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838027b05c4975fc5f55b86184077144347a1bad | 4f21e3301c1a8699745528177b3210b4f1a1f1d5 | /week10/project2/library/settings.py | 4dbdb1bf1faf2c3a9ac45fabe288d8e6aa05c0ca | []
| no_license | ndina/webdev2019 | 7fd0250b662b378d55e24e931f82d0b2538d63a5 | eae4808e2f0bfcdd5a366fd4692c041b96faaa0b | refs/heads/master | 2020-05-03T22:05:12.392913 | 2019-05-04T02:46:56 | 2019-05-04T02:46:56 | 167,550,783 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 2,688 | py | """
Django settings for library project.
Generated by 'django-admin startproject' using Django 1.8.7.
For more information on this file, see
https://docs.djangoproject.com/en/1.8/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.8/ref/settings/
"""
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
import os
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/1.8/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'u2iir6bmw(y%pu*23y%sm1u#8y#o7_qchko#=r*_rtqy_-ge+e'
# 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',
)
MIDDLEWARE_CLASSES = (
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.auth.middleware.SessionAuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
'django.middleware.security.SecurityMiddleware',
)
ROOT_URLCONF = 'library.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 = 'library.wsgi.application'
# Database
# https://docs.djangoproject.com/en/1.8/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Internationalization
# https://docs.djangoproject.com/en/1.8/topics/i18n/
LANGUAGE_CODE = 'en-us'
TIME_ZONE = 'UTC'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/1.8/howto/static-files/
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
| [
"[email protected]"
]
| |
cdc14ab92541df567c2da2c71eab1580caecb1c9 | 73dadaa1c10ba149cf42fe3600edee9565b2e418 | /pythonBasicsHandsOn.py | 92dfdf4b8dab6dd981c4aea7320a988e9e35c5e3 | []
| no_license | Sankarb475/Python_Learning | 078826e5087bf1c6d2e18e9176af1bad3e345eb1 | 1b0d929a66f99b86bfd41590c1ce8781385223a0 | refs/heads/master | 2022-06-14T07:45:10.053327 | 2022-06-09T05:11:44 | 2022-06-09T05:11:44 | 167,215,150 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 10,865 | py | # Shallow Copy and Deep Copy
# When you use "=" to create a copy of an object, It only creates a new variable that shares the reference of the original object.
a = [1,2,3,4]
b = a
a.append(5)
a[2] = 100
print(a,b)
=> [1, 2, 100, 4, 5] [1, 2, 100, 4, 5]
-- Shallow copy creates a copy of t
import copy
a = [1,2,3,4]
b = copy.copy(a)
b.append(5)
print(a,b)
-- [1, 2, 3, 4] [1, 2, 3, 4, 5]
import copy
a = [1,2,3,4]
b = copy.copy(a)
b[0] = 100
print(a,b)
-- [1, 2, 3, 4] [100, 2, 3, 4]
import copy
a = [[1],[2],[3],[4]]
b = copy.copy(a)
a.append(5)
a[0][0] = 100
print(a,b)
-- [[100], [2], [3], [4], 5] [[100], [2], [3], [4]]
-- Deep Copy
it creates a completely new object with the elements of the existing object and they have no relation at all.
import copy
old_list = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
new_list = copy.deepcopy(old_list)
old_list[1][0] = 'BB'
print("Old list:", old_list)
print("New list:", new_list)
Old list: [[1, 1, 1], ['BB', 2, 2], [3, 3, 3]]
New list: [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
================================================================================
# Python args and kargs
The special syntax **kwargs in function definitions in python is used to pass a keyworded, variable-length argument list.
def myFun(**kwargs):
for key, value in kwargs.items():
print ("%s == %s" %(key, value))
# Driver code
myFun(first ='Geeks', mid ='for', last='Geeks')
-- The special syntax *args in function definitions in python is used to pass a variable number of arguments to a function
You cant pass a key-worded parameter.
def myFun(*argv):
for arg in argv:
print (arg)
myFun('Hello', 'Welcome', 'to', 'GeeksforGeeks')
# Python Decorators
-- A design pattern to in python - takes in a function, adds some functionality and returns it.
-- This is also called metaprogramming because a part of the program tries to modify another part of the program at compile time
def make_pretty(func):
def inner():
print("I got decorated")
func()
return inner
def ordinary():
print("I am ordinary")
>>> ordinary()
I am ordinary
>>> # let's decorate this ordinary function
>>> pretty = make_pretty(ordinary)
>>> pretty()
I got decorated
I am ordinary
#Python Serialization
===============================================
Pickling is the process whereby a Python object hierarchy is converted into a byte stream (usually not human readable) to be written to a file,
this is also known as Serialization. Unpickling is the reverse operation, whereby a byte stream is converted back into a working Python object hierarchy.
import pickle
# serializes
pickle.dump()
#deserializes
pickle.load()
'''to run python in command prompt, use "python", (windows :considering you have set up environment variable)
The interactive prompt runs code and echoes results as you go, but it doesn’t save your code in a file
'''
# enumerate() in python ==> it will give you the index numbers while iterating
>>> for n,i in enumerate(arr):
... print(n,i)
...
0 6
1 4
2 2
3 1
4 3
5 5
6 7
>>> arr
[6, 4, 2, 1, 3, 5, 7]
#to get current working directory
>>> import os
>>> os.getcwd()
'/Users/sankar.biswas'
#changing current direcctory
>>> os.chdir('/Users/sankar.biswas/Desktop/Python/coding')
>>> os.getcwd()
'/Users/sankar.biswas/Desktop/Python/coding'
# to run a python script from command prompt
python file1.py
#saving the output in a file
python script1.py > saveit.txt
# "dir" - you can use it to fetch a list of all the names available inside a module
>>> import sys
>>> dir(sys)
['__breakpointhook__', '__displayhook__', '__doc__', '__excepthook__', '__interactivehook__', '__loader__', '__name__', '__package__', '__spec__', '__stderr__',
'__stdin__', '__stdout__', '_clear_type_cache', '_current_frames', '_debugmallocstats', '_framework', '_getframe', '_git',
'_home', '_xoptions', 'abiflags', 'api_version', 'argv', 'base_exec_prefix', 'base_prefix', 'breakpointhook', 'builtin_module_names',
'byteorder', 'call_tracing', 'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix', 'executable', 'exit',
'flags', 'float_info', 'float_repr_style', 'get_asyncgen_hooks', 'get_coroutine_origin_tracking_depth', 'get_coroutine_wrapper',
'getallocatedblocks', 'getcheckinterval', 'getdefaultencoding', 'getdlopenflags', 'getfilesystemencodeerrors', 'getfilesystemencoding',
'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info', 'intern', 'is_finalizing', 'maxsize', 'maxunicode',
'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'prefix', 'ps1', 'ps2', 'set_asyncgen_hooks',
'set_coroutine_origin_tracking_depth', 'set_coroutine_wrapper', 'setcheckinterval', 'setdlopenflags', 'setprofile', 'setrecursionlimit',
'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout', 'thread_info', 'version', 'version_info', 'warnoptions']
# the " exec(open('module.py').read())" built-in function call is another way to launch files from the interactive prompt without having to import and later reload
#you can also find out the functions you can apply on a variable using "dir"
>>> a = 234
>>> dir(a)
['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__',
'__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__gt__',
'__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__'
, '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__',
'__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__',
'__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__',
'__trunc__', '__xor__', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']
# this will help you get a knowledge on the functionality of the function, dial 'q' to escape
>>> help(a.__abs__)
# Pattern Matching
>>> match = re.match('Hello[ \t]*(.*)world', 'Hello Python world')
>>> match
<re.Match object; span=(0, 18), match='Hello Python world'>
>>> match.group(1)
'Python '
>>> match = re.match('[/:](.*)[/:](.*)[/:](.*)', '/usr/home:lumberjack')
>>> match.groups()
('usr', 'home', 'lumberjack')
>>> re.split('[/:]', '/usr/home/lumberjack')
['', 'usr', 'home', 'lumberjack']
#List Operations
>>> L = [123, 'spam', 1.23]
>>> len(L)
3
>>> L*2
[123, 'spam', 1.23, 123, 'spam', 1.23]
>>> L[:]
[123, 'spam', 1.23]
>>> L[2:]
[1.23]
>>> L[:-1]
[123, 'spam']
>>> L.append(23)
[123, 'spam', 1.23, 23]
>>> L.pop(2)
1.23
>>> L
[123, 'spam', 23]
>>> list = [1,23,4,56,33,656,564]
>>> list.sort()
>>> list
[1, 4, 23, 33, 56, 564, 656]
#adding multiple elements to an existing list
>>> L
[123, 'abc', 1.23, {}]
>>> L.extend([5,6,7])
>>> L
[123, 'abc', 1.23, {}, 5, 6, 7]
#deleting all the elements
>>> L.clear()
>>> L
[]
#deleting a single element by index
>>> L = [123, 'abc', 1.23, {}]
>>> del L[0]
>>> L
['abc', 1.23, {}]
#selecting a partcular column from a 2D list
>>> list2D = [[1,2,3],[4,5,6],[7,8,9]]
>>> list2D[1][2]
6
>>> col2 = [row[1] for row in list2D] #Give me row[1] (2nd element) for each row in matrix M, in a new list.
>>> col2
[2, 5, 8]
>>> M
['bb', 'aa', 'cc']
>>> M.sort()
>>> M
['aa', 'bb', 'cc']
>>> [row[1] for row in M if row[1] % 2 == 0] #Filter out odd items
[2, 8]
#diagonal matrix
>>> diag = [M[i][i] for i in [0, 1, 2]] >>> diag
[1, 5, 9]
# Repeat characters in a string
>>> doubles = [c * 2 for c in 'spam'] >>> doubles
['ss', 'pp', 'aa', 'mm']
>>> list(range(4))
[0, 1, 2, 3]
>>> a = list(range(-6,7,2))
>>> a
[-6, -4, -2, 0, 2, 4, 6]
>>> [[x ** 2, x **3] for x in range(4)]
[[0, 0], [1, 1], [4, 8], [9, 27]]
>>> [[x, x / 2, x * 2] for x in range(-6, 7, 2) if x > 0]
[[2, 1.0, 4], [4, 2.0, 8], [6, 3.0, 12]]
>>> [[x, int(x / 2), x * 2] for x in range(-6, 7, 2) if x > 0]
[[2, 1, 4], [4, 2, 8], [6, 3, 12]]
>>> G = (sum(row) for row in M)
>>> G
<generator object <genexpr> at 0x105b29408>
>>> next(G)
6
>>> next(G)
15
>>> next(G)
24
'''Dictionaries :: Dictionaries, the only mapping type (not a sequence) in Python’s core objects set, are also mutable '''
>>> D = {}
>>> type(D)
<class 'dict'>
>>> D = {'food': 'Spam', 'quantity': 4, 'color': 'pink'}
>>> D
{'food': 'Spam', 'quantity': 4, 'color': 'pink'}
#using dict to define a dictionary
>>> bob1 = dict(name='Bob', job='dev', age=40)
>>> bob1
{'age': 40, 'name': 'Bob', 'job': 'dev'}
#zipping way to define dictionary
>>> bob2 = dict(zip(['name', 'job', 'age'], ['Bob', 'dev', 40]))
>>> bob2
{'name': 'Bob', 'job': 'dev', 'age': 40}
#Complex nesting of different types in python - one of the advantage of using python, complex nesting is easy to implement
>>> rec = {'name': {'first': 'Bob', 'last': 'Smith'}, 'jobs': ['dev', 'mgr'], 'age': 40.5}
>>> rec['jobs'][1]
'mgr'
>>> rec['name']['last']
'Smith'
>>> rec['jobs'].append('support')
>>> rec
{'name': {'first': 'Bob', 'last': 'Smith'}, 'jobs': ['dev', 'mgr', 'support'], 'age': 40.5}
#In Python, when we lose the last reference to the object—by assigning its variable to something else
>>> rec = 0
#Python has a feature known as garbage collection that cleans up unused memory as your program runs and frees you from having to manage such details in your code.
>>> D = {'a': 1, 'b': 2, 'c': 3}
#so now, what ".get" does is it will select the data with the key 'x' in dictionary D, if it doesnyt find it, it will return 0
>>> value = D.get('x', 0)
>>> value
0
#Sorting Keys: for Loops
>>> sorted(D)
['a', 'b', 'c']
>>> Ks = list(D.keys())
>>> Ks
['a', 'c', 'b']
>>> Ks.sort()
>>> Ks
['a', 'b', 'c']
#Tuples :: tuples are sequences, like lists, but they are immutable. Functionally, they’re used to represent fixed collections of items.
>>> T = (1, 2, 3, 4, 5)
>>> len(T)
5
>>> T + (5,6)
(1, 2, 3, 4, 5, 5, 6)
>>> T
(1, 2, 3, 4, 5)
>>> T[0]
1
>>> T.index(4)
3
>>> T.count(4)
1
#tuples provide a sort of integrity constraint
#String slicing, so the last number is the gap of skipping, that is 1,3,5,... will be skipped
>>> S = "I a m s a d"
>>> S[::2]
'Iamsad'
#the third index if given negative will reverse the selection
>>> S[::-2]
'dasmaI'
>>> S
'I evol being alone'
>>> S[5:1:-1]
'love'
>>>
>>> S[::-1]
'enola gnieb love I'
#converting whatever we have into string
>>> repr(42)
'42'
#converting into ASCII
>>> ord('A')
65
#converting integer to binary
>>> bin(13)
'0b1101'
#converting binary to integer
>>> int('1101', 2)
13
| [
"[email protected]"
]
| |
aa08416cec64433ef17052cae24d44ab961b544f | 2d9a17e2b896d2f6a90913a4ba02d41f0ede5dd0 | /_gsinfo/qiyecxb-ct/qycxb_spider.py | c629ad7e0a0c3154e2b6a4ad7ae34b42379b6c08 | []
| no_license | wolfwhoami/xxxxx | 1cf2ed2c8ed78048d87cccf2953ca86c0871a783 | 670787ec71127bc05c1645cc3d8ef7c3a91fe84b | refs/heads/master | 2020-03-30T00:44:55.864817 | 2016-12-16T01:45:03 | 2016-12-16T01:45:03 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 17,218 | py | #!/usr/bin/env python
# -*- coding:utf8 -*-
import os
import sys
sys.path.append(sys.path[0]+"/..")
print sys.path
import time
from spider.spider import Spider, AccountErrors
import re
from spider.savebin import BinSaver
import random
import threading
import traceback
import spider.util
from spider.savebin import FileSaver
from qycxb_AES import CCIQ_AES
filter_name = set()
bloom = set()
class QycxbSpider(Spider):
"""
根据企业基本信息查询详情 121.40.186.237:18889:ipin:helloipin
"""
def __init__(self):
self._can_use_proxy_num = 0
self.is_debug = False
if self.is_debug:
Spider.__init__(self, 1)
#self.proxy_error_cnt = 0
else:
self.proxies_dict = []
self.read_proxy("../../_ct_proxy/proxy_all_filter.txt")
Spider.__init__(self, len(self.proxies_dict))
self._aes_ = CCIQ_AES()
#成功的
self.query_success = FileSaver("beijing_query_detail1.txt")
#失败的
self.query_failure = FileSaver("beijing_query_detail_failure1.txt")
#已经爬取过的
self.already_cname_list = FileSaver("beijing_already_detail1.txt")
#初始化已经爬过的公司
self.init_cname()
self.extJsons = ["Hoi6oX70l9whauZmjq8jVAmoe3UspXXhX9mPG+KAeqs1rKZVr/uapICH92P/Crryt63u28aP4QP665AzcT/jN5Go1o3bvwMvVIkuN9e60k6WI2pVFBrwZMvxwW6BnQukSzDSlyPvEhgpR5DIHQEV6C51hMgp4Zc3OkTSsyezAm4=",
"ctlCXDvoyaH2pCIArrgvXp7zrZTzpz2Q5rukh+aWvupEFABw6P2AvbmaN+HJ7IZgDJ/kgBkJt/rLppSGitYCPKGR2IGv6OXZsrJGgbRB3G3Ac4K8xpX3aMB5s8Ci2a/YpTpioZxAvptqJsQUCoNn0tLCOVM4XxMJQWbrErkOcl4=",
"ctlCXDvoyaH2pCIArrgvXp7zrZTzpz2Q5rukh+aWvupEFABw6P2AvbmaN+HJ7IZgDJ/kgBkJt/rLppSGitYCPKGR2IGv6OXZsrJGgbRB3G1U2wdOlL49/aDwt3NZNp4TGa5iBFpYLm69F/6PPFoXIR/Aw5p48//8OgZFpddDUwQ="]
self.user_agents = ["=CCIQ/2.0.1 (iPhone; iOS 9.1; Scale/2.00)",
"=CCIQ/2.0.1 (iPhone; iOS 8.1.3; Scale/2.00)",
"=CCIQ/2.0.1 (iPhone; iOS 8.4; Scale/2.00)"]
self.is_first = True
self.init_time = 0
def req_all(self, url, encryptedJson, retry=0):
number = random.randrange(0, 3, 1)
self.select_user_agent(self.user_agents[number])
param = spider.util.utf8str({"encryptedJson":self._aes_.encrypt(spider.util.utf8str(encryptedJson)), "extJson":self.extJsons[number]})
param = param.replace('/', "\/")
if self.is_first:
self.init_time = time.time()
print '初始化时间',self.init_time
self.is_first = False
if self.is_debug:
res = self.request_url(url, headers={"Content-Type": "application/json"}, data=param, proxies={'http': 'http://ipin:[email protected]:3428', 'https': 'https://ipin:[email protected]:3428'})
#res = self.request_url(url, headers={"Content-Type": "application/json"}, data=param, proxies={'http': 'http://137.135.166.225:8120', 'https': 'https://137.135.166.225:8120'})
else:
res = self.request_url(url, headers={"Content-Type": "application/json"}, data=param, proxies=self.proxies_dict[self.get_tid()])
if res is None:
if retry < 3:
time.sleep(3)
return self.req_all(url, encryptedJson, retry=(retry+1))
else:
return None
if res.code == 200:
time.sleep(random.randrange(30, 50, 1))
else:
time.sleep(5)
return res
def init_cname(self):
with open("beijing_already_detail1.txt", "r") as f:
for line in f:
filter_name.add(line.strip())
def wait_q_breakable(self):
lt = 0
while True:
if not self.job_queue.empty() or not self.job_queue2.empty() or not self.job_queue3.empty():
time.sleep(5)
if time.time() < lt + 1 and self._running_count == 0:
return True
time.sleep(2)
lt = time.time()
if self._worker_count == 0:
return False
def dispatch(self):
with open("all_company_list.txt", "r") as f:
cnt = 0
for line in f:
line = line.strip()
cnt += 1
if line in filter_name:
#print cnt, "already spider!!!"
continue
job = {"line": line, "cnt": cnt, "retry": 1}
self.add_job(job, True)
self.wait_q_breakable()
self.add_job(None, True)
def record_spider(self, line, cname):
"""
已经爬过的,无论成功失败都算爬过.
"""
filter_name.add(line)
self.already_cname_list.append(line)
bloom.add(cname)
def run_job(self, jobid):
line = jobid.get("line")
cnt = jobid.get("cnt")
retry = jobid.get("retry")
self.get_detail(line, cnt, retry)
#time.sleep(random.randrange(5, 11, 1))
def get_detail(self, line, cnt, retry):
tid = self.get_tid()
param = None
try:
param = eval(line)
except Exception as err:
print 'tid=%d --- cnt=%d --- data is not json, return'%(tid, cnt)
self.record_spider(line, 'UNKNOW')
return
cname = param['oc_name']
if cname in bloom:
cname = param['query_name']
if cname in bloom:
print 'query_name:%s aleready crawler...' % cname
return
ccode = param['oc_code']
carea = param['oc_area']
url = "http://appsvc.qiye.qianzhan.com/OrgCompany.svc/orgcompany/combine/detail"
encryptedJson = {
"bl_oc_code" : ccode,#code, #"71526726X"
"v1" : "QZOrgV005",
"isDirect" : "0",
"bl_oc_name" : cname,#cname, #"腾讯科技"
"bl_oc_area" : carea #area #"4403"
}
res = self.req_all(url, encryptedJson)
res_code = 0
if res is None:
if self.get_fail_cnt(1, 'failcount-none') < 10:
self.re_add_job({'line': line,'cnt': cnt, 'retry': retry})
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d " % (tid, cnt, cname, retry, res_code)
return
else:
self.re_add_job({'line': line, 'cnt': cnt, 'retry': (retry+1)})
self._can_use_proxy_num -= 1
raise AccountErrors.NoAccountError("Maybe the proxy invalid,failcount-none = [ %d ]" % self.get_fail_cnt(0, 'failcount-none'))
else:
setattr(self._curltls, 'failcount-none', 0)
res_code = res.code
if (res_code >= 400 and res_code < 500) or res_code == 202 :
self.re_add_job({'line': line,'cnt': cnt, 'retry': (retry+1)})
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d " % (tid, cnt, cname, retry, res_code)
if self.get_fail_cnt(1, 'failcount-400') > 5:
self._can_use_proxy_num -= 1
raise AccountErrors.NoAccountError("Maybe the proxy invalid,failcount-400 = [ %d ]" % self.get_fail_cnt(0, 'failcount-400'))
return
else:
setattr(self._curltls, 'failcount-400', 0)
if res_code >= 500:
self.re_add_job({'line': line, 'cnt': cnt, 'retry': (retry+1)})
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d " % (tid, cnt, cname, retry, res_code)
time.sleep(retry*2)
return
elif res_code == 200:
try:
c = eval(res.text)['c']
except Exception as err:
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d res.text exception " % (tid, cnt, cname, retry, res_code)#, "\n", res.text
#param["error_type"] = "res_text_error"
#self.query_failure.append(spider.util.utf8str(param))
#self.record_spider(line, cname)
self.re_add_job({'line': line, 'cnt': cnt, 'retry': retry})
return
if len(c) == 0:
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d --- exception 'C' IS NULL" % (tid, cnt, cname, retry, res_code)
param["error_type"] = "c=0"
self.query_failure.append(spider.util.utf8str(param))
self.record_spider(line, cname)
return
result = CCIQ_AES("BF1856A312580D41256311147089E1CC").decrypt(c)
try:
detail = eval(result)
except Exception as err:
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d --- exception result:%s" % (tid, cnt, cname, retry, res_code, result)
param["error_type"] = "result_error"
self.query_failure.append(spider.util.utf8str(param))
self.record_spider(line, cname)
return
#股东信息
listGD = self.get_gd(carea, ccode, cname)
if listGD is not None:
#print "tid=", tid, " listGD=", spider.util.utf8str(listGD)
detail['listGD'] = listGD['listGD']
#投资信息
list_inversted = self.get_inversted(cname)
if list_inversted is not None:
#print "tid=", tid, " list_inversted=", spider.util.utf8str(list_inversted)
detail['inversted'] = list_inversted['inversted']
#获取分支机构信息
branch = []
list_branch = self.get_branch(cname, list_branch=branch)
if list_branch is not None:
#print "tid=", tid, " list_branch=", spider.util.utf8str(list_branch)
detail['Branch'] = list_branch #['Branch']
self.query_success.append(spider.util.utf8str(detail))
self.record_spider(line, cname)
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d @@@ success:\n %s" % (tid, cnt, cname, retry, res_code, spider.util.utf8str(detail))
else:
param["error_type"] = "unknown_error:%d" % res_code
self.query_failure.append(spider.util.utf8str(param))
self.record_spider(line, cname)
print "tid=%d --- cnt=%d --- cname=%s --- retry=%d --- res.code=%d --- exception UNKNOW ERROR" % (tid, cnt, cname, retry, res_code)
return
def get_gd(self, area, code, cname, retry=0):
"""
获取股东信息
"""
url = "http://appsvc.qiye.qianzhan.com/OrgCompany.svc/orgcompany/gd/detail"
encryptedJson = {
"bl_oc_area" : area,
"v1" : "QZOrgV005",
"bl_oc_code" : code
}
res = self.req_all(url, encryptedJson)
if res is None:
return None
if res.code == 200:
try:
c = eval(res.text)['c']
if len(c) == 0:
print "get_gd --- cname=%s --- retry=%d --- reason:len(c)=0" % (cname, retry)
return None
result = CCIQ_AES("BF1856A312580D41256311147089E1CC").decrypt(c)
return eval(result)
except Exception as err:
print "get_gd --- cname=%s --- retry=%d --- reason:%s" % (cname, retry, err)
if retry < 5:
retry += 1
time.sleep(retry*1.5)
return self.get_gd(area, code, cname, retry=retry)
else:
return None
else:
print "get_gd --- cname=%s --- retry=%d --- res.code=%d" % (cname, retry, res.code)
if retry < 5:
retry += 1
time.sleep(retry*1.5)
return self.get_gd(area, code, cname, retry=retry)
else:
return None
def get_inversted(self, cname, retry=0):
"""
查询投资信息
"""
url = "http://appsvc.qiye.qianzhan.com/OrgCompany.svc/orgcompany/map/invesment"
encryptedJson = {
"input" : cname,
"v1" : "QZOrgV005"
}
res = self.req_all(url, encryptedJson)
if res is None:
return None
if res.code == 200:
try:
c = eval(res.text)['c']
if len(c) == 0:
print "get_inversted --- cname=%s --- retry=%d --- reason:len(c)=0" % (cname, retry)
return None
result = CCIQ_AES("BF1856A312580D41256311147089E1CC").decrypt(c)
return eval(result)
except Exception as err:
print "get_inversted --- cname=%s --- retry=%d --- reason:%s" % (cname, retry, err)
if retry < 5:
retry += 1
time.sleep(retry*1.5)
return self.get_inversted(cname, retry=retry)
else:
return None
else:
print "get_inversted --- cname=%s --- retry=%d --- res.code=%d" % (cname, retry, res.code)
if retry < 5:
retry += 1
time.sleep(retry*1.5)
return self.get_inversted(cname, retry=retry)
else:
return None
def get_branch(self,cname, now_page=1, list_branch=[], retry=0):
"""
查询分支机构
"""
url = "http://appsvc.qiye.qianzhan.com/OrgCompany.svc/orgcompany/branch/select/page"
encryptedJson = {
"companyName" : cname,
"v1" : "QZOrgV005",
"page" : now_page,
"pagesize" : "10"
}
res = self.req_all(url, encryptedJson)
if res is None:
return None
if res.code == 200:
try:
c = eval(res.text)['c']
if len(c) == 0:
print "get_branch --- cname=%s --- retry=%d --- reason:len(c)=0" % (cname, retry)
return None
result = CCIQ_AES("BF1856A312580D41256311147089E1CC").decrypt(c)
temp = eval(result)
if temp is not None:
for t in temp['Branch']:
list_branch.append(t)
if len(temp['Branch']) == 10:
if now_page > 3:
return list_branch
now_page += 1
print cname, "翻页 -----------------------------------> now_page", now_page
return self.get_branch(cname, now_page=now_page, list_branch=list_branch, retry=retry)
else:
return list_branch
else:
print "get_branch --- cname=%s --- retry=%d --- now_page=%d --- res.code=%d --- Branch is NULL" % (cname, retry, now_page)
return None
except Exception as err:
print "get_branch --- cname=%s --- retry=%d --- reason:%s" % (cname, retry, err)
if retry < 5:
retry += 1
time.sleep(retry*1.5)
return self.get_branch(cname, now_page=now_page, list_branch=list_branch, retry=retry)
else:
return None
else:
print "get_branch --- cname=%s --- retry=%d --- res.code=%d" % (cname, retry, res.code)
if retry < 5:
retry += 1
time.sleep(retry*1.5)
return self.get_branch(cname, now_page=now_page, list_branch=list_branch, retry=retry)
else:
return None
def get_fail_cnt(self, addv , type):
fc = getattr(self._curltls, type, 0)
if (addv):
fc += addv
setattr(self._curltls, type, fc)
return fc
def event_handler(self, evt, msg, **kwargs):
if evt == 'DONE':
msg += '企业查询宝APP公司详情detail查询已经停止...'
