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timestamp[us]date 2015-08-06 10:31:46
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132
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6640f7ae8b1855e37ca2bb49587ede0bf5f2a525
|
6a41dd36ddd3e501b62ff253b40bf9bbbaa722c2
|
/코딩오답/오답03.py
|
a9f6c42b3f113758b38a0c35737da719f644327e
|
[] |
no_license
|
skysamer/first_python
|
9ba79b194d838e0cdeab6f2e7a4207d71c73ed63
|
638622f51434eda65ef3300e3ce5db3a2a79db2a
|
refs/heads/master
| 2023-02-03T08:21:23.370285 | 2020-12-27T13:39:20 | 2020-12-27T13:39:20 | 307,953,829 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 175 |
py
|
list_of_list=[
[1, 2, 3],
[4, 5, 6, 7],
[8, 9],
]
for line in list_of_list:
for item in line:
print(item)
# 1
# 2
# 3
# 4
# 5
# 6
# 7
# 8
# 9
|
[
"[email protected]"
] | |
5db710d5daf2c06e17c9d537bf382a6aaf8987dc
|
1e7a2a03fef545619945e2dd881d9405c0959b31
|
/labman/gui/handlers/auth.py
|
aae0833e0d7ccc4c86cabf6106b3981e712cc1e2
|
[
"BSD-3-Clause"
] |
permissive
|
nreeve17/labman
|
bca300ee3ce52f96d83a866e5ede455fab70c2b8
|
cc643228f601236bbd7348f8215b00daa3e61358
|
refs/heads/master
| 2021-09-06T18:58:13.890245 | 2018-02-05T22:48:16 | 2018-02-05T22:48:16 | 120,044,962 | 0 | 0 | null | 2018-02-03T00:12:41 | 2018-02-03T00:12:41 | null |
UTF-8
|
Python
| false | false | 1,490 |
py
|
# ----------------------------------------------------------------------------
# Copyright (c) 2017-, labman development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ----------------------------------------------------------------------------
from tornado.escape import json_encode
from labman.gui.handlers.base import BaseHandler
from labman.db.user import User
from labman.db.exceptions import LabmanUnknownIdError, LabmanLoginError
class LoginHandler(BaseHandler):
def get(self):
self.redirect('/')
def post(self):
username = self.get_argument('username', '').strip().lower()
passwd = self.get_argument('password', '')
error_msg = ""
user = None
try:
user = User.login(username, passwd)
except LabmanUnknownIdError:
error_msg = "Unknown user name"
except LabmanLoginError:
error_msg = "Incorrect password"
if user:
self.set_current_user(username)
self.redirect("/")
else:
self.render("index.html", message=error_msg, level='danger')
def set_current_user(self, user):
if user:
self.set_secure_cookie("user", json_encode(user))
else:
self.clear_cookie("user")
class LogoutHandler(BaseHandler):
def get(self):
self.clear_cookie("user")
self.redirect("/")
|
[
"[email protected]"
] | |
a61f8c7141e4d1f4ac5757a1ba565e2c14914c9f
|
c2a03f1cdc338c9078534d8eb2213b214a251e73
|
/Pollapp/views.py
|
356b37b17d72c90fd4396def27f7776d9614820c
|
[] |
no_license
|
risification/onlinde_test
|
9a2db6a945734cc74ee8bc8408ac0ce39fa9d3b3
|
3e1e7e5aca4fa59db08f6394c85ce00652c0871b
|
refs/heads/master
| 2023-03-14T08:24:53.574738 | 2021-03-05T17:22:08 | 2021-03-05T17:22:08 | 344,850,651 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,225 |
py
|
from django.shortcuts import render
from .models import Poll, Question
from .forms import AnswerForm
from django.http import HttpResponse
# Create your views here.
def poll_page(request):
poll = Poll.objects.all()
return render(request, 'Poll/poll.html', {'poll': poll})
def question_page(request, id_poll):
poll = Poll.objects.get(id=id_poll)
question = poll.question_set.all()
return render(request, 'Poll/question.html', {'questions': question})
def choice_answer_page(request, id_question):
question = Question.objects.get(id=id_question)
choice = question.choiceansw_set.all()
form = AnswerForm(initial={'question': question})
if request.method == 'POST':
form = AnswerForm(request.POST)
if form.is_valid():
form.save()
if question.true_answer == form.cleaned_data['answer']:
question.poll.points+= 5
question.poll.save()
return HttpResponse('правильный ответ ')
else:
return HttpResponse('не правильно')
return render(request, 'Poll/choice_answer.html',
{'choices': choice, 'questions': question, 'form': form,})
|
[
"[email protected]"
] | |
6c93ea7887c227aa05f4563d5391ae3dc80941a3
|
0eaab1305900d8e70dd746d676126d1667d9c314
|
/winregrc/collector.py
|
00cd86f31d6a14c554b9132fd0852c63cb4f0d42
|
[
"Apache-2.0"
] |
permissive
|
scudette/winreg-kb
|
89ffc7f63c2630b266bed41d1c66dff64fd1d32d
|
f81b8bcaef8365d0c52bf3c87af2bccb4274bece
|
refs/heads/master
| 2020-06-08T20:51:37.427445 | 2019-06-14T06:47:16 | 2019-06-14T06:47:16 | 193,304,780 | 1 | 0 | null | 2019-06-23T04:07:02 | 2019-06-23T04:07:02 | null |
UTF-8
|
Python
| false | false | 3,913 |
py
|
# -*- coding: utf-8 -*-
"""Windows volume collector."""
from __future__ import unicode_literals
from dfvfs.helpers import volume_scanner as dfvfs_volume_scanner
from dfvfs.lib import definitions as dfvfs_definitions
from dfvfs.path import factory as dfvfs_path_spec_factory
from dfvfs.resolver import resolver as dfvfs_resolver
from dfwinreg import interface as dfwinreg_interface
from dfwinreg import regf as dfwinreg_regf
from dfwinreg import registry as dfwinreg_registry
class CollectorRegistryFileReader(dfwinreg_interface.WinRegistryFileReader):
"""Collector-based Windows Registry file reader."""
def __init__(self, volume_scanner):
"""Initializes a Windows Registry file reader object.
Args:
volume_scanner (dfvfs.WindowsVolumeScanner): Windows volume scanner.
"""
super(CollectorRegistryFileReader, self).__init__()
self._volume_scanner = volume_scanner
def Open(self, path, ascii_codepage='cp1252'):
"""Opens the Windows Registry file specified by the path.
Args:
path (str): path of the Windows Registry file. The path is a Windows
path relative to the root of the file system that contains the
specific Windows Registry file, such as:
C:\\Windows\\System32\\config\\SYSTEM
ascii_codepage (Optional[str]): ASCII string codepage.
Returns:
WinRegistryFile: Windows Registry file or None the file does not exist or
cannot be opened.
"""
file_object = self._volume_scanner.OpenFile(path)
if file_object is None:
return None
registry_file = dfwinreg_regf.REGFWinRegistryFile(
ascii_codepage=ascii_codepage)
try:
registry_file.Open(file_object)
except IOError:
file_object.close()
return None
return registry_file
class WindowsRegistryCollector(dfvfs_volume_scanner.WindowsVolumeScanner):
"""Windows Registry collector.
Attributes:
registry (dfwinreg.WinRegistry): Windows Registry.
"""
def __init__(self, mediator=None):
"""Initializes a Windows Registry collector.
Args:
mediator (Optional[dfvfs.VolumeScannerMediator]): a volume scanner
mediator.
"""
super(WindowsRegistryCollector, self).__init__(mediator=mediator)
self._single_file = False
registry_file_reader = CollectorRegistryFileReader(self)
self.registry = dfwinreg_registry.WinRegistry(
registry_file_reader=registry_file_reader)
def IsSingleFileRegistry(self):
"""Determines if the Registry consists of a single file.
Returns:
bool: True if the Registry consists of a single file.
"""
return self._single_file
def OpenFile(self, windows_path):
"""Opens the file specified by the Windows path.
Args:
windows_path (str): Windows path to the file.
Returns:
dfvfs.FileIO: file-like object or None if the file does not exist.
"""
if not self._single_file:
return super(WindowsRegistryCollector, self).OpenFile(windows_path)
# TODO: check name of single file.
path_spec = dfvfs_path_spec_factory.Factory.NewPathSpec(
dfvfs_definitions.TYPE_INDICATOR_OS, location=self._source_path)
if path_spec is None:
return None
return dfvfs_resolver.Resolver.OpenFileObject(path_spec)
def ScanForWindowsVolume(self, source_path):
"""Scans for a Windows volume.
Args:
source_path (str): source path.
Returns:
bool: True if a Windows volume was found.
Raises:
ScannerError: if the source path does not exists, or if the source path
is not a file or directory, or if the format of or within
the source file is not supported.
"""
result = super(WindowsRegistryCollector, self).ScanForWindowsVolume(
source_path)
if self._source_type == dfvfs_definitions.SOURCE_TYPE_FILE:
self._single_file = True
return True
return result
|
[
"[email protected]"
] | |
a013bfd614f01f8138748b177bd78152abdcc7a3
|
44b6bc41fe8e424196f98dbc5b2f050c1f9645f8
|
/platforms/windows/dos/17072.py
|
40119e550ee1da439adf6922d425147183333a93
|
[] |
no_license
|
angeloobeta/exploit-database
|
21283dd8549f47836a35af6f3ea7b63b8dba11ea
|
43f3d9e94c01a7f51e30561a96214af231dd9d36
|
refs/heads/master
| 2021-08-08T21:07:38.794539 | 2017-11-11T05:01:28 | 2017-11-11T05:01:28 | 110,380,452 | 0 | 1 | null | 2017-11-11T21:09:05 | 2017-11-11T21:09:04 | null |
UTF-8
|
Python
| false | false | 25,785 |
py
|
# done by BraniX <[email protected]>
# www.hackers.org.pl
# found: 2011.03.27
# published: 2011.03.29
# tested on: Windows XP SP3 Home Edition
# tested on: Windows XP SP3 Professional Edition
# App: Windows Explorer 6.0.2900.5512 (Shmedia.dll 6.0.2900.5512)
# App Url: http://www.micro$oft.com
# Shmedia.dll 6.0.2900.5512 MD5: 1b8182c8fe6172727c3cf47415cbad8f
# Explorer 6.0.2900.5512 MD5: c791ed9eac5e76d9525e157b1d7a599a
# DoS is caused by unhandled exception in module Shmedia.dll loaded via Explorer.exe
# 5CF4AC4A 8B4C24 14 MOV ECX,DWORD PTR SS:[ESP+14] ; ECX = 0
# 5CF4AC4E 8B4424 10 MOV EAX,DWORD PTR SS:[ESP+10] ; EAX = 1
# 5CF4AC52 33D2 XOR EDX,EDX ; EDX = 0
# 5CF4AC54 F7F1 DIV ECX ; Integer division by zero
filepath = "C:\\Windows Explorer 6.0.2900.5512 (Shmedia.dll 6.0.2900.5512) DoS PoC.avi"
f = open(filepath, "wb")
poc = 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f.write(poc)
f.close()
print "Done, 1 file generated on 'C:\\' ..."
print "Highlight (select) generated file in Explorer"
print "DoS is triggered when Explorer tries to render AVI file for preview"
|
[
"[email protected]"
] | |
a6e3e7723b1c6314f82564eb711a6401b7bd795b
|
e7af0c1de1185bdda5ff43669ca465e828332581
|
/public/fit.py
|
ffb1a4f76c2b4db8583c2b0b60173fb8995105b8
|
[
"MIT"
] |
permissive
|
KoshikawaShinya/ppwa
|
511f7dbe9818039bafb8cce4b469b3c3f7349423
|
b5278a9775ee12d1621021bebdcae2b271474958
|
refs/heads/master
| 2022-12-09T15:51:52.956493 | 2020-08-19T10:41:03 | 2020-08-19T10:41:03 | 287,700,134 | 0 | 0 | null | 2020-08-15T07:33:45 | 2020-08-15T07:33:44 | null |
UTF-8
|
Python
| false | false | 1,854 |
py
|
import glob
import os
import cv2
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from tensorflow.python import keras as K
img_size = 300
img_num = 2
with open('count.pickle', 'rb') as f:
count = pickle.load(f)
label = 0
datas = []
labels = []
# 画像をテンソル化
for fold_path in glob.glob('../storage/app/public/photo_images/resized/*'):
imgs = glob.glob(fold_path + '/*')
for img_path in imgs:
img = cv2.imread(img_path)
datas.append(img)
labels.append(label)
label += 1
image_datas = np.array(datas)
image_labels = np.array(labels)
x_train, x_test, y_train, y_test = train_test_split(image_datas, image_labels)
x_train = x_train / 255
x_test = x_test / 255
model = K.Sequential([
# K.layers.Conv2D(フィルタの枚数, フィルタサイズ(a,a), インプットの形, 活性化関数)
K.layers.Conv2D(32, kernel_size=(3, 3), strides=1, input_shape=(img_size, img_size, 3), activation="relu"),
K.layers.MaxPooling2D(pool_size=(2,2)),
K.layers.Conv2D(64, (3, 3), strides=1, activation="relu"),
K.layers.MaxPooling2D(pool_size=(2,2)),
K.layers.Conv2D(64, (3, 3), strides=1, activation="relu"),
# 一次元のベクトルに変換
K.layers.Flatten(),
K.layers.Dense(64, activation="relu"),
K.layers.Dense(img_num, activation="softmax")
])
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
model.fit(x_train, y_train, epochs=5)
predicts = model.predict(x_test)
predicts = np.argmax(predicts, axis=1)
print(classification_report(y_test, predicts))
count += 1
model.save('saved_model/PredictFruit_' + str(count) + '.h5')
with open('count.pickle', 'wb') as f:
pickle.dump(count, f)
|
[
"[email protected]"
] | |
56618462619a5fbdf553629f377f72a79e0f0732
|
07ec5a0b3ba5e70a9e0fb65172ea6b13ef4115b8
|
/lib/python3.6/site-packages/pip/_vendor/requests/packages/urllib3/connectionpool.py
|
944e403f7d222fde30692a4be7ee8280576fe50b
|
[] |
no_license
|
cronos91/ML-exercise
|
39c5cd7f94bb90c57450f9a85d40c2f014900ea4
|
3b7afeeb6a7c87384049a9b87cac1fe4c294e415
|
refs/heads/master
| 2021-05-09T22:02:55.131977 | 2017-12-14T13:50:44 | 2017-12-14T13:50:44 | 118,736,043 | 0 | 0 | null | 2018-01-24T08:30:23 | 2018-01-24T08:30:22 | null |
UTF-8
|
Python
| false | false | 130 |
py
|
version https://git-lfs.github.com/spec/v1
oid sha256:7e5b35f67d58e239f0381b19cfff45d358b4f311f68195c4232c8399677e98a5
size 33591
|
[
"[email protected]"
] | |
218858f59480e0196ac56ea89486303734741c50
|
c75ec82316ed5322c5844912ce9c528c24360b9f
|
/nsd1903/python01/day04/randpass.py
|
42d359e1d3b8ae650b80e350b9b857d65d06d6d2
|
[] |
no_license
|
MrZhangzhg/nsd2019
|
a94cde22f2e4bd648bb9e56ca63827f558f3c083
|
54f6d2c7b348a69f13ad5f38f2fbdc8207528749
|
refs/heads/master
| 2021-08-22T17:38:27.697675 | 2020-02-22T08:36:21 | 2020-02-22T08:36:21 | 183,539,489 | 21 | 24 | null | 2020-05-17T12:07:55 | 2019-04-26T02:06:16 |
HTML
|
UTF-8
|
Python
| false | false | 191 |
py
|
from random import choice
all_chs = '1234567890qwertyuiopasdfghjklzxcvbnmQWERTYUIOPASDFGHJKLZXCVBNM'
result = ''
for i in range(8):
ch = choice(all_chs)
result += ch
print(result)
|
[
"[email protected]"
] | |
1838ac019d3d1931bbc379fca3daf7b5a624bd1c
|
5a52ccea88f90dd4f1acc2819997fce0dd5ffb7d
|
/alipay/aop/api/request/AlipayMerchantComplainReconciliationQueryRequest.py
|
9ca5de878179b1a08db1e17407bb49c9a9377f13
|
[
"Apache-2.0"
] |
permissive
|
alipay/alipay-sdk-python-all
|
8bd20882852ffeb70a6e929038bf88ff1d1eff1c
|
1fad300587c9e7e099747305ba9077d4cd7afde9
|
refs/heads/master
| 2023-08-27T21:35:01.778771 | 2023-08-23T07:12:26 | 2023-08-23T07:12:26 | 133,338,689 | 247 | 70 |
Apache-2.0
| 2023-04-25T04:54:02 | 2018-05-14T09:40:54 |
Python
|
UTF-8
|
Python
| false | false | 4,045 |
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.FileItem import FileItem
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.AlipayMerchantComplainReconciliationQueryModel import AlipayMerchantComplainReconciliationQueryModel
class AlipayMerchantComplainReconciliationQueryRequest(object):
def __init__(self, biz_model=None):
self._biz_model = biz_model
self._biz_content = None
self._version = "1.0"
self._terminal_type = None
self._terminal_info = None
self._prod_code = None
self._notify_url = None
self._return_url = None
self._udf_params = None
self._need_encrypt = False
@property
def biz_model(self):
return self._biz_model
@biz_model.setter
def biz_model(self, value):
self._biz_model = value
@property
def biz_content(self):
return self._biz_content
@biz_content.setter
def biz_content(self, value):
if isinstance(value, AlipayMerchantComplainReconciliationQueryModel):
self._biz_content = value
else:
self._biz_content = AlipayMerchantComplainReconciliationQueryModel.from_alipay_dict(value)
@property
def version(self):
return self._version
@version.setter
def version(self, value):
self._version = value
@property
def terminal_type(self):
return self._terminal_type
@terminal_type.setter
def terminal_type(self, value):
self._terminal_type = value
@property
def terminal_info(self):
return self._terminal_info
@terminal_info.setter
def terminal_info(self, value):
self._terminal_info = value
@property
def prod_code(self):
return self._prod_code
@prod_code.setter
def prod_code(self, value):
self._prod_code = value
@property
def notify_url(self):
return self._notify_url
@notify_url.setter
def notify_url(self, value):
self._notify_url = value
@property
def return_url(self):
return self._return_url
@return_url.setter
def return_url(self, value):
self._return_url = value
@property
def udf_params(self):
return self._udf_params
@udf_params.setter
def udf_params(self, value):
if not isinstance(value, dict):
return
self._udf_params = value
@property
def need_encrypt(self):
return self._need_encrypt
@need_encrypt.setter
def need_encrypt(self, value):
self._need_encrypt = value
def add_other_text_param(self, key, value):
if not self.udf_params:
self.udf_params = dict()
self.udf_params[key] = value
def get_params(self):
params = dict()
params[P_METHOD] = 'alipay.merchant.complain.reconciliation.query'
params[P_VERSION] = self.version
if self.biz_model:
params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':'))
if self.biz_content:
if hasattr(self.biz_content, 'to_alipay_dict'):
params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':'))
else:
params['biz_content'] = self.biz_content
if self.terminal_type:
params['terminal_type'] = self.terminal_type
if self.terminal_info:
params['terminal_info'] = self.terminal_info
if self.prod_code:
params['prod_code'] = self.prod_code
if self.notify_url:
params['notify_url'] = self.notify_url
if self.return_url:
params['return_url'] = self.return_url
if self.udf_params:
params.update(self.udf_params)
return params
def get_multipart_params(self):
multipart_params = dict()
return multipart_params
|
[
"[email protected]"
] | |
786edfe6db7cbf98fc8b5771ee273030a79fe00a
|
79e19819aec49b500825f82a7de149eb6a0ba81d
|
/leetcode/27.py
|
2d8bf84dbb95a493a05cbdcf8d0945cb7dd76212
|
[] |
no_license
|
seoyeonhwng/algorithm
|
635e5dc4a2e9e1c50dc0c75d9a2a334110bb8e26
|
90406ee75de69996e666ea505ff5d9045c2ad941
|
refs/heads/master
| 2023-05-03T16:51:48.454619 | 2021-05-26T00:54:40 | 2021-05-26T00:54:40 | 297,548,218 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 389 |
py
|
# 삭제할 원소를 맨 뒤로 몰아넣고, 배열의 크기를 줄인다!
class Solution:
def removeElement(self, nums: List[int], val: int) -> int:
i = 0
n = len(nums)
while i < n:
if nums[i] == val:
nums[i] = nums[n - 1]
n -= 1
else:
i += 1
return n
|
[
"[email protected]"
] | |
9f5a61b76b64485a122ac7bc5c40f97ea8a1f888
|
f4b60f5e49baf60976987946c20a8ebca4880602
|
/lib/python2.7/site-packages/acimodel-1.3_2j-py2.7.egg/cobra/modelimpl/eqptcapacity/polentry1qtr.py
|
9cf4678534d481afb1da77b78f8111526bfa7dcc
|
[] |
no_license
|
cqbomb/qytang_aci
|
12e508d54d9f774b537c33563762e694783d6ba8
|
a7fab9d6cda7fadcc995672e55c0ef7e7187696e
|
refs/heads/master
| 2022-12-21T13:30:05.240231 | 2018-12-04T01:46:53 | 2018-12-04T01:46:53 | 159,911,666 | 0 | 0 | null | 2022-12-07T23:53:02 | 2018-12-01T05:17:50 |
Python
|
UTF-8
|
Python
| false | false | 11,650 |
py
|
# coding=UTF-8
# **********************************************************************
# Copyright (c) 2013-2016 Cisco Systems, Inc. All rights reserved
# written by zen warriors, do not modify!
# **********************************************************************
from cobra.mit.meta import ClassMeta
from cobra.mit.meta import StatsClassMeta
from cobra.mit.meta import CounterMeta
from cobra.mit.meta import PropMeta
from cobra.mit.meta import Category
from cobra.mit.meta import SourceRelationMeta
from cobra.mit.meta import NamedSourceRelationMeta
from cobra.mit.meta import TargetRelationMeta
from cobra.mit.meta import DeploymentPathMeta, DeploymentCategory
from cobra.model.category import MoCategory, PropCategory, CounterCategory
from cobra.mit.mo import Mo
# ##################################################
class PolEntry1qtr(Mo):
"""
A class that represents the most current statistics for Policy entry in a 1 quarter sampling interval. This class updates every day.
"""
meta = StatsClassMeta("cobra.model.eqptcapacity.PolEntry1qtr", "Policy entry")
counter = CounterMeta("normalized", CounterCategory.GAUGE, "percentage", "Policy CAM entries usage")
counter._propRefs[PropCategory.IMPLICIT_LASTREADING] = "normalizedLast"
counter._propRefs[PropCategory.IMPLICIT_MIN] = "normalizedMin"
counter._propRefs[PropCategory.IMPLICIT_MAX] = "normalizedMax"
counter._propRefs[PropCategory.IMPLICIT_AVG] = "normalizedAvg"
counter._propRefs[PropCategory.IMPLICIT_SUSPECT] = "normalizedSpct"
counter._propRefs[PropCategory.IMPLICIT_TOTAL] = "normalizedTtl"
counter._propRefs[PropCategory.IMPLICIT_THRESHOLDED] = "normalizedThr"
counter._propRefs[PropCategory.IMPLICIT_TREND_BASE] = "normalizedTrBase"
counter._propRefs[PropCategory.IMPLICIT_TREND] = "normalizedTr"
meta._counters.append(counter)
meta.moClassName = "eqptcapacityPolEntry1qtr"
meta.rnFormat = "CDeqptcapacityPolEntry1qtr"
meta.category = MoCategory.STATS_CURRENT
meta.label = "current Policy entry stats in 1 quarter"
meta.writeAccessMask = 0x1
meta.readAccessMask = 0x1
meta.isDomainable = False
meta.isReadOnly = True
meta.isConfigurable = False
meta.isDeletable = False
meta.isContextRoot = True
meta.parentClasses.add("cobra.model.eqptcapacity.Entity")
meta.superClasses.add("cobra.model.stats.Item")
meta.superClasses.add("cobra.model.stats.Curr")
meta.superClasses.add("cobra.model.eqptcapacity.PolEntry")
meta.rnPrefixes = [
('CDeqptcapacityPolEntry1qtr', False),
]
prop = PropMeta("str", "childAction", "childAction", 4, PropCategory.CHILD_ACTION)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop._addConstant("deleteAll", "deleteall", 16384)
prop._addConstant("deleteNonPresent", "deletenonpresent", 8192)
prop._addConstant("ignore", "ignore", 4096)
meta.props.add("childAction", prop)
prop = PropMeta("str", "cnt", "cnt", 16212, PropCategory.REGULAR)
prop.label = "Number of Collections During this Interval"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("cnt", prop)
prop = PropMeta("str", "dn", "dn", 1, PropCategory.DN)
prop.label = "None"
prop.isDn = True
prop.isImplicit = True
prop.isAdmin = True
prop.isCreateOnly = True
meta.props.add("dn", prop)
prop = PropMeta("str", "lastCollOffset", "lastCollOffset", 111, PropCategory.REGULAR)
prop.label = "Collection Length"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("lastCollOffset", prop)
prop = PropMeta("str", "modTs", "modTs", 7, PropCategory.REGULAR)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop.defaultValue = 0
prop.defaultValueStr = "never"
prop._addConstant("never", "never", 0)
meta.props.add("modTs", prop)
prop = PropMeta("str", "normalizedAvg", "normalizedAvg", 9095, PropCategory.IMPLICIT_AVG)
prop.label = "Policy CAM entries usage average value"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedAvg", prop)
prop = PropMeta("str", "normalizedLast", "normalizedLast", 9092, PropCategory.IMPLICIT_LASTREADING)
prop.label = "Policy CAM entries usage current value"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedLast", prop)
prop = PropMeta("str", "normalizedMax", "normalizedMax", 9094, PropCategory.IMPLICIT_MAX)
prop.label = "Policy CAM entries usage maximum value"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedMax", prop)
prop = PropMeta("str", "normalizedMin", "normalizedMin", 9093, PropCategory.IMPLICIT_MIN)
prop.label = "Policy CAM entries usage minimum value"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedMin", prop)
prop = PropMeta("str", "normalizedSpct", "normalizedSpct", 9096, PropCategory.IMPLICIT_SUSPECT)
prop.label = "Policy CAM entries usage suspect count"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedSpct", prop)
prop = PropMeta("str", "normalizedThr", "normalizedThr", 9098, PropCategory.IMPLICIT_THRESHOLDED)
prop.label = "Policy CAM entries usage thresholded flags"
prop.isOper = True
prop.isStats = True
prop.defaultValue = 0
prop.defaultValueStr = "unspecified"
prop._addConstant("avgCrit", "avg-severity-critical", 2199023255552)
prop._addConstant("avgHigh", "avg-crossed-high-threshold", 68719476736)
prop._addConstant("avgLow", "avg-crossed-low-threshold", 137438953472)
prop._addConstant("avgMajor", "avg-severity-major", 1099511627776)
prop._addConstant("avgMinor", "avg-severity-minor", 549755813888)
prop._addConstant("avgRecovering", "avg-recovering", 34359738368)
prop._addConstant("avgWarn", "avg-severity-warning", 274877906944)
prop._addConstant("cumulativeCrit", "cumulative-severity-critical", 8192)
prop._addConstant("cumulativeHigh", "cumulative-crossed-high-threshold", 256)
prop._addConstant("cumulativeLow", "cumulative-crossed-low-threshold", 512)
prop._addConstant("cumulativeMajor", "cumulative-severity-major", 4096)
prop._addConstant("cumulativeMinor", "cumulative-severity-minor", 2048)
prop._addConstant("cumulativeRecovering", "cumulative-recovering", 128)
prop._addConstant("cumulativeWarn", "cumulative-severity-warning", 1024)
prop._addConstant("lastReadingCrit", "lastreading-severity-critical", 64)
prop._addConstant("lastReadingHigh", "lastreading-crossed-high-threshold", 2)
prop._addConstant("lastReadingLow", "lastreading-crossed-low-threshold", 4)
prop._addConstant("lastReadingMajor", "lastreading-severity-major", 32)
prop._addConstant("lastReadingMinor", "lastreading-severity-minor", 16)
prop._addConstant("lastReadingRecovering", "lastreading-recovering", 1)
prop._addConstant("lastReadingWarn", "lastreading-severity-warning", 8)
prop._addConstant("maxCrit", "max-severity-critical", 17179869184)
prop._addConstant("maxHigh", "max-crossed-high-threshold", 536870912)
prop._addConstant("maxLow", "max-crossed-low-threshold", 1073741824)
prop._addConstant("maxMajor", "max-severity-major", 8589934592)
prop._addConstant("maxMinor", "max-severity-minor", 4294967296)
prop._addConstant("maxRecovering", "max-recovering", 268435456)
prop._addConstant("maxWarn", "max-severity-warning", 2147483648)
prop._addConstant("minCrit", "min-severity-critical", 134217728)
prop._addConstant("minHigh", "min-crossed-high-threshold", 4194304)
prop._addConstant("minLow", "min-crossed-low-threshold", 8388608)
prop._addConstant("minMajor", "min-severity-major", 67108864)
prop._addConstant("minMinor", "min-severity-minor", 33554432)
prop._addConstant("minRecovering", "min-recovering", 2097152)
prop._addConstant("minWarn", "min-severity-warning", 16777216)
prop._addConstant("periodicCrit", "periodic-severity-critical", 1048576)
prop._addConstant("periodicHigh", "periodic-crossed-high-threshold", 32768)
prop._addConstant("periodicLow", "periodic-crossed-low-threshold", 65536)
prop._addConstant("periodicMajor", "periodic-severity-major", 524288)
prop._addConstant("periodicMinor", "periodic-severity-minor", 262144)
prop._addConstant("periodicRecovering", "periodic-recovering", 16384)
prop._addConstant("periodicWarn", "periodic-severity-warning", 131072)
prop._addConstant("rateCrit", "rate-severity-critical", 36028797018963968)
prop._addConstant("rateHigh", "rate-crossed-high-threshold", 1125899906842624)
prop._addConstant("rateLow", "rate-crossed-low-threshold", 2251799813685248)
prop._addConstant("rateMajor", "rate-severity-major", 18014398509481984)
prop._addConstant("rateMinor", "rate-severity-minor", 9007199254740992)
prop._addConstant("rateRecovering", "rate-recovering", 562949953421312)
prop._addConstant("rateWarn", "rate-severity-warning", 4503599627370496)
prop._addConstant("trendCrit", "trend-severity-critical", 281474976710656)
prop._addConstant("trendHigh", "trend-crossed-high-threshold", 8796093022208)
prop._addConstant("trendLow", "trend-crossed-low-threshold", 17592186044416)
prop._addConstant("trendMajor", "trend-severity-major", 140737488355328)
prop._addConstant("trendMinor", "trend-severity-minor", 70368744177664)
prop._addConstant("trendRecovering", "trend-recovering", 4398046511104)
prop._addConstant("trendWarn", "trend-severity-warning", 35184372088832)
prop._addConstant("unspecified", None, 0)
meta.props.add("normalizedThr", prop)
prop = PropMeta("str", "normalizedTr", "normalizedTr", 9100, PropCategory.IMPLICIT_TREND)
prop.label = "Policy CAM entries usage trend"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedTr", prop)
prop = PropMeta("str", "normalizedTrBase", "normalizedTrBase", 9099, PropCategory.IMPLICIT_TREND_BASE)
prop.label = "Policy CAM entries usage trend baseline"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedTrBase", prop)
prop = PropMeta("str", "normalizedTtl", "normalizedTtl", 9097, PropCategory.IMPLICIT_TOTAL)
prop.label = "Policy CAM entries usage total sum"
prop.isOper = True
prop.isStats = True
meta.props.add("normalizedTtl", prop)
prop = PropMeta("str", "repIntvEnd", "repIntvEnd", 110, PropCategory.REGULAR)
prop.label = "Reporting End Time"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("repIntvEnd", prop)
prop = PropMeta("str", "repIntvStart", "repIntvStart", 109, PropCategory.REGULAR)
prop.label = "Reporting Start Time"
prop.isImplicit = True
prop.isAdmin = True
meta.props.add("repIntvStart", prop)
prop = PropMeta("str", "rn", "rn", 2, PropCategory.RN)
prop.label = "None"
prop.isRn = True
prop.isImplicit = True
prop.isAdmin = True
prop.isCreateOnly = True
meta.props.add("rn", prop)
prop = PropMeta("str", "status", "status", 3, PropCategory.STATUS)
prop.label = "None"
prop.isImplicit = True
prop.isAdmin = True
prop._addConstant("created", "created", 2)
prop._addConstant("deleted", "deleted", 8)
prop._addConstant("modified", "modified", 4)
meta.props.add("status", prop)
def __init__(self, parentMoOrDn, markDirty=True, **creationProps):
namingVals = []
Mo.__init__(self, parentMoOrDn, markDirty, *namingVals, **creationProps)
# End of package file
# ##################################################
|
[
"[email protected]"
] | |
aa6356be04c2d65cffc046602f1a25268f5b78cf
|
9743d5fd24822f79c156ad112229e25adb9ed6f6
|
/xai/brain/wordbase/verbs/_supply.py
|
3f1e672b7dd59f95b8e9b7d5379b7b0f045b0c6b
|
[
"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 | 384 |
py
|
#calss header
class _SUPPLY():
def __init__(self,):
self.name = "SUPPLY"
self.definitions = [u'to provide something that is wanted or needed, often in large quantities and over a long period of time: ']
self.parents = []
self.childen = []
self.properties = []
self.jsondata = {}
self.specie = 'verbs'
def run(self, obj1 = [], obj2 = []):
return self.jsondata
|
[
"[email protected]"
] | |
e99c0e9346727fd5be1ba28f95eca900b459bac4
|
dfb2c26b2657e7f5fc12657452fe8f06b15a5a7e
|
/thread_utils/__init__.py
|
364681f106006ec0946f73ecf5d9eafd9ead7440
|
[] |
no_license
|
sqlitech/PythonUtils
|
3b8d7cd7c71e74670bf2411118e98787e4a4fa2c
|
5eb9d5d651a803e54d4664cdd9d407a596bd3e31
|
refs/heads/master
| 2020-06-25T20:21:48.708676 | 2018-06-27T03:15:13 | 2018-06-27T03:15:13 | 199,413,166 | 0 | 1 | null | 2019-07-29T08:39:54 | 2019-07-29T08:39:54 | null |
UTF-8
|
Python
| false | false | 43 |
py
|
# -*-encoding:utf-8-*-
from async import *
|
[
"[email protected]"
] | |
663771fc38f04f61b54985175f6ebf9fecc4190c
|
35a398d96c8433eeb8d807f155504e072a098a04
|
/hwilib/devices/trezorlib/log.py
|
50f778a12dabeb0f0459e24400dbadbc90bc2acd
|
[
"MIT"
] |
permissive
|
Sjors/HWI
|
a98ea8dcd8655fc65d8a4225e1c0bf09462525d2
|
b3b9f8818d9a851e9a88368f83de77ce504c522c
|
refs/heads/master
| 2021-11-29T04:08:26.538699 | 2021-01-29T03:13:44 | 2021-01-29T03:14:02 | 148,819,675 | 2 | 0 |
MIT
| 2018-09-14T17:15:17 | 2018-09-14T17:15:17 | null |
UTF-8
|
Python
| false | false | 1,799 |
py
|
# This file is part of the Trezor project.
#
# Copyright (C) 2012-2018 SatoshiLabs and contributors
#
# This library is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License version 3
# as published by the Free Software Foundation.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the License along with this library.
# If not, see <https://www.gnu.org/licenses/lgpl-3.0.html>.
import logging
from typing import Optional, Set, Type
from . import protobuf
OMITTED_MESSAGES = set() # type: Set[Type[protobuf.MessageType]]
class PrettyProtobufFormatter(logging.Formatter):
def format(self, record: logging.LogRecord) -> str:
time = self.formatTime(record)
message = "[{time}] {source} {level}: {msg}".format(
time=time,
level=record.levelname.upper(),
source=record.name,
msg=super().format(record),
)
if hasattr(record, "protobuf"):
if type(record.protobuf) in OMITTED_MESSAGES:
message += " ({} bytes)".format(record.protobuf.ByteSize())
else:
message += "\n" + protobuf.format_message(record.protobuf)
return message
def enable_debug_output(handler: Optional[logging.Handler] = None):
if handler is None:
handler = logging.StreamHandler()
formatter = PrettyProtobufFormatter()
handler.setFormatter(formatter)
logger = logging.getLogger("trezorlib")
logger.setLevel(logging.DEBUG)
logger.addHandler(handler)
|
[
"[email protected]"
] | |
6ffb9cb32cac8e80fef5bec34b6dabd6cf1572ea
|
0e1e643e864bcb96cf06f14f4cb559b034e114d0
|
/Exps_7_v3/doc3d/I_w_M_to_Wxyz_focus_Z_ok_to_Cxy_focus/Pyr_wiDiv_ch032/pyr_5s/L7/step10_a.py
|
38fd5b1327ad6d9b6bbc1ac9811b71d71d296c9f
|
[] |
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 | 634,345 |
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_5side_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]
import Exps_7_v3.doc3d.I_w_M_to_W_focus_Zok_div.ch032.wiColorJ.Add2Loss.Sob_k09_s001_Mae_s001_good.pyr_Tcrop255_p20_j15.pyr_5s.L7.step10_a as I_w_M_to_W_p20_pyr
from Exps_7_v3.doc3d.W_w_Mgt_to_Cx_Cy_focus_Z_ok.Sob_k03t13_EroM_Mae_Tv_EroM.pyr_Tcrop255_pad20_jit15.step10_a import L5_ch032_2blk__Mae_s001_Sob_k09_s001 as W_w_M_to_C_p20_2s_L5_Mae_Sob_k09
#############################################################################################################################################################################################################
'''
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_v2
use_loss_obj = [G_mae_s001_loss_info_builder.set_loss_target("UNet_z").copy(), G_mae_s001_loss_info_builder.set_loss_target("UNet_y").copy(), G_mae_s001_loss_info_builder.set_loss_target("UNet_x").copy(), G_mae_s001_loss_info_builder.set_loss_target("UNet_Cx").copy(), G_mae_s001_loss_info_builder.set_loss_target("UNet_Cy").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("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=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")
##################################
### 1side1
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side1 OK 1
ch032_1side_1__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_1__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s1__2s1__3s1__4s1__5s1")
##################################
### 1side2
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side1 OK 1
ch032_1side_2__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_2__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s2__2s1__3s1__4s1__5s1")
# 1 "3" 6 10 15 21 28 36 45 55
# 2side2 OK 4
ch032_1side_2__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_2__2side_2__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s2__2s2__3s1__4s1__5s1")
ch032_1side_2__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_2__2side_2__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s2__2s2__3s2__4s1__5s1")
ch032_1side_2__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_2__2side_2__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s2__2s2__3s2__4s2__5s1")
ch032_1side_2__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_2__2side_2__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s2__2s2__3s2__4s2__5s2")
##################################
### 1side3
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side1 OK 1
ch032_1side_3__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s1__3s1__4s1__5s1")
# 1 "3" 6 10 15 21 28 36 45 55
# 2side2 OK 4
ch032_1side_3__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_2__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s2__3s1__4s1__5s1")
ch032_1side_3__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_2__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s2__3s2__4s1__5s1")
ch032_1side_3__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_2__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s2__3s2__4s2__5s1")
ch032_1side_3__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_2__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s2__3s2__4s2__5s2")
# 1 3 "6" 10 15 21 28 36 45 55
# 2side3 OK 10
ch032_1side_3__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s1__4s1__5s1")
ch032_1side_3__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s2__4s1__5s1")
ch032_1side_3__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s2__4s2__5s1")
ch032_1side_3__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s2__4s2__5s2")
ch032_1side_3__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s3__4s1__5s1")
ch032_1side_3__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s3__4s2__5s1")
ch032_1side_3__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s3__4s2__5s2")
ch032_1side_3__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s3__4s3__5s1")
ch032_1side_3__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s3__4s3__5s2")
ch032_1side_3__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_3__2side_3__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s3__2s3__3s3__4s3__5s3")
##################################
### 1side4
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side1 OK 1
ch032_1side_4__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s1__3s1__4s1__5s1")
# 1 "3" 6 10 15 21 28 36 45 55
# 2side2 OK 4
ch032_1side_4__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_2__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s2__3s1__4s1__5s1")
ch032_1side_4__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_2__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s2__3s2__4s1__5s1")
ch032_1side_4__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_2__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s2__3s2__4s2__5s1")
ch032_1side_4__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_2__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s2__3s2__4s2__5s2")
# 1 3 "6" 10 15 21 28 36 45 55
# 2side3 OK 10
ch032_1side_4__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s1__4s1__5s1")
ch032_1side_4__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s2__4s1__5s1")
ch032_1side_4__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s2__4s2__5s1")
ch032_1side_4__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s2__4s2__5s2")
ch032_1side_4__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s3__4s1__5s1")
ch032_1side_4__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s3__4s2__5s1")
ch032_1side_4__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s3__4s2__5s2")
ch032_1side_4__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s3__4s3__5s1")
ch032_1side_4__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s3__4s3__5s2")
ch032_1side_4__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_3__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s3__3s3__4s3__5s3")
# 1 3 6 "10" 15 21 28 36 45 55
# 2side4 OK 20
ch032_1side_4__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s1__4s1__5s1")
ch032_1side_4__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s2__4s1__5s1")
ch032_1side_4__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s2__4s2__5s1")
ch032_1side_4__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s2__4s2__5s2")
ch032_1side_4__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s3__4s1__5s1")
ch032_1side_4__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s3__4s2__5s1")
ch032_1side_4__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s3__4s2__5s2")
ch032_1side_4__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s3__4s3__5s1")
ch032_1side_4__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s3__4s3__5s2")
ch032_1side_4__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s3__4s3__5s3")
ch032_1side_4__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s1__5s1")
ch032_1side_4__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s2__5s1")
ch032_1side_4__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s2__5s2")
ch032_1side_4__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s3__5s1")
ch032_1side_4__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s3__5s2")
ch032_1side_4__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s3__5s3")
ch032_1side_4__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s4__5s1")
ch032_1side_4__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s4__5s2")
ch032_1side_4__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s4__5s3")
ch032_1side_4__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_4__2side_4__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s4__2s4__3s4__4s4__5s4")
##################################
### 1side5
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side1 OK 1
ch032_1side_5__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s1__3s1__4s1__5s1")
# 1 "3" 6 10 15 21 28 36 45 55
# 2side2 OK 4
ch032_1side_5__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_2__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s2__3s1__4s1__5s1")
ch032_1side_5__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_2__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s2__3s2__4s1__5s1")
ch032_1side_5__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_2__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s2__3s2__4s2__5s1")
ch032_1side_5__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_2__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s2__3s2__4s2__5s2")
# 1 3 "6" 10 15 21 28 36 45 55
# 2side3 OK 10
ch032_1side_5__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s1__4s1__5s1")
ch032_1side_5__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s2__4s1__5s1")
ch032_1side_5__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s2__4s2__5s1")
ch032_1side_5__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s2__4s2__5s2")
ch032_1side_5__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s3__4s1__5s1")
ch032_1side_5__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s3__4s2__5s1")
ch032_1side_5__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s3__4s2__5s2")
ch032_1side_5__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s3__4s3__5s1")
ch032_1side_5__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s3__4s3__5s2")
ch032_1side_5__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_3__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s3__3s3__4s3__5s3")
# 1 3 6 "10" 15 21 28 36 45 55
# 2side4 OK 20
ch032_1side_5__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s1__4s1__5s1")
ch032_1side_5__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s2__4s1__5s1")
ch032_1side_5__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s2__4s2__5s1")
ch032_1side_5__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s2__4s2__5s2")
ch032_1side_5__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s3__4s1__5s1")
ch032_1side_5__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s3__4s2__5s1")
ch032_1side_5__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s3__4s2__5s2")
ch032_1side_5__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s3__4s3__5s1")
ch032_1side_5__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s3__4s3__5s2")
ch032_1side_5__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s3__4s3__5s3")
ch032_1side_5__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s1__5s1")
ch032_1side_5__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s2__5s1")
ch032_1side_5__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s2__5s2")
ch032_1side_5__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s3__5s1")
ch032_1side_5__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s3__5s2")
ch032_1side_5__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s3__5s3")
ch032_1side_5__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s4__5s1")
ch032_1side_5__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s4__5s2")
ch032_1side_5__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s4__5s3")
ch032_1side_5__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_4__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s4__3s4__4s4__5s4")
# 1 3 6 10 "15" 21 28 36 45 55
# 2side5 OK 35
ch032_1side_5__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s1__4s1__5s1")
ch032_1side_5__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s2__4s1__5s1")
ch032_1side_5__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s2__4s2__5s1")
ch032_1side_5__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s2__4s2__5s2")
ch032_1side_5__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s3__4s1__5s1")
ch032_1side_5__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s3__4s2__5s1")
ch032_1side_5__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s3__4s2__5s2")
ch032_1side_5__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s3__4s3__5s1")
ch032_1side_5__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s3__4s3__5s2")
ch032_1side_5__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s3__4s3__5s3")
ch032_1side_5__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s1__5s1")
ch032_1side_5__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s2__5s1")
ch032_1side_5__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s2__5s2")
ch032_1side_5__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s3__5s1")
ch032_1side_5__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s3__5s2")
ch032_1side_5__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s3__5s3")
ch032_1side_5__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s4__5s1")
ch032_1side_5__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s4__5s2")
ch032_1side_5__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s4__5s3")
ch032_1side_5__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s4__4s4__5s4")
ch032_1side_5__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s1__5s1")
ch032_1side_5__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s2__5s1")
ch032_1side_5__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s2__5s2")
ch032_1side_5__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s3__5s1")
ch032_1side_5__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s3__5s2")
ch032_1side_5__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s3__5s3")
ch032_1side_5__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s4__5s1")
ch032_1side_5__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s4__5s2")
ch032_1side_5__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s4__5s3")
ch032_1side_5__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s4__5s4")
ch032_1side_5__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s5__5s1")
ch032_1side_5__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s5__5s2")
ch032_1side_5__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s5__5s3")
ch032_1side_5__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s5__5s4")
ch032_1side_5__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_5__2side_5__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s5__2s5__3s5__4s5__5s5")
##################################
### 5side6
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side1 OK 1
ch032_1side_6__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s1__3s1__4s1__5s1")
# 1 "3" 6 10 15 21 28 36 45 55
# 2side2 OK 4
ch032_1side_6__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_2__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s2__3s1__4s1__5s1")
ch032_1side_6__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_2__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s2__3s2__4s1__5s1")
ch032_1side_6__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_2__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s2__3s2__4s2__5s1")
ch032_1side_6__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_2__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s2__3s2__4s2__5s2")
# 1 3 "6" 10 15 21 28 36 45 55
# 2side3 OK 10
ch032_1side_6__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s1__4s1__5s1")
ch032_1side_6__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s2__4s1__5s1")
ch032_1side_6__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s2__4s2__5s1")
ch032_1side_6__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s2__4s2__5s2")
ch032_1side_6__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s3__4s1__5s1")
ch032_1side_6__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s3__4s2__5s1")
ch032_1side_6__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s3__4s2__5s2")
ch032_1side_6__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s3__4s3__5s1")
ch032_1side_6__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s3__4s3__5s2")
ch032_1side_6__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_3__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s3__3s3__4s3__5s3")
# 1 3 6 "10" 15 21 28 36 45 55
# 2side4 OK 20
ch032_1side_6__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s1__4s1__5s1")
ch032_1side_6__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s2__4s1__5s1")
ch032_1side_6__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s2__4s2__5s1")
ch032_1side_6__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s2__4s2__5s2")
ch032_1side_6__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s3__4s1__5s1")
ch032_1side_6__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s3__4s2__5s1")
ch032_1side_6__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s3__4s2__5s2")
ch032_1side_6__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s3__4s3__5s1")
ch032_1side_6__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s3__4s3__5s2")
ch032_1side_6__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s3__4s3__5s3")
ch032_1side_6__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s1__5s1")
ch032_1side_6__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s2__5s1")
ch032_1side_6__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s2__5s2")
ch032_1side_6__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s3__5s1")
ch032_1side_6__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s3__5s2")
ch032_1side_6__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s3__5s3")
ch032_1side_6__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s4__5s1")
ch032_1side_6__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s4__5s2")
ch032_1side_6__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s4__5s3")
ch032_1side_6__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_4__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s4__3s4__4s4__5s4")
# 1 3 6 10 "15" 21 28 36 45 55
# 2side5 OK 35
ch032_1side_6__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s1__4s1__5s1")
ch032_1side_6__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s2__4s1__5s1")
ch032_1side_6__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s2__4s2__5s1")
ch032_1side_6__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s2__4s2__5s2")
ch032_1side_6__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s3__4s1__5s1")
ch032_1side_6__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s3__4s2__5s1")
ch032_1side_6__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s3__4s2__5s2")
ch032_1side_6__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s3__4s3__5s1")
ch032_1side_6__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s3__4s3__5s2")
ch032_1side_6__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s3__4s3__5s3")
ch032_1side_6__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s1__5s1")
ch032_1side_6__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s2__5s1")
ch032_1side_6__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s2__5s2")
ch032_1side_6__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s3__5s1")
ch032_1side_6__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s3__5s2")
ch032_1side_6__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s3__5s3")
ch032_1side_6__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s4__5s1")
ch032_1side_6__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s4__5s2")
ch032_1side_6__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s4__5s3")
ch032_1side_6__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s4__4s4__5s4")
ch032_1side_6__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s1__5s1")
ch032_1side_6__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s2__5s1")
ch032_1side_6__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s2__5s2")
ch032_1side_6__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s3__5s1")
ch032_1side_6__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s3__5s2")
ch032_1side_6__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s3__5s3")
ch032_1side_6__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s4__5s1")
ch032_1side_6__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s4__5s2")
ch032_1side_6__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s4__5s3")
ch032_1side_6__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s4__5s4")
ch032_1side_6__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s5__5s1")
ch032_1side_6__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s5__5s2")
ch032_1side_6__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s5__5s3")
ch032_1side_6__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s5__5s4")
ch032_1side_6__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_5__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s5__3s5__4s5__5s5")
# 1 3 6 10 15 "21" 28 36 45 55
# 2side6 OK 56
ch032_1side_6__2side_6__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s1__4s1__5s1")
ch032_1side_6__2side_6__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s2__4s1__5s1")
ch032_1side_6__2side_6__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s2__4s2__5s1")
ch032_1side_6__2side_6__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s2__4s2__5s2")
ch032_1side_6__2side_6__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s3__4s1__5s1")
ch032_1side_6__2side_6__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s3__4s2__5s1")
ch032_1side_6__2side_6__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s3__4s2__5s2")
ch032_1side_6__2side_6__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s3__4s3__5s1")
ch032_1side_6__2side_6__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s3__4s3__5s2")
ch032_1side_6__2side_6__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s3__4s3__5s3")
ch032_1side_6__2side_6__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s1__5s1")
ch032_1side_6__2side_6__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s2__5s1")
ch032_1side_6__2side_6__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s2__5s2")
ch032_1side_6__2side_6__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s3__5s1")
ch032_1side_6__2side_6__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s3__5s2")
ch032_1side_6__2side_6__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s3__5s3")
ch032_1side_6__2side_6__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s4__5s1")
ch032_1side_6__2side_6__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s4__5s2")
ch032_1side_6__2side_6__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s4__5s3")
ch032_1side_6__2side_6__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s4__4s4__5s4")
ch032_1side_6__2side_6__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s1__5s1")
ch032_1side_6__2side_6__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s2__5s1")
ch032_1side_6__2side_6__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s2__5s2")
ch032_1side_6__2side_6__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s3__5s1")
ch032_1side_6__2side_6__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s3__5s2")
ch032_1side_6__2side_6__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s3__5s3")
ch032_1side_6__2side_6__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s4__5s1")
ch032_1side_6__2side_6__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s4__5s2")
ch032_1side_6__2side_6__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s4__5s3")
ch032_1side_6__2side_6__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s4__5s4")
ch032_1side_6__2side_6__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s5__5s1")
ch032_1side_6__2side_6__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s5__5s2")
ch032_1side_6__2side_6__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s5__5s3")
ch032_1side_6__2side_6__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s5__5s4")
ch032_1side_6__2side_6__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s5__4s5__5s5")
ch032_1side_6__2side_6__3side_6_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s1__5s1")
ch032_1side_6__2side_6__3side_6_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s2__5s1")
ch032_1side_6__2side_6__3side_6_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s2__5s2")
ch032_1side_6__2side_6__3side_6_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s3__5s1")
ch032_1side_6__2side_6__3side_6_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s3__5s2")
ch032_1side_6__2side_6__3side_6_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s3__5s3")
ch032_1side_6__2side_6__3side_6_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s4__5s1")
ch032_1side_6__2side_6__3side_6_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s4__5s2")
ch032_1side_6__2side_6__3side_6_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s4__5s3")
ch032_1side_6__2side_6__3side_6_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s4__5s4")
ch032_1side_6__2side_6__3side_6_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s5__5s1")
ch032_1side_6__2side_6__3side_6_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s5__5s2")
ch032_1side_6__2side_6__3side_6_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s5__5s3")
ch032_1side_6__2side_6__3side_6_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s5__5s4")
ch032_1side_6__2side_6__3side_6_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s5__5s5")
ch032_1side_6__2side_6__3side_6_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s6__5s1")
ch032_1side_6__2side_6__3side_6_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s6__5s2")
ch032_1side_6__2side_6__3side_6_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s6__5s3")
ch032_1side_6__2side_6__3side_6_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s6__5s4")
ch032_1side_6__2side_6__3side_6_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s6__5s5")
ch032_1side_6__2side_6__3side_6_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_6__2side_6__3side_6_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s6__2s6__3s6__4s6__5s6")
##################################
### 1side7
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side1 OK 1
ch032_1side_7__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s1__3s1__4s1__5s1")
# 1 "3" 6 10 15 21 28 36 45 55
# 2side2 OK 4
ch032_1side_7__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_2__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s2__3s1__4s1__5s1")
ch032_1side_7__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_2__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s2__3s2__4s1__5s1")
ch032_1side_7__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_2__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s2__3s2__4s2__5s1")
ch032_1side_7__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_2__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_2__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s2__3s2__4s2__5s2")
# 1 3 "6" 10 15 21 28 36 45 55
# 2side3 OK 10
ch032_1side_7__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s1__4s1__5s1")
ch032_1side_7__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s2__4s1__5s1")
ch032_1side_7__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s2__4s2__5s1")
ch032_1side_7__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s2__4s2__5s2")
ch032_1side_7__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s3__4s1__5s1")
ch032_1side_7__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s3__4s2__5s1")
ch032_1side_7__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s3__4s2__5s2")
ch032_1side_7__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s3__4s3__5s1")
ch032_1side_7__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s3__4s3__5s2")
ch032_1side_7__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_3__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_3__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s3__3s3__4s3__5s3")
# 1 3 6 "10" 15 21 28 36 45 55
# 2side4 OK 20
ch032_1side_7__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s1__4s1__5s1")
ch032_1side_7__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s2__4s1__5s1")
ch032_1side_7__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s2__4s2__5s1")
ch032_1side_7__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s2__4s2__5s2")
ch032_1side_7__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s3__4s1__5s1")
ch032_1side_7__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s3__4s2__5s1")
ch032_1side_7__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s3__4s2__5s2")
ch032_1side_7__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s3__4s3__5s1")
ch032_1side_7__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s3__4s3__5s2")
ch032_1side_7__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s3__4s3__5s3")
ch032_1side_7__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s1__5s1")
ch032_1side_7__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s2__5s1")
ch032_1side_7__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s2__5s2")
ch032_1side_7__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s3__5s1")
ch032_1side_7__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s3__5s2")
ch032_1side_7__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s3__5s3")
ch032_1side_7__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s4__5s1")
ch032_1side_7__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s4__5s2")
ch032_1side_7__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s4__5s3")
ch032_1side_7__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_4__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_4__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s4__3s4__4s4__5s4")
# 1 3 6 10 "15" 21 28 36 45 55
# 2side5 OK 35
ch032_1side_7__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s1__4s1__5s1")
ch032_1side_7__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s2__4s1__5s1")
ch032_1side_7__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s2__4s2__5s1")
ch032_1side_7__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s2__4s2__5s2")
ch032_1side_7__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s3__4s1__5s1")
ch032_1side_7__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s3__4s2__5s1")
ch032_1side_7__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s3__4s2__5s2")
ch032_1side_7__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s3__4s3__5s1")
ch032_1side_7__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s3__4s3__5s2")
ch032_1side_7__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s3__4s3__5s3")
ch032_1side_7__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s1__5s1")
ch032_1side_7__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s2__5s1")
ch032_1side_7__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s2__5s2")
ch032_1side_7__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s3__5s1")
ch032_1side_7__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s3__5s2")
ch032_1side_7__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s3__5s3")
ch032_1side_7__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s4__5s1")
ch032_1side_7__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s4__5s2")
ch032_1side_7__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s4__5s3")
ch032_1side_7__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s4__4s4__5s4")
ch032_1side_7__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s1__5s1")
ch032_1side_7__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s2__5s1")
ch032_1side_7__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s2__5s2")
ch032_1side_7__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s3__5s1")
ch032_1side_7__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s3__5s2")
ch032_1side_7__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s3__5s3")
ch032_1side_7__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s4__5s1")
ch032_1side_7__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s4__5s2")
ch032_1side_7__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s4__5s3")
ch032_1side_7__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s4__5s4")
ch032_1side_7__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s5__5s1")
ch032_1side_7__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s5__5s2")
ch032_1side_7__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s5__5s3")
ch032_1side_7__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s5__5s4")
ch032_1side_7__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_5__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_5__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s5__3s5__4s5__5s5")
# 1 3 6 10 15 "21" 28 36 45 55
# 2side6 OK 56
ch032_1side_7__2side_6__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s1__4s1__5s1")
ch032_1side_7__2side_6__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s2__4s1__5s1")
ch032_1side_7__2side_6__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s2__4s2__5s1")
ch032_1side_7__2side_6__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s2__4s2__5s2")
ch032_1side_7__2side_6__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s3__4s1__5s1")
ch032_1side_7__2side_6__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s3__4s2__5s1")
ch032_1side_7__2side_6__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s3__4s2__5s2")
ch032_1side_7__2side_6__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s3__4s3__5s1")
ch032_1side_7__2side_6__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s3__4s3__5s2")
ch032_1side_7__2side_6__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s3__4s3__5s3")
ch032_1side_7__2side_6__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s1__5s1")
ch032_1side_7__2side_6__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s2__5s1")
ch032_1side_7__2side_6__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s2__5s2")
ch032_1side_7__2side_6__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s3__5s1")
ch032_1side_7__2side_6__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s3__5s2")
ch032_1side_7__2side_6__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s3__5s3")
ch032_1side_7__2side_6__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s4__5s1")
ch032_1side_7__2side_6__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s4__5s2")
ch032_1side_7__2side_6__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s4__5s3")
ch032_1side_7__2side_6__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s4__4s4__5s4")
ch032_1side_7__2side_6__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s1__5s1")
ch032_1side_7__2side_6__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s2__5s1")
ch032_1side_7__2side_6__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s2__5s2")
ch032_1side_7__2side_6__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s3__5s1")
ch032_1side_7__2side_6__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s3__5s2")
ch032_1side_7__2side_6__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s3__5s3")
ch032_1side_7__2side_6__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s4__5s1")
ch032_1side_7__2side_6__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s4__5s2")
ch032_1side_7__2side_6__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s4__5s3")
ch032_1side_7__2side_6__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s4__5s4")
ch032_1side_7__2side_6__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s5__5s1")
ch032_1side_7__2side_6__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s5__5s2")
ch032_1side_7__2side_6__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s5__5s3")
ch032_1side_7__2side_6__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s5__5s4")
ch032_1side_7__2side_6__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s5__4s5__5s5")
ch032_1side_7__2side_6__3side_6_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s1__5s1")
ch032_1side_7__2side_6__3side_6_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s2__5s1")
ch032_1side_7__2side_6__3side_6_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s2__5s2")
ch032_1side_7__2side_6__3side_6_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s3__5s1")
ch032_1side_7__2side_6__3side_6_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s3__5s2")
ch032_1side_7__2side_6__3side_6_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s3__5s3")
ch032_1side_7__2side_6__3side_6_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s4__5s1")
ch032_1side_7__2side_6__3side_6_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s4__5s2")
ch032_1side_7__2side_6__3side_6_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s4__5s3")
ch032_1side_7__2side_6__3side_6_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s4__5s4")
ch032_1side_7__2side_6__3side_6_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s5__5s1")
ch032_1side_7__2side_6__3side_6_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s5__5s2")
ch032_1side_7__2side_6__3side_6_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s5__5s3")
ch032_1side_7__2side_6__3side_6_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s5__5s4")
ch032_1side_7__2side_6__3side_6_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s5__5s5")
ch032_1side_7__2side_6__3side_6_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s6__5s1")
ch032_1side_7__2side_6__3side_6_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s6__5s2")
ch032_1side_7__2side_6__3side_6_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s6__5s3")
ch032_1side_7__2side_6__3side_6_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s6__5s4")
ch032_1side_7__2side_6__3side_6_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s6__5s5")
ch032_1side_7__2side_6__3side_6_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_6__3side_6_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_6__3side_6_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s6__3s6__4s6__5s6")
# 1 3 6 10 15 21 "28" 36 45 55
# 2side7 OK 84
ch032_1side_7__2side_7__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s1__4s1__5s1")
ch032_1side_7__2side_7__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s2__4s1__5s1")
ch032_1side_7__2side_7__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s2__4s2__5s1")
ch032_1side_7__2side_7__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s2__4s2__5s2")
ch032_1side_7__2side_7__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s3__4s1__5s1")
ch032_1side_7__2side_7__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s3__4s2__5s1")
