / (1./v2-1./v1) - 0.5*d2*(1./v2-1./v1)
else:
d1 = (y1**2-y0**2) / (1./v1-1./v0) + 0.5*d2*(1./v1-1./v0)
d0 = y1**2
# calculate y
y = math.sqrt((d2/2.)*(1./v-1./v1)**2. + d1*(1./v-1./v1)+ d0)
return y
def _qsturng(p, r, v):
# r is interpolated through the q to y here we only need to
# account for when p and/or v are not found in the table.
global A, p_keys, v_keys
if p < .1 or p > .999:
raise ValueError('p must be between .1 and .999')
if p < .9:
if v < 2:
raise ValueError('v must be > 2 when p < .9')
else:
if v < 1:
raise ValueError('v must be > 1 when p >= .9')
if A.has_key((p,v)):
f = _func(A[(p,v)], p, r, v)
y = f + 1.
elif p not in p_keys and v not in v_keys+([],[1])[p>=.90]:
# apply bilinear (quadratic) interpolation
#
# p0,v2 + o + p1,v2 + p2,v2
# r2
#
# 1
# - (p,v)
# v x
#
# r1
# p0,v1 + o + p1,v1 + p2,v1
#
#
# p0,v0 + o r0 + p1,v0 + p2,v0
#
# _ptransform(p)
#
# (p1 and v1 may be below or above (p,v). The algorithm
# works in both cases. For diagramatic simplicity it is
# shown as above)
#
# 1. at v0, v1, and v2 use quadratic interpolation
# to find r0, r1, r2
#
# 2. use r0, r1, r2 and quadratic interpolaiton
# to find y and (p,v)
# find the 3 closest v values
v0, v1, v2 = _select_points(v, v_keys+([],[1])[p>=.90])
# find the 2 closest p values
p0, p1, p2 = _select_points(p, p_keys)
r0 = _interpolate_p(p, r, v0)
r1 = _interpolate_p(p, r, v1)
r2 = _interpolate_p(p, r, v2)
# calculate derivatives for quadratic interpolation
d2 = 2.*((r2**2-r1**2)/(1./v2-1./v1) - \
(r0**2-r1**2)/(1./v0-1./v1)) / (1./v2-1./v0)
if (1./v2 + 1./v0) >= (1./v1+1./v1):
d1 = (r2**2-r1**2) / (1./v2-1./v1) - 0.5*d2*(1./v2-1./v1)
else:
d1 = (r1**2-r0**2) / (1./v1-1./v0) + 0.5*d2*(1./v1-1./v0)
d0 = r1**2
# calculate y
y = math.sqrt((d2/2.)*(1./v-1./v1)**2. + d1*(1./v-1./v1)+ d0)
elif v not in v_keys+([],[1])[p>=.90]:
y = _interpolate_v(p, r, v)
elif p not in p_keys:
y = _interpolate_p(p, r, v)
return math.sqrt(2) * -y * \
scipy.stats.t.isf((1.+p)/2., (v,1e38)[v>1e38])
# make a qsturng functinon that will accept list-like objects
_vqsturng = np.vectorize(_qsturng)
def qsturng(p, r, v):
"""
returns the q-value of the Studentized Range q-distribution as a
function of the probability (p), number of sample means (r), and
the degrees of freedom (v).
"""
if all(map(_isfloat, [p, r, v])):
return _qsturng(p, r, v)
return _vqsturng(p, r, v)
import scipy.optimize
def _psturng(q, r, v):
opt_func = lambda p, r, v: abs(_qsturng(p, r, v) - q)
return 1. - scipy.optimize.fminbound(opt_func, .1, .999, args=(r,v))
_vpsturng = np.vectorize(_psturng)
def psturng(q, r, v):
"""
returns the probability for the Studentized q-distribution where
the value q cooresponds to qsturng(1 - p, r, v)
If .001 is returned the probability should be interpreted
as,
p <= .001.
Likewise if .9 is returned the probability should be
interpreted as,
p >= .9.