spider.util.sendmail('[email protected]', '%s DONE' % sys.argv[0], msg)
def read_proxy(self,fn):
with open(fn, 'r') as f:
for line in f:
line = line.strip()
self._match_proxy(line)
print " loaded [ %d ] proxis " % len(self.proxies_dict)
def _match_proxy(self,line):
m = re.match('([0-9.]+):(\d+):([a-z0-9]+):([a-z0-9._-]+)$', line, re.I)
m1 = re.match('([0-9.]+):(\d+):([a-z0-9]+)$', line, re.I)
if m:
prstr = '%s:%s@%s:%s' % (m.group(3), m.group(4), m.group(1), m.group(2))
proxies = {'http': 'http://' + prstr, 'https': 'https://' + prstr}
elif m1:
prstr = '%s:%s' % (m1.group(1), m1.group(2))
proxies = {'http': 'http://' + prstr, 'https': 'https://' + prstr}
else:
proxies = {'http': 'http://' + line, 'https': 'https://' + line}
self.proxies_dict.append(proxies)
if __name__ == "__main__":
s = QycxbSpider()
s.run()
#s.get_branch("江苏武进建工集团有限公司")
| [
"[email protected]"
]
| |
e3d781a3f7d2d498cb5c6001e32a838461a0daa6 | 53fab060fa262e5d5026e0807d93c75fb81e67b9 | /backup/user_054/ch2_2020_09_16_11_34_55_516156.py | cb769d528b4f741eaac3317840c0153eb23c362a | []
| no_license | gabriellaec/desoft-analise-exercicios | b77c6999424c5ce7e44086a12589a0ad43d6adca | 01940ab0897aa6005764fc220b900e4d6161d36b | refs/heads/main | 2023-01-31T17:19:42.050628 | 2020-12-16T05:21:31 | 2020-12-16T05:21:31 | 306,735,108 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 143 | py | # Está função é para calcular a velocidade média
def velocidade_media (d,t):
velocidade_media = d / t
return velocidade_média
| [
"[email protected]"
]
| |
c0e29612bc1ab99f21ed31d148930eda30c512c3 | 2a67dc681af4c4b9ef7a8e18c2ff75377dc5b44f | /aws.ses.EventDestination.sns-destination-python/__main__.py | d32f60d788281d4b38651670141a088b90714d15 | []
| no_license | ehubbard/templates-aws | e323b693a18234defe6bd56ffcc64095dc58e3a1 | 2ae2e7a5d05490078017fed6d132dcdde1f21c63 | refs/heads/master | 2022-11-17T13:53:14.531872 | 2020-07-10T21:56:27 | 2020-07-10T21:56:27 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 315 | py | import pulumi
import pulumi_aws as aws
sns = aws.ses.EventDestination("sns",
configuration_set_name=aws_ses_configuration_set["example"]["name"],
enabled=True,
matching_types=[
"bounce",
"send",
],
sns_destination={
"topic_arn": aws_sns_topic["example"]["arn"],
})
| [
"[email protected]"
]
| |
aa103ea582f1fe1dccda82638cc5841b408a0c7a | 321b4ed83b6874eeb512027eaa0b17b0daf3c289 | /988/988.smallest-string-starting-from-leaf.233252752.Accepted.leetcode.py | 22432d1b1812c2fa9c180ef407130c342025bc17 | []
| no_license | huangyingw/submissions | 7a610613bdb03f1223cdec5f6ccc4391149ca618 | bfac1238ecef8b03e54842b852f6fec111abedfa | refs/heads/master | 2023-07-25T09:56:46.814504 | 2023-07-16T07:38:36 | 2023-07-16T07:38:36 | 143,352,065 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 455 | py | class Solution(object):
def smallestFromLeaf(self, root):
self.result = "~"
def dfs(node, A):
if node:
A.append(chr(node.val + ord('a')))
if not node.left and not node.right:
self.result = min(self.result, "".join(reversed(A)))
dfs(node.left, A)
dfs(node.right, A)
A.pop()
dfs(root, [])
return self.result
| [
"[email protected]"
]
| |
5cb9c51015c50cab850bea8216889f5c99c937d9 | 487ce91881032c1de16e35ed8bc187d6034205f7 | /codes/CodeJamCrawler/16_0_2_neat/16_0_2_Jormungandr_Revenge_of_the_pancakes.py | d9925b4d479f3e794bba1c134eedd620908d2b23 | []
| no_license | DaHuO/Supergraph | 9cd26d8c5a081803015d93cf5f2674009e92ef7e | c88059dc66297af577ad2b8afa4e0ac0ad622915 | refs/heads/master | 2021-06-14T16:07:52.405091 | 2016-08-21T13:39:13 | 2016-08-21T13:39:13 | 49,829,508 | 2 | 0 | null | 2021-03-19T21:55:46 | 2016-01-17T18:23:00 | Python | UTF-8 | Python | false | false | 1,096 | py | #!/usr/bin/env python
__author__ = 'Bill'
def check_pancakes(n):
"""(check_pancakes):
function to test for all face up
:param n: the pancakes string
"""
for ch in n:
if ch == '-':
return False
return True
def flip_pancakes(n):
"""(flip_pancakes):
function to flip pancakes
:param n: the pancakes string
"""
n = list(n)
dict = {'+':'-', '-':'+'}
first = n[0]
i = 0
for ch in n:
if ch != first:
break
i += 1
for j in xrange(i):
n[j] = dict[first]
n = "".join(n)
return n
from misc import input_, output_
num_cases, cases = input_('B-large.in')
Results = []
for case in cases:
case = case.rstrip('\n')
i = 0
face_up = check_pancakes(case)
if face_up == True:
Results.append(i)
else:
while check_pancakes(case) == False:
case = flip_pancakes(case)
i += 1
Results.append(i)
output_(Results, 'Revenge_of_the_pancakes_large.out') | [
"[[email protected]]"
]
| |
43762e6631bb0431b80bd2656e2d2522d44b3bed | 3d228d5eac44b31d460dd81767b43309b7356577 | /extra/graph/company_tree.py | f1637b2f1a04bcbe1902f64e15364798ff383c47 | [
"BSD-3-Clause"
]
| permissive | lsbardel/mathfun | da65a6f09faacdb4815111dae287c9b974acf928 | 98e7c210409c2b5777e91059c3651cef4f3045dd | refs/heads/master | 2021-05-02T08:56:05.565539 | 2020-07-30T09:14:04 | 2020-07-30T09:14:04 | 26,242,622 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 41 | py | from mathfun.graph.template import Graph
| [
"[email protected]"
]
| |
3da8f7b040ba3e4364324d6671fd5826cd2494b7 | 1dacbf90eeb384455ab84a8cf63d16e2c9680a90 | /pkgs/odo-0.4.2-py27_0/lib/python2.7/site-packages/odo/backends/sparksql.py | 80e27ad318d1981f6194e3e20d84b782da1089e7 | [
"Apache-2.0",
"BSD-3-Clause",
"LicenseRef-scancode-unknown"
]
| permissive | wangyum/Anaconda | ac7229b21815dd92b0bd1c8b7ec4e85c013b8994 | 2c9002f16bb5c265e0d14f4a2314c86eeaa35cb6 | refs/heads/master | 2022-10-21T15:14:23.464126 | 2022-10-05T12:10:31 | 2022-10-05T12:10:31 | 76,526,728 | 11 | 10 | Apache-2.0 | 2022-10-05T12:10:32 | 2016-12-15T05:26:12 | Python | UTF-8 | Python | false | false | 9,639 | py | from __future__ import division, print_function, absolute_import
import os
import glob
import itertools
import tempfile
import shutil
from functools import partial
from collections import Iterator
from datetime import datetime, date
import pandas as pd
import toolz
from toolz.curried import get, map, memoize
from toolz import pipe, concat, curry
from pyspark import RDD, SQLContext, HiveContext
from pyspark.sql import SchemaRDD
from pyspark.rdd import PipelinedRDD
import datashape
from datashape import dshape, Record, DataShape, Option, Tuple
from datashape.predicates import isdimension, isrecord, iscollection
from .. import append, discover, convert
from ..core import ooc_types
from ..directory import Directory
from ..temp import Temp
from ..chunks import chunks
from .json import JSONLines, JSON
from .csv import CSV
from pyspark.sql import DataFrame as SparkDataFrame
from pyspark.sql.types import (
ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType,
StringType, BinaryType, BooleanType, TimestampType, DateType, ArrayType,
StructType, StructField
)
base = int, float, datetime, date, bool, str
_names = ('tmp%d' % i for i in itertools.count())
@append.register(SQLContext, object)
def iterable_to_sql_context(ctx, seq, **kwargs):
return append(ctx, append(ctx._sc, seq, **kwargs), **kwargs)
def register_table(ctx, srdd, name=None):
if name is None:
name = next(_names)
ctx.registerDataFrameAsTable(srdd, name)
@append.register(SQLContext, (JSONLines, Directory(JSONLines)))
def jsonlines_to_sparksql(ctx, json, dshape=None, name=None, schema=None,
samplingRatio=0.25, **kwargs):
# if we're passing in schema, assume that we know what we're doing and
# bypass any automated dshape inference
if dshape is not None and schema is None:
schema = dshape_to_schema(dshape.measure
if isrecord(dshape.measure) else dshape)
srdd = ctx.jsonFile(json.path, schema=schema, samplingRatio=samplingRatio)
register_table(ctx, srdd, name=name)
return srdd
@convert.register(list, (SparkDataFrame, SchemaRDD), cost=200.0)
def sparksql_dataframe_to_list(df, dshape=None, **kwargs):
result = df.collect()
if (dshape is not None and iscollection(dshape) and
not isrecord(dshape.measure)):
return list(map(get(0), result))
return result
@convert.register(base, (SparkDataFrame, SchemaRDD), cost=200.0)
def spark_df_to_base(df, **kwargs):
return df.collect()[0][0]
@append.register(SQLContext, RDD)
def rdd_to_sqlcontext(ctx, rdd, name=None, dshape=None, **kwargs):
""" Convert a normal PySpark RDD to a SparkSQL RDD or Spark DataFrame
Schema inferred by ds_to_sparksql. Can also specify it explicitly with
schema keyword argument.
"""
# TODO: assumes that we don't have e.g., 10 * 10 * {x: int, y: int}
if isdimension(dshape.parameters[0]):
dshape = dshape.measure
sql_schema = dshape_to_schema(dshape)
sdf = ctx.applySchema(rdd, sql_schema)
if name is None:
name = next(_names)
register_table(ctx, sdf, name=name)
ctx.cacheTable(name)
return sdf
def scala_set_to_set(ctx, x):
from py4j.java_gateway import java_import
# import scala
java_import(ctx._jvm, 'scala')
# grab Scala's set converter and convert to a Python set
return set(ctx._jvm.scala.collection.JavaConversions.setAsJavaSet(x))
@discover.register(SQLContext)
def discover_sqlcontext(ctx):
table_names = sorted(map(str, ctx.tableNames()))
dshapes = zip(table_names, map(discover, map(ctx.table, table_names)))
return datashape.DataShape(datashape.Record(dshapes))
@discover.register((SparkDataFrame, SchemaRDD))
def discover_spark_data_frame(df):
schema = df.schema() if callable(df.schema) else df.schema
return datashape.var * schema_to_dshape(schema)
def chunk_file(filename, chunksize):
"""Stream `filename` in chunks of size `chunksize`.
Parameters
----------
filename : str
File to chunk
chunksize : int
Number of bytes to hold in memory at a single time
"""
with open(filename, mode='rb') as f:
for chunk in iter(partial(f.read, chunksize), b''):
yield chunk
@append.register(JSONLines, (SparkDataFrame, SchemaRDD))
def spark_df_to_jsonlines(js, df,
pattern='part-*', chunksize=1 << 23, # 8MB
**kwargs):
tmpd = tempfile.mkdtemp()
try:
try:
df.save(tmpd, source='org.apache.spark.sql.json', mode='overwrite')
except AttributeError:
shutil.rmtree(tmpd)
df.toJSON().saveAsTextFile(tmpd)
except:
raise
else:
files = glob.glob(os.path.join(tmpd, pattern))
with open(js.path, mode='ab') as f:
pipe(files,
map(curry(chunk_file, chunksize=chunksize)),
concat,
map(f.write),
toolz.count)
finally:
shutil.rmtree(tmpd)
return js
@convert.register((SparkDataFrame, SchemaRDD), (RDD, PipelinedRDD))
def rdd_to_spark_df_or_srdd(rdd, **kwargs):
return append(HiveContext(rdd.context), rdd, **kwargs)
try:
from .hdfs import HDFS
except ImportError:
pass
else:
@append.register(HDFS(JSONLines),
(Iterator, object, SparkDataFrame, SchemaRDD))
@append.register(HDFS(JSON), (list, object))
@append.register(HDFS(CSV), (chunks(pd.DataFrame), pd.DataFrame, object))
def append_spark_to_hdfs(target, source, **kwargs):
tmp = convert(Temp(target.subtype), source, **kwargs)
return append(target, tmp, **kwargs)
def dshape_to_schema(ds):
"""Convert datashape to SparkSQL type system.
Examples
--------
>>> print(dshape_to_schema('int32')) # doctest: +SKIP
IntegerType
>>> print(dshape_to_schema('5 * int32') # doctest: +SKIP
ArrayType(IntegerType,false)
>>> print(dshape_to_schema('5 * ?int32')) # doctest: +SKIP
ArrayType(IntegerType,true)
>>> print(dshape_to_schema('{name: string, amount: int32}')) # doctest: +SKIP
StructType(List(StructField(name,StringType,false),StructField(amount,IntegerType,false) # doctest: +SKIP))
>>> print(dshape_to_schema('10 * {name: string, amount: ?int32}')) # doctest: +SKIP
ArrayType(StructType(List(StructField(name,StringType,false),StructField(amount,IntegerType,true))),false)
"""
if isinstance(ds, str):
return dshape_to_schema(dshape(ds))
if isinstance(ds, Tuple):
raise TypeError('Please provide a Record dshape for these column '
'types: %s' % (ds.dshapes,))
if isinstance(ds, Record):
return StructType([
StructField(name,
dshape_to_schema(deoption(typ)),
isinstance(typ, datashape.Option))
for name, typ in ds.fields])
if isinstance(ds, DataShape):
if isdimension(ds[0]):
elem = ds.subshape[0]
if isinstance(elem, DataShape) and len(elem) == 1:
elem = elem[0]
return ArrayType(dshape_to_schema(deoption(elem)),
isinstance(elem, Option))
else:
return dshape_to_schema(ds[0])
if ds in dshape_to_sparksql:
return dshape_to_sparksql[ds]
raise NotImplementedError()
def schema_to_dshape(schema):
if type(schema) in sparksql_to_dshape:
return sparksql_to_dshape[type(schema)]
if isinstance(schema, ArrayType):
dshape = schema_to_dshape(schema.elementType)
return datashape.var * (Option(dshape)
if schema.containsNull else dshape)
if isinstance(schema, StructType):
fields = [(field.name, Option(schema_to_dshape(field.dataType))
if field.nullable else schema_to_dshape(field.dataType))
for field in schema.fields]
return datashape.dshape(Record(fields))
raise NotImplementedError('SparkSQL type not known %r' %
type(schema).__name__)
def deoption(ds):
"""
>>> deoption('int32')
ctype("int32")
>>> deoption('?int32')
ctype("int32")
"""
if isinstance(ds, str):
ds = dshape(ds)
if isinstance(ds, DataShape) and not isdimension(ds[0]):
return deoption(ds[0])
if isinstance(ds, Option):
return ds.ty
else:
return ds
# see http://spark.apache.org/docs/latest/sql-programming-guide.html#spark-sql-datatype-reference
sparksql_to_dshape = {
ByteType: datashape.int8,
ShortType: datashape.int16,
IntegerType: datashape.int32,
LongType: datashape.int64,
FloatType: datashape.float32,
DoubleType: datashape.float64,
StringType: datashape.string,
BinaryType: datashape.bytes_,
BooleanType: datashape.bool_,
TimestampType: datashape.datetime_,
DateType: datashape.date_,
# sql.ArrayType: ?,
# sql.MapTYpe: ?,
# sql.StructType: ?
}
dshape_to_sparksql = {
datashape.int16: ShortType(),
datashape.int32: IntegerType(),
datashape.int64: LongType(),
datashape.float32: FloatType(),
datashape.float64: DoubleType(),
datashape.real: DoubleType(),
datashape.time_: TimestampType(),
datashape.date_: DateType(),
datashape.datetime_: TimestampType(),
datashape.bool_: BooleanType(),
datashape.string: StringType()
}
ooc_types |= set([SparkDataFrame, SchemaRDD])
SQLContext = memoize(SQLContext)
HiveContext = memoize(HiveContext)
| [
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]
| |
7e718881b9c46f43e2cc9329438179cd7fbc6988 | 85a9ffeccb64f6159adbd164ff98edf4ac315e33 | /pysnmp/CISCO-CDSTV-FSI-MIB.py | cb16a25f5a07fa321bae26f7dbc8039adcc9a510 | [
"Apache-2.0"
]
| permissive | agustinhenze/mibs.snmplabs.com | 5d7d5d4da84424c5f5a1ed2752f5043ae00019fb | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | refs/heads/master | 2020-12-26T12:41:41.132395 | 2019-08-16T15:51:41 | 2019-08-16T15:53:57 | 237,512,469 | 0 | 0 | Apache-2.0 | 2020-01-31T20:41:36 | 2020-01-31T20:41:35 | null | UTF-8 | Python | false | false | 6,231 | py | #
# PySNMP MIB module CISCO-CDSTV-FSI-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-CDSTV-FSI-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 17:35:42 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, ValueSizeConstraint, ConstraintsUnion, SingleValueConstraint, ValueRangeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueSizeConstraint", "ConstraintsUnion", "SingleValueConstraint", "ValueRangeConstraint")
ciscoMgmt, = mibBuilder.importSymbols("CISCO-SMI", "ciscoMgmt")
CiscoURLString, = mibBuilder.importSymbols("CISCO-TC", "CiscoURLString")
InetAddressType, InetPortNumber, InetAddress = mibBuilder.importSymbols("INET-ADDRESS-MIB", "InetAddressType", "InetPortNumber", "InetAddress")
SnmpAdminString, = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString")
NotificationGroup, ObjectGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ObjectGroup", "ModuleCompliance")
TimeTicks, Counter32, Integer32, ObjectIdentity, MibIdentifier, iso, NotificationType, MibScalar, MibTable, MibTableRow, MibTableColumn, IpAddress, ModuleIdentity, Counter64, Unsigned32, Bits, Gauge32 = mibBuilder.importSymbols("SNMPv2-SMI", "TimeTicks", "Counter32", "Integer32", "ObjectIdentity", "MibIdentifier", "iso", "NotificationType", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "IpAddress", "ModuleIdentity", "Counter64", "Unsigned32", "Bits", "Gauge32")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
ciscoCdstvFsiMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 9, 735))
ciscoCdstvFsiMIB.setRevisions(('2010-05-10 00:00',))
if mibBuilder.loadTexts: ciscoCdstvFsiMIB.setLastUpdated('201005100000Z')
if mibBuilder.loadTexts: ciscoCdstvFsiMIB.setOrganization('Cisco Systems, Inc.')
ciscoCdstvFsiMIBNotifs = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 735, 0))
ciscoCdstvFsiMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 735, 1))
ciscoCdstvFsiMIBConform = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 735, 2))
ciscoCdstvFsiMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 735, 2, 1))
cdstvFsiIpAddressType = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 1), InetAddressType()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiIpAddressType.setStatus('current')
cdstvFsiIpAddress = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 2), InetAddress()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiIpAddress.setStatus('current')
cdstvFsiServerPort = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 3), InetPortNumber()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiServerPort.setStatus('current')
cdstvFsiFtpClientPort = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 4), InetPortNumber()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiFtpClientPort.setStatus('current')
cdstvFsiFtpOutServerPort = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 5), InetPortNumber()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiFtpOutServerPort.setStatus('current')
cdstvFsiFtpOutLoginTTL = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 6), Unsigned32()).setUnits('hops').setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiFtpOutLoginTTL.setStatus('current')
cdstvFsiLogLevel = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("off", 1), ("low", 2), ("high", 3)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiLogLevel.setStatus('current')
cdstvFsiContentRootPath = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 8), SnmpAdminString()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiContentRootPath.setStatus('current')
cdstvFsiAsyncCallbackURL = MibScalar((1, 3, 6, 1, 4, 1, 9, 9, 735, 1, 9), CiscoURLString()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: cdstvFsiAsyncCallbackURL.setStatus('current')
ciscoCdstvFsiMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 735, 2, 2))
ciscoCdstvFsiMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 9, 9, 735, 2, 1, 1)).setObjects(("CISCO-CDSTV-FSI-MIB", "ciscoCdstvFsiMIBMainObjectGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoCdstvFsiMIBCompliance = ciscoCdstvFsiMIBCompliance.setStatus('current')
ciscoCdstvFsiMIBMainObjectGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 735, 2, 2, 1)).setObjects(("CISCO-CDSTV-FSI-MIB", "cdstvFsiIpAddress"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiServerPort"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiFtpClientPort"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiFtpOutServerPort"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiFtpOutLoginTTL"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiLogLevel"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiContentRootPath"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiAsyncCallbackURL"), ("CISCO-CDSTV-FSI-MIB", "cdstvFsiIpAddressType"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoCdstvFsiMIBMainObjectGroup = ciscoCdstvFsiMIBMainObjectGroup.setStatus('current')
mibBuilder.exportSymbols("CISCO-CDSTV-FSI-MIB", ciscoCdstvFsiMIBConform=ciscoCdstvFsiMIBConform, PYSNMP_MODULE_ID=ciscoCdstvFsiMIB, cdstvFsiFtpClientPort=cdstvFsiFtpClientPort, ciscoCdstvFsiMIBObjects=ciscoCdstvFsiMIBObjects, cdstvFsiIpAddress=cdstvFsiIpAddress, cdstvFsiServerPort=cdstvFsiServerPort, cdstvFsiContentRootPath=cdstvFsiContentRootPath, ciscoCdstvFsiMIB=ciscoCdstvFsiMIB, ciscoCdstvFsiMIBCompliance=ciscoCdstvFsiMIBCompliance, ciscoCdstvFsiMIBMainObjectGroup=ciscoCdstvFsiMIBMainObjectGroup, cdstvFsiIpAddressType=cdstvFsiIpAddressType, ciscoCdstvFsiMIBCompliances=ciscoCdstvFsiMIBCompliances, ciscoCdstvFsiMIBNotifs=ciscoCdstvFsiMIBNotifs, cdstvFsiAsyncCallbackURL=cdstvFsiAsyncCallbackURL, cdstvFsiFtpOutServerPort=cdstvFsiFtpOutServerPort, cdstvFsiFtpOutLoginTTL=cdstvFsiFtpOutLoginTTL, cdstvFsiLogLevel=cdstvFsiLogLevel, ciscoCdstvFsiMIBGroups=ciscoCdstvFsiMIBGroups)
| [
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]
| |
56749342e68294136dbbbacb342a3d9b2f01f30b | 18b3ad3b0e1f7f10969738251e1201d01dfbc6bf | /backup_files/samplepy/passbyvalue.py | 26689727f2944f32dee1688daef3ff1dc4632725 | []
| no_license | sahthi/backup2 | 11d509b980e731c73733b1399a8143780779e75a | 16bed38f0867fd7c766c2a008c8d43b0660f0cb0 | refs/heads/master | 2020-03-21T12:39:56.890129 | 2018-07-09T08:12:46 | 2018-07-09T08:12:46 | 138,565,151 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 180 | py | def changeme(mylist):
mylist = [1,2,3,4 ]
print "values inside the function",mylist
return
mylist = [10,20,30]
changeme(mylist)
print"values outside the function ",mylist
| [
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]
| |
057695d4910d814affa1cef49fbca93b9b520c88 | df690ac0484ff04cb63f71f528a9d0a0e557d6a3 | /.history/ws_20210608130810.py | 59216ed4c38672800e718b0909e4e451e853a45b | []
| no_license | khanhdk0000/Mqtt-Web-Socket | 437777c740c68d4197353e334f6fe6a629094afd | 4f9e49a3817baa9ebc4e4f8dcffc21b6ea9d0134 | refs/heads/master | 2023-06-20T17:08:09.447381 | 2021-06-08T17:42:37 | 2021-06-08T17:42:37 | 375,090,458 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,612 | py | from flask import Flask, jsonify, request
from flask_sock import Sock
import time
app = Flask(__name__)
sock = Sock(app)
import threading
BROKER = 'io.adafruit.com'
USER = 'khanhdk0000'
PASSWORD = 'aio_FfID10QWNVSKUC2j15nLtOSeckin'
TOPIC = 'khanhdk0000/feeds/'
LIGHT = 'light'
SOUND = 'sound'
TEMP = 'temp'
LCD = 'iot_led'
BUZZER = 'buzzer'
########
# USER = 'CSE_BBC'
# PASSWORD = 'aio_FfID10QWNVSKUC2j15nLtOSeckin'
# TOPIC = 'CSE_BBC/feeds/'
# USER1 = 'CSE_BBC1'
# PASSWORD1 = 'aio_FfID10QWNVSKUC2j15nLtOSeckin'
# TOPIC1 = 'CSE_BBC1/feeds/'
# LIGHT = 'bk-iot-light'
# SOUND = 'bk-iot-sound'
# TEMP = 'bk-iot-temp-humid'
# LCD = 'bk-iot-lcd'
# BUZZER = 'bk-iot-speaker'
resLight = '"id":"13","name":"LIGHT","data":"0","unit":""'
prevLight = resLight
resTemp = '"id":"7","name":"SOUND","data":"0","unit":""'
prevTemp = resTemp
resSound = '"id":"12","name":"TEMP-HUMID","data":"0","unit":""'
prevSound = resSound
def mqttGet(user, password,topic,device):
import paho.mqtt.client as mqtt
def on_connect(client, userdata, flags, rc):
print("Connected with result code "+str(rc))
if rc == 0:
print('good')
else:
print('no good')
def on_disconnect(client, userdata, flags, rc=0):
print("Disconnected result code " + str(rc))
def on_message(client, userdata, message):
if device == LIGHT:
global resLight
message = str(message.payload.decode("utf-8"))
print(message)
resLight = message
elif device == TEMP:
global resTemp
message = str(message.payload.decode("utf-8"))
print(message)
resTemp = message
elif device == SOUND:
global resSound
message = str(message.payload.decode("utf-8"))
print(message)
resSound = message
client = mqtt.Client()
client.username_pw_set(username=user,password=password)
client.on_connect = on_connect
client.on_disconnect = on_disconnect
client.on_message = on_message
client.connect(BROKER, 1883, 60)
client.subscribe(topic)
client.loop_forever()
t1 = threading.Thread(target=mqttGet, name=mqttGet, args=(USER, PASSWORD,TOPIC + LIGHT, LIGHT))
t1.start()
t2 = threading.Thread(target=mqttGet, name=mqttGet, args=(USER, PASSWORD,TOPIC + TEMP, TEMP))
t2.start()
t3 = threading.Thread(target=mqttGet, name=mqttGet, args=(USER, PASSWORD,TOPIC + SOUND, SOUND))
t3.start()
def mqttPost(topic, user,pass,payload):
import paho.mqtt.publish as publish
publish.single(topic,hostname="io.adafruit.com",auth={"username":user, "password":pass},payload = payload)
@sock.route('/light')
def light(ws):
global resLight, prevLight
while True:
if prevLight == resLight:
continue
else:
ws.send(resLight)
prevLight = resLight
@sock.route('/sound')
def sound(ws):
global resSound, prevSound
while True:
if prevSound == resSound:
continue
else:
ws.send(resSound)
prevSound = resSound
@sock.route('/temp')
def temp(ws):
global resTemp, prevTemp
while True:
if prevTemp == resTemp:
continue
else:
ws.send(resTemp)
prevTemp = resTemp
@app.route('/postlcd', methods=["POST"])
def testpost():
input_json = request.get_json(force=True)
domain = input_json['data']
print('receive data', domain)
mqttPost(TOPIC+LCD, U)
return 'yea:' + domain
if __name__ == '__main__':
app.run(debug=True) | [
"[email protected]"
]
| |
09f47ffa874febc1dd80bb23531d909ac281739b | 694c187c8a00bee8c670c1690170099bad9b16b3 | /hindex.py | edded2784cbd958ce569e1997c2a49c5589810d0 | []
| no_license | ajayvenkat10/Competitive | 301f220b6d296f7e34328f192c43c4d7ef208cb1 | 14f2ecebe10eb19f72cc412dd0c414b3b1de9b4d | refs/heads/master | 2022-11-20T14:31:33.590099 | 2020-07-23T15:39:14 | 2020-07-23T15:39:14 | 281,599,951 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 450 | py | t = int(input())
for _ in range(t):
n = int(input())
arr = list(map(int, input().split()))
final = [1]
val = 2
for i in range(1,len(arr)):
count = 0
for j in range(i+1):
if(arr[j] >= val):
count += 1
if(count>=val):
final.append(val)
val += 1
else:
final.append(val-1)
print("Case #%d: " % (_+1) , end="")
print(*final)
| [
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]
| |
6452090ca100845c839848f14ac2d04f85352f4d | 934235f70a390a3ba0d7b464cddd10872f31cda3 | /rango/server/.history/tango_with_django/rango/admin_20210103130028.py | 361f6ca167ae05dc1771706293718383039c718e | []
| no_license | deji100/Projects | 6919041ba23e77a5c74e5ab7692bfcee38ececcb | 17e64d954d1d7805be57ec5d8d4344e4944889e6 | refs/heads/master | 2023-04-30T05:25:03.143303 | 2021-05-20T15:00:43 | 2021-05-20T15:00:43 | 338,844,691 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 521 | py | from django.contrib import admin