ch032_1side_7__2side_7__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s3__4s2__5s2")
ch032_1side_7__2side_7__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s3__4s3__5s1")
ch032_1side_7__2side_7__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s3__4s3__5s2")
ch032_1side_7__2side_7__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s3__4s3__5s3")
ch032_1side_7__2side_7__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s1__5s1")
ch032_1side_7__2side_7__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s2__5s1")
ch032_1side_7__2side_7__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s2__5s2")
ch032_1side_7__2side_7__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s3__5s1")
ch032_1side_7__2side_7__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s3__5s2")
ch032_1side_7__2side_7__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s3__5s3")
ch032_1side_7__2side_7__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s4__5s1")
ch032_1side_7__2side_7__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s4__5s2")
ch032_1side_7__2side_7__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s4__5s3")
ch032_1side_7__2side_7__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s4__4s4__5s4")
ch032_1side_7__2side_7__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s1__5s1")
ch032_1side_7__2side_7__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s2__5s1")
ch032_1side_7__2side_7__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s2__5s2")
ch032_1side_7__2side_7__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s3__5s1")
ch032_1side_7__2side_7__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s3__5s2")
ch032_1side_7__2side_7__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s3__5s3")
ch032_1side_7__2side_7__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s4__5s1")
ch032_1side_7__2side_7__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s4__5s2")
ch032_1side_7__2side_7__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s4__5s3")
ch032_1side_7__2side_7__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s4__5s4")
ch032_1side_7__2side_7__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s5__5s1")
ch032_1side_7__2side_7__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s5__5s2")
ch032_1side_7__2side_7__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s5__5s3")
ch032_1side_7__2side_7__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s5__5s4")
ch032_1side_7__2side_7__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s5__4s5__5s5")
ch032_1side_7__2side_7__3side_6_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s1__5s1")
ch032_1side_7__2side_7__3side_6_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s2__5s1")
ch032_1side_7__2side_7__3side_6_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s2__5s2")
ch032_1side_7__2side_7__3side_6_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s3__5s1")
ch032_1side_7__2side_7__3side_6_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s3__5s2")
ch032_1side_7__2side_7__3side_6_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s3__5s3")
ch032_1side_7__2side_7__3side_6_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s4__5s1")
ch032_1side_7__2side_7__3side_6_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s4__5s2")
ch032_1side_7__2side_7__3side_6_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s4__5s3")
ch032_1side_7__2side_7__3side_6_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s4__5s4")
ch032_1side_7__2side_7__3side_6_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s5__5s1")
ch032_1side_7__2side_7__3side_6_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s5__5s2")
ch032_1side_7__2side_7__3side_6_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s5__5s3")
ch032_1side_7__2side_7__3side_6_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s5__5s4")
ch032_1side_7__2side_7__3side_6_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s5__5s5")
ch032_1side_7__2side_7__3side_6_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s6__5s1")
ch032_1side_7__2side_7__3side_6_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s6__5s2")
ch032_1side_7__2side_7__3side_6_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s6__5s3")
ch032_1side_7__2side_7__3side_6_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s6__5s4")
ch032_1side_7__2side_7__3side_6_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s6__5s5")
ch032_1side_7__2side_7__3side_6_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_6_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_6_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s6__4s6__5s6")
ch032_1side_7__2side_7__3side_7_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s1__5s1")
ch032_1side_7__2side_7__3side_7_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s2__5s1")
ch032_1side_7__2side_7__3side_7_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s2__5s2")
ch032_1side_7__2side_7__3side_7_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s3__5s1")
ch032_1side_7__2side_7__3side_7_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s3__5s2")
ch032_1side_7__2side_7__3side_7_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s3__5s3")
ch032_1side_7__2side_7__3side_7_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s4__5s1")
ch032_1side_7__2side_7__3side_7_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s4__5s2")
ch032_1side_7__2side_7__3side_7_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s4__5s3")
ch032_1side_7__2side_7__3side_7_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s4__5s4")
ch032_1side_7__2side_7__3side_7_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s5__5s1")
ch032_1side_7__2side_7__3side_7_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s5__5s2")
ch032_1side_7__2side_7__3side_7_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s5__5s3")
ch032_1side_7__2side_7__3side_7_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s5__5s4")
ch032_1side_7__2side_7__3side_7_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s5__5s5")
ch032_1side_7__2side_7__3side_7_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s6__5s1")
ch032_1side_7__2side_7__3side_7_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s6__5s2")
ch032_1side_7__2side_7__3side_7_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s6__5s3")
ch032_1side_7__2side_7__3side_7_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s6__5s4")
ch032_1side_7__2side_7__3side_7_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s6__5s5")
ch032_1side_7__2side_7__3side_7_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s6__5s6")
ch032_1side_7__2side_7__3side_7_4side_7_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_7_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s7__5s1")
ch032_1side_7__2side_7__3side_7_4side_7_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_7_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s7__5s2")
ch032_1side_7__2side_7__3side_7_4side_7_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_7_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s7__5s3")
ch032_1side_7__2side_7__3side_7_4side_7_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_7_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s7__5s4")
ch032_1side_7__2side_7__3side_7_4side_7_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_7_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s7__5s5")
ch032_1side_7__2side_7__3side_7_4side_7_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_7_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s7__5s6")
ch032_1side_7__2side_7__3side_7_4side_7_5s7 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_7__2side_7__3side_7_4side_7_5s7_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_7__2side_7__3side_7_4side_7_5s7, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s7__2s7__3s7__4s7__5s7")
##################################
### 1side8
##################################
# "1" 3 6 10 15 21 28 36 45 55
# 2side3 OK 1
ch032_1side_8__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_1__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_1__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s1__3s1__4s1__5s1")
# 1 "3" 6 10 15 21 28 36 45 55
# 2side3 OK 4
ch032_1side_8__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_2__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s2__3s1__4s1__5s1")
ch032_1side_8__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_2__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s2__3s2__4s1__5s1")
ch032_1side_8__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_2__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s2__3s2__4s2__5s1")
ch032_1side_8__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_2__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_2__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s2__3s2__4s2__5s2")
# 1 3 "6" 10 15 21 28 36 45 55
# 2side3 OK 10
ch032_1side_8__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s1__4s1__5s1")
ch032_1side_8__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s2__4s1__5s1")
ch032_1side_8__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s2__4s2__5s1")
ch032_1side_8__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s2__4s2__5s2")
ch032_1side_8__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s3__4s1__5s1")
ch032_1side_8__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s3__4s2__5s1")
ch032_1side_8__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s3__4s2__5s2")
ch032_1side_8__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s3__4s3__5s1")
ch032_1side_8__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s3__4s3__5s2")
ch032_1side_8__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_3__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_3__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s3__3s3__4s3__5s3")
# 1 3 6 "10" 15 21 28 36 45 55
# 2side4 OK 20
ch032_1side_8__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s1__4s1__5s1")
ch032_1side_8__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s2__4s1__5s1")
ch032_1side_8__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s2__4s2__5s1")
ch032_1side_8__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s2__4s2__5s2")
ch032_1side_8__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s3__4s1__5s1")
ch032_1side_8__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s3__4s2__5s1")
ch032_1side_8__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s3__4s2__5s2")
ch032_1side_8__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s3__4s3__5s1")
ch032_1side_8__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s3__4s3__5s2")
ch032_1side_8__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s3__4s3__5s3")
ch032_1side_8__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s1__5s1")
ch032_1side_8__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s2__5s1")
ch032_1side_8__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s2__5s2")
ch032_1side_8__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s3__5s1")
ch032_1side_8__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s3__5s2")
ch032_1side_8__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s3__5s3")
ch032_1side_8__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s4__5s1")
ch032_1side_8__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s4__5s2")
ch032_1side_8__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s4__5s3")
ch032_1side_8__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_4__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_4__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s4__3s4__4s4__5s4")
# 1 3 6 10 "15" 21 28 36 45 55
# 2side5 OK 35
ch032_1side_8__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s1__4s1__5s1")
ch032_1side_8__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s2__4s1__5s1")
ch032_1side_8__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s2__4s2__5s1")
ch032_1side_8__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s2__4s2__5s2")
ch032_1side_8__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s3__4s1__5s1")
ch032_1side_8__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s3__4s2__5s1")
ch032_1side_8__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s3__4s2__5s2")
ch032_1side_8__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s3__4s3__5s1")
ch032_1side_8__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s3__4s3__5s2")
ch032_1side_8__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s3__4s3__5s3")
ch032_1side_8__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s1__5s1")
ch032_1side_8__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s2__5s1")
ch032_1side_8__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s2__5s2")
ch032_1side_8__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s3__5s1")
ch032_1side_8__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s3__5s2")
ch032_1side_8__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s3__5s3")
ch032_1side_8__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s4__5s1")
ch032_1side_8__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s4__5s2")
ch032_1side_8__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s4__5s3")
ch032_1side_8__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s4__4s4__5s4")
ch032_1side_8__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s1__5s1")
ch032_1side_8__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s2__5s1")
ch032_1side_8__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s2__5s2")
ch032_1side_8__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s3__5s1")
ch032_1side_8__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s3__5s2")
ch032_1side_8__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s3__5s3")
ch032_1side_8__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s4__5s1")
ch032_1side_8__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s4__5s2")
ch032_1side_8__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s4__5s3")
ch032_1side_8__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s4__5s4")
ch032_1side_8__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s5__5s1")
ch032_1side_8__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s5__5s2")
ch032_1side_8__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s5__5s3")
ch032_1side_8__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s5__5s4")
ch032_1side_8__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_5__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_5__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s5__3s5__4s5__5s5")
# 1 3 6 10 15 "21" 28 36 45 55
# 2side6 OK 56
ch032_1side_8__2side_6__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s1__4s1__5s1")
ch032_1side_8__2side_6__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s2__4s1__5s1")
ch032_1side_8__2side_6__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s2__4s2__5s1")
ch032_1side_8__2side_6__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s2__4s2__5s2")
ch032_1side_8__2side_6__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s3__4s1__5s1")
ch032_1side_8__2side_6__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s3__4s2__5s1")
ch032_1side_8__2side_6__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s3__4s2__5s2")
ch032_1side_8__2side_6__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s3__4s3__5s1")
ch032_1side_8__2side_6__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s3__4s3__5s2")
ch032_1side_8__2side_6__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s3__4s3__5s3")
ch032_1side_8__2side_6__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s1__5s1")
ch032_1side_8__2side_6__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s2__5s1")
ch032_1side_8__2side_6__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s2__5s2")
ch032_1side_8__2side_6__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s3__5s1")
ch032_1side_8__2side_6__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s3__5s2")
ch032_1side_8__2side_6__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s3__5s3")
ch032_1side_8__2side_6__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s4__5s1")
ch032_1side_8__2side_6__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s4__5s2")
ch032_1side_8__2side_6__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s4__5s3")
ch032_1side_8__2side_6__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s4__4s4__5s4")
ch032_1side_8__2side_6__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s1__5s1")
ch032_1side_8__2side_6__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s2__5s1")
ch032_1side_8__2side_6__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s2__5s2")
ch032_1side_8__2side_6__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s3__5s1")
ch032_1side_8__2side_6__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s3__5s2")
ch032_1side_8__2side_6__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s3__5s3")
ch032_1side_8__2side_6__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s4__5s1")
ch032_1side_8__2side_6__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s4__5s2")
ch032_1side_8__2side_6__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s4__5s3")
ch032_1side_8__2side_6__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s4__5s4")
ch032_1side_8__2side_6__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s5__5s1")
ch032_1side_8__2side_6__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s5__5s2")
ch032_1side_8__2side_6__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s5__5s3")
ch032_1side_8__2side_6__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s5__5s4")
ch032_1side_8__2side_6__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s5__4s5__5s5")
ch032_1side_8__2side_6__3side_6_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s1__5s1")
ch032_1side_8__2side_6__3side_6_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s2__5s1")
ch032_1side_8__2side_6__3side_6_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s2__5s2")
ch032_1side_8__2side_6__3side_6_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s3__5s1")
ch032_1side_8__2side_6__3side_6_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s3__5s2")
ch032_1side_8__2side_6__3side_6_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s3__5s3")
ch032_1side_8__2side_6__3side_6_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s4__5s1")
ch032_1side_8__2side_6__3side_6_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s4__5s2")
ch032_1side_8__2side_6__3side_6_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s4__5s3")
ch032_1side_8__2side_6__3side_6_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s4__5s4")
ch032_1side_8__2side_6__3side_6_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s5__5s1")
ch032_1side_8__2side_6__3side_6_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s5__5s2")
ch032_1side_8__2side_6__3side_6_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s5__5s3")
ch032_1side_8__2side_6__3side_6_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s5__5s4")
ch032_1side_8__2side_6__3side_6_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s5__5s5")
ch032_1side_8__2side_6__3side_6_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s6__5s1")
ch032_1side_8__2side_6__3side_6_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s6__5s2")
ch032_1side_8__2side_6__3side_6_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s6__5s3")
ch032_1side_8__2side_6__3side_6_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s6__5s4")
ch032_1side_8__2side_6__3side_6_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s6__5s5")
ch032_1side_8__2side_6__3side_6_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_6__3side_6_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_6__3side_6_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s6__3s6__4s6__5s6")
# 1 3 6 10 15 21 "28" 36 45 55
# 2side7 OK 84
ch032_1side_8__2side_7__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s1__4s1__5s1")
ch032_1side_8__2side_7__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s2__4s1__5s1")
ch032_1side_8__2side_7__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s2__4s2__5s1")
ch032_1side_8__2side_7__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s2__4s2__5s2")
ch032_1side_8__2side_7__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s3__4s1__5s1")
ch032_1side_8__2side_7__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s3__4s2__5s1")
ch032_1side_8__2side_7__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s3__4s2__5s2")
ch032_1side_8__2side_7__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s3__4s3__5s1")
ch032_1side_8__2side_7__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s3__4s3__5s2")
ch032_1side_8__2side_7__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s3__4s3__5s3")
ch032_1side_8__2side_7__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s1__5s1")
ch032_1side_8__2side_7__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s2__5s1")
ch032_1side_8__2side_7__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s2__5s2")
ch032_1side_8__2side_7__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s3__5s1")
ch032_1side_8__2side_7__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s3__5s2")
ch032_1side_8__2side_7__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s3__5s3")
ch032_1side_8__2side_7__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s4__5s1")
ch032_1side_8__2side_7__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s4__5s2")
ch032_1side_8__2side_7__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s4__5s3")
ch032_1side_8__2side_7__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s4__4s4__5s4")
ch032_1side_8__2side_7__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s1__5s1")
ch032_1side_8__2side_7__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s2__5s1")
ch032_1side_8__2side_7__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s2__5s2")
ch032_1side_8__2side_7__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s3__5s1")
ch032_1side_8__2side_7__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s3__5s2")
ch032_1side_8__2side_7__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s3__5s3")
ch032_1side_8__2side_7__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s4__5s1")
ch032_1side_8__2side_7__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s4__5s2")
ch032_1side_8__2side_7__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s4__5s3")
ch032_1side_8__2side_7__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s4__5s4")
ch032_1side_8__2side_7__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s5__5s1")
ch032_1side_8__2side_7__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s5__5s2")
ch032_1side_8__2side_7__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s5__5s3")
ch032_1side_8__2side_7__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s5__5s4")
ch032_1side_8__2side_7__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s5__4s5__5s5")
ch032_1side_8__2side_7__3side_6_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s1__5s1")
ch032_1side_8__2side_7__3side_6_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s2__5s1")
ch032_1side_8__2side_7__3side_6_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s2__5s2")
ch032_1side_8__2side_7__3side_6_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s3__5s1")
ch032_1side_8__2side_7__3side_6_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s3__5s2")
ch032_1side_8__2side_7__3side_6_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s3__5s3")
ch032_1side_8__2side_7__3side_6_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s4__5s1")
ch032_1side_8__2side_7__3side_6_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s4__5s2")
ch032_1side_8__2side_7__3side_6_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s4__5s3")
ch032_1side_8__2side_7__3side_6_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s4__5s4")
ch032_1side_8__2side_7__3side_6_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s5__5s1")
ch032_1side_8__2side_7__3side_6_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s5__5s2")
ch032_1side_8__2side_7__3side_6_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s5__5s3")
ch032_1side_8__2side_7__3side_6_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s5__5s4")
ch032_1side_8__2side_7__3side_6_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s5__5s5")
ch032_1side_8__2side_7__3side_6_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s6__5s1")
ch032_1side_8__2side_7__3side_6_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s6__5s2")
ch032_1side_8__2side_7__3side_6_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s6__5s3")
ch032_1side_8__2side_7__3side_6_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s6__5s4")
ch032_1side_8__2side_7__3side_6_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s6__5s5")
ch032_1side_8__2side_7__3side_6_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_6_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_6_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s6__4s6__5s6")
ch032_1side_8__2side_7__3side_7_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s1__5s1")
ch032_1side_8__2side_7__3side_7_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s2__5s1")
ch032_1side_8__2side_7__3side_7_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s2__5s2")
ch032_1side_8__2side_7__3side_7_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s3__5s1")
ch032_1side_8__2side_7__3side_7_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s3__5s2")
ch032_1side_8__2side_7__3side_7_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s3__5s3")
ch032_1side_8__2side_7__3side_7_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s4__5s1")
ch032_1side_8__2side_7__3side_7_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s4__5s2")
ch032_1side_8__2side_7__3side_7_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s4__5s3")
ch032_1side_8__2side_7__3side_7_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s4__5s4")
ch032_1side_8__2side_7__3side_7_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s5__5s1")
ch032_1side_8__2side_7__3side_7_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s5__5s2")
ch032_1side_8__2side_7__3side_7_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s5__5s3")
ch032_1side_8__2side_7__3side_7_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s5__5s4")
ch032_1side_8__2side_7__3side_7_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s5__5s5")
ch032_1side_8__2side_7__3side_7_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s6__5s1")
ch032_1side_8__2side_7__3side_7_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s6__5s2")
ch032_1side_8__2side_7__3side_7_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s6__5s3")
ch032_1side_8__2side_7__3side_7_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s6__5s4")
ch032_1side_8__2side_7__3side_7_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s6__5s5")
ch032_1side_8__2side_7__3side_7_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s6__5s6")
ch032_1side_8__2side_7__3side_7_4side_7_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_7_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s7__5s1")
ch032_1side_8__2side_7__3side_7_4side_7_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_7_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s7__5s2")
ch032_1side_8__2side_7__3side_7_4side_7_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_7_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s7__5s3")
ch032_1side_8__2side_7__3side_7_4side_7_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_7_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s7__5s4")
ch032_1side_8__2side_7__3side_7_4side_7_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_7_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s7__5s5")
ch032_1side_8__2side_7__3side_7_4side_7_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_7_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s7__5s6")
ch032_1side_8__2side_7__3side_7_4side_7_5s7 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_7__3side_7_4side_7_5s7_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_7__3side_7_4side_7_5s7, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s7__3s7__4s7__5s7")
# 1 3 6 10 15 21 28 "36" 45 55
# 2side8 OK 120
ch032_1side_8__2side_8__3side_1_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_1_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_1_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s1__4s1__5s1")
ch032_1side_8__2side_8__3side_2_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_2_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s2__4s1__5s1")
ch032_1side_8__2side_8__3side_2_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_2_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s2__4s2__5s1")
ch032_1side_8__2side_8__3side_2_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_2_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_2_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s2__4s2__5s2")
ch032_1side_8__2side_8__3side_3_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_3_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s3__4s1__5s1")
ch032_1side_8__2side_8__3side_3_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_3_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s3__4s2__5s1")
ch032_1side_8__2side_8__3side_3_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_3_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s3__4s2__5s2")
ch032_1side_8__2side_8__3side_3_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_3_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s3__4s3__5s1")
ch032_1side_8__2side_8__3side_3_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_3_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s3__4s3__5s2")
ch032_1side_8__2side_8__3side_3_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_3_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_3_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s3__4s3__5s3")
ch032_1side_8__2side_8__3side_4_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s1__5s1")
ch032_1side_8__2side_8__3side_4_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s2__5s1")
ch032_1side_8__2side_8__3side_4_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s2__5s2")
ch032_1side_8__2side_8__3side_4_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s3__5s1")
ch032_1side_8__2side_8__3side_4_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s3__5s2")
ch032_1side_8__2side_8__3side_4_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s3__5s3")
ch032_1side_8__2side_8__3side_4_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s4__5s1")
ch032_1side_8__2side_8__3side_4_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s4__5s2")
ch032_1side_8__2side_8__3side_4_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s4__5s3")
ch032_1side_8__2side_8__3side_4_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_4_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_4_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s4__4s4__5s4")
ch032_1side_8__2side_8__3side_5_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s1__5s1")
ch032_1side_8__2side_8__3side_5_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s2__5s1")
ch032_1side_8__2side_8__3side_5_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s2__5s2")
ch032_1side_8__2side_8__3side_5_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s3__5s1")
ch032_1side_8__2side_8__3side_5_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s3__5s2")
ch032_1side_8__2side_8__3side_5_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s3__5s3")
ch032_1side_8__2side_8__3side_5_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s4__5s1")
ch032_1side_8__2side_8__3side_5_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s4__5s2")
ch032_1side_8__2side_8__3side_5_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s4__5s3")
ch032_1side_8__2side_8__3side_5_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s4__5s4")
ch032_1side_8__2side_8__3side_5_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s5__5s1")
ch032_1side_8__2side_8__3side_5_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s5__5s2")
ch032_1side_8__2side_8__3side_5_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s5__5s3")
ch032_1side_8__2side_8__3side_5_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s5__5s4")
ch032_1side_8__2side_8__3side_5_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_5_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_5_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s5__4s5__5s5")
ch032_1side_8__2side_8__3side_6_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s1__5s1")
ch032_1side_8__2side_8__3side_6_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s2__5s1")
ch032_1side_8__2side_8__3side_6_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s2__5s2")
ch032_1side_8__2side_8__3side_6_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s3__5s1")
ch032_1side_8__2side_8__3side_6_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s3__5s2")
ch032_1side_8__2side_8__3side_6_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s3__5s3")
ch032_1side_8__2side_8__3side_6_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s4__5s1")
ch032_1side_8__2side_8__3side_6_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s4__5s2")
ch032_1side_8__2side_8__3side_6_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s4__5s3")
ch032_1side_8__2side_8__3side_6_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s4__5s4")
ch032_1side_8__2side_8__3side_6_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s5__5s1")
ch032_1side_8__2side_8__3side_6_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s5__5s2")
ch032_1side_8__2side_8__3side_6_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s5__5s3")
ch032_1side_8__2side_8__3side_6_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s5__5s4")
ch032_1side_8__2side_8__3side_6_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s5__5s5")
ch032_1side_8__2side_8__3side_6_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s6__5s1")
ch032_1side_8__2side_8__3side_6_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s6__5s2")
ch032_1side_8__2side_8__3side_6_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s6__5s3")
ch032_1side_8__2side_8__3side_6_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s6__5s4")
ch032_1side_8__2side_8__3side_6_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s6__5s5")
ch032_1side_8__2side_8__3side_6_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_6_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_6_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s6__4s6__5s6")
ch032_1side_8__2side_8__3side_7_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s1__5s1")
ch032_1side_8__2side_8__3side_7_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s2__5s1")
ch032_1side_8__2side_8__3side_7_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s2__5s2")
ch032_1side_8__2side_8__3side_7_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s3__5s1")
ch032_1side_8__2side_8__3side_7_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s3__5s2")
ch032_1side_8__2side_8__3side_7_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s3__5s3")
ch032_1side_8__2side_8__3side_7_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s4__5s1")
ch032_1side_8__2side_8__3side_7_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s4__5s2")
ch032_1side_8__2side_8__3side_7_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s4__5s3")
ch032_1side_8__2side_8__3side_7_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s4__5s4")
ch032_1side_8__2side_8__3side_7_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s5__5s1")
ch032_1side_8__2side_8__3side_7_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s5__5s2")
ch032_1side_8__2side_8__3side_7_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s5__5s3")
ch032_1side_8__2side_8__3side_7_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s5__5s4")
ch032_1side_8__2side_8__3side_7_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s5__5s5")
ch032_1side_8__2side_8__3side_7_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s6__5s1")
ch032_1side_8__2side_8__3side_7_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s6__5s2")
ch032_1side_8__2side_8__3side_7_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s6__5s3")
ch032_1side_8__2side_8__3side_7_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s6__5s4")
ch032_1side_8__2side_8__3side_7_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s6__5s5")
ch032_1side_8__2side_8__3side_7_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s6__5s6")
ch032_1side_8__2side_8__3side_7_4side_7_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_7_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s7__5s1")
ch032_1side_8__2side_8__3side_7_4side_7_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_7_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s7__5s2")
ch032_1side_8__2side_8__3side_7_4side_7_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_7_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s7__5s3")
ch032_1side_8__2side_8__3side_7_4side_7_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_7_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s7__5s4")
ch032_1side_8__2side_8__3side_7_4side_7_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_7_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s7__5s5")
ch032_1side_8__2side_8__3side_7_4side_7_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_7_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s7__5s6")
ch032_1side_8__2side_8__3side_7_4side_7_5s7 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_7_4side_7_5s7_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_7_4side_7_5s7, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s7__4s7__5s7")
ch032_1side_8__2side_8__3side_8_4side_1_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_1_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_1_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s1__5s1")
ch032_1side_8__2side_8__3side_8_4side_2_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_2_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s2__5s1")
ch032_1side_8__2side_8__3side_8_4side_2_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_2_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_2_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s2__5s2")
ch032_1side_8__2side_8__3side_8_4side_3_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_3_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s3__5s1")
ch032_1side_8__2side_8__3side_8_4side_3_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_3_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s3__5s2")
ch032_1side_8__2side_8__3side_8_4side_3_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_3_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_3_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s3__5s3")
ch032_1side_8__2side_8__3side_8_4side_4_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_4_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s4__5s1")
ch032_1side_8__2side_8__3side_8_4side_4_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_4_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s4__5s2")
ch032_1side_8__2side_8__3side_8_4side_4_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_4_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s4__5s3")
ch032_1side_8__2side_8__3side_8_4side_4_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_4_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_4_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s4__5s4")
ch032_1side_8__2side_8__3side_8_4side_5_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_5_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s5__5s1")
ch032_1side_8__2side_8__3side_8_4side_5_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_5_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s5__5s2")
ch032_1side_8__2side_8__3side_8_4side_5_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_5_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s5__5s3")
ch032_1side_8__2side_8__3side_8_4side_5_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_5_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s5__5s4")
ch032_1side_8__2side_8__3side_8_4side_5_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_5_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_5_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s5__5s5")
ch032_1side_8__2side_8__3side_8_4side_6_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_6_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s6__5s1")
ch032_1side_8__2side_8__3side_8_4side_6_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_6_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s6__5s2")
ch032_1side_8__2side_8__3side_8_4side_6_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_6_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s6__5s3")
ch032_1side_8__2side_8__3side_8_4side_6_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_6_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s6__5s4")
ch032_1side_8__2side_8__3side_8_4side_6_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_6_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s6__5s5")
ch032_1side_8__2side_8__3side_8_4side_6_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_6_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_6_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s6__5s6")
ch032_1side_8__2side_8__3side_8_4side_7_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_7_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s7__5s1")
ch032_1side_8__2side_8__3side_8_4side_7_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_7_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s7__5s2")
ch032_1side_8__2side_8__3side_8_4side_7_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_7_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s7__5s3")
ch032_1side_8__2side_8__3side_8_4side_7_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_7_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s7__5s4")
ch032_1side_8__2side_8__3side_8_4side_7_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_7_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s7__5s5")
ch032_1side_8__2side_8__3side_8_4side_7_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_7_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s7__5s6")
ch032_1side_8__2side_8__3side_8_4side_7_5s7 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_7_5s7_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_7_5s7, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s7__5s7")
ch032_1side_8__2side_8__3side_8_4side_8_5s1 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s1_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s1, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s1")
ch032_1side_8__2side_8__3side_8_4side_8_5s2 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s2_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s2, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s2")
ch032_1side_8__2side_8__3side_8_4side_8_5s3 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s3_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s3, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s3")
ch032_1side_8__2side_8__3side_8_4side_8_5s4 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s4_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s4, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s4")
ch032_1side_8__2side_8__3side_8_4side_8_5s5 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s5_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s5, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s5")
ch032_1side_8__2side_8__3side_8_4side_8_5s6 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s6_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s6, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s6")
ch032_1side_8__2side_8__3side_8_4side_8_5s7 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s7_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s7, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s7")
ch032_1side_8__2side_8__3side_8_4side_8_5s8 = Exp_builder().set_basic("test_Kong_Crop_p60_gt_DewarpNet_p60_then_Use_KModel5_FBA", use_db_obj, ch032_pyramid_1side_8__2side_8__3side_8_4side_8_5s8_and_1s6_2s6, 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.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_multi_model_reload_exp_builders_dict(I_to_Wx_Wy_Wz=I_w_M_to_W_p20_pyr.ch032_1side_8__2side_8__3side_8_4side_8_5s8, W_to_Cx_Cy=W_w_M_to_C_p20_2s_L5_Mae_Sob_k09).set_result_name(result_name="p20_L5-ch032_1s8__2s8__3s8__4s8__5s8")
#############################################################
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.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])
|
[
"[email protected]"
] | |
ed9f248c4a3a90143c4c4a9fa970e44a0d7b69c9
|
a4ea525e226d6c401fdb87a6e9adfdc5d07e6020
|
/src/azure-cli/azure/cli/command_modules/network/azure_stack/profile_2019_03_01_hybrid/operations/_util.py
|
7f0f515e93c51f2802407cbe7f176fe90553fb6c
|
[
"MIT",
"BSD-3-Clause",
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MPL-2.0",
"LGPL-2.1-only",
"Apache-2.0",
"LGPL-2.1-or-later",
"BSD-2-Clause"
] |
permissive
|
Azure/azure-cli
|
13340eeca2e288e66e84d393fa1c8a93d46c8686
|
a40fd14ad0b6e89720a2e58d4d9be3a6ce1535ca
|
refs/heads/dev
| 2023-08-17T06:25:37.431463 | 2023-08-17T06:00:10 | 2023-08-17T06:00:10 | 51,040,886 | 4,018 | 3,310 |
MIT
| 2023-09-14T11:11:05 | 2016-02-04T00:21:51 |
Python
|
UTF-8
|
Python
| false | false | 592 |
py
|
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
import importlib
def import_aaz_by_profile(module_name):
# use aaz in 2018-03-01-hybrid profile, because apis are the some.
return importlib.import_module(f"azure.cli.command_modules.network.aaz.profile_2018_03_01_hybrid.{module_name}")
|
[
"[email protected]"
] | |
cedc36ff24c7551d29d2d758e7ec05c63bdeb54f
|
860c31e414c4c280b70ec0872042d715a2d56978
|
/torch_ecg/augmenters/random_flip.py
|
8e568a5e53279bd331a03438f99ca8d76c73c9f6
|
[
"MIT"
] |
permissive
|
DeepPSP/torch_ecg
|
255e49ff436e13044a1f049141f982680e56970e
|
a40c65f4fefa83ba7d3d184072a4c05627b7e226
|
refs/heads/master
| 2023-09-01T06:47:17.153216 | 2023-08-31T18:00:47 | 2023-08-31T18:00:47 | 298,482,237 | 111 | 16 |
MIT
| 2023-08-21T11:25:07 | 2020-09-25T06:03:17 |
Python
|
UTF-8
|
Python
| false | false | 3,705 |
py
|
"""
"""
from numbers import Real
from typing import Any, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch import Tensor
from .base import Augmenter
__all__ = [
"RandomFlip",
]
class RandomFlip(Augmenter):
"""Randomly flip the ECGs along the voltage axis.
Parameters
----------
fs : int, optional
Sampling frequency of the ECGs to be augmented
per_channel : bool, default True
Whether to flip each channel independently.
prob : float or Sequence[float], default ``[0.4, 0.2]``
Probability of performing flip,
the first probality is for the batch dimension,
the second probability is for the lead dimension.
inplace : bool, default True
If True, ECG signal tensors will be modified inplace.
kwargs : dict, optional
Additional keyword arguments.
Examples
--------
.. code-block:: python
rf = RandomFlip()
sig = torch.randn(32, 12, 5000)
sig, _ = rf(sig, None)
"""
__name__ = "RandomFlip"
def __init__(
self,
fs: Optional[int] = None,
per_channel: bool = True,
prob: Union[Sequence[float], float] = [0.4, 0.2],
inplace: bool = True,
**kwargs: Any
) -> None:
super().__init__()
self.fs = fs
self.per_channel = per_channel
self.inplace = inplace
self.prob = prob
if isinstance(self.prob, Real):
self.prob = np.array([self.prob, self.prob])
else:
self.prob = np.array(self.prob)
assert (self.prob >= 0).all() and (
self.prob <= 1
).all(), "Probability must be between 0 and 1"
def forward(
self,
sig: Tensor,
label: Optional[Tensor],
*extra_tensors: Sequence[Tensor],
**kwargs: Any
) -> Tuple[Tensor, ...]:
"""Forward function of the RandomFlip augmenter.
Parameters
----------
sig : torch.Tensor
The ECGs to be augmented, of shape ``(batch, lead, siglen)``.
label : torch.Tensor, optional
Label tensor of the ECGs.
Not used, but kept for consistency with other augmenters.
extra_tensors : Sequence[torch.Tensor], optional
Not used, but kept for consistency with other augmenters.
kwargs : dict, optional
Additional keyword arguments.
Not used, but kept for consistency with other augmenters.
Returns
-------
sig : torch.Tensor
The augmented ECGs.
label : torch.Tensor
The label tensor of the augmented ECGs, unchanged.
extra_tensors : Sequence[torch.Tensor], optional
Unchanged extra tensors.
"""
batch, lead, siglen = sig.shape
if not self.inplace:
sig = sig.clone()
if self.prob[0] == 0:
return (sig, label, *extra_tensors)
if self.per_channel:
flip = torch.ones((batch, lead, 1), dtype=sig.dtype, device=sig.device)
for i in self.get_indices(prob=self.prob[0], pop_size=batch):
flip[i, self.get_indices(prob=self.prob[1], pop_size=lead), ...] = -1
sig = sig.mul_(flip)
else:
flip = torch.ones((batch, 1, 1), dtype=sig.dtype, device=sig.device)
flip[self.get_indices(prob=self.prob[0], pop_size=batch), ...] = -1
sig = sig.mul_(flip)
return (sig, label, *extra_tensors)
def extra_repr_keys(self) -> List[str]:
return [
"per_channel",
"prob",
"inplace",
] + super().extra_repr_keys()
|
[
"[email protected]"
] | |
4fdb87404cde51d55e157b2a585ba2b14eeeef11
|
9c58a1f594e18cee20128f2c8dad8257429b10d1
|
/returns_management/models/amz_return_message.py
|
efebf7e1850f51ccbec5bee4b9d2b212c023cdc5
|
[] |
no_license
|
gastonfeng/Odoo-eBay-Amazon
|
e8919768b2a1500209f209ee3aecc7f2fb10cda7
|
a9c4a8a7548b19027bc0fd904f8ae9249248a293
|
refs/heads/master
| 2022-04-05T00:23:50.483430 | 2020-02-19T04:58:56 | 2020-02-19T04:58:56 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 13,401 |
py
|
# -*- coding: utf-8 -*-
from datetime import datetime
from odoo import models, fields, api
from odoo.tools import DEFAULT_SERVER_DATE_FORMAT
import logging
import locale
import re
_logger = logging.getLogger(__name__)
class AmzReturnMessage(models.Model):
_name = 'sale.store.amz.return.message'
_inherit = ['mail.thread']
_order = "id"
name = fields.Char('Subject', required=True)
store_id = fields.Many2one('sale.store', 'Store')
raw_content = fields.Text('Raw Content')
return_id = fields.Many2one('sale.return', string='Return')
@api.model
def message_new(self, msg_dict, custom_values=None):
self = self.with_context(default_user_id=False)
defaults = {
'name': msg_dict.get('subject') or "No Subject"
}
# _logger.info('Amazon message: %s' % msg_dict)
try:
store_email = msg_dict['to'].split('<')[1][:-1]
except Exception as e:
_logger.error(e)
return super(AmzReturnMessage, self).message_new(msg_dict, custom_values=defaults)
store_id = self.env['sale.store'].search([('amz_returns_email', '=', store_email)])
if store_id:
defaults['store_id'] = store_id.id
return super(AmzReturnMessage, self).message_new(msg_dict, custom_values=defaults)
@api.multi
def process_message(self):
_logger.info('Processing Amazon return message. %s' % self)
if self.name.startswith('Return authorization notification'):
if self.message_ids:
content = self.message_ids[0].body.replace('<br>', '\n').split('\n')
# Just for testing purpose
if len(content) < 5:
test_content = self.message_ids[0].body.replace('<br style="font-size: 12.8px">', '').replace('</span>', '')
content = test_content.split('<span style="font-size: 12.8px">')
if len(content) < 5:
test_content = self.message_ids[0].body.replace('<br style="font-size:12.8px">', '').replace('</span>', '')
content = test_content.split('<span style="font-size:12.8px">')
values = {'lines': []}
searchstrings = [
['web_order_id', 'Order ID: #'],
['item', 'Item:'],
['product_uom_qty', 'Quantity:'],
['return_reason', 'Return reason:'],
['customer_comments', 'Customer comments:'],
['carrier_id', 'Return Shipping Carrier:'],
['tracking_number', 'Tracking ID:'],
['amz_return_request_details', 'Return request details:'],
['request_date', 'Request received:']
]
for ind, line in enumerate(content):
for searchstring in searchstrings:
if line.strip().startswith(searchstring[1]):
val = line[len(searchstring[1]):].strip()
if searchstring[0] == "item":
values['lines'].append({'item': val})
elif searchstring[0] == "product_uom_qty":
values['lines'][-1]['product_uom_qty'] = float(val)
elif searchstring[0] == "return_reason":
values['lines'][-1]['return_reason'] = val
elif searchstring[0] == "customer_comments":
values['lines'][-1]['customer_comments'] = val
elif searchstring[0] == "request_date":
values['request_date'] = datetime.strptime(val.strip(), '%B %d, %Y').strftime(DEFAULT_SERVER_DATE_FORMAT)
elif searchstring[0] == "carrier_id":
values['carrier_id'] = line[len(searchstring[1]) + 2:].strip()
elif searchstring[0].strip() == "amz_return_request_details":
if re.findall("<a.*>(.*?)</a>", content[ind + 1]):
values['amz_return_request_details'] = re.findall("<a.*>(.*?)</a>", content[ind + 1])[0]
elif re.findall("http.*", content[ind + 1]):
values['amz_return_request_details'] = re.findall("http.*", content[ind + 1])[0]
else:
values[searchstring[0]] = line[len(searchstring[1]):].strip()
break
if 'carrier_id' in values and values['carrier_id']:
carrier_id = self.env['ship.carrier'].search([('name', '=', values['carrier_id'])])
if carrier_id:
values['carrier_id'] = carrier_id.id
else:
values.pop('carrier_id')
values['amz_related_message_id'] = self.id
values['store_id'] = self.store_id.id
lines = values['lines']
values.pop('lines')
if values.get('web_order_id'):
sale_order_id = self.env['sale.order'].search([('web_order_id', '=', values['web_order_id']), ('state', '!=', 'cancel')], limit=1)
if not sale_order_id:
_logger.error('Cant find order %s %s', values['web_order_id'], self)
if len(sale_order_id.order_line) == len(lines) and len(lines) == 1:
values['partner_id'] = sale_order_id.partner_id.id
values['sale_order_id'] = sale_order_id.id
values['type'] = 'amazon'
old_return = self.env['sale.return'].search([('web_order_id', '=', values['web_order_id'])])
if old_return:
self.proceed_old_return(old_return, lines)
else:
self.proceed_new_return(sale_order_id, values, lines)
else:
_logger.error('No web_order_id: %s' % self)
@api.multi
def create_return(self):
locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')
_logger.info('Manual creation of return using Amazon return message. %s' % self)
if self.message_ids and self.store_id:
body = self.message_ids[0].body
content = body.replace('<br>', '\n').split('\n')
values = {'lines': []}
searchstrings = [
['web_order_id', 'Order ID: # '],
['item', 'Item: '],
['product_uom_qty', 'Quantity: '],
['return_reason', 'Return reason: '],
['customer_comments', 'Customer comments: '],
['carrier_id', 'Return Shipping Carrier:'],
['tracking_number', 'Tracking ID: '],
['amz_return_request_details', 'Return request details:'],
['request_date', 'Request received: ']
]
for ind, line in enumerate(content):
for searchstring in searchstrings:
if line.startswith(searchstring[1]):
val = line[len(searchstring[1]):].replace('\n', '').strip()
if searchstring[0] == "item":
values['lines'].append({'item': val})
elif searchstring[0] == "product_uom_qty":
values['lines'][-1]['product_uom_qty'] = float(val)
elif searchstring[0] == "return_reason":
values['lines'][-1]['return_reason'] = val
elif searchstring[0] == "customer_comments":
values['lines'][-1]['customer_comments'] = val
elif searchstring[0] == "request_date":
values['request_date'] = datetime.strptime(val, '%B %d, %Y').strftime(DEFAULT_SERVER_DATE_FORMAT)
elif searchstring[0] == "carrier_id":
values['carrier_id'] = line[len(searchstring[1]) + 2:].strip()
elif searchstring[0].strip() == "amz_return_request_details":
if re.findall("<a.*>(.*?)</a>", content[ind + 1]):
values['amz_return_request_details'] = re.findall("<a.*>(.*?)</a>", content[ind + 1])[0]
elif re.findall("http.*", content[ind + 1]):
values['amz_return_request_details'] = re.findall("http.*", content[ind + 1])[0]
else:
values[searchstring[0]] = line[len(searchstring[1]):].strip()
break
if 'carrier_id' in values and values['carrier_id']:
carrier_id = self.env['ship.carrier'].search([('name', '=', values['carrier_id'])])
if carrier_id:
values['carrier_id'] = carrier_id.id
else:
values.pop('carrier_id')
values['amz_related_message_id'] = self.id
values['store_id'] = self.store_id.id
lines = values['lines']
values.pop('lines')
if values.get('web_order_id'):
sale_order_id = self.env['sale.order'].search([('web_order_id', '=', values['web_order_id']), ('state', '!=', 'cancel')], limit=1)
if not sale_order_id:
_logger.error('Cant find order %s %s', values['web_order_id'], self)
# if len(sale_order_id.order_line) == len(lines) and len(lines) == 1:
values['partner_id'] = sale_order_id.partner_id.id
values['sale_order_id'] = sale_order_id.id
values['type'] = 'amazon'
old_return = self.env['sale.return'].search([('web_order_id', '=', values['web_order_id'])])
if old_return:
self.proceed_old_return(old_return, lines, values)
else:
self.proceed_new_return(sale_order_id, values, lines)
else:
_logger.error('No web_order_id: %s' % self)
@api.model
def proceed_old_return(self, old_return, lines, values):
self.return_id = old_return.id
old_return.return_reason = lines[0].get('return_reason')
old_return.customer_comments = lines[0].get('customer_comments')
old_return.amz_return_request_details = values.get('amz_return_request_details')
old_return.amz_related_message_id = self.id
@api.model
def proceed_new_return(self, sale_order_id, values, lines):
return_id = self.env['sale.return'].create(values)
self.return_id = return_id.id
ok = False
if len(sale_order_id.order_line) == len(lines) and len(lines) == 1: # If single line just pick it up
values['partner_id'] = sale_order_id.partner_id.id
values['sale_order_id'] = sale_order_id.id
values['type'] = 'amazon'
return_id = self.env['sale.return'].create(values)
self.return_id = return_id.id
lines[0]['name'] = sale_order_id.order_line[0].product_id.name
lines[0]['return_id'] = return_id.id
lines[0]['product_id'] = sale_order_id.order_line[0].product_id.id
lines[0]['sale_line_id'] = sale_order_id.order_line[0].id
lines[0]['product_uom'] = sale_order_id.order_line[0].product_uom.id
self.env['sale.return.line'].create(lines[0])
else: # Few lines. Lets try to match using partslink
for index, ln in enumerate(lines):
for order_line in sale_order_id.order_line:
if order_line.product_id.partslink in ln['item']:
ok = True
ln['name'] = order_line.name
ln['return_id'] = return_id.id
ln['product_id'] = order_line.product_id.id
ln['sale_line_id'] = order_line.id
ln['product_uom'] = order_line.product_uom.id
self.env['sale.return.line'].create(ln)
if not ok:
msg = {
'name': 'Achtung ! Cant match return item',
'type': 'ir.actions.act_window',
'view_type': 'form',
'view_mode': 'form',
'res_model': 'custom.message',
'target': 'new',
'context': {'default_text': 'Return is created, but please check lines manually cuz I cant match it with message text.'}
}
return msg
msg = 'Amazon return created using return message. Order:%s Return:%s' % (values['web_order_id'], return_id.name)
_logger.info(msg)
attachment = {
'color': '#7CD197',
'fallback': 'Amazon return',
'title': 'Amazon return. No:%s' % return_id.name,
'text': msg
}
slack_returns_channel = self.env['ir.config_parameter'].get_param('slack_returns_channel')
self.env['slack.calls'].notify_slack('Amazon return', '', slack_returns_channel, attachment)
|
[
"[email protected]"
] | |
02477477bced69dfa14b2f606809e5fa1938461d
|
f9d564f1aa83eca45872dab7fbaa26dd48210d08
|
/huaweicloud-sdk-apig/huaweicloudsdkapig/v2/model/update_backend_instances_v2_request.py
|
474669c5d89f50b1eda068f6274602635a9fff48
|
[
"Apache-2.0"
] |
permissive
|
huaweicloud/huaweicloud-sdk-python-v3
|
cde6d849ce5b1de05ac5ebfd6153f27803837d84
|
f69344c1dadb79067746ddf9bfde4bddc18d5ecf
|
refs/heads/master
| 2023-09-01T19:29:43.013318 | 2023-08-31T08:28:59 | 2023-08-31T08:28:59 | 262,207,814 | 103 | 44 |
NOASSERTION
| 2023-06-22T14:50:48 | 2020-05-08T02:28:43 |
Python
|
UTF-8
|
Python
| false | false | 5,109 |
py
|
# coding: utf-8
import six
from huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization
class UpdateBackendInstancesV2Request:
"""
Attributes:
openapi_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.
"""
sensitive_list = []
openapi_types = {
'instance_id': 'str',
'vpc_channel_id': 'str',
'body': 'VpcMemberModify'
}
attribute_map = {
'instance_id': 'instance_id',
'vpc_channel_id': 'vpc_channel_id',
'body': 'body'
}
def __init__(self, instance_id=None, vpc_channel_id=None, body=None):
"""UpdateBackendInstancesV2Request
The model defined in huaweicloud sdk
:param instance_id: 实例ID，在API网关控制台的“实例信息”中获取。
:type instance_id: str
:param vpc_channel_id: VPC通道的编号
:type vpc_channel_id: str
:param body: Body of the UpdateBackendInstancesV2Request
:type body: :class:`huaweicloudsdkapig.v2.VpcMemberModify`
"""
self._instance_id = None
self._vpc_channel_id = None
self._body = None
self.discriminator = None
self.instance_id = instance_id
self.vpc_channel_id = vpc_channel_id
if body is not None:
self.body = body
@property
def instance_id(self):
"""Gets the instance_id of this UpdateBackendInstancesV2Request.
实例ID，在API网关控制台的“实例信息”中获取。
:return: The instance_id of this UpdateBackendInstancesV2Request.
:rtype: str
"""
return self._instance_id
@instance_id.setter
def instance_id(self, instance_id):
"""Sets the instance_id of this UpdateBackendInstancesV2Request.
实例ID，在API网关控制台的“实例信息”中获取。
:param instance_id: The instance_id of this UpdateBackendInstancesV2Request.
:type instance_id: str
"""
self._instance_id = instance_id
@property
def vpc_channel_id(self):
"""Gets the vpc_channel_id of this UpdateBackendInstancesV2Request.
VPC通道的编号
:return: The vpc_channel_id of this UpdateBackendInstancesV2Request.
:rtype: str
"""
return self._vpc_channel_id
@vpc_channel_id.setter
def vpc_channel_id(self, vpc_channel_id):
"""Sets the vpc_channel_id of this UpdateBackendInstancesV2Request.
VPC通道的编号
:param vpc_channel_id: The vpc_channel_id of this UpdateBackendInstancesV2Request.
:type vpc_channel_id: str
"""
self._vpc_channel_id = vpc_channel_id
@property
def body(self):
"""Gets the body of this UpdateBackendInstancesV2Request.
:return: The body of this UpdateBackendInstancesV2Request.
:rtype: :class:`huaweicloudsdkapig.v2.VpcMemberModify`
"""
return self._body
@body.setter
def body(self, body):
"""Sets the body of this UpdateBackendInstancesV2Request.
:param body: The body of this UpdateBackendInstancesV2Request.
:type body: :class:`huaweicloudsdkapig.v2.VpcMemberModify`
"""
self._body = body
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.openapi_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:
if attr in self.sensitive_list:
result[attr] = "****"
else:
result[attr] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
import simplejson as json
if six.PY2:
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
return json.dumps(sanitize_for_serialization(self), ensure_ascii=False)
def __repr__(self):
"""For `print`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, UpdateBackendInstancesV2Request):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
|
[
"[email protected]"
] | |
53cf32460a454ad7d0d66d9f77cabc86af34ee10
|
f576f0ea3725d54bd2551883901b25b863fe6688
|
/sdk/sql/azure-mgmt-sql/azure/mgmt/sql/aio/operations/_sql_vulnerability_assessment_baselines_operations.py
|
e3200f1df96e49a046add9c8f03244b987031569
|
[
"MIT",
"LicenseRef-scancode-generic-cla",
"LGPL-2.1-or-later"
] |
permissive
|
Azure/azure-sdk-for-python
|
02e3838e53a33d8ba27e9bcc22bd84e790e4ca7c
|
c2ca191e736bb06bfbbbc9493e8325763ba990bb
|
refs/heads/main
| 2023-09-06T09:30:13.135012 | 2023-09-06T01:08:06 | 2023-09-06T01:08:06 | 4,127,088 | 4,046 | 2,755 |
MIT
| 2023-09-14T21:48:49 | 2012-04-24T16:46:12 |
Python
|
UTF-8
|
Python
| false | false | 11,794 |
py
|
# pylint: disable=too-many-lines
# 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 io import IOBase
import sys
from typing import Any, Callable, Dict, IO, Optional, TypeVar, Union, overload
from azure.core.exceptions import (
ClientAuthenticationError,
HttpResponseError,
ResourceExistsError,
ResourceNotFoundError,
ResourceNotModifiedError,
map_error,
)
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import AsyncHttpResponse
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator_async import distributed_trace_async
from azure.core.utils import case_insensitive_dict
from azure.mgmt.core.exceptions import ARMErrorFormat
from ... import models as _models
from ..._vendor import _convert_request
from ...operations._sql_vulnerability_assessment_baselines_operations import build_create_or_update_request
if sys.version_info >= (3, 8):
from typing import Literal # pylint: disable=no-name-in-module, ungrouped-imports
else:
from typing_extensions import Literal # type: ignore # pylint: disable=ungrouped-imports
T = TypeVar("T")
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class SqlVulnerabilityAssessmentBaselinesOperations:
"""
.. warning::
**DO NOT** instantiate this class directly.
Instead, you should access the following operations through
:class:`~azure.mgmt.sql.aio.SqlManagementClient`'s
:attr:`sql_vulnerability_assessment_baselines` attribute.
"""
models = _models
def __init__(self, *args, **kwargs) -> None:
input_args = list(args)
self._client = input_args.pop(0) if input_args else kwargs.pop("client")
self._config = input_args.pop(0) if input_args else kwargs.pop("config")
self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
@overload
async def create_or_update(
self,
resource_group_name: str,
server_name: str,
vulnerability_assessment_name: Union[str, _models.VulnerabilityAssessmentName],
baseline_name: Union[str, _models.BaselineName],
parameters: _models.DatabaseSqlVulnerabilityAssessmentRuleBaselineListInput,
*,
content_type: str = "application/json",
**kwargs: Any
) -> _models.DatabaseSqlVulnerabilityAssessmentBaselineSet:
"""Add a database's vulnerability assessment rule baseline list.
:param resource_group_name: The name of the resource group that contains the resource. You can
obtain this value from the Azure Resource Manager API or the portal. Required.
:type resource_group_name: str
:param server_name: The name of the server. Required.
:type server_name: str
:param vulnerability_assessment_name: The name of the vulnerability assessment. "default"
Required.
:type vulnerability_assessment_name: str or ~azure.mgmt.sql.models.VulnerabilityAssessmentName
:param baseline_name: "default" Required.
:type baseline_name: str or ~azure.mgmt.sql.models.BaselineName
:param parameters: The requested rule baseline resource. Required.
:type parameters:
~azure.mgmt.sql.models.DatabaseSqlVulnerabilityAssessmentRuleBaselineListInput
:keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
Default value is "application/json".
:paramtype content_type: str
:keyword system_database_name: The vulnerability assessment system database name. Default value
is "master". Note that overriding this default value may result in unsupported behavior.
:paramtype system_database_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: DatabaseSqlVulnerabilityAssessmentBaselineSet or the result of cls(response)
:rtype: ~azure.mgmt.sql.models.DatabaseSqlVulnerabilityAssessmentBaselineSet
:raises ~azure.core.exceptions.HttpResponseError:
"""
@overload
async def create_or_update(
self,
resource_group_name: str,
server_name: str,
vulnerability_assessment_name: Union[str, _models.VulnerabilityAssessmentName],
baseline_name: Union[str, _models.BaselineName],
parameters: IO,
*,
content_type: str = "application/json",
**kwargs: Any
) -> _models.DatabaseSqlVulnerabilityAssessmentBaselineSet:
"""Add a database's vulnerability assessment rule baseline list.
:param resource_group_name: The name of the resource group that contains the resource. You can
obtain this value from the Azure Resource Manager API or the portal. Required.
:type resource_group_name: str
:param server_name: The name of the server. Required.
:type server_name: str
:param vulnerability_assessment_name: The name of the vulnerability assessment. "default"
Required.
:type vulnerability_assessment_name: str or ~azure.mgmt.sql.models.VulnerabilityAssessmentName
:param baseline_name: "default" Required.
:type baseline_name: str or ~azure.mgmt.sql.models.BaselineName
:param parameters: The requested rule baseline resource. Required.
:type parameters: IO
:keyword content_type: Body Parameter content-type. Content type parameter for binary body.
Default value is "application/json".
:paramtype content_type: str
:keyword system_database_name: The vulnerability assessment system database name. Default value
is "master". Note that overriding this default value may result in unsupported behavior.
:paramtype system_database_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: DatabaseSqlVulnerabilityAssessmentBaselineSet or the result of cls(response)
:rtype: ~azure.mgmt.sql.models.DatabaseSqlVulnerabilityAssessmentBaselineSet
:raises ~azure.core.exceptions.HttpResponseError:
"""
@distributed_trace_async
async def create_or_update(
self,
resource_group_name: str,
server_name: str,
vulnerability_assessment_name: Union[str, _models.VulnerabilityAssessmentName],
baseline_name: Union[str, _models.BaselineName],
parameters: Union[_models.DatabaseSqlVulnerabilityAssessmentRuleBaselineListInput, IO],
**kwargs: Any
) -> _models.DatabaseSqlVulnerabilityAssessmentBaselineSet:
"""Add a database's vulnerability assessment rule baseline list.
:param resource_group_name: The name of the resource group that contains the resource. You can
obtain this value from the Azure Resource Manager API or the portal. Required.
:type resource_group_name: str
:param server_name: The name of the server. Required.
:type server_name: str
:param vulnerability_assessment_name: The name of the vulnerability assessment. "default"
Required.
:type vulnerability_assessment_name: str or ~azure.mgmt.sql.models.VulnerabilityAssessmentName
:param baseline_name: "default" Required.