"""
if all(map(_isfloat, [q, r, v])):
return _psturng(q, r, v)
return _vpsturng(q, r, v)
##p, r, v = .9, 10, 20
##print
##print 'p and v interpolation'
##print '\t20\t22\t24'
##print '.75',qsturng(.75, r, 20),qsturng(.75, r, 22),qsturng(.75, r, 24)
##print '.85',qsturng(.85, r, 20),qsturng(.85, r, 22),qsturng(.85, r, 24)
##print '.90',qsturng(.90, r, 20),qsturng(.90, r, 22),qsturng(.90, r, 24)
##print
##print 'p and v interpolation'
##print '\t120\t500\tinf'
##print '.950',qsturng(.95, r, 120),qsturng(.95, r, 500),qsturng(.95, r, inf)
##print '.960',qsturng(.96, r, 120),qsturng(.96, r, 500),qsturng(.96, r, inf)
##print '.975',qsturng(.975, r, 120),qsturng(.975, r, 500),qsturng(.975, r, inf)
##print
##print 'p and v interpolation'
##print '\t40\t50\t60'
##print '.950',qsturng(.95, r, 40),qsturng(.95, r, 50),qsturng(.95, r, 60)
##print '.960',qsturng(.96, r, 40),qsturng(.96, r, 50),qsturng(.96, r, 60)
##print '.975',qsturng(.975, r, 40),qsturng(.975, r, 50),qsturng(.975, r, 60)
##print
##print 'p and v interpolation'
##print '\t20\t22\t24'
##print '.50',qsturng(.5, r, 20),qsturng(.5, r, 22),qsturng(.5, r, 24)
##print '.60',qsturng(.6, r, 20),qsturng(.6, r, 22),qsturng(.6, r, 24)
##print '.75',qsturng(.75, r, 20),qsturng(.75, r, 22),qsturng(.75, r, 24)
| [
"[email protected]"
]
| |
11b9bf5a469cbefb5d55ecbc166fdf0b95d5e6a5 | d2bb13cec7faf28e3d268312298f03c99806bd8b | /IPTS-16891-Dy2Ti2O7/norm_mesh_symm_All_rwp_100mK_7.py | d66eaa8f6dc140ec0ed3f53c2db9c0369b379c0f | []
| no_license | rosswhitfield/corelli | 06a91c26556ea788f20f973a1018a56e82a8c09a | d9e47107e3272c4457aa0d2e0732fc0446f54279 | refs/heads/master | 2021-08-07T14:04:24.426151 | 2021-08-03T19:19:05 | 2021-08-03T19:19:05 | 51,771,543 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,488 | py | from mantid.simpleapi import *
from mantid.geometry import SymmetryOperationFactory
import numpy as np
# about information on where the data are and where to save
iptsfolder= "/SNS/CORELLI/IPTS-16891/"
outputdir="/SNS/users/rwp/corelli/IPTS-16891-Dy2Ti2O7/"
nxfiledir=iptsfolder + "nexus/"
ccfiledir = iptsfolder +"shared/autoreduce/"
UBfile = iptsfolder+"shared/DTO_UB_111Vertical.mat"
reducedfile_prefix = "DTO_cc"
LoadNexus(Filename='/SNS/CORELLI/shared/Vanadium/2016B/SolidAngle20160720NoCC.nxs', OutputWorkspace='sa')
LoadNexus(Filename='/SNS/CORELLI/shared/Vanadium/2016B/Spectrum20160720NoCC.nxs', OutputWorkspace='flux')
MaskBTP(Workspace='sa',Bank="1-30,62-91")
MaskBTP(workspace='sa',Pixel='1-16,200-256') #Mask the magnet
MaskBTP(Workspace='sa',Bank="49",Tube="1")
MaskBTP(Workspace='sa',Bank="54",Tube="1")
MaskBTP(Workspace='sa',Bank="58",Tube="13-16",Pixel="80-130")
MaskBTP(Workspace='sa',Bank="59",Tube="1-4",Pixel="80-130")
# Get UBs
LoadEmptyInstrument(Filename='/SNS/CORELLI/shared/Calibration/CORELLI_Definition_cal_20160310.xml', OutputWorkspace='ub')
LoadIsawUB(InputWorkspace='ub', Filename=UBfile)
ub=mtd['ub'].sample().getOrientedLattice().getUB()
print "Starting UB :"
print ub
#DTO Fd-3m (227) general position has 192 symmety operations.