from .models import Category, Page, User
# Register your models here.
class PageInline(admin.StackedInline):
list_display = ('title', 'category', 'url')
# fields = ('title', 'url', 'category')
model
class CategoryAdmin(admin.ModelAdmin):
list_display = ('name', 'views', 'likes')
# prepopulated_fields = {'slug': ('name',)}
inlines = [PageInline]
admin.site.register(Category, CategoryAdmin)
admin.site.register(Page, PageAdmin)
admin.site.register(User) | [
"[email protected]"
]
| |
0ea09ec878674f42ce2fb633727af303b0ff9662 | 830398bc5ae951b153ff695a40be7239742bc73e | /exercises/parse_dhcp_snooping.py | 27114f9e94d30bcea5c6296a1383f9c2e461987f | []
| no_license | dmikos/pyneng | ff67f1d617a97d73103a7785a7bf86140e7baa82 | 543fb0d9fc63a2afee45d2465af3a4c3966e4a86 | refs/heads/master | 2021-01-25T14:56:44.181140 | 2018-04-23T04:31:00 | 2018-04-23T04:31:00 | 123,739,447 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 669 | py | # -*- coding: utf-8 -*-
import re
#'00:09:BB:3D:D6:58 10.1.10.2 86250 dhcp-snooping 10 FastEthernet0/1'
regex = re.compile('(?P<mac>\S+) +(?P<ip>\S+) +\d+ +\S+ +(?P<vlan>\d+) +(?P<port>\S+)')
result = []
with open('dhcp_snooping.txt') as data:
for line in data:
match = regex.search(line)
if match:
result.append(match.groupdict())
print('К коммутатору подключено {} устройства'.format(len(result)))
for num, comp in enumerate(result, 1):
print('Параметры устройства {}:'.format(num))
for key in comp:
print('{:10}: {:10}'.format(key,comp[key]))
| [
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]
| |
8435baa0b8beaab331ff8904a8889f896a8d23c0 | 9ae6ce54bf9a2a86201961fdbd5e7b0ec913ff56 | /google/ads/googleads/v9/services/services/third_party_app_analytics_link_service/transports/__init__.py | 502d5cf2169f355fb53779b340f3900e0e913770 | [
"Apache-2.0"
]
| permissive | GerhardusM/google-ads-python | 73b275a06e5401e6b951a6cd99af98c247e34aa3 | 676ac5fcb5bec0d9b5897f4c950049dac5647555 | refs/heads/master | 2022-07-06T19:05:50.932553 | 2022-06-17T20:41:17 | 2022-06-17T20:41:17 | 207,535,443 | 0 | 0 | Apache-2.0 | 2019-09-10T10:58:55 | 2019-09-10T10:58:55 | null | UTF-8 | Python | false | false | 1,141 | py | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from collections import OrderedDict
from typing import Dict, Type
from .base import ThirdPartyAppAnalyticsLinkServiceTransport
from .grpc import ThirdPartyAppAnalyticsLinkServiceGrpcTransport
# Compile a registry of transports.
_transport_registry = (
OrderedDict()
) # type: Dict[str, Type[ThirdPartyAppAnalyticsLinkServiceTransport]]
_transport_registry["grpc"] = ThirdPartyAppAnalyticsLinkServiceGrpcTransport
__all__ = (
"ThirdPartyAppAnalyticsLinkServiceTransport",
"ThirdPartyAppAnalyticsLinkServiceGrpcTransport",
)
| [
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]
| |
ce7050ab38a7683c7b476a80901ac6beac9d0799 | 4fbd844113ec9d8c526d5f186274b40ad5502aa3 | /algorithms/python3/maximize_distance_to_closest_person.py | 37e744aa546a7f515c70e1f156bc63f0f499ee8d | []
| no_license | capric8416/leetcode | 51f9bdc3fa26b010e8a1e8203a7e1bcd70ace9e1 | 503b2e303b10a455be9596c31975ee7973819a3c | refs/heads/master | 2022-07-16T21:41:07.492706 | 2020-04-22T06:18:16 | 2020-04-22T06:18:16 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,203 | py | # !/usr/bin/env python
# -*- coding: utf-8 -*-
"""
In a row of seats, 1 represents a person sitting in that seat, and 0 represents that the seat is empty.
There is at least one empty seat, and at least one person sitting.
Alex wants to sit in the seat such that the distance between him and the closest person to him is maximized.
Return that maximum distance to closest person.
Example 1:
Input: [1,0,0,0,1,0,1]
Output: 2
Explanation:
If Alex sits in the second open seat (seats[2]), then the closest person has distance 2.
If Alex sits in any other open seat, the closest person has distance 1.
Thus, the maximum distance to the closest person is 2.
Example 2:
Input: [1,0,0,0]
Output: 3
Explanation:
If Alex sits in the last seat, the closest person is 3 seats away.
This is the maximum distance possible, so the answer is 3.
Note:
1 <= seats.length <= 20000
seats contains only 0s or 1s, at least one 0, and at least one 1.
"""
""" ==================== body ==================== """
class Solution:
def maxDistToClosest(self, seats):
"""
:type seats: List[int]
:rtype: int
"""
""" ==================== body ==================== """
| [
"[email protected]"
]
| |
d86e1749616a76d2d38d3047025c8bd2f53d42fd | 53438732c6bc70b0d15eea99d961d6036f8839df | /Auth1/venv/bin/pip3.7 | 7e7d4a7ec99e985fd6466a5c34625337413e6453 | []
| no_license | Amarjeet2629/MyPycharmProjects | 6e07c972dce8ef12453ae0246bcbfcfd03cba1fb | 179a87f327d7c036a6192d0c6e372f2f1e3588ff | refs/heads/master | 2023-05-07T20:32:22.091132 | 2021-04-20T17:06:15 | 2021-04-20T17:06:15 | 224,671,445 | 0 | 0 | null | 2023-04-21T20:51:29 | 2019-11-28T14:32:13 | Python | UTF-8 | Python | false | false | 410 | 7 | #!/home/amarjeet-pc/PycharmProjects/Auth1/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip3.7'
__requires__ = 'pip==19.0.3'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('pip==19.0.3', 'console_scripts', 'pip3.7')()
)
| [
"[email protected]"
]
| |
a4a27e3eb0c39273105293f96a89dc9b05e6f10a | b6a84594f8c29d968014faaddd49abeb7537a5fc | /python/1040.moving-stones-until-consecutive-ii.py | 799deed3b361b4636ffa827b1e859308649b708d | []
| no_license | nickyfoto/lc | 8a6af3df114e693e265d0ede03f4d4e1283e010e | 3633b4df3e24968057c7d684689b931c5a8032d3 | refs/heads/master | 2020-09-16T19:23:07.765917 | 2020-06-07T17:18:06 | 2020-06-07T17:18:06 | 223,866,098 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,913 | py | #
# @lc app=leetcode id=1040 lang=python3
#
# [1040] Moving Stones Until Consecutive II
#
# https://leetcode.com/problems/moving-stones-until-consecutive-ii/description/
#
# algorithms
# Medium (52.07%)
# Likes: 152
# Dislikes: 231
# Total Accepted: 4.5K
# Total Submissions: 8.7K
# Testcase Example: '[7,4,9]'
#
# On an infinite number line, the position of the i-th stone is given by
# stones[i]. Call a stone an endpoint stone if it has the smallest or largest
# position.
#
# Each turn, you pick up an endpoint stone and move it to an unoccupied
# position so that it is no longer an endpoint stone.
#
# In particular, if the stones are at say, stones = [1,2,5], you cannot move
# the endpoint stone at position 5, since moving it to any position (such as 0,
# or 3) will still keep that stone as an endpoint stone.
#
# The game ends when you cannot make any more moves, ie. the stones are in
# consecutive positions.
#
# When the game ends, what is the minimum and maximum number of moves that you
# could have made? Return the answer as an length 2 array: answer =
# [minimum_moves, maximum_moves]
#
#
#
# Example 1:
#
#
# Input: [7,4,9]
# Output: [1,2]
# Explanation:
# We can move 4 -> 8 for one move to finish the game.
# Or, we can move 9 -> 5, 4 -> 6 for two moves to finish the game.
#
#
#
# Example 2:
#
#
# Input: [6,5,4,3,10]
# Output: [2,3]
# We can move 3 -> 8 then 10 -> 7 to finish the game.
# Or, we can move 3 -> 7, 4 -> 8, 5 -> 9 to finish the game.
# Notice we cannot move 10 -> 2 to finish the game, because that would be an
# illegal move.
#
#
#
# Example 3:
#
#
# Input: [100,101,104,102,103]
# Output: [0,0]
#
#
#
#
#
# Note:
#
#
# 3 <= stones.length <= 10^4
# 1 <= stones[i] <= 10^9
# stones[i] have distinct values.
#
#
#
#
#
#
#
#
#
# @lc code=start
class Solution:
def numMovesStonesII(self, stones):
pass
# @lc code=end
| [
"[email protected]"
]
| |
9adf50d27141869fb0693ddeb11ca31431191545 | bd93fa910151c278be8249055bc084e5a5c35a6a | /Python/DjangoTest2/booktest/models.py | 3735ebb85498b072da8a92f26cec7a80e612790c | []
| no_license | ahojcn/practice-code | bd81595b80239cd2550183093566bd536a83ed3f | b65f4e76271479269463e92fd3fd41585c2ac792 | refs/heads/master | 2021-07-10T14:15:08.036592 | 2020-07-09T11:32:16 | 2020-07-09T11:32:16 | 153,059,349 | 2 | 2 | null | null | null | null | UTF-8 | Python | false | false | 704 | py | from django.db import models
# Create your models here.
# 创建模型
class BookInfo(models.Model):
"""图书模型类"""
# 图书名
btitle = models.CharField(max_length=20)
# 出版日期
bpub_date = models.DateField()
def __str__(self):
return self.btitle
class HeroInfo(models.Model):
"""书中的英雄人物类"""
# 英雄名
hname = models.CharField(max_length=20)
# 性别 Boolean 类型，默认 False 代表男
hgender = models.BooleanField(default=False)
# 备注
hcomment = models.CharField(max_length=128)
# 关系
hbook = models.ForeignKey(BookInfo, on_delete=None)
def __str__(self):
return self.hname
| [
"[email protected]"
]
| |
8d1a3522d4cfd4b873a8f1089516307ab89e8605 | d0281cecabd070c399d18612bbb3ba11913c0ab1 | /venv/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py | 2f6941ef65e3378a7104657698538b9f1db55d8d | [
"MIT"
]
| permissive | yuxuan1995liu/darkflowyolo_detection | f0b7aa0a667591da9736fb2860d6080b2fc41577 | a7807e9b85833e3f877d46bb60e8fa7d0596a10b | refs/heads/master | 2022-11-03T04:00:42.996414 | 2019-05-10T01:58:59 | 2019-05-10T01:58:59 | 185,880,108 | 0 | 1 | MIT | 2022-10-30T16:38:49 | 2019-05-09T22:28:01 | Python | UTF-8 | Python | false | false | 514,131 | py | """Python wrappers around TensorFlow ops.
This file is MACHINE GENERATED! Do not edit.
Original C++ source file: array_ops.cc
"""
import collections as _collections
import six as _six
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
from tensorflow.python.eager import context as _context
from tensorflow.python.eager import core as _core
from tensorflow.python.eager import execute as _execute
from tensorflow.python.framework import dtypes as _dtypes
from tensorflow.python.framework import errors as _errors
from tensorflow.python.framework import tensor_shape as _tensor_shape
from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
# Needed to trigger the call to _set_call_cpp_shape_fn.
from tensorflow.python.framework import common_shapes as _common_shapes
from tensorflow.python.framework import op_def_registry as _op_def_registry
from tensorflow.python.framework import ops as _ops
from tensorflow.python.framework import op_def_library as _op_def_library
from tensorflow.python.util.deprecation import deprecated_endpoints
from tensorflow.python.util import dispatch as _dispatch
from tensorflow.python.util.tf_export import tf_export
def batch_matrix_band_part(input, num_lower, num_upper, name=None):
r"""TODO: add doc.
Args:
input: A `Tensor`.
num_lower: A `Tensor` of type `int64`.
num_upper: A `Tensor` of type `int64`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"BatchMatrixBandPart", name, _ctx._post_execution_callbacks, input,
num_lower, num_upper)
return _result
except _core._FallbackException:
try:
return batch_matrix_band_part_eager_fallback(
input, num_lower, num_upper, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"BatchMatrixBandPart", input=input, num_lower=num_lower,
num_upper=num_upper, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"BatchMatrixBandPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_matrix_band_part_eager_fallback(input, num_lower, num_upper, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function batch_matrix_band_part
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
num_lower = _ops.convert_to_tensor(num_lower, _dtypes.int64)
num_upper = _ops.convert_to_tensor(num_upper, _dtypes.int64)
_inputs_flat = [input, num_lower, num_upper]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"BatchMatrixBandPart", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BatchMatrixBandPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_matrix_diag(diagonal, name=None):
r"""TODO: add doc.
Args:
diagonal: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `diagonal`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"BatchMatrixDiag", name, _ctx._post_execution_callbacks, diagonal)
return _result
except _core._FallbackException:
try:
return batch_matrix_diag_eager_fallback(
diagonal, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"BatchMatrixDiag", diagonal=diagonal, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"BatchMatrixDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_matrix_diag_eager_fallback(diagonal, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function batch_matrix_diag
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (diagonal,) = _execute.args_to_matching_eager([diagonal], _ctx)
_inputs_flat = [diagonal]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"BatchMatrixDiag", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BatchMatrixDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_matrix_diag_part(input, name=None):
r"""TODO: add doc.
Args:
input: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"BatchMatrixDiagPart", name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return batch_matrix_diag_part_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"BatchMatrixDiagPart", input=input, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"BatchMatrixDiagPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_matrix_diag_part_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function batch_matrix_diag_part
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"BatchMatrixDiagPart", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BatchMatrixDiagPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_matrix_set_diag(input, diagonal, name=None):
r"""TODO: add doc.
Args:
input: A `Tensor`.
diagonal: A `Tensor`. Must have the same type as `input`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"BatchMatrixSetDiag", name, _ctx._post_execution_callbacks, input,
diagonal)
return _result
except _core._FallbackException:
try:
return batch_matrix_set_diag_eager_fallback(
input, diagonal, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"BatchMatrixSetDiag", input=input, diagonal=diagonal, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"BatchMatrixSetDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_matrix_set_diag_eager_fallback(input, diagonal, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function batch_matrix_set_diag
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([input, diagonal], _ctx)
(input, diagonal) = _inputs_T
_inputs_flat = [input, diagonal]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"BatchMatrixSetDiag", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BatchMatrixSetDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_to_space(input, crops, block_size, name=None):
r"""BatchToSpace for 4-D tensors of type T.
This is a legacy version of the more general BatchToSpaceND.
Rearranges (permutes) data from batch into blocks of spatial data, followed by
cropping. This is the reverse transformation of SpaceToBatch. More specifically,
this op outputs a copy of the input tensor where values from the `batch`
dimension are moved in spatial blocks to the `height` and `width` dimensions,
followed by cropping along the `height` and `width` dimensions.
Args:
input: A `Tensor`. 4-D tensor with shape
`[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
depth]`. Note that the batch size of the input tensor must be divisible by
`block_size * block_size`.
crops: A `Tensor`. Must be one of the following types: `int32`, `int64`.
2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
how many elements to crop from the intermediate result across the spatial
dimensions as follows:
crops = [[crop_top, crop_bottom], [crop_left, crop_right]]
block_size: An `int` that is `>= 2`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "BatchToSpace",
name, _ctx._post_execution_callbacks, input, crops, "block_size",
block_size)
return _result
except _core._FallbackException:
try:
return batch_to_space_eager_fallback(
input, crops, block_size=block_size, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
block_size = _execute.make_int(block_size, "block_size")
_, _, _op = _op_def_lib._apply_op_helper(
"BatchToSpace", input=input, crops=crops, block_size=block_size,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "block_size", _op.get_attr("block_size"),
"Tidx", _op.get_attr("Tidx"))
_execute.record_gradient(
"BatchToSpace", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_to_space_eager_fallback(input, crops, block_size, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function batch_to_space
"""
_ctx = ctx if ctx else _context.context()
block_size = _execute.make_int(block_size, "block_size")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tidx, (crops,) = _execute.args_to_matching_eager([crops], _ctx, _dtypes.int32)
_inputs_flat = [input, crops]
_attrs = ("T", _attr_T, "block_size", block_size, "Tidx", _attr_Tidx)
_result = _execute.execute(b"BatchToSpace", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BatchToSpace", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export(v1=['batch_to_space_nd', 'manip.batch_to_space_nd'])
@deprecated_endpoints('batch_to_space_nd', 'manip.batch_to_space_nd')
def batch_to_space_nd(input, block_shape, crops, name=None):
r"""BatchToSpace for N-D tensors of type T.
This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape
`block_shape + [batch]`, interleaves these blocks back into the grid defined by
the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as
the input. The spatial dimensions of this intermediate result are then
optionally cropped according to `crops` to produce the output. This is the
reverse of SpaceToBatch. See below for a precise description.
Args:
input: A `Tensor`.
N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
where spatial_shape has M dimensions.
block_shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
1-D with shape `[M]`, all values must be >= 1.
crops: A `Tensor`. Must be one of the following types: `int32`, `int64`.
2-D with shape `[M, 2]`, all values must be >= 0.
`crops[i] = [crop_start, crop_end]` specifies the amount to crop from input
dimension `i + 1`, which corresponds to spatial dimension `i`. It is
required that
`crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.
This operation is equivalent to the following steps:
1. Reshape `input` to `reshaped` of shape:
[block_shape[0], ..., block_shape[M-1],
batch / prod(block_shape),
input_shape[1], ..., input_shape[N-1]]
2. Permute dimensions of `reshaped` to produce `permuted` of shape
[batch / prod(block_shape),
input_shape[1], block_shape[0],
...,
input_shape[M], block_shape[M-1],
input_shape[M+1], ..., input_shape[N-1]]
3. Reshape `permuted` to produce `reshaped_permuted` of shape
[batch / prod(block_shape),
input_shape[1] * block_shape[0],
...,
input_shape[M] * block_shape[M-1],
input_shape[M+1],
...,
input_shape[N-1]]
4. Crop the start and end of dimensions `[1, ..., M]` of
`reshaped_permuted` according to `crops` to produce the output of shape:
[batch / prod(block_shape),
input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
...,
input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
input_shape[M+1], ..., input_shape[N-1]]
Some examples:
(1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and
`crops = [[0, 0], [0, 0]]`:
```
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
```
The output tensor has shape `[1, 2, 2, 1]` and value:
```
x = [[[[1], [2]], [[3], [4]]]]
```
(2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and
`crops = [[0, 0], [0, 0]]`:
```
[[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
```
The output tensor has shape `[1, 2, 2, 3]` and value:
```
x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
```
(3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and
`crops = [[0, 0], [0, 0]]`:
```
x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
```
The output tensor has shape `[1, 4, 4, 1]` and value:
```
x = [[[1], [2], [3], [4]],
[[5], [6], [7], [8]],
[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]
```
(4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and
`crops = [[0, 0], [2, 0]]`:
```
x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
[[[0], [2], [4]]], [[[0], [10], [12]]],
[[[0], [5], [7]]], [[[0], [13], [15]]],
[[[0], [6], [8]]], [[[0], [14], [16]]]]
```
The output tensor has shape `[2, 2, 4, 1]` and value:
```
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]]],
[[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
```
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"BatchToSpaceND", name, _ctx._post_execution_callbacks, input,
block_shape, crops)
return _result
except _core._FallbackException:
try:
return batch_to_space_nd_eager_fallback(
input, block_shape, crops, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
batch_to_space_nd, input=input, block_shape=block_shape,
crops=crops, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"BatchToSpaceND", input=input, block_shape=block_shape, crops=crops,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
batch_to_space_nd, input=input, block_shape=block_shape,
crops=crops, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tblock_shape",
_op.get_attr("Tblock_shape"), "Tcrops", _op.get_attr("Tcrops"))
_execute.record_gradient(
"BatchToSpaceND", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def batch_to_space_nd_eager_fallback(input, block_shape, crops, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function batch_to_space_nd
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tblock_shape, (block_shape,) = _execute.args_to_matching_eager([block_shape], _ctx, _dtypes.int32)
_attr_Tcrops, (crops,) = _execute.args_to_matching_eager([crops], _ctx, _dtypes.int32)
_inputs_flat = [input, block_shape, crops]
_attrs = ("T", _attr_T, "Tblock_shape", _attr_Tblock_shape, "Tcrops",
_attr_Tcrops)
_result = _execute.execute(b"BatchToSpaceND", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BatchToSpaceND", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('bitcast')
def bitcast(input, type, name=None):
r"""Bitcasts a tensor from one type to another without copying data.
Given a tensor `input`, this operation returns a tensor that has the same buffer
data as `input` with datatype `type`.
If the input datatype `T` is larger than the output datatype `type` then the
shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)].
If `T` is smaller than `type`, the operator requires that the rightmost
dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from
[..., sizeof(`type`)/sizeof(`T`)] to [...].
*NOTE*: Bitcast is implemented as a low-level cast, so machines with different
endian orderings will give different results.
Args:
input: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int64`, `int32`, `uint8`, `uint16`, `uint32`, `uint64`, `int8`, `int16`, `complex64`, `complex128`, `qint8`, `quint8`, `qint16`, `quint16`, `qint32`.
type: A `tf.DType` from: `tf.bfloat16, tf.half, tf.float32, tf.float64, tf.int64, tf.int32, tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.int8, tf.int16, tf.complex64, tf.complex128, tf.qint8, tf.quint8, tf.qint16, tf.quint16, tf.qint32`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `type`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Bitcast",
name, _ctx._post_execution_callbacks, input, "type", type)
return _result
except _core._FallbackException:
try:
return bitcast_eager_fallback(
input, type=type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
bitcast, input=input, type=type, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
type = _execute.make_type(type, "type")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"Bitcast", input=input, type=type, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
bitcast, input=input, type=type, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "type", _op.get_attr("type"))
_execute.record_gradient(
"Bitcast", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def bitcast_eager_fallback(input, type, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function bitcast
"""
_ctx = ctx if ctx else _context.context()
type = _execute.make_type(type, "type")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T, "type", type)
_result = _execute.execute(b"Bitcast", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Bitcast", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def broadcast_args(s0, s1, name=None):
r"""Return the shape of s0 op s1 with broadcast.
Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the
broadcasted shape. `s0`, `s1` and `r0` are all integer vectors.
Args:
s0: A `Tensor`. Must be one of the following types: `int32`, `int64`.
s1: A `Tensor`. Must have the same type as `s0`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `s0`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"BroadcastArgs", name, _ctx._post_execution_callbacks, s0, s1)
return _result
except _core._FallbackException:
try:
return broadcast_args_eager_fallback(
s0, s1, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"BroadcastArgs", s0=s0, s1=s1, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"BroadcastArgs", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def broadcast_args_eager_fallback(s0, s1, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function broadcast_args
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([s0, s1], _ctx, _dtypes.int32)
(s0, s1) = _inputs_T
_inputs_flat = [s0, s1]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"BroadcastArgs", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BroadcastArgs", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
_broadcast_gradient_args_outputs = ["r0", "r1"]
_BroadcastGradientArgsOutput = _collections.namedtuple(
"BroadcastGradientArgs", _broadcast_gradient_args_outputs)
def broadcast_gradient_args(s0, s1, name=None):
r"""Return the reduction indices for computing gradients of s0 op s1 with broadcast.
This is typically used by gradient computations for a broadcasting operation.