:type baseline_name: str or ~azure.mgmt.sql.models.BaselineName
:param parameters: The requested rule baseline resource. Is either a
DatabaseSqlVulnerabilityAssessmentRuleBaselineListInput type or a IO type. Required.
:type parameters:
~azure.mgmt.sql.models.DatabaseSqlVulnerabilityAssessmentRuleBaselineListInput or IO
:keyword system_database_name: The vulnerability assessment system database name. Default value
is "master". Note that overriding this default value may result in unsupported behavior.
:paramtype system_database_name: str
:keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
Default value is None.
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: DatabaseSqlVulnerabilityAssessmentBaselineSet or the result of cls(response)
:rtype: ~azure.mgmt.sql.models.DatabaseSqlVulnerabilityAssessmentBaselineSet
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
system_database_name: Literal["master"] = kwargs.pop(
"system_database_name", _params.pop("systemDatabaseName", "master")
)
api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2022-11-01-preview"))
content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
cls: ClsType[_models.DatabaseSqlVulnerabilityAssessmentBaselineSet] = kwargs.pop("cls", None)
content_type = content_type or "application/json"
_json = None
_content = None
if isinstance(parameters, (IOBase, bytes)):
_content = parameters
else:
_json = self._serialize.body(parameters, "DatabaseSqlVulnerabilityAssessmentRuleBaselineListInput")
request = build_create_or_update_request(
resource_group_name=resource_group_name,
server_name=server_name,
vulnerability_assessment_name=vulnerability_assessment_name,
baseline_name=baseline_name,
subscription_id=self._config.subscription_id,
system_database_name=system_database_name,
api_version=api_version,
content_type=content_type,
json=_json,
content=_content,
template_url=self.create_or_update.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url)
_stream = False
pipeline_response: PipelineResponse = await self._client._pipeline.run( # pylint: disable=protected-access
request, stream=_stream, **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)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize("DatabaseSqlVulnerabilityAssessmentBaselineSet", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_or_update.metadata = {
"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Sql/servers/{serverName}/sqlVulnerabilityAssessments/{vulnerabilityAssessmentName}/baselines/{baselineName}"
}
|
[
"[email protected]"
] | |
a6dbd80f6affd77ffea6e3c619cbf570c687d395
|
c64bb34a3dde14d3c9bf813bde414a7b3f10611d
|
/ommat_addons/manufacturing_change_two/models/models.py
|
54d7e25e2870844b7639a07f30f6e9c82cc8b2d4
|
[] |
no_license
|
sm2x/my_work
|
ebf2e1abd06191ee59b0d82a23534274a81a3195
|
efc469aee4cd20b038d48d4c09f8257f3f04ba1c
|
refs/heads/master
| 2021-01-07T20:41:45.254025 | 2020-02-12T16:02:46 | 2020-02-12T16:02:46 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 28,505 |
py
|
# -*- coding: utf-8 -*-
from dateutil.relativedelta import relativedelta
from odoo import models, fields, api, _
from odoo.exceptions import ValidationError
class new_fields_mrp_bom(models.Model):
_inherit = 'mrp.bom'
pro = fields.Boolean(related=False, string='Production')
lab = fields.Boolean(related=False, string='Lab')
farming = fields.Boolean(default=False, string='Farming')
cleaning = fields.Boolean(default=False, string='Cleaning')
@api.multi
def action_mrp_production(self):
product = self.product_tmpl_id
if self.product_tmpl_id.product_variant_ids:
for p in self.product_tmpl_id.product_variant_ids:
product = p
mrp_obj = self.env['mrp.production']
mo = mrp_obj.create({
'product_id': product.id,
'product_qty': self.product_qty,
'product_uom_id': product.uom_id.id,
'bom_id': self.id,
})
return {
'type': 'ir.actions.act_window',
'name': _('Manufacturing Orders'),
'view_mode': 'form',
'view_type': 'form',
'res_model': 'mrp.production',
'res_id': mo.id,
'target': 'current',
}
class new_fields_mrp_production(models.Model):
_inherit = 'mrp.production'
pro = fields.Boolean(related=False, string='Production')
lab = fields.Boolean(related=False, string='Lab')
dynasty = fields.Many2one('dynasty.model', related=False, string='Dynasty')
gender = fields.Selection([('female', 'Female'),
('male', 'Male')], related=False, copy=False)
farming = fields.Boolean(default=False, string='Farming')
cleaning = fields.Boolean(default=False, string='Cleaning')
type_l_b = fields.Selection([('land', 'Land'), ('bat', 'Battery')], copy=False, default='land', string="Loc Type 1")
# week_no = fields.Integer('Week No', compute=False, store=True)
@api.onchange('product_id')
def onchange_product(self):
if self.product_id and self.availability == 'waiting':
self.dynasty = self.product_id.dynasty.id
@api.onchange('bom_id')
def onchange_bom(self):
if self.bom_id and self.availability not in ('assigned', 'partially_available'):
self.pro = self.bom_id.pro
self.lab = self.bom_id.lab
self.farming = self.bom_id.farming
self.cleaning = self.bom_id.cleaning
self.gender = self.bom_id.gender
@api.model
def create(self, val):
rec = super(new_fields_mrp_production, self).create(val)
if rec.bom_id.lab or rec.bom_id.farming or rec.bom_id.pro:
if rec.product_id:
if rec.product_id.dynasty:
rec.dynasty = rec.product_id.dynasty.id
catalogue = rec.env['ommat.catalogue'].search([('dynasty', '=', rec.dynasty.id),
('state', '=', 'in_progress')])
if rec.type_l_b == 'land':
if catalogue and catalogue.land_week_ids:
if rec.date_planned_start:
p_date = (rec.date_planned_start).date()
for line in catalogue.land_week_ids:
print(line.l_code)
if line.l_date_to and line.l_date_from:
date_f = (line.l_date_from).date()
date_t = (line.l_date_to).date()
if date_f <= p_date <= date_t:
rec.week_no = line.l_code
if rec.week_no == 0:
raise ValidationError(_('please update catalogue dates'))
else:
raise ValidationError(_('please update catalogue dates'))
elif rec.type_l_b == 'bat':
if catalogue and catalogue.bat_week_ids:
if rec.date_planned_start:
p_date = (rec.date_planned_start).date()
for line in catalogue.bat_week_ids:
print(line.b_code)
if line.b_date_to and line.b_date_from:
date_f = (line.b_date_from).date()
date_t = (line.b_date_to).date()
if date_f <= p_date <= date_t:
rec.week_no = line.b_code
if rec.week_no == 0:
raise ValidationError(_('please update catalogue dates'))
else:
raise ValidationError(_('please update catalogue dates'))
else:
raise ValidationError(_('please set dynasty for product'))
return rec
@api.onchange('location_src_id')
def onchange_location_src_id(self):
if self.location_src_id and self.availability not in ('assigned', 'partially_available'):
for line in self.move_raw_ids:
line.write({'location_id': self.location_src_id.id})
if self.location_src_id.type_l_b:
self.type_l_b = self.location_src_id.type_l_b
def action_assign(self):
rec = super(new_fields_mrp_production, self).action_assign()
if self.availability != 'waiting':
self.pro = self.bom_id.pro
self.lab = self.bom_id.lab
self.farming = self.bom_id.farming
self.cleaning = self.bom_id.cleaning
self.gender = self.bom_id.gender
self.dynasty = self.product_id.dynasty.id
self.type_l_b = self.location_src_id.type_l_b
return rec
@api.one
@api.depends('date_planned_start')
def get_week_no(self):
if self.product_id:
if self.product_id.dynasty:
self.dynasty = self.product_id.dynasty.id
catalogue = self.env['ommat.catalogue'].search([('dynasty', '=', self.dynasty.id),
('state', '=', 'in_progress')])
if self.type_l_b == 'land':
if catalogue and catalogue.land_week_ids:
if self.date_planned_start:
p_date = (self.date_planned_start).date()
for line in catalogue.land_week_ids:
print(line.l_code)
if line.l_date_to and line.l_date_from:
date_f = (line.l_date_from).date()
date_t = (line.l_date_to).date()
if date_f <= p_date <= date_t:
self.week_no = line.l_code
if self.week_no == 0:
raise ValidationError(_('please update catalogue dates'))
else:
raise ValidationError(_('please update catalogue dates'))
elif self.type_l_b == 'bat':
if catalogue and catalogue.bat_week_ids:
if self.date_planned_start:
p_date = (self.date_planned_start).date()
for line in catalogue.bat_week_ids:
print(line.b_code)
if line.b_date_to and line.b_date_from:
date_f = (line.b_date_from).date()
date_t = (line.b_date_to).date()
if date_f <= p_date <= date_t:
self.week_no = line.b_code
if self.week_no == 0:
raise ValidationError(_('please update catalogue dates'))
else:
raise ValidationError(_('please update catalogue dates'))
else:
raise ValidationError(_('please set dynasty for produced product'))
# class change_ommat_catalogue(models.Model):
# _inherit = 'ommat.catalogue'
#
# date_to_first_week = fields.Date('إلى اولاسبوع')
#
# @api.multi
# def upload_weeks(self):
# if self.flock_id:
# land_week_line = []
# bat_week_line = []
# w_land_week_line = []
# w_bat_week_line = []
# last_wn_land_week = 0
# last_wn_bat_week_line = 0
# last_wn_w_land_week_line = 0
# last_wn_w_bat_week_line = 0
#
# if self.land_week_ids:
#
# for check_line in self.land_week_ids:
# if last_wn_land_week < check_line.l_code:
# last_wn_land_week = check_line.l_code
# l_date_from = check_line.l_date_from
# l_date_to = check_line.l_date_to
# else:
# l_date_from = str(self.date_from)
# l_date_to = str(self.date_to_first_week)
# check = False
# for line in self.flock_id.land_week_ids:
# if line.l_code > last_wn_land_week:
#
# if last_wn_land_week != 0:
# l_date_from = l_date_to+relativedelta(days=1)
# l_date_to = l_date_from+relativedelta(days=7)
#
# elif check == True:
# l_date_from = fields.Datetime.from_string(l_date_to)+relativedelta(days=1)
# l_date_to = fields.Datetime.from_string(l_date_from)+relativedelta(days=7)
#
# land_week_line.append((0, 0, {
# 'l_code': line.l_code,
# # 'l_catalogue_id': self.id,
# # 'l_flock_id': line.l_flock_id.id,
# 'l_date_from': l_date_from,
# 'l_date_to': l_date_to,
# 'l_total_age': line.l_total_age,
# 'l_productive_age': line.l_productive_age,
# 'l_scraped_f': line.l_scraped_f,
# 'l_scraped_m': line.l_scraped_m,
# 'l_daily_feed_f': line.l_daily_feed_f,
# 'l_daily_feed_m': line.l_daily_feed_m,
# 'l_total_weekly_production_f': line.l_total_weekly_production_f,
# 'l_evacuation_weekly_production_f': line.l_evacuation_weekly_production_f,
# 'l_hatching_f': line.l_hatching_f,
# }))
# check = True
#
# if self.bat_week_ids:
# for check_line in self.bat_week_ids:
# if last_wn_bat_week_line < check_line.b_code:
# last_wn_bat_week_line = check_line.b_code
# b_date_from = check_line.b_date_from
# b_date_to = check_line.b_date_to
# else:
# b_date_from = str(self.date_from)
# b_date_to = str(self.date_to_first_week)
# check = False
# for line in self.flock_id.bat_week_ids:
# if line.b_code > last_wn_bat_week_line:
# if last_wn_bat_week_line != 0:
# b_date_from = b_date_to+relativedelta(days=1)
# b_date_to = b_date_from+relativedelta(days=7)
#
# elif check == True:
# b_date_from = fields.Datetime.from_string(b_date_to)+relativedelta(days=1)
# b_date_to = fields.Datetime.from_string(b_date_from)+relativedelta(days=7)
#
# bat_week_line.append((0, 0, {
# 'b_date_from': b_date_from,
# 'b_date_to': b_date_to,
# 'b_total_age': line.b_total_age,
# 'b_productive_age': line.b_productive_age,
# 'b_scraped_f': line.b_scraped_f,
# 'b_scraped_m': line.b_scraped_m,
# 'b_daily_feed_f': line.b_daily_feed_f,
# 'b_daily_feed_m': line.b_daily_feed_m,
# 'b_total_weekly_production_f': line.b_total_weekly_production_f,
# 'b_evacuation_weekly_production_f': line.b_evacuation_weekly_production_f,
# 'b_hatching_f': line.b_hatching_f,
# }))
# check = True
#
# if self.w_land_week_ids:
# for check_line in self.w_land_week_ids:
# if last_wn_w_land_week_line < check_line.l_code:
# last_wn_w_land_week_line = check_line.l_code
#
# for line in self.flock_id.w_land_week_ids:
# if line.l_code > last_wn_w_land_week_line:
# w_land_week_line.append((0, 0, {
# 'l_total_age_ww': line.l_total_age_ww,
# 'l_productive_age_ww': line.l_productive_age_ww,
# 'l_scraped_f_ww': line.l_scraped_f_ww,
# 'l_scraped_m_ww': line.l_scraped_m_ww,
# 'l_daily_feed_f_ww': line.l_daily_feed_f_ww,
# 'l_daily_feed_m_ww': line.l_daily_feed_m_ww,
# 'l_total_weekly_production_f_ww': line.l_total_weekly_production_f_ww,
# 'l_evacuation_weekly_production_f_ww': line.l_evacuation_weekly_production_f_ww,
# # 'l_catalogue_id': self.id,
# 'l_hatching_f_ww': line.l_hatching_f_ww,
# }))
#
# if self.w_bat_week_ids:
# for check_line in self.w_bat_week_ids:
# if last_wn_w_bat_week_line < check_line.b_co22de:
# last_wn_w_bat_week_line = check_line.b_code
# for line in self.flock_id.w_bat_week_ids:
# if line.b_code > last_wn_land_week:
# w_bat_week_line.append((0, 0, {
# 'b_total_age_ww': line.b_total_age_ww,
# 'b_productive_age_ww': line.b_productive_age_ww,
# 'b_scraped_f_ww': line.b_scraped_f_ww,
# 'b_scraped_m_ww': line.b_scraped_m_ww,
# 'b_daily_feed_f_ww': line.b_daily_feed_f_ww,
# 'b_daily_feed_m_ww': line.b_daily_feed_m_ww,
# 'b_total_weekly_production_f_ww': line.b_total_weekly_production_f_ww,
# 'b_evacuation_weekly_production_f_ww': line.b_evacuation_weekly_production_f_ww,
# 'b_hatching_f_ww': line.b_hatching_f_ww,
# }))
# self.update({'land_week_ids': land_week_line,
# 'bat_week_ids': bat_week_line,
# 'w_land_week_ids': w_land_week_line,
# 'w_bat_week_ids': w_bat_week_line
# })
#
# self.land_get_rested_ommat()
# self.bat_get_rested_ommat()
# class change_LandWeek(models.Model):
# _inherit = 'land.week'
# l_actual_ommat_f = fields.Float('عدد النافق الفعلى -انثى', compute='all_real_land_no', digits=(16, 4))
# l_actual_ommat_m = fields.Float('عدد النافق الفعلى -ذكر', compute='all_real_land_no', digits=(16, 4))
# l_actual_daily_feed_f = fields.Float('العلف الفعلى اليومى -أنثى', compute='all_real_land_no', digits=(16, 4))
# l_actual_daily_feed_m = fields.Float('العلف الفعلى اليومى -ذكر', compute='all_real_land_no', digits=(16, 4))
# l_total_weekly_production_pro = fields.Float('الكلى الاسبوعى الفعلى', compute='all_real_land_no', digits=(16, 4))
# l_evacuation_weekly_production_lab = fields.Float('التفريغ الاسبوعى الفعلى', compute='all_real_land_no',
# digits=(16, 4))
# @api.multi
# @api.depends('l_code')
# def all_real_land_no(self):
#
# scrap_products = self.env['product.product'].search([('scrap', '=', True)])
# feed_products = self.env['product.product'].search([('feed_type', '=', 'feed')])
#
# for rec in self:
# female_scrap_r_total = 0.0000
# feed_female_r_total = 0.0000
# male_scrap_r_total = 0.0000
# feed_male_r_total = 0.0000
#
# mo_farming_female = rec.env['mrp.production'].search([('state', '=', 'done'),
# ('farming', '=', True),
#
# ('dynasty', '=', rec.l_catalogue_id.dynasty.id),
# ('week_no', '=', rec.l_code),
# ('type_l_b', '=', 'land')])
#
# if mo_farming_female:
# for mo in mo_farming_female:
# if mo.gender == 'female':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id in scrap_products.ids:
# female_scrap_r_total = female_scrap_r_total+finish.qty_done
#
# for mat in mo.move_raw_ids:
# if mat.product_id.id in feed_products.ids:
# feed_female_r_total = feed_female_r_total+mat.quantity_done
# elif mo.gender == 'male':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id in scrap_products.ids:
# male_scrap_r_total = male_scrap_r_total+finish.qty_done
#
# for mat in mo.move_raw_ids:
# if mat.product_id.id in feed_products.ids:
# feed_male_r_total = feed_male_r_total+mat.quantity_done
#
# production_r_total = 0.000
#
# mo_production_total = rec.env['mrp.production'].search([('state', '=', 'done'),
# ('pro', '=', True),
#
# ('dynasty', '=', rec.l_catalogue_id.dynasty.id),
# ('week_no', '=', rec.l_code),
# ('type_l_b', '=', 'land')])
#
# if mo_production_total:
# for mo in mo_production_total:
# if mo.gender == 'female':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# production_r_total = production_r_total+finish.qty_done
#
# elif finish.product_id.id in scrap_products.ids:
# female_scrap_r_total = female_scrap_r_total+finish.qty_done
#
# elif mo.gender == 'male':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# production_r_total = production_r_total+finish.qty_done
#
# elif finish.product_id.id in scrap_products.ids:
# male_scrap_r_total = male_scrap_r_total+finish.qty_done
#
# evac_r_total = 0.000
# mo_evac_total = rec.env['mrp.production'].search([('state', '=', 'done'),
# ('lab', '=', True),
#
# ('dynasty', '=', rec.l_catalogue_id.dynasty.id),
# ('week_no', '=', rec.l_code),
# ('type_l_b', '=', 'land')])
#
# if mo_evac_total:
# for mo in mo_evac_total:
# if mo.gender == 'female':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# evac_r_total = evac_r_total+finish.qty_done
#
#
# elif finish.product_id.id in scrap_products.ids:
# female_scrap_r_total = female_scrap_r_total+finish.qty_done
#
# elif mo.gender == 'male':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# evac_r_total = evac_r_total+finish.qty_done
#
# elif finish.product_id.id in scrap_products.ids:
# male_scrap_r_total = male_scrap_r_total+finish.qty_done
#
# rec.l_actual_ommat_f = female_scrap_r_total
# rec.l_actual_ommat_m = male_scrap_r_total
# rec.l_actual_daily_feed_f = feed_female_r_total
# rec.l_actual_daily_feed_m = feed_male_r_total
# rec.l_total_weekly_production_pro = production_r_total
# rec.l_evacuation_weekly_production_lab = evac_r_total
# class change_BatteryWeek(models.Model):
# _inherit = 'bat.week'
#
# b_actual_ommat_f = fields.Float('عدد النافق الفعلى -انثى', compute='all_real_bat_no', digits=(16, 4))
# b_actual_ommat_m = fields.Float('عدد النافق الفعلى -ذكر', compute='all_real_bat_no', digits=(16, 4))
# b_actual_daily_feed_f = fields.Float('العلف الفعلى اليومى -أنثى', compute='all_real_bat_no', digits=(16, 4))
# b_actual_daily_feed_m = fields.Float('العلف الفعلى اليومى -ذكر', compute='all_real_bat_no', digits=(16, 4))
# b_total_weekly_production_pro = fields.Float('الكلى الاسبوعى الفعلى', compute='all_real_bat_no', digits=(16, 4))
# b_evacuation_weekly_production_lab = fields.Float('التفريغ الاسبوعى الفعلى', compute='all_real_bat_no',
# digits=(16, 4))
#
# @api.multi
# @api.depends('b_code')
# def all_real_bat_no(self):
#
# scrap_products = self.env['product.product'].search([('scrap', '=', True)])
# feed_products = self.env['product.product'].search([('feed_type', '=', 'feed')])
#
# for rec in self:
# female_scrap_r_total = 0.0000
# feed_female_r_total = 0.0000
# male_scrap_r_total = 0.0000
# feed_male_r_total = 0.0000
#
# mo_farming_female = rec.env['mrp.production'].search([('state', '=', 'done'),
# ('farming', '=', True),
#
# ('dynasty', '=', rec.b_catalogue_id.dynasty.id),
# ('week_no', '=', rec.b_code),
# ('type_l_b', '=', 'bat')])
#
# if mo_farming_female:
# for mo in mo_farming_female:
# if mo.gender == 'female':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id in scrap_products.ids:
# female_scrap_r_total = female_scrap_r_total+finish.qty_done
#
# for mat in mo.move_raw_ids:
# if mat.product_id.id in feed_products.ids:
# feed_female_r_total = feed_female_r_total+mat.quantity_done
# elif mo.gender == 'male':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id in scrap_products.ids:
# male_scrap_r_total = male_scrap_r_total+finish.qty_done
#
# for mat in mo.move_raw_ids:
# if mat.product_id.id in feed_products.ids:
# feed_male_r_total = feed_male_r_total+mat.quantity_done
#
# production_r_total = 0.000
#
# mo_production_total = rec.env['mrp.production'].search([('state', '=', 'done'),
# ('pro', '=', True),
#
# ('dynasty', '=', rec.b_catalogue_id.dynasty.id),
# ('week_no', '=', rec.b_code),
# ('type_l_b', '=', 'bat')])
#
# if mo_production_total:
# for mo in mo_production_total:
# if mo.gender == 'female':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# production_r_total = production_r_total+finish.qty_done
#
# elif finish.product_id.id in scrap_products.ids:
# female_scrap_r_total = female_scrap_r_total+finish.qty_done
#
# elif mo.gender == 'male':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# production_r_total = production_r_total+finish.qty_done
#
# elif finish.product_id.id in scrap_products.ids:
# male_scrap_r_total = male_scrap_r_total+finish.qty_done
#
# evac_r_total = 0.000
# mo_evac_total = rec.env['mrp.production'].search([('state', '=', 'done'),
# ('lab', '=', True),
#
# ('dynasty', '=', rec.b_catalogue_id.dynasty.id),
# ('week_no', '=', rec.b_code),
# ('type_l_b', '=', 'bat')])
#
# if mo_evac_total:
# for mo in mo_evac_total:
# if mo.gender == 'female':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# evac_r_total = evac_r_total+finish.qty_done
#
#
# elif finish.product_id.id in scrap_products.ids:
# female_scrap_r_total = female_scrap_r_total+finish.qty_done
#
# elif mo.gender == 'male':
# for finish in mo.finished_move_line_ids:
# if finish.product_id.id not in scrap_products.ids:
# evac_r_total = evac_r_total+finish.qty_done
#
# elif finish.product_id.id in scrap_products.ids:
# male_scrap_r_total = male_scrap_r_total+finish.qty_done
#
# rec.b_actual_ommat_f = female_scrap_r_total
# rec.b_actual_ommat_m = male_scrap_r_total
# rec.b_actual_daily_feed_f = feed_female_r_total
# rec.b_actual_daily_feed_m = feed_male_r_total
# rec.b_total_weekly_production_pro = production_r_total
# rec.b_evacuation_weekly_production_lab = evac_r_total
class changeMrpSubProduct(models.Model):
_inherit = 'mrp.subproduct'
mrp_id = fields.Many2one('mrp.production', compute=False)
|
[
"[email protected]"
] | |
269a453e77012b0ffb441e1a0f07293fec6eb70c
|
afc85d75ac2115f33a00d535a0f08104571a1e4a
|
/Ex87.py
|
404c5a22a285e0a880bf24c609529823cf1c801d
|
[] |
no_license
|
BrunoCerberus/Algoritmo
|
aa80651920705a88248fa32d700555672964dae4
|
8fad13cf936eb7120f26a699bca4a8ad76d1a53f
|
refs/heads/master
| 2016-09-08T01:23:09.210110 | 2015-07-08T06:39:52 | 2015-07-08T06:39:52 | 29,315,141 | 4 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 567 |
py
|
"""
Programa que calcula o total de elementos separadamente
por linha, e os exibe.
"""
a = [[1,2],[3,4,5],[6,7]]
total1, total2, total3 = 0,0,0
# percorre os elementos da primeira liha
for column in range(len(a[0])):
total1 += a[0][column]
# percorre os elementos da segunda linha
for column in range(len(a[1])):
total2 += a[1][column]
# percorre os elementos da terceira linha
for column in range(len(a[2])):
total3 += a[2][column]
print("Total da primeira linha e",total1)
print("Total da segunda linha e",total2)
print("Total da terceira linha e",total3)
|
[
"[email protected]"
] | |
a8a774633139c03938895ba6eb2851db9c02b02f
|
495531870c08ea3495bb45393b05f907366f052e
|
/x7-src/dashboard/steer/steer/dashboards/engine/instances_and_volumes/tests.py
|
7aca763ebbbf750b2f4f736e1d46d747c87480ad
|
[
"Apache-2.0"
] |
permissive
|
wendy-king/x7_venv
|
5fcb326cf3ecaa26d3b839af743b027d23af29e0
|
d8266c1dc474935c54126ce36d1a6410a7e452f5
|
refs/heads/master
| 2021-01-01T06:33:24.605851 | 2012-01-19T15:54:44 | 2012-01-19T15:54:44 | 3,209,071 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,638 |
py
|
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2011 United States Government as represented by the
# Administrator of the National Aeronautics and Space Administration.
# All Rights Reserved.
#
# Copyright 2011 Nebula, Inc.
#
# 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.
import datetime
from django import http
from django.contrib import messages
from django.core.urlresolvers import reverse
from mox import IsA, IgnoreArg
from x7x.api import exceptions as api_exceptions
from steer import api
from steer import test
class InstancesAndVolumesViewTest(test.BaseViewTests):
def setUp(self):
super(InstancesAndVolumesViewTest, self).setUp()
server = api.Server(None, self.request)
server.id = 1
server.name = 'serverName'
volume = api.Volume(self.request)
volume.id = 1
self.servers = (server,)
self.volumes = (volume,)
def test_index(self):
self.mox.StubOutWithMock(api, 'server_list')
self.mox.StubOutWithMock(api, 'volume_list')
api.server_list(IsA(http.HttpRequest)).AndReturn(self.servers)
api.volume_list(IsA(http.HttpRequest)).AndReturn(self.volumes)
self.mox.ReplayAll()
res = self.client.get(
reverse('steer:engine:instances_and_volumes:index'))
self.assertTemplateUsed(res,
'engine/instances_and_volumes/index.html')
self.assertItemsEqual(res.context['instances'], self.servers)
def test_index_server_list_exception(self):
self.mox.StubOutWithMock(api, 'server_list')
self.mox.StubOutWithMock(api, 'volume_list')
exception = api_exceptions.ApiException('apiException')
api.server_list(IsA(http.HttpRequest)).AndRaise(exception)
api.volume_list(IsA(http.HttpRequest)).AndReturn(self.volumes)
self.mox.ReplayAll()
res = self.client.get(
reverse('steer:engine:instances_and_volumes:index'))
self.assertTemplateUsed(res,
'engine/instances_and_volumes/index.html')
self.assertEqual(len(res.context['instances']), 0)
|
[
"[email protected]"
] | |
e280b5a3a149a8222d07e449c825e541ea713970
|
3058fa7653137ea32b552d800377e19927dbc86b
|
/Subject4_Science/E3_Sci_StreetView/3_StreetView_Sci_CNN.py
|
4ae55feeff5676dcf1e76772514b879b58452f22
|
[] |
no_license
|
heatherbaier/schoolCNN
|
9b1a3301b8e49294f298c384e9f69fe25c1bf4eb
|
df1120d07b37881df801a2a828fc7715b1ea74af
|
refs/heads/master
| 2020-12-09T04:31:40.800407 | 2020-01-27T08:24:40 | 2020-01-27T08:24:40 | 232,407,094 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 7,460 |
py
|
from __future__ import print_function, division
from torchvision import datasets, models, transforms
from imgaug import parameters as iap
from imgaug import augmenters as iaa
from torch.optim import lr_scheduler
import matplotlib.pyplot as plt
import torch.optim as optim
import torch.nn as nn
import imgaug as ia
import numpy as np
import torchvision
import pickle
import joblib
import torch
import copy
import time
import os
plt.ion() # interactive mode
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
epoch_num = 0
since = time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
model.train() # Set model to training mode
else:
model.eval() # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for inputs, labels in dataloaders[phase]:
inputs = inputs.to(device)
labels = labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward
# track history if only in train
with torch.set_grad_enabled(phase == 'train'):
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)
# backward + optimize only if in training phase
if phase == 'train':
loss.backward()
optimizer.step()
# statistics
running_loss += loss.item() * inputs.size(0)
running_corrects += torch.sum(preds == labels.data)
if phase == 'train':
scheduler.step()
epoch_loss = running_loss / dataset_sizes[phase]
epoch_acc = running_corrects.double() / dataset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
# deep copy the model
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model_wts = copy.deepcopy(model.state_dict())
# Save each epoch that achieves a higher accuracy than the current best_acc in case the model crashes mid-training
model_name = './clean/Subject4_Science/E3_Sci_StreetView/epochs/StreetViewResNeXt101_Sci_Epoch' + str(epoch_num) + '.sav'
pickle.dump(model, open(model_name, 'wb'))
epoch_num += 1
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
# load best model weights
model.load_state_dict(best_model_wts)
return model
def visualize_model(model, num_images=6):
was_training = model.training
model.eval()
images_so_far = 0
fig = plt.figure()
with torch.no_grad():
for i, (inputs, labels) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
for j in range(inputs.size()[0]):
images_so_far += 1
ax = plt.subplot(num_images//2, 2, images_so_far)
ax.axis('off')
ax.set_title('predicted: {}'.format(class_names[preds[j]]))
imshow(inputs.cpu().data[j])
if images_so_far == num_images:
model.train(mode=was_training)
return
model.train(mode=was_training)
def imshow(inp, title=None):
"""Imshow for Tensor."""
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
inp = std * inp + mean
inp = np.clip(inp, 0, 1)
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
#### TRANSFORM DATA ####
class ImgAugTransform:
def __init__(self):
self.aug = iaa.Sequential([
iaa.Scale((224, 224)),
iaa.Sometimes(0.30, iaa.GaussianBlur(sigma=(0, 3.0))),
iaa.Sometimes(0.25, iaa.Multiply((0.5, 1.5), per_channel=0.5)),
iaa.Sometimes(0.20, iaa.Invert(0.25, per_channel=0.5)),
iaa.Sometimes(0.25, iaa.ReplaceElementwise(
iap.FromLowerResolution(iap.Binomial(0.1), size_px=8),
iap.Normal(128, 0.4*128),
per_channel=0.5)
),
iaa.Sometimes(0.30, iaa.AdditivePoissonNoise(40)),
iaa.Fliplr(0.5),
iaa.Affine(rotate=(-20, 20), mode='symmetric'),
iaa.Sometimes(0.30,
iaa.OneOf([iaa.Dropout(p=(0, 0.1)),
iaa.CoarseDropout(0.1, size_percent=0.5)])),
iaa.AddToHueAndSaturation(value=(-10, 10), per_channel=True)
])
def __call__(self, img):
img = np.array(img)
return self.aug.augment_image(img)
data_transforms = {
'train': transforms.Compose([
ImgAugTransform(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
ImgAugTransform(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}
data_dir = './clean/Subject4_Science/E3_Sci_StreetView/data/'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
data_transforms[x])
for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=100,
shuffle=True, num_workers=0)
for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Get a batch of training data
inputs, classes = next(iter(dataloaders['train']))
# Make a grid from batch
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])
model_ft = models.resnext101_32x8d(pretrained=True)
num_ftrs = model_ft.fc.in_features
# Here the size of each output sample is set to 2.
# Alternatively, it can be generalized to nn.Linear(num_ftrs, len(class_names)).
model_ft.fc = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,
num_epochs=50)
visualize_model(model_ft)
final_model_name = './clean/Subject4_Science/E3_Sci_StreetView/models/StreetViewResNeXt101_Sci_10epoch.sav'
pickle.dump(model_ft, open(final_model_name, 'wb'))
|
[
"[email protected]"
] | |
63f452ce469a6a29fa2d5230bc8d393acbd2e94e
|
a1f6290c078b3d9bd004c777972ce4d5bc8af749
|
/IVote/.history/app_20211026171321.py
|
474758214e2ca9afa1695dc9d67f4dcbd11c788a
|
[] |
no_license
|
CS699-IITB-Autumn-2021/project-alpha_team
|
2803b99b49dcfe6f1acdcdf768791d58e0441d05
|
d3a7105d6d0d702d4b31a80a331b3772a03f2428
|
refs/heads/master
| 2023-08-19T17:32:01.401161 | 2021-10-27T19:14:08 | 2021-10-27T19:14:08 | 413,135,878 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 8,535 |
py
|
from posixpath import lexists
import re
import sqlite3
import random
import os.path
from flask import Flask, render_template, request,redirect,session
from flask.helpers import url_for
from datetime import date
from datetime import datetime
from pathlib import Path
from werkzeug.utils import redirect
from generateResult import generateResults
from blockchainImp import Block
app = Flask(__name__)
app.secret_key="ivote"
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("CREATE TABLE IF NOT EXISTS Voters(name TEXT,email TEXT,cardno TEXT,password TEXT,voted TEXT)")
c.execute("CREATE TABLE IF NOT EXISTS admin(email TEXT,password TEXT)")
c.execute("CREATE TABLE IF NOT EXISTS election(electionid INTEGER,topic TEXT,startdate TEXT,enddate TEXT,numcand INTEGER,ended Text)")
c.execute("CREATE TABLE IF NOT EXISTS candidate(name TEXT,electionid INTEGER,candidateid TEXT,age INTEGER,mobno INTEGER,email TEXT)")
c.execute("CREATE TABLE IF NOT EXISTS result(election_id Text,cand_id Text, noofvotes Number)")
c.execute("SELECT electionid FROM election")
r = c.fetchall()
for i in r:
fle = Path("static/blockchain/"+str(i[0])+".txt")
c.execute("CREATE TABLE IF NOT EXISTS election"+str(i[0])+"(secret_code TEXT ,name_of_blockchain TEXT,voter_id TEXT,vote_given TEXT)")
fle.touch(exist_ok=True)
f = open(fle)
conn.commit()
conn.close()
@app.route('/',methods=['GET','POST'])
def login():
r = ""
if request.method=="POST":
email = request.form["email"]
password = request.form["password"]
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * FROM Voters WHERE email='"+email+"' and password='"+password+"'")
r = c.fetchall()
for i in r:
if email==i[1] and password == i[3]:
#session[]
return redirect(url_for("voter"))
return render_template('home.html')
@app.route('/signup.html',methods=['GET','POST'])
def signup():
if request.method=="POST":
name = request.form["name"]
email = request.form["email"]
cardno = request.form["id"]
password = request.form["password"]
confirm = request.form["confirm"]
if password==confirm:
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("INSERT INTO Voters VALUES('"+name+"','"+email+"','"+cardno+"','"+password+"','True')")
conn.commit()
conn.close()
return render_template('login.html')
return render_template('signup.html')
@app.route('/Login.html',methods=['GET','POST'])
def adminlogin():
r = ""
if request.method=="POST":
email = request.form["email"]
password = request.form["password"]
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * FROM admin WHERE email='"+email+"' and password='"+password+"'")
r = c.fetchall()
for i in r:
if email==i[0] and password == i[1]:
return redirect(url_for("admin"))
return render_template('Login.html')
@app.route('/forgotPassword.html',methods=['GET','POST'])
def forgot():
return render_template('forgotPassword.html')
@app.route('/admin.html',methods = ['GET','POST'])
def admin():
msg = None
if request.method=="POST":
id = request.form['id']
topic = request.form['topic']
start = request.form['startdate']
end = request.form['enddate']
numcand = request.form['numcand']
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * from election WHERE electionid = '"+id+"'")
r = c.fetchall()
if len(r)>=1:
msg = "Election with this id already exist"
else :
c.execute("INSERT INTO election VALUES('"+id+"','"+topic+"','"+start+"','"+end+"','"+numcand+"','T')")
conn.commit()
conn.close()
msg = "Election created"
return render_template('admin.html',msg = msg)
@app.route("/addcandidate.html",methods = ['GET','POST'])
def add():
if request.method=="POST":
name = request.form['name1']
id = request.form['id']
candid = request.form['candid']
age = request.form['age']
mobile = request.form['mobile']
email = request.form['email']
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("INSERT INTO candidate VALUES('"+name+"','"+id+"','"+candid+"','"+age+"','"+mobile+"','"+email+"')")
conn.commit()
conn.close()
return render_template('addcandidate.html')
@app.route("/results.html",methods=['GET','POST'])
def result():
msg = None
print("Working")
if request.method=="POST":
id = request.form['id']
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * from election WHERE electionid = '"+id+"'")
r = c.fetchall()
if len(r) >= 1:
print("Working")
return redirect(url_for("viewresults",id = id))
else:
msg = "Please enter correct ID"
return render_template('results.html',msg = msg)
@app.route("/election",methods=['GET','POST'])
def election():
id = request.form.get("id",None)
return render_template('election.html')
@app.route("/voter.html",methods=['GET','POST'])
def voter():
if request.method=="POST":
id = request.form['id']
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * from election WHERE electionid = '"+id+"'")
r = c.fetchall()
if len(r) >= 1:
return redirect(url_for("showelectionvoter",id = id))
return render_template('voter.html')
@app.route("/voterresult.html")
def results():
msg = None
print("Working")
if request.method=="POST":
id = request.form['id']
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * from election WHERE electionid = '"+id+"'")
r = c.fetchall()
if len(r) >= 1:
print("Working")
return redirect(url_for("viewresults",id = id))
else:
msg = "Please enter correct ID"
return render_template("voterresult.html",msg=msg)
@app.route("/view",methods=["GET","POST"])
def viewresults():
id = request.form.get('id',None)
print(id)
return render_template("view.html")
@app.route("/logout")
def logout():
return redirect(url_for("login"))
@app.route("/genResult.html",methods=["GET","POST"])
def genresult():
msg=""
if request.method=="POST":
GR = generateResults()
id = request.form['id1']
msg=GR.genResult(id)
return render_template("genResult.html",msg=msg)
@app.route("/viewblockchain.html")
def viewblockchain():
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT electionid FROM election")
r = c.fetchall()
allbc=[]
for i in r:
fle = Path("static/blockchain/"+str(i[0])+".txt")
allbc.append("static/blockchain/"+str(i[0])+".txt")
fle.touch(exist_ok=True)
f = open(fle)
conn.commit()
conn.close()
return render_template('viewblockchain.html',allbc=allbc)
@app.route("/Voting.html")
def Vote():
if request.method=="POST":
id = request.form['id']
id2 = request.form['id2']
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("CREATE TABLE IF NOT EXISTS election"+id2+"(secret_code TEXT ,name_of_blockchain TEXT,voter_id TEXT,vote_given TEXT)")
r1 = random.randint(0, 100000)
B=Block(1,0,0,0)
hash=B.addBlock(r1,str(id),1,str(id2))
return render_template('voter.html',r1=r1,hash=hash)
return render_template('Voting.html')
@app.route("/show<int:id>")
def showelectionvoter(id):
p=str(id)
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * from election WHERE electionid = '"+p+"'")
r = c.fetchall()
return render_template("showelectionvoter.html",r =r )
@app.route("/show<int:id>")
def showresultvoter(id):
p=str(id)
conn = sqlite3.connect("ivote.db")
c = conn.cursor()
c.execute("SELECT * from election WHERE electionid = '"+p+"'")
r = c.fetchall()
return render_template("showelectionvoter.html",r =r )
if __name__=="__main__":
app.run(debug=True)
|
[
"[email protected]"
] | |
50f7434229ae157f44b1726a7e098f852970cab7
|
78f43f8bd07ae0fc91738a63cd7bbca08ae26066
|
/leetcode/twopointer/two_sum_ii_input_array_is_sorted_twopointer.py
|
d3fbe0fe52b8997b0ce0589ddcc703213e1ff0cf
|
[] |
no_license
|
hanrick2000/LeetcodePy
|
2f3a841f696005e8f0bf4cd33fe586f97173731f
|
b24fb0e7403606127d26f91ff86ddf8d2b071318
|
refs/heads/master
| 2022-04-14T01:34:05.044542 | 2020-04-12T06:11:29 | 2020-04-12T06:11:29 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 442 |
py
|
from typing import List
class Solution:
def twoSum(self, numbers: List[int], target: int) -> List[int]:
L = len(numbers)
p1, p2 = 0, L-1
if L <= 1: return []
while p1 < p2:
diff = target - numbers[p1]
if diff < numbers[p2]:
p2 -= 1
elif diff > numbers[p2]:
p1 += 1
else:
return [p1+1,p2+1]
return []
|
[
"[email protected]"
] | |
14f4eccc41465e450e48c6dc0efbf946375796c6
|
50f15c1d9bdd580dcd7d082d723a7f78aae696de
|
/ave/components/requirements.py
|
b859c2eab7dc231c498eb07a62aa651b0db0f099
|
[
"MIT"
] |
permissive
|
AVEgame/AVE
|
78107942056eafd0dced09f3927d45b092727984
|
d9292af865ddebe125ede1aabba9fb360bfa03f7
|
refs/heads/master
| 2021-01-22T07:07:22.164595 | 2020-07-24T13:40:16 | 2020-07-24T13:40:16 | 29,246,001 | 3 | 0 |
MIT
| 2020-07-24T13:40:18 | 2015-01-14T13:43:17 |
Python
|
UTF-8
|
Python
| false | false | 3,483 |
py
|
"""Conditions that are required to show a line, option or item name."""
from .numbers import Constant
class Requirement:
"""A base requirement."""
def has(self, character):
"""Check if the character satisifies this."""
raise NotImplementedError()
def get_all(self):
"""Get all items involved in this requirement."""
raise NotImplementedError()
class RequiredItem(Requirement):
"""The character must have an item."""
def __init__(self, item):
"""Make the requirement."""
self.item = item
def has(self, character):
"""Check if the character satisifies this."""
return character.has(self.item)
def get_all(self):
"""Get all items involved in this requirement."""
return [self.item]
class RequiredNumber(Requirement):
"""A numerical variable must satisfy a condition."""
def __init__(self, v1, sign=">", v2=Constant(0)):
"""Make the requirement."""
self.v1 = v1
self.sign = sign
self.v2 = v2
def has(self, character):
"""Check if the character satisifies this."""
v1 = self.v1.get_value(character)
v2 = self.v2.get_value(character)
if self.sign == ">":
return v1 > v2
if self.sign == "<":
return v1 < v2
if self.sign == ">=":
return v1 >= v2
if self.sign == "<=":
return v1 <= v2
if self.sign == "=" or self.sign == "==":
return v1 == v2
def get_all(self):
"""Get all items involved in this requirement."""
return self.v1.get_all_variables() + self.v1.get_all_variables()
class Or(Requirement):
"""One of a set of Requirements must be satisfied."""
def __init__(self, *items):
"""Make the requirement."""
self.items = items
def has(self, character):
"""Check if the character satisifies this."""
for i in self.items:
if i.has(character):
return True
return False
def get_all(self):
"""Get all items involved in this requirement."""
out = []
for i in self.items:
out += i.get_all()
return out
class And(Requirement):
"""A set of Requirements must all be satisfied."""
def __init__(self, *items):
"""Make the requirement."""
self.items = items
def has(self, character):
"""Check if the character satisifies this."""
for i in self.items:
if not i.has(character):
return False
return True
def get_all(self):
"""Get all items involved in this requirement."""
out = []
for i in self.items:
out += i.get_all()
return out
class Not(Requirement):
"""The negation of another Requirement."""
def __init__(self, item):
"""Make the requirement."""
self.item = item
def has(self, character):
"""Check if the character satisifies this."""
return not self.item.has(character)
def get_all(self):
"""Get all items involved in this requirement."""
return self.item.get_all()
class Satisfied(Requirement):
"""This requirement is always satisfied."""
def has(self, character):
"""Check if the character satisifies this."""
return True
def get_all(self):
"""Get all items involved in this requirement."""
return []
|
[
"[email protected]"
] | |
d6f0c8d246d7f204951c27dc7bfcad1a0ce9d702
|
5e557741c8867bca4c4bcf2d5e67409211d059a3
|
/benchmarks/fastrnns/bench.py
|
b7c315b27fefecee093b530fcd7614d04f5672d7
|
[
"BSD-2-Clause",
"BSD-3-Clause",
"LicenseRef-scancode-generic-cla",
"BSL-1.0",
"Apache-2.0"
] |
permissive
|
Pandinosaurus/pytorch
|
a2bb724cfc548f0f2278b5af2fd8b1d2758adb76
|
bb8978f605e203fbb780f03010fefbece35ac51c
|
refs/heads/master
| 2023-05-02T20:07:23.577610 | 2021-11-05T14:01:30 | 2021-11-05T14:04:40 | 119,666,381 | 2 | 0 |
NOASSERTION
| 2021-11-05T19:55:56 | 2018-01-31T09:37:34 |
C++
|
UTF-8
|
Python
| false | false | 10,272 |
py
|
import argparse
from collections import namedtuple
import torch
import gc
import sys
import json
import copy
import time
from .fuser import set_fuser
from .runner import get_nn_runners
BenchResult = namedtuple('BenchResult', [
'name', 'avg_fwd', 'std_fwd', 'info_fwd', 'avg_bwd', 'std_bwd', 'info_bwd',
])
def fit_str(string, colwidth=16):
if len(string) < colwidth:
return (colwidth - len(string)) * ' ' + string
else:
return string[:colwidth]
def to_str(item):
if isinstance(item, float):
return '%.4g' % item
return str(item)
def print_header(colwidth=16, sep=' '):
items = []
for item in BenchResult._fields:
items.append(fit_str(item))
return sep.join(items)
def pretty_print(benchresult, colwidth=16, sep=' '):
items = []
for thing in benchresult:
items.append(fit_str(to_str(thing)))
return sep.join(items)
# shim for torch.cuda.Event when running on cpu
class Event(object):
def __init__(self, enable_timing):
pass
def record(self):
self.time = time.perf_counter()
def elapsed_time(self, end_event):
assert isinstance(end_event, Event)
return end_event.time - self.time
def trainbench(name, rnn_creator, nloops=100, warmup=10,
seqLength=100, numLayers=1, inputSize=512, hiddenSize=512,
miniBatch=64, device='cuda', seed=None):
def train_batch(modeldef):
# CUDA events for timing
if device == 'cuda':
timer_class = torch.cuda.Event
else:
timer_class = Event
fwd_start_event = timer_class(enable_timing=True)
fwd_end_event = timer_class(enable_timing=True)
bwd_start_event = timer_class(enable_timing=True)
bwd_end_event = timer_class(enable_timing=True)
gc.collect()
fwd_start_event.record()
forward_output = modeldef.forward(*modeldef.inputs)
fwd_end_event.record()
# XXX: Use if need to print something
# print(modeldef.forward.graph_for(*modeldef.inputs))
if modeldef.backward_setup is not None:
backward_input = modeldef.backward_setup(forward_output)
else:
backward_input = forward_output
gc.collect()
bwd_start_event.record()
if modeldef.backward is not None:
modeldef.backward(*backward_input)
bwd_end_event.record()
if modeldef.backward is not None:
with torch.no_grad():
for param in modeldef.params:
assert param.grad is not None
param.grad.zero_()
if device == 'cuda':
torch.cuda.synchronize()
fwd_time = fwd_start_event.elapsed_time(fwd_end_event)
bwd_time = bwd_start_event.elapsed_time(bwd_end_event)
return fwd_time, bwd_time
creator_args = creator_args = {
'seqLength': seqLength, 'numLayers': numLayers,
'inputSize': inputSize, 'hiddenSize': hiddenSize,
'miniBatch': miniBatch, 'device': device, 'seed': seed
}
modeldef = rnn_creator(**creator_args)
[train_batch(modeldef) for _ in range(warmup)]
results = [train_batch(modeldef) for _ in range(nloops)]
fwd_times, bwd_times = zip(*results)
fwd_times = torch.tensor(fwd_times)
bwd_times = torch.tensor(bwd_times)
return BenchResult(name=name,
avg_fwd=fwd_times.mean().item(),
std_fwd=fwd_times.std().item(),
info_fwd=fwd_times,
avg_bwd=bwd_times.mean().item(),
std_bwd=bwd_times.std().item(),
info_bwd=bwd_times)
def print_stderr(*args, **kwargs):
kwargs['file'] = sys.stderr
return print(*args, **kwargs)
def print_json_oss_format(results):
oss_results = {}
for group_name, group_val in results.items():
oss_results[group_name] = {}
for model_name, run_time in group_val.items():
# Output for OSS
oss_results[group_name][model_name] = run_time['avg']
print(json.dumps(oss_results))
def print_json_pep_format(results):
# print the AI-PEP format json string for each model
for group_name, group_val in results.items():
for model_name, run_time in group_val.items():
# Output for AI-PEP
num_iters = len(run_time['info'])
info = run_time['info'].tolist()
for i in range(num_iters):
print("Caffe2Observer " + json.dumps(
{
"type": "NET",
"metric": group_name + "-" + model_name,
"unit": "ms",
"value": str(info[i])
}
))
def bench(rnn_runners, group_name, print_json=False, sep=' ', **params):
print_stderr(print_header(sep=sep))
results = {}
for name, creator, context in rnn_runners:
with context():
try:
result = trainbench(name, creator, **params)
# Replace the value of info_fwd and info_bwd to None
result_with_no_info = result._replace(
info_fwd='None', info_bwd='None')
print_stderr(pretty_print(result_with_no_info, sep=sep))
results[name] = result
except Exception as e:
if not print_json:
raise
return {
group_name: {k: {"avg": v.avg_fwd, "std": v.std_fwd, "info": v.info_fwd} for k, v in results.items()},
group_name + '-backward': {k: {"avg": v.avg_bwd, "std": v.std_bwd, "info": v.info_bwd} for k, v in results.items()},
}
def bench_group(model_list, bench_name, bench_group, bench_args):
print_stderr('Benchmarking {}s...'.format(bench_name))
nn_results = bench(get_nn_runners(*model_list), bench_group, **bench_args)
print_stderr('')
return nn_results
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Profile RNNs')
# groups help control which test group you want to run
# if you only want to run one/two benchmark, run it with
# e.g: python -m fastrnns.bench --rnns jit and --group rnns
default_groups = ['cnns', 'rnns']
parser.add_argument('--seqLength', default='100', type=int)
parser.add_argument('--numLayers', default='1', type=int)
parser.add_argument('--inputSize', default='512', type=int)
parser.add_argument('--hiddenSize', default='512', type=int)
parser.add_argument('--miniBatch', default='64', type=int)
parser.add_argument('--warmup', default='10', type=int)
parser.add_argument('--nloops', default='100', type=int)
parser.add_argument('--device', default='cuda', type=str)
parser.add_argument('--variable_lstms', action='store_true',
help='Also benchmark variable sequence length lstms '
'Note that some of these run really slowly '
'and that the `seqLength` flag will be ignored.')
parser.add_argument('--sep', default=' ', type=str)
parser.add_argument('--print-json', nargs='?', default=None, const='oss')
parser.add_argument('--rnns', nargs='*',
help='What to run. cudnn, aten, jit, etc')
parser.add_argument('--cnns', nargs='*',
help='What to run. resnet18, resnet18_jit, resnet50, etc')
parser.add_argument('--group', nargs='*', default=default_groups, help='Which group to run. cnns, rnns, etc.')
parser.add_argument('--fuser', default='te', type=str,
help='The fuser backend to use. One of: te, old, or none')
parser.add_argument('--executor', default=None, type=str,
help='The executor to use. One of: legacy, simple, profiling')
parser.add_argument('--cuda_pointwise_loop_level', default=None, type=int)
parser.add_argument('--cuda_pointwise_block_count', default=None, type=int)
parser.add_argument('--cuda_pointwise_block_size', default=None, type=int)
args = parser.parse_args()
set_fuser(args.fuser, args.executor)
if args.cuda_pointwise_loop_level:
torch._C._jit_set_te_cuda_pointwise_loop_levels(args.cuda_pointwise_loop_level)
if args.cuda_pointwise_block_count:
torch._C._jit_set_te_cuda_pointwise_block_count(args.cuda_pointwise_block_count)
if args.cuda_pointwise_block_size:
torch._C._jit_set_te_cuda_pointwise_block_size(args.cuda_pointwise_block_size)
rnns = args.rnns or ['cudnn', 'aten', 'jit', 'jit_premul', 'jit_premul_bias', 'jit_simple',
'jit_multilayer', 'py']
cnns = args.cnns or ['resnet18', 'resnet18_jit', 'resnet50', 'resnet50_jit']
# TODO: Maybe add a separate section for the layernorm/dropout lstms
# 'cudnn_layernorm', jit_layernorm', 'jit_layernom_decom',
# 'jit', 'jit_dropout', 'cudnn_dropout'
vlrnns = ['vl_cudnn', 'vl_jit', 'vl_py']
if args.print_json:
print_stderr = lambda *args, **kwargs: None # noqa: E731,F811
print_stderr(args)
bench_args = copy.deepcopy(vars(args))
should_bench_varlen_lstms = args.variable_lstms
del bench_args['group']
del bench_args['rnns']
del bench_args['cnns']
del bench_args['variable_lstms']
del bench_args['fuser']
del bench_args['executor']
del bench_args['cuda_pointwise_loop_level']
del bench_args['cuda_pointwise_block_count']
del bench_args['cuda_pointwise_block_size']
results = {}
if should_bench_varlen_lstms:
if args.nloops + args.warmup > 30:
print_stderr(
'WARNING: some of the variable sequence length lstms are '
'very unoptimized and therefore take forever to run.')
results.update(bench_group(vlrnns, 'variable-length sequence LSTM', 'vl_lstm', bench_args))
if 'rnns' in args.group:
results.update(bench_group(rnns, 'LSTM', 'lstm', bench_args))
if 'cnns' in args.group:
results.update(bench_group(cnns, 'ResNet', 'resnet', bench_args))
if args.print_json == 'oss':
print_json_oss_format(results)
elif args.print_json == 'pep':
print_json_pep_format(results)
|
[
"[email protected]"
] | |
7975bc63573e9c2bae7ca54b0b9772152873656a
|
12ecd25d75023b7269e9eb103c5cab01b9798859
|
/questions/migrations/0001_initial.py
|
3fb589ac7a7434f86b4ca8c86b18895480b46ae1
|
[
"MIT"
] |
permissive
|
alexyvassili/otuspy-hasker
|
dfe8881cc6d150dd98298cd308af19bc3d06a068
|
b094401fd863eab8006c6d4a92d2c08efb815f95
|
refs/heads/master
| 2023-02-17T18:28:00.002133 | 2022-06-17T21:10:49 | 2022-06-17T21:10:49 | 133,680,455 | 0 | 1 |
MIT
| 2023-02-15T18:45:59 | 2018-05-16T14:44:23 |
JavaScript
|
UTF-8
|
Python
| false | false | 781 |
py
|
# Generated by Django 2.0.5 on 2018-05-23 09:05
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='UserProfile',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('avatar', models.ImageField(default='users/avatar.jpg', upload_to='users/')),
('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
|
[
"[email protected]"
] | |
f2e2e84612c442fb0b08b491827aeed5390ab984
|
bc539788b876773e294383863252c1637de9eb7f
|
/scrapy/PycharmProjects/Reptile/ven/6-python发送邮件-测试.py
|
cb5c6ed0e152de7335e4a7db2b59cd04278b645c
|
[] |
no_license
|
umsung/scrapy
|
4eb56bf74f3e617e49dcdec61cf77010eb912f4f
|
deacd9f289159c5af114b0dd3110448ad7eb43e8
|
refs/heads/master
| 2020-05-31T14:11:46.530793 | 2019-10-16T01:32:25 | 2019-10-16T01:32:25 | 190,321,772 | 3 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,812 |
py
|
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
def send_mail(subject='subject', content='content'):
# mail server
mail_host = 'smtp.sina.com'
mail_port = 25
mail_user = '' # 账号
mail_pwd = '' # 密码
# mail message
# email模块 负责构造邮件
# 三个参数：第一个为邮件正文文本内容，第二个 plain 设置文本格式，第三个 utf-8 设置编码
message = MIMEText(content, 'plain', 'utf-8')
message['Subject'] = subject # 邮件的主题
message['From'] = '' # 发送者 填写发送邮箱者地址
message['To'] = '' # 接收者 填写接收邮箱者地址, #收件人为多个收件人,通过join将列表转换为以;为间隔的字符串
# send mail
# smtplib模块负责发送邮件，是一个发送邮件的动作
try:
# 实例化SMTP()
smtp_obj = smtplib.SMTP()
# 实例化SMTP(),连接邮箱服务器, mail_host是邮箱服务器地址，mail_port是端口，
# 新浪邮箱：smtp.sina.com,
# 新浪VIP：smtp.vip.sina.com,
# 搜狐邮箱：smtp.sohu.com，
# 126邮箱：smtp.126.com,
# 139邮箱：smtp.139.com,
# 163网易邮箱：smtp.163.com。
smtp_obj.connect(mail_host, mail_port) # SMTP协议默认端口是25
smtp_obj.login(mail_user, mail_pwd)
# as_string()message(MIMEText对象或者MIMEMultipart对象)变为str
smtp_obj.sendmail(message['From'], message['To'], message.as_string())
smtp_obj.quit()
except smtplib.SMTPException as e:
print(e)
s = 'Please study hard'
c = 'My name is Teacher hou, I teach python'
send_mail(subject=s, content=c)
|
[
"[email protected]"
] | |
23cac2a90471800dcb06f401d6accba6a9b068b5
|
49a6f4076d287af69834f22b9af0b4c05ad32556
|
/docs/conf.py
|
6005be4b757601f5a22187dc8949749ba13bfcea
|
[
"Zlib"
] |
permissive
|
ywg121020/libusb
|
fcd9597c10a9a857fa4934cb4c94296813a64820
|
0755dc25c60bfb92fa41dfe460c1b9fa638be913
|
refs/heads/master
| 2020-06-11T00:34:04.840221 | 2019-02-15T13:47:05 | 2019-02-15T13:47:05 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 6,320 |
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/master/config
import sys
import os
top_dir = os.path.join(os.path.dirname(__file__), os.pardir)
with open(os.path.join(top_dir, "src", "libusb", "__about__.py")) as f:
class about: exec(f.read(), None)
def setup(app):
pass
# -- 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('.'))
# -- Project information -----------------------------------------------------
project = about.__title__
project_ = project.replace('.', '_')
copyright = about.__copyright__
author = about.__author__
# The short X.Y version
version = '{0.major}.{0.minor}'.format(about.__version_info__)
# The full version, including alpha/beta/rc tags
release = about.__version__
# -- 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.autosummary',
'sphinx.ext.doctest',
'sphinx.ext.intersphinx',
#'sphinx.ext.todo',
#'sphinx.ext.coverage',
'sphinx.ext.ifconfig',
'sphinx.ext.napoleon',
]
# 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 encoding of source files.
#
source_encoding = 'utf-8'
# 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 = ['_build', 'Thumbs.db', '.DS_Store']
# 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 = 'sphinxdoc' # 'alabaster'
# 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 = project_ + '_' + 'doc'
# -- 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, project_ + '.tex', project + ' Documentation',
author, '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, project_.lower(), project + ' 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, project_, project + ' Documentation',
author, project_, about.__summary__,
'Miscellaneous'),
]
# -- Options for Epub output -------------------------------------------------
# Bibliographic Dublin Core info.
epub_title = project
epub_author = author
epub_publisher = author
epub_copyright = copyright
# The unique identifier of the text. This can be a ISBN number
# or the project homepage.
#
# epub_identifier = ''
# A unique identification for the text.
#
# epub_uid = ''
# A list of files that should not be packed into the epub file.
epub_exclude_files = ['search.html']
# -- Extension configuration -------------------------------------------------
# -- Options for intersphinx extension ---------------------------------------
# Example configuration for intersphinx: refer to the Python standard library.
# intersphinx_mapping = {'https://docs.python.org/': None}
# -- Options for todo extension ----------------------------------------------
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False
|
[
"[email protected]"
] | |
8d5788663ac2a8631c337da7c2251331c5de6e80
|
3fbd26091ebbc13913f9c7be1aaf10d477c79536
|
/week01/zuoye/requests_maoyan/.history/maoyan_20200628012930.py
|
b4d45d5f82cd1ac6af42e71cf24f78dcfed08446
|
[] |
no_license
|
shuncon/Python001-class01
|
d28faf3d5d8e9ea4cee93bcae7143a26fd8c472e
|
df19758181cdaf37f30d4b518600fc4612590499
|
refs/heads/master
| 2022-11-13T19:31:27.019214 | 2020-07-10T14:58:25 | 2020-07-10T14:58:25 | 273,135,541 | 0 | 0 | null | 2020-06-18T03:46:56 | 2020-06-18T03:46:55 | null |
UTF-8
|
Python
| false | false | 1,583 |
py
|
#-*-conding：utf-8 -*-
import requests
import lxml
from bs4 import BeautifulSoup as bfs
headers = {
'user-agent' :'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36',
'Cookie': '__mta=143555850.1593182389402.1593186588642.1593186951752.13; _lxsdk_cuid=172f111f51562-0c65d9babc0209-3a65460c-1fa400-172f111f516c8; uuid_n_v=v1; uuid=E1D4A130B7BA11EAB4E28FB161D5B82BB28615396FEA473FAA79466FF93A0ADC; _lxsdk=E1D4A130B7BA11EAB4E28FB161D5B82BB28615396FEA473FAA79466FF93A0ADC; mojo-uuid=8bd33533b8dd1a759d17cadf1be0eefb; _lx_utm=utm_source%3DBaidu%26utm_medium%3Dorganic; _csrf=0d6a79576c8af13e864ce4bc16256224b781a4cddb5105facfa01b80cc9314b6; Hm_lvt_703e94591e87be68cc8da0da7cbd0be2=1593182389,1593186370,1593276349; mojo-session-id={"id":"e7c501439a3b4a3b79e13dd84a3f3791","time":1593276349044}; mojo-trace-id=3; Hm_lpvt_703e94591e87be68cc8da0da7cbd0be2=1593276558; __mta=143555850.1593182389402.1593186951752.1593276557953.14; _lxsdk_s=172f6abba85-f3d-41e-9d2%7C%7C6'
}
myurl = 'https://maoyan.com/films?showType=3'
response = requests.get(myurl,headers=headers)
print(f'返回状态码：{response.status_code}')
selector = lxml.etree.HTML(response.text)
bs_info = bfs(response.text,'html.parser')
# for tags in bs_info.find_all('div', arrts={'class':'movie-item-title'}):
# for ttags in tags.find_all('p' ,arrts={'class' : 'name'}):
# for atag in ttags.find_all('a',):
# print (atag.get('href'))
# print (atag.get('titile'))
for ttitle in bs_info.find_all()('div',arrts={})
|
[
"[email protected]"
] | |
7073414c50df76a49a5045cbf9c5f48b0a0da33a
|
1bc41de1c91561f37284d7de25c01fb1ff0373a4
|
/django_core_utils/constants.py
|
cbbf827350f7f9ca3dce81c4eabbe604652680e6
|
[
"MIT"
] |
permissive
|
ajaniv/django-core-utils
|
7e36022956849cb0fc5afdc1440fd3413740cf02
|
4ae9fc325f4bc9e3a3e723207d48133f772804c3
|
refs/heads/master
| 2020-04-04T21:17:45.840269 | 2019-03-11T23:35:31 | 2019-03-11T23:35:31 | 52,732,860 | 1 | 0 |
MIT
| 2019-03-11T23:35:32 | 2016-02-28T17:12:35 |
Python
|
UTF-8
|
Python
| false | false | 313 |
py
|
"""
.. module:: django_core_utils.constants
:synopsis: django_core_utils constants module.
django_core_utils constants module.