symOps = SymmetryOperationFactory.createSymOps(\
"x,y,z; -x,-y,z; -x,y,-z; x,-y,-z;\
z,x,y; z,-x,-y; -z,-x,y; -z,x,-y;\
y,z,x; -y,z,-x; y,-z,-x; -y,-z,x;\
y,x,-z; -y,-x,-z; y,-x,z; -y,x,z;\
x,z,-y; -x,z,y; -x,-z,-y; x,-z,y;\
z,y,-x; z,-y,x; -z,y,x; -z,-y,-x;\
-x,-y,-z; x,y,-z; x,-y,z; -x,y,z;\
-z,-x,-y; -z,x,y; z,x,-y; z,-x,y;\
-y,-z,-x; y,-z,x; -y,z,x; y,z,-x;\
-y,-x,z; y,x,z; -y,x,-z; y,-x,-z;\
-x,-z,y; x,-z,-y; x,z,y; -x,z,-y;\
-z,-y,x; -z,y,-x; z,-y,-x; z,y,x")
ub_list=[]
for sym in symOps:
UBtrans = np.zeros((3,3))
UBtrans[0] = sym.transformHKL([1,0,0])
UBtrans[1] = sym.transformHKL([0,1,0])
UBtrans[2] = sym.transformHKL([0,0,1])
UBtrans=np.matrix(UBtrans.T)
new_ub = ub*UBtrans
print "Symmetry transform for "+sym.getIdentifier()
print UBtrans
print "New UB:"
print new_ub
ub_list.append(new_ub)
#load in background
#bkg=LoadEventNexus('/SNS/CORELLI/IPTS-15796/nexus/CORELLI_28124.nxs.h5')
#bkg=LoadNexus('/SNS/CORELLI/IPTS-15796/shared/autoreduce/CORELLI_28124_elastic.nxs')
#MaskDetectors(Workspace=bkg,MaskedWorkspace='sa')
#pc_bkg=sum(bkg.getRun()['proton_charge'].value)
#print 'pc_bkg=:'+str(pc_bkg)
#T=1.8 K
runs = range(34599,34635,1)
#T=100 mK
runs = range(34635,34653,1)
totalrun = len(runs)
print "Total number of runs %d" %totalrun
if mtd.doesExist('normMD'):
DeleteWorkspace('normMD')
if mtd.doesExist('dataMD'):
DeleteWorkspace('dataMD')
#for r in runs:
for index, r in enumerate(runs):
print index, ' Processing run : %s' %r
num=0
print 'Loading run number:'+ str(r)
#filename='/SNS/CORELLI/IPTS-15526/nexus/CORELLI_'+str(r)+'.nxs.h5'
#dataR=LoadEventNexus(Filename=filename)
filename=ccfiledir+'CORELLI_'+str(r)+'_elastic.nxs'
dataR=LoadNexus(Filename=filename)
LoadInstrument(Workspace= dataR, Filename='/SNS/CORELLI/shared/Calibration/CORELLI_Definition_cal_20160310.xml',RewriteSpectraMap=False)
MaskDetectors(Workspace=dataR,MaskedWorkspace='sa')
pc_data=sum(dataR.getRun()['proton_charge'].value)
print 'pc_data=:'+str(pc_data)
#dataR=dataR - bkg*pc_data/pc_bkg
# subtract the background if a background file was provided. Please make sure that the data were treated in the same way in terms of proton charge.
if mtd.doesExist('Bkg'):
bkg = mtd['Bkg']
ratio = pc_data/pc_bkg
bkg_c = bkg*ratio
Minus(LHSWorkspace=dataR, RHSWorkspace=bkg_c, OutputWorkspace=dataR)
dataR=ConvertUnits(dataR,Target="Momentum",EMode="Elastic")
dataR=CropWorkspace(dataR,XMin=2.5,XMax=10)
SetGoniometer(dataR,Axis0="BL9:Mot:Sample:Axis2,0,1,0,1")
LoadIsawUB(InputWorkspace=dataR,Filename=UBfile)
for ub in ub_list:
#for index, ub in enumerate(ub_list):
#print "index, using UB ", (index+1), ":"
num += 1
print "Run number"+str(r)+" Using UB:"+str(num)
print ub
SetUB(dataR, UB=ub)
md=ConvertToMD(InputWorkspace=dataR,QDimensions='Q3D',dEAnalysisMode='Elastic', Q3DFrames='HKL',
QConversionScales='HKL',MinValues='-7.1,-7.1,-7.1',MaxValues='7.1,7.1,7.1')
a1,b1=MDNormSCD(InputWorkspace='md',FluxWorkspace='flux',SolidAngleWorkspace='sa',
AlignedDim0="[H,0,0],-7.01,7.01,701",
AlignedDim1="[0,K,0],-7.01,7.01,701",
AlignedDim2="[0,0,L],-7.01,7.01,701")
if mtd.doesExist('dataMD'):
dataMD=dataMD+a1
else:
dataMD=CloneMDWorkspace(a1)
if mtd.doesExist('normMD'):
normMD=normMD+b1
else:
normMD=CloneMDWorkspace(b1)
normData_CC=dataMD/normMD
SaveMD('dataMD',Filename=outputdir+'DTO_datacc_48sym_Temp100mK_7.nxs')
SaveMD('normMD',Filename=outputdir+'DTO_normcc_48sym_Temp100mK_7.nxs')
SaveMD('normData_CC',Filename=outputdir+'DTO_normdatacc_48sym_Temp100mK_7.nxs')
# group the data
#data6K=GroupWorkspaces(datatoMerge)
#md6K=GroupWorkspaces(mdtoMerge)
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