Args:
s0: A `Tensor`. Must be one of the following types: `int32`, `int64`.
s1: A `Tensor`. Must have the same type as `s0`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (r0, r1).
r0: A `Tensor`. Has the same type as `s0`.
r1: A `Tensor`. Has the same type as `s0`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"BroadcastGradientArgs", name, _ctx._post_execution_callbacks, s0, s1)
_result = _BroadcastGradientArgsOutput._make(_result)
return _result
except _core._FallbackException:
try:
return broadcast_gradient_args_eager_fallback(
s0, s1, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"BroadcastGradientArgs", s0=s0, s1=s1, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"BroadcastGradientArgs", _inputs_flat, _attrs, _result, name)
_result = _BroadcastGradientArgsOutput._make(_result)
return _result
def broadcast_gradient_args_eager_fallback(s0, s1, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function broadcast_gradient_args
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([s0, s1], _ctx, _dtypes.int32)
(s0, s1) = _inputs_T
_inputs_flat = [s0, s1]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"BroadcastGradientArgs", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BroadcastGradientArgs", _inputs_flat, _attrs, _result, name)
_result = _BroadcastGradientArgsOutput._make(_result)
return _result
@_dispatch.add_dispatch_list
@tf_export('broadcast_to')
def broadcast_to(input, shape, name=None):
r"""Broadcast an array for a compatible shape.
Broadcasting is the process of making arrays to have compatible shapes
for arithmetic operations. Two shapes are compatible if for each
dimension pair they are either equal or one of them is one. When trying
to broadcast a Tensor to a shape, it starts with the trailing dimensions,
and works its way forward.
For example,
```
>>> x = tf.constant([1, 2, 3])
>>> y = tf.broadcast_to(x, [3, 3])
>>> sess.run(y)
array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]], dtype=int32)
```
In the above example, the input Tensor with the shape of `[1, 3]`
is broadcasted to output Tensor with shape of `[3, 3]`.
Args:
input: A `Tensor`. A Tensor to broadcast.
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
An 1-D `int` Tensor. The shape of the desired output.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "BroadcastTo",
name, _ctx._post_execution_callbacks, input, shape)
return _result
except _core._FallbackException:
try:
return broadcast_to_eager_fallback(
input, shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
broadcast_to, input=input, shape=shape, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"BroadcastTo", input=input, shape=shape, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
broadcast_to, input=input, shape=shape, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tidx", _op.get_attr("Tidx"))
_execute.record_gradient(
"BroadcastTo", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def broadcast_to_eager_fallback(input, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function broadcast_to
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tidx, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int32)
_inputs_flat = [input, shape]
_attrs = ("T", _attr_T, "Tidx", _attr_Tidx)
_result = _execute.execute(b"BroadcastTo", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"BroadcastTo", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('debugging.check_numerics', v1=['debugging.check_numerics', 'check_numerics'])
@deprecated_endpoints('check_numerics')
def check_numerics(tensor, message, name=None):
r"""Checks a tensor for NaN and Inf values.
When run, reports an `InvalidArgument` error if `tensor` has any values
that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is.
Args:
tensor: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
message: A `string`. Prefix of the error message.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"CheckNumerics", name, _ctx._post_execution_callbacks, tensor,
"message", message)
return _result
except _core._FallbackException:
try:
return check_numerics_eager_fallback(
tensor, message=message, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
check_numerics, tensor=tensor, message=message, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
message = _execute.make_str(message, "message")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"CheckNumerics", tensor=tensor, message=message, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
check_numerics, tensor=tensor, message=message, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "message", _op.get_attr("message"))
_execute.record_gradient(
"CheckNumerics", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def check_numerics_eager_fallback(tensor, message, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function check_numerics
"""
_ctx = ctx if ctx else _context.context()
message = _execute.make_str(message, "message")
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx)
_inputs_flat = [tensor]
_attrs = ("T", _attr_T, "message", message)
_result = _execute.execute(b"CheckNumerics", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"CheckNumerics", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def concat(concat_dim, values, name=None):
r"""Concatenates tensors along one dimension.
Args:
concat_dim: A `Tensor` of type `int32`.
0-D. The dimension along which to concatenate. Must be in the
range [0, rank(values)).
values: A list of at least 2 `Tensor` objects with the same type.
The `N` Tensors to concatenate. Their ranks and types must match,
and their sizes must match in all dimensions except `concat_dim`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `values`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Concat", name,
_ctx._post_execution_callbacks, concat_dim, values)
return _result
except _core._FallbackException:
try:
return concat_eager_fallback(
concat_dim, values, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'concat' Op, not %r." % values)
_attr_N = len(values)
_, _, _op = _op_def_lib._apply_op_helper(
"Concat", concat_dim=concat_dim, values=values, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"), "T", _op.get_attr("T"))
_execute.record_gradient(
"Concat", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def concat_eager_fallback(concat_dim, values, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function concat
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'concat' Op, not %r." % values)
_attr_N = len(values)
_attr_T, values = _execute.args_to_matching_eager(list(values), _ctx)
concat_dim = _ops.convert_to_tensor(concat_dim, _dtypes.int32)
_inputs_flat = [concat_dim] + list(values)
_attrs = ("N", _attr_N, "T", _attr_T)
_result = _execute.execute(b"Concat", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Concat", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def concat_offset(concat_dim, shape, name=None):
r"""Computes offsets of concat inputs within its output.
For example:
```
# 'x' is [2, 2, 7]
# 'y' is [2, 3, 7]
# 'z' is [2, 5, 7]
concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0]
```
This is typically used by gradient computations for a concat operation.
Args:
concat_dim: A `Tensor` of type `int32`.
The dimension along which to concatenate.
shape: A list of at least 2 `Tensor` objects with type `int32`.
The `N` int32 vectors representing shape of tensors being concatenated.
name: A name for the operation (optional).
Returns:
A list with the same length as `shape` of `Tensor` objects with type `int32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ConcatOffset",
name, _ctx._post_execution_callbacks, concat_dim, shape)
return _result
except _core._FallbackException:
try:
return concat_offset_eager_fallback(
concat_dim, shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(shape, (list, tuple)):
raise TypeError(
"Expected list for 'shape' argument to "
"'concat_offset' Op, not %r." % shape)
_attr_N = len(shape)
_, _, _op = _op_def_lib._apply_op_helper(
"ConcatOffset", concat_dim=concat_dim, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"))
_execute.record_gradient(
"ConcatOffset", _inputs_flat, _attrs, _result, name)
return _result
def concat_offset_eager_fallback(concat_dim, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function concat_offset
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(shape, (list, tuple)):
raise TypeError(
"Expected list for 'shape' argument to "
"'concat_offset' Op, not %r." % shape)
_attr_N = len(shape)
concat_dim = _ops.convert_to_tensor(concat_dim, _dtypes.int32)
shape = _ops.convert_n_to_tensor(shape, _dtypes.int32)
_inputs_flat = [concat_dim] + list(shape)
_attrs = ("N", _attr_N)
_result = _execute.execute(b"ConcatOffset", _attr_N, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ConcatOffset", _inputs_flat, _attrs, _result, name)
return _result
def concat_v2(values, axis, name=None):
r"""Concatenates tensors along one dimension.
Args:
values: A list of at least 2 `Tensor` objects with the same type.
List of `N` Tensors to concatenate. Their ranks and types must match,
and their sizes must match in all dimensions except `concat_dim`.
axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
0-D. The dimension along which to concatenate. Must be in the
range [-rank(values), rank(values)).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `values`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ConcatV2",
name, _ctx._post_execution_callbacks, values, axis)
return _result
except _core._FallbackException:
try:
return concat_v2_eager_fallback(
values, axis, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'concat_v2' Op, not %r." % values)
_attr_N = len(values)
_, _, _op = _op_def_lib._apply_op_helper(
"ConcatV2", values=values, axis=axis, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"), "T", _op.get_attr("T"), "Tidx",
_op.get_attr("Tidx"))
_execute.record_gradient(
"ConcatV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def concat_v2_eager_fallback(values, axis, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function concat_v2
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'concat_v2' Op, not %r." % values)
_attr_N = len(values)
_attr_T, values = _execute.args_to_matching_eager(list(values), _ctx)
_attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], _ctx, _dtypes.int32)
_inputs_flat = list(values) + [axis]
_attrs = ("N", _attr_N, "T", _attr_T, "Tidx", _attr_Tidx)
_result = _execute.execute(b"ConcatV2", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ConcatV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def conjugate_transpose(x, perm, name=None):
r"""Shuffle dimensions of x according to a permutation and conjugate the result.
The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
`y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
`y[i,j,k,...,s,t,u] == conj(x[perm[i], perm[j], perm[k],...,perm[s], perm[t], perm[u]])`
Args:
x: A `Tensor`.
perm: A `Tensor`. Must be one of the following types: `int32`, `int64`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ConjugateTranspose", name, _ctx._post_execution_callbacks, x, perm)
return _result
except _core._FallbackException:
try:
return conjugate_transpose_eager_fallback(
x, perm, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ConjugateTranspose", x=x, perm=perm, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tperm", _op.get_attr("Tperm"))
_execute.record_gradient(
"ConjugateTranspose", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def conjugate_transpose_eager_fallback(x, perm, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function conjugate_transpose
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_attr_Tperm, (perm,) = _execute.args_to_matching_eager([perm], _ctx, _dtypes.int32)
_inputs_flat = [x, perm]
_attrs = ("T", _attr_T, "Tperm", _attr_Tperm)
_result = _execute.execute(b"ConjugateTranspose", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ConjugateTranspose", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def const(value, dtype, name=None):
r"""Returns a constant tensor.
Args:
value: A `tf.TensorProto`. Attr `value` is the tensor to return.
dtype: A `tf.DType`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `dtype`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Const", name,
_ctx._post_execution_callbacks, "value", value, "dtype", dtype)
return _result
except _core._FallbackException:
try:
return const_eager_fallback(
value=value, dtype=dtype, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
value = _execute.make_tensor(value, "value")
dtype = _execute.make_type(dtype, "dtype")
_, _, _op = _op_def_lib._apply_op_helper(
"Const", value=value, dtype=dtype, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("value", _op.get_attr("value"), "dtype", _op.get_attr("dtype"))
_execute.record_gradient(
"Const", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def const_eager_fallback(value, dtype, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function const
"""
_ctx = ctx if ctx else _context.context()
value = _execute.make_tensor(value, "value")
dtype = _execute.make_type(dtype, "dtype")
_inputs_flat = []
_attrs = ("value", value, "dtype", dtype)
_result = _execute.execute(b"Const", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Const", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def debug_gradient_identity(input, name=None):
r"""Identity op for gradient debugging.
This op is hidden from public in Python. It is used by TensorFlow Debugger to
register gradient tensors for gradient debugging.
This op operates on non-reference-type tensors.
Args:
input: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"DebugGradientIdentity", name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return debug_gradient_identity_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"DebugGradientIdentity", input=input, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"DebugGradientIdentity", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def debug_gradient_identity_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function debug_gradient_identity
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"DebugGradientIdentity", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DebugGradientIdentity", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def debug_gradient_ref_identity(input, name=None):
r"""Identity op for gradient debugging.
This op is hidden from public in Python. It is used by TensorFlow Debugger to
register gradient tensors for gradient debugging.
This op operates on reference-type tensors.
Args:
input: A mutable `Tensor`.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
raise RuntimeError("debug_gradient_ref_identity op does not support eager execution. Arg 'output' is a ref.")
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"DebugGradientRefIdentity", input=input, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"DebugGradientRefIdentity", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def debug_gradient_ref_identity_eager_fallback(input, name=None, ctx=None):
raise RuntimeError("debug_gradient_ref_identity op does not support eager execution. Arg 'output' is a ref.")
def deep_copy(x, name=None):
r"""Makes a copy of `x`.
Args:
x: A `Tensor`. The source tensor of type `T`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "DeepCopy",
name, _ctx._post_execution_callbacks, x)
return _result
except _core._FallbackException:
try:
return deep_copy_eager_fallback(
x, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"DeepCopy", x=x, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"DeepCopy", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def deep_copy_eager_fallback(x, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function deep_copy
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_inputs_flat = [x]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"DeepCopy", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DeepCopy", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def depth_to_space(input, block_size, data_format="NHWC", name=None):
r"""DepthToSpace for tensors of type T.
Rearranges data from depth into blocks of spatial data.
This is the reverse transformation of SpaceToDepth. More specifically,
this op outputs a copy of the input tensor where values from the `depth`
dimension are moved in spatial blocks to the `height` and `width` dimensions.
The attr `block_size` indicates the input block size and how the data is moved.
* Chunks of data of size `block_size * block_size` from depth are rearranged
into non-overlapping blocks of size `block_size x block_size`
* The width the output tensor is `input_depth * block_size`, whereas the
height is `input_height * block_size`.
* The Y, X coordinates within each block of the output image are determined
by the high order component of the input channel index.
* The depth of the input tensor must be divisible by
`block_size * block_size`.
The `data_format` attr specifies the layout of the input and output tensors
with the following options:
"NHWC": `[ batch, height, width, channels ]`
"NCHW": `[ batch, channels, height, width ]`
"NCHW_VECT_C":
`qint8 [ batch, channels / 4, height, width, 4 ]`
It is useful to consider the operation as transforming a 6-D Tensor.
e.g. for data_format = NHWC,
Each element in the input tensor can be specified via 6 coordinates,
ordered by decreasing memory layout significance as:
n,iY,iX,bY,bX,oC (where n=batch index, iX, iY means X or Y coordinates
within the input image, bX, bY means coordinates
within the output block, oC means output channels).
The output would be the input transposed to the following layout:
n,iY,bY,iX,bX,oC
This operation is useful for resizing the activations between convolutions
(but keeping all data), e.g. instead of pooling. It is also useful for training
purely convolutional models.
For example, given an input of shape `[1, 1, 1, 4]`, data_format = "NHWC" and
block_size = 2:
```
x = [[[[1, 2, 3, 4]]]]
```
This operation will output a tensor of shape `[1, 2, 2, 1]`:
```
[[[[1], [2]],
[[3], [4]]]]
```
Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`,
the corresponding output will have 2x2 elements and will have a depth of
1 channel (1 = `4 / (block_size * block_size)`).
The output element shape is `[2, 2, 1]`.
For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g.
```
x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
```
This operation, for block size of 2, will return the following tensor of shape
`[1, 2, 2, 3]`
```
[[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
```
Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2:
```
x = [[[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[9, 10, 11, 12],
[13, 14, 15, 16]]]]
```
the operator will return the following tensor of shape `[1 4 4 1]`:
```
x = [[[ [1], [2], [5], [6]],
[ [3], [4], [7], [8]],
[ [9], [10], [13], [14]],
[ [11], [12], [15], [16]]]]
```
Args:
input: A `Tensor`.
block_size: An `int` that is `>= 2`.
The size of the spatial block, same as in Space2Depth.
data_format: An optional `string` from: `"NHWC", "NCHW", "NCHW_VECT_C"`. Defaults to `"NHWC"`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "DepthToSpace",
name, _ctx._post_execution_callbacks, input, "block_size", block_size,
"data_format", data_format)
return _result
except _core._FallbackException:
try:
return depth_to_space_eager_fallback(
input, block_size=block_size, data_format=data_format, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
block_size = _execute.make_int(block_size, "block_size")
if data_format is None:
data_format = "NHWC"
data_format = _execute.make_str(data_format, "data_format")
_, _, _op = _op_def_lib._apply_op_helper(
"DepthToSpace", input=input, block_size=block_size,
data_format=data_format, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "block_size", _op.get_attr("block_size"),
"data_format", _op.get_attr("data_format"))
_execute.record_gradient(
"DepthToSpace", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def depth_to_space_eager_fallback(input, block_size, data_format="NHWC", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function depth_to_space
"""
_ctx = ctx if ctx else _context.context()
block_size = _execute.make_int(block_size, "block_size")
if data_format is None:
data_format = "NHWC"
data_format = _execute.make_str(data_format, "data_format")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T, "block_size", block_size, "data_format",
data_format)
_result = _execute.execute(b"DepthToSpace", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DepthToSpace", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('quantization.dequantize', v1=['quantization.dequantize', 'dequantize'])
@deprecated_endpoints('dequantize')
def dequantize(input, min_range, max_range, mode="MIN_COMBINED", name=None):
r"""Dequantize the 'input' tensor into a float Tensor.
[min_range, max_range] are scalar floats that specify the range for
the 'input' data. The 'mode' attribute controls exactly which calculations are
used to convert the float values to their quantized equivalents.
In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
```
if T == qint8: in[i] += (range(T) + 1)/ 2.0
out[i] = min_range + (in[i]* (max_range - min_range) / range(T))
```
here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
*MIN_COMBINED Mode Example*
If the input comes from a QuantizedRelu6, the output type is
quint8 (range of 0-255) but the possible range of QuantizedRelu6 is
0-6. The min_range and max_range values are therefore 0.0 and 6.0.
Dequantize on quint8 will take each value, cast to float, and multiply
by 6 / 255.
Note that if quantizedtype is qint8, the operation will additionally add
each value by 128 prior to casting.
If the mode is 'MIN_FIRST', then this approach is used:
```c++
num_discrete_values = 1 << (# of bits in T)
range_adjust = num_discrete_values / (num_discrete_values - 1)
range = (range_max - range_min) * range_adjust
range_scale = range / num_discrete_values
const double offset_input = static_cast<double>(input) - lowest_quantized;
result = range_min + ((input - numeric_limits<T>::min()) * range_scale)
```
*SCALED mode Example*
`SCALED` mode matches the quantization approach used in
`QuantizeAndDequantize{V2|V3}`.
If the mode is `SCALED`, we do not use the full range of the output type,
choosing to elide the lowest possible value for symmetry (e.g., output range is
-127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
0.
We first find the range of values in our tensor. The
range we use is always centered on 0, so we find m such that
```c++
m = max(abs(input_min), abs(input_max))
```
Our input tensor range is then `[-m, m]`.
Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.
If T is signed, this is
```
num_bits = sizeof(T) * 8
[min_fixed, max_fixed] =
[-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
```
Otherwise, if T is unsigned, the fixed-point range is
```
[min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
```
From this we compute our scaling factor, s:
```c++
s = (2 * m) / (max_fixed - min_fixed)
```
Now we can dequantize the elements of our tensor:
```c++
result = input * s
```
Args:
input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
min_range: A `Tensor` of type `float32`.
The minimum scalar value possibly produced for the input.
max_range: A `Tensor` of type `float32`.
The maximum scalar value possibly produced for the input.
mode: An optional `string` from: `"MIN_COMBINED", "MIN_FIRST", "SCALED"`. Defaults to `"MIN_COMBINED"`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Dequantize",
name, _ctx._post_execution_callbacks, input, min_range, max_range,
"mode", mode)
return _result
except _core._FallbackException:
try:
return dequantize_eager_fallback(
input, min_range, max_range, mode=mode, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
dequantize, input=input, min_range=min_range,
max_range=max_range, mode=mode, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if mode is None:
mode = "MIN_COMBINED"
mode = _execute.make_str(mode, "mode")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"Dequantize", input=input, min_range=min_range, max_range=max_range,
mode=mode, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
dequantize, input=input, min_range=min_range, max_range=max_range,
mode=mode, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "mode", _op.get_attr("mode"))
_execute.record_gradient(
"Dequantize", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def dequantize_eager_fallback(input, min_range, max_range, mode="MIN_COMBINED", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function dequantize
"""
_ctx = ctx if ctx else _context.context()
if mode is None:
mode = "MIN_COMBINED"
mode = _execute.make_str(mode, "mode")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
min_range = _ops.convert_to_tensor(min_range, _dtypes.float32)
max_range = _ops.convert_to_tensor(max_range, _dtypes.float32)
_inputs_flat = [input, min_range, max_range]
_attrs = ("T", _attr_T, "mode", mode)
_result = _execute.execute(b"Dequantize", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Dequantize", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('linalg.tensor_diag', v1=['linalg.tensor_diag', 'diag'])
@deprecated_endpoints('diag')
def diag(diagonal, name=None):
r"""Returns a diagonal tensor with a given diagonal values.
Given a `diagonal`, this operation returns a tensor with the `diagonal` and
everything else padded with zeros. The diagonal is computed as follows:
Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of
rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where:
`output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else.
For example:
```
# 'diagonal' is [1, 2, 3, 4]
tf.diag(diagonal) ==> [[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]]
```
Args:
diagonal: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
Rank k tensor where k is at most 1.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `diagonal`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Diag", name,
_ctx._post_execution_callbacks, diagonal)
return _result
except _core._FallbackException:
try:
return diag_eager_fallback(
diagonal, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
diag, diagonal=diagonal, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"Diag", diagonal=diagonal, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
diag, diagonal=diagonal, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"Diag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def diag_eager_fallback(diagonal, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function diag
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (diagonal,) = _execute.args_to_matching_eager([diagonal], _ctx)
_inputs_flat = [diagonal]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"Diag", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Diag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('linalg.tensor_diag_part', v1=['linalg.tensor_diag_part', 'diag_part'])
@deprecated_endpoints('diag_part')
def diag_part(input, name=None):
r"""Returns the diagonal part of the tensor.
This operation returns a tensor with the `diagonal` part
of the `input`. The `diagonal` part is computed as follows:
Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a
tensor of rank `k` with dimensions `[D1,..., Dk]` where:
`diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.
For example:
```
# 'input' is [[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]]
tf.diag_part(input) ==> [1, 2, 3, 4]
```
Args:
input: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
Rank k tensor where k is even and not zero.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "DiagPart",
name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return diag_part_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
diag_part, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"DiagPart", input=input, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
diag_part, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"DiagPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def diag_part_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function diag_part
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"DiagPart", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"DiagPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def edit_distance(hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape, normalize=True, name=None):
r"""Computes the (possibly normalized) Levenshtein Edit Distance.
The inputs are variable-length sequences provided by SparseTensors
(hypothesis_indices, hypothesis_values, hypothesis_shape)
and
(truth_indices, truth_values, truth_shape).
The inputs are:
Args:
hypothesis_indices: A `Tensor` of type `int64`.
The indices of the hypothesis list SparseTensor.
This is an N x R int64 matrix.
hypothesis_values: A `Tensor`.
The values of the hypothesis list SparseTensor.
This is an N-length vector.
hypothesis_shape: A `Tensor` of type `int64`.
The shape of the hypothesis list SparseTensor.
This is an R-length vector.
truth_indices: A `Tensor` of type `int64`.
The indices of the truth list SparseTensor.
This is an M x R int64 matrix.
truth_values: A `Tensor`. Must have the same type as `hypothesis_values`.
The values of the truth list SparseTensor.
This is an M-length vector.
truth_shape: A `Tensor` of type `int64`. truth indices, vector.
normalize: An optional `bool`. Defaults to `True`.
boolean (if true, edit distances are normalized by length of truth).
The output is:
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "EditDistance",
name, _ctx._post_execution_callbacks, hypothesis_indices,
hypothesis_values, hypothesis_shape, truth_indices, truth_values,
truth_shape, "normalize", normalize)
return _result
except _core._FallbackException:
try:
return edit_distance_eager_fallback(
hypothesis_indices, hypothesis_values, hypothesis_shape,
truth_indices, truth_values, truth_shape, normalize=normalize,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if normalize is None:
normalize = True
normalize = _execute.make_bool(normalize, "normalize")
_, _, _op = _op_def_lib._apply_op_helper(
"EditDistance", hypothesis_indices=hypothesis_indices,
hypothesis_values=hypothesis_values,
hypothesis_shape=hypothesis_shape,
truth_indices=truth_indices,
truth_values=truth_values, truth_shape=truth_shape,
normalize=normalize, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("normalize", _op.get_attr("normalize"), "T", _op.get_attr("T"))
_execute.record_gradient(
"EditDistance", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def edit_distance_eager_fallback(hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape, normalize=True, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function edit_distance
"""
_ctx = ctx if ctx else _context.context()
if normalize is None:
normalize = True
normalize = _execute.make_bool(normalize, "normalize")
_attr_T, _inputs_T = _execute.args_to_matching_eager([hypothesis_values, truth_values], _ctx)
(hypothesis_values, truth_values) = _inputs_T
hypothesis_indices = _ops.convert_to_tensor(hypothesis_indices, _dtypes.int64)
hypothesis_shape = _ops.convert_to_tensor(hypothesis_shape, _dtypes.int64)
truth_indices = _ops.convert_to_tensor(truth_indices, _dtypes.int64)
truth_shape = _ops.convert_to_tensor(truth_shape, _dtypes.int64)
_inputs_flat = [hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape]
_attrs = ("normalize", normalize, "T", _attr_T)
_result = _execute.execute(b"EditDistance", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"EditDistance", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def empty(shape, dtype, init=False, name=None):
r"""Creates a tensor with the given shape.
This operation creates a tensor of `shape` and `dtype`.
Args:
shape: A `Tensor` of type `int32`.
1-D. Represents the shape of the output tensor.
dtype: A `tf.DType`.
init: An optional `bool`. Defaults to `False`.
If True, initialize the returned tensor with the default value of dtype. Otherwise, the implementation is free not to initializethe tensor's content.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `dtype`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Empty", name,
_ctx._post_execution_callbacks, shape, "dtype", dtype, "init", init)
return _result
except _core._FallbackException:
try:
return empty_eager_fallback(
shape, dtype=dtype, init=init, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
dtype = _execute.make_type(dtype, "dtype")
if init is None:
init = False
init = _execute.make_bool(init, "init")
_, _, _op = _op_def_lib._apply_op_helper(
"Empty", shape=shape, dtype=dtype, init=init, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("dtype", _op.get_attr("dtype"), "init", _op.get_attr("init"))
_execute.record_gradient(
"Empty", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def empty_eager_fallback(shape, dtype, init=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function empty
"""
_ctx = ctx if ctx else _context.context()
dtype = _execute.make_type(dtype, "dtype")
if init is None:
init = False
init = _execute.make_bool(init, "init")
shape = _ops.convert_to_tensor(shape, _dtypes.int32)
_inputs_flat = [shape]
_attrs = ("dtype", dtype, "init", init)
_result = _execute.execute(b"Empty", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Empty", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ensure_shape(input, shape, name=None):
r"""Ensures that the tensor's shape matches the expected shape.
Raises an error if the input tensor's shape does not match the specified shape.
Returns the input tensor otherwise.
Args:
input: A `Tensor`. A tensor, whose shape is to be validated.
shape: A `tf.TensorShape` or list of `ints`.
The expected (possibly partially specified) shape of the input tensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "EnsureShape",
name, _ctx._post_execution_callbacks, input, "shape", shape)
return _result
except _core._FallbackException:
try:
return ensure_shape_eager_fallback(
input, shape=shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
shape = _execute.make_shape(shape, "shape")
_, _, _op = _op_def_lib._apply_op_helper(
"EnsureShape", input=input, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("shape", _op.get_attr("shape"), "T", _op.get_attr("T"))
_execute.record_gradient(
"EnsureShape", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ensure_shape_eager_fallback(input, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function ensure_shape
"""
_ctx = ctx if ctx else _context.context()
shape = _execute.make_shape(shape, "shape")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("shape", shape, "T", _attr_T)
_result = _execute.execute(b"EnsureShape", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"EnsureShape", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def expand_dims(input, axis, name=None):
r"""Inserts a dimension of 1 into a tensor's shape.
Given a tensor `input`, this operation inserts a dimension of 1 at the
dimension index `axis` of `input`'s shape. The dimension index `axis` starts at
zero; if you specify a negative number for `axis` it is counted backward from
the end.