The *constants* module contains public constant definitions.
"""
HTTP_GET = "GET"
HTTP_POST = "POST"
HTTP_DELETE = "DELETE"
HTTP_PUT = "PUT"
SITE_LABEL = "sl"
UNKNOWN = "UNKNOWN"
|
[
"[email protected]"
] | |
76d55cda48cfc76fb1f21ee550827b7267464301
|
9c315e3762961668a1fe58ad811ae87c5fbf7539
|
/apertium-tools/scraper/scrp-azathabar.py
|
0f0d7586b0f74692b378218abc3e342939da4b40
|
[] |
no_license
|
frankier/apertium
|
f2b893115c413203b1194e5c0d4feb0adf2b1b3e
|
d3f5515bf2455f3046314a62ea564457bcf504b8
|
refs/heads/gnulib
| 2021-01-20T21:00:53.139135 | 2016-05-27T17:30:01 | 2016-05-27T17:30:01 | 59,847,975 | 0 | 1 | null | 2016-07-07T12:39:01 | 2016-05-27T16:21:14 |
HTML
|
UTF-8
|
Python
| false | false | 5,564 |
py
|
#!/usr/bin/env python3
from datetime import date, timedelta
from urllib import request
#from lxml import etree
import lxml.html
import lxml.html.clean
from scrapers import ScraperAzathabar
from scraper_classes import Source, Writer
import urllib.error
import http.client
import curses
import sys
import signal
domain = "www.azathabar.com"
urltemplate = "/archive/%s/%s%02d%02d/%s/%s.html"
topics = { 2239: ("news", "day"),
2246: ("Turkmenistan", "days"),
2247: ("asia", "days"),
2248: ("international", "days"),
2275: ("special_programs", "days"),
2289: ("Multimedia", "days"),
2756: ("Blogs", "days"),
#2240: ("commentary", "month"),
#2244: ("interview", "month"),
2241: ("politics", "days"),
2242: ("economics", "days"),
2243: ("society", "days"),
#2245: ("culture", "month"),
#2800: ("History", "year"),
#2276: ("newscast", "month"),
#2249: ("analytical", "month"),
2250: ("neighbors", "days"),
#2251: ("style", "month"),
#2252: ("youth", "month"),
#2253: ("economy", "month"),
2254: ("spotlight", "days"),
#2255: ("women", "month"),
#2256: ("rights", "month"),
2257: ("health", "days"),
2258: ("time", "days"),
#2259: ("review", "month"),
#2261: ("music", "month"),
#2263: ("personalities", "month"),
#2984: ("No_Comment", "??"),
3264: ("Voice_of_people", "days"),
}
startyear = 2010
endyear = 2010
minmonth = 1
maxmonth = 12
startnum = 1
def get_urls(monthurl, pagetype): # get the URLS for a given month
global domain
sys.stdout.write("\rGetting %s." % monthurl)
sys.stdout.flush()
conn = http.client.HTTPConnection(domain)
conn.request("GET", monthurl)
res = conn.getresponse()
if res.status != 200:
print(monthurl, res.status, res.reason)
return
contents = res.read().decode('utf-8')
sys.stdout.write(".")
sys.stdout.flush()
doc = lxml.html.fromstring(contents)
mid = doc.find_class("middlePart")[0]
curdate = ""
urls = []
for el in mid.findall(".//li"):
#if "class" in el.attrib:
# if "archive_listrow_date" in el.attrib['class'].split():
# curdate = el.text
#if curdate != "":
if "class" in el.attrib:
classes = el.attrib['class'].split()
if pagetype == "days":
if "archive_listrow_date" in classes:
curdate = el.text
#if "archive_listrow_date" in el.attrib['class'].split():
# curdate = el.text
#if curdate != "":
# elif "zoomMe" in classes and "date" not in classes:
if "zoomMe" in classes and "date" not in classes:
title = None
url = None
for a in el.findall(".//a"):
if "style" not in a.attrib:
title = a.text
url = a.attrib["href"]
if title == None or url == None:
for a in el.findall(".//a"):
title = a.text
url = a.attrib["href"]
#print(lxml.html.tostring(el)) #lxml.html.document_fromstring(lxml.html.clean.clean_html(lxml.html.tostring(el).decode('utf-8'))))
if title != None and url != None:
urls.append((url, title))
#print(url, title)
sys.stdout.write("%s urls" % len(urls))
sys.stdout.write(".\n")
sys.stdout.flush()
conn.close()
return urls
def get_allurls(startyear, endyear, minmonth, maxmonth): # get all urls for given date range
allurls = []
for year in range(startyear, endyear+1):
for month in range(minmonth, maxmonth+1):
for num, (topic, pagetype) in topics.items():
if pagetype=="day":
for day in range(1, 32):
dayurl = urltemplate % (topic, year, month, day, num, num)
urls = get_urls(dayurl, pagetype)
if urls is not None:
for url in urls:
allurls.append(url)
elif pagetype=="days":
dayurl = urltemplate % (topic, year, month, 1, num, num)
urls = get_urls(dayurl, pagetype)
if urls is not None:
for url in urls:
allurls.append(url)
return allurls
def main():
global startyear, endyear, minmonth, maxmonth, domain
sys.stdout.write("\rGenerating urls...\n")
sys.stdout.flush()
allurls = get_allurls(startyear, endyear, minmonth, maxmonth)
sys.stdout.write("\r%d articles total\n" % len(allurls))
conn = http.client.HTTPConnection(domain)
ids = None
root = None
this = 0
w = Writer()
def term_handler(sigNum, frame):
print("\nReceived a SIGTERM signal. Closing the program.")
w.close()
sys.exit(0)
signal.signal(signal.SIGTERM, term_handler)
try:
for (url, title) in allurls:
#sys.stdout.write("\r"+url+" "+title+"\n")
#sys.stdout.flush()
this += 1
try:
source = Source(url, title=title, scraper=ScraperAzathabar, conn=conn)
source.makeRoot("./", ids=ids, root=root, lang="tuk")
msg = "(%s/%s)" % (this, len(allurls))
source.add_to_archive(msg=msg)
if ids is None: # if not ids:
ids = source.ids
if root is None: # if not root:
root = source.root
except Exception as e:
sys.stdout.write(" — %s \n" % e)
sys.stdout.flush()
raise
except KeyboardInterrupt:
print("\nReceived a keyboard interrupt. Closing the program.")
w.close()
conn.close()
def tryOneArticle(url):
global domain
root = None
ids = None
conn = http.client.HTTPConnection(domain)
w = Writer()
source = Source(url, title="", scraper=ScraperAzathabar, conn=conn)
source.makeRoot("./", ids=ids, root=root, lang="tuk")
source.add_to_archive()
w.close()
conn.close()
main()
#tryOneArticle("http://www.azathabar.com/archive/news/20111231/2239/2239.html?id=24439101")
#tryOneArticle("http://www.azathabar.com/content/article/24437444.html")
#tryOneArticle("http://www.azathabar.com/content/article/24425908.html")
#tryOneArticle("http://www.azathabar.com//content/article/2306850.html")
|
[
"unhammer@72bbbca6-d526-0410-a7d9-f06f51895060"
] |
unhammer@72bbbca6-d526-0410-a7d9-f06f51895060
|
0dba12150c173f3c6fc7ce27a5c922cd86afe827
|
6fcfb638fa725b6d21083ec54e3609fc1b287d9e
|
/python/automl_HPOlib/HPOlib-master/HPOlib/Plotting/plot_util.py
|
09677ec7ca5947ace327c84039e55c32120354a1
|
[] |
no_license
|
LiuFang816/SALSTM_py_data
|
6db258e51858aeff14af38898fef715b46980ac1
|
d494b3041069d377d6a7a9c296a14334f2fa5acc
|
refs/heads/master
| 2022-12-25T06:39:52.222097 | 2019-12-12T08:49:07 | 2019-12-12T08:49:07 | 227,546,525 | 10 | 7 | null | 2022-12-19T02:53:01 | 2019-12-12T07:29:39 |
Python
|
UTF-8
|
Python
| false | false | 15,727 |
py
|
##
# wrapping: A program making it easy to use hyperparameter
# optimization software.
# Copyright (C) 2013 Katharina Eggensperger and Matthias Feurer
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import cPickle
import itertools
import logging
import os
import numpy as np
import sys
import HPOlib.wrapping_util
__authors__ = ["Katharina Eggensperger", "Matthias Feurer"]
__contact__ = "automl.org"
logging.basicConfig(format='[%(levelname)s] [%(asctime)s:%(name)s] %('
'message)s', datefmt='%H:%M:%S')
hpolib_logger = logging.getLogger("HPOlib")
hpolib_logger.setLevel(logging.INFO)
logger = logging.getLogger("HPOlib.plot_util")
# A super-simple cache for unpickled objects...
cache = dict()
def get_empty_iterator():
return itertools.cycle([None])
def get_plot_markers():
return itertools.cycle(['o', 's', 'x', '^', 'p', 'v', '>', '<', '8', '*',
'+', 'D'])
def get_plot_linestyles():
return itertools.cycle(['-', '--', '-.', ':', ])
def get_single_linestyle():
return itertools.cycle(['-'])
def get_plot_colors():
# color brewer, 2nd qualitative 9 color scheme (http://colorbrewer2.org/)
return itertools.cycle(["#e41a1c", # Red
"#377eb8", # Blue
"#4daf4a", # Green
"#984ea3", # Purple
"#ff7f00", # Orange
"#ffff33", # Yellow
"#a65628", # Brown
"#f781bf", # Pink
"#999999"]) # Grey
def load_pickles(name_list, pkl_list):
pickles = dict()
for i in range(len(name_list)):
key = name_list[i][0]
pickles[key] = list()
for pkl in pkl_list[i]:
if cache.get(pkl) is None:
fh = open(pkl)
pickles[key].append(cPickle.load(fh))
fh.close()
cache[pkl] = pickles[key][-1]
else:
pickles[key].append(cache.get(pkl))
return pickles
def get_pkl_and_name_list(argument_list):
name_list = list()
pkl_list = list()
now_data = False
for i in range(len(argument_list)):
if not ".pkl" in argument_list[i] and now_data:
raise ValueError("You need at least on .pkl file per Experiment, %s has none" % name_list[-1])
elif not ".pkl" in argument_list[i] and not now_data:
# print "Adding", argument_list[i]
name_list.append([argument_list[i], 0])
pkl_list.append(list())
now_data = True
continue
else:
if os.path.exists(argument_list[i]):
now_data = False
name_list[-1][1] += 1
pkl_list[-1].append(argument_list[i])
else:
raise ValueError("%s is not a valid file" % argument_list[i])
if now_data:
raise ValueError("You need at least one .pkl file per Experiment, %s has none" % name_list[-1])
return pkl_list, name_list
def get_best_dict(name_list, pickles, cut=sys.maxint):
"""
Get the best values of many experiments.
Input
* name_list: A list with of tuples of kind (optimizer_name, num_pickles)
* pickles: A dictionary with a list of all pickle files for an
optimizer_name
* cut: How many iterations should be considered
Returns:
* best_dict: A dictionary with a list of the best response value for every
optimizer
* idx_dict: A dictionary with a list the number of iterations needed to
find the optimum
* keys: A list with optimizer names.
"""
best_dict = dict()
idx_dict = dict()
keys = list()
for i in range(len(name_list)):
keys.append(name_list[i][0])
best_dict[name_list[i][0]] = list()
idx_dict[name_list[i][0]] = list()
for pkl in pickles[name_list[i][0]]:
best, idx = get_best_value_and_index(pkl, cut)
best_dict[name_list[i][0]].append(best)
idx_dict[name_list[i][0]].append(idx)
return best_dict, idx_dict, keys
def fill_trajectories(trace_list, times_list):
""" Each trajectory must have the exact same number of entries
and timestamps
trace_list: list of n lists with y values
times_list: list of n lists with x values
returns a list of n lists where for each x value and each y-list an
entry exists. time list will always start at 0
Example:
trace_list = [[5,3], [5,2,1]]
times_list = [[1,2], [1,3,5]]
returns:
trajectories = [[5, 5, 3, 3, 3], [5, 5, 5, 2, 1]]
times = [0,1,2,3,5]
"""
# We need to define the max value =
# what is measured before the first evaluation
max_value = np.max([np.max(ls) for ls in trace_list])
for idx in range(len(trace_list)):
assert len(trace_list[idx]) == len(times_list[idx]), \
"%d != %d" % (len(trace_list[idx]), len(times_list[idx]))
number_exp = len(trace_list)
new_trajectories = list()
new_times = list()
for i in range(number_exp):
new_trajectories.append(list())
new_times.append(list())
# noinspection PyUnusedLocal
counter = [1 for i in range(number_exp)]
finish = False
# We need to insert the max values in the beginning
# and the min values in the end
for i in range(number_exp):
trace_list[i].insert(0, max_value)
trace_list[i].append(np.min(trace_list[i]))
times_list[i].insert(0, 0)
times_list[i].append(sys.maxint)
# Add all possible time values
while not finish:
min_idx = np.argmin([times_list[idx][counter[idx]]
for idx in range(number_exp)])
counter[min_idx] += 1
for idx in range(number_exp):
new_times[idx].append(times_list[min_idx][counter[min_idx] - 1])
new_trajectories[idx].append(trace_list[idx][counter[idx] - 1])
# Check if we're finished
for i in range(number_exp):
finish = True
if counter[i] < len(trace_list[i]) - 1:
finish = False
break
times = new_times
trajectories = new_trajectories
tmp_times = list()
# Sanitize lists and delete double entries
for i in range(number_exp):
tmp_times = list()
tmp_traj = list()
for t in range(len(times[i]) - 1):
if times[i][t + 1] != times[i][t] and not np.isnan(times[i][t]):
tmp_times.append(times[i][t])
tmp_traj.append(trajectories[i][t])
tmp_times.append(times[i][-1])
tmp_traj.append(trajectories[i][-1])
times[i] = tmp_times
trajectories[i] = tmp_traj
# We need only one list for all times
times = tmp_times
# Now clean data as sometimes the best val doesn't change over time
last_perf = [i*10 for i in range(number_exp)] # dummy entry
time_ = list()
performance = list([list() for i in range(number_exp)])
for idx, t in enumerate(times):
# print t, idx, last_perf, perf_list[0][idx], perf_list[1][idx]
diff = sum([np.abs(last_perf[i] - trajectories[i][idx]) for i in range(number_exp)])
if diff != 0 or idx == 0 or idx == len(times) - 1:
# always use first and last entry
time_.append(t)
[performance[i].append(trajectories[i][idx]) for i in range(number_exp)]
last_perf = [p[idx] for p in trajectories]
trajectories = performance
times = time_
return trajectories, times
def extract_trajectory(experiment, cut=sys.maxint, maxvalue=sys.maxint,
test=False):
"""
Extract a list where the value at position i is the current best after i
configurations. Starts with maxvalue, as at timestep 0 there is no known
performance value
"""
if not isinstance(cut, int):
raise ValueError("Argument cut must be an Integer value but is %s" %
type(cut))
if cut <= 0:
raise ValueError("Argument cut cannot be zero or negative.")
trace = list([maxvalue, ])
test_results = None
if test:
test_results = list([maxvalue, ])
currentbest = experiment['trials'][0]["result"]
if not np.isfinite(currentbest):
currentbest = maxvalue
for result in [trial for trial in experiment['trials'][:cut]]:
if result["status"] != 3 or not np.isfinite(result["result"]):
# Ignore this trial, it is not valid/finished
# add previous result
trace.append(currentbest)
continue
if result["result"] < currentbest:
currentbest = min(maxvalue, result["result"])
trace.append(currentbest)
if test and np.isfinite(result["test_result"]):
test_results.append([len(trace) - 1, min(maxvalue, result["test_result"])])
if test:
return trace, test_results
else:
return trace
#def extract_trajectory(trials, cut=sys.maxint):
# trace = list()
# currentbest = trials['trials'][0]
# for result in [trial["result"] for trial in trials['trials'][:cut]]:
# if result < currentbest:
# currentbest = result
# trace.append(currentbest)
# return trace
def extract_results(experiment, cut=sys.maxint):
"""Extract a list with all results.
If `cut` is given, return up to `cut` results. Raise ValueError if cut
is equal or less than zero."""
if not isinstance(cut, int):
raise ValueError("Argument cut must be an Integer value but is %s" %
type(cut))
if cut <= 0:
raise ValueError("Argument cut cannot be zero or negative.")
trl = [trial["result"] for trial in experiment['trials'][:cut]]
return trl
def extract_runtime_timestamps(trials, cut=sys.maxint, conf_overhead=False):
"""Extracts timesteps for a list of trials
trials = list of trials as in a HPOlib.pkl
cut = consider only that many trials
conf_overhead = add conf overhead, if false only add up target algorithm time
return a list like (0, 20, 53, 101, 200)
"""
# (TODO): This does not work for crossvalidation + intensify
time_list = list()
time_list.append(0)
for idx, trial in enumerate(trials["trials"][:cut+1]):
if conf_overhead:
if len(trials["starttime"]) > 1:
raise ValueError("Cannot extract runtimes for restarted "
"experiments, please implement me")
if trial["status"] != 3:
# Although this trial is crashed, we add some minor timestep
logger.critical("%d: trial is crashed, status %d" % (idx, trial["status"]))
t = time_list[-1] + np.sum(trial["instance_durations"])
if np.isnan(t):
logger.critical("Trying to use instance durations failed")
if len(trials["trials"]) == len(trials["cv_endtime"]):
t = time_list[-1] + trials["cv_endtime"][idx] - trials["cv_starttime"][idx]
logging.critical("Use 'cv_starttime' and 'cv_endtime': %d" % t)
elif idx == len(trials["trials"][:cut+1]):
t = trials["total_wallclock_time"]
logging.critical("Assuming last trial, use 'max_wallclock_time' ")
else:
t = trials["cv_starttime"][idx] - trials["starttime"][0] + trial["duration"]
if np.isnan(t):
logger.critical("%d: Cannot extract sample as trial is broken. "
"Assuming it is the last one and returning "
"'total_wallclock_time'" % idx)
logger.critical("%d: Obtain duration failed, use %f" % (idx, 0.1))
t = time_list[-1] + 0.1
time_list.append(t)
else:
time_list.append(np.sum(trial["instance_durations"]) + time_list[-1])
return time_list
def get_best(experiment, cut=sys.maxint):
"""Return the best value found in experiment.
If `cut` is given, look at the first `cut` results. Raise ValueError if cut
is equal or less than zero."""
if not isinstance(cut, int):
raise ValueError("Argument cut must be an Integer value but is %s" %
type(cut))
if cut <= 0:
raise ValueError("Argument cut cannot be zero or negative.")
# returns the best value found in this experiment
traj = extract_trajectory(experiment)
if cut < len(traj):
best_value = traj[cut-1]
else:
best_value = traj[-1]
return best_value
def get_best_value_and_index(trials, cut=sys.maxint):
"""Return the best value found and its index in experiment.
If `cut` is given, look at the first `cut` results. Raise ValueError if cut
is equal or less than zero. Important: The index is zero-based!
if test is given, look for the best trial and report testperformance
"""
if not isinstance(cut, int):
raise ValueError("Argument cut must be an Integer value but is %s" %
type(cut))
if cut <= 0:
raise ValueError("Argument cut cannot be zero or negative.")
traj = extract_trajectory(experiment=trials, cut=cut, maxvalue=sys.maxint)
if traj[0] == sys.maxint:
traj = traj[1:]
if cut < len(traj):
best_value = traj[cut-1]
best_index = np.argmin(traj[:cut])
else:
best_value = traj[-1]
best_index = np.argmin(traj)
return best_value, best_index
def get_Trace_cv(trials, maxvalue=sys.maxint):
trace = list()
trials_list = trials['trials']
instance_order = trials['instance_order']
instance_mean = np.ones([len(trials_list), 1]) * np.inf
instance_val = np.ones([len(trials_list), len(trials_list[0]['instance_results'])]) * np.nan
for tr_idx, in_idx in instance_order:
instance_val[tr_idx, in_idx] = trials_list[tr_idx]['instance_results'][in_idx]
val = HPOlib.wrapping_util.nan_mean(instance_val[tr_idx, :])
if np.isnan(val):
val = np.inf
instance_mean[tr_idx] = val
trace.append(np.min(instance_mean, axis=0)[0])
if np.isnan(trace[-1]):
del trace[-1]
trace = [min(maxvalue, entry) for entry in trace]
return trace
def get_defaults():
default = {"linestyles": get_single_linestyle(),
"colors": get_plot_colors(),
"markers": get_empty_iterator(),
"markersize": 6,
"labelfontsize": 12,
"linewidth": 1,
"titlefontsize": 15,
"gridcolor": 'lightgrey',
"gridalpha": 0.5,
"dpi": 100
}
return default
def fill_with_defaults(def_dict):
defaults = get_defaults()
for key in defaults:
if key not in def_dict:
def_dict[key] = defaults[key]
elif def_dict[key] is None:
def_dict[key] = defaults[key]
else:
pass
return def_dict
|
[
"[email protected]"
] | |
abd9ecd9cb7b6e414f4ff573b0d132b752e8c1a2
|
68f7087d91b6cbaeb10e7aeeb383fcea20d6759d
|
/VistekDeviceManage/site-packages/vistek_onvif/onvif_wrap.py
|
991e9f2ac4bf92b2002ae09981f775c8abf5dfcf
|
[] |
no_license
|
yuetianle-Media/VistekDeviceManage
|
7cd05d0e0582dc342150307afdfcf6a9b63bc371
|
e0e83d4de01657faf409fa8141645d5b3a3a0812
|
refs/heads/master
| 2020-07-14T08:41:48.513315 | 2016-08-29T06:09:19 | 2016-08-29T06:09:19 | 66,067,186 | 0 | 0 | null | 2016-08-19T08:39:51 | 2016-08-19T08:39:51 | null |
UTF-8
|
Python
| false | false | 16,735 |
py
|
#!/usr/bin/env python
# -*- coding:utf-8 -*-
import onvif, os, sys, collections, threading, time, logging, traceback,logging.handlers
from onvif import ONVIFCamera
import Vistek.Data as v_data
import eventlet
import onvif_global_value
try:
import Queue
except:
import queue as Queue
if not os.path.exists("log"):
os.mkdir("log")
file_name = "{0}-{1}.log".format(__name__, os.getpid())
file_path = os.path.join("log", str(os.getpid()))
try:
if not os.path.exists(file_path):
os.makedirs(file_path)
except:
traceback.print_exc()
log_file = os.path.join(file_path, file_name)
log_level = logging.DEBUG
logger = logging.getLogger(file_name)
handler = logging.handlers.TimedRotatingFileHandler(log_file, when="D", interval=1)
formatter = logging.Formatter("[%(asctime)s] [%(levelname)s] [%(name)s] [%(filename)s:%(funcName)s:%(lineno)s] [%(message)s]")
logger.disabled = False
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(log_level)
import vistek_util.workTemplate as work_template
try:
import xml.etree.cElementTree as ET
except:
import xml.etree.ElementTree as ET
Session = collections.namedtuple('Session','client ip port user pwd')
RemoteSession = collections.namedtuple('RemoteSession', 'device_id ip port user pwd wsdl_path')
is_start = False
def register_device(device_id, ip, port, user_name, user_pwd):
global is_start
register_success_dev_list = onvif_global_value.get_register_success_device_list()
register_faile_dev_list = onvif_global_value.get_register_fail_device_list()
status_queue = onvif_global_value.get_device_status_queue()
register_xml_node = onvif_global_value.get_register_xml_node()
dev_status_list = onvif_global_value.get_device_status_list()
device_node = ET.SubElement(register_xml_node, "device")
device_node.set("device_id", device_id)
device_node.set("ip", ip)
device_node.set("port", str(port))
device_node.set("user", user_name)
device_node.set("pwd", user_pwd)
device_list = onvif_global_value.get_device_list()
register_node = ET.Element('register')
ip_node = ET.SubElement(register_node, 'ip')
ip_node.text = ip
session_node = ET.SubElement(register_node, 'session')
if not device_list.has_key(device_id):
if sys.platform == 'win32':
wsdl_path = os.path.join(os.path.dirname(onvif.__file__), os.path.pardir, "wsdl")
try:
cache_location= os.path.dirname(onvif.__file__)
client = ONVIFCamera(ip, port, user_name, user_pwd, wsdl_path\
, cache_location=cache_location, cache_duration="d"\
, no_cache=False)
# client = ONVIFCamera(ip, port, user_name, user_pwd, wsdl_path)
session = Session(client=client, ip=ip, port=port, user=user_name, pwd=user_pwd)
device_list[device_id] = session
if client is not None:
logger.info("register success id:{0} ip:{1}.".format(device_id, ip))
status_xml = get_device_status(device_id)
if status_xml is not None and isinstance(status_xml, tuple) and 0 < len(status_xml[0]):
status_queue.put(status_xml[0])
if device_id not in register_success_dev_list:
register_success_dev_list[device_id] = session
if device_id not in dev_status_list:
dev_status_list[device_id] = True
except:
logger.warn("register fail id:{0} ip:{1}".format(device_id, ip))
# if device_id not in register_faile_dev_list:
# register_faile_dev_list[device_id] = session
# if device_id not in dev_status_list:
# dev_status_list[device_id] = True
traceback.print_exc()
else:
client = ONVIFCamera(ip, port, user_name, user_pwd)
if not is_start:
t = StartServerThread()
t.start()
push_thread = CheckStatusChangeThread()
push_thread.start()
is_start = True
session_node.text = ip
else:
session_node.text = str(device_list.get(device_id).ip)
ET.ElementTree(register_xml_node).write("onvif_device_lists.xml", encoding="UTF-8", method="xml")
session_xml = ET.tostring(register_node, encoding='UTF-8', method='xml')
return (session_xml, len(session_xml))
def un_register_device(device_id):
device_list = onvif_global_value.get_device_list()
device_status_list = onvif_global_value.get_device_status_list()
if device_id in device_list:
device_list.pop(device_id)
if device_id in device_status_list:
device_status_list.pop(device_id)
def get_stream_url(device_id, channel=None):
device_list = onvif_global_value.get_device_list()
urls = ET.Element('stream_url_lists')
urls_xml = ""
if not device_list.has_key(device_id ):
urls_xml = ET.tostring(urls, encoding='UTF-8', method='xml')
logger.warning("device_id:{0} get_stream_url failed, dev_id not exists.".format(str(device_id)))
return (urls_xml, len(urls_xml))
else:
session = device_list.get(device_id)
media_capability_name ='Media'
request = session.client.devicemgmt.create_type("GetCapabilities")
request.Category=media_capability_name
media_info = session.client.devicemgmt.GetCapabilities(request)
#media_info = session.client.devicemgmt.GetCapabilities({'Categroy':media_capability_name})
if media_info.Media.StreamingCapabilities.RTP_RTSP_TCP or media_info.Media.StreamingCapabilities.RTP_TCP:
#media_service = session.client.create_media_service()
media_service = session.client.get_service(media_capability_name)
profiles = media_service.GetProfiles()
#video_sources = media_service.GetVideoSources()
for profile in profiles:
media_params = media_service.create_type("GetStreamUri")
media_params.ProfileToken = profile._token
media_params.StreamSetup.Stream.value = "RTP-Unicast"
media_params.StreamSetup.Transport.Protocol.value = "RTSP"
stream_url = media_service.GetStreamUri(media_params)
url_node = ET.SubElement(urls,'stream_url')
url_node.text = stream_url.Uri
urls_xml = ET.tostring(urls, encoding='UTF-8', method='xml')
logger.info("device_id:{0} get_stream_url success, value:{1}.".format(str(device_id), str(urls_xml)))
return (urls_xml, len(urls_xml))
def get_device_status(device_id, channel=None):
device_list = onvif_global_value.get_device_list()
device_status = ET.Element('device_status')
if not device_list.has_key(device_id) :
logger.error("device:{0} not register.".format(device_id))
return ("", 0)
else:
remote_session = device_list.get(device_id)
client = None
client = remote_session.client
time = client.devicemgmt.GetSystemDateAndTime()
if time is not None:
device_status.text = str(True)
else:
device_status.text = str(False)
device_status.set('ip', remote_session.ip)
device_status.set('port', str(remote_session.port))
device_status.set('device_id', str(device_id))
logger.info("dev_id:{0} ip:{1} get status success".format(device_id, str(remote_session.ip)))
status_xml = ET.tostring(device_status, encoding='UTF-8', method='xml')
return (status_xml, len(status_xml))
def get_event_service(device_id):
global device_list
device_status = ET.Element('device_status')
if device_id in device_list:
event_service = device_list[device_id].client.create_events_service()
#properties = event_service.GetEventProperties()
params = {'InitialTerminationTime':'PT20s'}
wrap = event_service.CreatePullPointSubscription(params)
#print('event service:', event_service,'func:',dir(event_service), 'wrap:', wrap, 'type:', type(wrap))
#print('is instance', isinstance(wrap,PullPointSubscription))
pull_params = {'Timeout':'PT30s', 'MessageLimit':2}
msg = wrap.SubscriptionReference.PullMessages(pull_params)
#print('msg:', msg)
def ptz(device_id, cmd, *args, **kwargs):
dev_lists = onvif_global_value.get_device_list()
if device_id in dev_lists:
session_info = dev_lists.get(device_id)
ptz_name = "PTZ"
request = session_info.client.devicemgmt.create_type("GetCapabilities")
request.Category = ptz_name
try:
ret = session_info.client.devicemgmt.GetCapabilities(request)
except:
traceback.print_exc()
def start_check_all_staus():
global device_lists
global device_status_lists
logger.info("onvif start check all device status")
device_status_manager = work_template.WorkerManager(10, 5)
while True:
for device_id, login_info in device_list.items():
device_status_manager.add_job(get_device_status, device_id)
device_status_manager.wait_for_complete()
out_queue = onvif_global_value.get_device_status_queue()
while not device_status_manager.result_queue_empty():
out_str = device_status_manager.get_result()
if 1 > len(out_str[0]):
continue
device_status_node = ET.fromstring(out_str[0])
dev_node_id = device_status_node.get('device_id')
logger.info("onvif status change queue size:{0}".format(out_queue.qsize()))
if device_status_list.has_key(dev_node_id ) and device_status_node.text != str(device_status_list.get(dev_node_id)):
out_queue.put(out_str)
time.sleep(5)
def start_get_all_status_by_eventlet():
task_pool = eventlet.GreenPool()
device_list = onvif_global_value.get_device_list()
cur_status_queue = onvif_global_value.get_cur_status_queue()
while True:
begin_time = time.time()
results = task_pool.imap(get_device_status, device_list.keys())
task_pool.waitall()
end_time = time.time()
print("total get all status time:{0}".format(end_time - begin_time))
insert_list = []
for result in results:
insert_list.append(result)
cur_status_queue.put(insert_list)
time.sleep(5)
class StartServerThread(threading.Thread):
def run(self):
#start_check_all_staus()
start_get_all_status_by_eventlet()
def check_status_change_one(status_xml):
status_queue = onvif_global_value.get_device_status_queue()
device_status_lists = onvif_global_value.get_device_status_list()
if isinstance(status_xml, tuple) and 0 < len(status_xml[0]):
device_status_list_node = ET.fromstring(status_xml[0])
if device_status_list_node is not None:
for device_status_node in device_status_list_node.iterfind("device_status"):
dev_status_id = device_status_node.get('status_id')
cur_status = str(device_status_node.text)
if dev_status_id in device_status_lists and str(device_status_node.text) != str(device_status_lists.get(dev_status_id)):
node_str = ET.tostring(device_status_node, encoding="UTF-8", method="xml")
status_queue.put(node_str)
logger.info("status change:{0}".format(node_str))
device_status_lists[dev_status_id] = cur_status
def check_status_change():
cur_status_queue = onvif_global_value.get_cur_status_queue()
task_pool = eventlet.GreenPool()
while True:
if not cur_status_queue.empty():
begin_time = time.time()
all_cur_status = cur_status_queue.get()
for item in all_cur_status:
task_pool.spawn(check_status_change_one, item)
task_pool.waitall()
end_time = time.time()
print("total check change time:{0}".format(end_time - begin_time))
time.sleep(0.01)
class CheckStatusChangeThread(threading.Thread):
def run(self):
check_status_change()
def try_get_device_info(device):
try:
client = None
if sys.platform == 'win32':
wsdl_path = os.path.join(os.path.dirname(onvif.__file__), os.path.pardir, "wsdl")
client = ONVIFCamera(device.IP, device.Port, device.Username, device.Password, wsdl_path)
#out_data = client.devicemgmt.GetDeviceInformation()
media_svc = client.get_service("Media")
channel_list = []
if media_svc is not None:
# video_sources = media_svc.GetVideoSources()
profiles = media_svc.GetProfiles()
channel_list = []
source_token = []
for profile in profiles:
source_token.append(profile.VideoSourceConfiguration._token)
unique_source_token = set(source_token)
for index, token in enumerate(unique_source_token):
channel = v_data.DmDeviceVideoChannel()
if device.DeviceID is not None:
channel.DeviceID = device.DeviceID
channel.Name = "{0}-{1}".format(device.IP, index)
channel.ChannelIndex = index
channel_list.append(channel)
if not device.ChannelList:
device.ChannelList = channel_list
if profiles is not None:
return (device.DeviceID, True, 2, "onvif")
logger.info("try device success id:{0} ip:{1} pid:{2} threadid:{3}.".format(device.DeviceID\
, device.IP\
, os.getpid()\
, threading.currentThread().ident))
logger.error("try device id:{0} ip:{1} pid:{2} threadid:{3} fail!!!".format(device.DeviceID\
, device.IP\
, os.getpid()\
, threading.currentThread().ident))
return (device.DeviceID, False, 0, None)
except:
logger.error("try device id:{0} ip:{1} pid:{2} threadid:{3} exception fail!!!".format(device.DeviceID \
, device.IP \
, os.getpid() \
, threading.currentThread().ident))
return (device.DeviceID, False, 0, None)
finally:
del client
# def try_get_device_info(device_id, ip, port, user, pwd):
# try:
# client = None
# if sys.platform == 'win32':
# wsdl_path = os.path.join(os.path.dirname(onvif.__file__), os.path.pardir, "wsdl")
# client = ONVIFCamera(ip, port, user, pwd, wsdl_path)
# out_data = client.devicemgmt.GetDeviceInformation()
# if out_data:
# return (device_id, True, 2, str(out_data.Manufacturer).lower())
# else:
# return (device_id, False, 0, None)
# except:
# traceback.print_exc()
# return (device_id, False, 0, None)
def test_nvr():
# camera_ip = "221.2.91.54"
# out = register_device(camera_ip, camera_ip, 80, 'admin', 'admin12345')
# print('out:', out, 'type:', type(out))
# out = get_stream_url(camera_ip)
camera_ip = "140.246.230.64"
out = register_device(camera_ip, camera_ip, 80, 'admin', '12345')
print('out:', out, 'type:', type(out))
out = get_stream_url(camera_ip)
def test_ipc():
begin_time = time.time()
camera_ip = "172.16.1.191"
out = register_device(camera_ip, camera_ip, 80, 'admin', '12345')
print('out:', out, 'type:', type(out))
out = get_stream_url(camera_ip)
end_time = time.time()
print("total time:{0}".format((end_time-begin_time)))
if __name__ == '__main__':
# try_get_device_info("172.16.2.190", "172.16.2.190", 85, "888888", "888888")
# test_nvr()
logging.getLogger("suds.client").setLevel(level=logging.DEBUG)
test_ipc()
while 1:
time.sleep(1)
|
[
"[email protected]"
] | |
d736a4ccfdcc6273173962b84962e7197e204b18
|
866dee1b3d01b863c31332ec81330d1b5ef5c6fa
|
/openquake.hazardlib/openquake/hazardlib/tests/gsim/raghukanth_iyengar_2007_test.py
|
ade25256d8d0e00119510016353fae82d27aa2b9
|
[
"MIT",
"AGPL-3.0-only"
] |
permissive
|
rainzhop/ConvNetQuake
|
3e2e1a040952bd5d6346905b83f39889c6a2e51a
|
a3e6de3f7992eac72f1b9883fec36b8c7fdefd48
|
refs/heads/master
| 2020-08-07T16:41:03.778293 | 2019-11-01T01:49:00 | 2019-11-01T01:49:00 | 213,527,701 | 0 | 0 |
MIT
| 2019-10-08T02:08:00 | 2019-10-08T02:08:00 | null |
UTF-8
|
Python
| false | false | 4,430 |
py
|
# The Hazard Library
# Copyright (C) 2012-2016 GEM Foundation
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
Module
:mod:`openquake.hazardlib.gsim.raghukanth_iyengar_2007_test`
defines
:class:`RaghukanthIyengar2007TestCase`
:class:`RaghukanthIyengar2007KoynaWarnaTestCase`
:class:`RaghukanthIyengar2007SouthernTestCase`
:class:`RaghukanthIyengar2007WesternCentralTestCase`
for testing of
:class:`openquake.hazardlib.gsim.raghukanth_iyengar_2007.RaghukanthIyengar2007`
and subclasses of same.
"""
import warnings
import numpy as np
from openquake.hazardlib import gsim
from openquake.hazardlib.tests.gsim.utils import BaseGSIMTestCase
from openquake.hazardlib.gsim.raghukanth_iyengar_2007 import (
RaghukanthIyengar2007,
RaghukanthIyengar2007KoynaWarna,
RaghukanthIyengar2007Southern,
RaghukanthIyengar2007WesternCentral,
)
class RaghukanthIyengar2007TestCase(BaseGSIMTestCase):
"""
Mean value data obtained by digitizing Figure 5 using
http://arohatgi.info/WebPlotDigitizer/app/ .
"""
GSIM_CLASS = RaghukanthIyengar2007
MEAN_FILE = 'RAIY07/RAIY07_PI_MEAN.csv'
SIGMA_FILE = 'RAIY07/RAIY07_PI_STD_TOTAL.csv'
TOL_PERCENT = 11.
def test_mean(self):
"""
Ensure that means match reference dataset.
"""
self.check(self.MEAN_FILE, max_discrep_percentage=self.TOL_PERCENT)
def test_std_total(self):
"""
Ensure that standard deviations match reference dataset.
"""
self.check(self.SIGMA_FILE, max_discrep_percentage=self.TOL_PERCENT)
def test_warning(self):
"""
Warning should be thrown for any vs30 below limit for NEHRP class D.
"""
rctx = gsim.base.RuptureContext()
sctx = gsim.base.SitesContext()
dctx = gsim.base.DistancesContext()
# set reasonable default values
gmpe = self.GSIM_CLASS()
rctx.mag = np.array([6.5])
dctx.rhypo = np.array([100.])
im_type = sorted(gmpe.COEFFS_BEDROCK.sa_coeffs.keys())[0]
std_types = list(gmpe.DEFINED_FOR_STANDARD_DEVIATION_TYPES)
# set critical value to trigger warning
sctx.vs30 = np.array([170.])
with warnings.catch_warnings(record=True) as warning_stream:
warnings.simplefilter('always')
mean = gmpe.get_mean_and_stddevs(
sctx, rctx, dctx, im_type, std_types)[0]
# confirm type and content of warning
assert len(warning_stream) == 1
assert issubclass(warning_stream[-1].category, UserWarning)
assert 'not supported' in str(warning_stream[-1].message).lower()
assert np.all(np.isnan(mean))
class RaghukanthIyengar2007KoynaWarnaTestCase(RaghukanthIyengar2007TestCase):
"""
Mean bedrock motions obtained by digitizing Figure 3 using
http://arohatgi.info/WebPlotDigitizer/app/ .
"""
GSIM_CLASS = RaghukanthIyengar2007KoynaWarna
MEAN_FILE = 'RAIY07/RAIY07_KW_MEAN.csv'
SIGMA_FILE = 'RAIY07/RAIY07_KW_STD_TOTAL.csv'
TOL_PERCENT = 1.5
class RaghukanthIyengar2007SouthernTestCase(RaghukanthIyengar2007TestCase):
"""
Mean bedrock motions obtained by digitizing Figure 3 using
http://arohatgi.info/WebPlotDigitizer/app/ .
"""
GSIM_CLASS = RaghukanthIyengar2007Southern
MEAN_FILE = 'RAIY07/RAIY07_SI_MEAN.csv'
SIGMA_FILE = 'RAIY07/RAIY07_SI_STD_TOTAL.csv'
TOL_PERCENT = 10.
class RaghukanthIyengar2007WesternCentralTestCase(
RaghukanthIyengar2007TestCase):
"""
Mean bedrock motions obtained by digitizing Figure 3 using
http://arohatgi.info/WebPlotDigitizer/app/ .
"""
GSIM_CLASS = RaghukanthIyengar2007WesternCentral
MEAN_FILE = 'RAIY07/RAIY07_WC_MEAN.csv'
SIGMA_FILE = 'RAIY07/RAIY07_WC_STD_TOTAL.csv'
TOL_PERCENT = 2.
|
[
"[email protected]"
] | |
0540326a34c8eea969ab2b250c2b3f1ec56b7c6a
|
289e17a9b3d0cf187f403894ebfb1007dcb1b3dc
|
/old-leetcode/older/455AssignCookies.py
|
675b3deca4cadfed6019c625c6b235707590c23c
|
[] |
no_license
|
congyingTech/Basic-Algorithm
|
7ddb376e49ef3b1c0d989fb1d4a4949d2d121d63
|
18c06a96bb14688e4a1d5fb6baf235a6b53bd3ae
|
refs/heads/master
| 2021-11-27T07:01:05.474609 | 2021-11-15T07:16:31 | 2021-11-15T07:16:31 | 224,206,231 | 10 | 3 | null | null | null | null |
UTF-8
|
Python
| false | false | 288 |
py
|
class Solution(object):
def findContentChildren(self, g, s):
"""
:type g: List[int]
:type s: List[int]
:rtype: int
"""
if __name__ == '__main__':
a = Solution()
g = [1,2,3,4]
s = [2,4,6,8]
print(a.findContentChildren(g, s))
|
[
"[email protected]"
] | |
ebebf49828ccb7b90e9a49da0fb21464b90fbbe0
|
5c29360b142773c87cae6b245b48cefa7cce708c
|
/inventory/inventory/report/alokasi_barang_terkirim/alokasi_barang_terkirim.py
|
9186e5fdbc59477a87e9214eeccd3b9205565207
|
[
"MIT"
] |
permissive
|
bobzz-zone/inventory
|
4f496d57189496f0cdc58c5510b51d26f5ad8a68
|
0cf5f3c5cc5e77a577605f99b8210fb0178aea30
|
refs/heads/master
| 2020-05-22T09:30:20.896237 | 2016-10-28T14:28:31 | 2016-10-28T14:28:31 | 65,599,131 | 0 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,859 |
py
|
# Copyright (c) 2013, Myme and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
def execute(filters=None):
columns, data = [], []
columns = []
select_field = ""
group_clause = ""
order_clause = ""
left_join = ""
if filters.get("group_by") == "Customer" :
columns = ["Customer:Link/Customer:100","Item Code:Link/Item:100","Colour:Data:100","Yard/Meter per Roll:Float:150","Qty Pending Order:Float:150","Qty Sisa di Pending Order:Float:200",
"Qty Terkirim:Float:150"]
select_field = " ab.`customer`,abd.`item_code_roll`,abd.`colour`,abd.`yard_atau_meter_per_roll`,abd.`roll_qty`,abd.`qty_sisa`,abd.`qty_terkirim` "
order_clause = " ORDER BY ab.`customer` "
elif filters.get("group_by") == "Item" :
columns = ["Item Code:Link/Item:100","Colour:Data:100","Yard/Meter per Roll:Float:150","Qty Pending Order:Float:150","Qty Sisa di Pending Order:Float:200",
"Qty Terkirim:Float:150"]
select_field = " abd.`item_code_roll`,abd.`colour`,abd.`yard_atau_meter_per_roll`,abd.`roll_qty`,abd.`qty_sisa`,abd.`qty_terkirim` "
iorder_clause = " ORDER BY abd.`item_code_roll` "
elif filters.get("group_by") == "Colour":
columns = ["Colour:Data:100","Item Code:Link/Item:100","Yard/Meter per Roll:Float:150","Qty Pending Order:Float:150","Qty Sisa di Pending Order:Float:200",
"Qty Terkirim:Float:150"]
select_field = " abd.`colour`,abd.`item_code_roll`,abd.`yard_atau_meter_per_roll`,abd.`roll_qty`,abd.`qty_sisa`,abd.`qty_terkirim` "
order_clause = " ORDER BY abd.`colour` "
elif filters.get("group_by") == "Alokasi Barang" :
columns = ["Alokasi Barang No.:Link/Alokasi Barang:100","Item Code:Link/Item:100","Colour:Data:100","Yard/Meter per Roll:Float:150","Qty Pending Order:Float:150","Qty Sisa di Pending Order:Float:200",
"Alokasi No.:Link/Alokasi Barang:100","Qty Delivery:Float:100"]
select_field = " ab.`name`,abd.`item_code_roll`,abd.`colour`,abd.`yard_atau_meter_per_roll`,abd.`roll_qty`,abd.`qty_sisa`,pld.`name`,pldd.`roll_qty` "
left_join = """ LEFT JOIN `tabPacking List Delivery`pld ON pld.`alokasi_barang`=ab.`name` AND pld.`docstatus`=1
LEFT JOIN `tabPacking List Delivery Data`pldd ON pld.`name`=pldd.`parent`
AND pldd.`item_code_roll`=abd.`item_code_roll` AND pldd.`colour`=abd.`colour` AND pldd.`yard_atau_meter_per_roll`=abd.`yard_atau_meter_per_roll`
AND pldd.`group`=abd.`group`
"""
order_clause = " ORDER BY ab.`name` "
else :
return [],[]
ab_clause = ""
if filters.get("alokasi_barang") :
ab_clause = """ AND ab.`name`="{0}" """.format(filters.get("alokasi_barang"))
item_clause = ""
if filters.get("item") :
item_clause = """ AND abd.`item_code_roll`="{0}" """.format(filters.get("item"))
customer_clause = ""
if filters.get("customer") :
customer_clause = """ AND ab.`customer`="{0}" """.format(filters.get("customer"))
colour_clause = ""
if filters.get("colour") :
colour_clause = """ AND abd.`colour`="{0}" """.format(filters.get("colour"))
delivery_clause = ""
if filters.get("delivery_from_date") and filters.get("delivery_to_date"):
delivery_clause = """ AND ab.`expected_delivery_date` BETWEEN "{0}" AND "{1}" """.format(filters.get("delivery_from_date"),filters.get("delivery_to_date"))
date_clause = ""
if filters.get("posting_from_date") and filters.get("posting_to_date"):
delivery_clause = """ AND ab.`posting_date` BETWEEN "{0}" AND "{1}" """.format(filters.get("posting_from_date"),filters.get("posting_to_date"))
data = frappe.db.sql(""" SELECT {0}
FROM `tabAlokasi Barang`ab
JOIN `tabAlokasi Barang Data`abd ON abd.`parent`=ab.`name`
{1}
WHERE ab.`docstatus`=1
{2} {3} {4} {5} {6} {7}
{8} """.format(select_field,left_join,ab_clause,item_clause,customer_clause,colour_clause,delivery_clause,date_clause,order_clause))
return columns, data
|
[
"[email protected]"
] | |
5cd5c23e6afd2f10efdc27cbbfc89a7c2f9d17d9
|
b5a31a9b0827232fb8efa2a56db90d396abbbcef
|
/Data Structures/Stack.py
|
84e9a0f480d06fa2389166dc1b9da774001d5e87
|
[] |
no_license
|
Storiesbyharshit/Competetive-Coding
|
3a693b2b3193df6fea1b9bc3bbbbb2dfb79eecfd
|
722c216ee8931754eb7de0660d2aeb21544cf48b
|
refs/heads/master
| 2022-12-12T03:50:59.348383 | 2020-09-09T17:26:01 | 2020-09-09T17:26:01 | 281,312,841 | 2 | 2 | null | 2020-08-28T05:06:11 | 2020-07-21T06:20:05 |
Python
|
UTF-8
|
Python
| false | false | 326 |
py
|
class Stack:
def __init__(self):
self.stack = []
def isEmpty(self):
return self.stack == []
def push(self, data):
self.stack.append(data)
def pop(self):
data = self.stack[-1]
del self.stack[-1]
return data
def peek(self):
return self.stack[-1]
def sizeStack(self):
return len(self.stack)
|
[
"[email protected]"
] | |
514679e96bcbea4084d4e89e8174f9f6f3c1c9db
|
b3729186993105603a77016d6a911932fec06630
|
/manage.py
|
c08ce0ec89afe776811b072eec0aabbcb0ea1511
|
[] |
no_license
|
MichalDrosio/grades
|
05425498bbee3873ebe7128f96227491861b1395
|
a6fa146ef1ac06f23f423eaf2de43c23f0faf754
|
refs/heads/main
| 2022-12-30T06:31:04.100722 | 2020-10-16T13:22:10 | 2020-10-16T13:22:10 | 302,673,025 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 659 |
py
|
#!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'can.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]"
] | |
da7ce1aef3312b4041c16a690b5921d4eebe669f
|
c97b9ae1bf06757ba61f90905e4d9b9dd6498700
|
/venv/Lib/site-packages/shapely/algorithms/polylabel.py
|
2869a0ab011af07a9cb05229a701e2b1ba8fd562
|
[] |
no_license
|
Rahulk1p/image-processor
|
f7ceee2e3f66d10b2889b937cdfd66a118df8b5d
|
385f172f7444bdbf361901108552a54979318a2d
|
refs/heads/main
| 2023-03-27T10:09:46.080935 | 2021-03-16T13:04:02 | 2021-03-16T13:04:02 | 348,115,443 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 129 |
py
|
version https://git-lfs.github.com/spec/v1
oid sha256:597fdfa5049872301df5a7b2eaca0b4d5ce5dd97bdfc31f574663654b2341f22
size 4675
|
[
"[email protected]"
] | |
68989c58d3143706f0ff6d4c919e22855714c894
|
52389ba81fa5abe009b2dee5deb83fabe56982a8
|
/kgtk/cli/tail.py
|
bf327303d5eb480123f504ed88c4d47fdef28569
|
[
"MIT"
] |
permissive
|
Qanu-survey/kgtk
|
4111a817bd81e9d48b0b0b421f733f2f25e45d7a
|
95024bfc61c12282c75d53d256115cdf41837f04
|
refs/heads/master
| 2023-09-01T03:17:34.712634 | 2021-10-23T00:30:45 | 2021-10-23T00:30:45 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 7,058 |
py
|
"""This utility is analogous to the POSIX 'tail' command.
For "-n N", pass just the last N data edges of a KGTK input file to the KGTK output file.
The POSIX 'tail' command's notion of '-n +N' is not supported.
The header record, cotaining the column names, is always passed and is not
included in N.
Multiplier suffixes are not supported.
--mode=NONE is default.
TODO: Need KgtkWriterOptions
"""
from argparse import Namespace, SUPPRESS
from kgtk.cli_argparse import KGTKArgumentParser, KGTKFiles
def parser():
return {
'help': 'Pass the tail (last records) of a KGTK file.',
'description': 'This utility is analogous to the POSIX "head" command. ' +
'\n\nFor "-n N", pass just the last N data edges of a KGTK input file to the KGTK output file. ' +
'\n\n"-n +N" does not have the special meaning it has in the POSIC "tail" command. ' +
'\n\nThe header record, cotaining the column names, is always passed and is not included in N. ' +
'\n\nMultiplier suffixes are not supported. ' +
'\n\nUse this command to filter the output of any KGTK command: ' +
'\n\nkgtk xxx / tail -n 20 ' +
'\n\nUse it to limit the records in a file: ' +
'\n\nkgtk tail -i file.tsv -o file.html' +
'\n\nThis command defaults to --mode=NONE so it will work with TSV files that do not follow KGTK column naming conventions.' +
'\n\nAdditional options are shown in expert help.\nkgtk --expert html --help'
}
def add_arguments_extended(parser: KGTKArgumentParser, parsed_shared_args: Namespace):
"""
Parse arguments
Args:
parser (argparse.ArgumentParser)
"""
from kgtk.io.kgtkreader import KgtkReader, KgtkReaderOptions, KgtkReaderMode
from kgtk.io.kgtkwriter import KgtkWriter
from kgtk.value.kgtkvalueoptions import KgtkValueOptions
_expert: bool = parsed_shared_args._expert
# This helper function makes it easy to suppress options from
# The help message. The options are still there, and initialize
# what they need to initialize.
def h(msg: str)->str:
if _expert:
return msg
else:
return SUPPRESS
parser.add_input_file()
parser.add_output_file()
parser.add_argument("-n", "--edges", dest="edge_limit", type=int, default=10,
help="The number of records to pass (default=%(default)d).")
parser.add_argument( "--output-format", dest="output_format", help=h("The file format (default=kgtk)"), type=str,
choices=KgtkWriter.OUTPUT_FORMAT_CHOICES)
KgtkReader.add_debug_arguments(parser, expert=_expert)
KgtkReaderOptions.add_arguments(parser, mode_options=True, default_mode=KgtkReaderMode.NONE, expert=_expert)
KgtkValueOptions.add_arguments(parser, expert=_expert)
def run(input_file: KGTKFiles,
output_file: KGTKFiles,
edge_limit: int,
output_format: str,
errors_to_stdout: bool = False,
errors_to_stderr: bool = True,
show_options: bool = False,
verbose: bool = False,
very_verbose: bool = False,
**kwargs # Whatever KgtkFileOptions and KgtkValueOptions want.
)->int:
# import modules locally
from collections import deque
from pathlib import Path
import sys
import typing
from kgtk.exceptions import KGTKException
from kgtk.io.kgtkreader import KgtkReader, KgtkReaderOptions, KgtkReaderMode
from kgtk.io.kgtkwriter import KgtkWriter
from kgtk.join.kgtkcat import KgtkCat
from kgtk.value.kgtkvalueoptions import KgtkValueOptions
input_file_path: Path = KGTKArgumentParser.get_input_file(input_file)
output_file_path: Path = KGTKArgumentParser.get_output_file(output_file)
# Select where to send error messages, defaulting to stderr.
error_file: typing.TextIO = sys.stdout if errors_to_stdout else sys.stderr
# TODO: check that at most one input file is stdin?
# Build the option structures.
reader_options: KgtkReaderOptions = KgtkReaderOptions.from_dict(kwargs, mode=KgtkReaderMode.NONE)
value_options: KgtkValueOptions = KgtkValueOptions.from_dict(kwargs)
# Show the final option structures for debugging and documentation.
if show_options:
print("--input-file=%s" % str(input_file_path), file=error_file, flush=True)
print("--output-file=%s" % str(output_file_path), file=error_file, flush=True)
print("--edges=%s" % str(edge_limit), file=error_file, flush=True)
reader_options.show(out=error_file)
value_options.show(out=error_file)
print("=======", file=error_file, flush=True)
try:
kr: KgtkReader = KgtkReader.open(input_file_path,
options=reader_options,
value_options = value_options,
error_file=error_file,
verbose=verbose,
very_verbose=very_verbose,
)
output_mode: KgtkWriter.Mode = KgtkWriter.Mode.NONE
if kr.is_edge_file:
output_mode = KgtkWriter.Mode.EDGE
if verbose:
print("Opening the output edge file: %s" % str(output_file_path), file=error_file, flush=True)
elif kr.is_node_file:
output_mode = KgtkWriter.Mode.NODE
if verbose:
print("Opening the output node file: %s" % str(output_file_path), file=error_file, flush=True)
else:
if verbose:
print("Opening the output file: %s" % str(output_file_path), file=error_file, flush=True)
kw: KgtkWriter = KgtkWriter.open(kr.column_names,
output_file_path,
use_mgzip=reader_options.use_mgzip, # Hack!
mgzip_threads=reader_options.mgzip_threads, # Hack!
gzip_in_parallel=False,
mode=output_mode,
output_format=output_format,
error_file=error_file,
verbose=verbose,
very_verbose=very_verbose)
edge_count: int = 0
row: typing.List[str]
edge_buffer: deque = deque()
for row in kr:
edge_buffer.append(row)
if len(edge_buffer) > edge_limit:
edge_buffer.popleft()
while len(edge_buffer) > 0:
edge_count += 1
kw.write(edge_buffer.popleft())
kw.close()
if verbose:
print("Copied %d edges." % edge_count, file=error_file, flush=True)
except SystemExit as e:
raise KGTKException("Exit requested")
except Exception as e:
raise KGTKException(str(e))
|
[
"[email protected]"
] | |
67fe1507b996d283661d40cca7f5ffaec7b9ad5c
|
1f006f0c7871fcde10986c4f5cec916f545afc9f
|
/apps/ice/plugins/required/plugin_auth_ldap_test.py
|
790563538a3ae1cca495208198b7d61596441ab3
|
[] |
no_license
|
ptsefton/integrated-content-environment
|
248b8cd29b29e8989ec1a154dd373814742a38c1
|
c1d6b5a1bea3df4dde10cb582fb0da361dd747bc
|
refs/heads/master
| 2021-01-10T04:46:09.319989 | 2011-05-05T01:42:52 | 2011-05-05T01:42:52 | 36,273,470 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,214 |
py
|
#!/usr/bin/env python
# Copyright (C) 2007 Distance and e-Learning Centre,
# University of Southern Queensland
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
from unittest import TestCase
import sys
from plugin_auth_ldap import AuthLDAP
## ===============================
## TESTS
## ===============================
class AuthLDAPTests(TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def testCheckAuthentication(self):
class Object(object): pass
context = Object()
context.settings = {"ldapUrl":None}
#context.settings["ldapOU"] = "Staff"
#context.settings["ldapDC"] = "dc=usq,dc=edu,dc=au"
auth = AuthLDAP(context)
self.assertFalse(auth.checkAuthentication("userId", None))
self.assertFalse(auth.checkAuthentication("userId", ""))
self.assertFalse(auth.checkAuthentication("userId", "pw"))
# Note: can not automate testing for 'True' without a test LDAP server!
def runUnitTests(locals):
print "\n\n\n\n"
if sys.platform=="cli":
import clr
import System.Console
System.Console.Clear()
print "---- Testing under IronPython ----"
else:
print "---- Testing ----"
# Run only the selected tests
args = list(sys.argv)
sys.argv = sys.argv[:1]
args.pop(0)
runTests = args
runTests = [ i.lower().strip(", ") for i in runTests]
runTests = ["test"+i for i in runTests if not i.startswith("test")] + \
[i for i in runTests if i.startswith("test")]
if runTests!=[]:
testClasses = [i for i in locals.values() \
if hasattr(i, "__bases__") and \
(TestCase in i.__bases__)]
testing = []
for x in testClasses:
l = dir(x)
l = [ i for i in l if i.startswith("test") and callable(getattr(x, i))]
for i in l:
if i.lower() not in runTests:
delattr(x, i)
else:
testing.append(i)
x = None
num = len(testing)
if num<1:
print "No selected tests found! - %s" % str(args)[1:-1]
elif num==1:
print "Running selected test - %s" % (str(testing)[1:-1])
else:
print "Running %s selected tests - %s" % (num, str(testing)[1:-1])
from unittest import main
main()
if __name__=="__main__":
runUnitTests(locals())
sys.exit(0)
|
[
"[email protected]@110e3293-9ef9-cb8f-f479-66bdb1942d05"
] |
[email protected]@110e3293-9ef9-cb8f-f479-66bdb1942d05
|
7b0151f365485c4c49e2ff58ec33293dc6a48996
|
eb99769b7c9e0eb1cf3b88878934a400ba42f0bf
|
/news/migrations/0002_favoriteitem.py
|
054ed15c2d05e485c9d310ce8e7432b2650c6d91
|
[] |
no_license
|
Levalife/petsterr2.0
|
3657b200b9e236b81896f4ac104932e85517ceb3
|
43d20e65362596d72942fe624c29fd4f84d90f9a
|
refs/heads/master
| 2023-01-13T04:58:23.496527 | 2018-09-13T09:50:48 | 2018-09-13T09:50:48 | 203,134,329 | 0 | 0 | null | 2023-01-05T21:55:18 | 2019-08-19T08:48:32 |
Python
|
UTF-8
|
Python
| false | false | 1,358 |
py
|
# Generated by Django 2.0.6 on 2018-07-09 10:06
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('news', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='FavoriteItem',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
('is_deleted', models.BooleanField(default=False)),
('source_id', models.IntegerField(blank=True, null=True)),
('type', models.CharField(blank=True, choices=[('kennel', 'kennel'), ('animal', 'animal'), ('litter', 'litter')], max_length=100, null=True)),
('follower', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL)),
],
options={
'verbose_name': 'Favorite Items',
'verbose_name_plural': 'Favorite Item',
'db_table': 'favorite_item',
},
),
]
|
[
"[email protected]"
] | |
94e03b2534aad88510168b1b286c1c69c38c9c59
|
7170e8a71c85bf88c43ae7524ffab25cf853b916
|
/awswrangler/__metadata__.py
|
b3ae6c0df2a77d84e65a606e7447b3fca0127a9e
|
[
"Apache-2.0"
] |
permissive
|
ibanmarco/aws-data-wrangler
|
b18aa898e2c0f33f225c44cdebf11b25f6637f63
|
e99937296075c671e5f8a0998b430879c808687d
|
refs/heads/master
| 2022-12-29T21:18:18.351632 | 2020-10-19T19:13:58 | 2020-10-19T19:13:58 | 289,549,549 | 0 | 0 |
Apache-2.0
| 2020-10-19T19:13:59 | 2020-08-22T19:01:24 |
Python
|
UTF-8
|
Python
| false | false | 286 |
py
|
"""Metadata Module.
Source repository: https://github.com/awslabs/aws-data-wrangler
Documentation: https://aws-data-wrangler.readthedocs.io/
"""
__title__: str = "awswrangler"
__description__: str = "Pandas on AWS."
__version__: str = "1.9.6"
__license__: str = "Apache License 2.0"
|
[
"[email protected]"
] | |
280b193fda89b737c832f4360d1e6a627aa012db
|
d48b89048d4fe8f09d1fcc1702f89b195186e025
|
/portfolio/settings.py
|
1bc72b7660ac6fbed74985a0433e7bf586ff4454
|
[] |
no_license
|
Aitodev/portfoliotestwithstudents
|
833e32f80afc5361204880031bdcbba3af580421
|
fb8e220bb504253ce47856ea46e1a28d3b6e46ef
|
refs/heads/master
| 2022-12-11T23:13:00.170658 | 2020-09-24T04:20:11 | 2020-09-24T04:20:11 | 298,162,892 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,179 |
py
|
import os
from pathlib import Path
# Build paths inside the project like this: BASE_DIR / 'subdir'.
BASE_DIR = Path(__file__).resolve().parent.parent
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'f-b%v$a9svi8d+xm%a_68)m)9x&mm3_+-v_1+dccxw1lj-p8)j'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'mainapp.apps.MainappConfig',
]
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'portfolio.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'portfolio.wsgi.application'
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': BASE_DIR / 'db.sqlite3',
}
}
# Password validation
# https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/3.1/topics/i18n/
LANGUAGE_CODE = 'ru'
TIME_ZONE = 'Asia/Bishkek'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/3.1/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'mainapp/media')
EMAIL_HOST = 'smtp.gmail.com'
EMAIL_HOST_USER = '[email protected]'
EMAIL_HOST_PASSWORD = 'aitodevguruit'
EMAIL_PORT = 465
EMAIL_USE_SSL = True
DEFAULT_FROM_EMAIL = '[email protected]'
DEFAULT_TO_EMAIL = '[email protected]'
|
[
"[email protected]"
] | |
4b61cdfe5342b7c9620f14ce0febe8663b51fffd
|
f0d713996eb095bcdc701f3fab0a8110b8541cbb
|
/2t6NvMe27HtSmqC4F_9.py
|
5a367fa275b81bfe1cef01467c4a3e4dccdd6e0e
|
[] |
no_license
|
daniel-reich/turbo-robot
|
feda6c0523bb83ab8954b6d06302bfec5b16ebdf
|
a7a25c63097674c0a81675eed7e6b763785f1c41
|
refs/heads/main
| 2023-03-26T01:55:14.210264 | 2021-03-23T16:08:01 | 2021-03-23T16:08:01 | 350,773,815 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,153 |
py
|
"""
Write three functions:
1. boolean_and
2. boolean_or
3. boolean_xor
These functions should evaluate a list of `True` and `False` values, starting
from the leftmost element and evaluating pairwise.
### Examples
boolean_and([True, True, False, True]) ➞ False
# [True, True, False, True] => [True, False, True] => [False, True] => False
boolean_or([True, True, False, False]) ➞ True
# [True, True, False, True] => [True, False, False] => [True, False] => True
boolean_xor([True, True, False, False]) ➞ False
# [True, True, False, False] => [False, False, False] => [False, False] => False
### Notes
* `XOR` is the same as `OR`, except that it excludes `[True, True]`.
* Each time you evaluate an element at 0 and at 1, you collapse it into the single result.
"""
def boolean_and(lst):
return not False in lst
def boolean_or(lst):
return True in lst
def boolean_xor(lst):
while len(lst) > 1:
tmp = []
for i in range(len(lst)-1):
a, b = lst[i:i+2]
if (a or b) and not (a and b): tmp.append(True)
else: tmp.append(False)
lst = tmp[:]
return tmp[0]
|
[
"[email protected]"
] | |
41f654ef6d53ddd5b0dd9ad92e5c7c59858f5527
|
741c5c70bf4a0adb05db6b0777c8d07e28eb9cf6
|
/lib/python3.4/site-packages/IPython/nbformat/v3/validator.py
|
bc037cce2b4a6337067ab018bbfd06fe6ddc9505
|
[] |
no_license
|
andybp85/hyLittleSchemer
|
e686d2dc0f9067562367ea1173f275e8e2d2cb85
|
af5cb6adf6a196cc346aa7d14d7f9509e084c414
|
refs/heads/master
| 2021-01-19T07:48:31.309949 | 2015-01-04T00:57:30 | 2015-01-04T00:57:30 | 28,496,304 | 6 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,786 |
py
|
from __future__ import print_function
#!/usr/bin/env python
# -*- coding: utf8 -*-
import argparse
import traceback
import json
from IPython.external.jsonschema import Draft3Validator, validate, ValidationError
import IPython.external.jsonpointer as jsonpointer
from IPython.utils.py3compat import iteritems
def nbvalidate(nbjson, schema='v3.withref.json', key=None,verbose=True):
v3schema = resolve_ref(json.load(open(schema,'r')))
if key :
v3schema = jsonpointer.resolve_pointer(v3schema,key)
errors = 0
v = Draft3Validator(v3schema);
for error in v.iter_errors(nbjson):
errors = errors + 1
if verbose:
print(error)
return errors
def resolve_ref(json, base=None):
"""return a json with resolved internal references
only support local reference to the same json
"""
if not base :
base = json
temp = None
if type(json) is list:
temp = [];
for item in json:
temp.append(resolve_ref(item, base=base))
elif type(json) is dict:
temp = {};
for key,value in iteritems(json):
if key == '$ref':
return resolve_ref(jsonpointer.resolve_pointer(base,value), base=base)
else :
temp[key]=resolve_ref(value, base=base)
else :
return json
return temp
def convert(namein, nameout, indent=2):
"""resolve the references of namein, save the result in nameout"""
jsn = None
with open(namein) as file :
jsn = json.load(file)
v = resolve_ref(jsn, base=jsn)
x = jsonpointer.resolve_pointer(v, '/notebook')
with open(nameout,'w') as file:
json.dump(x,file,indent=indent)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--schema',
type=str, default='v3.withref.json')
parser.add_argument('-k', '--key',
type=str, default='/notebook',
help='subkey to extract json schema from json file')
parser.add_argument("-v", "--verbose", action="store_true",
help="increase output verbosity")
parser.add_argument('filename',
type=str,
help="file to validate",
nargs='*',
metavar='names')
args = parser.parse_args()
for name in args.filename :
nerror = nbvalidate(json.load(open(name,'r')),
schema=args.schema,
key=args.key,
verbose=args.verbose)
if nerror is 0:
print(u"[Pass]",name)
else :
print(u"[ ]",name,'(%d)'%(nerror))
if args.verbose :
print('==================================================')
|
[
"[email protected]"
] | |
101791f75248e23711a3f1cccf9fa0bdf8c2ba68
|
c4b8e1e09dedbccd37ca008ecaaca4438610bbaf
|
/google_or_tools/circuit.py
|
6b66df28031dfa54488a90ae0f6ab74b12c7d1e5
|
[
"MIT"
] |
permissive
|
hakank/hakank
|
4806598b98cb36dd51b24b0ab688f52dadfe9626
|
c337aaf8187f15dcdc4d5b09cd2ed0dbdb2e72c2
|
refs/heads/master
| 2023-08-15T00:21:52.750270 | 2023-07-27T16:21:40 | 2023-07-27T16:21:40 | 11,933,517 | 336 | 97 |
MIT
| 2023-07-27T11:19:42 | 2013-08-06T20:12:10 |
JavaScript
|
UTF-8
|
Python
| false | false | 3,206 |
py
|
# Copyright 2010 Hakan Kjellerstrand [email protected]
#
# 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.
"""
Decomposition of the circuit constraint in Google CP Solver.
Cf Global constraint catalog:
http://www.emn.fr/x-info/sdemasse/gccat/Ccircuit.html
Solution of n=4:
x: [2, 0, 3, 1]
x: [3, 0, 1, 2]
x: [1, 3, 0, 2]
x: [3, 2, 0, 1]
x: [1, 2, 3, 0]
x: [2, 3, 1, 0]
The 'orbit' method that is used here is based on some
observations on permutation orbits.
Compare with the following models:
* MiniZinc: http://www.hakank.org/minizinc/circuit_test.mzn
* Gecode: http://www.hakank.org/gecode/circuit_orbit.mzn
This model was created by Hakan Kjellerstrand ([email protected])
Also see my other Google CP Solver models:
http://www.hakank.org/google_or_tools/
"""
from __future__ import print_function
import sys
from ortools.constraint_solver import pywrapcp
#
# circuit(x)
# constraints x to be an circuit
#
# Note: This assumes that x is has the domain 0..len(x)-1,
# i.e. 0-based.
#
def circuit(solver, x):
n = len(x)
z = [solver.IntVar(0, n - 1, "z%i" % i) for i in range(n)]
solver.Add(solver.AllDifferent(x))
solver.Add(solver.AllDifferent(z))
# put the orbit of x[0] in in z[0..n-1]
solver.Add(z[0] == x[0])
for i in range(1, n - 1):
# The following constraint give the error
# "TypeError: list indices must be integers, not IntVar"
# solver.Add(z[i] == x[z[i-1]])
# solution: use Element instead
solver.Add(z[i] == solver.Element(x, z[i - 1]))
#
# Note: At least one of the following two constraint must be set.
#
# may not be 0 for i < n-1
for i in range(1, n - 1):
solver.Add(z[i] != 0)
# when i = n-1 it must be 0
solver.Add(z[n - 1] == 0)
def main(n=5):
# Create the solver.
solver = pywrapcp.Solver("Send most money")
# data
print("n:", n)
# declare variables
# Note: domain should be 0..n-1
x = [solver.IntVar(0, n - 1, "x%i" % i) for i in range(n)]
#
# constraints
#
circuit(solver, x)
#
# solution and search
#
solution = solver.Assignment()
solution.Add(x)
collector = solver.AllSolutionCollector(solution)
solver.Solve(
solver.Phase(x, solver.CHOOSE_FIRST_UNBOUND, solver.ASSIGN_MIN_VALUE),
[collector])
num_solutions = collector.SolutionCount()
for s in range(num_solutions):
print("x:", [collector.Value(s, x[i]) for i in range(len(x))])
print()
print("num_solutions:", num_solutions)
print("failures:", solver.Failures())
print("branches:", solver.Branches())
print("WallTime:", solver.WallTime())
print()
n = 5
if __name__ == "__main__":
if len(sys.argv) > 1:
n = int(sys.argv[1])
main(n)
|
[
"[email protected]"
] | |
aa76d1630dc7831741c9ddd5d4369448e608559c
|
750f6d44fc8aa00a2011070af681936c53a6127e
|
/Question_11_20/q19.py
|
4702fdfae98fa1fe8953c0bd6c0cf364aa89eb71
|
[] |
no_license
|
ryuseiasumo/Gazoshori100knock
|
2aab4bf625183e597a7e8401af4ec1dc67d334ad
|
71ae0ce1d712ae5979aa5be3d088290934e26369
|
refs/heads/master
| 2022-06-19T10:06:16.169369 | 2020-05-10T03:48:17 | 2020-05-10T03:48:17 | 254,810,301 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,616 |
py
|
import cv2
import sys
sys.path.append("..")
import numpy as np
from Question_01_10.q2 import Gray
def zero_padding(_img, K_size = 5):
pad = K_size//2
out = np.pad(_img, ([pad,pad],[pad,pad]), "constant")
return out
def Log_fillter(_img, K_size = 5, sigma = 3):
img = _img.copy()
pad = K_size//2
if len(_img.shape) == 3:
H, W, C = _img.shape
else:
_img = np.expand_dims(_img, axis = -1)
H, W, C = _img.shape
img_zero = zero_padding(img).astype(np.float)
tmp = img_zero.copy()
out = np.zeros_like(tmp).astype(np.float)
print(tmp)
print("ーーーーーーーーー")
#prepare kernel
K = np.zeros((K_size, K_size), dtype = np.float)
for y in range(-pad, -pad + K_size):
for x in range(-pad, -pad + K_size):
K[y + pad, x + pad] = (x ** 2 + y ** 2 - 2 * (sigma ** 2)) * np.exp( - (x ** 2 + y ** 2) / (2 * (sigma ** 2)))
K /= (2 * np.pi * (sigma ** 6))
K /= K.sum()
# filtering
for y in range(H):
for x in range(W):
out[pad + y, pad + x] = np.sum(K * tmp[y: y + K_size, x: x + K_size])
print(out)
print("ーーーーーーーーーーーーーー")
out = np.clip(out, 0, 255)
print(out)
print("ーーーーーーーーーーーーーー")
out = out[pad:pad+H, pad:pad+W].astype(np.uint8)
return out
img = cv2.imread("./image_11_20/imori_noise.jpg").astype(np.float)
img_gray = Gray(img)
img_ans = Log_fillter(img_gray)
cv2.imwrite("./image_11_20/answer19.jpg", img_ans)
cv2.imshow("result", img_ans)
cv2.waitKey(0)
cv2.destroyAllWindows()
|
[
"[email protected]"
] | |
29c2efec1dab3982a89e48c821ee9c8b852062a3
|
f12f299984060186ad8422913ed591cac1fd918d
|
/miscellaneous/odd_even_jump.py
|
44b387b4c86d4f79aee8830bc8d895e058b8bb36
|
[] |
no_license
|
pitikdmitry/leetcode
|
775f9163850dd5d6cdb971603c6fd4615c7f89d7
|
f82a36be6d8dcf842354d759bab98dd915173fd5
|
refs/heads/master
| 2023-01-23T23:42:01.568390 | 2020-12-07T09:03:45 | 2020-12-07T09:03:45 | 240,458,017 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,812 |
py
|
'''
You are given an integer array A. From some starting index, you can make a series of jumps.
The (1st, 3rd, 5th, ...) jumps in the series are called odd numbered jumps, and the (2nd, 4th, 6th, ...) jumps in the series are called even numbered jumps.
You may from index i jump forward to index j (with i < j) in the following way:
During odd numbered jumps (ie. jumps 1, 3, 5, ...), you jump to the index j such that A[i] <= A[j] and A[j] is the smallest possible value. If there are multiple such indexes j, you can only jump to the smallest such index j.
During even numbered jumps (ie. jumps 2, 4, 6, ...), you jump to the index j such that A[i] >= A[j] and A[j] is the largest possible value. If there are multiple such indexes j, you can only jump to the smallest such index j.
(It may be the case that for some index i, there are no legal jumps.)
A starting index is good if, starting from that index, you can reach the end of the array (index A.length - 1) by jumping some number of times (possibly 0 or more than once.)
Return the number of good starting indexes.
Example 1:
Input: [10,13,12,14,15]
Output: 2
Explanation:
From starting index i = 0, we can jump to i = 2 (since A[2]
is the smallest among A[1], A[2], A[3], A[4] that is greater or equal to A[0]),
then we can't jump any more.
From starting index i = 1 and i = 2, we can jump to i = 3, then we can't jump any more.
From starting index i = 3, we can jump to i = 4, so we've reached the end.
From starting index i = 4, we've reached the end already.
In total, there are 2 different starting indexes (i = 3, i = 4)
where we can reach the end with some number of jumps.
'''
from typing import List
# The problem is how to find next greater element in array, that is righter
# We will use decreasing stack and process elements in sorted order
class Solution:
def build_next_greater_el_array(self, increasing_elements_idxs, next_bigger_el_idxs):
decr_stack = []
for idx in increasing_elements_idxs:
while len(decr_stack) > 0 and decr_stack[-1] < idx:
prev_idx = decr_stack.pop()
next_bigger_el_idxs[prev_idx] = idx
decr_stack.append(idx)
def oddEvenJumps(self, arr: List[int]) -> int:
if len(arr) == 0:
return 0
arr_with_idx = [[arr[i], i] for i in range(len(arr))]
# for next bigger element
arr_with_idx_sorted = sorted(arr_with_idx, key=lambda x: x[0])
increasing_elements_idxs = list(map(lambda x: x[1], arr_with_idx_sorted))
next_bigger_el_idxs = [None for i in range(len(arr))]
self.build_next_greater_el_array(increasing_elements_idxs, next_bigger_el_idxs)
# for next smaller element
arr_with_idx_sorted_desc = sorted(arr_with_idx, key=lambda x: x[0], reverse=True)
increasing_elements_idxs_desc = list(map(lambda x: x[1], arr_with_idx_sorted_desc))
next_smaller_el_idxs = [None for i in range(len(arr))]
self.build_next_greater_el_array(increasing_elements_idxs_desc, next_smaller_el_idxs)
# process elements
higher = [False for i in range(len(arr))]
lower = [False for i in range(len(arr))]
higher[-1] = True
lower[-1] = True
result = 1
for i in range(len(arr) - 2, -1, -1):
next_bigger_el_idx = next_bigger_el_idxs[i]
next_smaller_el_idx = next_smaller_el_idxs[i]
if next_bigger_el_idx is not None:
lower[i] = higher[next_bigger_el_idx]
if next_smaller_el_idx is not None:
higher[i] = lower[next_smaller_el_idx]
if lower[i] is True:
result += 1
return result
solution = Solution()
arr = [10,13,12,14,15]
print(solution.oddEvenJumps(arr))
|
[
"[email protected]"
] | |
5658fc51036576756e2e818068317ea39e8b4c02
|
fa5510464ba1573f41c94b4dd652fc684036beac
|
/FlaskURLShortener/main.py
|
f965c3e14b301c251cb9108cd21ff0bc8a3700e2
|
[] |
no_license
|
AndrewErmakov/WebDevelopmentTraining
|
5371265f1737f929c454134a8ed032bdeda5e8ef
|
86aeab91157dab57be41b7a10b60535a39ea0a84
|
refs/heads/master
| 2023-05-11T06:43:14.422330 | 2021-06-27T18:56:13 | 2021-06-27T18:56:13 | 246,019,245 | 1 | 0 | null | 2023-05-01T22:52:00 | 2020-03-09T11:35:41 |
Python
|
UTF-8
|
Python
| false | false | 1,626 |
py
|
from flask import (
Flask,
render_template,
request,
redirect,
url_for,
flash,
abort,
session,
jsonify)
import json
import os.path
app = Flask(__name__)
app.secret_key = 'shtopor'
@app.route('/')
def index():
return render_template('index.html', codes=session.keys())
@app.route('/your-url', methods=['POST', 'GET'])
def your_url():
if request.method == 'POST':
urls = {}
if os.path.exists('urls.json'):
with open('urls.json') as file:
urls = json.load(file)
if request.form['code'] in urls.keys():
flash('That short name has already been taken')
return redirect(url_for('index'))
urls[request.form.get('code')] = {'url': request.form['url']}
with open('urls.json', 'w') as file:
json.dump(urls, file)
session[request.form['code']] = True
return render_template('your_url.html', code=request.form.get('code'))
else:
return redirect(url_for('index'))
@app.route('/<string:code>')
def redirect_to_url(code):
if os.path.exists('urls.json'):
with open('urls.json') as file:
urls = json.load(file)
if code in urls.keys():
if 'url' in urls[code].keys():
return redirect(urls[code]['url'])
return abort(404)
# custom handler 404 error
@app.errorhandler(404)
def page_not_found(error):
return render_template('page_not_found.html'), 404
@app.route('/api')
def session_api():
return jsonify(list(session.keys()))
if __name__ == "__main__":
app.run()
|
[
"[email protected]"
] | |
f509745a558fc1e998a4da4875a540a353adf7ab
|
afc677459e46635ceffccf60d1daf50e62694557
|
/ACME/transform/RandomScaling.py
|
e48ad04f0eca3ce1ef9b0a164f92ce91f4ee8876
|
[
"MIT"
] |
permissive
|
mauriziokovacic/ACME
|
056b06da4bf66d89087fcfcbe0fd0a2e255d09f3
|
2615b66dd4addfd5c03d9d91a24c7da414294308
|
refs/heads/master
| 2020-05-23T23:40:06.667416 | 2020-01-10T14:42:01 | 2020-01-10T14:42:01 | 186,997,977 | 3 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 821 |
py
|
import torch
from ..utility.islist import *
from ..math.normvec import *
from .Transform import *
class RandomScaling(Transform):
def __init__(self, attr=['pos', 'norm']):
super(RandomScaling, self).__init__()
self.attr = attr if islist(attr) else [attr]
def __eval__(self, x, *args, **kwargs):
T = torch.diag(torch.rand(3, x.pos.device) * 2 - 1)
for attr in self.attr:
if hasattr(x, attr):
d = getattr(x, attr)
if d:
if attr == 'norm':
setattr(x, attr, normr(torch.matmul(d, T)))
else:
setattr(x, attr, torch.matmul(d, T))
def __extra_repr__(self):
return 'attr={}'.format(self.attr if len(self.attr) > 1 else self.attr[0])
|
[
"[email protected]"
] | |
71665b70d45138e620cfc606a18cb9ff5b7b5ff2
|
11f7207d5e7c1c6c3329f7ae9d4fa1507c806110
|
/test_faker_producer.py
|
c862e7e93684590ba0067889bc781f55559f764b
|
[] |
no_license
|
gmdmgithub/pandas-playground
|
4a7a654a27acb0d14b442afdee6af22d67f64852
|
a24267ab6b79c2351e9220bd11e028a190df21c2
|
refs/heads/master
| 2022-12-09T20:26:55.233005 | 2019-10-07T15:02:38 | 2019-10-07T15:02:38 | 205,238,397 | 0 | 0 | null | 2022-12-08T06:36:42 | 2019-08-29T19:42:31 |
Python
|
UTF-8
|
Python
| false | false | 233 |
py
|
import unittest
import faker_producer as fp
class TestFakerProducer(unittest.TestCase):
def test_first_name(self):
self.assertIsNone(fp.first_name(None))
self.assertIsNotNone(fp.first_name('Alex'))
|
[
"[email protected]"
] | |
bc0cc3c965bd0986cdcb2ad70947c4f1c090a79b
|
ca7aa979e7059467e158830b76673f5b77a0f5a3
|
/Python_codes/p02233/s629912444.py
|
b438e72f5bac040431451dc0f4cb4283a499aed9
|
[] |
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 | 127 |
py
|
n=int(input())
a,b=1,1
if n<=1:
print(1)
else:
for i in range(n-1):
c=a+b
a=b
b=c
print(c)
|
[
"[email protected]"
] | |
c3ab16220b5902f75045e7bbb8d1a43f0bd8deb9
|
49600905e4aaa4929758997c5d1df09ff693534a
|
/njunmt/data/text_inputter.py
|
b6c2046d7ca7f1578310ce5af9768c88b61673fe
|
[
"Apache-2.0"
] |
permissive
|
zhaocq-nlp/NJUNMT-tf
|
3466d967cdc96b2dc6b0fb6a3e769ec1b83010d2
|
01155c740705f1641ebf3134829cea0e212f2d28
|
refs/heads/v0.6
| 2018-12-04T18:55:36.641444 | 2018-01-26T06:39:42 | 2018-01-26T06:39:42 | 115,672,915 | 114 | 44 |
Apache-2.0
| 2018-01-27T13:54:31 | 2017-12-29T01:17:43 |
Python
|
UTF-8
|
Python
| false | false | 29,753 |
py
|
# Copyright 2017 Natural Language Processing Group, Nanjing University, [email protected].
#
# 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.
""" Classes for reading in data. """
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from abc import ABCMeta, abstractmethod
import numpy
import six
import tensorflow as tf
from tensorflow import gfile
from njunmt.utils.global_names import GlobalNames
from njunmt.utils.misc import open_file, close_file
from njunmt.utils.misc import shuffle_data
from njunmt.utils.misc import padding_batch_data
@six.add_metaclass(ABCMeta)
class TextInputter(object):
"""Base class for inputters. """
def __init__(self,
dataset,
batch_size=None):
""" Initializes common attributes of inputters.
Args:
dataset: A `Dataset` object.
batch_size: An integer value indicating the number of
sentences passed into one step.
"""
self._dataset = dataset
self._vocab_source = self._dataset.vocab_source
self._vocab_target = self._dataset.vocab_target
self._batch_size = batch_size
@property
def input_fields(self):
""" Returns the dictionary of placeholders. """
return self._dataset._input_fields
@abstractmethod
def make_feeding_data(self, *args, **kwargs):
""" Processes the data file and return an iterable instance for loop. """
raise NotImplementedError
class TextLineInputter(TextInputter):
""" Class for reading in source side lines or target side lines. """
def __init__(self,
dataset,
data_field_name,
batch_size):
""" Initializes the parameters for this inputter.
Args:
dataset: A `Dataset` object.
data_field_name: The attribute name of dataset that has
access to a data file.
batch_size: An integer value indicating the number of
sentences passed into one step. Sentences will be padded by EOS.
Raises:
ValueError: if `batch_size` is None, or if `dataset` has no
attribute named `data_field_name`, or if the attribute
`data_field_name` has error type (only str and list available).
"""
super(TextLineInputter, self).__init__(dataset, batch_size)
if self._batch_size is None:
raise ValueError("batch_size should be provided.")
if not hasattr(dataset, data_field_name):
raise ValueError("dataset object has no attribute named \"{}\""
.format(data_field_name))
self._data_files = getattr(dataset, data_field_name)
if not (isinstance(self._data_files, str) or isinstance(self._data_files, list)):
raise ValueError("error type with for attribute \"{}\" of dataset, "
"which should be str or list".format(data_field_name))
if "features" in data_field_name:
self._vocab = dataset.vocab_source
else:
self._vocab = dataset.vocab_target
def _make_feeding_data_from(self,
filename,
maximum_line_length=None,
maximum_encoded_length=None):
""" Processes the data file and return an iterable instance for loop.
Args:
filename: A specific data file.
maximum_line_length: The maximum sequence length. If provided,
sentences exceeding this value will be ignore.
maximum_encoded_length: The maximum length of symbols (especially
after BPE is applied). If provided symbols of one sentence exceeding
this value will be ignore.
Returns: An iterable instance that packs feeding dictionary
for `tf.Session().run` according to the `filename`.
"""
features = open_file(filename, encoding="utf-8")
str_buf = []
ss_buf = []
for ss in features:
if maximum_line_length and len(ss.strip().split()) > maximum_line_length:
continue
encoded_ss = self._vocab.convert_to_idlist(ss.strip().split(" "))
if maximum_encoded_length and len(encoded_ss) - 1 > maximum_encoded_length:
continue
bpe_ss = self._vocab.bpe_encode(ss.strip())
str_buf.append(bpe_ss)
ss_buf.append(encoded_ss)
close_file(features)
data = []
batch_data_idx = 0
while batch_data_idx < len(ss_buf):
x, len_x = padding_batch_data(
ss_buf[batch_data_idx: batch_data_idx + self._batch_size],
self._vocab.eos_id)
str_x = str_buf[batch_data_idx: batch_data_idx + self._batch_size]
batch_data_idx += self._batch_size
data.append((
str_x, len_x,
{self.input_fields[GlobalNames.PH_FEATURE_IDS_NAME]: x,
self.input_fields[GlobalNames.PH_FEATURE_LENGTH_NAME]: len_x}))
return data
def make_feeding_data(self, maximum_line_length=None, maximum_encoded_length=None):
""" Processes the data file(s) and return an iterable
instance for loop.
Args:
maximum_line_length: The maximum sequence length. If provided,
sentences exceeding this value will be ignore.
maximum_encoded_length: The maximum length of symbols (especially
after BPE is applied). If provided symbols of one sentence exceeding
this value will be ignore.
Returns: An iterable instance or a list of iterable
instances according to the `data_field_name`
in the constructor.
"""
if isinstance(self._data_files, list):
return [self._make_feeding_data_from(filename) for filename in self._data_files]
return self._make_feeding_data_from(self._data_files)
class ParallelTextInputter(TextInputter):
""" Class for reading in parallel texts. """
def __init__(self,
dataset,
features_field_name,
labels_field_name,
batch_size=None,
batch_tokens_size=None,
shuffle_every_epoch=None,
bucketing=True):
""" Initializes the parameters for this inputter.
Args:
dataset: A `Dataset` object.
features_field_name: The attribute name of dataset that has
access to a features file.
labels_field_name: The attribute name of dataset that has
access to a labels file.
batch_size: An integer value indicating the number of
sentences passed into one step. Sentences will be padded by EOS.
batch_tokens_size: An integer value indicating the number of
words of each batch. If provided, sentence pairs will be batched
together by approximate sequence length.
shuffle_every_epoch: A string type. If provided, use it as postfix
of shuffled data file name.
bucketing: Whether to sort the sentences by length of labels.
Raises:
ValueError: if both `batch_size` and `batch_tokens_size` are
not provided, or if `dataset` has no attribute name
`features_field_name` or `labels_field_name`.
"""
super(ParallelTextInputter, self).__init__(
dataset, batch_size)
self._batch_tokens_size = batch_tokens_size
self._shuffle_every_epoch = shuffle_every_epoch
if not hasattr(dataset, features_field_name):
raise ValueError("dataset object has no attribute named \"{}\""
.format(features_field_name))
if not hasattr(dataset, labels_field_name):
raise ValueError("dataset object has no attribute named \"{}\""
.format(labels_field_name))
self._features_file = getattr(self._dataset, features_field_name)
self._labels_file = getattr(self._dataset, labels_field_name)
self._bucketing = bucketing
if self._batch_size is None and self._batch_tokens_size is None:
raise ValueError("Either batch_size or batch_tokens_size should be provided.")
if (self._batch_size is not None) and (self._batch_tokens_size is not None):
tf.logging.info("batching data according to batch_tokens_size={}, "
"and use batch_size={} as an auxiliary variable.".format(batch_tokens_size, batch_size))
if batch_tokens_size is None:
self._cache_size = self._batch_size * 128 # 80 * 128 = 10240
else:
self._cache_size = self._batch_tokens_size * 6 # 4096 * 6 := 25000
if batch_size is None:
self._batch_size = 32
def make_feeding_data(self,
maximum_features_length=None,
maximum_labels_length=None,
maximum_encoded_features_length=None,
maximum_encoded_labels_length=None):
""" Processes the data files and return an iterable
instance for loop.
Args:
maximum_features_length: The maximum sequence length of "features" field.
If provided, sentences exceeding this value will be ignore.
maximum_labels_length: The maximum sequence length of "labels" field.
If provided, sentences exceeding this value will be ignore.
maximum_encoded_features_length: The maximum length of feature symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
maximum_encoded_labels_length: The maximum length of label symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
Returns: An iterable instance or a list of iterable instances.
"""
if self._features_file is None or self._labels_file is None:
raise ValueError("Both _features_file and _labels_file should be provided.")
if isinstance(self._features_file, list):
return [self._make_feeding_data(f, l, maximum_features_length, maximum_labels_length,
maximum_encoded_features_length, maximum_encoded_labels_length)
for f, l in zip(self._features_file, self._labels_file)]
return self._make_feeding_data(
self._features_file, self._labels_file,
maximum_features_length, maximum_labels_length,
maximum_encoded_features_length, maximum_encoded_labels_length)
def _make_feeding_data(self,
features_file,
labels_file,
maximum_features_length=None,
maximum_labels_length=None,
maximum_encoded_features_length=None,
maximum_encoded_labels_length=None):
""" Processes the data files and return an iterable
instance for loop.
Args:
features_file: The path of features file.
labels_file: The path of labels file.
maximum_features_length: The maximum sequence length of "features" field.
If provided, sentences exceeding this value will be ignore.
maximum_labels_length: The maximum sequence length of "labels" field.
If provided, sentences exceeding this value will be ignore.
maximum_encoded_features_length: The maximum length of feature symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
maximum_encoded_labels_length: The maximum length of label symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
Returns: An iterable instance.
"""
if features_file is None or labels_file is None:
raise ValueError("Both features_file and labels_file should be provided.")
line_count = 0
with gfile.GFile(features_file) as fp:
for _ in fp:
line_count += 1
if line_count > self._cache_size or self._batch_tokens_size is not None:
return self._BigParallelData(
self, features_file, labels_file,
maximum_features_length, maximum_labels_length,
maximum_encoded_features_length, maximum_encoded_labels_length)
return self._SmallParallelData(
features_file, labels_file,
maximum_features_length, maximum_labels_length,
maximum_encoded_features_length, maximum_encoded_labels_length)
def make_eval_feeding_data(self):
""" Processes the data files and return an iterable instance for loop,
especially for output_attention when EVAL.
Returns: An iterable instance or a list of iterable instances.
"""
if self._features_file is None or self._labels_file is None:
raise ValueError("Both _features_file and _labels_file should be provided.")
if isinstance(self._features_file, list):
return [self._EvalParallelData(f, l)
for f, l in zip(self._features_file, self._labels_file)]
return self._EvalParallelData(
self._features_file, self._labels_file)
def _EvalParallelData(self,
features_file,
labels_file):
""" Function for reading small scale parallel data for evaluation.
Args:
features_file: The path of features file.
labels_file: The path of labels file.
Returns: A list of feeding data.
"""
eval_features = open_file(features_file, encoding="utf-8")
if gfile.Exists(labels_file):
eval_labels = open_file(labels_file, encoding="utf-8")
else:
eval_labels = open_file(labels_file + "0", encoding="utf-8")
ss_buf = []
tt_buf = []
ss_str_buf = []
tt_str_buf = []
for ss, tt in zip(eval_features, eval_labels):
ss_str = ss.strip().split(" ")
tt_str = tt.strip().split(" ")
ss_str_buf.append(ss_str)
tt_str_buf.append(tt_str)
ss_buf.append(self._vocab_source.convert_to_idlist(ss_str))
tt_buf.append(self._vocab_target.convert_to_idlist(tt_str))
close_file(eval_features)
close_file(eval_labels)
if self._bucketing:
tlen = numpy.array([len(t) for t in tt_buf])
tidx = tlen.argsort()
_ss_buf = [ss_buf[i] for i in tidx]
_tt_buf = [tt_buf[i] for i in tidx]
_ss_str_buf = [ss_str_buf[i] for i in tidx]
_tt_str_buf = [tt_str_buf[i] for i in tidx]
ss_buf = _ss_buf
tt_buf = _tt_buf
ss_str_buf = _ss_str_buf
tt_str_buf = _tt_str_buf
data = []
batch_data_idx = 0
while batch_data_idx < len(ss_buf):
x, len_x = padding_batch_data(
ss_buf[batch_data_idx: batch_data_idx + self._batch_size],
self._vocab_source.eos_id)
y, len_y = padding_batch_data(
tt_buf[batch_data_idx: batch_data_idx + self._batch_size],
self._vocab_target.eos_id)
data.append((
ss_str_buf[batch_data_idx: batch_data_idx + self._batch_size],
tt_str_buf[batch_data_idx: batch_data_idx + self._batch_size], {
self.input_fields[GlobalNames.PH_FEATURE_IDS_NAME]: x,
self.input_fields[GlobalNames.PH_FEATURE_LENGTH_NAME]: len_x,
self.input_fields[GlobalNames.PH_LABEL_IDS_NAME]: y,
self.input_fields[GlobalNames.PH_LABEL_LENGTH_NAME]: len_y}))
batch_data_idx += self._batch_size
return data
def _SmallParallelData(self,
features_file,
labels_file,
maximum_features_length=None,
maximum_labels_length=None,
maximum_encoded_features_length=None,
maximum_encoded_labels_length=None):
""" Function for reading small scale parallel data.
Args:
features_file: The path of features file.
labels_file: The path of labels file.
maximum_features_length: The maximum sequence length of "features" field.
If provided, sentences exceeding this value will be ignore.
maximum_labels_length: The maximum sequence length of "labels" field.
If provided, sentences exceeding this value will be ignore.
maximum_encoded_features_length: The maximum length of feature symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
maximum_encoded_labels_length: The maximum length of label symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
Returns: A list of feeding data.
"""
eval_features = open_file(features_file, encoding="utf-8")
if gfile.Exists(labels_file):
eval_labels = open_file(labels_file, encoding="utf-8")
else:
eval_labels = open_file(labels_file + "0", encoding="utf-8")
ss_buf = []
tt_buf = []
for ss, tt in zip(eval_features, eval_labels):
if maximum_features_length and len(ss.strip().split()) > maximum_features_length:
continue
if maximum_labels_length and len(tt.strip().split()) > maximum_labels_length:
continue
encoded_ss = self._vocab_source.convert_to_idlist(ss.strip().split(" "))
if maximum_encoded_features_length and len(encoded_ss) - 1 > maximum_encoded_features_length:
continue
encoded_tt = self._vocab_target.convert_to_idlist(tt.strip().split(" "))
if maximum_encoded_labels_length and len(encoded_tt) - 1 > maximum_encoded_labels_length:
continue
ss_buf.append(encoded_ss)
tt_buf.append(encoded_tt)
close_file(eval_features)
close_file(eval_labels)
if self._bucketing:
tlen = numpy.array([len(t) for t in tt_buf])
tidx = tlen.argsort()
_ss_buf = [ss_buf[i] for i in tidx]
_tt_buf = [tt_buf[i] for i in tidx]
ss_buf = _ss_buf
tt_buf = _tt_buf
data = []
batch_data_idx = 0
while batch_data_idx < len(ss_buf):
x, len_x = padding_batch_data(
ss_buf[batch_data_idx: batch_data_idx + self._batch_size],
self._vocab_source.eos_id)
y, len_y = padding_batch_data(
tt_buf[batch_data_idx: batch_data_idx + self._batch_size],
self._vocab_target.eos_id)
batch_data_idx += self._batch_size
data.append((len(len_x), {
self.input_fields[GlobalNames.PH_FEATURE_IDS_NAME]: x,
self.input_fields[GlobalNames.PH_FEATURE_LENGTH_NAME]: len_x,
self.input_fields[GlobalNames.PH_LABEL_IDS_NAME]: y,
self.input_fields[GlobalNames.PH_LABEL_LENGTH_NAME]: len_y}))
return data
class _BigParallelData(object):
""" An iterator class for reading parallel data. """
def __init__(self,
parent,
features_file,
labels_file,
maximum_features_length=None,
maximum_labels_length=None,
maximum_encoded_features_length=None,
maximum_encoded_labels_length=None):
""" Initializes.
Args:
parent: A `ParallelTextInputter` object.
features_file: The path of features file.
labels_file: The path of labels file.
maximum_features_length: The maximum sequence length of "features" field.
If provided, sentences exceeding this value will be ignore.
maximum_labels_length: The maximum sequence length of "labels" field.
If provided, sentences exceeding this value will be ignore.
maximum_encoded_features_length: The maximum length of feature symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
maximum_encoded_labels_length: The maximum length of label symbols (especially
after BPE is applied) . If provided, the number of symbols of one sentence
exceeding this value will be ignore.
"""
self._parent = parent
self._features_file = features_file
self._labels_file = labels_file
if not gfile.Exists(self._labels_file):
self._labels_file = self._labels_file + "0"
self._maximum_features_length = maximum_features_length
self._maximum_labels_length = maximum_labels_length
self._maximum_encoded_features_length = maximum_encoded_features_length
self._maximum_encoded_labels_length = maximum_encoded_labels_length
if self._parent._shuffle_every_epoch:
self._shuffle_features_file = self._features_file.strip().split("/")[-1] \
+ "." + self._parent._shuffle_every_epoch
self._shuffle_labels_file = self._labels_file.strip().split("/")[-1] \
+ "." + self._parent._shuffle_every_epoch
self._shuffle()
self._features = open_file(self._features_file, encoding="utf-8")
self._labels = open_file(self._labels_file, encoding="utf-8")
self._features_buffer = []
self._labels_buffer = []
self._features_len_buffer = []
self._labels_len_buffer = []
self._end_of_data = False
def __iter__(self):
return self
def _reset(self):
if self._parent._shuffle_every_epoch:
close_file(self._features)
close_file(self._labels)
self._shuffle()
self._features = open_file(self._features_file, encoding="utf-8")
self._labels = open_file(self._labels_file, encoding="utf-8")
self._features.seek(0)
self._labels.seek(0)
def __next__(self):
""" capable for python3
:return:
"""
return self.next()
def _next_features(self):
ss_tmp = self._features.readline()
if ss_tmp == "":
return ""
ss_tmp = ss_tmp.strip().split(" ")
if self._maximum_features_length and len(ss_tmp) > self._maximum_features_length:
return None
encoded_ss = self._parent._vocab_source.convert_to_idlist(ss_tmp)
if self._maximum_encoded_features_length and len(
encoded_ss) - 1 > self._maximum_encoded_features_length:
return None
return encoded_ss
def _next_labels(self):
tt_tmp = self._labels.readline()
if tt_tmp == "":
return ""
tt_tmp = tt_tmp.strip().split(" ")
if self._maximum_labels_length and len(tt_tmp) > self._maximum_labels_length:
return None
encoded_tt = self._parent._vocab_target.convert_to_idlist(tt_tmp)
if self._maximum_encoded_labels_length and len(
encoded_tt) - 1 > self._maximum_encoded_labels_length:
return None
return encoded_tt
def next(self):
if self._end_of_data:
self._end_of_data = False
self._reset()
raise StopIteration
assert len(self._features_buffer) == len(self._labels_buffer), "Buffer size mismatch"
if len(self._features_buffer) < self._parent._batch_size:
cnt = len(self._features_buffer)
while cnt < self._parent._cache_size:
ss = self._next_features()
tt = self._next_labels()
if ss == "" or tt == "":
break
if ss is None or tt is None:
continue
cnt += 1
self._features_buffer.append(ss)
self._labels_buffer.append(tt)
if len(self._features_buffer) == 0 or len(self._labels_buffer) == 0:
self._end_of_data = False
self._reset()
raise StopIteration
if self._parent._bucketing:
# sort by len
tlen = numpy.array([len(t) for t in self._labels_buffer])
tidx = tlen.argsort()
_fbuf = [self._features_buffer[i] for i in tidx]
_lbuf = [self._labels_buffer[i] for i in tidx]
self._features_buffer = _fbuf
self._labels_buffer = _lbuf
self._features_len_buffer = [len(s) for s in self._features_buffer]
self._labels_len_buffer = [len(t) for t in self._labels_buffer]
local_batch_size = self._parent._batch_size
if self._parent._batch_tokens_size is not None: # batching data by num of tokens
sum_s = numpy.sum(self._features_len_buffer[: local_batch_size])
sum_t = numpy.sum(self._labels_len_buffer[: local_batch_size])
while True:
if sum_s >= self._parent._batch_tokens_size or sum_t >= self._parent._batch_tokens_size:
break
if self._parent._batch_tokens_size - sum_s < 20 or self._parent._batch_tokens_size - sum_t < 20:
break
if local_batch_size >= len(self._features_len_buffer):
break
sum_s += self._features_len_buffer[local_batch_size]
sum_t += self._labels_len_buffer[local_batch_size]
local_batch_size += 1
features = self._features_buffer[:local_batch_size]
labels = self._labels_buffer[:local_batch_size]
if len(features) < local_batch_size:
del self._features_buffer[:]
del self._labels_buffer[:]
del self._features_len_buffer[:]
del self._labels_len_buffer[:]
else:
del self._features_buffer[:local_batch_size]
del self._labels_buffer[:local_batch_size]
del self._features_len_buffer[:local_batch_size]
del self._labels_len_buffer[:local_batch_size]
if len(features) <= 0 or len(labels) <= 0:
self._end_of_data = False
self._reset()
raise StopIteration
return len(features), self._make_inputs(features, labels)
def _make_inputs(self, features, labels):
x, len_x = padding_batch_data(features, self._parent._vocab_source.eos_id)
y, len_y = padding_batch_data(labels, self._parent._vocab_target.eos_id)
return {
self._parent.input_fields[GlobalNames.PH_FEATURE_IDS_NAME]: x,
self._parent.input_fields[GlobalNames.PH_FEATURE_LENGTH_NAME]: len_x,
self._parent.input_fields[GlobalNames.PH_LABEL_IDS_NAME]: y,
self._parent.input_fields[GlobalNames.PH_LABEL_LENGTH_NAME]: len_y}
def _shuffle(self):
""" shuffle features & labels file
:return:
"""
tf.logging.info("shuffling data\n\t{} ==> {}\n\t{} ==> {}"
.format(self._features_file, self._shuffle_features_file,
self._labels_file, self._shuffle_labels_file))
shuffle_data([self._features_file, self._labels_file],
[self._shuffle_features_file, self._shuffle_labels_file])
self._features_file = self._shuffle_features_file
self._labels_file = self._shuffle_labels_file
|
[
"[email protected]"
] | |
93f2537480291a57fcaebe66bd44da2a87e8be9e
|
2002be85574bcce8b74b4230c8cfef295a104f92
|
/deliravision/torch/models/gans/enhanced_super_resolution/__init__.py
|
bb48bc57ae5a961c578af544e76d84e92f2169f1
|
[
"BSD-2-Clause"
] |
permissive
|
delira-dev/vision_torch
|
062b259bd1600be75af201ca9649d6582a5c93ac
|
d944aa67d319bd63a2add5cb89e8308413943de6
|
refs/heads/master
| 2022-03-05T11:04:39.035448 | 2019-09-27T19:24:50 | 2019-09-27T19:24:50 | 200,359,229 | 5 | 0 |
BSD-2-Clause
| 2019-08-13T18:33:58 | 2019-08-03T09:52:39 |
Python
|
UTF-8
|
Python
| false | false | 98 |
py
|
from deliravision.models.gans.enhanced_super_resolution.esr_gan import EnhancedSuperResolutionGAN
|
[
"[email protected]"
] | |
b370208f59708c4250a1eb9f3c929edf4c85b117
|
b4849ca0f38c29407a9a88007a7ecb11035851eb
|
/setup.py
|
011c251fcb84d42621397affd3b8ebe1d7cad831
|
[
"MIT"
] |
permissive
|
qianshuqinghan/dicom2nifti
|
e85619051470d01f0db11deffa821edac2cf4c67
|
79ed535835bd3be003d27a03c119d3a1cb46a0ac
|
refs/heads/master
| 2020-04-01T03:58:12.176680 | 2018-10-11T17:39:24 | 2018-10-11T17:39:24 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,743 |
py
|
from distutils.core import setup
from setuptools import find_packages
version = '2.0.4'
long_description = """
With this package you can convert dicom images to nifti files.
There is support for most anatomical CT and MR data.