This operation is useful if you want to add a batch dimension to a single
element. For example, if you have a single image of shape `[height, width,
channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
which will make the shape `[1, height, width, channels]`.
Other examples:
```
# 't' is a tensor of shape [2]
shape(expand_dims(t, 0)) ==> [1, 2]
shape(expand_dims(t, 1)) ==> [2, 1]
shape(expand_dims(t, -1)) ==> [2, 1]
# 't2' is a tensor of shape [2, 3, 5]
shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5]
shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5]
shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1]
```
This operation requires that:
`-1-input.dims() <= dim <= input.dims()`
This operation is related to `squeeze()`, which removes dimensions of
size 1.
Args:
input: A `Tensor`.
axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
0-D (scalar). Specifies the dimension index at which to
expand the shape of `input`. Must be in the range
`[-rank(input) - 1, rank(input)]`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ExpandDims",
name, _ctx._post_execution_callbacks, input, axis)
return _result
except _core._FallbackException:
try:
return expand_dims_eager_fallback(
input, axis, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ExpandDims", input=input, dim=axis, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tdim", _op.get_attr("Tdim"))
_execute.record_gradient(
"ExpandDims", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def expand_dims_eager_fallback(input, axis, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function expand_dims
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tdim, (axis,) = _execute.args_to_matching_eager([axis], _ctx, _dtypes.int32)
_inputs_flat = [input, axis]
_attrs = ("T", _attr_T, "Tdim", _attr_Tdim)
_result = _execute.execute(b"ExpandDims", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ExpandDims", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def extract_image_patches(images, ksizes, strides, rates, padding, name=None):
r"""Extract `patches` from `images` and put them in the "depth" output dimension.
Args:
images: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
4-D Tensor with shape `[batch, in_rows, in_cols, depth]`.
ksizes: A list of `ints` that has length `>= 4`.
The size of the sliding window for each dimension of `images`.
strides: A list of `ints` that has length `>= 4`.
1-D of length 4. How far the centers of two consecutive patches are in
the images. Must be: `[1, stride_rows, stride_cols, 1]`.
rates: A list of `ints` that has length `>= 4`.
1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the
input stride, specifying how far two consecutive patch samples are in the
input. Equivalent to extracting patches with
`patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by
subsampling them spatially by a factor of `rates`. This is equivalent to
`rate` in dilated (a.k.a. Atrous) convolutions.
padding: A `string` from: `"SAME", "VALID"`.
The type of padding algorithm to use.
We specify the size-related attributes as:
```python
ksizes = [1, ksize_rows, ksize_cols, 1]
strides = [1, strides_rows, strides_cols, 1]
rates = [1, rates_rows, rates_cols, 1]
```
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `images`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ExtractImagePatches", name, _ctx._post_execution_callbacks, images,
"ksizes", ksizes, "strides", strides, "rates", rates, "padding",
padding)
return _result
except _core._FallbackException:
try:
return extract_image_patches_eager_fallback(
images, ksizes=ksizes, strides=strides, rates=rates,
padding=padding, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(ksizes, (list, tuple)):
raise TypeError(
"Expected list for 'ksizes' argument to "
"'extract_image_patches' Op, not %r." % ksizes)
ksizes = [_execute.make_int(_i, "ksizes") for _i in ksizes]
if not isinstance(strides, (list, tuple)):
raise TypeError(
"Expected list for 'strides' argument to "
"'extract_image_patches' Op, not %r." % strides)
strides = [_execute.make_int(_i, "strides") for _i in strides]
if not isinstance(rates, (list, tuple)):
raise TypeError(
"Expected list for 'rates' argument to "
"'extract_image_patches' Op, not %r." % rates)
rates = [_execute.make_int(_i, "rates") for _i in rates]
padding = _execute.make_str(padding, "padding")
_, _, _op = _op_def_lib._apply_op_helper(
"ExtractImagePatches", images=images, ksizes=ksizes, strides=strides,
rates=rates, padding=padding, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("ksizes", _op.get_attr("ksizes"), "strides",
_op.get_attr("strides"), "rates", _op.get_attr("rates"), "T",
_op.get_attr("T"), "padding", _op.get_attr("padding"))
_execute.record_gradient(
"ExtractImagePatches", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def extract_image_patches_eager_fallback(images, ksizes, strides, rates, padding, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function extract_image_patches
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(ksizes, (list, tuple)):
raise TypeError(
"Expected list for 'ksizes' argument to "
"'extract_image_patches' Op, not %r." % ksizes)
ksizes = [_execute.make_int(_i, "ksizes") for _i in ksizes]
if not isinstance(strides, (list, tuple)):
raise TypeError(
"Expected list for 'strides' argument to "
"'extract_image_patches' Op, not %r." % strides)
strides = [_execute.make_int(_i, "strides") for _i in strides]
if not isinstance(rates, (list, tuple)):
raise TypeError(
"Expected list for 'rates' argument to "
"'extract_image_patches' Op, not %r." % rates)
rates = [_execute.make_int(_i, "rates") for _i in rates]
padding = _execute.make_str(padding, "padding")
_attr_T, (images,) = _execute.args_to_matching_eager([images], _ctx)
_inputs_flat = [images]
_attrs = ("ksizes", ksizes, "strides", strides, "rates", rates, "T",
_attr_T, "padding", padding)
_result = _execute.execute(b"ExtractImagePatches", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ExtractImagePatches", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('extract_volume_patches')
def extract_volume_patches(input, ksizes, strides, padding, name=None):
r"""Extract `patches` from `input` and put them in the "depth" output dimension. 3D extension of `extract_image_patches`.
Args:
input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
5-D Tensor with shape `[batch, in_planes, in_rows, in_cols, depth]`.
ksizes: A list of `ints` that has length `>= 5`.
The size of the sliding window for each dimension of `input`.
strides: A list of `ints` that has length `>= 5`.
1-D of length 5. How far the centers of two consecutive patches are in
`input`. Must be: `[1, stride_planes, stride_rows, stride_cols, 1]`.
padding: A `string` from: `"SAME", "VALID"`.
The type of padding algorithm to use.
We specify the size-related attributes as:
```python
ksizes = [1, ksize_planes, ksize_rows, ksize_cols, 1]
strides = [1, stride_planes, strides_rows, strides_cols, 1]
```
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ExtractVolumePatches", name, _ctx._post_execution_callbacks, input,
"ksizes", ksizes, "strides", strides, "padding", padding)
return _result
except _core._FallbackException:
try:
return extract_volume_patches_eager_fallback(
input, ksizes=ksizes, strides=strides, padding=padding, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
extract_volume_patches, input=input, ksizes=ksizes,
strides=strides, padding=padding,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(ksizes, (list, tuple)):
raise TypeError(
"Expected list for 'ksizes' argument to "
"'extract_volume_patches' Op, not %r." % ksizes)
ksizes = [_execute.make_int(_i, "ksizes") for _i in ksizes]
if not isinstance(strides, (list, tuple)):
raise TypeError(
"Expected list for 'strides' argument to "
"'extract_volume_patches' Op, not %r." % strides)
strides = [_execute.make_int(_i, "strides") for _i in strides]
padding = _execute.make_str(padding, "padding")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"ExtractVolumePatches", input=input, ksizes=ksizes, strides=strides,
padding=padding, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
extract_volume_patches, input=input, ksizes=ksizes, strides=strides,
padding=padding, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("ksizes", _op.get_attr("ksizes"), "strides",
_op.get_attr("strides"), "T", _op.get_attr("T"), "padding",
_op.get_attr("padding"))
_execute.record_gradient(
"ExtractVolumePatches", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def extract_volume_patches_eager_fallback(input, ksizes, strides, padding, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function extract_volume_patches
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(ksizes, (list, tuple)):
raise TypeError(
"Expected list for 'ksizes' argument to "
"'extract_volume_patches' Op, not %r." % ksizes)
ksizes = [_execute.make_int(_i, "ksizes") for _i in ksizes]
if not isinstance(strides, (list, tuple)):
raise TypeError(
"Expected list for 'strides' argument to "
"'extract_volume_patches' Op, not %r." % strides)
strides = [_execute.make_int(_i, "strides") for _i in strides]
padding = _execute.make_str(padding, "padding")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("ksizes", ksizes, "strides", strides, "T", _attr_T, "padding",
padding)
_result = _execute.execute(b"ExtractVolumePatches", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ExtractVolumePatches", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('quantization.fake_quant_with_min_max_args', v1=['quantization.fake_quant_with_min_max_args', 'fake_quant_with_min_max_args'])
@deprecated_endpoints('fake_quant_with_min_max_args')
def fake_quant_with_min_max_args(inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None):
r"""Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
Attributes `[min; max]` define the clamping range for the `inputs` data.
`inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
then de-quantized and output as floats in `[min; max]` interval.
`num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
Quantization is called fake since the output is still in floating point.
Args:
inputs: A `Tensor` of type `float32`.
min: An optional `float`. Defaults to `-6`.
max: An optional `float`. Defaults to `6`.
num_bits: An optional `int`. Defaults to `8`.
narrow_range: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"FakeQuantWithMinMaxArgs", name, _ctx._post_execution_callbacks,
inputs, "min", min, "max", max, "num_bits", num_bits, "narrow_range",
narrow_range)
return _result
except _core._FallbackException:
try:
return fake_quant_with_min_max_args_eager_fallback(
inputs, min=min, max=max, num_bits=num_bits,
narrow_range=narrow_range, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_args, inputs=inputs, min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if min is None:
min = -6
min = _execute.make_float(min, "min")
if max is None:
max = 6
max = _execute.make_float(max, "max")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"FakeQuantWithMinMaxArgs", inputs=inputs, min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_args, inputs=inputs, min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("min", _op.get_attr("min"), "max", _op.get_attr("max"),
"num_bits", _op.get_attr("num_bits"), "narrow_range",
_op.get_attr("narrow_range"))
_execute.record_gradient(
"FakeQuantWithMinMaxArgs", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def fake_quant_with_min_max_args_eager_fallback(inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function fake_quant_with_min_max_args
"""
_ctx = ctx if ctx else _context.context()
if min is None:
min = -6
min = _execute.make_float(min, "min")
if max is None:
max = 6
max = _execute.make_float(max, "max")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
inputs = _ops.convert_to_tensor(inputs, _dtypes.float32)
_inputs_flat = [inputs]
_attrs = ("min", min, "max", max, "num_bits", num_bits, "narrow_range",
narrow_range)
_result = _execute.execute(b"FakeQuantWithMinMaxArgs", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"FakeQuantWithMinMaxArgs", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('quantization.fake_quant_with_min_max_args_gradient', v1=['quantization.fake_quant_with_min_max_args_gradient', 'fake_quant_with_min_max_args_gradient'])
@deprecated_endpoints('fake_quant_with_min_max_args_gradient')
def fake_quant_with_min_max_args_gradient(gradients, inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None):
r"""Compute gradients for a FakeQuantWithMinMaxArgs operation.
Args:
gradients: A `Tensor` of type `float32`.
Backpropagated gradients above the FakeQuantWithMinMaxArgs operation.
inputs: A `Tensor` of type `float32`.
Values passed as inputs to the FakeQuantWithMinMaxArgs operation.
min: An optional `float`. Defaults to `-6`.
max: An optional `float`. Defaults to `6`.
num_bits: An optional `int`. Defaults to `8`.
narrow_range: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"FakeQuantWithMinMaxArgsGradient", name,
_ctx._post_execution_callbacks, gradients, inputs, "min", min, "max",
max, "num_bits", num_bits, "narrow_range", narrow_range)
return _result
except _core._FallbackException:
try:
return fake_quant_with_min_max_args_gradient_eager_fallback(
gradients, inputs, min=min, max=max, num_bits=num_bits,
narrow_range=narrow_range, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_args_gradient, gradients=gradients,
inputs=inputs, min=min,
max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if min is None:
min = -6
min = _execute.make_float(min, "min")
if max is None:
max = 6
max = _execute.make_float(max, "max")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"FakeQuantWithMinMaxArgsGradient", gradients=gradients, inputs=inputs,
min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_args_gradient, gradients=gradients,
inputs=inputs, min=min,
max=max, num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("min", _op.get_attr("min"), "max", _op.get_attr("max"),
"num_bits", _op.get_attr("num_bits"), "narrow_range",
_op.get_attr("narrow_range"))
_execute.record_gradient(
"FakeQuantWithMinMaxArgsGradient", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def fake_quant_with_min_max_args_gradient_eager_fallback(gradients, inputs, min=-6, max=6, num_bits=8, narrow_range=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function fake_quant_with_min_max_args_gradient
"""
_ctx = ctx if ctx else _context.context()
if min is None:
min = -6
min = _execute.make_float(min, "min")
if max is None:
max = 6
max = _execute.make_float(max, "max")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
gradients = _ops.convert_to_tensor(gradients, _dtypes.float32)
inputs = _ops.convert_to_tensor(inputs, _dtypes.float32)
_inputs_flat = [gradients, inputs]
_attrs = ("min", min, "max", max, "num_bits", num_bits, "narrow_range",
narrow_range)
_result = _execute.execute(b"FakeQuantWithMinMaxArgsGradient", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"FakeQuantWithMinMaxArgsGradient", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('quantization.fake_quant_with_min_max_vars', v1=['quantization.fake_quant_with_min_max_vars', 'fake_quant_with_min_max_vars'])
@deprecated_endpoints('fake_quant_with_min_max_vars')
def fake_quant_with_min_max_vars(inputs, min, max, num_bits=8, narrow_range=False, name=None):
r"""Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
and `max` to 'outputs' tensor of same shape as `inputs`.
`[min; max]` define the clamping range for the `inputs` data.
`inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
then de-quantized and output as floats in `[min; max]` interval.
`num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
This operation has a gradient and thus allows for training `min` and `max`
values.
Args:
inputs: A `Tensor` of type `float32`.
min: A `Tensor` of type `float32`.
max: A `Tensor` of type `float32`.
num_bits: An optional `int`. Defaults to `8`.
narrow_range: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"FakeQuantWithMinMaxVars", name, _ctx._post_execution_callbacks,
inputs, min, max, "num_bits", num_bits, "narrow_range", narrow_range)
return _result
except _core._FallbackException:
try:
return fake_quant_with_min_max_vars_eager_fallback(
inputs, min, max, num_bits=num_bits, narrow_range=narrow_range,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars, inputs=inputs, min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"FakeQuantWithMinMaxVars", inputs=inputs, min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars, inputs=inputs, min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num_bits", _op.get_attr("num_bits"), "narrow_range",
_op.get_attr("narrow_range"))
_execute.record_gradient(
"FakeQuantWithMinMaxVars", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def fake_quant_with_min_max_vars_eager_fallback(inputs, min, max, num_bits=8, narrow_range=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function fake_quant_with_min_max_vars
"""
_ctx = ctx if ctx else _context.context()
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
inputs = _ops.convert_to_tensor(inputs, _dtypes.float32)
min = _ops.convert_to_tensor(min, _dtypes.float32)
max = _ops.convert_to_tensor(max, _dtypes.float32)
_inputs_flat = [inputs, min, max]
_attrs = ("num_bits", num_bits, "narrow_range", narrow_range)
_result = _execute.execute(b"FakeQuantWithMinMaxVars", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"FakeQuantWithMinMaxVars", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
_fake_quant_with_min_max_vars_gradient_outputs = ["backprops_wrt_input",
"backprop_wrt_min",
"backprop_wrt_max"]
_FakeQuantWithMinMaxVarsGradientOutput = _collections.namedtuple(
"FakeQuantWithMinMaxVarsGradient",
_fake_quant_with_min_max_vars_gradient_outputs)
@_dispatch.add_dispatch_list
@tf_export('quantization.fake_quant_with_min_max_vars_gradient', v1=['quantization.fake_quant_with_min_max_vars_gradient', 'fake_quant_with_min_max_vars_gradient'])
@deprecated_endpoints('fake_quant_with_min_max_vars_gradient')
def fake_quant_with_min_max_vars_gradient(gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None):
r"""Compute gradients for a FakeQuantWithMinMaxVars operation.
Args:
gradients: A `Tensor` of type `float32`.
Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
inputs: A `Tensor` of type `float32`.
Values passed as inputs to the FakeQuantWithMinMaxVars operation.
min, max: Quantization interval, scalar floats.
min: A `Tensor` of type `float32`.
max: A `Tensor` of type `float32`.
num_bits: An optional `int`. Defaults to `8`.
The bitwidth of the quantization; between 2 and 8, inclusive.
narrow_range: An optional `bool`. Defaults to `False`.
Whether to quantize into 2^num_bits - 1 distinct values.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max).
backprops_wrt_input: A `Tensor` of type `float32`.
backprop_wrt_min: A `Tensor` of type `float32`.
backprop_wrt_max: A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"FakeQuantWithMinMaxVarsGradient", name,
_ctx._post_execution_callbacks, gradients, inputs, min, max,
"num_bits", num_bits, "narrow_range", narrow_range)
_result = _FakeQuantWithMinMaxVarsGradientOutput._make(_result)
return _result
except _core._FallbackException:
try:
return fake_quant_with_min_max_vars_gradient_eager_fallback(
gradients, inputs, min, max, num_bits=num_bits,
narrow_range=narrow_range, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars_gradient, gradients=gradients,
inputs=inputs, min=min,
max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"FakeQuantWithMinMaxVarsGradient", gradients=gradients, inputs=inputs,
min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars_gradient, gradients=gradients,
inputs=inputs, min=min,
max=max, num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num_bits", _op.get_attr("num_bits"), "narrow_range",
_op.get_attr("narrow_range"))
_execute.record_gradient(
"FakeQuantWithMinMaxVarsGradient", _inputs_flat, _attrs, _result, name)
_result = _FakeQuantWithMinMaxVarsGradientOutput._make(_result)
return _result
def fake_quant_with_min_max_vars_gradient_eager_fallback(gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function fake_quant_with_min_max_vars_gradient
"""
_ctx = ctx if ctx else _context.context()
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
gradients = _ops.convert_to_tensor(gradients, _dtypes.float32)
inputs = _ops.convert_to_tensor(inputs, _dtypes.float32)
min = _ops.convert_to_tensor(min, _dtypes.float32)
max = _ops.convert_to_tensor(max, _dtypes.float32)
_inputs_flat = [gradients, inputs, min, max]
_attrs = ("num_bits", num_bits, "narrow_range", narrow_range)
_result = _execute.execute(b"FakeQuantWithMinMaxVarsGradient", 3,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"FakeQuantWithMinMaxVarsGradient", _inputs_flat, _attrs, _result, name)
_result = _FakeQuantWithMinMaxVarsGradientOutput._make(_result)
return _result
@_dispatch.add_dispatch_list
@tf_export('quantization.fake_quant_with_min_max_vars_per_channel', v1=['quantization.fake_quant_with_min_max_vars_per_channel', 'fake_quant_with_min_max_vars_per_channel'])
@deprecated_endpoints('fake_quant_with_min_max_vars_per_channel')
def fake_quant_with_min_max_vars_per_channel(inputs, min, max, num_bits=8, narrow_range=False, name=None):
r"""Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
`[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]`
to 'outputs' tensor of same shape as `inputs`.
`[min; max]` define the clamping range for the `inputs` data.
`inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]`
when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and
then de-quantized and output as floats in `[min; max]` interval.
`num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
This operation has a gradient and thus allows for training `min` and `max`
values.
Args:
inputs: A `Tensor` of type `float32`.
min: A `Tensor` of type `float32`.
max: A `Tensor` of type `float32`.
num_bits: An optional `int`. Defaults to `8`.
narrow_range: An optional `bool`. Defaults to `False`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"FakeQuantWithMinMaxVarsPerChannel", name,
_ctx._post_execution_callbacks, inputs, min, max, "num_bits",
num_bits, "narrow_range", narrow_range)
return _result
except _core._FallbackException:
try:
return fake_quant_with_min_max_vars_per_channel_eager_fallback(
inputs, min, max, num_bits=num_bits, narrow_range=narrow_range,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars_per_channel, inputs=inputs,
min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"FakeQuantWithMinMaxVarsPerChannel", inputs=inputs, min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars_per_channel, inputs=inputs, min=min,
max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num_bits", _op.get_attr("num_bits"), "narrow_range",
_op.get_attr("narrow_range"))
_execute.record_gradient(
"FakeQuantWithMinMaxVarsPerChannel", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def fake_quant_with_min_max_vars_per_channel_eager_fallback(inputs, min, max, num_bits=8, narrow_range=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function fake_quant_with_min_max_vars_per_channel
"""
_ctx = ctx if ctx else _context.context()
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
inputs = _ops.convert_to_tensor(inputs, _dtypes.float32)
min = _ops.convert_to_tensor(min, _dtypes.float32)
max = _ops.convert_to_tensor(max, _dtypes.float32)
_inputs_flat = [inputs, min, max]
_attrs = ("num_bits", num_bits, "narrow_range", narrow_range)
_result = _execute.execute(b"FakeQuantWithMinMaxVarsPerChannel", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"FakeQuantWithMinMaxVarsPerChannel", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
_fake_quant_with_min_max_vars_per_channel_gradient_outputs = ["backprops_wrt_input",
"backprop_wrt_min",
"backprop_wrt_max"]
_FakeQuantWithMinMaxVarsPerChannelGradientOutput = _collections.namedtuple(
"FakeQuantWithMinMaxVarsPerChannelGradient",
_fake_quant_with_min_max_vars_per_channel_gradient_outputs)
@_dispatch.add_dispatch_list
@tf_export('quantization.fake_quant_with_min_max_vars_per_channel_gradient', v1=['quantization.fake_quant_with_min_max_vars_per_channel_gradient', 'fake_quant_with_min_max_vars_per_channel_gradient'])
@deprecated_endpoints('fake_quant_with_min_max_vars_per_channel_gradient')
def fake_quant_with_min_max_vars_per_channel_gradient(gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None):
r"""Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
Args:
gradients: A `Tensor` of type `float32`.
Backpropagated gradients above the FakeQuantWithMinMaxVars operation,
shape one of: `[d]`, `[b, d]`, `[b, h, w, d]`.
inputs: A `Tensor` of type `float32`.
Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape
same as `gradients`.
min, max: Quantization interval, floats of shape `[d]`.
min: A `Tensor` of type `float32`.
max: A `Tensor` of type `float32`.
num_bits: An optional `int`. Defaults to `8`.
The bitwidth of the quantization; between 2 and 16, inclusive.
narrow_range: An optional `bool`. Defaults to `False`.
Whether to quantize into 2^num_bits - 1 distinct values.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max).
backprops_wrt_input: A `Tensor` of type `float32`.
backprop_wrt_min: A `Tensor` of type `float32`.
backprop_wrt_max: A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"FakeQuantWithMinMaxVarsPerChannelGradient", name,
_ctx._post_execution_callbacks, gradients, inputs, min, max,
"num_bits", num_bits, "narrow_range", narrow_range)
_result = _FakeQuantWithMinMaxVarsPerChannelGradientOutput._make(_result)
return _result
except _core._FallbackException:
try:
return fake_quant_with_min_max_vars_per_channel_gradient_eager_fallback(
gradients, inputs, min, max, num_bits=num_bits,
narrow_range=narrow_range, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars_per_channel_gradient, gradients=gradients,
inputs=inputs,
min=min,
max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"FakeQuantWithMinMaxVarsPerChannelGradient", gradients=gradients,
inputs=inputs, min=min,
max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
fake_quant_with_min_max_vars_per_channel_gradient, gradients=gradients,
inputs=inputs,
min=min, max=max,
num_bits=num_bits,
narrow_range=narrow_range,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num_bits", _op.get_attr("num_bits"), "narrow_range",
_op.get_attr("narrow_range"))
_execute.record_gradient(
"FakeQuantWithMinMaxVarsPerChannelGradient", _inputs_flat, _attrs, _result, name)
_result = _FakeQuantWithMinMaxVarsPerChannelGradientOutput._make(_result)
return _result
def fake_quant_with_min_max_vars_per_channel_gradient_eager_fallback(gradients, inputs, min, max, num_bits=8, narrow_range=False, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function fake_quant_with_min_max_vars_per_channel_gradient
"""
_ctx = ctx if ctx else _context.context()
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if narrow_range is None:
narrow_range = False
narrow_range = _execute.make_bool(narrow_range, "narrow_range")
gradients = _ops.convert_to_tensor(gradients, _dtypes.float32)
inputs = _ops.convert_to_tensor(inputs, _dtypes.float32)
min = _ops.convert_to_tensor(min, _dtypes.float32)
max = _ops.convert_to_tensor(max, _dtypes.float32)
_inputs_flat = [gradients, inputs, min, max]
_attrs = ("num_bits", num_bits, "narrow_range", narrow_range)
_result = _execute.execute(b"FakeQuantWithMinMaxVarsPerChannelGradient", 3,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"FakeQuantWithMinMaxVarsPerChannelGradient", _inputs_flat, _attrs, _result, name)
_result = _FakeQuantWithMinMaxVarsPerChannelGradientOutput._make(_result)
return _result
@_dispatch.add_dispatch_list
@tf_export('fill')
def fill(dims, value, name=None):
r"""Creates a tensor filled with a scalar value.
This operation creates a tensor of shape `dims` and fills it with `value`.
For example:
```
# Output tensor has shape [2, 3].
fill([2, 3], 9) ==> [[9, 9, 9]
[9, 9, 9]]
```
`tf.fill` differs from `tf.constant` in a few ways:
* `tf.fill` only supports scalar contents, whereas `tf.constant` supports
Tensor values.
* `tf.fill` creates an Op in the computation graph that constructs the actual
Tensor value at runtime. This is in contrast to `tf.constant` which embeds
the entire Tensor into the graph with a `Const` node.
* Because `tf.fill` evaluates at graph runtime, it supports dynamic shapes
based on other runtime Tensors, unlike `tf.constant`.
Args:
dims: A `Tensor`. Must be one of the following types: `int32`, `int64`.
1-D. Represents the shape of the output tensor.
value: A `Tensor`. 0-D (scalar). Value to fill the returned tensor.
@compatibility(numpy)
Equivalent to np.full
@end_compatibility
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `value`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Fill", name,
_ctx._post_execution_callbacks, dims, value)
return _result
except _core._FallbackException:
try:
return fill_eager_fallback(
dims, value, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
fill, dims=dims, value=value, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"Fill", dims=dims, value=value, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
fill, dims=dims, value=value, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "index_type", _op.get_attr("index_type"))
_execute.record_gradient(
"Fill", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def fill_eager_fallback(dims, value, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function fill
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (value,) = _execute.args_to_matching_eager([value], _ctx)
_attr_index_type, (dims,) = _execute.args_to_matching_eager([dims], _ctx, _dtypes.int32)
_inputs_flat = [dims, value]
_attrs = ("T", _attr_T, "index_type", _attr_index_type)
_result = _execute.execute(b"Fill", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Fill", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def gather(params, indices, validate_indices=True, name=None):
r"""Gather slices from `params` according to `indices`.
`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
Produces an output tensor with shape `indices.shape + params.shape[1:]` where:
```python
# Scalar indices
output[:, ..., :] = params[indices, :, ... :]
# Vector indices
output[i, :, ..., :] = params[indices[i], :, ... :]
# Higher rank indices
output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :]
```
If `indices` is a permutation and `len(indices) == params.shape[0]` then
this operation will permute `params` accordingly.
`validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in
`indices` are always validated to be within range. If assigned to GPU,
out-of-bound indices result in safe but unspecified behavior, which may include
raising an error.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
</div>
Args:
params: A `Tensor`.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
validate_indices: An optional `bool`. Defaults to `True`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `params`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Gather", name,
_ctx._post_execution_callbacks, params, indices, "validate_indices",
validate_indices)
return _result
except _core._FallbackException:
try:
return gather_eager_fallback(
params, indices, validate_indices=validate_indices, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if validate_indices is None:
validate_indices = True
validate_indices = _execute.make_bool(validate_indices, "validate_indices")
_, _, _op = _op_def_lib._apply_op_helper(
"Gather", params=params, indices=indices,
validate_indices=validate_indices, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("validate_indices", _op.get_attr("validate_indices"), "Tparams",
_op.get_attr("Tparams"), "Tindices", _op.get_attr("Tindices"))
_execute.record_gradient(
"Gather", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def gather_eager_fallback(params, indices, validate_indices=True, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function gather
"""
_ctx = ctx if ctx else _context.context()
if validate_indices is None:
validate_indices = True
validate_indices = _execute.make_bool(validate_indices, "validate_indices")
_attr_Tparams, (params,) = _execute.args_to_matching_eager([params], _ctx)
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
_inputs_flat = [params, indices]
_attrs = ("validate_indices", validate_indices, "Tparams", _attr_Tparams,
"Tindices", _attr_Tindices)
_result = _execute.execute(b"Gather", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Gather", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('gather_nd', v1=['gather_nd', 'manip.gather_nd'])
@deprecated_endpoints('manip.gather_nd')
def gather_nd(params, indices, name=None):
r"""Gather slices from `params` into a Tensor with shape specified by `indices`.
`indices` is an K-dimensional integer tensor, best thought of as a
(K-1)-dimensional tensor of indices into `params`, where each element defines a
slice of `params`:
output[\\(i_0, ..., i_{K-2}\\)] = params[indices[\\(i_0, ..., i_{K-2}\\)]]
Whereas in `tf.gather` `indices` defines slices into the first
dimension of `params`, in `tf.gather_nd`, `indices` defines slices into the
first `N` dimensions of `params`, where `N = indices.shape[-1]`.
The last dimension of `indices` can be at most the rank of
`params`:
indices.shape[-1] <= params.rank
The last dimension of `indices` corresponds to elements
(if `indices.shape[-1] == params.rank`) or slices
(if `indices.shape[-1] < params.rank`) along dimension `indices.shape[-1]`
of `params`. The output tensor has shape
indices.shape[:-1] + params.shape[indices.shape[-1]:]
Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, a 0 is stored in the
corresponding output value.
Some examples below.
Simple indexing into a matrix:
```python
indices = [[0, 0], [1, 1]]
params = [['a', 'b'], ['c', 'd']]
output = ['a', 'd']
```
Slice indexing into a matrix:
```python
indices = [[1], [0]]
params = [['a', 'b'], ['c', 'd']]
output = [['c', 'd'], ['a', 'b']]
```
Indexing into a 3-tensor:
```python
indices = [[1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[['a1', 'b1'], ['c1', 'd1']]]
indices = [[0, 1], [1, 0]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [['c0', 'd0'], ['a1', 'b1']]
indices = [[0, 0, 1], [1, 0, 1]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = ['b0', 'b1']
```
Batched indexing into a matrix:
```python
indices = [[[0, 0]], [[0, 1]]]
params = [['a', 'b'], ['c', 'd']]
output = [['a'], ['b']]
```
Batched slice indexing into a matrix:
```python
indices = [[[1]], [[0]]]
params = [['a', 'b'], ['c', 'd']]
output = [[['c', 'd']], [['a', 'b']]]
```
Batched indexing into a 3-tensor:
```python
indices = [[[1]], [[0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[[['a1', 'b1'], ['c1', 'd1']]],
[[['a0', 'b0'], ['c0', 'd0']]]]
indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [[['c0', 'd0'], ['a1', 'b1']],
[['a0', 'b0'], ['c1', 'd1']]]
indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]
params = [[['a0', 'b0'], ['c0', 'd0']],
[['a1', 'b1'], ['c1', 'd1']]]
output = [['b0', 'b1'], ['d0', 'c1']]
```
See also `tf.gather` and `tf.batch_gather`.
Args:
params: A `Tensor`. The tensor from which to gather values.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Index tensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `params`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "GatherNd",
name, _ctx._post_execution_callbacks, params, indices)
return _result
except _core._FallbackException:
try:
return gather_nd_eager_fallback(
params, indices, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
gather_nd, params=params, indices=indices, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"GatherNd", params=params, indices=indices, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
gather_nd, params=params, indices=indices, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("Tparams", _op.get_attr("Tparams"), "Tindices",
_op.get_attr("Tindices"))
_execute.record_gradient(
"GatherNd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def gather_nd_eager_fallback(params, indices, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function gather_nd
"""
_ctx = ctx if ctx else _context.context()
_attr_Tparams, (params,) = _execute.args_to_matching_eager([params], _ctx)
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
_inputs_flat = [params, indices]
_attrs = ("Tparams", _attr_Tparams, "Tindices", _attr_Tindices)
_result = _execute.execute(b"GatherNd", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"GatherNd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def gather_v2(params, indices, axis, name=None):
r"""Gather slices from `params` axis `axis` according to `indices`.
`indices` must be an integer tensor of any dimension (usually 0-D or 1-D).
Produces an output tensor with shape `params.shape[:axis] + indices.shape +
params.shape[axis + 1:]` where:
```python
# Scalar indices (output is rank(params) - 1).
output[a_0, ..., a_n, b_0, ..., b_n] =
params[a_0, ..., a_n, indices, b_0, ..., b_n]
# Vector indices (output is rank(params)).
output[a_0, ..., a_n, i, b_0, ..., b_n] =
params[a_0, ..., a_n, indices[i], b_0, ..., b_n]
# Higher rank indices (output is rank(params) + rank(indices) - 1).
output[a_0, ..., a_n, i, ..., j, b_0, ... b_n] =
params[a_0, ..., a_n, indices[i, ..., j], b_0, ..., b_n]
```
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/Gather.png" alt>
</div>
Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, a 0 is stored in the
corresponding output value.
See also `tf.batch_gather` and `tf.gather_nd`.
Args:
params: A `Tensor`.
The tensor from which to gather values. Must be at least rank
`axis + 1`.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Index tensor. Must be in range `[0, params.shape[axis])`.
axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
The axis in `params` to gather `indices` from. Defaults to the first
dimension. Supports negative indexes.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `params`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "GatherV2",
name, _ctx._post_execution_callbacks, params, indices, axis)
return _result
except _core._FallbackException:
try:
return gather_v2_eager_fallback(
params, indices, axis, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"GatherV2", params=params, indices=indices, axis=axis, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("Tparams", _op.get_attr("Tparams"), "Tindices",
_op.get_attr("Tindices"), "Taxis", _op.get_attr("Taxis"))
_execute.record_gradient(
"GatherV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def gather_v2_eager_fallback(params, indices, axis, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function gather_v2
"""
_ctx = ctx if ctx else _context.context()
_attr_Tparams, (params,) = _execute.args_to_matching_eager([params], _ctx)
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
_attr_Taxis, (axis,) = _execute.args_to_matching_eager([axis], _ctx)
_inputs_flat = [params, indices, axis]
_attrs = ("Tparams", _attr_Tparams, "Tindices", _attr_Tindices, "Taxis",
_attr_Taxis)
_result = _execute.execute(b"GatherV2", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"GatherV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('guarantee_const')
def guarantee_const(input, name=None):
r"""Gives a guarantee to the TF runtime that the input tensor is a constant.
The runtime is then free to make optimizations based on this.
Only accepts value typed tensors as inputs and rejects resource variable handles
as input.
Returns the input tensor without modification.
Args:
input: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"GuaranteeConst", name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return guarantee_const_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
guarantee_const, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"GuaranteeConst", input=input, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
guarantee_const, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"GuaranteeConst", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def guarantee_const_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function guarantee_const
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"GuaranteeConst", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"GuaranteeConst", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def identity(input, name=None):
r"""Return a tensor with the same shape and contents as the input tensor or value.
Args:
input: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Identity",
name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return identity_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Identity", input=input, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"Identity", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def identity_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function identity
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"Identity", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Identity", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('identity_n')
def identity_n(input, name=None):
r"""Returns a list of tensors with the same shapes and contents as the input
tensors.
This op can be used to override the gradient for complicated functions. For
example, suppose y = f(x) and we wish to apply a custom function g for backprop
such that dx = g(dy). In Python,
```python
with tf.get_default_graph().gradient_override_map(
{'IdentityN': 'OverrideGradientWithG'}):
y, _ = identity_n([f(x), x])
@tf.RegisterGradient('OverrideGradientWithG')
def ApplyG(op, dy, _):
return [None, g(dy)] # Do not backprop to f(x).
```
Args:
input: A list of `Tensor` objects.
name: A name for the operation (optional).
Returns:
A list of `Tensor` objects. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "IdentityN",
name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return identity_n_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
identity_n, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"IdentityN", input=input, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
identity_n, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"IdentityN", _inputs_flat, _attrs, _result, name)
return _result
def identity_n_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function identity_n
"""
_ctx = ctx if ctx else _context.context()
_attr_T, input = _execute.convert_to_mixed_eager_tensors(input, _ctx)
_inputs_flat = list(input)
_attrs = ("T", _attr_T)
_result = _execute.execute(b"IdentityN", len(input), inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"IdentityN", _inputs_flat, _attrs, _result, name)
return _result
def immutable_const(dtype, shape, memory_region_name, name=None):
r"""Returns immutable tensor from memory region.
The current implementation memmaps the tensor from a file.
Args:
dtype: A `tf.DType`. Type of the returned tensor.
shape: A `tf.TensorShape` or list of `ints`. Shape of the returned tensor.
memory_region_name: A `string`.
Name of readonly memory region used by the tensor, see
NewReadOnlyMemoryRegionFromFile in tensorflow::Env.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `dtype`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ImmutableConst", name, _ctx._post_execution_callbacks, "dtype",
dtype, "shape", shape, "memory_region_name", memory_region_name)
return _result
except _core._FallbackException:
try:
return immutable_const_eager_fallback(
dtype=dtype, shape=shape, memory_region_name=memory_region_name,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
dtype = _execute.make_type(dtype, "dtype")
shape = _execute.make_shape(shape, "shape")
memory_region_name = _execute.make_str(memory_region_name, "memory_region_name")
_, _, _op = _op_def_lib._apply_op_helper(
"ImmutableConst", dtype=dtype, shape=shape,
memory_region_name=memory_region_name, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("dtype", _op.get_attr("dtype"), "shape", _op.get_attr("shape"),
"memory_region_name", _op.get_attr("memory_region_name"))
_execute.record_gradient(
"ImmutableConst", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def immutable_const_eager_fallback(dtype, shape, memory_region_name, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function immutable_const
"""
_ctx = ctx if ctx else _context.context()
dtype = _execute.make_type(dtype, "dtype")
shape = _execute.make_shape(shape, "shape")
memory_region_name = _execute.make_str(memory_region_name, "memory_region_name")
_inputs_flat = []
_attrs = ("dtype", dtype, "shape", shape, "memory_region_name",
memory_region_name)
_result = _execute.execute(b"ImmutableConst", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ImmutableConst", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def inplace_add(x, i, v, name=None):
r""" Adds v into specified rows of x.
Computes y = x; y[i, :] += v; return y.
Args:
x: A `Tensor`. A `Tensor` of type T.
i: A `Tensor` of type `int32`.
A vector. Indices into the left-most dimension of `x`.
v: A `Tensor`. Must have the same type as `x`.
A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "InplaceAdd",
name, _ctx._post_execution_callbacks, x, i, v)
return _result
except _core._FallbackException:
try:
return inplace_add_eager_fallback(
x, i, v, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"InplaceAdd", x=x, i=i, v=v, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"InplaceAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def inplace_add_eager_fallback(x, i, v, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function inplace_add
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([x, v], _ctx)
(x, v) = _inputs_T
i = _ops.convert_to_tensor(i, _dtypes.int32)
_inputs_flat = [x, i, v]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"InplaceAdd", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"InplaceAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def inplace_sub(x, i, v, name=None):
r""" Subtracts `v` into specified rows of `x`.
Computes y = x; y[i, :] -= v; return y.
Args:
x: A `Tensor`. A `Tensor` of type T.
i: A `Tensor` of type `int32`.
A vector. Indices into the left-most dimension of `x`.
v: A `Tensor`. Must have the same type as `x`.
A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "InplaceSub",
name, _ctx._post_execution_callbacks, x, i, v)
return _result
except _core._FallbackException:
try:
return inplace_sub_eager_fallback(
x, i, v, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"InplaceSub", x=x, i=i, v=v, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"InplaceSub", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def inplace_sub_eager_fallback(x, i, v, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function inplace_sub
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([x, v], _ctx)
(x, v) = _inputs_T
i = _ops.convert_to_tensor(i, _dtypes.int32)
_inputs_flat = [x, i, v]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"InplaceSub", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"InplaceSub", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def inplace_update(x, i, v, name=None):
r""" Updates specified rows with values in `v`.
Computes `x[i, :] = v; return x`.
Args:
x: A `Tensor`. A tensor of type `T`.
i: A `Tensor` of type `int32`.
A vector. Indices into the left-most dimension of `x`.
v: A `Tensor`. Must have the same type as `x`.
A `Tensor` of type T. Same dimension sizes as x except the first dimension, which must be the same as i's size.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"InplaceUpdate", name, _ctx._post_execution_callbacks, x, i, v)
return _result
except _core._FallbackException:
try:
return inplace_update_eager_fallback(
x, i, v, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"InplaceUpdate", x=x, i=i, v=v, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"InplaceUpdate", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def inplace_update_eager_fallback(x, i, v, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function inplace_update
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([x, v], _ctx)
(x, v) = _inputs_T
i = _ops.convert_to_tensor(i, _dtypes.int32)
_inputs_flat = [x, i, v]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"InplaceUpdate", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"InplaceUpdate", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('math.invert_permutation', v1=['math.invert_permutation', 'invert_permutation'])
@deprecated_endpoints('invert_permutation')
def invert_permutation(x, name=None):
r"""Computes the inverse permutation of a tensor.
This operation computes the inverse of an index permutation. It takes a 1-D
integer tensor `x`, which represents the indices of a zero-based array, and
swaps each value with its index position. In other words, for an output tensor
`y` and an input tensor `x`, this operation computes the following:
`y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`
The values must include 0. There can be no duplicate values or negative values.
For example:
```
# tensor `x` is [3, 4, 0, 2, 1]
invert_permutation(x) ==> [2, 4, 3, 0, 1]
```
Args:
x: A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"InvertPermutation", name, _ctx._post_execution_callbacks, x)
return _result
except _core._FallbackException:
try:
return invert_permutation_eager_fallback(
x, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
invert_permutation, x=x, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"InvertPermutation", x=x, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
invert_permutation, x=x, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"InvertPermutation", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def invert_permutation_eager_fallback(x, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function invert_permutation
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx, _dtypes.int32)
_inputs_flat = [x]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"InvertPermutation", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"InvertPermutation", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
_list_diff_outputs = ["out", "idx"]
_ListDiffOutput = _collections.namedtuple(
"ListDiff", _list_diff_outputs)
def list_diff(x, y, out_idx=_dtypes.int32, name=None):
r"""Computes the difference between two lists of numbers or strings.
Given a list `x` and a list `y`, this operation returns a list `out` that
represents all values that are in `x` but not in `y`. The returned list `out`
is sorted in the same order that the numbers appear in `x` (duplicates are
preserved). This operation also returns a list `idx` that represents the
position of each `out` element in `x`. In other words:
`out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]`
For example, given this input:
```
x = [1, 2, 3, 4, 5, 6]
y = [1, 3, 5]
```
This operation would return:
```
out ==> [2, 4, 6]
idx ==> [1, 3, 5]
```
Args:
x: A `Tensor`. 1-D. Values to keep.
y: A `Tensor`. Must have the same type as `x`. 1-D. Values to remove.
out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (out, idx).
out: A `Tensor`. Has the same type as `x`.
idx: A `Tensor` of type `out_idx`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ListDiff",
name, _ctx._post_execution_callbacks, x, y, "out_idx", out_idx)
_result = _ListDiffOutput._make(_result)
return _result
except _core._FallbackException:
try:
return list_diff_eager_fallback(
x, y, out_idx=out_idx, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_, _, _op = _op_def_lib._apply_op_helper(
"ListDiff", x=x, y=y, out_idx=out_idx, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "out_idx", _op.get_attr("out_idx"))
_execute.record_gradient(
"ListDiff", _inputs_flat, _attrs, _result, name)
_result = _ListDiffOutput._make(_result)
return _result
def list_diff_eager_fallback(x, y, out_idx=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function list_diff
"""
_ctx = ctx if ctx else _context.context()
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], _ctx)
(x, y) = _inputs_T
_inputs_flat = [x, y]
_attrs = ("T", _attr_T, "out_idx", out_idx)
_result = _execute.execute(b"ListDiff", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ListDiff", _inputs_flat, _attrs, _result, name)
_result = _ListDiffOutput._make(_result)
return _result
def lower_bound(sorted_inputs, values, out_type=_dtypes.int32, name=None):
r"""Applies lower_bound(sorted_search_values, values) along each row.
Each set of rows with the same index in (sorted_inputs, values) is treated
independently. The resulting row is the equivalent of calling
`np.searchsorted(sorted_inputs, values, side='left')`.
The result is not a global index to the entire
`Tensor`, but rather just the index in the last dimension.
A 2-D example:
sorted_sequence = [[0, 3, 9, 9, 10],
[1, 2, 3, 4, 5]]
values = [[2, 4, 9],
[0, 2, 6]]
result = LowerBound(sorted_sequence, values)
result == [[1, 2, 2],
[0, 1, 5]]
Args:
sorted_inputs: A `Tensor`. 2-D Tensor where each row is ordered.
values: A `Tensor`. Must have the same type as `sorted_inputs`.
2-D Tensor with the same numbers of rows as `sorted_search_values`. Contains
the values that will be searched for in `sorted_search_values`.
out_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `out_type`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "LowerBound",
name, _ctx._post_execution_callbacks, sorted_inputs, values,
"out_type", out_type)
return _result
except _core._FallbackException:
try:
return lower_bound_eager_fallback(
sorted_inputs, values, out_type=out_type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_, _, _op = _op_def_lib._apply_op_helper(
"LowerBound", sorted_inputs=sorted_inputs, values=values,
out_type=out_type, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "out_type", _op.get_attr("out_type"))
_execute.record_gradient(
"LowerBound", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def lower_bound_eager_fallback(sorted_inputs, values, out_type=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function lower_bound
"""
_ctx = ctx if ctx else _context.context()
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_attr_T, _inputs_T = _execute.args_to_matching_eager([sorted_inputs, values], _ctx)
(sorted_inputs, values) = _inputs_T
_inputs_flat = [sorted_inputs, values]
_attrs = ("T", _attr_T, "out_type", out_type)
_result = _execute.execute(b"LowerBound", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"LowerBound", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('linalg.band_part', v1=['linalg.band_part', 'matrix_band_part'])
@deprecated_endpoints('matrix_band_part')
def matrix_band_part(input, num_lower, num_upper, name=None):
r"""Copy a tensor setting everything outside a central band in each innermost matrix
to zero.
The `band` part is computed as follows:
Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
tensor with the same shape where
`band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`.
The indicator function
`in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) &&
(num_upper < 0 || (n-m) <= num_upper)`.
For example:
```
# if 'input' is [[ 0, 1, 2, 3]
[-1, 0, 1, 2]
[-2, -1, 0, 1]
[-3, -2, -1, 0]],
tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3]
[-1, 0, 1, 2]
[ 0, -1, 0, 1]
[ 0, 0, -1, 0]],
tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0]
[-1, 0, 1, 0]
[-2, -1, 0, 1]
[ 0, -2, -1, 0]]
```
Useful special cases:
```
tf.matrix_band_part(input, 0, -1) ==> Upper triangular part.
tf.matrix_band_part(input, -1, 0) ==> Lower triangular part.
tf.matrix_band_part(input, 0, 0) ==> Diagonal.
```
Args:
input: A `Tensor`. Rank `k` tensor.
num_lower: A `Tensor`. Must be one of the following types: `int32`, `int64`.
0-D tensor. Number of subdiagonals to keep. If negative, keep entire
lower triangle.
num_upper: A `Tensor`. Must have the same type as `num_lower`.
0-D tensor. Number of superdiagonals to keep. If negative, keep
entire upper triangle.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"MatrixBandPart", name, _ctx._post_execution_callbacks, input,
num_lower, num_upper)
return _result
except _core._FallbackException:
try:
return matrix_band_part_eager_fallback(
input, num_lower, num_upper, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_band_part, input=input, num_lower=num_lower,
num_upper=num_upper, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"MatrixBandPart", input=input, num_lower=num_lower,
num_upper=num_upper, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_band_part, input=input, num_lower=num_lower,
num_upper=num_upper, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindex", _op.get_attr("Tindex"))
_execute.record_gradient(
"MatrixBandPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def matrix_band_part_eager_fallback(input, num_lower, num_upper, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function matrix_band_part
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tindex, _inputs_Tindex = _execute.args_to_matching_eager([num_lower, num_upper], _ctx, _dtypes.int64)
(num_lower, num_upper) = _inputs_Tindex
_inputs_flat = [input, num_lower, num_upper]
_attrs = ("T", _attr_T, "Tindex", _attr_Tindex)
_result = _execute.execute(b"MatrixBandPart", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"MatrixBandPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('linalg.diag', v1=['linalg.diag', 'matrix_diag'])
@deprecated_endpoints('matrix_diag')
def matrix_diag(diagonal, name=None):
r"""Returns a batched diagonal tensor with a given batched diagonal values.
Given a `diagonal`, this operation returns a tensor with the `diagonal` and
everything else padded with zeros. The diagonal is computed as follows:
Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a
tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where:
`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.
For example:
```
# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
and diagonal.shape = (2, 4)
tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]],
[[5, 0, 0, 0]
[0, 6, 0, 0]
[0, 0, 7, 0]
[0, 0, 0, 8]]]
which has shape (2, 4, 4)
```
Args:
diagonal: A `Tensor`. Rank `k`, where `k >= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `diagonal`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "MatrixDiag",
name, _ctx._post_execution_callbacks, diagonal)
return _result
except _core._FallbackException:
try:
return matrix_diag_eager_fallback(
diagonal, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_diag, diagonal=diagonal, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"MatrixDiag", diagonal=diagonal, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_diag, diagonal=diagonal, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"MatrixDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def matrix_diag_eager_fallback(diagonal, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function matrix_diag
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (diagonal,) = _execute.args_to_matching_eager([diagonal], _ctx)
_inputs_flat = [diagonal]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"MatrixDiag", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"MatrixDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('linalg.diag_part', v1=['linalg.diag_part', 'matrix_diag_part'])
@deprecated_endpoints('matrix_diag_part')
def matrix_diag_part(input, name=None):
r"""Returns the batched diagonal part of a batched tensor.
This operation returns a tensor with the `diagonal` part
of the batched `input`. The `diagonal` part is computed as follows:
Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a
tensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where:
`diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.
The input must be at least a matrix.
For example:
```
# 'input' is [[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]],
[[5, 0, 0, 0]
[0, 6, 0, 0]
[0, 0, 7, 0]
[0, 0, 0, 8]]]
and input.shape = (2, 4, 4)
tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
which has shape (2, 4)
```
Args:
input: A `Tensor`. Rank `k` tensor where `k >= 2`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"MatrixDiagPart", name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return matrix_diag_part_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_diag_part, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"MatrixDiagPart", input=input, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_diag_part, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"MatrixDiagPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def matrix_diag_part_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function matrix_diag_part
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"MatrixDiagPart", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"MatrixDiagPart", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('linalg.set_diag', v1=['linalg.set_diag', 'matrix_set_diag'])
@deprecated_endpoints('matrix_set_diag')
def matrix_set_diag(input, diagonal, name=None):
r"""Returns a batched matrix tensor with new batched diagonal values.
Given `input` and `diagonal`, this operation returns a tensor with the
same shape and values as `input`, except for the main diagonal of the
innermost matrices. These will be overwritten by the values in `diagonal`.
The output is computed as follows:
Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has
`k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a
tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where:
* `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`.
* `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`.
Args:
input: A `Tensor`. Rank `k+1`, where `k >= 1`.
diagonal: A `Tensor`. Must have the same type as `input`.
Rank `k`, where `k >= 1`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"MatrixSetDiag", name, _ctx._post_execution_callbacks, input,
diagonal)
return _result
except _core._FallbackException:
try:
return matrix_set_diag_eager_fallback(
input, diagonal, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_set_diag, input=input, diagonal=diagonal, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"MatrixSetDiag", input=input, diagonal=diagonal, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
matrix_set_diag, input=input, diagonal=diagonal, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"MatrixSetDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def matrix_set_diag_eager_fallback(input, diagonal, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function matrix_set_diag
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([input, diagonal], _ctx)
(input, diagonal) = _inputs_T
_inputs_flat = [input, diagonal]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"MatrixSetDiag", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"MatrixSetDiag", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def mirror_pad(input, paddings, mode, name=None):
r"""Pads a tensor with mirrored values.
This operation pads a `input` with mirrored values according to the `paddings`
you specify. `paddings` is an integer tensor with shape `[n, 2]`, where n is
the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
how many values to add before the contents of `input` in that dimension, and
`paddings[D, 1]` indicates how many values to add after the contents of `input`
in that dimension. Both `paddings[D, 0]` and `paddings[D, 1]` must be no greater
than `input.dim_size(D)` (or `input.dim_size(D) - 1`) if `copy_border` is true
(if false, respectively).
The padded size of each dimension D of the output is:
`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
For example:
```
# 't' is [[1, 2, 3], [4, 5, 6]].
# 'paddings' is [[1, 1]], [2, 2]].
# 'mode' is SYMMETRIC.
# rank of 't' is 2.
pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2]
[2, 1, 1, 2, 3, 3, 2]
[5, 4, 4, 5, 6, 6, 5]
[5, 4, 4, 5, 6, 6, 5]]
```
Args:
input: A `Tensor`. The input tensor to be padded.
paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A two-column matrix specifying the padding sizes. The number of
rows must be the same as the rank of `input`.
mode: A `string` from: `"REFLECT", "SYMMETRIC"`.