For MR specifically there is support for most 4D data (like DTI and fMRI)
"""
setup(
name='dicom2nifti',
packages=find_packages(exclude=['contrib', 'docs', 'tests*']),
version=version,
description='package for converting dicom files to nifti',
long_description=long_description,
license='MIT',
author='icometrix NV',
author_email='[email protected]',
maintainer="icometrix NV",
maintainer_email="[email protected]",
url='https://github.com/icometrix/dicom2nifti',
download_url='https://github.com/icometrix/dicom2nifti/tarball/%s' % version,
keywords=['dicom', 'nifti', 'medical imaging'],
scripts=['scripts/dicom2nifti'],
# https://pypi.python.org/pypi?%3Aaction=list_classifiers
classifiers=[
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Healthcare Industry',
'Intended Audience :: Science/Research',
'Topic :: Scientific/Engineering :: Medical Science Apps.',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Operating System :: MacOS :: MacOS X',
'Operating System :: POSIX :: Linux'],
install_requires=['six', 'future', 'nibabel', 'numpy', 'scipy', 'pydicom>=1.0.1'],
setup_requires=['nose', 'coverage']
)
|
[
"[email protected]"
] | |
9dabdb65c1ec3b2d7f17d7d6a72ad18da35458b0
|
93db77d922572c51678d68ca6c62b04b6be0d83a
|
/code/文本摘要/analyser.py
|
25beafdb91bb39225fe2bbf26ba06dd7d1bf7e70
|
[] |
no_license
|
gmftbyGMFTBY/BITNLP
|
9b80900d9da39fa27fc1cbe25d7329b448e3fcf1
|
41ddcd4df1277ef429d70ddab8cde751cbecced0
|
refs/heads/master
| 2021-05-08T23:04:36.954316 | 2018-01-31T14:27:46 | 2018-01-31T14:27:46 | 114,972,685 | 2 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,270 |
py
|
#!/usr/bin/python3
# Author : GMFTBY
# Time : 2017.1.23
'''
This script try to use the Edmundson Algorithm to analyse the result of the summary
'''
import TFIDF
def Edmundson(result, answer):
# result 机器摘要
# answer 参考摘要
r_s = set(TFIDF.cut_by_sentence(result))
r_a = set(TFIDF.cut_by_sentence(answer))
share = r_s & r_a
return len(share) / len(r_a)
if __name__ == "__main__":
answer = '一度疯狂的ＩＰＯ申报脚步于近日呈大幅放缓趋势。上周主板无新增初审企业。山东龙大肉食品、福建安溪铁观音集团、广东台城制药和海洋王照明科技４家拟主板企业于上周进行预披露。 创业板 方面，南京宝色、丹东欣泰电气和深圳市凯立德科技也已公布招股说明书。'
summary = '３０６家（包括１３家中止审查企业）。上周主板无新增初审企业。ＩＰＯ申报企业基本信息显示。仅有３家拟上市企业进入候审队伍。山东龙大肉食品、福建安溪铁观音集团、广东台城制药和海洋王照明科技４家拟主板企业于上周进行预披露。南京宝色、丹东欣泰电气和深圳市凯立德科技也已公布招股说明书。'
print(Edmundson(summary, answer))
|
[
"[email protected]"
] | |
f7e3df9d2c601ce3a1dde43c93a654fc9db98c82
|
2a28a94fc8eb08961e76c61ab73889135153502b
|
/test/tests_file_controller.py
|
7790e78dace3fe43f4a65345c6179958af38d99a
|
[
"MIT"
] |
permissive
|
aspose-cells-cloud/aspose-cells-cloud-python
|
45fc7e686b442302a29a8223e7dbddb71950438c
|
270d70ce7f8f3f2ecd9370b1dacfc4789293097e
|
refs/heads/master
| 2023-09-04T01:29:44.242037 | 2023-08-23T13:13:30 | 2023-08-23T13:13:30 | 123,092,364 | 6 | 5 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,261 |
py
|
# coding: utf-8
from __future__ import absolute_import
import os
import sys
import unittest
import warnings
ABSPATH = os.path.abspath(os.path.realpath(os.path.dirname(__file__)) + "/..")
sys.path.append(ABSPATH)
from asposecellscloud.rest import ApiException
from asposecellscloud.apis.cells_api import CellsApi
import AuthUtil
from asposecellscloud.models import *
from asposecellscloud.requests import *
global_api = None
class TestFileControllerApi(unittest.TestCase):
def setUp(self):
warnings.simplefilter('ignore', ResourceWarning)
global global_api
if global_api is None:
global_api = CellsApi(AuthUtil.GetClientId(),AuthUtil.GetClientSecret(),"v3.0",AuthUtil.GetBaseUrl())
self.api = global_api
def tearDown(self):
pass
def test_download_file(self):
remote_folder = 'TestData/In'
local_name = 'Book1.xlsx'
remote_name = 'Book1.xlsx'
result = AuthUtil.Ready(self.api, local_name, remote_folder + '/' + remote_name , '')
self.assertTrue(len(result.uploaded)>0)
request = DownloadFileRequest( remote_folder + '/' + remote_name,storage_name= '',version_id= '')
self.api.download_file(request)
def test_upload_file(self):
remote_folder = 'TestData/In'
local_name = 'Book1.xlsx'
remote_name = 'Book1.xlsx'
mapFiles = {
local_name: os.path.dirname(os.path.realpath(__file__)) + "/../TestData/" +local_name
}
result = AuthUtil.Ready(self.api, local_name, remote_folder + '/' + remote_name , '')
self.assertTrue(len(result.uploaded)>0)
request = UploadFileRequest( mapFiles, remote_folder + '/' + remote_name,storage_name= '')
self.api.upload_file(request)
def test_copy_file(self):
remote_folder = 'TestData/In'
local_name = 'Book1.xlsx'
remote_name = 'Book1.xlsx'
result = AuthUtil.Ready(self.api, local_name, remote_folder + '/' + remote_name , '')
self.assertTrue(len(result.uploaded)>0)
request = CopyFileRequest( remote_folder + '/' + remote_name, 'OutResult/' + remote_name,src_storage_name= '',dest_storage_name= '',version_id= '')
self.api.copy_file(request)
def test_move_file(self):
remote_folder = 'TestData/In'
local_name = 'Book1.xlsx'
remote_name = 'Book1.xlsx'
result = AuthUtil.Ready(self.api, local_name, remote_folder + '/' + remote_name , '')
self.assertTrue(len(result.uploaded)>0)
request = MoveFileRequest( remote_folder + '/' + remote_name, 'OutResult/' + remote_name,src_storage_name= '',dest_storage_name= '',version_id= '')
self.api.move_file(request)
def test_delete_file(self):
remote_folder = 'TestData/In'
local_name = 'Book1.xlsx'
remote_name = 'Book1.xlsx'
result = AuthUtil.Ready(self.api, local_name, remote_folder + '/' + remote_name , '')
self.assertTrue(len(result.uploaded)>0)
request = DeleteFileRequest( remote_folder + '/' + remote_name,storage_name= '',version_id= '')
self.api.delete_file(request)
if __name__ == '__main__':
unittest.main()
|
[
"[email protected]"
] | |
3bcf02b6585973dc6d8ead444f231a4e92e2c4c0
|
79030ecbe234e6906ec4925f0f816e626e30734a
|
/QQSpider2_new/mongo_temp.py
|
ac447a05a4be2424ae27873bddaef5746699b13b
|
[] |
no_license
|
TSLNIHAOGIT/QQSpider
|
b6789e53aedb17e8f98c73ef83c15e7a1a6986d1
|
6f5c22e0ad0c22d193ec777e9dcb6294a2536934
|
refs/heads/master
| 2020-04-14T19:09:49.638777 | 2019-01-04T02:39:13 | 2019-01-04T02:39:13 | 164,047,349 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 588 |
py
|
from pymongo import MongoClient
import datetime
class MongoManager(object):
def __init__(self, server_ip='localhost', client=None):
print(server_ip)
self.client = MongoClient(server_ip, 27017) if client is None else client
# self.redis_client = redis.StrictRedis(host=server_ip, port=6379, db=0)
self.mongo_db = self.client.QQ
#有插入操作后才会在数据库中产生
self.mongo_db.query.insert({
# 'time': datetime.utcnow(),
'prediction': '''[prediction]'''})
if __name__=='__main__':
MongoManager()
|
[
"[email protected]"
] | |
18e718a6321ba39deab46746fac8de2a9e46c65e
|
5dc77586e3e0f9de1f032fd2ca68494d8e58928f
|
/tests/execution_engine/test_pandas_execution_engine.py
|
0410690db1a47d44ad4c414f4ffa0ebb01a80696
|
[
"Apache-2.0"
] |
permissive
|
great-expectations/great_expectations
|
dd7c22e6277d6b08bee3ff38a015e6e8cd434df6
|
b0290e2fd2aa05aec6d7d8871b91cb4478e9501d
|
refs/heads/develop
| 2023-09-04T09:30:26.395518 | 2023-09-02T00:00:13 | 2023-09-02T00:00:13 | 103,071,520 | 8,931 | 1,535 |
Apache-2.0
| 2023-09-14T19:57:16 | 2017-09-11T00:18:46 |
Python
|
UTF-8
|
Python
| false | false | 23,770 |
py
|
import os
from typing import Dict, Tuple
from unittest import mock
import pandas as pd
import pytest
import great_expectations.exceptions as gx_exceptions
from great_expectations.compatibility import aws, azure, google
from great_expectations.core.batch_spec import RuntimeDataBatchSpec, S3BatchSpec
# noinspection PyBroadException
from great_expectations.core.metric_domain_types import MetricDomainTypes
from great_expectations.execution_engine.pandas_execution_engine import (
PandasExecutionEngine,
)
from great_expectations.util import is_library_loadable
from great_expectations.validator.computed_metric import MetricValue
from great_expectations.validator.metric_configuration import MetricConfiguration
from tests.expectations.test_util import get_table_columns_metric
@pytest.mark.unit
def test_constructor_with_boto3_options():
# default instantiation
PandasExecutionEngine()
# instantiation with custom parameters
engine = PandasExecutionEngine(discard_subset_failing_expectations=True)
assert "discard_subset_failing_expectations" in engine.config
assert engine.config.get("discard_subset_failing_expectations") is True
custom_boto3_options = {"region_name": "us-east-1"}
engine = PandasExecutionEngine(boto3_options=custom_boto3_options)
assert "boto3_options" in engine.config
assert engine.config.get("boto3_options")["region_name"] == "us-east-1"
@pytest.mark.unit
def test_reader_fn():
engine = PandasExecutionEngine()
# Testing that can recognize basic excel file
fn = engine._get_reader_fn(path="myfile.xlsx")
assert "<function read_excel" in str(fn)
# Testing that can recognize basic sas7bdat file
fn_read_sas7bdat = engine._get_reader_fn(path="myfile.sas7bdat")
assert "<function read_sas" in str(fn_read_sas7bdat)
# Testing that can recognize basic SAS xpt file
fn_read_xpt = engine._get_reader_fn(path="myfile.xpt")
assert "<function read_sas" in str(fn_read_xpt)
# Ensuring that other way around works as well - reader_method should always override path
fn_new = engine._get_reader_fn(reader_method="read_csv")
assert "<function" in str(fn_new)
@pytest.mark.unit
def test_get_domain_records_with_column_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame(
{"a": [1, 2, 3, 4, 5], "b": [2, 3, 4, 5, None], "c": [1, 2, 3, 4, None]}
)
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data = engine.get_domain_records(
domain_kwargs={
"column": "a",
"row_condition": "b<5",
"condition_parser": "pandas",
}
)
expected_column_df = df.iloc[:3]
assert data.equals(
expected_column_df
), "Data does not match after getting full access compute domain"
@pytest.mark.unit
def test_get_domain_records_with_column_pair_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5, 6],
"b": [2, 3, 4, 5, None, 6],
"c": [1, 2, 3, 4, 5, None],
}
)
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data = engine.get_domain_records(
domain_kwargs={
"column_A": "a",
"column_B": "b",
"row_condition": "b>2",
"condition_parser": "pandas",
"ignore_row_if": "both_values_are_missing",
}
)
expected_column_pair_df = pd.DataFrame(
{
"a": [2, 3, 4, 6],
"b": [3.0, 4.0, 5.0, 6.0],
"c": [2.0, 3.0, 4.0, None],
},
index=[1, 2, 3, 5],
)
assert data.equals(
expected_column_pair_df
), "Data does not match after getting full access compute domain"
data = engine.get_domain_records(
domain_kwargs={
"column_A": "b",
"column_B": "c",
"row_condition": "b>2",
"condition_parser": "pandas",
"ignore_row_if": "either_value_is_missing",
}
)
data = data.astype(int)
expected_column_pair_df = pd.DataFrame(
{"a": [2, 3, 4], "b": [3, 4, 5], "c": [2, 3, 4]}, index=[1, 2, 3]
)
assert data.equals(
expected_column_pair_df
), "Data does not match after getting full access compute domain"
data = engine.get_domain_records(
domain_kwargs={
"column_A": "b",
"column_B": "c",
"row_condition": "a<6",
"condition_parser": "pandas",
"ignore_row_if": "neither",
}
)
expected_column_pair_df = pd.DataFrame(
{
"a": [1, 2, 3, 4, 5],
"b": [2.0, 3.0, 4.0, 5.0, None],
"c": [1.0, 2.0, 3.0, 4.0, 5.0],
}
)
assert data.equals(
expected_column_pair_df
), "Data does not match after getting full access compute domain"
@pytest.mark.unit
def test_get_domain_records_with_multicolumn_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame(
{
"a": [1, 2, 3, 4, None, 5],
"b": [2, 3, 4, 5, 6, 7],
"c": [1, 2, 3, 4, None, 6],
}
)
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data = engine.get_domain_records(
domain_kwargs={
"column_list": ["a", "c"],
"row_condition": "b>2",
"condition_parser": "pandas",
"ignore_row_if": "all_values_are_missing",
}
)
data = data.astype(int)
expected_multicolumn_df = pd.DataFrame(
{"a": [2, 3, 4, 5], "b": [3, 4, 5, 7], "c": [2, 3, 4, 6]}, index=[1, 2, 3, 5]
)
assert data.equals(
expected_multicolumn_df
), "Data does not match after getting full access compute domain"
data = engine.get_domain_records(
domain_kwargs={
"column_list": ["b", "c"],
"row_condition": "a<5",
"condition_parser": "pandas",
"ignore_row_if": "any_value_is_missing",
}
)
data = data.astype(int)
expected_multicolumn_df = pd.DataFrame(
{"a": [1, 2, 3, 4], "b": [2, 3, 4, 5], "c": [1, 2, 3, 4]}, index=[0, 1, 2, 3]
)
assert data.equals(
expected_multicolumn_df
), "Data does not match after getting full access compute domain"
engine = PandasExecutionEngine()
df = pd.DataFrame(
{
"a": [1, 2, 3, 4, None, 5],
"b": [2, 3, 4, 5, 6, 7],
"c": [1, 2, 3, 4, None, 6],
}
)
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data = engine.get_domain_records(
domain_kwargs={
"column_list": ["b", "c"],
"ignore_row_if": "never",
}
)
expected_multicolumn_df = pd.DataFrame(
{
"a": [1, 2, 3, 4, None, 5],
"b": [2, 3, 4, 5, 6, 7],
"c": [1, 2, 3, 4, None, 6],
},
index=[0, 1, 2, 3, 4, 5],
)
assert data.equals(
expected_multicolumn_df
), "Data does not match after getting full access compute domain"
@pytest.mark.unit
def test_get_compute_domain_with_no_domain_kwargs():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={}, domain_type="table"
)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
# Trying same test with enum form of table domain - should work the same way
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={}, domain_type=MetricDomainTypes.TABLE
)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
@pytest.mark.unit
def test_get_compute_domain_with_column_pair_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, 5], "c": [1, 2, 3, 4]})
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column_A": "a", "column_B": "b"}, domain_type="column_pair"
)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {
"column_A": "a",
"column_B": "b",
}, "Accessor kwargs have been modified"
@pytest.mark.unit
def test_get_compute_domain_with_multicolumn_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame(
{"a": [1, 2, 3, 4], "b": [2, 3, 4, None], "c": [1, 2, 2, 3], "d": [2, 7, 9, 2]}
)
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column_list": ["a", "b", "c"]}, domain_type="multicolumn"
)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {
"column_list": ["a", "b", "c"]
}, "Accessor kwargs have been modified"
@pytest.mark.unit
def test_get_compute_domain_with_column_domain():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"column": "a"}, domain_type=MetricDomainTypes.COLUMN
)
assert data.equals(df), "Data does not match after getting compute domain"
assert compute_kwargs == {}, "Compute domain kwargs should be existent"
assert accessor_kwargs == {"column": "a"}, "Accessor kwargs have been modified"
@pytest.mark.unit
def test_get_compute_domain_with_row_condition():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
expected_df = df[df["b"] > 2]
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={"row_condition": "b > 2", "condition_parser": "pandas"},
domain_type="table",
)
# Ensuring data has been properly queried
assert data["b"].equals(
expected_df["b"]
), "Data does not match after getting compute domain"
# Ensuring compute kwargs have not been modified
assert (
"row_condition" in compute_kwargs.keys()
), "Row condition should be located within compute kwargs"
assert accessor_kwargs == {}, "Accessor kwargs have been modified"
# What happens when we filter such that no value meets the condition?
@pytest.mark.unit
def test_get_compute_domain_with_unmeetable_row_condition():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4], "b": [2, 3, 4, None]})
expected_df = df[df["b"] > 24]
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
data, compute_kwargs, accessor_kwargs = engine.get_compute_domain(
domain_kwargs={
"column": "a",
"row_condition": "b > 24",
"condition_parser": "pandas",
},
domain_type="column",
)
# Ensuring data has been properly queried
assert data["b"].equals(
expected_df["b"]
), "Data does not match after getting compute domain"
# Ensuring compute kwargs have not been modified
assert (
"row_condition" in compute_kwargs.keys()
), "Row condition should be located within compute kwargs"
assert accessor_kwargs == {"column": "a"}, "Accessor kwargs have been modified"
# Just checking that the Pandas Execution Engine can perform these in sequence
@pytest.mark.unit
def test_resolve_metric_bundle():
df = pd.DataFrame({"a": [1, 2, 3, None]})
# Building engine and configurations in attempt to resolve metrics
engine = PandasExecutionEngine(batch_data_dict={"made-up-id": df})
metrics: Dict[Tuple[str, str, str], MetricValue] = {}
table_columns_metric: MetricConfiguration
results: Dict[Tuple[str, str, str], MetricValue]
table_columns_metric, results = get_table_columns_metric(execution_engine=engine)
metrics.update(results)
mean = MetricConfiguration(
metric_name="column.mean",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
)
mean.metric_dependencies = {
"table.columns": table_columns_metric,
}
stdev = MetricConfiguration(
metric_name="column.standard_deviation",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
)
stdev.metric_dependencies = {
"table.columns": table_columns_metric,
}
desired_metrics = (mean, stdev)
results = engine.resolve_metrics(
metrics_to_resolve=desired_metrics, metrics=metrics
)
metrics.update(results)
# Ensuring metrics have been properly resolved
assert (
metrics[("column.mean", "column=a", ())] == 2.0
), "mean metric not properly computed"
assert metrics[("column.standard_deviation", "column=a", ())] == 1.0, (
"standard deviation " "metric not properly computed"
)
# Ensuring that we can properly inform user when metric doesn't exist - should get a metric provider error
@pytest.mark.unit
def test_resolve_metric_bundle_with_nonexistent_metric():
df = pd.DataFrame({"a": [1, 2, 3, None]})
# Building engine and configurations in attempt to resolve metrics
engine = PandasExecutionEngine(batch_data_dict={"made_up_id": df})
mean = MetricConfiguration(
metric_name="column.i_don't_exist",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
)
stdev = MetricConfiguration(
metric_name="column.nonexistent",
metric_domain_kwargs={"column": "a"},
metric_value_kwargs=None,
)
desired_metrics = (mean, stdev)
# noinspection PyUnusedLocal
with pytest.raises(gx_exceptions.MetricProviderError):
# noinspection PyUnusedLocal
engine.resolve_metrics(metrics_to_resolve=desired_metrics)
# Making sure dataframe property is functional
@pytest.mark.unit
def test_dataframe_property_given_loaded_batch():
engine = PandasExecutionEngine()
df = pd.DataFrame({"a": [1, 2, 3, 4]})
# Loading batch data
engine.load_batch_data(batch_data=df, batch_id="1234")
# Ensuring Data not distorted
assert engine.dataframe.equals(df)
@pytest.mark.unit
def test_get_batch_data(test_df):
split_df = PandasExecutionEngine().get_batch_data(
RuntimeDataBatchSpec(
batch_data=test_df,
)
)
assert split_df.dataframe.shape == (120, 10)
# No dataset passed to RuntimeDataBatchSpec
with pytest.raises(gx_exceptions.InvalidBatchSpecError):
PandasExecutionEngine().get_batch_data(RuntimeDataBatchSpec())
@pytest.mark.skipif(
not aws.boto3,
reason="Unable to load AWS connection object. Please install boto3 and botocore.",
)
@pytest.mark.big
def test_get_batch_s3_compressed_files(test_s3_files_compressed, test_df_small):
bucket, keys = test_s3_files_compressed
path = keys[0]
full_path = f"s3a://{os.path.join(bucket, path)}" # noqa: PTH118
batch_spec = S3BatchSpec(path=full_path, reader_method="read_csv")
df = PandasExecutionEngine().get_batch_data(batch_spec=batch_spec)
assert df.dataframe.shape == test_df_small.shape
@pytest.mark.skipif(
not aws.boto3
or (
not is_library_loadable(library_name="pyarrow")
and not is_library_loadable(library_name="fastparquet")
),
reason="pyarrow and fastparquet are not installed",
)
@pytest.mark.big
def test_get_batch_s3_parquet(test_s3_files_parquet, test_df_small):
bucket, keys = test_s3_files_parquet
path = [key for key in keys if key.endswith(".parquet")][0]
full_path = f"s3a://{os.path.join(bucket, path)}" # noqa: PTH118
batch_spec = S3BatchSpec(path=full_path, reader_method="read_parquet")
df = PandasExecutionEngine().get_batch_data(batch_spec=batch_spec)
assert df.dataframe.shape == test_df_small.shape
@pytest.mark.skipif(
not aws.boto3,
reason="Unable to load AWS connection object. Please install boto3 and botocore.",
)
@pytest.mark.big
def test_get_batch_with_no_s3_configured():
batch_spec = S3BatchSpec(
path="s3a://i_dont_exist",
reader_method="read_csv",
splitter_method="_split_on_whole_table",
)
# if S3 was not configured
execution_engine_no_s3 = PandasExecutionEngine()
with pytest.raises(gx_exceptions.ExecutionEngineError):
execution_engine_no_s3.get_batch_data(batch_spec=batch_spec)
@pytest.mark.big
def test_get_batch_with_split_on_divided_integer_and_sample_on_list(test_df):
split_df = PandasExecutionEngine().get_batch_data(
RuntimeDataBatchSpec(
batch_data=test_df,
splitter_method="_split_on_divided_integer",
splitter_kwargs={
"column_name": "id",
"divisor": 10,
"batch_identifiers": {"id": 5},
},
sampling_method="_sample_using_mod",
sampling_kwargs={
"column_name": "id",
"mod": 5,
"value": 4,
},
)
)
assert split_df.dataframe.shape == (2, 10)
assert split_df.dataframe.id.min() == 54
assert split_df.dataframe.id.max() == 59
# noinspection PyUnusedLocal
@pytest.mark.skipif(
not (azure.storage and azure.BlobServiceClient),
reason='Could not import "azure.storage.blob" from Microsoft Azure cloud',
)
@mock.patch(
"great_expectations.execution_engine.pandas_execution_engine.azure.BlobServiceClient",
)
@pytest.mark.big
def test_constructor_with_azure_options(mock_azure_conn):
# default instantiation
PandasExecutionEngine()
# instantiation with custom parameters
engine = PandasExecutionEngine(discard_subset_failing_expectations=True)
assert "discard_subset_failing_expectations" in engine.config
assert engine.config.get("discard_subset_failing_expectations") is True
custom_azure_options = {"account_url": "my_account_url"}
engine = PandasExecutionEngine(azure_options=custom_azure_options)
assert "azure_options" in engine.config
assert engine.config.get("azure_options")["account_url"] == "my_account_url"
@pytest.mark.skipif(
not (azure.storage and azure.BlobServiceClient),
reason='Could not import "azure.storage.blob" from Microsoft Azure cloud',
)
@mock.patch(
"great_expectations.execution_engine.pandas_execution_engine.azure.BlobServiceClient",
)
@pytest.mark.big
def test_get_batch_data_with_azure_batch_spec(
mock_azure_conn,
azure_batch_spec,
):
mock_blob_client = mock_azure_conn().get_blob_client()
mock_azure_obj = mock_blob_client.download_blob()
mock_azure_obj.readall.return_value = (
b"colA,colB,colC\n1,2,3\n4,5,6\n7,8,9" # (3,3) CSV for testing
)
df = PandasExecutionEngine().get_batch_data(batch_spec=azure_batch_spec)
mock_azure_conn().get_blob_client.assert_called_with(
container="test_container", blob="path/A-100.csv"
)
mock_azure_obj.readall.assert_called_once()
assert df.dataframe.shape == (3, 3)
@pytest.mark.big
def test_get_batch_with_no_azure_configured(azure_batch_spec):
# if Azure BlobServiceClient was not configured
execution_engine_no_azure = PandasExecutionEngine()
execution_engine_no_azure._azure = None
# Raises error due the connection object not being set
with pytest.raises(gx_exceptions.ExecutionEngineError):
execution_engine_no_azure.get_batch_data(batch_spec=azure_batch_spec)
@pytest.mark.skipif(
not google.storage,
reason="Could not import 'storage' from google.cloud in pandas_execution_engine.py",
)
@mock.patch(
"great_expectations.execution_engine.pandas_execution_engine.google.service_account",
)
@mock.patch(
"great_expectations.execution_engine.pandas_execution_engine.google.storage.Client",
)
@pytest.mark.big
def test_constructor_with_gcs_options(mock_gcs_conn, mock_auth_method):
# default instantiation
PandasExecutionEngine()
# instantiation with custom parameters
engine = PandasExecutionEngine(discard_subset_failing_expectations=True)
assert "discard_subset_failing_expectations" in engine.config
assert engine.config.get("discard_subset_failing_expectations") is True
custom_gcs_options = {"filename": "a/b/c/my_gcs_credentials.json"}
engine = PandasExecutionEngine(gcs_options=custom_gcs_options)
assert "gcs_options" in engine.config
assert "filename" in engine.config.get("gcs_options")
@pytest.mark.skipif(
not google.storage,
reason="Could not import 'storage' from google.cloud in pandas_execution_engine.py",
)
@mock.patch(
"great_expectations.execution_engine.pandas_execution_engine.google.storage.Client",
)
@pytest.mark.big
def test_get_batch_data_with_gcs_batch_spec(
mock_gcs_conn,
gcs_batch_spec,
):
mock_gcs_bucket = mock_gcs_conn().get_bucket()
mock_gcs_blob = mock_gcs_bucket.blob()
mock_gcs_blob.download_as_bytes.return_value = (
b"colA,colB,colC\n1,2,3\n4,5,6\n7,8,9" # (3,3) CSV for testing
)
# Necessary to pass kwargs to bypass "os.getenv | gcs_options == {}" check
kwargs = {"gcs_options": {"my_option": "my_value"}}
df = PandasExecutionEngine(**kwargs).get_batch_data(batch_spec=gcs_batch_spec)
mock_gcs_conn().get_bucket.assert_called_with("test_bucket")
mock_gcs_bucket.blob.assert_called_with("path/A-100.csv")
mock_gcs_blob.download_as_bytes.assert_called_once()
assert df.dataframe.shape == (3, 3)
@pytest.mark.skipif(
not google.storage,
reason="Could not import 'storage' from google.cloud in pandas_execution_engine.py",
)
@pytest.mark.big
def test_get_batch_data_with_gcs_batch_spec_no_credentials(gcs_batch_spec, monkeypatch):
# If PandasExecutionEngine contains no credentials for GCS, we will still instantiate _gcs engine,
# but will raise Exception when trying get_batch_data(). The only situation where it would work is if we are running in a Google Cloud container.
# TODO : Determine how we can test the scenario where we are running PandasExecutionEngine from within Google Cloud env.
monkeypatch.delenv("GOOGLE_APPLICATION_CREDENTIALS", raising=False)
with pytest.raises(gx_exceptions.ExecutionEngineError):
PandasExecutionEngine().get_batch_data(batch_spec=gcs_batch_spec)
@pytest.mark.skipif(
not google.storage,
reason="Could not import 'storage' from google.cloud in pandas_execution_engine.py",
)
@pytest.mark.big
def test_get_batch_with_gcs_misconfigured(gcs_batch_spec):
# gcs_batchspec point to data that the ExecutionEngine does not have access to
execution_engine_no_gcs = PandasExecutionEngine()
# Raises error if batch_spec causes ExecutionEngine error
with pytest.raises(gx_exceptions.ExecutionEngineError):
execution_engine_no_gcs.get_batch_data(batch_spec=gcs_batch_spec)
|
[
"[email protected]"
] | |
2440df8c04a8ff17110262f019f91d6b325a3e50
|
716abd9e5ba4b72b72cc5f724a6cc0a6ad4390d1
|
/8-Working with Python Modules/37-getpass-module.py
|
9b32db78d59efce0394ef03d7cefa784cd9b1224
|
[] |
no_license
|
devopstasks/PythonScripting
|
ac45edd72dc134ec3539b962f02dfc866f365ecf
|
48bc37733ae6b3be4e2d64909ffe0962b6908518
|
refs/heads/master
| 2023-03-29T11:18:01.329452 | 2021-04-07T03:25:20 | 2021-04-07T03:25:20 | 350,388,744 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 609 |
py
|
'''
============================
getpass() prompts the usser for a password without echoing.The getpass module provides a secure
way to handle the passsword prompts where program interact with the users via the terminal.
getuser() function displays the login name of the user. This function checks the environment variables
LOGNAME, USER, LNAME and USERNAME, in order, and returns the value of the first non-empty string.
============================
'''
'''
import getpass
#db_pass=getpass.getpass()
db_pass=getpass.getpass(prompt="Enter your DB Password: ")
print(f"The entered password is {db_pass}")
'''
|
[
"[email protected]"
] | |
dc5f089560924477896016bb260ab44854dd8657
|
95c09d4e6d9396a0b2c20c14b56d3eb15ba4f20e
|
/pdm/installers/uninstallers.py
|
b0e68354e0e49d2b4131d9b4945ee14e161779ca
|
[
"MIT"
] |
permissive
|
frafra/pdm
|
0bc901bd7f4d3cd784fb42051591e95239d03f60
|
12c5c4f91bbb7260be7d93f3e3914ba708309032
|
refs/heads/main
| 2023-07-13T16:38:47.512887 | 2021-08-20T00:46:27 | 2021-08-20T00:46:27 | 398,244,097 | 0 | 0 |
MIT
| 2021-08-20T10:48:23 | 2021-08-20T10:48:22 | null |
UTF-8
|
Python
| false | false | 10,490 |
py
|
from __future__ import annotations
import abc
import glob
import os
import shutil
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Iterable, Type, TypeVar, cast
from pdm import termui
from pdm._types import Distribution
from pdm.exceptions import UninstallError
from pdm.installers.packages import CachedPackage
from pdm.models.environment import Environment
from pdm.utils import is_egg_link
_T = TypeVar("_T", bound="BaseRemovePaths")
def renames(old: str, new: str) -> None:
"""Like os.renames(), but handles renaming across devices."""
# Implementation borrowed from os.renames().
head, tail = os.path.split(new)
if head and tail and not os.path.exists(head):
os.makedirs(head)
shutil.move(old, new)
head, tail = os.path.split(old)
if head and tail:
try:
os.removedirs(head)
except OSError:
pass
def compress_for_rename(paths: Iterable[str]) -> set[str]:
"""Returns a set containing the paths that need to be renamed.
This set may include directories when the original sequence of paths
included every file on disk.
"""
case_map = {os.path.normcase(p): p for p in paths}
remaining = set(case_map)
unchecked = sorted({os.path.split(p)[0] for p in case_map.values()}, key=len)
wildcards: set[str] = set()
def norm_join(*a):
# type: (str) -> str
return os.path.normcase(os.path.join(*a))
for root in unchecked:
if any(os.path.normcase(root).startswith(w) for w in wildcards):
# This directory has already been handled.
continue
all_files: set[str] = set()
all_subdirs: set[str] = set()
for dirname, subdirs, files in os.walk(root):
all_subdirs.update(norm_join(root, dirname, d) for d in subdirs)
all_files.update(norm_join(root, dirname, f) for f in files)
# If all the files we found are in our remaining set of files to
# remove, then remove them from the latter set and add a wildcard
# for the directory.
if not (all_files - remaining):
remaining.difference_update(all_files)
wildcards.add(root + os.sep)
return set(map(case_map.__getitem__, remaining)) | wildcards
def _script_names(script_name: str, is_gui: bool) -> Iterable[str]:
yield script_name
if os.name == "nt":
yield script_name + ".exe"
yield script_name + ".exe.manifest"
if is_gui:
yield script_name + "-script.pyw"
else:
yield script_name + "-script.py"
def _cache_file_from_source(py_file: str) -> Iterable[str]:
py2_cache = py_file[:-3] + ".pyc"
if os.path.isfile(py2_cache):
yield py2_cache
parent, base = os.path.split(py_file)
cache_dir = os.path.join(parent, "__pycache__")
for path in glob.glob(os.path.join(cache_dir, base[:-3] + ".*.pyc")):
yield path
def _get_file_root(path: str, base: str) -> str | None:
try:
rel_path = Path(path).relative_to(base)
except ValueError:
return None
else:
root = rel_path.parts[0] if len(rel_path.parts) > 1 else ""
return os.path.normcase(os.path.join(base, root))
class BaseRemovePaths(abc.ABC):
"""A collection of paths and/or pth entries to remove"""
def __init__(self, dist: Distribution, envrionment: Environment) -> None:
self.dist = dist
self.envrionment = envrionment
self._paths: set[str] = set()
self._pth_entries: set[str] = set()
self.refer_to: str | None = None
@abc.abstractmethod
def remove(self) -> None:
"""Remove the files"""
def commit(self) -> None:
"""Commit the removal"""
def rollback(self) -> None:
"""Roll back the removal operations"""
@classmethod
def from_dist(cls: Type[_T], dist: Distribution, envrionment: Environment) -> _T:
"""Create an instance from the distribution"""
scheme = envrionment.get_paths()
instance = cls(dist, envrionment)
meta_location = os.path.normcase(dist._path.absolute()) # type: ignore
dist_location = os.path.dirname(meta_location)
if is_egg_link(dist):
egg_link_path = cast("Path | None", getattr(dist, "link_file", None))
if not egg_link_path:
termui.logger.warn(
"No egg link is found for editable distribution %s, do nothing.",
dist.metadata["Name"],
)
else:
link_pointer = os.path.normcase(
egg_link_path.open("rb").readline().decode().strip()
)
if link_pointer != dist_location:
raise UninstallError(
f"The link pointer in {egg_link_path} doesn't match "
f"the location of {dist.metadata['Name']}(at {dist_location}"
)
instance.add_path(str(egg_link_path))
instance.add_pth(link_pointer)
elif dist.files:
for file in dist.files:
location = dist.locate_file(file)
instance.add_path(str(location))
bare_name, ext = os.path.splitext(location)
if ext == ".py":
# .pyc files are added by add_path()
instance.add_path(bare_name + ".pyo")
bin_dir = scheme["scripts"]
if os.path.isdir(os.path.join(meta_location, "scripts")):
for script in os.listdir(os.path.join(meta_location, "scripts")):
instance.add_path(os.path.join(bin_dir, script))
if os.name == "nt":
instance.add_path(os.path.join(bin_dir, script) + ".bat")
# find console_scripts
_scripts_to_remove: list[str] = []
for ep in dist.entry_points:
if ep.group == "console_scripts":
_scripts_to_remove.extend(_script_names(ep.name, False))
elif ep.group == "gui_scripts":
_scripts_to_remove.extend(_script_names(ep.name, True))
for s in _scripts_to_remove:
instance.add_path(os.path.join(bin_dir, s))
return instance
def add_pth(self, line: str) -> None:
self._pth_entries.add(line)
def add_path(self, path: str) -> None:
normalized_path = os.path.normcase(os.path.expanduser(os.path.abspath(path)))
self._paths.add(normalized_path)
if path.endswith(".py"):
self._paths.update(_cache_file_from_source(normalized_path))
elif path.replace("\\", "/").endswith(".dist-info/REFER_TO"):
line = open(path, "rb").readline().decode().strip()
if line:
self.refer_to = line
class StashedRemovePaths(BaseRemovePaths):
"""Stash the paths to temporarily location and remove them after commit"""
PTH_REGISTRY = "easy-install.pth"
def __init__(self, dist: Distribution, environment: Environment) -> None:
super().__init__(dist, environment)
self._pth_file = os.path.join(
self.envrionment.get_paths()["purelib"], self.PTH_REGISTRY
)
self._saved_pth: bytes | None = None
self._stashed: list[tuple[str, str]] = []
self._tempdirs: dict[str, TemporaryDirectory] = {}
def remove(self) -> None:
self._remove_pth()
self._stash_files()
def _remove_pth(self) -> None:
if not self._pth_entries:
return
self._saved_pth = open(self._pth_file, "rb").read()
endline = "\r\n" if b"\r\n" in self._saved_pth else "\n"
lines = self._saved_pth.decode().splitlines()
for item in self._pth_entries:
termui.logger.debug("Removing pth entry: %s", item)
lines.remove(item)
with open(self._pth_file, "wb") as f:
f.write((endline.join(lines) + endline).encode("utf8"))
def _stash_files(self) -> None:
paths_to_rename = compress_for_rename(self._paths)
for old_path in paths_to_rename:
if not os.path.exists(old_path):
continue
is_dir = os.path.isdir(old_path) and not os.path.islink(old_path)
termui.logger.debug(
"Removing %s %s", "directory" if is_dir else "file", old_path
)
if old_path.endswith(".pyc"):
# Don't stash cache files, remove them directly
os.unlink(old_path)
root = _get_file_root(
old_path, os.path.abspath(self.envrionment.get_paths()["prefix"])
)
if root is None:
termui.logger.debug(
"File path %s is not under packages root, skip", old_path
)
continue
if root not in self._tempdirs:
self._tempdirs[root] = TemporaryDirectory("-uninstall", "pdm-")
new_root = self._tempdirs[root].name
relpath = os.path.relpath(old_path, root)
new_path = os.path.join(new_root, relpath)
if is_dir and os.path.isdir(new_path):
os.rmdir(new_path)
renames(old_path, new_path)
self._stashed.append((old_path, new_path))
def commit(self) -> None:
for tempdir in self._tempdirs.values():
try:
tempdir.cleanup()
except FileNotFoundError:
pass
self._tempdirs.clear()
self._stashed.clear()
self._saved_pth = None
if self.refer_to:
termui.logger.debug("Unlink from cached package %s", self.refer_to)
CachedPackage(self.refer_to).remove_referrer(os.path.dirname(self.refer_to))
self.refer_to = None
def rollback(self) -> None:
if not self._stashed:
termui.logger.error("Can't rollback, not uninstalled yet")
return
if self._saved_pth is not None:
with open(self._pth_file, "wb") as f:
f.write(self._saved_pth)
for old_path, new_path in self._stashed:
termui.logger.debug("Rollback %s\n from %s", old_path, new_path)
if os.path.isfile(old_path) or os.path.islink(old_path):
os.unlink(old_path)
elif os.path.isdir(old_path):
shutil.rmtree(old_path)
renames(new_path, old_path)
self.commit()
|
[
"[email protected]"
] | |
af02d32f9a6520f9d256420f9097e7778dad4acd
|
39689ee725bc7183d5d59fb34f7d2ffe5fd6ad36
|
/ABC_A/ABC142A.py
|
826ae07f226d78522817a44bd69684709a6e7e52
|
[] |
no_license
|
yu5shi8/AtCoder
|
b6eb920a9046bdfa98012dd3fc65f75f16214ffe
|
f9ca69001ece8379e3a70c993c44b540f8be2217
|
refs/heads/master
| 2021-06-15T17:58:07.027699 | 2021-03-20T14:04:03 | 2021-03-20T14:04:03 | 177,757,053 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 245 |
py
|
# -*- coding: utf-8 -*-
# A - Odds of Oddness
# https://atcoder.jp/contests/abc142/tasks/abc142_a
N = int(input())
if N % 2 != 0:
num = N // 2 + 1
else:
num = N // 2
ans = num / N
print('{:.06f}'.format(ans))
# 21:00 - 21:05（AC）
|
[
"[email protected]"
] | |
ed8a4c0c136e477c942f8c3a86c55171d995ac60
|
d5be74d2de6fa0ded61d6c3ee7c91a403c0f90db
|
/tests/behave/features/steps/qrhei.py
|
d2b7556a64001e67f250b41cc98083845db657ff
|
[
"MIT"
] |
permissive
|
tmancal74/quantarhei
|
43cf9d4be857b8e6db1274ebb8a384f1545cd9ad
|
fa3042d809005d47106e53609e6a63aa780c477c
|
refs/heads/master
| 2023-05-11T06:57:36.368595 | 2023-05-02T13:10:18 | 2023-05-02T13:10:18 | 63,804,925 | 20 | 22 |
MIT
| 2022-12-21T14:10:00 | 2016-07-20T18:30:25 |
Python
|
UTF-8
|
Python
| false | false | 1,870 |
py
|
"""
Autogenerated by ghenerate script, part of Quantarhei
http://github.com/tmancal74/quantarhei
Tomas Mancal, [email protected]
Generated on: 2018-06-06 15:00:05
Edit the functions below to give them desired functionality.
In present version of `ghenerate`, no edits or replacements
are perfomed in the feature file text.
"""
import os
from behave import given
from behave import when
from behave import then
import quantarhei.testing.behave as bhv
#
# Given ...
#
@given('that I have a list of examples from qrhei list')
def step_given_1(context):
"""
Given that I have a list of examples from qrhei list
"""
bhv.secure_temp_dir(context)
with bhv.testdir(context):
bhv.shell_command(context, "qrhei list --examples")
text = context.output.decode("utf-8")
items = text.split()
files_to_fetch = []
for item in items:
if item.startswith("ex_"):
files_to_fetch.append(item)
context.files = files_to_fetch
#
# When ...
#
@when('I fetch all examples one by one')
def step_when_2(context):
"""
When I fetch all examples one by one
"""
failures = []
with bhv.testdir(context):
for file in context.files:
bhv.shell_command(context, "qrhei fetch --examples "+file)
print(context.output.decode("utf-8"))
if not os.path.isfile(file):
failures.append("File: "+file+" was not fetched")
context.failures = failures
#
# Then ...
#
@then('examples are all fetchable')
def step_then_3(context):
"""
Then examples are all fetchable
"""
if len(context.failures) > 0:
raise Exception("some examples are not fetchable: "
+str(context.failures))
|
[
"[email protected]"
] | |
ba9ff63d687e7e07f7e50782cbf97d733c1fef07
|
f1a1e764c4ed1238c63c3f4908bd8bebdfb3197f
|
/opencv-starter/udemy/noise.py
|
731911a56b3499f4dbbc9b86c308053b60761107
|
[] |
no_license
|
razmik/computer-vision-python
|
e4eadac1fae4fd0483189a23958ba7c794cbd68e
|
21f5557af9804fa01bcfddc085504678828c94ef
|
refs/heads/master
| 2021-01-25T06:35:55.600991 | 2017-06-13T01:40:59 | 2017-06-13T01:40:59 | 93,591,304 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 3,281 |
py
|
import numpy as np
import os
from skimage.util import random_noise
import matplotlib.pyplot as plt
import scipy.misc
import cv2
"""
Parameters
----------
image : ndarray
Input image data. Will be converted to float.
mode : str
One of the following strings, selecting the type of noise to add:
'gauss' Gaussian-distributed additive noise.
'poisson' Poisson-distributed noise generated from the data.
's&p' Replaces random pixels with 0 or 1.
'speckle' Multiplicative noise using out = image + n*image,where
n is uniform noise with specified mean & variance.
"""
def noisy(image, noise_typ='gauss', var=0.1):
if noise_typ == "gauss":
if len(image.shape) == 2:
row, col = image.shape
mean = 0
sigma = var ** 0.5
gauss = np.random.normal(mean, sigma, (row, col))
gauss = gauss.reshape(row, col)
noisy = image + gauss
else:
row, col, ch = image.shape
mean = 0
sigma = var ** 0.5
gauss = np.random.normal(mean, sigma, (row, col, ch))
gauss = gauss.reshape(row, col, ch)
noisy = image + gauss
return noisy
elif noise_typ == "s&p":
row, col, ch = image.shape
s_vs_p = 0.5
amount = 0.004
out = np.copy(image)
# Salt mode
num_salt = np.ceil(amount * image.size * s_vs_p)
coords = [np.random.randint(0, i - 1, int(num_salt))
for i in image.shape]
out[coords] = 1
# Pepper mode
num_pepper = np.ceil(amount * image.size * (1. - s_vs_p))
coords = [np.random.randint(0, i - 1, int(num_pepper))
for i in image.shape]
out[coords] = 0
return out
elif noise_typ == "poisson":
vals = len(np.unique(image))
vals = 2 ** np.ceil(np.log2(vals))
noisy = np.random.poisson(image * vals) / float(vals)
return noisy
elif noise_typ == "speckle":
row, col, ch = image.shape
gauss = np.random.randn(row, col, ch)
gauss = gauss.reshape(row, col, ch)
noisy = image + image * gauss
return noisy
"""
Noising image
"""
# filepath = "..\data\group.jpg".replace('\\', '/')
#
# img = cv2.imread(filepath)
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#
# noised_img = noisy(gray, var=1000)
#
# # plt.imshow(gray)
# # plt.show()
#
# # noised_img = random_noise(img) # skimage - add noise function
#
# scipy.misc.imsave('outfile.jpg', noised_img)
"""
Denoising
"""
filepath = 'outfile.jpg'
img = cv2.imread(filepath)
plt.figure(1)
plt.title('Original noised image')
plt.imshow(img)
# plt.figure(2)
# plt.title('2D Convolution ( Image Filtering )')
# kernal = np.ones((5, 5), np.float32) / 25
# filtered_image = cv2.filter2D(img, -1, kernal)
# plt.imshow(filtered_image)
#
# plt.figure(3)
# plt.title('Averaging')
# blur = cv2.blur(img, (5,5))
# plt.imshow(blur)
plt.figure(4)
plt.title('Gaussian blur')
blur = cv2.GaussianBlur(img, (5,5), 0)
# kernal = cv2.getGaussianKernel(10, 100)
# blur = cv2.filter2D(img, -1, kernal)
plt.imshow(blur)
# plt.figure(5)
# plt.title('Bilateral filtering')
# blur = cv2.bilateralFilter(img,9,75,75)
# plt.imshow(blur)
plt.show()
|
[
"[email protected]"
] | |
40c3c590320e9ad5cfb2362493d179d28943aeaa
|
510a042cc6ead9ee708a85e431bd5d271102da9c
|
/backend/admin/macro.py
|
759fe7ba7c87e9c15bbdb860994805de91adf74c
|
[
"MIT"
] |
permissive
|
rushilsrivastava/flask-react-spa
|
8ad7683f9fc5a8fc2cbc551df6e135b82a8aef19
|
7cf45c92c0db411156fd6fd53c3febc84f81eba7
|
refs/heads/master
| 2022-11-17T19:03:16.447828 | 2020-07-18T22:44:52 | 2020-07-18T22:44:52 | 265,141,635 | 1 | 0 |
MIT
| 2020-05-19T04:12:11 | 2020-05-19T04:12:10 | null |
UTF-8
|
Python
| false | false | 691 |
py
|
def macro(name):
"""Replaces flask_admin.model.template.macro, adding support for using
macros imported from another file
For Example::
class FooAdmin(ModelAdmin):
column_formatters = {
'col_name': macro('<macro_import_name_inside_template>.<macro_name>')
}
"""
def wrapper(view, context, model, column):
if '.' in name:
macro_import_name, macro_name = name.split('.')
m = getattr(context.get(macro_import_name), macro_name, None)
else:
m = context.resolve(name)
if not m:
return m
return m(model=model, column=column)
return wrapper
|
[
"[email protected]"
] | |
27cdacfee09e4e1c2c9057af17e1eb5ef6004409
|
6f05f7d5a67b6bb87956a22b988067ec772ba966
|
/data/test/python/b63a17321b0865613f47eca68b65ccbb2894fa9erun.py
|
b63a17321b0865613f47eca68b65ccbb2894fa9e
|
[
"MIT"
] |
permissive
|
harshp8l/deep-learning-lang-detection
|
93b6d24a38081597c610ecf9b1f3b92c7d669be5
|
2a54293181c1c2b1a2b840ddee4d4d80177efb33
|
refs/heads/master
| 2020-04-07T18:07:00.697994 | 2018-11-29T23:21:23 | 2018-11-29T23:21:23 | 158,597,498 | 0 | 0 |
MIT
| 2018-11-21T19:36:42 | 2018-11-21T19:36:41 | null |
UTF-8
|
Python
| false | false | 1,027 |
py
|
"""myapp.py
Usage:
(window1)$ python myapp.py -l info
(window2)$ python
>>> from myapp import add
>>> add.delay(16, 16).get()
32
You can also specify the app to use with celeryd::
$ celeryd -l info --app=myapp.celery
"""
import sys
sys.path.insert(0,'lib')
sys.path.insert(0, 'lib/celery')
sys.path.insert(0, 'lib/kombu')
from celery import Celery
BROKER_BACKEND = 'redis'
BROKER_HOST = '127.0.0.1'
BROKER_PORT = 6379
BROKER_VHOST = '12'
CELERY_RESULT_BACKEND = "redis"
REDIS_HOST = '127.0.0.1'
REDIS_PORT = 6379
REDIS_DB = '100'
REDIS_CONNECT_RETRY = True
celery = Celery("myapp")
#celery.conf.update(BROKER_URL="amqp://guest:guest@localhost:5672//")
celery.conf.update(BROKER_BACKEND='redis')
celery.conf.update(BROKER_HOST='127.0.0.1')
celery.conf.update(BROKER_PORT=6379)
celery.conf.update(BROKER_VHOST='12')
print celery.conf
@celery.task
def add(x, y):
print 'i am doing work'
return x + y
if __name__ == "__main__":
import celery.bin.celeryd
celery.bin.celeryd.main()
|
[
"[email protected]"
] | |
857719d772c77bc910a2a9a26c7b07c6b1f1ca34
|
3395a234e7c80d011607e79c49cd48bf516f256b
|
/dependencies/jedi/third_party/typeshed/third_party/2/six/moves/xmlrpc_client.pyi
|
1b3bd7468cfa146462d762a2a5e7d14d1f238ffb
|
[
"MIT",
"Apache-2.0"
] |
permissive
|
srusskih/SublimeJEDI
|
67329b72e184bc9584843968dcc534a002c797a1
|
95c185d778425c04536d53517b0e3fe6dedf8e59
|
refs/heads/master
| 2023-08-24T11:30:37.801834 | 2022-08-30T09:04:17 | 2022-08-30T09:04:17 | 6,241,108 | 669 | 125 |
MIT
| 2022-08-30T09:04:18 | 2012-10-16T08:23:57 |
Python
|
UTF-8
|
Python
| false | false | 24 |
pyi
|
from xmlrpclib import *
|
[
"[email protected]"
] | |
4789e8cbb3ac096ea2f1fcca098891ef05488484
|
4e5141121d8b4015db233cbc71946ec3cfbe5fe6
|
/samples/basic/crud/models/cisco-ios-xr/Cisco-IOS-XR-ipv4-arp-cfg/nc-update-xr-ipv4-arp-cfg-10-ydk.py
|
8fa6211b688d0ec4c961bf8810acadbbe1a699e5
|
[
"Apache-2.0"
] |
permissive
|
itbj/ydk-py-samples
|
898c6c9bad9d6f8072892300d42633d82ec38368
|
c5834091da0ebedbb11af7bbf780f268aad7040b
|
refs/heads/master
| 2022-11-20T17:44:58.844428 | 2020-07-25T06:18:02 | 2020-07-25T06:18:02 | 282,382,442 | 1 | 0 | null | 2020-07-25T06:04:51 | 2020-07-25T06:04:50 | null |
UTF-8
|
Python
| false | false | 2,663 |
py
|
#!/usr/bin/env python
#
# Copyright 2016 Cisco Systems, Inc.
#
# 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.
#
"""
Update configuration for model Cisco-IOS-XR-ipv4-arp-cfg.
usage: nc-update-xr-ipv4-arp-cfg-10-ydk.py [-h] [-v] device
positional arguments:
device NETCONF device (ssh://user:password@host:port)
optional arguments:
-h, --help show this help message and exit
-v, --verbose print debugging messages
"""
from argparse import ArgumentParser
from urlparse import urlparse
from ydk.services import CRUDService
from ydk.providers import NetconfServiceProvider
from ydk.models.cisco_ios_xr import Cisco_IOS_XR_ipv4_arp_cfg \
as xr_ipv4_arp_cfg
import logging
def config_arp(arp):
"""Add config data to arp object."""
pass
if __name__ == "__main__":
"""Execute main program."""
parser = ArgumentParser()
parser.add_argument("-v", "--verbose", help="print debugging messages",
action="store_true")
parser.add_argument("device",
help="NETCONF device (ssh://user:password@host:port)")
args = parser.parse_args()
device = urlparse(args.device)
# log debug messages if verbose argument specified
if args.verbose:
logger = logging.getLogger("ydk")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter(("%(asctime)s - %(name)s - "
"%(levelname)s - %(message)s"))
handler.setFormatter(formatter)
logger.addHandler(handler)
# create NETCONF provider
provider = NetconfServiceProvider(address=device.hostname,
port=device.port,
username=device.username,
password=device.password,
protocol=device.scheme)
# create CRUD service
crud = CRUDService()
arp = xr_ipv4_arp_cfg.Arp() # create object
config_arp(arp) # add object configuration
# update configuration on NETCONF device
# crud.update(provider, arp)
exit()
# End of script
|
[
"[email protected]"
] | |
e05e89a8cc45a7fd835f8907cb25a8e9832090b9
|
f576f0ea3725d54bd2551883901b25b863fe6688
|
/sdk/ml/azure-ai-ml/azure/ai/ml/_utils/_storage_utils.py
|
abe32c22964c58192cf3d71b3014bfcb4b142c57
|
[
"LicenseRef-scancode-generic-cla",
"MIT",
"LGPL-2.1-or-later",
"LicenseRef-scancode-python-cwi",
"PSF-2.0",
"LGPL-2.0-or-later",
"GPL-3.0-or-later",
"GPL-1.0-or-later",
"LicenseRef-scancode-warranty-disclaimer",
"LGPL-2.1-only",
"Python-2.0",
"MPL-2.0",
"LicenseRef-scancode-other-copyleft",
"HPND",
"ODbL-1.0",
"GPL-3.0-only",
"ZPL-2.1",
"Apache-2.0",
"BSD-2-Clause",
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown"
] |
permissive
|
Azure/azure-sdk-for-python
|
02e3838e53a33d8ba27e9bcc22bd84e790e4ca7c
|
c2ca191e736bb06bfbbbc9493e8325763ba990bb
|
refs/heads/main
| 2023-09-06T09:30:13.135012 | 2023-09-06T01:08:06 | 2023-09-06T01:08:06 | 4,127,088 | 4,046 | 2,755 |
MIT
| 2023-09-14T21:48:49 | 2012-04-24T16:46:12 |
Python
|
UTF-8
|
Python
| false | false | 8,103 |
py
|
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
import re
from typing import Optional, Tuple, Union
from azure.ai.ml._artifacts._blob_storage_helper import BlobStorageClient
from azure.ai.ml._artifacts._constants import STORAGE_URI_REGEX
from azure.ai.ml._artifacts._fileshare_storage_helper import FileStorageClient
from azure.ai.ml._artifacts._gen2_storage_helper import Gen2StorageClient
from azure.ai.ml._azure_environments import _get_storage_endpoint_from_metadata
from azure.ai.ml._restclient.v2022_10_01.models import DatastoreType
from azure.ai.ml.constants._common import (
FILE_PREFIX,
FOLDER_PREFIX,
JOB_URI_REGEX_FORMAT,
LONG_URI_FORMAT,
LONG_URI_REGEX_FORMAT,
MLFLOW_URI_REGEX_FORMAT,
OUTPUT_URI_REGEX_FORMAT,
SHORT_URI_FORMAT,
SHORT_URI_REGEX_FORMAT,
STORAGE_ACCOUNT_URLS,
)
from azure.ai.ml.exceptions import ErrorTarget, ValidationErrorType, ValidationException
module_logger = logging.getLogger(__name__)
class AzureMLDatastorePathUri:
"""Parser for an azureml:// datastore path URI, e.g.: azureml://datastores/mydatastore/paths/images/dogs'.
:param uri: The AzureML datastore path URI.
:type uri: str
:raises ~azure.ai.ml.exceptions.ValidationException: Raised if the AzureML datastore
path URI is incorrectly formatted.
'
"""
def __init__(self, uri: str):
if uri.startswith(FILE_PREFIX):
uri = uri[len(FILE_PREFIX) :]
elif uri.startswith(FOLDER_PREFIX):
uri = uri[len(FOLDER_PREFIX) :]
self.uri = uri
short_uri_match = re.match(SHORT_URI_REGEX_FORMAT, uri)
ml_flow_uri_match = re.match(MLFLOW_URI_REGEX_FORMAT, uri)
job_uri_match = re.match(JOB_URI_REGEX_FORMAT, uri)
long_uri_match = re.match(LONG_URI_REGEX_FORMAT, uri)
output_uri_match = re.match(OUTPUT_URI_REGEX_FORMAT, uri)
if short_uri_match:
self.datastore = short_uri_match.group(1)
self.path = short_uri_match.group(2)
self.uri_type = "Datastore"
self.workspace_name = None
self.resource_group = None
self.subscription_id = None
elif ml_flow_uri_match:
self.datastore = ml_flow_uri_match.group(1)
self.path = ml_flow_uri_match.group(2)
self.uri_type = "MlFlow"
self.workspace_name = None
self.resource_group = None
self.subscription_id = None
elif job_uri_match:
self.datastore = job_uri_match.group(1)
self.path = job_uri_match.group(2)
self.uri_type = "Job"
self.workspace_name = None
self.resource_group = None
self.subscription_id = None
elif output_uri_match:
self.datastore = output_uri_match.group(1)
self.path = output_uri_match.group(2)
self.uri_type = None
self.workspace_name = None
self.resource_group = None
self.subscription_id = None
elif long_uri_match:
self.datastore = long_uri_match.group(4)
self.path = long_uri_match.group(5)
self.uri_type = "Datastore"
self.workspace_name = long_uri_match.group(3)
self.resource_group = long_uri_match.group(2)
self.subscription_id = long_uri_match.group(1)
else:
msg = "Invalid AzureML datastore path URI {}"
raise ValidationException(
message=msg.format(uri),
no_personal_data_message=msg.format("[uri]"),
target=ErrorTarget.DATASTORE,
error_type=ValidationErrorType.INVALID_VALUE,
)
def to_short_uri(self) -> str:
return SHORT_URI_FORMAT.format(self.datastore, self.path)
def to_long_uri(self, subscription_id: str, resource_group_name: str, workspace_name: str) -> str:
return LONG_URI_FORMAT.format(
subscription_id,
resource_group_name,
workspace_name,
self.datastore,
self.path,
)
def get_uri_type(self) -> str:
if self.uri[0:20] == "azureml://datastores":
return "Datastore"
if self.uri[0:14] == "azureml://jobs":
return "Jobs"
if self.uri[0 : self.uri.find(":")] == "runs":
return "MLFlow"
msg = "Invalid uri format for {}. URI must start with 'azureml://' or 'runs:/'"
raise ValidationException(
message=msg.format(self.uri),
no_personal_data_message=msg.format("[self.uri]"),
target=ErrorTarget.DATASTORE,
error_type=ValidationErrorType.INVALID_VALUE,
)
def get_storage_client(
credential: str,
storage_account: str,
storage_type: Union[DatastoreType, str] = DatastoreType.AZURE_BLOB,
account_url: Optional[str] = None,
container_name: Optional[str] = None,
) -> Union[BlobStorageClient, FileStorageClient, Gen2StorageClient]:
"""Return a storage client class instance based on the storage account type.
:param credential: The credential
:type credential: str
:param storage_account: The storage_account name
:type storage_account: str
:param storage_type: The storage type
:type storage_type: Union[DatastoreType, str]
:param account_url: The account url
:type account_url: Optional[str]
:param container_name: The container name
:type container_name: Optional[str]
:return: The storage client
:rtype: Union[BlobStorageClient, FileStorageClient, Gen2StorageClient]
"""
client_builders = {
DatastoreType.AZURE_BLOB: lambda credential, container_name, account_url: BlobStorageClient(
credential=credential, account_url=account_url, container_name=container_name
),
DatastoreType.AZURE_DATA_LAKE_GEN2: lambda credential, container_name, account_url: Gen2StorageClient(
credential=credential, file_system=container_name, account_url=account_url
),
DatastoreType.AZURE_FILE: lambda credential, container_name, account_url: FileStorageClient(
credential=credential, file_share_name=container_name, account_url=account_url
),
}
if storage_type not in client_builders:
msg = (
f"Datastore type {storage_type} is not supported. Supported storage"
+ f"types for artifact upload include: {*client_builders,}"
)
raise ValidationException(
message=msg,
no_personal_data_message=msg,
target=ErrorTarget.DATASTORE,
error_type=ValidationErrorType.INVALID_VALUE,
)
storage_endpoint = _get_storage_endpoint_from_metadata()
if not account_url and storage_endpoint:
account_url = STORAGE_ACCOUNT_URLS[storage_type].format(storage_account, storage_endpoint)
return client_builders[storage_type](credential, container_name, account_url)
def get_artifact_path_from_storage_url(blob_url: str, container_name: dict) -> str:
split_blob_url = blob_url.split(container_name)
if len(split_blob_url) > 1:
path = split_blob_url[-1]
if path.startswith("/"):
return path[1:]
return path
return blob_url
def get_ds_name_and_path_prefix(asset_uri: str, registry_name: Optional[str] = None) -> Tuple[str, str]:
if registry_name:
try:
split_paths = re.findall(STORAGE_URI_REGEX, asset_uri)
path_prefix = split_paths[0][3]
except Exception as e:
raise Exception("Registry asset URI could not be parsed.") from e
ds_name = None
else:
try:
ds_name = asset_uri.split("paths")[0].split("/")[-2]
path_prefix = asset_uri.split("paths")[1][1:]
except Exception as e:
raise Exception("Workspace asset URI could not be parsed.") from e
return ds_name, path_prefix
|
[
"[email protected]"
] | |
74df06b01faeb3ebf08331a61bb0ae9a7f7e4705
|
2b0654193b3090b309a7ea6240fc57be01c0aa43
|
/xam/linear_model/auc_regressor.py
|
a4052bde26ea46ba09fbb8a0517cc1eb01753302
|
[
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
Python-Repository-Hub/xam
|
86043af1cc9edd2c779ecb76e2ad5fe20044d3b0
|
93c066990d976c7d4d74b63fb6fb3254ee8d9b48
|
refs/heads/master
| 2022-04-07T11:33:10.455356 | 2020-02-04T20:38:45 | 2020-02-04T20:38:45 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 729 |
py
|
from functools import partial
import scipy as sp
from sklearn import linear_model
from sklearn import metrics
class AUCRegressor():
def _auc_loss(self, coef, X, y):
fpr, tpr, _ = metrics.roc_curve(y, sp.dot(X, coef))
return -metrics.auc(fpr, tpr)
def fit(self, X, y, verbose=False):
lr = linear_model.LinearRegression()
auc_partial = partial(self._auc_loss, X=X, y=y)
initial_coef = lr.fit(X, y).coef_
self.coef_ = sp.optimize.fmin(auc_partial, initial_coef, disp=verbose)
def predict(self, X):
return sp.dot(X, self.coef_)
def score(self, X, y):
fpr, tpr, _ = metrics.roc_curve(y, sp.dot(X, self.coef_))
return metrics.auc(fpr, tpr)
|
[
"[email protected]"
] | |
87288fea8165e3ccfadfccce0ad9128d031a9760
|
0644c03cc3f89b0fc22d9e548a2d06e6a594f1b4
|
/pabi_base/models/res_investment_structure.py
|
c3ed23de10b6fea7458a17f6f6a0e81a64860fa6
|
[] |
no_license
|
phongyanon/pb2_addons
|
552fbf4cd904c81a1fd0ac5817dc1cf8f3377096
|
4c69002eeda2de8e806c8a168d8ba9f28527c8d2
|
refs/heads/master
| 2021-01-19T13:20:53.749866 | 2017-12-20T11:12:51 | 2017-12-20T11:12:51 | 97,184,424 | 0 | 0 | null | 2017-07-14T02:29:53 | 2017-07-14T02:29:52 | null |
UTF-8
|
Python
| false | false | 3,469 |
py
|
# -*- coding: utf-8 -*-
from openerp import fields, models, api
from openerp.addons.pabi_base.models.res_common import ResCommon
CONSTRUCTION_PHASE = {
'1-design': '1-Design',
'2-control': '2-Control',
'3-construct': '3-Construct',
'4-procure': '4-Procurement',
'5-other': '5-Others',
}
# Investment - Asset
class ResInvestAsset(ResCommon, models.Model):
_name = 'res.invest.asset'
_description = 'Investment Asset'
invest_asset_categ_id = fields.Many2one(
'res.invest.asset.category',
string='Investment Asset Category'
)
org_id = fields.Many2one(
'res.org',
string='Org',
required=False,
)
costcenter_id = fields.Many2one(
'res.costcenter',
string='Costcenter',
required=True,
)
name_common = fields.Char(
string='Common Name',
)
fund_ids = fields.Many2many(
'res.fund',
'res_fund_invest_asset_rel',
'invest_asset_id', 'fund_id',
string='Funds',
default=lambda self: self.env.ref('base.fund_nstda'),
)
objective = fields.Char(
string='Objective',
)
owner_section_id = fields.Many2one(
'res.section',
string='Owner Section',
help="Not related to budgeting, this field hold the "
"section owner of this asset",
)
class ResInvestAssetCategory(ResCommon, models.Model):
_name = 'res.invest.asset.category'
_description = 'Investment Asset Category'
# Investment - Construction
class ResInvestConstruction(ResCommon, models.Model):
_name = 'res.invest.construction'
_description = 'Investment Construction'
phase_ids = fields.One2many(
'res.invest.construction.phase',
'invest_construction_id',
string='Phases',
)
org_id = fields.Many2one(
'res.org',
string='Org',
required=False,
)
costcenter_id = fields.Many2one(
'res.costcenter',
string='Costcenter',
required=True,
)
fund_ids = fields.Many2many(
'res.fund',
'res_fund_invest_construction_rel',
'invest_construction_id', 'fund_id',
string='Funds',
default=lambda self: self.env.ref('base.fund_nstda'),
)
class ResInvestConstructionPhase(ResCommon, models.Model):
_name = 'res.invest.construction.phase'
_description = 'Investment Construction Phase'
_order = 'sequence, id'
sequence = fields.Integer(
string='Sequence',
default=10,
)
invest_construction_id = fields.Many2one(
'res.invest.construction',
string='Investment Construction',
index=True,
ondelete='cascade',
)
phase = fields.Selection(
sorted(CONSTRUCTION_PHASE.items()),
string='Phase',
required=True,
)
fund_ids = fields.Many2many(
'res.fund',
related='invest_construction_id.fund_ids',
string='Funds',
)
_sql_constraints = [
('phase_uniq', 'unique(invest_construction_id, phase)',
'Phase must be unique for a construction project!'),
]
@api.multi
def name_get(self):
result = []
for rec in self:
result.append((rec.id, "[%s] %s - %s" %
(rec.invest_construction_id.code,
rec.invest_construction_id.name,
CONSTRUCTION_PHASE[rec.phase])))
return result
|
[
"[email protected]"
] | |
667b89dec3e842d6015cbe4a72ffe366da95bcf0
|
25ebc03b92df764ff0a6c70c14c2848a49fe1b0b
|
/daily/20161010/example_yaml/01cc.py
|
e9af9b204c556569002ac8f1244ad7c6ae27567b
|
[] |
no_license
|
podhmo/individual-sandbox
|
18db414fafd061568d0d5e993b8f8069867dfcfb
|
cafee43b4cf51a321f4e2c3f9949ac53eece4b15
|
refs/heads/master
| 2023-07-23T07:06:57.944539 | 2023-07-09T11:45:53 | 2023-07-09T11:45:53 | 61,940,197 | 6 | 0 | null | 2022-10-19T05:01:17 | 2016-06-25T11:27:04 |
Python
|
UTF-8
|
Python
| false | false | 2,719 |
py
|
SafeConstructor.add_constructor(
'tag:yaml.org,2002:null',
SafeConstructor.construct_yaml_null)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:bool',
SafeConstructor.construct_yaml_bool)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:int',
SafeConstructor.construct_yaml_int)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:float',
SafeConstructor.construct_yaml_float)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:binary',
SafeConstructor.construct_yaml_binary)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:timestamp',
SafeConstructor.construct_yaml_timestamp)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:omap',
SafeConstructor.construct_yaml_omap)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:pairs',
SafeConstructor.construct_yaml_pairs)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:set',
SafeConstructor.construct_yaml_set)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:str',
SafeConstructor.construct_yaml_str)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:seq',
SafeConstructor.construct_yaml_seq)
SafeConstructor.add_constructor(
'tag:yaml.org,2002:map',
SafeConstructor.construct_yaml_map)
SafeConstructor.add_constructor(None,
SafeConstructor.construct_undefined)
Constructor.add_constructor(
'tag:yaml.org,2002:python/none',
Constructor.construct_yaml_null)
Constructor.add_constructor(
'tag:yaml.org,2002:python/bool',
Constructor.construct_yaml_bool)
Constructor.add_constructor(
'tag:yaml.org,2002:python/str',
Constructor.construct_python_str)
Constructor.add_constructor(
'tag:yaml.org,2002:python/unicode',
Constructor.construct_python_unicode)
Constructor.add_constructor(
'tag:yaml.org,2002:python/bytes',
Constructor.construct_python_bytes)
Constructor.add_constructor(
'tag:yaml.org,2002:python/int',
Constructor.construct_yaml_int)
Constructor.add_constructor(
'tag:yaml.org,2002:python/long',
Constructor.construct_python_long)
Constructor.add_constructor(
'tag:yaml.org,2002:python/float',
Constructor.construct_yaml_float)
Constructor.add_constructor(
'tag:yaml.org,2002:python/complex',
Constructor.construct_python_complex)
Constructor.add_constructor(
'tag:yaml.org,2002:python/list',
Constructor.construct_yaml_seq)
Constructor.add_constructor(
'tag:yaml.org,2002:python/tuple',
Constructor.construct_python_tuple)
Constructor.add_constructor(
'tag:yaml.org,2002:python/dict',
Constructor.construct_yaml_map)
|
[
"[email protected]"
] | |
4746f82e2d5ad8079a4557a45cd4a09eba253752
|
50948d4cb10dcb1cc9bc0355918478fb2841322a
|
/azure-mgmt-policyinsights/azure/mgmt/policyinsights/models/policy_definition_summary_py3.py
|
6ed5afbba2da97206e215dc580d8c25f7cbcb7ab
|
[
"MIT"
] |
permissive
|
xiafu-msft/azure-sdk-for-python
|
de9cd680b39962702b629a8e94726bb4ab261594
|
4d9560cfd519ee60667f3cc2f5295a58c18625db
|
refs/heads/master
| 2023-08-12T20:36:24.284497 | 2019-05-22T00:55:16 | 2019-05-22T00:55:16 | 187,986,993 | 1 | 0 |
MIT
| 2020-10-02T01:17:02 | 2019-05-22T07:33:46 |
Python
|
UTF-8
|
Python
| false | false | 1,765 |
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 msrest.serialization import Model
class PolicyDefinitionSummary(Model):
"""Policy definition summary.
:param policy_definition_id: Policy definition ID.
:type policy_definition_id: str
:param policy_definition_reference_id: Policy definition reference ID.
:type policy_definition_reference_id: str
:param effect: Policy effect, i.e. policy definition action.
:type effect: str
:param results: Non-compliance summary for the policy definition.
:type results: ~azure.mgmt.policyinsights.models.SummaryResults
"""
_attribute_map = {
'policy_definition_id': {'key': 'policyDefinitionId', 'type': 'str'},
'policy_definition_reference_id': {'key': 'policyDefinitionReferenceId', 'type': 'str'},
'effect': {'key': 'effect', 'type': 'str'},
'results': {'key': 'results', 'type': 'SummaryResults'},
}
def __init__(self, *, policy_definition_id: str=None, policy_definition_reference_id: str=None, effect: str=None, results=None, **kwargs) -> None:
super(PolicyDefinitionSummary, self).__init__(**kwargs)
self.policy_definition_id = policy_definition_id
self.policy_definition_reference_id = policy_definition_reference_id
self.effect = effect
self.results = results
|
[
"[email protected]"
] | |
ac521f40077e998cd74f25609bde0b95df5e5258
|
8cdc63b549f5a7f1aca7b476a5a918e5c05e38c5
|
/app/account/authentication.py
|
8a8033971e3e0561994788b43e15e5dada3c0e08
|
[
"MIT"
] |
permissive
|
rogeriopaulos/gep
|
984e3bcd8bd4569031577e1d28a8c47c6aace91f
|
e56fd0450bdb8f572e2e35cc59a74ab0f0b372e2
|
refs/heads/main
| 2023-08-14T08:41:19.558899 | 2021-09-15T02:51:46 | 2021-09-15T02:51:46 | 402,270,601 | 0 | 1 | null | null | null | null |
UTF-8
|
Python
| false | false | 536 |
py
|
# -*- coding: utf-8 -*-
from django.contrib.auth.models import User
class EmailAuthBackend(object):
def authenticate(self, username=None, password=None):
try:
user = User.objects.get(email=username)
if user.check_password(password):
return user
return None
except User.DoesNotExist:
return None
def get_user(self, user_id):
try:
return User.objects.get(pk=user_id)
except User.DoesNotExist:
return None
|
[
"[email protected]"
] | |
06ae159ec81acf80cbb7b8a38ccee9c9863f9f61
|
c22da67f4b1ac5e7fc28a9f81bf9fccefb33308e
|
/campus/mi/2019_fall_1.py
|
6ed4262c1b44d7c133b63eba4ff47ead975e8d70
|
[] |
no_license
|
iamkissg/nowcoder
|
3b9d7ffffaba2c1ee43647595ae86619e2efb504
|
9b7e590d8f2e200d1ac98672d10f3ae9216a13e1
|
refs/heads/master
| 2020-05-04T10:35:55.606425 | 2020-04-28T12:49:52 | 2020-04-28T12:49:52 | 179,091,187 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 372 |
py
|
def is_huiwen(ll):
len_ll = len(ll)
if len_ll == 0:
# 假定空链表是回文的
# raise ValueError('Empty input.')
return True
if len_ll == 1:
return True
for i in range(len_ll//2+1):
if ll[i] != ll[len_ll-1-i]:
return False
else:
return True
ll = input().split()
print(is_huiwen(ll))
|
[
"[email protected]"
] | |
93facca6d7d15082320c04bb9f392522ee8a120d
|
cf9103d28a1c09bd9d7aeffdb43e95961ae50f5d
|
/LSTM/data.py
|
00861d2151263c1a890e2879b64ba862b2e9ca6a
|
[] |
no_license
|
AhmedSSoliman/NLP_models
|
d308993c07edb62809140952be304f222328b46c
|
c223d2398e55a6a87d45cf8c3ffed543a649bda4
|
refs/heads/master
| 2023-04-17T05:41:38.261185 | 2021-04-23T11:06:17 | 2021-04-23T11:06:17 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 5,978 |
py
|
import os
import torch
#from .tokenizer import tokenize
from collections import defaultdict
import logging
from tqdm import tqdm
#------------------------------------------------------------------------
from nltk.tokenize import sent_tokenize
from nltk.tokenize import word_tokenize
import os
import re
import inflect
from tqdm import tqdm
special_words = {
'english': {
'grown-ups': 'grownups',
'grown-up': 'grownup',
'hasn\'t': 'hasnt',
'hasn‘t': 'hasnt'
},
'french': {
}
}
def tokenize(path, language, vocab=None, path_like=True, train=False):
print('Tokenizing...')
if path_like:
assert os.path.exists(path)
path = open(path, 'r', encoding='utf8').read()
if not train:
print('Preprocessing...')
text = preprocess(path, special_words, language)
print('Preprocessed.')
else:
text = path
# iterator = [unk_transform(item, vocab).lower() for item in text.split()]
iterator = [unk_transform(item, vocab) for item in tqdm(text.split())] # vocab words not lowered
print('Tokenized.')
return iterator
def unk_transform(word, vocab=None):
if word == 'unk':
return '<unk>'
elif not vocab:
return word
elif word in vocab.idx2word:
return word
else:
return '<unk>'
def preprocess(text, special_words, language):
text = text.replace('\n', '')
text = text.replace('<unk>', 'unk')
for word in special_words[language].keys():
text = text.replace(word, special_words[language][word])
transf = inflect.engine()
numbers = re.findall('\d+', text)
for number in numbers:
text = text.replace(number, transf.number_to_words(number))
punctuation = ['.', '\'', ',', ';', ':', '!', '?', '/', '-', '"', '‘', '’', '(', ')', '{', '}', '[', ']', '`', '“', '”', '—']
for item in punctuation:
text = text.replace(item, ' '+ item + ' ')
text = text.replace('. . .', '...')
### tokenize without punctuation ###
# for item in punctuation:
# text = text.replace(item, ' ')
### tokenize with punctuation ###
# ### tokenize thanks to usual tools for text without strange characters ###
# tokenized = sent_tokenize(text, language=language)
# tokenized = [word_tokenize(sentence, language=language) + ['<eos>'] for sentence in tokenized]
# iterator = [unk_transform(item, vocab).lower() for sublist in tokenized for item in sublist]
return text
#------------------------------------------------------------------------
class Dictionary(object):
def __init__(self, path, language):
self.word2idx = {}
self.idx2word = []
self.language = language
self.word2freq = defaultdict(int)
vocab_path = os.path.join(path, 'vocab.txt')
try:
vocab = open(vocab_path, encoding="utf8").read()
self.word2idx = {w: i for i, w in enumerate(vocab.split())}
self.idx2word = [w for w in vocab.split()]
self.vocab_file_exists = True
except FileNotFoundError:
logging.info("Vocab file not found, creating new vocab file.")
self.create_vocab(os.path.join(path, 'train.txt'))
open(vocab_path,"w").write("\n".join([w for w in self.idx2word]))
def add_word(self, word):
self.word2freq[word] += 1
if word not in self.word2idx:
self.idx2word.append(word)
self.word2idx[word] = len(self.idx2word) - 1
def __len__(self):
return len(self.idx2word)
def create_vocab(self, path):
iterator = tokenize(path, self.language, train=True)
for item in tqdm(iterator):
self.add_word(item)
self.add_word('<unk>')
class Corpus(object):
def __init__(self, path, language):
print('Building dictionary...')
self.dictionary = Dictionary(path, language)
print('Dictionary built.')
train_path = os.path.join(path, 'train.txt')
valid_path = os.path.join(path, 'valid.txt')
test_path = os.path.join(path, 'test.txt')
train_tensor = os.path.join(path, 'train.pkl')
valid_tensor = os.path.join(path, 'valid.pkl')
test_tensor = os.path.join(path, 'test.pkl')
try:
with open(train_tensor, 'rb') as f:
self.train = torch.load(f)
with open(valid_tensor, 'rb') as f:
self.valid = torch.load(f)
with open(test_tensor, 'rb') as f:
self.test = torch.load(f)
except FileNotFoundError:
logging.info("Tensor files not found, creating new tensor files.")
print('Computing train tensor...')
self.train = create_tokenized_tensor(tokenize(train_path, language, self.dictionary, train=True), self.dictionary)
print('Train tensor computed.')
print('Computing valid tensor...')
self.valid = create_tokenized_tensor(tokenize(valid_path, language, self.dictionary, train=True), self.dictionary)
print('Valid tensor computed.')
print('Computing test tensor...')
self.test = create_tokenized_tensor(tokenize(test_path, language, self.dictionary, train=True), self.dictionary)
print('Test tensor computed.')
with open(train_tensor, 'wb') as f:
torch.save(self.train, f)
with open(valid_tensor, 'wb') as f:
torch.save(self.valid, f)
with open(test_tensor, 'wb') as f:
torch.save(self.test, f)
def create_tokenized_tensor(iterator, dictionary):
"""Create tensor of embeddings from word iterator."""
tensor = torch.LongTensor(len(iterator))
token = 0
for item in tqdm(iterator):
tensor[token] = dictionary.word2idx[item] if item in dictionary.word2idx else dictionary.word2idx['<unk>']
token += 1
return tensor
|
[
"[email protected]"
] | |
d6bebea6f5eb3b2edc9a167664fd7feeb9a2d97a
|
24d8cf871b092b2d60fc85d5320e1bc761a7cbe2
|
/Wicd/rev519-568/right-branch-568/wicd/backends/be-ip4network/wired/ui/gtkui.py
|
cdc56a51e6c38b0cc00182b964bced86b604f3bc
|
[] |
no_license
|
joliebig/featurehouse_fstmerge_examples
|
af1b963537839d13e834f829cf51f8ad5e6ffe76
|
1a99c1788f0eb9f1e5d8c2ced3892d00cd9449ad
|
refs/heads/master
| 2016-09-05T10:24:50.974902 | 2013-03-28T16:28:47 | 2013-03-28T16:28:47 | 9,080,611 | 3 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 459 |
py
|
import sys, os
import logging
sys.path.insert(0, os.path.abspath(
os.path.join(os.path.dirname(
os.path.realpath(__file__)
),
'../..')))
logging.debug(sys.path[0])
from gtkuibase import ShortInterfaceUiBase
sys.path.pop(0)
class WiredShortInterfaceUi(ShortInterfaceUiBase):
def __init__(self, interface):
ShortInterfaceUiBase.__init__(self, interface)
self.image.set_from_icon_name('network-wired', 6)
|
[
"[email protected]"
] | |
668473f0b568f0a3a6cf3a6c8bf0d3e79566f6c3
|
0bc777a57e39c466a9482af9a6eda698ab3c1437
|
/HeavyIonsAnalysis/JetAnalysis/python/jets/akPuSoftDropZ05B153PFJetSequence_pPb_data_cff.py
|
be94241948e1f5b33e3b6f3cb936c7128ae735a8
|
[] |
no_license
|
stahlleiton/cmssw
|
3c78d80b9372fdf2a37f424372504b23c9dc4f78
|
fcfda663dc8c315b505eb6bcc7e936401c01c4d1
|
refs/heads/EWQAnalysis2017_8030
| 2023-08-23T13:50:40.837198 | 2017-11-09T17:45:31 | 2017-11-09T17:45:31 | 45,795,305 | 0 | 3 | null | 2021-04-30T07:36:28 | 2015-11-08T19:28:54 |
C++
|
UTF-8
|
Python
| false | false | 17,572 |
py
|
import FWCore.ParameterSet.Config as cms
from HeavyIonsAnalysis.JetAnalysis.patHeavyIonSequences_cff import patJetGenJetMatch, patJetPartonMatch, patJetCorrFactors, patJets
from HeavyIonsAnalysis.JetAnalysis.inclusiveJetAnalyzer_cff import *
from HeavyIonsAnalysis.JetAnalysis.bTaggers_cff import *
from RecoJets.JetProducers.JetIDParams_cfi import *
from RecoJets.JetProducers.nJettinessAdder_cfi import Njettiness
akPuSoftDropZ05B153PFmatch = patJetGenJetMatch.clone(
src = cms.InputTag("akPuSoftDropZ05B153PFJets"),
matched = cms.InputTag("ak3HiSignalGenJets"),
resolveByMatchQuality = cms.bool(False),
maxDeltaR = 0.3
)
akPuSoftDropZ05B153PFmatchGroomed = patJetGenJetMatch.clone(
src = cms.InputTag("akSoftDropZ05B153HiSignalGenJets"),
matched = cms.InputTag("ak3HiSignalGenJets"),
resolveByMatchQuality = cms.bool(False),
maxDeltaR = 0.3
)
akPuSoftDropZ05B153PFparton = patJetPartonMatch.clone(src = cms.InputTag("akPuSoftDropZ05B153PFJets")
)
akPuSoftDropZ05B153PFcorr = patJetCorrFactors.clone(
useNPV = cms.bool(False),
useRho = cms.bool(False),
# primaryVertices = cms.InputTag("hiSelectedVertex"),
levels = cms.vstring('L2Relative','L3Absolute'),
src = cms.InputTag("akPuSoftDropZ05B153PFJets"),
payload = "AKPu3PF_offline"
)
akPuSoftDropZ05B153PFJetID= cms.EDProducer('JetIDProducer', JetIDParams, src = cms.InputTag('akPuSoftDropZ05B153CaloJets'))
#akPuSoftDropZ05B153PFclean = heavyIonCleanedGenJets.clone(src = cms.InputTag('ak3HiSignalGenJets'))
akPuSoftDropZ05B153PFbTagger = bTaggers("akPuSoftDropZ05B153PF",0.3)
#create objects locally since they dont load properly otherwise
#akPuSoftDropZ05B153PFmatch = akPuSoftDropZ05B153PFbTagger.match
akPuSoftDropZ05B153PFparton = patJetPartonMatch.clone(src = cms.InputTag("akPuSoftDropZ05B153PFJets"), matched = cms.InputTag("genParticles"))
akPuSoftDropZ05B153PFPatJetFlavourAssociationLegacy = akPuSoftDropZ05B153PFbTagger.PatJetFlavourAssociationLegacy
akPuSoftDropZ05B153PFPatJetPartons = akPuSoftDropZ05B153PFbTagger.PatJetPartons
akPuSoftDropZ05B153PFJetTracksAssociatorAtVertex = akPuSoftDropZ05B153PFbTagger.JetTracksAssociatorAtVertex
akPuSoftDropZ05B153PFJetTracksAssociatorAtVertex.tracks = cms.InputTag("highPurityTracks")
akPuSoftDropZ05B153PFSimpleSecondaryVertexHighEffBJetTags = akPuSoftDropZ05B153PFbTagger.SimpleSecondaryVertexHighEffBJetTags
akPuSoftDropZ05B153PFSimpleSecondaryVertexHighPurBJetTags = akPuSoftDropZ05B153PFbTagger.SimpleSecondaryVertexHighPurBJetTags
akPuSoftDropZ05B153PFCombinedSecondaryVertexBJetTags = akPuSoftDropZ05B153PFbTagger.CombinedSecondaryVertexBJetTags
akPuSoftDropZ05B153PFCombinedSecondaryVertexV2BJetTags = akPuSoftDropZ05B153PFbTagger.CombinedSecondaryVertexV2BJetTags
akPuSoftDropZ05B153PFJetBProbabilityBJetTags = akPuSoftDropZ05B153PFbTagger.JetBProbabilityBJetTags
akPuSoftDropZ05B153PFSoftPFMuonByPtBJetTags = akPuSoftDropZ05B153PFbTagger.SoftPFMuonByPtBJetTags
akPuSoftDropZ05B153PFSoftPFMuonByIP3dBJetTags = akPuSoftDropZ05B153PFbTagger.SoftPFMuonByIP3dBJetTags
akPuSoftDropZ05B153PFTrackCountingHighEffBJetTags = akPuSoftDropZ05B153PFbTagger.TrackCountingHighEffBJetTags
akPuSoftDropZ05B153PFTrackCountingHighPurBJetTags = akPuSoftDropZ05B153PFbTagger.TrackCountingHighPurBJetTags
akPuSoftDropZ05B153PFPatJetPartonAssociationLegacy = akPuSoftDropZ05B153PFbTagger.PatJetPartonAssociationLegacy
akPuSoftDropZ05B153PFImpactParameterTagInfos = akPuSoftDropZ05B153PFbTagger.ImpactParameterTagInfos
akPuSoftDropZ05B153PFImpactParameterTagInfos.primaryVertex = cms.InputTag("offlinePrimaryVertices")
akPuSoftDropZ05B153PFJetProbabilityBJetTags = akPuSoftDropZ05B153PFbTagger.JetProbabilityBJetTags
akPuSoftDropZ05B153PFSecondaryVertexTagInfos = akPuSoftDropZ05B153PFbTagger.SecondaryVertexTagInfos
akPuSoftDropZ05B153PFSimpleSecondaryVertexHighEffBJetTags = akPuSoftDropZ05B153PFbTagger.SimpleSecondaryVertexHighEffBJetTags
akPuSoftDropZ05B153PFSimpleSecondaryVertexHighPurBJetTags = akPuSoftDropZ05B153PFbTagger.SimpleSecondaryVertexHighPurBJetTags
akPuSoftDropZ05B153PFCombinedSecondaryVertexBJetTags = akPuSoftDropZ05B153PFbTagger.CombinedSecondaryVertexBJetTags
akPuSoftDropZ05B153PFCombinedSecondaryVertexV2BJetTags = akPuSoftDropZ05B153PFbTagger.CombinedSecondaryVertexV2BJetTags
akPuSoftDropZ05B153PFSecondaryVertexNegativeTagInfos = akPuSoftDropZ05B153PFbTagger.SecondaryVertexNegativeTagInfos
akPuSoftDropZ05B153PFNegativeSimpleSecondaryVertexHighEffBJetTags = akPuSoftDropZ05B153PFbTagger.NegativeSimpleSecondaryVertexHighEffBJetTags
akPuSoftDropZ05B153PFNegativeSimpleSecondaryVertexHighPurBJetTags = akPuSoftDropZ05B153PFbTagger.NegativeSimpleSecondaryVertexHighPurBJetTags
akPuSoftDropZ05B153PFNegativeCombinedSecondaryVertexBJetTags = akPuSoftDropZ05B153PFbTagger.NegativeCombinedSecondaryVertexBJetTags
akPuSoftDropZ05B153PFPositiveCombinedSecondaryVertexBJetTags = akPuSoftDropZ05B153PFbTagger.PositiveCombinedSecondaryVertexBJetTags
akPuSoftDropZ05B153PFNegativeCombinedSecondaryVertexV2BJetTags = akPuSoftDropZ05B153PFbTagger.NegativeCombinedSecondaryVertexV2BJetTags
akPuSoftDropZ05B153PFPositiveCombinedSecondaryVertexV2BJetTags = akPuSoftDropZ05B153PFbTagger.PositiveCombinedSecondaryVertexV2BJetTags
akPuSoftDropZ05B153PFSoftPFMuonsTagInfos = akPuSoftDropZ05B153PFbTagger.SoftPFMuonsTagInfos
akPuSoftDropZ05B153PFSoftPFMuonsTagInfos.primaryVertex = cms.InputTag("offlinePrimaryVertices")
akPuSoftDropZ05B153PFSoftPFMuonBJetTags = akPuSoftDropZ05B153PFbTagger.SoftPFMuonBJetTags
akPuSoftDropZ05B153PFSoftPFMuonByIP3dBJetTags = akPuSoftDropZ05B153PFbTagger.SoftPFMuonByIP3dBJetTags
akPuSoftDropZ05B153PFSoftPFMuonByPtBJetTags = akPuSoftDropZ05B153PFbTagger.SoftPFMuonByPtBJetTags
akPuSoftDropZ05B153PFNegativeSoftPFMuonByPtBJetTags = akPuSoftDropZ05B153PFbTagger.NegativeSoftPFMuonByPtBJetTags
akPuSoftDropZ05B153PFPositiveSoftPFMuonByPtBJetTags = akPuSoftDropZ05B153PFbTagger.PositiveSoftPFMuonByPtBJetTags
akPuSoftDropZ05B153PFPatJetFlavourIdLegacy = cms.Sequence(akPuSoftDropZ05B153PFPatJetPartonAssociationLegacy*akPuSoftDropZ05B153PFPatJetFlavourAssociationLegacy)
#Not working with our PU sub, but keep it here for reference
#akPuSoftDropZ05B153PFPatJetFlavourAssociation = akPuSoftDropZ05B153PFbTagger.PatJetFlavourAssociation
#akPuSoftDropZ05B153PFPatJetFlavourId = cms.Sequence(akPuSoftDropZ05B153PFPatJetPartons*akPuSoftDropZ05B153PFPatJetFlavourAssociation)
akPuSoftDropZ05B153PFJetBtaggingIP = cms.Sequence(akPuSoftDropZ05B153PFImpactParameterTagInfos *
(akPuSoftDropZ05B153PFTrackCountingHighEffBJetTags +
akPuSoftDropZ05B153PFTrackCountingHighPurBJetTags +
akPuSoftDropZ05B153PFJetProbabilityBJetTags +
akPuSoftDropZ05B153PFJetBProbabilityBJetTags
)
)
akPuSoftDropZ05B153PFJetBtaggingSV = cms.Sequence(akPuSoftDropZ05B153PFImpactParameterTagInfos
*
akPuSoftDropZ05B153PFSecondaryVertexTagInfos
* (akPuSoftDropZ05B153PFSimpleSecondaryVertexHighEffBJetTags+
akPuSoftDropZ05B153PFSimpleSecondaryVertexHighPurBJetTags+
akPuSoftDropZ05B153PFCombinedSecondaryVertexBJetTags+
akPuSoftDropZ05B153PFCombinedSecondaryVertexV2BJetTags
)
)
akPuSoftDropZ05B153PFJetBtaggingNegSV = cms.Sequence(akPuSoftDropZ05B153PFImpactParameterTagInfos
*
akPuSoftDropZ05B153PFSecondaryVertexNegativeTagInfos
* (akPuSoftDropZ05B153PFNegativeSimpleSecondaryVertexHighEffBJetTags+
akPuSoftDropZ05B153PFNegativeSimpleSecondaryVertexHighPurBJetTags+
akPuSoftDropZ05B153PFNegativeCombinedSecondaryVertexBJetTags+
akPuSoftDropZ05B153PFPositiveCombinedSecondaryVertexBJetTags+
akPuSoftDropZ05B153PFNegativeCombinedSecondaryVertexV2BJetTags+
akPuSoftDropZ05B153PFPositiveCombinedSecondaryVertexV2BJetTags
)
)
akPuSoftDropZ05B153PFJetBtaggingMu = cms.Sequence(akPuSoftDropZ05B153PFSoftPFMuonsTagInfos * (akPuSoftDropZ05B153PFSoftPFMuonBJetTags
+
akPuSoftDropZ05B153PFSoftPFMuonByIP3dBJetTags
+
akPuSoftDropZ05B153PFSoftPFMuonByPtBJetTags
+
akPuSoftDropZ05B153PFNegativeSoftPFMuonByPtBJetTags
+
akPuSoftDropZ05B153PFPositiveSoftPFMuonByPtBJetTags
)
)
akPuSoftDropZ05B153PFJetBtagging = cms.Sequence(akPuSoftDropZ05B153PFJetBtaggingIP
*akPuSoftDropZ05B153PFJetBtaggingSV
*akPuSoftDropZ05B153PFJetBtaggingNegSV
# *akPuSoftDropZ05B153PFJetBtaggingMu
)
akPuSoftDropZ05B153PFpatJetsWithBtagging = patJets.clone(jetSource = cms.InputTag("akPuSoftDropZ05B153PFJets"),
genJetMatch = cms.InputTag("akPuSoftDropZ05B153PFmatch"),
genPartonMatch = cms.InputTag("akPuSoftDropZ05B153PFparton"),
jetCorrFactorsSource = cms.VInputTag(cms.InputTag("akPuSoftDropZ05B153PFcorr")),
JetPartonMapSource = cms.InputTag("akPuSoftDropZ05B153PFPatJetFlavourAssociationLegacy"),
JetFlavourInfoSource = cms.InputTag("akPuSoftDropZ05B153PFPatJetFlavourAssociation"),
trackAssociationSource = cms.InputTag("akPuSoftDropZ05B153PFJetTracksAssociatorAtVertex"),
useLegacyJetMCFlavour = True,
discriminatorSources = cms.VInputTag(cms.InputTag("akPuSoftDropZ05B153PFSimpleSecondaryVertexHighEffBJetTags"),
cms.InputTag("akPuSoftDropZ05B153PFSimpleSecondaryVertexHighPurBJetTags"),
cms.InputTag("akPuSoftDropZ05B153PFCombinedSecondaryVertexBJetTags"),
cms.InputTag("akPuSoftDropZ05B153PFCombinedSecondaryVertexV2BJetTags"),
cms.InputTag("akPuSoftDropZ05B153PFJetBProbabilityBJetTags"),
cms.InputTag("akPuSoftDropZ05B153PFJetProbabilityBJetTags"),
#cms.InputTag("akPuSoftDropZ05B153PFSoftPFMuonByPtBJetTags"),
#cms.InputTag("akPuSoftDropZ05B153PFSoftPFMuonByIP3dBJetTags"),
cms.InputTag("akPuSoftDropZ05B153PFTrackCountingHighEffBJetTags"),
cms.InputTag("akPuSoftDropZ05B153PFTrackCountingHighPurBJetTags"),
),
jetIDMap = cms.InputTag("akPuSoftDropZ05B153PFJetID"),
addBTagInfo = True,
addTagInfos = True,
addDiscriminators = True,
addAssociatedTracks = True,
addJetCharge = False,
addJetID = False,
getJetMCFlavour = False,
addGenPartonMatch = False,
addGenJetMatch = False,
embedGenJetMatch = False,
embedGenPartonMatch = False,
# embedCaloTowers = False,
# embedPFCandidates = True
)
akPuSoftDropZ05B153PFNjettiness = Njettiness.clone(
src = cms.InputTag("akPuSoftDropZ05B153PFJets"),
R0 = cms.double( 0.3)
)
akPuSoftDropZ05B153PFpatJetsWithBtagging.userData.userFloats.src += ['akPuSoftDropZ05B153PFNjettiness:tau1','akPuSoftDropZ05B153PFNjettiness:tau2','akPuSoftDropZ05B153PFNjettiness:tau3']
akPuSoftDropZ05B153PFJetAnalyzer = inclusiveJetAnalyzer.clone(jetTag = cms.InputTag("akPuSoftDropZ05B153PFpatJetsWithBtagging"),
genjetTag = 'ak3HiSignalGenJets',
rParam = 0.3,
matchJets = cms.untracked.bool(False),
matchTag = 'patJetsWithBtagging',
pfCandidateLabel = cms.untracked.InputTag('particleFlow'),
trackTag = cms.InputTag("generalTracks"),
fillGenJets = False,
isMC = False,
doSubEvent = False,
useHepMC = cms.untracked.bool(False),
genParticles = cms.untracked.InputTag("genParticles"),
eventInfoTag = cms.InputTag("generator"),
doLifeTimeTagging = cms.untracked.bool(True),
doLifeTimeTaggingExtras = cms.untracked.bool(False),
bTagJetName = cms.untracked.string("akPuSoftDropZ05B153PF"),
jetName = cms.untracked.string("akPuSoftDropZ05B153PF"),
genPtMin = cms.untracked.double(5),
hltTrgResults = cms.untracked.string('TriggerResults::'+'HISIGNAL'),
doTower = cms.untracked.bool(True),
doSubJets = cms.untracked.bool(True),
doGenSubJets = cms.untracked.bool(False),
subjetGenTag = cms.untracked.InputTag("akSoftDropZ05B153GenJets"),
doGenTaus = cms.untracked.bool(False),
genTau1 = cms.InputTag("akSoftDropZ05B153GenNjettiness","tau1"),
genTau2 = cms.InputTag("akSoftDropZ05B153GenNjettiness","tau2"),
genTau3 = cms.InputTag("akSoftDropZ05B153GenNjettiness","tau3"),
doGenSym = cms.untracked.bool(False),
genSym = cms.InputTag("akSoftDropZ05B153GenJets","sym"),
genDroppedBranches = cms.InputTag("akSoftDropZ05B153GenJets","droppedBranches")
)
akPuSoftDropZ05B153PFJetSequence_mc = cms.Sequence(
#akPuSoftDropZ05B153PFclean
#*
akPuSoftDropZ05B153PFmatch
#*
#akPuSoftDropZ05B153PFmatchGroomed
*
akPuSoftDropZ05B153PFparton
*
akPuSoftDropZ05B153PFcorr
*
#akPuSoftDropZ05B153PFJetID
#*
akPuSoftDropZ05B153PFPatJetFlavourIdLegacy
#*
#akPuSoftDropZ05B153PFPatJetFlavourId # Use legacy algo till PU implemented
*
akPuSoftDropZ05B153PFJetTracksAssociatorAtVertex
*
akPuSoftDropZ05B153PFJetBtagging
*
akPuSoftDropZ05B153PFNjettiness #No constituents for calo jets in pp. Must be removed for pp calo jets but I'm not sure how to do this transparently (Marta)
*
akPuSoftDropZ05B153PFpatJetsWithBtagging
*
akPuSoftDropZ05B153PFJetAnalyzer
)
akPuSoftDropZ05B153PFJetSequence_data = cms.Sequence(akPuSoftDropZ05B153PFcorr
*
#akPuSoftDropZ05B153PFJetID
#*
akPuSoftDropZ05B153PFJetTracksAssociatorAtVertex
*
akPuSoftDropZ05B153PFJetBtagging
*
akPuSoftDropZ05B153PFNjettiness
*
akPuSoftDropZ05B153PFpatJetsWithBtagging
*
akPuSoftDropZ05B153PFJetAnalyzer
)
akPuSoftDropZ05B153PFJetSequence_jec = cms.Sequence(akPuSoftDropZ05B153PFJetSequence_mc)
akPuSoftDropZ05B153PFJetSequence_mb = cms.Sequence(akPuSoftDropZ05B153PFJetSequence_mc)
akPuSoftDropZ05B153PFJetSequence = cms.Sequence(akPuSoftDropZ05B153PFJetSequence_data)
akPuSoftDropZ05B153PFpatJetsWithBtagging.userData.userFloats.src += ['akPuSoftDropZ05B153PFJets:sym']
akPuSoftDropZ05B153PFpatJetsWithBtagging.userData.userInts.src += ['akPuSoftDropZ05B153PFJets:droppedBranches']
|
[
"[email protected]"
] | |
634bb7fc2f31ce753fb555853c2a0acf4d53bf66
|
d7a68c636e6128533b17975655bd6b46ed222916
|
/adapter-transformers-adapters3.1.0/src/transformers/models/vit/convert_dino_to_pytorch.py
|
8922684594a59e4e60aad1a5961fa1ac3a7bfe5f
|
[
"Apache-2.0"
] |
permissive
|
cambridgeltl/autopeft
|
69179f8faf2cc4d2164ff78e544dc3fe2d39c331
|
d8ad6bea93aa413a54d0e09fe25bdd62b46cfcf5
|
refs/heads/main
| 2023-05-23T09:21:59.912941 | 2023-04-25T14:35:31 | 2023-04-25T14:35:31 | 594,316,585 | 26 | 4 |
Apache-2.0
| 2023-04-25T14:35:32 | 2023-01-28T06:39:25 |
Python
|
UTF-8
|
Python
| false | false | 8,856 |
py
|
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
"""Convert ViT checkpoints trained with the DINO method."""
import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import ViTConfig, ViTFeatureExtractor, ViTForImageClassification, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
def create_rename_keys(config, base_model=False):
rename_keys = []
for i in range(config.num_hidden_layers):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight"))
rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias"))
rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight"))
rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias"))
rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight"))
rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias"))
rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight"))
rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias"))
rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight"))
rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias"))
# projection layer + position embeddings
rename_keys.extend(
[
("cls_token", "vit.embeddings.cls_token"),
("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
("pos_embed", "vit.embeddings.position_embeddings"),
]
)
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
]
)
# if just the base model, we should remove "vit" from all keys that start with "vit"
rename_keys = [(pair[0], pair[1][4:]) if pair[1].startswith("vit") else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("norm.weight", "vit.layernorm.weight"),
("norm.bias", "vit.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
]
)
return rename_keys
# we split up the matrix of each encoder layer into queries, keys and values
def read_in_q_k_v(state_dict, config, base_model=False):
for i in range(config.num_hidden_layers):
if base_model:
prefix = ""
else:
prefix = "vit."
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
in_proj_weight = state_dict.pop(f"blocks.{i}.attn.qkv.weight")
in_proj_bias = state_dict.pop(f"blocks.{i}.attn.qkv.bias")
# next, add query, keys and values (in that order) to the state dict
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.weight"] = in_proj_weight[
: config.hidden_size, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.query.bias"] = in_proj_bias[: config.hidden_size]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.weight"] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.key.bias"] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.weight"] = in_proj_weight[
-config.hidden_size :, :
]
state_dict[f"{prefix}encoder.layer.{i}.attention.attention.value.bias"] = in_proj_bias[-config.hidden_size :]
def remove_classification_head_(state_dict):
ignore_keys = ["head.weight", "head.bias"]
for k in ignore_keys:
state_dict.pop(k, None)
def rename_key(dct, old, new):
val = dct.pop(old)
dct[new] = val
# We will verify our results on an image of cute cats
def prepare_img():
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
im = Image.open(requests.get(url, stream=True).raw)
return im
@torch.no_grad()
def convert_vit_checkpoint(model_name, pytorch_dump_folder_path, base_model=True):
"""
Copy/paste/tweak model's weights to our ViT structure.
"""
# define default ViT configuration
config = ViTConfig()
# patch_size
if model_name[-1] == "8":
config.patch_size = 8
# set labels if required
if not base_model:
config.num_labels = 1000
repo_id = "datasets/huggingface/label-files"
filename = "imagenet-1k-id2label.json"
id2label = json.load(open(hf_hub_download(repo_id, filename), "r"))
id2label = {int(k): v for k, v in id2label.items()}
config.id2label = id2label
config.label2id = {v: k for k, v in id2label.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
config.hidden_size = 384
config.intermediate_size = 1536
config.num_hidden_layers = 12
config.num_attention_heads = 6
# load original model from torch hub
original_model = torch.hub.load("facebookresearch/dino:main", model_name)
original_model.eval()
# load state_dict of original model, remove and rename some keys
state_dict = original_model.state_dict()
if base_model:
remove_classification_head_(state_dict)
rename_keys = create_rename_keys(config, base_model=base_model)
for src, dest in rename_keys:
rename_key(state_dict, src, dest)
read_in_q_k_v(state_dict, config, base_model)
# load HuggingFace model
if base_model:
model = ViTModel(config, add_pooling_layer=False).eval()
else:
model = ViTForImageClassification(config).eval()
model.load_state_dict(state_dict)
# Check outputs on an image, prepared by ViTFeatureExtractor
feature_extractor = ViTFeatureExtractor()
encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
pixel_values = encoding["pixel_values"]
outputs = model(pixel_values)
if base_model:
final_hidden_state_cls_token = original_model(pixel_values)
assert torch.allclose(final_hidden_state_cls_token, outputs.last_hidden_state[:, 0, :], atol=1e-1)
else:
logits = original_model(pixel_values)
assert logits.shape == outputs.logits.shape
assert torch.allclose(logits, outputs.logits, atol=1e-3)
Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
model.save_pretrained(pytorch_dump_folder_path)
print(f"Saving feature extractor to {pytorch_dump_folder_path}")
feature_extractor.save_pretrained(pytorch_dump_folder_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="dino_vitb16",
type=str,
help="Name of the model trained with DINO you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--base_model",
action="store_true",
help="Whether to only convert the base model (no projection head weights).",
)
parser.set_defaults(base_model=True)
args = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
|
[
"[email protected]"
] | |
15fd3e2083ed3d05dd57c9731f3d952acc1107d6
|
674e5072af9433f0f41d9520a260acf4ac4616f8
|
/mysite/mysite/views.py
|
ef985e7f90c71c47ff414656b28b0326f0966563
|
[] |
no_license
|
dh-linux/eg_django
|
0446b62e2a691951ae7de30bfd3e2d1b526dd112
|
ed225eedc50255303a8305b667c5f57ec367ae71
|
refs/heads/master
| 2021-04-15T03:33:38.693205 | 2016-08-03T09:08:53 | 2016-08-03T09:08:53 | 64,833,308 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 459 |
py
|
from django.http import HttpResponse
from django.shortcuts import render_to_response, render
def search_form(request):
return render_to_response('search_form.html')
def search(request):
if 'q' in request.GET:
message = 'You search : %s' % request.GET['q']
else:
message = 'You submitted an empty form.'
return HttpResponse(message)
def ua(request):
ua = request.META.get('HTTP_USER_AGENT', 'unknown')
return HttpResponse("Your brower is %s" % ua)
|
[
"[email protected]"
] | |
9fe28abefb259f74efd5f4ae8626643f992ca4fd
|
60ca69e2a4c6b05e6df44007fd9e4a4ed4425f14
|
/beginner_contest/165/B.py
|
1933a54f16ef1d57c4ed38dcabb1ebdb098de06d
|
[
"MIT"
] |
permissive
|
FGtatsuro/myatcoder
|
12a9daafc88efbb60fc0cd8840e594500fc3ee55
|
25a3123be6a6311e7d1c25394987de3e35575ff4
|
refs/heads/master
| 2021-06-13T15:24:07.906742 | 2021-05-16T11:47:09 | 2021-05-16T11:47:09 | 195,441,531 | 0 | 0 |
MIT
| 2021-05-16T11:47:10 | 2019-07-05T16:47:58 |
Python
|
UTF-8
|
Python
| false | false | 210 |
py
|
import sys
input = sys.stdin.readline
sys.setrecursionlimit(10 ** 7)
x = int(input())
year = 1
total = 100
while True:
total = int(total * 1.01)
if total >= x:
break
year += 1
print(year)
|
[
"[email protected]"
] | |
3bd4055b0aefecf40596614a4fb6c677aafaabd1
|
4ef31d0f04f4d6d7725a530bffb1a4b115283d6f
|
/site/_build/jupyter_execute/notebooks/08-intro-nlp/05-bag-popcorn-bag-words.py
|
a280b560fcd100111038cdb1565d87ecf0e99d42
|
[
"MIT"
] |
permissive
|
rpi-techfundamentals/introml_website_fall_2020
|
98bb1cc4712f416b393b996b849f39c660167057
|
b85e5c297954bcaae565a8d25a18d2904d40f543
|
refs/heads/master
| 2023-07-14T16:49:21.625260 | 2020-12-10T17:51:34 | 2020-12-10T17:51:34 | 287,033,509 | 2 | 3 | null | null | null | null |
UTF-8
|
Python
| false | false | 11,803 |
py
|
[](http://rpi.analyticsdojo.com)
<center><h1> Bag of Words</h1></center>
<center><h3><a href = 'http://rpi.analyticsdojo.com'>rpi.analyticsdojo.com</a></h3></center>
This is adopted from: [Bag of Words Meets Bags of Popcorn](https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words)
[https://github.com/wendykan/DeepLearningMovies](https://github.com/wendykan/DeepLearningMovies)
# Bag of Words
import nltk
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
!wget https://github.com/rpi-techfundamentals/spring2019-materials/raw/master/input/labeledTrainData.tsv
!wget https://github.com/rpi-techfundamentals/spring2019-materials/raw/master/input/unlabeledTrainData.tsv
!wget https://github.com/rpi-techfundamentals/spring2019-materials/raw/master/input/testData.tsv
train = pd.read_csv('labeledTrainData.tsv', header=0, \
delimiter="\t", quoting=3)
unlabeled_train= pd.read_csv('unlabeledTrainData.tsv', header=0, \
delimiter="\t", quoting=3)
test = pd.read_csv('testData.tsv', header=0, \
delimiter="\t", quoting=3)
import os
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np
print(train.columns.values, test.columns.values)
train.head()
print('The train shape is: ', train.shape)
print('The train shape is: ', test.shape)
print('The first review is:')
print(train["review"][0])
# Import BeautifulSoup into your workspace
from bs4 import BeautifulSoup
# Initialize the BeautifulSoup object on a single movie review
example1 = BeautifulSoup(train["review"][0], "html.parser" )
print(example1.get_text())
import re
# Use regular expressions to do a find-and-replace
letters_only = re.sub("[^a-zA-Z]", # The pattern to search for
" ", # The pattern to replace it with
example1.get_text() ) # The text to search
print (letters_only)
lower_case = letters_only.lower() # Convert to lower case
words = lower_case.split() # Split into words
# Enter Download then stopwords.
nltk.download('stopwords')
print (stopwords.words("english"))
# Remove stop words from "words"
words = [w for w in words if not w in stopwords.words("english")]
print (words)
#Now we are going to do our first class
class KaggleWord2VecUtility(object):
"""KaggleWord2VecUtility is a utility class for processing raw HTML text into segments for further learning"""
@staticmethod
def review_to_wordlist( review, remove_stopwords=False ):
# Function to convert a document to a sequence of words,
# optionally removing stop words. Returns a list of words.
#
# 1. Remove HTML
review_text = BeautifulSoup(review,"html.parser" ).get_text()
#
# 2. Remove non-letters
review_text = re.sub("[^a-zA-Z]"," ", review_text)
#
# 3. Convert words to lower case and split them
words = review_text.lower().split()
#
# 4. Optionally remove stop words (false by default)
if remove_stopwords:
stops = set(stopwords.words("english"))
words = [w for w in words if not w in stops]
#
# 5. Return a list of words
return(words)
# Define a function to split a review into parsed sentences
@staticmethod
def review_to_sentences( review, tokenizer, remove_stopwords=False ):
# Function to split a review into parsed sentences. Returns a
# list of sentences, where each sentence is a list of words
#
# 1. Use the NLTK tokenizer to split the paragraph into sentences
raw_sentences = tokenizer.tokenize(review.strip())
#
# 2. Loop over each sentence
sentences = []
for raw_sentence in raw_sentences:
# If a sentence is empty, skip it
if len(raw_sentence) > 0:
# Otherwise, call review_to_wordlist to get a list of words
sentences.append( KaggleWord2VecUtility.review_to_wordlist( raw_sentence, remove_stopwords ))
#
# Return the list of sentences (each sentence is a list of words,
# so this returns a list of lists
return sentences
clean_review_word = KaggleWord2VecUtility.review_to_wordlist \
( train["review"][0], True )
clean_review_sentence = KaggleWord2VecUtility.review_to_wordlist \
( train["review"][0], True )
# Get the number of reviews based on the dataframe column size
num_reviews = train["review"].size
print ("Cleaning and parsing the training set movie reviews...\n")
clean_train_reviews = []
for i in range( 0, len(train["review"])):
if( (i+1)%1000 == 0 ):
print ("Review %d of %d\n" % ( i+1, num_reviews ) )
clean_train_reviews.append(" ".join(KaggleWord2VecUtility.review_to_wordlist(train["review"][i], True)))
clean_train_reviews[0:5]
print ("Creating the bag of words...\n")
from sklearn.feature_extraction.text import CountVectorizer
# Initialize the "CountVectorizer" object, which is scikit-learn's
# bag of words tool.
vectorizer = CountVectorizer(analyzer = "word", \
tokenizer = None, \
preprocessor = None, \
stop_words = None, \
max_features = 5000)
train_data_features = vectorizer.fit_transform(clean_train_reviews)
train_data_features = train_data_features.toarray()
print ("Training the random forest (this may take a while)...")
# Initialize a Random Forest classifier with 100 trees
forest = RandomForestClassifier(n_estimators = 100)
# Fit the forest to the training set, using the bag of words as
# features and the sentiment labels as the response variable
#
# This may take a few minutes to run
forest = forest.fit( train_data_features, train["sentiment"] )
# Create an empty list and append the clean reviews one by one
clean_test_reviews = []
print ("Cleaning and parsing the test set movie reviews...\n")
for i in range(0,len(test["review"])):
clean_test_reviews.append(" ".join(KaggleWord2VecUtility.review_to_wordlist(test["review"][i], True)))
# Get a bag of words for the test set, and convert to a numpy array
test_data_features = vectorizer.transform(clean_test_reviews)
test_data_features = test_data_features.toarray()
# Use the random forest to make sentiment label predictions
print ("Predicting test labels...\n")
result = forest.predict(test_data_features)
# Copy the results to a pandas dataframe with an "id" column and
# a "sentiment" column
output = pd.DataFrame( data={"id":test["id"], "sentiment":result} )
# Use pandas to write the comma-separated output file
output.to_csv('Bag_of_Words_model.csv', index=False, quoting=3)
print ("Wrote results to Bag_of_Words_model.csv")
### Word2Vec
#!pip install gensim
import pandas as pd
import os
from nltk.corpus import stopwords
import nltk.data
import logging
import numpy as np # Make sure that numpy is imported
from gensim.models import Word2Vec
from sklearn.ensemble import RandomForestClassifier
"In lexical analysis, tokenization is the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens. The list of tokens becomes input for further processing such as parsing or text mining." -[Wikipedia](https://en.wikipedia.org/wiki/Tokenization_(lexical_analysis)
Punkt is a specific tokenizer.
[http://www.nltk.org/_modules/nltk/tokenize/punkt.html](http://www.nltk.org/_modules/nltk/tokenize/punkt.html)
# download punkt
nltk.download('punkt')
#What is a tokenizer
# http://www.nltk.org/_modules/nltk/tokenize/punkt.html
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
# ****** Split the labeled and unlabeled training sets into clean sentences
# Note this will take a while and produce some warnings.
sentences = [] # Initialize an empty list of sentences
print ("Parsing sentences from training set")
for review in train["review"]:
sentences += KaggleWord2VecUtility.review_to_sentences(review, tokenizer)
print ("Parsing sentences from unlabeled set")
for review in unlabeled_train["review"]:
sentences += KaggleWord2VecUtility.review_to_sentences(review, tokenizer)
# ****** Define functions to create average word vectors
#
def makeFeatureVec(words, model, num_features):
# Function to average all of the word vectors in a given
# paragraph
#
# Pre-initialize an empty numpy array (for speed)
featureVec = np.zeros((num_features,),dtype="float32")
#
nwords = 0.
#
# Index2word is a list that contains the names of the words in
# the model's vocabulary. Convert it to a set, for speed
index2word_set = set(model.wv.index2word)
#
# Loop over each word in the review and, if it is in the model's
# vocaublary, add its feature vector to the total
for word in words:
if word in index2word_set:
nwords = nwords + 1.
featureVec = np.add(featureVec,model[word])
#
# Divide the result by the number of words to get the average
featureVec = np.divide(featureVec,nwords)
return featureVec
def getAvgFeatureVecs(reviews, model, num_features):
# Given a set of reviews (each one a list of words), calculate
# the average feature vector for each one and return a 2D numpy array
#
# Initialize a counter
counter = 0.