Either `REFLECT` or `SYMMETRIC`. In reflect mode the padded regions
do not include the borders, while in symmetric mode the padded regions
do include the borders. For example, if `input` is `[1, 2, 3]` and `paddings`
is `[0, 2]`, then the output is `[1, 2, 3, 2, 1]` in reflect mode, and
it is `[1, 2, 3, 3, 2]` in symmetric mode.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "MirrorPad",
name, _ctx._post_execution_callbacks, input, paddings, "mode", mode)
return _result
except _core._FallbackException:
try:
return mirror_pad_eager_fallback(
input, paddings, mode=mode, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
mode = _execute.make_str(mode, "mode")
_, _, _op = _op_def_lib._apply_op_helper(
"MirrorPad", input=input, paddings=paddings, mode=mode, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tpaddings", _op.get_attr("Tpaddings"),
"mode", _op.get_attr("mode"))
_execute.record_gradient(
"MirrorPad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def mirror_pad_eager_fallback(input, paddings, mode, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function mirror_pad
"""
_ctx = ctx if ctx else _context.context()
mode = _execute.make_str(mode, "mode")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tpaddings, (paddings,) = _execute.args_to_matching_eager([paddings], _ctx, _dtypes.int32)
_inputs_flat = [input, paddings]
_attrs = ("T", _attr_T, "Tpaddings", _attr_Tpaddings, "mode", mode)
_result = _execute.execute(b"MirrorPad", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"MirrorPad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def mirror_pad_grad(input, paddings, mode, name=None):
r"""Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor.
This operation folds the padded areas of `input` by `MirrorPad` according to the
`paddings` you specify. `paddings` must be the same as `paddings` argument
given to the corresponding `MirrorPad` op.
The folded size of each dimension D of the output is:
`input.dim_size(D) - paddings(D, 0) - paddings(D, 1)`
For example:
```
# 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]].
# 'paddings' is [[0, 1]], [0, 1]].
# 'mode' is SYMMETRIC.
# rank of 't' is 2.
pad(t, paddings) ==> [[ 1, 5]
[11, 28]]
```
Args:
input: A `Tensor`. The input tensor to be folded.
paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A two-column matrix specifying the padding sizes. The number of
rows must be the same as the rank of `input`.
mode: A `string` from: `"REFLECT", "SYMMETRIC"`.
The mode used in the `MirrorPad` op.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"MirrorPadGrad", name, _ctx._post_execution_callbacks, input,
paddings, "mode", mode)
return _result
except _core._FallbackException:
try:
return mirror_pad_grad_eager_fallback(
input, paddings, mode=mode, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
mode = _execute.make_str(mode, "mode")
_, _, _op = _op_def_lib._apply_op_helper(
"MirrorPadGrad", input=input, paddings=paddings, mode=mode, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tpaddings", _op.get_attr("Tpaddings"),
"mode", _op.get_attr("mode"))
_execute.record_gradient(
"MirrorPadGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def mirror_pad_grad_eager_fallback(input, paddings, mode, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function mirror_pad_grad
"""
_ctx = ctx if ctx else _context.context()
mode = _execute.make_str(mode, "mode")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tpaddings, (paddings,) = _execute.args_to_matching_eager([paddings], _ctx, _dtypes.int32)
_inputs_flat = [input, paddings]
_attrs = ("T", _attr_T, "Tpaddings", _attr_Tpaddings, "mode", mode)
_result = _execute.execute(b"MirrorPadGrad", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"MirrorPadGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def one_hot(indices, depth, on_value, off_value, axis=-1, name=None):
r"""Returns a one-hot tensor.
The locations represented by indices in `indices` take value `on_value`,
while all other locations take value `off_value`.
If the input `indices` is rank `N`, the output will have rank `N+1`,
The new axis is created at dimension `axis` (default: the new axis is
appended at the end).
If `indices` is a scalar the output shape will be a vector of length `depth`.
If `indices` is a vector of length `features`, the output shape will be:
```
features x depth if axis == -1
depth x features if axis == 0
```
If `indices` is a matrix (batch) with shape `[batch, features]`,
the output shape will be:
```
batch x features x depth if axis == -1
batch x depth x features if axis == 1
depth x batch x features if axis == 0
```
Examples
=========
Suppose that
```
indices = [0, 2, -1, 1]
depth = 3
on_value = 5.0
off_value = 0.0
axis = -1
```
Then output is `[4 x 3]`:
```
output =
[5.0 0.0 0.0] // one_hot(0)
[0.0 0.0 5.0] // one_hot(2)
[0.0 0.0 0.0] // one_hot(-1)
[0.0 5.0 0.0] // one_hot(1)
```
Suppose that
```
indices = [0, 2, -1, 1]
depth = 3
on_value = 0.0
off_value = 3.0
axis = 0
```
Then output is `[3 x 4]`:
```
output =
[0.0 3.0 3.0 3.0]
[3.0 3.0 3.0 0.0]
[3.0 3.0 3.0 3.0]
[3.0 0.0 3.0 3.0]
// ^ one_hot(0)
// ^ one_hot(2)
// ^ one_hot(-1)
// ^ one_hot(1)
```
Suppose that
```
indices = [[0, 2], [1, -1]]
depth = 3
on_value = 1.0
off_value = 0.0
axis = -1
```
Then output is `[2 x 2 x 3]`:
```
output =
[
[1.0, 0.0, 0.0] // one_hot(0)
[0.0, 0.0, 1.0] // one_hot(2)
][
[0.0, 1.0, 0.0] // one_hot(1)
[0.0, 0.0, 0.0] // one_hot(-1)
]
```
Args:
indices: A `Tensor`. Must be one of the following types: `uint8`, `int32`, `int64`.
A tensor of indices.
depth: A `Tensor` of type `int32`.
A scalar defining the depth of the one hot dimension.
on_value: A `Tensor`.
A scalar defining the value to fill in output when `indices[j] = i`.
off_value: A `Tensor`. Must have the same type as `on_value`.
A scalar defining the value to fill in output when `indices[j] != i`.
axis: An optional `int`. Defaults to `-1`.
The axis to fill (default: -1, a new inner-most axis).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `on_value`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "OneHot", name,
_ctx._post_execution_callbacks, indices, depth, on_value, off_value,
"axis", axis)
return _result
except _core._FallbackException:
try:
return one_hot_eager_fallback(
indices, depth, on_value, off_value, axis=axis, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if axis is None:
axis = -1
axis = _execute.make_int(axis, "axis")
_, _, _op = _op_def_lib._apply_op_helper(
"OneHot", indices=indices, depth=depth, on_value=on_value,
off_value=off_value, axis=axis, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("axis", _op.get_attr("axis"), "T", _op.get_attr("T"), "TI",
_op.get_attr("TI"))
_execute.record_gradient(
"OneHot", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def one_hot_eager_fallback(indices, depth, on_value, off_value, axis=-1, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function one_hot
"""
_ctx = ctx if ctx else _context.context()
if axis is None:
axis = -1
axis = _execute.make_int(axis, "axis")
_attr_T, _inputs_T = _execute.args_to_matching_eager([on_value, off_value], _ctx)
(on_value, off_value) = _inputs_T
_attr_TI, (indices,) = _execute.args_to_matching_eager([indices], _ctx, _dtypes.int64)
depth = _ops.convert_to_tensor(depth, _dtypes.int32)
_inputs_flat = [indices, depth, on_value, off_value]
_attrs = ("axis", axis, "T", _attr_T, "TI", _attr_TI)
_result = _execute.execute(b"OneHot", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"OneHot", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ones_like(x, name=None):
r"""Returns a tensor of ones with the same shape and type as x.
Args:
x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`, `complex64`, `complex128`, `bool`.
a tensor of type T.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "OnesLike",
name, _ctx._post_execution_callbacks, x)
return _result
except _core._FallbackException:
try:
return ones_like_eager_fallback(
x, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"OnesLike", x=x, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"OnesLike", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ones_like_eager_fallback(x, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function ones_like
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_inputs_flat = [x]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"OnesLike", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"OnesLike", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def pack(values, axis=0, name=None):
r"""Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Packs the `N` tensors in `values` into a tensor with rank one higher than each
tensor in `values`, by packing them along the `axis` dimension.
Given a list of tensors of shape `(A, B, C)`;
if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
Etc.
For example:
```
# 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]
```
This is the opposite of `unpack`.
Args:
values: A list of at least 1 `Tensor` objects with the same type.
Must be of same shape and type.
axis: An optional `int`. Defaults to `0`.
Dimension along which to pack. Negative values wrap around, so the
valid range is `[-(R+1), R+1)`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `values`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Pack", name,
_ctx._post_execution_callbacks, values, "axis", axis)
return _result
except _core._FallbackException:
try:
return pack_eager_fallback(
values, axis=axis, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'pack' Op, not %r." % values)
_attr_N = len(values)
if axis is None:
axis = 0
axis = _execute.make_int(axis, "axis")
_, _, _op = _op_def_lib._apply_op_helper(
"Pack", values=values, axis=axis, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"), "T", _op.get_attr("T"), "axis",
_op.get_attr("axis"))
_execute.record_gradient(
"Pack", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def pack_eager_fallback(values, axis=0, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function pack
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'pack' Op, not %r." % values)
_attr_N = len(values)
if axis is None:
axis = 0
axis = _execute.make_int(axis, "axis")
_attr_T, values = _execute.args_to_matching_eager(list(values), _ctx)
_inputs_flat = list(values)
_attrs = ("N", _attr_N, "T", _attr_T, "axis", axis)
_result = _execute.execute(b"Pack", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Pack", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def pad(input, paddings, name=None):
r"""Pads a tensor with zeros.
This operation pads a `input` with zeros according to the `paddings` you
specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the
rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
how many zeros to add before the contents of `input` in that dimension, and
`paddings[D, 1]` indicates how many zeros to add after the contents of `input`
in that dimension.
The padded size of each dimension D of the output is:
`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
For example:
```
# 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
[0, 0, 1, 1, 0, 0]
[0, 0, 2, 2, 0, 0]
[0, 0, 0, 0, 0, 0]]
```
Args:
input: A `Tensor`.
paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Pad", name,
_ctx._post_execution_callbacks, input, paddings)
return _result
except _core._FallbackException:
try:
return pad_eager_fallback(
input, paddings, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Pad", input=input, paddings=paddings, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tpaddings", _op.get_attr("Tpaddings"))
_execute.record_gradient(
"Pad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def pad_eager_fallback(input, paddings, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function pad
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tpaddings, (paddings,) = _execute.args_to_matching_eager([paddings], _ctx, _dtypes.int32)
_inputs_flat = [input, paddings]
_attrs = ("T", _attr_T, "Tpaddings", _attr_Tpaddings)
_result = _execute.execute(b"Pad", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Pad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def pad_v2(input, paddings, constant_values, name=None):
r"""Pads a tensor.
This operation pads `input` according to the `paddings` and `constant_values`
you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is
the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates
how many padding values to add before the contents of `input` in that dimension,
and `paddings[D, 1]` indicates how many padding values to add after the contents
of `input` in that dimension. `constant_values` is a scalar tensor of the same
type as `input` that indicates the value to use for padding `input`.
The padded size of each dimension D of the output is:
`paddings(D, 0) + input.dim_size(D) + paddings(D, 1)`
For example:
```
# 't' is [[1, 1], [2, 2]]
# 'paddings' is [[1, 1], [2, 2]]
# 'constant_values' is 0
# rank of 't' is 2
pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0]
[0, 0, 1, 1, 0, 0]
[0, 0, 2, 2, 0, 0]
[0, 0, 0, 0, 0, 0]]
```
Args:
input: A `Tensor`.
paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.
constant_values: A `Tensor`. Must have the same type as `input`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "PadV2", name,
_ctx._post_execution_callbacks, input, paddings, constant_values)
return _result
except _core._FallbackException:
try:
return pad_v2_eager_fallback(
input, paddings, constant_values, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"PadV2", input=input, paddings=paddings,
constant_values=constant_values, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tpaddings", _op.get_attr("Tpaddings"))
_execute.record_gradient(
"PadV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def pad_v2_eager_fallback(input, paddings, constant_values, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function pad_v2
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([input, constant_values], _ctx)
(input, constant_values) = _inputs_T
_attr_Tpaddings, (paddings,) = _execute.args_to_matching_eager([paddings], _ctx, _dtypes.int32)
_inputs_flat = [input, paddings, constant_values]
_attrs = ("T", _attr_T, "Tpaddings", _attr_Tpaddings)
_result = _execute.execute(b"PadV2", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"PadV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def parallel_concat(values, shape, name=None):
r"""Concatenates a list of `N` tensors along the first dimension.
The input tensors are all required to have size 1 in the first dimension.
For example:
```
# 'x' is [[1, 4]]
# 'y' is [[2, 5]]
# 'z' is [[3, 6]]
parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim.
```
The difference between concat and parallel_concat is that concat requires all
of the inputs be computed before the operation will begin but doesn't require
that the input shapes be known during graph construction. Parallel concat
will copy pieces of the input into the output as they become available, in
some situations this can provide a performance benefit.
Args:
values: A list of at least 1 `Tensor` objects with the same type.
Tensors to be concatenated. All must have size 1 in the first dimension
and same shape.
shape: A `tf.TensorShape` or list of `ints`.
the final shape of the result; should be equal to the shapes of any input
but with the number of input values in the first dimension.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `values`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ParallelConcat", name, _ctx._post_execution_callbacks, values,
"shape", shape)
return _result
except _core._FallbackException:
try:
return parallel_concat_eager_fallback(
values, shape=shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'parallel_concat' Op, not %r." % values)
_attr_N = len(values)
shape = _execute.make_shape(shape, "shape")
_, _, _op = _op_def_lib._apply_op_helper(
"ParallelConcat", values=values, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"), "T", _op.get_attr("T"), "shape",
_op.get_attr("shape"))
_execute.record_gradient(
"ParallelConcat", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def parallel_concat_eager_fallback(values, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function parallel_concat
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'parallel_concat' Op, not %r." % values)
_attr_N = len(values)
shape = _execute.make_shape(shape, "shape")
_attr_T, values = _execute.args_to_matching_eager(list(values), _ctx)
_inputs_flat = list(values)
_attrs = ("N", _attr_N, "T", _attr_T, "shape", shape)
_result = _execute.execute(b"ParallelConcat", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ParallelConcat", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def placeholder(dtype, shape=None, name=None):
r"""A placeholder op for a value that will be fed into the computation.
N.B. This operation will fail with an error if it is executed. It is
intended as a way to represent a value that will always be fed, and to
provide attrs that enable the fed value to be checked at runtime.
Args:
dtype: A `tf.DType`. The type of elements in the tensor.
shape: An optional `tf.TensorShape` or list of `ints`. Defaults to `None`.
(Optional) The shape of the tensor. If the shape has 0 dimensions, the
shape is unconstrained.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `dtype`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Placeholder",
name, _ctx._post_execution_callbacks, "dtype", dtype, "shape", shape)
return _result
except _core._FallbackException:
try:
return placeholder_eager_fallback(
dtype=dtype, shape=shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
dtype = _execute.make_type(dtype, "dtype")
if shape is None:
shape = None
shape = _execute.make_shape(shape, "shape")
_, _, _op = _op_def_lib._apply_op_helper(
"Placeholder", dtype=dtype, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("dtype", _op.get_attr("dtype"), "shape", _op.get_attr("shape"))
_execute.record_gradient(
"Placeholder", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def placeholder_eager_fallback(dtype, shape=None, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function placeholder
"""
_ctx = ctx if ctx else _context.context()
dtype = _execute.make_type(dtype, "dtype")
if shape is None:
shape = None
shape = _execute.make_shape(shape, "shape")
_inputs_flat = []
_attrs = ("dtype", dtype, "shape", shape)
_result = _execute.execute(b"Placeholder", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Placeholder", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def placeholder_v2(dtype, shape, name=None):
r"""A placeholder op for a value that will be fed into the computation.
N.B. This operation will fail with an error if it is executed. It is
intended as a way to represent a value that will always be fed, and to
provide attrs that enable the fed value to be checked at runtime.
Args:
dtype: A `tf.DType`. The type of elements in the tensor.
shape: A `tf.TensorShape` or list of `ints`.
The shape of the tensor. The shape can be any partially-specified
shape. To be unconstrained, pass in a shape with unknown rank.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `dtype`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"PlaceholderV2", name, _ctx._post_execution_callbacks, "dtype", dtype,
"shape", shape)
return _result
except _core._FallbackException:
try:
return placeholder_v2_eager_fallback(
dtype=dtype, shape=shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
dtype = _execute.make_type(dtype, "dtype")
shape = _execute.make_shape(shape, "shape")
_, _, _op = _op_def_lib._apply_op_helper(
"PlaceholderV2", dtype=dtype, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("dtype", _op.get_attr("dtype"), "shape", _op.get_attr("shape"))
_execute.record_gradient(
"PlaceholderV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def placeholder_v2_eager_fallback(dtype, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function placeholder_v2
"""
_ctx = ctx if ctx else _context.context()
dtype = _execute.make_type(dtype, "dtype")
shape = _execute.make_shape(shape, "shape")
_inputs_flat = []
_attrs = ("dtype", dtype, "shape", shape)
_result = _execute.execute(b"PlaceholderV2", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"PlaceholderV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def placeholder_with_default(input, shape, name=None):
r"""A placeholder op that passes through `input` when its output is not fed.
Args:
input: A `Tensor`. The default value to produce when `output` is not fed.
shape: A `tf.TensorShape` or list of `ints`.
The (possibly partial) shape of the tensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"PlaceholderWithDefault", name, _ctx._post_execution_callbacks, input,
"shape", shape)
return _result
except _core._FallbackException:
try:
return placeholder_with_default_eager_fallback(
input, shape=shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
shape = _execute.make_shape(shape, "shape")
_, _, _op = _op_def_lib._apply_op_helper(
"PlaceholderWithDefault", input=input, shape=shape, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("dtype", _op.get_attr("dtype"), "shape", _op.get_attr("shape"))
_execute.record_gradient(
"PlaceholderWithDefault", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def placeholder_with_default_eager_fallback(input, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function placeholder_with_default
"""
_ctx = ctx if ctx else _context.context()
shape = _execute.make_shape(shape, "shape")
_attr_dtype, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("dtype", _attr_dtype, "shape", shape)
_result = _execute.execute(b"PlaceholderWithDefault", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"PlaceholderWithDefault", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def prevent_gradient(input, message="", name=None):
r"""An identity op that triggers an error if a gradient is requested.
When executed in a graph, this op outputs its input tensor as-is.
When building ops to compute gradients, the TensorFlow gradient system
will return an error when trying to lookup the gradient of this op,
because no gradient must ever be registered for this function. This
op exists to prevent subtle bugs from silently returning unimplemented
gradients in some corner cases.
Args:
input: A `Tensor`. any tensor.
message: An optional `string`. Defaults to `""`.
Will be printed in the error when anyone tries to differentiate
this operation.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"PreventGradient", name, _ctx._post_execution_callbacks, input,
"message", message)
return _result
except _core._FallbackException:
try:
return prevent_gradient_eager_fallback(
input, message=message, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if message is None:
message = ""
message = _execute.make_str(message, "message")
_, _, _op = _op_def_lib._apply_op_helper(
"PreventGradient", input=input, message=message, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "message", _op.get_attr("message"))
_execute.record_gradient(
"PreventGradient", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def prevent_gradient_eager_fallback(input, message="", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function prevent_gradient
"""
_ctx = ctx if ctx else _context.context()
if message is None:
message = ""
message = _execute.make_str(message, "message")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T, "message", message)
_result = _execute.execute(b"PreventGradient", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"PreventGradient", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def quantize_and_dequantize(input, signed_input=True, num_bits=8, range_given=False, input_min=0, input_max=0, name=None):
r"""Use QuantizeAndDequantizeV2 instead.
Args:
input: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
signed_input: An optional `bool`. Defaults to `True`.
num_bits: An optional `int`. Defaults to `8`.
range_given: An optional `bool`. Defaults to `False`.
input_min: An optional `float`. Defaults to `0`.
input_max: An optional `float`. Defaults to `0`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"QuantizeAndDequantize", name, _ctx._post_execution_callbacks, input,
"signed_input", signed_input, "num_bits", num_bits, "range_given",
range_given, "input_min", input_min, "input_max", input_max)
return _result
except _core._FallbackException:
try:
return quantize_and_dequantize_eager_fallback(
input, signed_input=signed_input, num_bits=num_bits,
range_given=range_given, input_min=input_min, input_max=input_max,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if signed_input is None:
signed_input = True
signed_input = _execute.make_bool(signed_input, "signed_input")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if range_given is None:
range_given = False
range_given = _execute.make_bool(range_given, "range_given")
if input_min is None:
input_min = 0
input_min = _execute.make_float(input_min, "input_min")
if input_max is None:
input_max = 0
input_max = _execute.make_float(input_max, "input_max")
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizeAndDequantize", input=input, signed_input=signed_input,
num_bits=num_bits, range_given=range_given,
input_min=input_min, input_max=input_max,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("signed_input", _op.get_attr("signed_input"), "num_bits",
_op.get_attr("num_bits"), "range_given",
_op.get_attr("range_given"), "input_min",
_op.get_attr("input_min"), "input_max", _op.get_attr("input_max"),
"T", _op.get_attr("T"))
_execute.record_gradient(
"QuantizeAndDequantize", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def quantize_and_dequantize_eager_fallback(input, signed_input=True, num_bits=8, range_given=False, input_min=0, input_max=0, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function quantize_and_dequantize
"""
_ctx = ctx if ctx else _context.context()
if signed_input is None:
signed_input = True
signed_input = _execute.make_bool(signed_input, "signed_input")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if range_given is None:
range_given = False
range_given = _execute.make_bool(range_given, "range_given")
if input_min is None:
input_min = 0
input_min = _execute.make_float(input_min, "input_min")
if input_max is None:
input_max = 0
input_max = _execute.make_float(input_max, "input_max")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("signed_input", signed_input, "num_bits", num_bits, "range_given",
range_given, "input_min", input_min, "input_max", input_max, "T", _attr_T)
_result = _execute.execute(b"QuantizeAndDequantize", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"QuantizeAndDequantize", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('quantization.quantize_and_dequantize')
def quantize_and_dequantize_v2(input, input_min, input_max, signed_input=True, num_bits=8, range_given=False, round_mode="HALF_TO_EVEN", name=None):
r"""Quantizes then dequantizes a tensor.
This op simulates the precision loss from the quantized forward pass by:
1. Quantizing the tensor to fixed point numbers, which should match the target
quantization method when it is used in inference.
2. Dequantizing it back to floating point numbers for the following ops, most
likely matmul.
There are different ways to quantize. This version uses only scaling, so 0.0
maps to 0.
From the specified 'num_bits' in the quantized output type, it determines
minimum and maximum representable quantized values.
e.g.
* [-128, 127] for signed, num_bits = 8, or
* [0, 255] for unsigned, num_bits = 8.
If range_given == False, the initial input_min, input_max will be determined
automatically as the minimum and maximum values in the input tensor, otherwise
the specified values of input_min, input_max are used.
Note: If the input_min, input_max are specified, they do not need to equal the
actual minimum and maximum values in the tensor. e.g. in some cases it may be
beneficial to specify these values such that the low probability extremes of the
input distribution are clipped.
This op determines the maximum scale_factor that would map the initial
[input_min, input_max] range to a range that lies within the representable
quantized range.
It determines the scale from one of input_min and input_max, then updates the
other one to maximize the respresentable range.
e.g.
* if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0,
5.0]: it would use a scale_factor of -128 / -10.0 = 12.8 In this case, it
would update input_max to be 127 / 12.8 = 9.921875
* if the output is signed, num_bits = 8, [input_min, input_max] = [-10.0,
10.0]: it would use a scale_factor of 127 / 10.0 = 12.7 In this case, it
would update input_min to be 128.0 / 12.7 = -10.07874
* if the output is unsigned, input_min is forced to be 0, and only the
specified input_max is used.
After determining the scale_factor and updating the input range, it applies the
following to each value in the 'input' tensor.
output = round(clamp(value, input_min, input_max) * scale_factor) / scale_factor.
The above round function rounds the value based on the given round_mode.
Args:
input: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
Tensor to quantize and then dequantize.
input_min: A `Tensor`. Must have the same type as `input`.
If `range_given == True`, this specifies the minimum input value that needs to
be represented, otherwise it is determined from the min value of the `input`
tensor.
input_max: A `Tensor`. Must have the same type as `input`.
If `range_given == True`, this specifies the maximum input value that needs to
be represented, otherwise it is determined from the max value of the `input`
tensor.
signed_input: An optional `bool`. Defaults to `True`.
Whether the quantization is signed or unsigned. (actually this parameter should
have been called <b>`signed_output`</b>)
num_bits: An optional `int`. Defaults to `8`.
The bitwidth of the quantization.
range_given: An optional `bool`. Defaults to `False`.
Whether the range is given or should be determined from the `input` tensor.
round_mode: An optional `string` from: `"HALF_TO_EVEN", "HALF_UP"`. Defaults to `"HALF_TO_EVEN"`.
The 'round_mode' attribute controls which rounding tie-breaking algorithm is
used when rounding float values to their quantized equivalents. The following
rounding modes are currently supported:
* HALF_TO_EVEN: this is the default round_mode.
* HALF_UP: round towards positive. In this mode 7.5 rounds up to 8 and -7.5
rounds up to -7.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"QuantizeAndDequantizeV2", name, _ctx._post_execution_callbacks,
input, input_min, input_max, "signed_input", signed_input, "num_bits",
num_bits, "range_given", range_given, "round_mode", round_mode)
return _result
except _core._FallbackException:
try:
return quantize_and_dequantize_v2_eager_fallback(
input, input_min, input_max, signed_input=signed_input,
num_bits=num_bits, range_given=range_given, round_mode=round_mode,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
quantize_and_dequantize_v2, input=input, input_min=input_min,
input_max=input_max,
signed_input=signed_input,
num_bits=num_bits,
range_given=range_given,
round_mode=round_mode, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if signed_input is None:
signed_input = True
signed_input = _execute.make_bool(signed_input, "signed_input")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if range_given is None:
range_given = False
range_given = _execute.make_bool(range_given, "range_given")
if round_mode is None:
round_mode = "HALF_TO_EVEN"
round_mode = _execute.make_str(round_mode, "round_mode")
try:
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizeAndDequantizeV2", input=input, input_min=input_min,
input_max=input_max,
signed_input=signed_input,
num_bits=num_bits, range_given=range_given,
round_mode=round_mode, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
quantize_and_dequantize_v2, input=input, input_min=input_min,
input_max=input_max,
signed_input=signed_input,
num_bits=num_bits,
range_given=range_given,
round_mode=round_mode, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("signed_input", _op.get_attr("signed_input"), "num_bits",
_op.get_attr("num_bits"), "range_given",
_op.get_attr("range_given"), "T", _op.get_attr("T"), "round_mode",
_op.get_attr("round_mode"))
_execute.record_gradient(
"QuantizeAndDequantizeV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def quantize_and_dequantize_v2_eager_fallback(input, input_min, input_max, signed_input=True, num_bits=8, range_given=False, round_mode="HALF_TO_EVEN", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function quantize_and_dequantize_v2
"""
_ctx = ctx if ctx else _context.context()
if signed_input is None:
signed_input = True
signed_input = _execute.make_bool(signed_input, "signed_input")
if num_bits is None:
num_bits = 8
num_bits = _execute.make_int(num_bits, "num_bits")
if range_given is None:
range_given = False
range_given = _execute.make_bool(range_given, "range_given")
if round_mode is None:
round_mode = "HALF_TO_EVEN"
round_mode = _execute.make_str(round_mode, "round_mode")
_attr_T, _inputs_T = _execute.args_to_matching_eager([input, input_min, input_max], _ctx)
(input, input_min, input_max) = _inputs_T
_inputs_flat = [input, input_min, input_max]
_attrs = ("signed_input", signed_input, "num_bits", num_bits, "range_given",
range_given, "T", _attr_T, "round_mode", round_mode)
_result = _execute.execute(b"QuantizeAndDequantizeV2", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"QuantizeAndDequantizeV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def quantize_and_dequantize_v3(input, input_min, input_max, num_bits, signed_input=True, range_given=True, name=None):
r"""Quantizes then dequantizes a tensor.