#
# Preallocate a 2D numpy array, for speed
reviewFeatureVecs = np.zeros((len(reviews),num_features),dtype="float32")
#
# Loop through the reviews
for review in reviews:
# Print a status message every 1000th review
if counter%1000. == 0.:
print ("Review %d of %d" % (counter, len(reviews)))
# Call the function (defined above) that makes average feature vectors
reviewFeatureVecs[counter] = makeFeatureVec(review, model, num_features)
# Increment the counter
counter = counter + 1.
return reviewFeatureVecs
def getCleanReviews(reviews):
clean_reviews = []
for review in reviews["review"]:
clean_reviews.append( KaggleWord2VecUtility.review_to_wordlist( review, remove_stopwords=True ))
return clean_reviews
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',level=logging.INFO)
# Set values for various parameters
num_features = 300 # Word vector dimensionality
min_word_count = 40 # Minimum word count
num_workers = 4 # Number of threads to run in parallel
context = 10 # Context window size
downsampling = 1e-3 # Downsample setting for frequent words
# Initialize and train the model (this will take some time)
print ("Training Word2Vec model...")
model = Word2Vec(sentences, workers=num_workers, \
size=num_features, min_count = min_word_count, \
window = context, sample = downsampling, seed=1)
# If you don't plan to train the model any further, calling
# init_sims will make the model much more memory-efficient.
model.init_sims(replace=True)
# It can be helpful to create a meaningful model name and
# save the model for later use. You can load it later using Word2Vec.load()
model_name = "300features_40minwords_10context"
model.save(model_name)
model.doesnt_match("man woman child kitchen".split())
model.doesnt_match("france england germany soccer".split())
model.most_similar("soccer")
model.most_similar("man")
model["computer"]
model.most_similar("car")
|
[
"[email protected]"
] | |
02662542389abbb0e92a4e8e6133371ba0804813
|
41209325da09107de74e5864821e7e429f16df6b
|
/h2o-py/tests/testdir_demos/notebooks/pyunit_prep_airlines.py
|
2392e0832111bebbb52d2087ddd2c48f25f9a74b
|
[
"Apache-2.0"
] |
permissive
|
Sam7/h2o-3
|
8719fdced9f738db95f525165806dd7e585c53c6
|
c107d383ea4e201eea6e3e30129ed3d2748d6e61
|
refs/heads/master
| 2021-01-18T06:49:02.662728 | 2015-08-04T03:28:38 | 2015-08-04T03:28:38 | 40,160,767 | 0 | 0 | null | 2015-08-04T03:14:37 | 2015-08-04T03:14:37 | null |
UTF-8
|
Python
| false | false | 342 |
py
|
import sys
sys.path.insert(1, "../../../")
import h2o
def prep_airlines(ip,port):
# Connect to a pre-existing cluster
h2o.init(ip,port)
# execute ipython notebook
h2o.ipy_notebook_exec(h2o.locate("h2o-py/demos/prep_airlines.ipynb"),save_and_norun=False)
if __name__ == "__main__":
h2o.run_test(sys.argv, prep_airlines)
|
[
"[email protected]"
] | |
66f954f76279951de08e260c1e5652866f7842e0
|
b3879bc761ac38dab903da57c4061ad79fd70c6d
|
/курсы пайтон модуль 4/задание 26.py
|
2b39f54e860189efac740823746dfecbc9b1c91b
|
[] |
no_license
|
Ruslan5252/all-of-my-projects-byPyCharm
|
4df70cc3a31c4a5d97560fa858a706edcc856299
|
817d5f711408590ea141590ae52c6d888dfa2015
|
refs/heads/master
| 2023-05-03T01:06:30.156731 | 2021-05-29T13:51:16 | 2021-05-29T13:51:16 | 371,970,160 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 345 |
py
|
n = int(input("Введите количество элементов массива = "))
b = []
m=""
sum = 0
count = 0
for i in range(n):
a = int(input("введите число = "))
b.append(a)
print(b)
m=int(input("введите число m = "))
for i in b:
if i>m:
count+=1
sum+=i
aver=sum/count
print(aver)
|
[
"[email protected]"
] | |
3e54db497d49da0138de012bb12ff3abbe2a31b7
|
08401cff6a54ca358d3d563c0cbc1acf26e9960d
|
/Simulation_Tool/New_SimulationEnvironment_Ladybug/Sensitivity/SensitivityEnvelopAnnual.py
|
99fc1eb259284e048e510c5edccd8aab1fe972b9
|
[] |
no_license
|
architecture-building-systems/ASF_Simulation
|
5f55ba474a06d48e4e629db77e794874cc376d44
|
8cabb20da689f61891966dfa5d15cc82771050d3
|
refs/heads/main
| 2022-10-28T08:27:10.781156 | 2022-10-03T19:19:19 | 2022-10-03T19:19:19 | 46,800,943 | 9 | 3 | null | 2022-10-03T19:19:20 | 2015-11-24T15:36:58 |
HTML
|
UTF-8
|
Python
| false | false | 6,604 |
py
|
"""
Created on March 20 2017
@author: Prageeth Jayathissa
"""
import unittest
import os
import sys
import pandas as pd
import time
import numpy as np
from j_paths import PATHS
paths = PATHS()
sys.path.insert(0, paths['5R1C_ISO_simulator'])
sys.path.insert(0, paths['main'])
from SimulationClass import ASF_Simulation
from supplySystem import *
from emissionSystem import *
class TestMainSimulation(unittest.TestCase):
def test_Standard(self):
"""
Runs the ASF Simulation Analysis for multiple archetypes
TODO: Archetypes can only be chosen in the BuildArchetypeDict function. This should be moved here as an input parameter
:Output: all_results: A dataframe of building energy requirements for each archetype evaluated
"""
paths = PATHS()
# SimulationData = {
# 'optimizationTypes' : ['E_total'], #, 'Cooling', 'Heating', 'SolarEnergy', 'Lighting', 'E_HCL'
# 'DataFolderName' : 'ZH13_49comb', #'ZH13_49comb',
# 'FileName': 'ZH13_49comb',
# 'geoLocation' : 'Zuerich_Kloten_2013',
# 'EPWfile': 'Zuerich_Kloten_2013.epw',
# 'Save' : False,
# 'ShowFig': False}
# # Set Building Parameters in [mm]
# BuildingData = {
# "room_width": 4900,
# "room_height": 3100,
# "room_depth": 7000,
# "glazing_percentage_w": 1.0, #0.92
# "glazing_percentage_h": 1.0} #0.97
# PanelData = {
# "XANGLES": [0, 15, 30, 45, 60, 75, 90],
# "YANGLES" : [-45, -30,-15,0, 15, 30, 45],
# "NoClusters":1,
# "numberHorizontal":6,
# "numberVertical":9,
# "panelOffset":400,
# "panelSize":400,
# "panelSpacing":500,
# "panelGridSize" : 25}
##-----Static Simulatioh----
# SimulationData = {
# 'optimizationTypes' : ['E_total'], #, 'Cooling', 'Heating', 'SolarEnergy', 'Lighting', 'E_HCL'
# 'DataFolderName' : 'ZH13_49comb_static_45_0', #'ZH13_49comb_static_45_0',
# 'FileName': 'ZH13_49comb_static_45_0',
# 'geoLocation' : 'Zuerich_Kloten_2013',
# 'EPWfile': 'Zuerich_Kloten_2013.epw',
# 'Save' : False,
# 'ShowFig': False}
# # Set Building Parameters in [mm]
# BuildingData = {
# "room_width": 4900,
# "room_height": 3100,
# "room_depth": 7000,
# "glazing_percentage_w": 0.92,
# "glazing_percentage_h": 0.97}
# PanelData = {
# "XANGLES": [45],
# "YANGLES" : [0],
# "NoClusters":1,
# "numberHorizontal":6,
# "numberVertical":9,
# "panelOffset":400,
# "panelSize":400,
# "panelSpacing":500,
# "panelGridSize" : 25}
###----No ASF Simulatin -----
SimulationData = {
'optimizationTypes' : ['E_total'], #, 'Cooling', 'Heating', 'SolarEnergy', 'Lighting', 'E_HCL'
'DataFolderName' : 'ZH13_NoASF', #'ZH13_49comb_static_45_0',
'FileName': 'ZH13_NoASF',
'geoLocation' : 'Zuerich_Kloten_2013',
'EPWfile': 'Zuerich_Kloten_2013.epw',
'Save' : False,
'ShowFig': False}
# Set Building Parameters in [mm]
BuildingData = {
"room_width": 4900,
"room_height": 3100,
"room_depth": 7000,
"glazing_percentage_w": 0.92,
"glazing_percentage_h": 0.97}
PanelData = {
"XANGLES": [0],
"YANGLES" : [0],
"NoClusters":1,
"numberHorizontal":0,
"numberVertical":0,
"panelOffset":400,
"panelSize":400,
"panelSpacing":500,
"panelGridSize" : 25}
#Set building properties for RC-Model simulator
#Set simulation Properties
SimulationOptions= {
'setBackTempH' : 4.,
'setBackTempC' : 4.,
'Occupancy' : 'Occupancy_COM.csv',
'ActuationEnergy' : False,
"Temp_start" : 20,
'human_heat_emission' : 0.12,}
#U_Range=np.arange(0.2,4.1,0.2)
U_Range=np.arange(0.2,2.1,0.2)
all_results=pd.DataFrame({'Infiltration': []})
all_results.set_index(['Infiltration'], inplace=True)
# loop through building properties and simulation options dictionaries:
for ii,sens in enumerate(U_Range): #range(0, len(runlist)):
BuildingProperties={
"glass_solar_transmittance" : 0.687 ,
"glass_light_transmittance" : 0.744 ,
"lighting_load" : 11.74 ,
"lighting_control" : 300,
"Lighting_Utilisation_Factor" : 0.45,
"Lighting_Maintenance_Factor" : 0.9,
"U_em" : 0.2,
"U_w" : 1.2,
"ACH_vent" : 1.5,
"ACH_infl" :sens,
"ventilation_efficiency" : 0.6 ,
"c_m_A_f" : 165 * 10**3,
"theta_int_h_set" : 20,
"theta_int_c_set" : 26,
"phi_c_max_A_f": -np.inf,
"phi_h_max_A_f": np.inf,
"heatingSupplySystem" : DirectHeater,
"coolingSupplySystem" : COP3Cooler,
"heatingEmissionSystem" : AirConditioning,
"coolingEmissionSystem" : AirConditioning,
}
# Run ASF simulation
ASF_archetype = ASF_Simulation(SimulationData=SimulationData, BuildingData=BuildingData,
BuildingProperties=BuildingProperties, SimulationOptions=SimulationOptions, PanelData=PanelData)
ASF_archetype.SolveASF()
# Add building U_envelope to dataframe and append subsequent iterations:
current_result = ASF_archetype.yearlyData.T
current_result['Infiltration'] = sens
current_result.set_index(['Infiltration'], inplace=True)
temp_list = [all_results, current_result] #TODO: Change this to one line
all_results = pd.concat(temp_list)
print '--simulations complete--'
# write results to csv:
timestr = time.strftime("%d%m%Y_%H%M")
name = 'Sensitivity_' + SimulationData.get('DataFolderName') + '_' + timestr + '.csv'
all_results.to_csv(name)
print all_results
if __name__ == '__main__':
unittest.main()
|
[
"[email protected]"
] | |
2317d058180aaf1d421414b1a7f8fef85d9d7ffc
|
fe9573bad2f6452ad3e2e64539361b8bc92c1030
|
/Assignment/data_analysis/proving_fairness.py
|
f22b4ba82046ae8cb0861fd1f78fe43ddc168c11
|
[] |
no_license
|
OceanicSix/Python_program
|
e74c593e2e360ae22a52371af6514fcad0e8f41f
|
2716646ce02db00306b475bad97105b260b6cd75
|
refs/heads/master
| 2022-01-25T16:59:31.212507 | 2022-01-09T02:01:58 | 2022-01-09T02:01:58 | 149,686,276 | 1 | 2 | null | null | null | null |
UTF-8
|
Python
| false | false | 132 |
py
|
from card_analyze import Card_analysis
#create a deck with 4 suits in it
a=Card_analysis(4)
#show the diagram
a.draw_average_value()
|
[
"[email protected]"
] | |
d459d0fd5da7c1b4733bab84227575675b742b81
|
3a8110706a67e111305a943ab7590d94782b0f6a
|
/temp_file.py
|
32a89fc94f6b8f71abd85a5d234030af2d27d891
|
[] |
no_license
|
pawwahn/python_practice
|
41fac14f7107fd8f7c8a33fa7e09561f24bf9376
|
9e6564582abeb9f65c95de86121199939d0ee388
|
refs/heads/master
| 2022-10-04T10:32:49.952690 | 2022-09-15T09:43:18 | 2022-09-15T09:43:18 | 223,134,205 | 1 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,237 |
py
|
text = 'asd sdhosa afo hasdfoi fasd '
pattern = 'afo'
def KnuthMorrisPratt(text, pattern):
'''Yields all starting positions of copies of the pattern in the text.
Calling conventions are similar to string.find, but its arguments can be
lists or iterators, not just strings, it returns all matches, not just
the first one, and it does not need the whole text in memory at once.
Whenever it yields, it will have read the text exactly up to and including
the match that caused the yield.'''
# allow indexing into pattern and protect against change during yield
pattern = list(pattern)
# build table of shift amounts
shifts = [1] * (len(pattern) + 1)
shift = 1
for pos in range(len(pattern)):
while shift <= pos and pattern[pos] != pattern[pos-shift]:
shift += shifts[pos-shift]
shifts[pos+1] = shift
# do the actual search
startPos = 0
matchLen = 0
for c in text:
while matchLen == len(pattern) or \
matchLen >= 0 and pattern[matchLen] != c:
startPos += shifts[matchLen]
matchLen -= shifts[matchLen]
matchLen += 1
if matchLen == len(pattern):
yield startPos
|
[
"[email protected]"
] | |
a5256482ac3f5b173c2f34566009130210b5e73d
|
ef187d259d33e97c7b9ed07dfbf065cec3e41f59
|
/work/atcoder/arc/arc044/D/answers/693063_tnk0812.py
|
808ab63297d877c466eb4ff8d1c1b5f6b9ceab7a
|
[] |
no_license
|
kjnh10/pcw
|
847f7295ea3174490485ffe14ce4cdea0931c032
|
8f677701bce15517fb9362cc5b596644da62dca8
|
refs/heads/master
| 2020-03-18T09:54:23.442772 | 2018-07-19T00:26:09 | 2018-07-19T00:26:09 | 134,586,379 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 517 |
py
|
def main():
R = 10**9+7
N = int(input())
a = list(map(int, input().split(" ")))
if a[0] != 0:
return 0
amax = max(a)
h = [0] * (amax+1)
for i in a:
h[i] += 1
if h[0] != 1:
return 0
ans = 1
b = 1
for i in h[1:]:
if i == 0:
return 0
ans *= pow(2, i * (i - 1) // 2, R)
ans %= R
ans *= pow(pow(2, b, R) - 1, i, R)
ans %= R
b = i
return ans
if __name__ == '__main__':
print(main())
|
[
"[email protected]"
] | |
bc41d44f837d58b0e1bf29a353682983da671142
|
863bd9a0e98ea98615296bc498f0225d5864406a
|
/fhir/resources/STU3/structuredefinition.py
|
abeb94053768539c4b83bd8055197f3f8ec16474
|
[
"BSD-3-Clause"
] |
permissive
|
BTsykaniuk/fhir.resources
|
ec6087514c65a10941a79716b7eb6ae1ed1745ef
|
4e78732950150de3de98698fab02d9aee5e1f3e4
|
refs/heads/main
| 2023-04-14T14:41:56.046039 | 2021-05-03T09:32:43 | 2021-05-03T09:32:43 | 363,876,237 | 0 | 0 |
NOASSERTION
| 2021-05-03T09:17:12 | 2021-05-03T09:17:11 | null |
UTF-8
|
Python
| false | false | 30,076 |
py
|
# -*- coding: utf-8 -*-
"""
Profile: http://hl7.org/fhir/StructureDefinition/StructureDefinition
Release: STU3
Version: 3.0.2
Revision: 11917
Last updated: 2019-10-24T11:53:00+11:00
"""
import typing
from pydantic import Field, root_validator
from pydantic.error_wrappers import ErrorWrapper, ValidationError
from pydantic.errors import MissingError, NoneIsNotAllowedError
from . import backboneelement, domainresource, fhirtypes
class StructureDefinition(domainresource.DomainResource):
"""Disclaimer: Any field name ends with ``__ext`` doesn't part of
Resource StructureDefinition, instead used to enable Extensibility feature
for FHIR Primitive Data Types.
Structural Definition.
A definition of a FHIR structure. This resource is used to describe the
underlying resources, data types defined in FHIR, and also for describing
extensions and constraints on resources and data types.
"""
resource_type = Field("StructureDefinition", const=True)
abstract: bool = Field(
None,
alias="abstract",
title="Whether the structure is abstract",
description=(
"Whether structure this definition describes is abstract or not - that"
" is, whether the structure is not intended to be instantiated. For "
"Resources and Data types, abstract types will never be exchanged "
"between systems."
),
# if property is element of this resource.
element_property=True,
element_required=True,
)
abstract__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_abstract", title="Extension field for ``abstract``."
)
baseDefinition: fhirtypes.Uri = Field(
None,
alias="baseDefinition",
title="Definition that this type is constrained/specialized from",
description=(
"An absolute URI that is the base structure from which this type is "
"derived, either by specialization or constraint."
),
# if property is element of this resource.
element_property=True,
)
baseDefinition__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_baseDefinition", title="Extension field for ``baseDefinition``."
)
contact: typing.List[fhirtypes.ContactDetailType] = Field(
None,
alias="contact",
title="Contact details for the publisher",
description=(
"Contact details to assist a user in finding and communicating with the"
" publisher."
),
# if property is element of this resource.
element_property=True,
)
context: typing.List[fhirtypes.String] = Field(
None,
alias="context",
title="Where the extension can be used in instances",
description=(
"Identifies the types of resource or data type elements to which the "
"extension can be applied."
),
# if property is element of this resource.
element_property=True,
)
context__ext: typing.List[
typing.Union[fhirtypes.FHIRPrimitiveExtensionType, None]
] = Field(None, alias="_context", title="Extension field for ``context``.")
contextInvariant: typing.List[fhirtypes.String] = Field(
None,
alias="contextInvariant",
title="FHIRPath invariants - when the extension can be used",
description=(
"A set of rules as Fluent Invariants about when the extension can be "
"used (e.g. co-occurrence variants for the extension)."
),
# if property is element of this resource.
element_property=True,
)
contextInvariant__ext: typing.List[
typing.Union[fhirtypes.FHIRPrimitiveExtensionType, None]
] = Field(
None,
alias="_contextInvariant",
title="Extension field for ``contextInvariant``.",
)
contextType: fhirtypes.Code = Field(
None,
alias="contextType",
title="resource | datatype | extension",
description=(
"If this is an extension, Identifies the context within FHIR resources "
"where the extension can be used."
),
# if property is element of this resource.
element_property=True,
# note: Enum values can be used in validation,
# but use in your own responsibilities, read official FHIR documentation.
enum_values=["resource", "datatype", "extension"],
)
contextType__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_contextType", title="Extension field for ``contextType``."
)
copyright: fhirtypes.Markdown = Field(
None,
alias="copyright",
title="Use and/or publishing restrictions",
description=(
"A copyright statement relating to the structure definition and/or its "
"contents. Copyright statements are generally legal restrictions on the"
" use and publishing of the structure definition."
),
# if property is element of this resource.
element_property=True,
)
copyright__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_copyright", title="Extension field for ``copyright``."
)
date: fhirtypes.DateTime = Field(
None,
alias="date",
title="Date this was last changed",
description=(
"The date (and optionally time) when the structure definition was "
"published. The date must change if and when the business version "
"changes and it must change if the status code changes. In addition, it"
" should change when the substantive content of the structure "
"definition changes."
),
# if property is element of this resource.
element_property=True,
)
date__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_date", title="Extension field for ``date``."
)
derivation: fhirtypes.Code = Field(
None,
alias="derivation",
title="specialization | constraint - How relates to base definition",
description="How the type relates to the baseDefinition.",
# if property is element of this resource.
element_property=True,
# note: Enum values can be used in validation,
# but use in your own responsibilities, read official FHIR documentation.
enum_values=["specialization", "constraint"],
)
derivation__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_derivation", title="Extension field for ``derivation``."
)
description: fhirtypes.Markdown = Field(
None,
alias="description",
title="Natural language description of the structure definition",
description=(
"A free text natural language description of the structure definition "
"from a consumer's perspective."
),
# if property is element of this resource.
element_property=True,
)
description__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_description", title="Extension field for ``description``."
)
differential: fhirtypes.StructureDefinitionDifferentialType = Field(
None,
alias="differential",
title="Differential view of the structure",
description=(
"A differential view is expressed relative to the base "
"StructureDefinition - a statement of differences that it applies."
),
# if property is element of this resource.
element_property=True,
)
experimental: bool = Field(
None,
alias="experimental",
title="For testing purposes, not real usage",
description=(
"A boolean value to indicate that this structure definition is authored"
" for testing purposes (or education/evaluation/marketing), and is not "
"intended to be used for genuine usage."
),
# if property is element of this resource.
element_property=True,
)
experimental__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_experimental", title="Extension field for ``experimental``."
)
fhirVersion: fhirtypes.Id = Field(
None,
alias="fhirVersion",
title="FHIR Version this StructureDefinition targets",
description=(
"The version of the FHIR specification on which this "
"StructureDefinition is based - this is the formal version of the "
"specification, without the revision number, e.g. "
"[publication].[major].[minor], which is 3.0.2 for this version."
),
# if property is element of this resource.
element_property=True,
)
fhirVersion__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_fhirVersion", title="Extension field for ``fhirVersion``."
)
identifier: typing.List[fhirtypes.IdentifierType] = Field(
None,
alias="identifier",
title="Additional identifier for the structure definition",
description=(
"A formal identifier that is used to identify this structure definition"
" when it is represented in other formats, or referenced in a "
"specification, model, design or an instance."
),
# if property is element of this resource.
element_property=True,
)
jurisdiction: typing.List[fhirtypes.CodeableConceptType] = Field(
None,
alias="jurisdiction",
title="Intended jurisdiction for structure definition (if applicable)",
description=(
"A legal or geographic region in which the structure definition is "
"intended to be used."
),
# if property is element of this resource.
element_property=True,
)
keyword: typing.List[fhirtypes.CodingType] = Field(
None,
alias="keyword",
title="Assist with indexing and finding",
description=(
"A set of key words or terms from external terminologies that may be "
"used to assist with indexing and searching of templates."
),
# if property is element of this resource.
element_property=True,
)
kind: fhirtypes.Code = Field(
None,
alias="kind",
title="primitive-type | complex-type | resource | logical",
description="Defines the kind of structure that this definition is describing.",
# if property is element of this resource.
element_property=True,
element_required=True,
# note: Enum values can be used in validation,
# but use in your own responsibilities, read official FHIR documentation.
enum_values=["primitive-type", "complex-type", "resource", "logical"],
)
kind__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_kind", title="Extension field for ``kind``."
)
mapping: typing.List[fhirtypes.StructureDefinitionMappingType] = Field(
None,
alias="mapping",
title="External specification that the content is mapped to",
description="An external specification that the content is mapped to.",
# if property is element of this resource.
element_property=True,
)
name: fhirtypes.String = Field(
None,
alias="name",
title="Name for this structure definition (computer friendly)",
description=(
"A natural language name identifying the structure definition. This "
"name should be usable as an identifier for the module by machine "
"processing applications such as code generation."
),
# if property is element of this resource.
element_property=True,
element_required=True,
)
name__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_name", title="Extension field for ``name``."
)
publisher: fhirtypes.String = Field(
None,
alias="publisher",
title="Name of the publisher (organization or individual)",
description=(
"The name of the individual or organization that published the "
"structure definition."
),
# if property is element of this resource.
element_property=True,
)
publisher__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_publisher", title="Extension field for ``publisher``."
)
purpose: fhirtypes.Markdown = Field(
None,
alias="purpose",
title="Why this structure definition is defined",
description=(
"Explaination of why this structure definition is needed and why it has"
" been designed as it has."
),
# if property is element of this resource.
element_property=True,
)
purpose__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_purpose", title="Extension field for ``purpose``."
)
snapshot: fhirtypes.StructureDefinitionSnapshotType = Field(
None,
alias="snapshot",
title="Snapshot view of the structure",
description=(
"A snapshot view is expressed in a stand alone form that can be used "
"and interpreted without considering the base StructureDefinition."
),
# if property is element of this resource.
element_property=True,
)
status: fhirtypes.Code = Field(
None,
alias="status",
title="draft | active | retired | unknown",
description=(
"The status of this structure definition. Enables tracking the life-"
"cycle of the content."
),
# if property is element of this resource.
element_property=True,
element_required=True,
# note: Enum values can be used in validation,
# but use in your own responsibilities, read official FHIR documentation.
enum_values=["draft", "active", "retired", "unknown"],
)
status__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_status", title="Extension field for ``status``."
)
title: fhirtypes.String = Field(
None,
alias="title",
title="Name for this structure definition (human friendly)",
description=(
"A short, descriptive, user-friendly title for the structure " "definition."
),
# if property is element of this resource.
element_property=True,
)
title__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_title", title="Extension field for ``title``."
)
type: fhirtypes.Code = Field(
None,
alias="type",
title="Type defined or constrained by this structure",
description=(
"The type this structure describes. If the derivation kind is "
"'specialization' then this is the master definition for a type, and "
"there is always one of these (a data type, an extension, a resource, "
"including abstract ones). Otherwise the structure definition is a "
"constraint on the stated type (and in this case, the type cannot be an"
" abstract type)."
),
# if property is element of this resource.
element_property=True,
element_required=True,
)
type__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_type", title="Extension field for ``type``."
)
url: fhirtypes.Uri = Field(
None,
alias="url",
title="Logical URI to reference this structure definition (globally unique)",
description=(
"An absolute URI that is used to identify this structure definition "
"when it is referenced in a specification, model, design or an "
"instance. This SHALL be a URL, SHOULD be globally unique, and SHOULD "
"be an address at which this structure definition is (or will be) "
"published. The URL SHOULD include the major version of the structure "
"definition. For more information see [Technical and Business "
"Versions](resource.html#versions)."
),
# if property is element of this resource.
element_property=True,
element_required=True,
)
url__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_url", title="Extension field for ``url``."
)
useContext: typing.List[fhirtypes.UsageContextType] = Field(
None,
alias="useContext",
title="Context the content is intended to support",
description=(
"The content was developed with a focus and intent of supporting the "
"contexts that are listed. These terms may be used to assist with "
"indexing and searching for appropriate structure definition instances."
),
# if property is element of this resource.
element_property=True,
)
version: fhirtypes.String = Field(
None,
alias="version",
title="Business version of the structure definition",
description=(
"The identifier that is used to identify this version of the structure "
"definition when it is referenced in a specification, model, design or "
"instance. This is an arbitrary value managed by the structure "
"definition author and is not expected to be globally unique. For "
"example, it might be a timestamp (e.g. yyyymmdd) if a managed version "
"is not available. There is also no expectation that versions can be "
"placed in a lexicographical sequence."
),
# if property is element of this resource.
element_property=True,
)
version__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_version", title="Extension field for ``version``."
)
@classmethod
def elements_sequence(cls):
"""returning all elements names from
``StructureDefinition`` according specification,
with preserving original sequence order.
"""
return [
"id",
"meta",
"implicitRules",
"language",
"text",
"contained",
"extension",
"modifierExtension",
"url",
"identifier",
"version",
"name",
"title",
"status",
"experimental",
"date",
"publisher",
"contact",
"description",
"useContext",
"jurisdiction",
"purpose",
"copyright",
"keyword",
"fhirVersion",
"mapping",
"kind",
"abstract",
"contextType",
"context",
"contextInvariant",
"type",
"baseDefinition",
"derivation",
"snapshot",
"differential",
]
@root_validator(pre=True, allow_reuse=True)
def validate_required_primitive_elements_2203(
cls, values: typing.Dict[str, typing.Any]
) -> typing.Dict[str, typing.Any]:
"""https://www.hl7.org/fhir/extensibility.html#Special-Case
In some cases, implementers might find that they do not have appropriate data for
an element with minimum cardinality = 1. In this case, the element must be present,
but unless the resource or a profile on it has made the actual value of the primitive
data type mandatory, it is possible to provide an extension that explains why
the primitive value is not present.
"""
required_fields = [
("abstract", "abstract__ext"),
("kind", "kind__ext"),
("name", "name__ext"),
("status", "status__ext"),
("type", "type__ext"),
("url", "url__ext"),
]
_missing = object()
def _fallback():
return ""
errors: typing.List["ErrorWrapper"] = []
for name, ext in required_fields:
field = cls.__fields__[name]
ext_field = cls.__fields__[ext]
value = values.get(field.alias, _missing)
if value not in (_missing, None):
continue
ext_value = values.get(ext_field.alias, _missing)
missing_ext = True
if ext_value not in (_missing, None):
if isinstance(ext_value, dict):
missing_ext = len(ext_value.get("extension", [])) == 0
elif (
getattr(ext_value.__class__, "get_resource_type", _fallback)()
== "FHIRPrimitiveExtension"
):
if ext_value.extension and len(ext_value.extension) > 0:
missing_ext = False
else:
validate_pass = True
for validator in ext_field.type_.__get_validators__():
try:
ext_value = validator(v=ext_value)
except ValidationError as exc:
errors.append(ErrorWrapper(exc, loc=ext_field.alias))
validate_pass = False
if not validate_pass:
continue
if ext_value.extension and len(ext_value.extension) > 0:
missing_ext = False
if missing_ext:
if value is _missing:
errors.append(ErrorWrapper(MissingError(), loc=field.alias))
else:
errors.append(
ErrorWrapper(NoneIsNotAllowedError(), loc=field.alias)
)
if len(errors) > 0:
raise ValidationError(errors, cls) # type: ignore
return values
class StructureDefinitionDifferential(backboneelement.BackboneElement):
"""Disclaimer: Any field name ends with ``__ext`` doesn't part of
Resource StructureDefinition, instead used to enable Extensibility feature
for FHIR Primitive Data Types.
Differential view of the structure.
A differential view is expressed relative to the base StructureDefinition -
a statement of differences that it applies.
"""
resource_type = Field("StructureDefinitionDifferential", const=True)
element: typing.List[fhirtypes.ElementDefinitionType] = Field(
...,
alias="element",
title="Definition of elements in the resource (if no StructureDefinition)",
description="Captures constraints on each element within the resource.",
# if property is element of this resource.
element_property=True,
)
@classmethod
def elements_sequence(cls):
"""returning all elements names from
``StructureDefinitionDifferential`` according specification,
with preserving original sequence order.
"""
return ["id", "extension", "modifierExtension", "element"]
class StructureDefinitionMapping(backboneelement.BackboneElement):
"""Disclaimer: Any field name ends with ``__ext`` doesn't part of
Resource StructureDefinition, instead used to enable Extensibility feature
for FHIR Primitive Data Types.
External specification that the content is mapped to.
An external specification that the content is mapped to.
"""
resource_type = Field("StructureDefinitionMapping", const=True)
comment: fhirtypes.String = Field(
None,
alias="comment",
title="Versions, Issues, Scope limitations etc.",
description=(
"Comments about this mapping, including version notes, issues, scope "
"limitations, and other important notes for usage."
),
# if property is element of this resource.
element_property=True,
)
comment__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_comment", title="Extension field for ``comment``."
)
identity: fhirtypes.Id = Field(
None,
alias="identity",
title="Internal id when this mapping is used",
description=(
"An Internal id that is used to identify this mapping set when specific"
" mappings are made."
),
# if property is element of this resource.
element_property=True,
element_required=True,
)
identity__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_identity", title="Extension field for ``identity``."
)
name: fhirtypes.String = Field(
None,
alias="name",
title="Names what this mapping refers to",
description="A name for the specification that is being mapped to.",
# if property is element of this resource.
element_property=True,
)
name__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_name", title="Extension field for ``name``."
)
uri: fhirtypes.Uri = Field(
None,
alias="uri",
title="Identifies what this mapping refers to",
description=(
"An absolute URI that identifies the specification that this mapping is"
" expressed to."
),
# if property is element of this resource.
element_property=True,
)
uri__ext: fhirtypes.FHIRPrimitiveExtensionType = Field(
None, alias="_uri", title="Extension field for ``uri``."
)
@classmethod
def elements_sequence(cls):
"""returning all elements names from
``StructureDefinitionMapping`` according specification,
with preserving original sequence order.
"""
return [
"id",
"extension",
"modifierExtension",
"identity",
"uri",
"name",
"comment",
]
@root_validator(pre=True, allow_reuse=True)
def validate_required_primitive_elements_2912(
cls, values: typing.Dict[str, typing.Any]
) -> typing.Dict[str, typing.Any]:
"""https://www.hl7.org/fhir/extensibility.html#Special-Case
In some cases, implementers might find that they do not have appropriate data for
an element with minimum cardinality = 1. In this case, the element must be present,
but unless the resource or a profile on it has made the actual value of the primitive
data type mandatory, it is possible to provide an extension that explains why
the primitive value is not present.
"""
required_fields = [("identity", "identity__ext")]
_missing = object()
def _fallback():
return ""
errors: typing.List["ErrorWrapper"] = []
for name, ext in required_fields:
field = cls.__fields__[name]
ext_field = cls.__fields__[ext]
value = values.get(field.alias, _missing)
if value not in (_missing, None):
continue
ext_value = values.get(ext_field.alias, _missing)
missing_ext = True
if ext_value not in (_missing, None):
if isinstance(ext_value, dict):
missing_ext = len(ext_value.get("extension", [])) == 0
elif (
getattr(ext_value.__class__, "get_resource_type", _fallback)()
== "FHIRPrimitiveExtension"
):
if ext_value.extension and len(ext_value.extension) > 0:
missing_ext = False
else:
validate_pass = True
for validator in ext_field.type_.__get_validators__():
try:
ext_value = validator(v=ext_value)
except ValidationError as exc:
errors.append(ErrorWrapper(exc, loc=ext_field.alias))
validate_pass = False
if not validate_pass:
continue
if ext_value.extension and len(ext_value.extension) > 0:
missing_ext = False
if missing_ext:
if value is _missing:
errors.append(ErrorWrapper(MissingError(), loc=field.alias))
else:
errors.append(
ErrorWrapper(NoneIsNotAllowedError(), loc=field.alias)
)
if len(errors) > 0:
raise ValidationError(errors, cls) # type: ignore
return values
class StructureDefinitionSnapshot(backboneelement.BackboneElement):
"""Disclaimer: Any field name ends with ``__ext`` doesn't part of
Resource StructureDefinition, instead used to enable Extensibility feature
for FHIR Primitive Data Types.
Snapshot view of the structure.
A snapshot view is expressed in a stand alone form that can be used and
interpreted without considering the base StructureDefinition.
"""
resource_type = Field("StructureDefinitionSnapshot", const=True)
element: typing.List[fhirtypes.ElementDefinitionType] = Field(
...,
alias="element",
title="Definition of elements in the resource (if no StructureDefinition)",
description="Captures constraints on each element within the resource.",
# if property is element of this resource.
element_property=True,
)
@classmethod
def elements_sequence(cls):
"""returning all elements names from
``StructureDefinitionSnapshot`` according specification,
with preserving original sequence order.
"""
return ["id", "extension", "modifierExtension", "element"]
|
[
"[email protected]"
] | |
339ee1a6ae6f55d7ed98ae1c6906e31fd243914d
|
91438802ee114b2fb945aae4105a17993dd6953d
|
/build/learning_ros_noetic/Part_1/example_ros_service/catkin_generated/pkg.installspace.context.pc.py
|
f8ed8bfecc5e6967674ef6da63cd664c6b2eeaca
|
[] |
no_license
|
AlexLam616/Baxter-robot
|
3a4cef31fe46da0fdb23c0e3b5808d84b412d037
|
d10fdcd35f29427ca14bb75f14fa9c64af3b028c
|
refs/heads/master
| 2023-05-12T01:25:56.454549 | 2021-05-25T02:02:09 | 2021-05-25T02:02:09 | 367,070,028 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 469 |
py
|
# generated from catkin/cmake/template/pkg.context.pc.in
CATKIN_PACKAGE_PREFIX = ""
PROJECT_PKG_CONFIG_INCLUDE_DIRS = "${prefix}/include".split(';') if "${prefix}/include" != "" else []
PROJECT_CATKIN_DEPENDS = "roscpp;std_msgs;nav_msgs;geometry_msgs;message_runtime".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else []
PROJECT_NAME = "example_ros_service"
PROJECT_SPACE_DIR = "/home/alex/workspace/install"
PROJECT_VERSION = "0.0.0"
|
[
"[email protected]"
] | |
ac99a5886f3a2a1b61a3b4229237a77c3f47b84d
|
c7014b5d347d63db9293260173642e41a3c73ccc
|
/.history/bacalab/settings/production_20190521163656.py
|
b2c5a0106006969ed89b327922ea11aafcd1ea18
|
[] |
no_license
|
helder-a-reis/bacalab
|
6f03d218d6234fc656814f443afecf9b28f67e59
|
2b34c9ff6f303561d7b787d766d6f20d849bc3f1
|
refs/heads/master
| 2023-04-27T09:52:29.493627 | 2019-06-13T14:06:38 | 2019-06-13T14:06:38 | 171,745,411 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 455 |
py
|
from .base import *
import django_heroku
ALLOWED_HOSTS = ['bacalab.herokuapp.com']
# Database
# https://docs.djangoproject.com/en/2.2/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': 'bacalab',
'USER': os.environ.get('DB_USER'),
'PASSWORD': os.environ.get('DB_PWD'),
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
django_heroku.settings(locals())
|
[
"[email protected]"
] | |
72b60e128ce87535ce290e408646476df218ff65
|
3ea75e35408de10bba250f52120b5424bd50fdd9
|
/py/plotDensComparisonDF.py
|
307a27df27548ae09a70af92516d47b63d31d9a7
|
[] |
no_license
|
jobovy/segue-maps
|
9848fe59ee24a11a751df4f8855c40f2480aef23
|
ed20b1058a98618700a20da5aa9b5ebd2ea7719b
|
refs/heads/main
| 2022-11-30T15:27:08.079999 | 2016-12-20T04:28:26 | 2016-12-20T04:28:26 | 40,663,061 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 21,592 |
py
|
#ython plotDensComparisonDF.py --type=z --dfeh=1. --dafe=0.6 -f ../fakeDF/fakeDF_dfeh1._dafe0.6_q0.7.fits --savenorm=../fakeDF/fakeDFFit_dfeh1._dafe0.6_q0.7_justpot_norm.sav --nmcv=100 --minndata=10000 ../figs/fakeDFFit_dfeh1._dafe0.6_q0.7_
import os, os.path
import numpy
import cPickle as pickle
from optparse import OptionParser
from galpy.util import bovy_plot, bovy_coords
import segueSelect
import compareDataModel
from segueSelect import read_gdwarfs, read_kdwarfs, _GDWARFFILE, _KDWARFFILE, \
segueSelect, _ERASESTR
from fitDensz import cb, _ZSUN, DistSpline, _ivezic_dist, _NDS
from pixelFitDens import pixelAfeFeh
from pixelFitDF import *
from pixelFitDF import _SURFNRS, _SURFNZS, _PRECALCVSAMPLES, _REFR0, _REFV0
from plotDensComparisonDFMulti4gridall import calc_model
_NOTDONEYET= True
_RRANGES= False
_VARYHSZ= True
def plotDensComparisonDF(options,args):
#Read data etc.
print "Reading the data ..."
raw= read_rawdata(options)
#Bin the data
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
#Map the bins with ndata > minndata in 1D
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
#print binned.feh(ii), binned.afe(jj), len(data)
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
if not options.singlefeh is None:
if True: #Just to keep indentation the same
#Set up single feh
indx= binned.callIndx(options.singlefeh,options.singleafe)
if numpy.sum(indx) == 0:
raise IOError("Bin corresponding to singlefeh and singleafe is empty ...")
allraw= copy.copy(raw)
raw= copy.copy(binned.data[indx])
#newerrstuff= []
#for ii in range(len(binned.data)):
# if indx[ii]: newerrstuff.append(errstuff[ii])
#errstuff= newerrstuff
print "Using %i data points ..." % (len(data))
#Bin again
binned= pixelAfeFeh(raw,dfeh=options.dfeh,dafe=options.dafe)
fehs, afes= [], []
for ii in range(len(binned.fehedges)-1):
for jj in range(len(binned.afeedges)-1):
data= binned(binned.feh(ii),binned.afe(jj))
if len(data) < options.minndata:
continue
fehs.append(binned.feh(ii))
afes.append(binned.afe(jj))
nabundancebins= len(fehs)
fehs= numpy.array(fehs)
afes= numpy.array(afes)
if options.singles:
run_abundance_singles_plotdens(options,args,fehs,afes)
return None
if options.andistances:
data= binned(fehs[0],afes[0])
distfac= AnDistance.AnDistance(data.dered_g-data.dered_r,
data.feh)
if options.fixdm is None:
options.fixdm= numpy.log10(distfac)*5.
else:
options.fixdm= options.fixdm+numpy.log10(distfac)*5.
options.andistances= False
#Start over
plotDensComparisonDF(options,args)
return None
#Setup everything for the selection function
print "Setting up stuff for the normalization integral ..."
normintstuff= setup_normintstuff(options,raw,binned,fehs,afes,allraw)
##########POTENTIAL PARAMETERS####################
potparams1= numpy.array([numpy.log(2.5/8.),options.fixvc/220.,
numpy.log(400./8000.),0.2,0.])
potparams2= numpy.array([numpy.log(3./8.),options.fixvc/220.,
numpy.log(400./8000.),0.466666666,0.])
potparams3= numpy.array([numpy.log(2.5/8.),options.fixvc/220.,
numpy.log(400./8000.),0.8,0.])
options.potential= 'dpdiskplhalofixbulgeflatwgasalt'
#Set up density models and their parameters
pop= 0 #assume first population
#Load savefile
if not options.init is None:
#Load initial parameters from file
savename= options.init
# spl= savename.split('.')
# newname= ''
# for ll in range(len(spl)-1):
# newname+= spl[ll]
# if not ll == len(spl)-2: newname+= '.'
# newname+= '_%i.' % pop
# newname+= spl[-1]
savefile= open(savename,'rb')
try:
if not _NOTDONEYET:
params= pickle.load(savefile)
mlogl= pickle.load(savefile)
logl= pickle.load(savefile)
except:
if savetopickle:
save_pickles(tmpfiles[jj],None)
return None
else:
return None
finally:
savefile.close()
else:
raise IOError("base filename not specified ...")
#First model is best-fit for this particular bin
marglogl= numpy.zeros((8,16))
for ll in range(8):
for kk in range(16):
marglogl[ll,kk]= logsumexp(logl[ll,0,0,kk,:,:,:,0])
indx= numpy.unravel_index(numpy.nanargmax(marglogl),(8,16))
print "Maximum for %i at %i,%i" % (pop,indx[0],indx[1])
rds= numpy.linspace(2.0,3.4,options.nrds)/_REFR0
rds= numpy.log(rds)
fhs= numpy.linspace(0.,1.,options.nfhs)
potparams1[0]= rds[indx[0]]
potparams1[3]= fhs[indx[1]]
#######DF PARAMETER RANGES###########
hrs, srs, szs= setup_dfgrid(fehs,afes,options)
dfindx= numpy.unravel_index(numpy.nanargmax(logl[indx[0],0,0,indx[1],:,:,:,0]),
(8,8,16))
print "Maximum for %i at %i,%i,%i" % (pop,dfindx[0],dfindx[1],dfindx[2])
tparams= initialize(options,fehs,afes)
startindx= 0
if options.fitdvt: startindx+= 1
tparams[startindx]= hrs[dfindx[0]]
tparams[startindx+4]= srs[dfindx[1]]
tparams[startindx+2]= szs[dfindx[2]]
tparams[startindx+5]= 0. #outlier fraction
tparams= set_potparams(potparams1,tparams,options,1)
#Set up density models and their parameters
model1= interpDens#woutlier
print "Working on model 1 ..."
paramsInterp= calc_model(tparams,options,0,_retsurfz=False,
normintstuff=normintstuff)
params1= paramsInterp
if False:
indx0= numpy.argmin((potparams2[0]-rds)**2.)
indx1= numpy.argmin((potparams2[3]-fhs)**2.)
#indx0= indx[0]
#indx1= indx[1]
dfindx= numpy.unravel_index(numpy.argmax(logl[indx0,0,0,indx1,:,:,:,0]),
(8,8,16))
tparams[startindx]= hrs[dfindx[0]]
tparams[startindx+4]= srs[dfindx[1]]
tparams[startindx+2]= szs[dfindx[2]]
#print "BOVY: YOU HAVE MESSED WITH MODEL 2"
tparams= set_potparams(potparams2,tparams,toptions,1)
model2= interpDens
print "Working on model 2 ..."
paramsInterp, surfz= calc_model(tparams,toptions,0,_retsurfz=True)
params2= paramsInterp
indx0= numpy.argmin((potparams3[0]-rds)**2.)
indx1= numpy.argmin((potparams3[3]-fhs)**2.)
dfindx= numpy.unravel_index(numpy.argmax(logl[indx0,0,0,indx1,:,:,:,0]),
(8,8,16))
tparams[startindx]= hrs[dfindx[0]]
tparams[startindx+4]= srs[dfindx[1]]
tparams[startindx+2]= szs[dfindx[2]]
tparams= set_potparams(potparams3,tparams,toptions,1)
model3= interpDens
print "Working on model 3 ..."
paramsInterp, surfz= calc_model(tparams,toptions,0,_retsurfz=True)
params3= paramsInterp
else:
model2= None
params2= None
model3= None
params3= None
data= binned(fehs[pop],afes[pop])
#Setup everything for selection function
thisnormintstuff= normintstuff[pop]
if _PRECALCVSAMPLES:
sf, plates,platel,plateb,platebright,platefaint,grmin,grmax,rmin,rmax,fehmin,fehmax,feh,colordist,fehdist,gr,rhogr,rhofeh,mr,dmin,dmax,ds, surfscale, hr, hz, colorfehfac,normR, normZ,surfnrs, surfnzs, surfRgrid, surfzgrid, surfvrs, surfvts, surfvzs= unpack_normintstuff(thisnormintstuff,options)
else:
sf, plates,platel,plateb,platebright,platefaint,grmin,grmax,rmin,rmax,fehmin,fehmax,feh,colordist,fehdist,gr,rhogr,rhofeh,mr,dmin,dmax,ds, surfscale, hr, hz, colorfehfac, normR, normZ= unpack_normintstuff(thisnormintstuff,options)
if True:
#Cut out bright stars on faint plates and vice versa
indx= []
nfaintbright, nbrightfaint= 0, 0
for ii in range(len(data.feh)):
if sf.platebright[str(data[ii].plate)] and data[ii].dered_r >= 17.8:
indx.append(False)
nbrightfaint+= 1
elif not sf.platebright[str(data[ii].plate)] and data[ii].dered_r < 17.8:
indx.append(False)
nfaintbright+= 1
else:
indx.append(True)
print "nbrightfaint, nfaintbright", nbrightfaint, nfaintbright
indx= numpy.array(indx,dtype='bool')
data= data[indx]
#Ranges
if options.type == 'z':
xrange= [-0.1,5.]
elif options.type == 'R':
xrange= [4.8,14.2]
elif options.type == 'r':
xrange= [14.2,20.1]
#We do bright/faint for 4 directions and all, all bright, all faint
ls= [180,180,45,45]
bs= [0,90,-23,23]
bins= 21
#Set up comparison
if options.type == 'r':
compare_func= compareDataModel.comparerdistPlate
elif options.type == 'z':
compare_func= compareDataModel.comparezdistPlate
elif options.type == 'R':
compare_func= compareDataModel.compareRdistPlate
#First do R ranges for z
if options.type.lower() == 'z' and _RRANGES:
bins=21
Rmins= [None,7.,9.]
Rmaxs= [7.,9.,None]
nameRmins= [4,7,9]
nameRmaxs= [7,9,13]
for ii in range(len(Rmins)):
plate= 'all'
if Rmins[ii] is None:
thisleft_legend= r'$R \leq 7\,\mathrm{kpc\ plates}$'
elif Rmins[ii] == 7.:
thisleft_legend= r'$7\,\mathrm{kpc} < R \leq 9\,\mathrm{kpc\ plates}$'
elif Rmaxs[ii] is None:
thisleft_legend= r'$R \geq 9\,\mathrm{kpc\ plates}$'
thisright_legend= None
bovy_plot.bovy_print()
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
bins=bins,ls='-',left_legend=thisleft_legend,
right_legend=thisright_legend,
Rmin=Rmins[ii],Rmax=Rmaxs[ii])
if not params2 is None:
compare_func(model2,params2,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--',
Rmin=Rmins[ii],Rmax=Rmaxs[ii])
if not params3 is None:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':',
Rmin=Rmins[ii],Rmax=Rmaxs[ii])
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_%iR%i' % (nameRmins[ii],nameRmaxs[ii])+'.'+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_%iR%i' % (nameRmins[ii],nameRmaxs[ii])+'.'+options.ext)
#all, faint, bright
bins= [31,31,31]
plates= ['all','bright','faint']
for ii in range(len(plates)):
plate= plates[ii]
if plate == 'all':
# thisleft_legend= left_legend
# thisright_legend= right_legend
thisleft_legend= None
thisright_legend= None
else:
thisleft_legend= None
thisright_legend= None
bovy_plot.bovy_print()
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
bins=bins[ii],ls='-',left_legend=thisleft_legend,
right_legend=thisright_legend)
if not params2 is None:
compare_func(model2,params2,sf,colordist,fehdist,
data,plate,color='k',bins=bins[ii],
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins[ii],
rmin=14.5,rmax=rmax,
grmin=grmin,grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':')
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+plate+'.'+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_'+plate+'.'+options.ext)
if options.all: return None
bins= 16
for ii in range(len(ls)):
nodata= False
#Bright
plate= compareDataModel.similarPlatesDirection(ls[ii],bs[ii],20.,
sf,data,
faint=False)
bovy_plot.bovy_print()
try:
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
bins=bins,ls='-')
except IndexError:
#no data
nodata= True
if not params2 is None and not nodata:
compare_func(model2,params2,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None and not nodata:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':')
if not nodata:
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+'l%i_b%i_bright.' % (ls[ii],bs[ii])+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_l%i_b%i_bright.' % (ls[ii],bs[ii])+options.ext)
#Faint
plate= compareDataModel.similarPlatesDirection(ls[ii],bs[ii],20.,
sf,data,
bright=False)
bovy_plot.bovy_print()
try:
compare_func(model1,params1,sf,colordist,fehdist,
data,plate,color='k',
rmin=14.5,rmax=rmax,
grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
bins=bins,ls='-')
except IndexError:
#No data
continue
if not params2 is None:
compare_func(model2,params2,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls='--')
if not params3 is None:
compare_func(model3,params3,sf,colordist,fehdist,
data,plate,color='k',bins=bins,
rmin=14.5,rmax=rmax,grmin=grmin,
grmax=grmax,
fehmin=fehmin,fehmax=fehmax,feh=feh,
xrange=xrange,
overplot=True,ls=':')
if options.type == 'r':
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+'l%i_b%i_faint.' % (ls[ii],bs[ii])+options.ext)
else:
bovy_plot.bovy_end_print(args[0]+'model_data_g_'+options.type+'dist_l%i_b%i_faint.' % (ls[ii],bs[ii])+options.ext)
return None
def old_calc_model(params,options,pop):
nrs, nzs= _SURFNRS, _SURFNZS
thisrmin, thisrmax= 4./_REFR0, 15./_REFR0
thiszmin, thiszmax= 0., .8
Rgrid= numpy.linspace(thisrmin,thisrmax,nrs)
zgrid= numpy.linspace(thiszmin,thiszmax,nzs)
#Model 1
vo= get_vo(params,options,1)
ro= get_ro(params,options)
pot= setup_potential(params,options,1)
aA= setup_aA(pot,options)
dfparams= get_dfparams(params,pop,options,log=False)
if options.dfmodel.lower() == 'qdf':
#Normalize
hr= dfparams[0]/ro
sr= dfparams[1]/vo
sz= dfparams[2]/vo
hsr= dfparams[3]/ro
hsz= dfparams[4]/ro
#Setup
qdf= quasiisothermaldf(hr,sr,sz,hsr,hsz,pot=pot,aA=aA,cutcounter=True)
surfgrid= numpy.empty((nrs,nzs))
for ii in range(nrs):
for jj in range(nzs):
surfgrid[ii,jj]= qdf.density(Rgrid[ii],zgrid[jj],
nmc=options.nmcv,
ngl=options.ngl)
surfInterp= interpolate.RectBivariateSpline(Rgrid,zgrid,
numpy.log(surfgrid),
kx=3,ky=3,
s=0.)
return surfInterp
##RUNNING SINGLE BINS IN A SINGLE CALL
def run_abundance_singles_plotdens(options,args,fehs,afes):
options.singles= False #First turn this off!
savename= args[0]
initname= options.init
normname= options.savenorm
if not options.multi is None:
dummy= multi.parallel_map((lambda x: run_abundance_singles_plotdens_single(options,args,fehs,afes,x,
savename,initname,normname)),
range(len(fehs)),
numcores=numpy.amin([len(fehs),
multiprocessing.cpu_count(),
options.multi]))
else:
for ii in range(len(fehs)):
run_abundance_singles_plotdens_single(options,args,fehs,afes,ii,
savename,initname,normname)
return None
def run_abundance_singles_plotdens_single(options,args,fehs,afes,ii,savename,
initname,
normname):
if numpy.log(monoAbundanceMW.hr(fehs[ii],afes[ii],
k=(options.sample.lower() == 'k'))/8.) > -0.5 and not options.conditionalr:
#We didn't run, because we cannot model these populations with our model
return None
#Prepare args and options
spl= savename.split('.')
newname= savename+'%i_' % ii
args[0]= newname
if not initname is None:
#Do the same for init
spl= initname.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
options.init= newname
if not normname is None:
#Do the same for init
spl= normname.split('.')
newname= ''
for jj in range(len(spl)-1):
newname+= spl[jj]
if not jj == len(spl)-2: newname+= '.'
newname+= '_%i.' % ii
newname+= spl[-1]
options.savenorm= newname
options.singlefeh= fehs[ii]
options.singleafe= afes[ii]
#Now run
plotDensComparisonDF(options,args)
if __name__ == '__main__':
(options,args)= get_options().parse_args()
plotDensComparisonDF(options,args)
|
[
"[email protected]"
] | |
ec626dc70cae86ce5e4964a304e8be838b6eefa0
|
8ce656578e04369cea75c81b529b977fb1d58d94
|
/bank_guarantee/management/commands/export_bank_config.py
|
c52f95d875669bff83d718689ce7e6d95ae76981
|
[] |
no_license
|
JJvzd/django_exp
|
f9a08c40a6a7535777a8b5005daafe581d8fe1dc
|
b1df4681e67aad49a1ce6426682df66b81465cb6
|
refs/heads/master
| 2023-05-31T13:21:24.178394 | 2021-06-22T10:19:43 | 2021-06-22T10:19:43 | 379,227,324 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 2,898 |
py
|
from django.core.management import BaseCommand
from bank_guarantee.models import RequestPrintForm, OfferPrintForm
from clients.models import Bank
class Command(BaseCommand):
""" команда в процессе разработки """
help = 'Экспортирует конфиг банка по коду банка'
def add_arguments(self, parser):
parser.add_argument('bank_code', nargs='?', type=str, default=0)
def pack_users(self, bank):
return [
{
'first_name': user.first_name,
'last_name': user.last_name,
'middle_name': user.middle_name,
'email': user.email,
'username': user.username,
'is_active': user.is_active,
'roles': [role.name for role in user.roles.all()]
}
for user in bank.user_set.all()
]
def pack_print_forms(self, bank):
result = []
for pf in RequestPrintForm.objects.all():
if bank in pf.banks.all():
result.append({
'name': pf.name,
'download_name': pf.download_name,
'type': pf.type,
'filename': pf.filename,
'active': pf.active,
'readonly': pf.readonly,
'in_conclusions': pf.in_conclusions,
'roles': pf.roles,
})
return result
def pack_offer_categories(self, bank):
return [
{
'first_name': user.first_name,
'last_name': user.last_name,
'middle_name': user.middle_name,
'email': user.email,
'username': user.username,
'is_active': user.is_active,
'roles': [role.name for role in user.roles.all()]
}
for user in bank.user_set.all()
]
def pack_offer_print_forms(self, bank):
return [{
'name': pf.name,
'filename': pf.filename,
'type': pf.type,
'download_name': pf.download_name,
'active': pf.active,
} for pf in OfferPrintForm.objects.all()]
def handle(self, *args, **options):
bank_code = options['bank_code'] or None
print(bank_code)
bank = Bank.objects.get(code=bank_code)
data = {
'full_name': bank.full_name,
'short_name': bank.short_name,
'inn': bank.inn,
'ogrn': bank.ogrn,
'settings': {
'': ''
},
'users': self.pack_users(bank),
'print_forms': self.pack_print_forms(bank),
'offer_categories': self.pack_offer_categories(bank),
'offer_print_forms': self.pack_offer_print_forms(bank)
}
print(data)
|
[
"[email protected]"
] | |
7fad11fabe4b33ddbf34788cfb4383664f2e3679
|
298266f026dd1762f4469f3b1343d0fbde51076c
|
/core/environnement/base/order_n_price_managment.py
|
1a3c681f97e27bcec4209428ba0883e1b93e1b2c
|
[
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] |
permissive
|
elvis121193/TradzQAI
|
721f7e57cfb9fc1014fc0f7dc4936f9fe6978b43
|
5cb8775833cb438e7e57a676702d05ab1733edb6
|
refs/heads/master
| 2020-04-01T20:46:49.304246 | 2018-10-08T10:52:11 | 2018-10-08T10:52:11 | null | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,937 |
py
|
import time
class onpm(object):
def __init__(self):
self.orders = []
self.order_number = 0
self.decay = 0.01
self.max_pip_diff = 5
def bestPriceFunc(self, bids, asks, order, side, tid):
id = tid
if not tid:
id = self.order_number
self.order_number += 1
ordr = dict(
current_order = order,
id = id,
last_price = 0,
start_price = 0,
start_time = time.time()
)
self.orders.append(ordr)
self.orders[id]['current_order'] = order
if (time.time() - self.orders[id]['start_time']) / 60 >= 10:
cancel = True
else:
cancel = False
best_ask, best_bid = None, None
# Mutation protection
while True:
try:
best_bid = float(bids(0)[0][0]['price'])
best_ask = float(asks(0)[0][0]['price'])
break
except:
print ("rofl")
pass
spread = round(best_ask - best_bid, 2)
price = self.orders[id]['last_price']
if side == "buy":
if self.orders[id]['start_price'] - self.orders[id]['last_price'] <= -self.max_pip_diff:
cancel = True
elif spread == 0.01:
price = best_bid
else:
price = best_bid + self.decay
elif side == "sell":
if self.orders[id]['last_price'] - self.orders[id]['start_price'] <= -self.max_pip_diff:
cancel = True
elif spread == 0.01:
price = best_ask
else:
price = best_ask + self.decay
price = round(price, 2)
self.orders[id]['last_price'] = price
if not tid:
self.orders[id]['start_price'] = price
return price, cancel, id
|
[
"[email protected]"
] | |
40e5c01b7b17b21b34690cf16d24daa0d37bb3b8
|
023763d9f86116381f5765c51fb8b403e8eef527
|
/BootCamp_easy/agc012_a.py
|
c741541fb1fcb7ee964e0dfb2106f92bccd6d35c
|
[] |
no_license
|
Hilary02/atcoder
|
d45589682159c0f838561fc7d0bd25f0828e578b
|
879c74f3acc7befce75abd10abf1ab43967fc3c7
|
refs/heads/master
| 2021-07-18T11:34:22.702502 | 2021-07-11T09:04:12 | 2021-07-11T09:04:12 | 144,648,001 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 131 |
py
|
n = int(input())
la = [int(w) for w in input().split()]
la.sort()
ans = 0
for i in range(1, n+1):
ans += la[-2 * i]
print(ans)
|
[
"[email protected]"
] | |
9c5152d4fff88be75490b75d261de4694934beb3
|
4fc016459e4c78680c61488c771eb6b7eb20d5fe
|
/Python-Algorithms-DataStructure/src/leet/166_FractiontoRecurringDecimal.py
|
b2a0d7150c268c7ce26934a19ec65bceec14faa7
|
[] |
no_license
|
coremedy/Python-Algorithms-DataStructure
|
7c318de68fd9694377a0a4369d8dbeb49e1e17aa
|
3873502679a5def6af4be03028542f07d059d1a9
|
refs/heads/master
| 2021-01-25T07:34:17.714241 | 2015-11-05T10:17:40 | 2015-11-05T10:17:40 | 27,949,498 | 0 | 0 | null | null | null | null |
UTF-8
|
Python
| false | false | 1,209 |
py
|
'''
Created on 2015-08-30
'''
class Solution(object):
def fractionToDecimal(self, numerator, denominator):
"""
:type numerator: int
:type denominator: int
:rtype: str
"""
if denominator == 0:
return None
sign, numerator, denominator = "" if numerator * denominator >= 0 else "-", numerator if numerator >= 0 else -numerator, denominator if denominator >= 0 else -denominator
result, nums, d, index, location = str(numerator // denominator), [], dict(), 0, None
if numerator % denominator == 0:
return sign + result
while True:
numerator, index = 10 * (numerator % denominator), index + 1
nums.append(str(numerator // denominator))
if numerator == 0:
break
location = d.get(numerator)
if location is not None:
break
d[numerator] = index - 1
remainder = ''.join(nums[:index - 1]) if numerator == 0 else ''.join(nums[:location]) + '(' + ''.join(nums[location: index - 1]) + ')'
return sign + result + '.' + remainder
if __name__ == '__main__':
pass
|
[
"[email protected]"
] |
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