This is almost identical to QuantizeAndDequantizeV2, except that num_bits is a
tensor, so its value can change during training.
Args:
input: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
input_min: A `Tensor`. Must have the same type as `input`.
input_max: A `Tensor`. Must have the same type as `input`.
num_bits: A `Tensor` of type `int32`.
signed_input: An optional `bool`. Defaults to `True`.
range_given: An optional `bool`. Defaults to `True`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"QuantizeAndDequantizeV3", name, _ctx._post_execution_callbacks,
input, input_min, input_max, num_bits, "signed_input", signed_input,
"range_given", range_given)
return _result
except _core._FallbackException:
try:
return quantize_and_dequantize_v3_eager_fallback(
input, input_min, input_max, num_bits, signed_input=signed_input,
range_given=range_given, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if signed_input is None:
signed_input = True
signed_input = _execute.make_bool(signed_input, "signed_input")
if range_given is None:
range_given = True
range_given = _execute.make_bool(range_given, "range_given")
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizeAndDequantizeV3", input=input, input_min=input_min,
input_max=input_max, num_bits=num_bits,
signed_input=signed_input,
range_given=range_given, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("signed_input", _op.get_attr("signed_input"), "range_given",
_op.get_attr("range_given"), "T", _op.get_attr("T"))
_execute.record_gradient(
"QuantizeAndDequantizeV3", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def quantize_and_dequantize_v3_eager_fallback(input, input_min, input_max, num_bits, signed_input=True, range_given=True, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function quantize_and_dequantize_v3
"""
_ctx = ctx if ctx else _context.context()
if signed_input is None:
signed_input = True
signed_input = _execute.make_bool(signed_input, "signed_input")
if range_given is None:
range_given = True
range_given = _execute.make_bool(range_given, "range_given")
_attr_T, _inputs_T = _execute.args_to_matching_eager([input, input_min, input_max], _ctx)
(input, input_min, input_max) = _inputs_T
num_bits = _ops.convert_to_tensor(num_bits, _dtypes.int32)
_inputs_flat = [input, input_min, input_max, num_bits]
_attrs = ("signed_input", signed_input, "range_given", range_given, "T",
_attr_T)
_result = _execute.execute(b"QuantizeAndDequantizeV3", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"QuantizeAndDequantizeV3", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
_quantize_v2_outputs = ["output", "output_min", "output_max"]
_QuantizeV2Output = _collections.namedtuple(
"QuantizeV2", _quantize_v2_outputs)
def quantize_v2(input, min_range, max_range, T, mode="MIN_COMBINED", round_mode="HALF_AWAY_FROM_ZERO", name=None):
r"""Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
[min_range, max_range] are scalar floats that specify the range for
the 'input' data. The 'mode' attribute controls exactly which calculations are
used to convert the float values to their quantized equivalents. The
'round_mode' attribute controls which rounding tie-breaking algorithm is used
when rounding float values to their quantized equivalents.
In 'MIN_COMBINED' mode, each value of the tensor will undergo the following:
```
out[i] = (in[i] - min_range) * range(T) / (max_range - min_range)
if T == qint8: out[i] -= (range(T) + 1) / 2.0
```
here `range(T) = numeric_limits<T>::max() - numeric_limits<T>::min()`
*MIN_COMBINED Mode Example*
Assume the input is type float and has a possible range of [0.0, 6.0] and the
output type is quint8 ([0, 255]). The min_range and max_range values should be
specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each
value of the input by 255/6 and cast to quint8.
If the output type was qint8 ([-128, 127]), the operation will additionally
subtract each value by 128 prior to casting, so that the range of values aligns
with the range of qint8.
If the mode is 'MIN_FIRST', then this approach is used:
```
num_discrete_values = 1 << (# of bits in T)
range_adjust = num_discrete_values / (num_discrete_values - 1)
range = (range_max - range_min) * range_adjust
range_scale = num_discrete_values / range
quantized = round(input * range_scale) - round(range_min * range_scale) +
numeric_limits<T>::min()
quantized = max(quantized, numeric_limits<T>::min())
quantized = min(quantized, numeric_limits<T>::max())
```
The biggest difference between this and MIN_COMBINED is that the minimum range
is rounded first, before it's subtracted from the rounded value. With
MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing
and dequantizing will introduce a larger and larger error.
*SCALED mode Example*
`SCALED` mode matches the quantization approach used in
`QuantizeAndDequantize{V2|V3}`.
If the mode is `SCALED`, we do not use the full range of the output type,
choosing to elide the lowest possible value for symmetry (e.g., output range is
-127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to
0.
We first find the range of values in our tensor. The
range we use is always centered on 0, so we find m such that
```c++
m = max(abs(input_min), abs(input_max))
```
Our input tensor range is then `[-m, m]`.
Next, we choose our fixed-point quantization buckets, `[min_fixed, max_fixed]`.
If T is signed, this is
```
num_bits = sizeof(T) * 8
[min_fixed, max_fixed] =
[-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]
```
Otherwise, if T is unsigned, the fixed-point range is
```
[min_fixed, max_fixed] = [0, (1 << num_bits) - 1]
```
From this we compute our scaling factor, s:
```c++
s = (max_fixed - min_fixed) / (2 * m)
```
Now we can quantize the elements of our tensor:
```c++
result = round(input * s)
```
One thing to watch out for is that the operator may choose to adjust the
requested minimum and maximum values slightly during the quantization process,
so you should always use the output ports as the range for further calculations.
For example, if the requested minimum and maximum values are close to equal,
they will be separated by a small epsilon value to prevent ill-formed quantized
buffers from being created. Otherwise, you can end up with buffers where all the
quantized values map to the same float value, which causes problems for
operations that have to perform further calculations on them.
Args:
input: A `Tensor` of type `float32`.
min_range: A `Tensor` of type `float32`.
The minimum scalar value possibly produced for the input.
max_range: A `Tensor` of type `float32`.
The maximum scalar value possibly produced for the input.
T: A `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`.
mode: An optional `string` from: `"MIN_COMBINED", "MIN_FIRST", "SCALED"`. Defaults to `"MIN_COMBINED"`.
round_mode: An optional `string` from: `"HALF_AWAY_FROM_ZERO", "HALF_TO_EVEN"`. Defaults to `"HALF_AWAY_FROM_ZERO"`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output, output_min, output_max).
output: A `Tensor` of type `T`.
output_min: A `Tensor` of type `float32`.
output_max: A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "QuantizeV2",
name, _ctx._post_execution_callbacks, input, min_range, max_range,
"T", T, "mode", mode, "round_mode", round_mode)
_result = _QuantizeV2Output._make(_result)
return _result
except _core._FallbackException:
try:
return quantize_v2_eager_fallback(
input, min_range, max_range, T=T, mode=mode,
round_mode=round_mode, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
T = _execute.make_type(T, "T")
if mode is None:
mode = "MIN_COMBINED"
mode = _execute.make_str(mode, "mode")
if round_mode is None:
round_mode = "HALF_AWAY_FROM_ZERO"
round_mode = _execute.make_str(round_mode, "round_mode")
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizeV2", input=input, min_range=min_range, max_range=max_range,
T=T, mode=mode, round_mode=round_mode, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "mode", _op.get_attr("mode"),
"round_mode", _op.get_attr("round_mode"))
_execute.record_gradient(
"QuantizeV2", _inputs_flat, _attrs, _result, name)
_result = _QuantizeV2Output._make(_result)
return _result
def quantize_v2_eager_fallback(input, min_range, max_range, T, mode="MIN_COMBINED", round_mode="HALF_AWAY_FROM_ZERO", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function quantize_v2
"""
_ctx = ctx if ctx else _context.context()
T = _execute.make_type(T, "T")
if mode is None:
mode = "MIN_COMBINED"
mode = _execute.make_str(mode, "mode")
if round_mode is None:
round_mode = "HALF_AWAY_FROM_ZERO"
round_mode = _execute.make_str(round_mode, "round_mode")
input = _ops.convert_to_tensor(input, _dtypes.float32)
min_range = _ops.convert_to_tensor(min_range, _dtypes.float32)
max_range = _ops.convert_to_tensor(max_range, _dtypes.float32)
_inputs_flat = [input, min_range, max_range]
_attrs = ("T", T, "mode", mode, "round_mode", round_mode)
_result = _execute.execute(b"QuantizeV2", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"QuantizeV2", _inputs_flat, _attrs, _result, name)
_result = _QuantizeV2Output._make(_result)
return _result
_quantized_concat_outputs = ["output", "output_min", "output_max"]
_QuantizedConcatOutput = _collections.namedtuple(
"QuantizedConcat", _quantized_concat_outputs)
@_dispatch.add_dispatch_list
@tf_export('quantization.quantized_concat', v1=['quantization.quantized_concat', 'quantized_concat'])
@deprecated_endpoints('quantized_concat')
def quantized_concat(concat_dim, values, input_mins, input_maxes, name=None):
r"""Concatenates quantized tensors along one dimension.
Args:
concat_dim: A `Tensor` of type `int32`.
0-D. The dimension along which to concatenate. Must be in the
range [0, rank(values)).
values: A list of at least 2 `Tensor` objects with the same type.
The `N` Tensors to concatenate. Their ranks and types must match,
and their sizes must match in all dimensions except `concat_dim`.
input_mins: A list with the same length as `values` of `Tensor` objects with type `float32`.
The minimum scalar values for each of the input tensors.
input_maxes: A list with the same length as `values` of `Tensor` objects with type `float32`.
The maximum scalar values for each of the input tensors.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output, output_min, output_max).
output: A `Tensor`. Has the same type as `values`.
output_min: A `Tensor` of type `float32`.
output_max: A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"QuantizedConcat", name, _ctx._post_execution_callbacks, concat_dim,
values, input_mins, input_maxes)
_result = _QuantizedConcatOutput._make(_result)
return _result
except _core._FallbackException:
try:
return quantized_concat_eager_fallback(
concat_dim, values, input_mins, input_maxes, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
quantized_concat, concat_dim=concat_dim, values=values,
input_mins=input_mins,
input_maxes=input_maxes, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'quantized_concat' Op, not %r." % values)
_attr_N = len(values)
if not isinstance(input_mins, (list, tuple)):
raise TypeError(
"Expected list for 'input_mins' argument to "
"'quantized_concat' Op, not %r." % input_mins)
if len(input_mins) != _attr_N:
raise ValueError(
"List argument 'input_mins' to 'quantized_concat' Op with length %d "
"must match length %d of argument 'values'." %
(len(input_mins), _attr_N))
if not isinstance(input_maxes, (list, tuple)):
raise TypeError(
"Expected list for 'input_maxes' argument to "
"'quantized_concat' Op, not %r." % input_maxes)
if len(input_maxes) != _attr_N:
raise ValueError(
"List argument 'input_maxes' to 'quantized_concat' Op with length %d "
"must match length %d of argument 'values'." %
(len(input_maxes), _attr_N))
try:
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizedConcat", concat_dim=concat_dim, values=values,
input_mins=input_mins, input_maxes=input_maxes,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
quantized_concat, concat_dim=concat_dim, values=values,
input_mins=input_mins, input_maxes=input_maxes,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"), "T", _op.get_attr("T"))
_execute.record_gradient(
"QuantizedConcat", _inputs_flat, _attrs, _result, name)
_result = _QuantizedConcatOutput._make(_result)
return _result
def quantized_concat_eager_fallback(concat_dim, values, input_mins, input_maxes, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function quantized_concat
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(values, (list, tuple)):
raise TypeError(
"Expected list for 'values' argument to "
"'quantized_concat' Op, not %r." % values)
_attr_N = len(values)
if not isinstance(input_mins, (list, tuple)):
raise TypeError(
"Expected list for 'input_mins' argument to "
"'quantized_concat' Op, not %r." % input_mins)
if len(input_mins) != _attr_N:
raise ValueError(
"List argument 'input_mins' to 'quantized_concat' Op with length %d "
"must match length %d of argument 'values'." %
(len(input_mins), _attr_N))
if not isinstance(input_maxes, (list, tuple)):
raise TypeError(
"Expected list for 'input_maxes' argument to "
"'quantized_concat' Op, not %r." % input_maxes)
if len(input_maxes) != _attr_N:
raise ValueError(
"List argument 'input_maxes' to 'quantized_concat' Op with length %d "
"must match length %d of argument 'values'." %
(len(input_maxes), _attr_N))
_attr_T, values = _execute.args_to_matching_eager(list(values), _ctx)
concat_dim = _ops.convert_to_tensor(concat_dim, _dtypes.int32)
input_mins = _ops.convert_n_to_tensor(input_mins, _dtypes.float32)
input_maxes = _ops.convert_n_to_tensor(input_maxes, _dtypes.float32)
_inputs_flat = [concat_dim] + list(values) + list(input_mins) + list(input_maxes)
_attrs = ("N", _attr_N, "T", _attr_T)
_result = _execute.execute(b"QuantizedConcat", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"QuantizedConcat", _inputs_flat, _attrs, _result, name)
_result = _QuantizedConcatOutput._make(_result)
return _result
_quantized_instance_norm_outputs = ["y", "y_min", "y_max"]
_QuantizedInstanceNormOutput = _collections.namedtuple(
"QuantizedInstanceNorm", _quantized_instance_norm_outputs)
def quantized_instance_norm(x, x_min, x_max, output_range_given=False, given_y_min=0, given_y_max=0, variance_epsilon=1e-05, min_separation=0.001, name=None):
r"""Quantized Instance normalization.
Args:
x: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
A 4D input Tensor.
x_min: A `Tensor` of type `float32`.
The value represented by the lowest quantized input.
x_max: A `Tensor` of type `float32`.
The value represented by the highest quantized input.
output_range_given: An optional `bool`. Defaults to `False`.
If True, `given_y_min` and `given_y_min`
and `given_y_max` are used as the output range. Otherwise,
the implementation computes the output range.
given_y_min: An optional `float`. Defaults to `0`.
Output in `y_min` if `output_range_given` is True.
given_y_max: An optional `float`. Defaults to `0`.
Output in `y_max` if `output_range_given` is True.
variance_epsilon: An optional `float`. Defaults to `1e-05`.
A small float number to avoid dividing by 0.
min_separation: An optional `float`. Defaults to `0.001`.
Minimum value of `y_max - y_min`
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (y, y_min, y_max).
y: A `Tensor`. Has the same type as `x`.
y_min: A `Tensor` of type `float32`.
y_max: A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"QuantizedInstanceNorm", name, _ctx._post_execution_callbacks, x,
x_min, x_max, "output_range_given", output_range_given, "given_y_min",
given_y_min, "given_y_max", given_y_max, "variance_epsilon",
variance_epsilon, "min_separation", min_separation)
_result = _QuantizedInstanceNormOutput._make(_result)
return _result
except _core._FallbackException:
try:
return quantized_instance_norm_eager_fallback(
x, x_min, x_max, output_range_given=output_range_given,
given_y_min=given_y_min, given_y_max=given_y_max,
variance_epsilon=variance_epsilon, min_separation=min_separation,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if output_range_given is None:
output_range_given = False
output_range_given = _execute.make_bool(output_range_given, "output_range_given")
if given_y_min is None:
given_y_min = 0
given_y_min = _execute.make_float(given_y_min, "given_y_min")
if given_y_max is None:
given_y_max = 0
given_y_max = _execute.make_float(given_y_max, "given_y_max")
if variance_epsilon is None:
variance_epsilon = 1e-05
variance_epsilon = _execute.make_float(variance_epsilon, "variance_epsilon")
if min_separation is None:
min_separation = 0.001
min_separation = _execute.make_float(min_separation, "min_separation")
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizedInstanceNorm", x=x, x_min=x_min, x_max=x_max,
output_range_given=output_range_given,
given_y_min=given_y_min,
given_y_max=given_y_max,
variance_epsilon=variance_epsilon,
min_separation=min_separation, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "output_range_given",
_op.get_attr("output_range_given"), "given_y_min",
_op.get_attr("given_y_min"), "given_y_max",
_op.get_attr("given_y_max"), "variance_epsilon",
_op.get_attr("variance_epsilon"), "min_separation",
_op.get_attr("min_separation"))
_execute.record_gradient(
"QuantizedInstanceNorm", _inputs_flat, _attrs, _result, name)
_result = _QuantizedInstanceNormOutput._make(_result)
return _result
def quantized_instance_norm_eager_fallback(x, x_min, x_max, output_range_given=False, given_y_min=0, given_y_max=0, variance_epsilon=1e-05, min_separation=0.001, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function quantized_instance_norm
"""
_ctx = ctx if ctx else _context.context()
if output_range_given is None:
output_range_given = False
output_range_given = _execute.make_bool(output_range_given, "output_range_given")
if given_y_min is None:
given_y_min = 0
given_y_min = _execute.make_float(given_y_min, "given_y_min")
if given_y_max is None:
given_y_max = 0
given_y_max = _execute.make_float(given_y_max, "given_y_max")
if variance_epsilon is None:
variance_epsilon = 1e-05
variance_epsilon = _execute.make_float(variance_epsilon, "variance_epsilon")
if min_separation is None:
min_separation = 0.001
min_separation = _execute.make_float(min_separation, "min_separation")
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
x_min = _ops.convert_to_tensor(x_min, _dtypes.float32)
x_max = _ops.convert_to_tensor(x_max, _dtypes.float32)
_inputs_flat = [x, x_min, x_max]
_attrs = ("T", _attr_T, "output_range_given", output_range_given,
"given_y_min", given_y_min, "given_y_max", given_y_max, "variance_epsilon",
variance_epsilon, "min_separation", min_separation)
_result = _execute.execute(b"QuantizedInstanceNorm", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"QuantizedInstanceNorm", _inputs_flat, _attrs, _result, name)
_result = _QuantizedInstanceNormOutput._make(_result)
return _result
_quantized_reshape_outputs = ["output", "output_min", "output_max"]
_QuantizedReshapeOutput = _collections.namedtuple(
"QuantizedReshape", _quantized_reshape_outputs)
def quantized_reshape(tensor, shape, input_min, input_max, name=None):
r"""Reshapes a quantized tensor as per the Reshape op.
```
Args:
tensor: A `Tensor`.
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Defines the shape of the output tensor.
input_min: A `Tensor` of type `float32`. The minimum value of the input.
input_max: A `Tensor` of type `float32`. The maximum value of the input.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (output, output_min, output_max).
output: A `Tensor`. Has the same type as `tensor`.
output_min: A `Tensor` of type `float32`.
output_max: A `Tensor` of type `float32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"QuantizedReshape", name, _ctx._post_execution_callbacks, tensor,
shape, input_min, input_max)
_result = _QuantizedReshapeOutput._make(_result)
return _result
except _core._FallbackException:
try:
return quantized_reshape_eager_fallback(
tensor, shape, input_min, input_max, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"QuantizedReshape", tensor=tensor, shape=shape, input_min=input_min,
input_max=input_max, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tshape", _op.get_attr("Tshape"))
_execute.record_gradient(
"QuantizedReshape", _inputs_flat, _attrs, _result, name)
_result = _QuantizedReshapeOutput._make(_result)
return _result
def quantized_reshape_eager_fallback(tensor, shape, input_min, input_max, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function quantized_reshape
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx)
_attr_Tshape, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int32)
input_min = _ops.convert_to_tensor(input_min, _dtypes.float32)
input_max = _ops.convert_to_tensor(input_max, _dtypes.float32)
_inputs_flat = [tensor, shape, input_min, input_max]
_attrs = ("T", _attr_T, "Tshape", _attr_Tshape)
_result = _execute.execute(b"QuantizedReshape", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"QuantizedReshape", _inputs_flat, _attrs, _result, name)
_result = _QuantizedReshapeOutput._make(_result)
return _result
def rank(input, name=None):
r"""Returns the rank of a tensor.
This operation returns an integer representing the rank of `input`.
For example:
```
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
# shape of tensor 't' is [2, 2, 3]
rank(t) ==> 3
```
**Note**: The rank of a tensor is not the same as the rank of a matrix. The rank
of a tensor is the number of indices required to uniquely select each element
of the tensor. Rank is also known as "order", "degree", or "ndims."
Args:
input: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int32`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Rank", name,
_ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return rank_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Rank", input=input, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"Rank", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def rank_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function rank
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"Rank", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Rank", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ref_identity(input, name=None):
r"""Return the same ref tensor as the input ref tensor.
Args:
input: A mutable `Tensor`.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
raise RuntimeError("ref_identity op does not support eager execution. Arg 'output' is a ref.")
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"RefIdentity", input=input, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"RefIdentity", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def ref_identity_eager_fallback(input, name=None, ctx=None):
raise RuntimeError("ref_identity op does not support eager execution. Arg 'output' is a ref.")
@_dispatch.add_dispatch_list
@tf_export('reshape', v1=['reshape', 'manip.reshape'])
@deprecated_endpoints('manip.reshape')
def reshape(tensor, shape, name=None):
r"""Reshapes a tensor.
Given `tensor`, this operation returns a tensor that has the same values
as `tensor` with shape `shape`.
If one component of `shape` is the special value -1, the size of that dimension
is computed so that the total size remains constant. In particular, a `shape`
of `[-1]` flattens into 1-D. At most one component of `shape` can be -1.
If `shape` is 1-D or higher, then the operation returns a tensor with shape
`shape` filled with the values of `tensor`. In this case, the number of elements
implied by `shape` must be the same as the number of elements in `tensor`.
For example:
```
# tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]
# tensor 't' has shape [9]
reshape(t, [3, 3]) ==> [[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
# tensor 't' is [[[1, 1], [2, 2]],
# [[3, 3], [4, 4]]]
# tensor 't' has shape [2, 2, 2]
reshape(t, [2, 4]) ==> [[1, 1, 2, 2],
[3, 3, 4, 4]]
# tensor 't' is [[[1, 1, 1],
# [2, 2, 2]],
# [[3, 3, 3],
# [4, 4, 4]],
# [[5, 5, 5],
# [6, 6, 6]]]
# tensor 't' has shape [3, 2, 3]
# pass '[-1]' to flatten 't'
reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]
# -1 can also be used to infer the shape
# -1 is inferred to be 9:
reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 2:
reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],
[4, 4, 4, 5, 5, 5, 6, 6, 6]]
# -1 is inferred to be 3:
reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],
[2, 2, 2],
[3, 3, 3]],
[[4, 4, 4],
[5, 5, 5],
[6, 6, 6]]]
# tensor 't' is [7]
# shape `[]` reshapes to a scalar
reshape(t, []) ==> 7
```
Args:
tensor: A `Tensor`.
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Defines the shape of the output tensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Reshape",
name, _ctx._post_execution_callbacks, tensor, shape)
return _result
except _core._FallbackException:
try:
return reshape_eager_fallback(
tensor, shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
reshape, tensor=tensor, shape=shape, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"Reshape", tensor=tensor, shape=shape, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
reshape, tensor=tensor, shape=shape, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tshape", _op.get_attr("Tshape"))
_execute.record_gradient(
"Reshape", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def reshape_eager_fallback(tensor, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function reshape
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx)
_attr_Tshape, (shape,) = _execute.args_to_matching_eager([shape], _ctx, _dtypes.int32)
_inputs_flat = [tensor, shape]
_attrs = ("T", _attr_T, "Tshape", _attr_Tshape)
_result = _execute.execute(b"Reshape", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Reshape", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def resource_strided_slice_assign(ref, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None):
r"""Assign `value` to the sliced l-value reference of `ref`.
The values of `value` are assigned to the positions in the variable
`ref` that are selected by the slice parameters. The slice parameters
`begin, `end`, `strides`, etc. work exactly as in `StridedSlice`.
NOTE this op currently does not support broadcasting and so `value`'s
shape must be exactly the shape produced by the slice of `ref`.
Args:
ref: A `Tensor` of type `resource`.
begin: A `Tensor`. Must be one of the following types: `int32`, `int64`.
end: A `Tensor`. Must have the same type as `begin`.
strides: A `Tensor`. Must have the same type as `begin`.
value: A `Tensor`.
begin_mask: An optional `int`. Defaults to `0`.
end_mask: An optional `int`. Defaults to `0`.
ellipsis_mask: An optional `int`. Defaults to `0`.
new_axis_mask: An optional `int`. Defaults to `0`.
shrink_axis_mask: An optional `int`. Defaults to `0`.
name: A name for the operation (optional).
Returns:
The created Operation.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ResourceStridedSliceAssign", name, _ctx._post_execution_callbacks,
ref, begin, end, strides, value, "begin_mask", begin_mask, "end_mask",
end_mask, "ellipsis_mask", ellipsis_mask, "new_axis_mask",
new_axis_mask, "shrink_axis_mask", shrink_axis_mask)
return _result
except _core._FallbackException:
try:
return resource_strided_slice_assign_eager_fallback(
ref, begin, end, strides, value, begin_mask=begin_mask,
end_mask=end_mask, ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask, shrink_axis_mask=shrink_axis_mask,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if begin_mask is None:
begin_mask = 0
begin_mask = _execute.make_int(begin_mask, "begin_mask")
if end_mask is None:
end_mask = 0
end_mask = _execute.make_int(end_mask, "end_mask")
if ellipsis_mask is None:
ellipsis_mask = 0
ellipsis_mask = _execute.make_int(ellipsis_mask, "ellipsis_mask")
if new_axis_mask is None:
new_axis_mask = 0
new_axis_mask = _execute.make_int(new_axis_mask, "new_axis_mask")
if shrink_axis_mask is None:
shrink_axis_mask = 0
shrink_axis_mask = _execute.make_int(shrink_axis_mask, "shrink_axis_mask")
_, _, _op = _op_def_lib._apply_op_helper(
"ResourceStridedSliceAssign", ref=ref, begin=begin, end=end,
strides=strides, value=value,
begin_mask=begin_mask,
end_mask=end_mask,
ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask,
name=name)
return _op
_result = None
return _result
def resource_strided_slice_assign_eager_fallback(ref, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function resource_strided_slice_assign
"""
_ctx = ctx if ctx else _context.context()
if begin_mask is None:
begin_mask = 0
begin_mask = _execute.make_int(begin_mask, "begin_mask")
if end_mask is None:
end_mask = 0
end_mask = _execute.make_int(end_mask, "end_mask")
if ellipsis_mask is None:
ellipsis_mask = 0
ellipsis_mask = _execute.make_int(ellipsis_mask, "ellipsis_mask")
if new_axis_mask is None:
new_axis_mask = 0
new_axis_mask = _execute.make_int(new_axis_mask, "new_axis_mask")
if shrink_axis_mask is None:
shrink_axis_mask = 0
shrink_axis_mask = _execute.make_int(shrink_axis_mask, "shrink_axis_mask")
_attr_T, (value,) = _execute.args_to_matching_eager([value], _ctx)
_attr_Index, _inputs_Index = _execute.args_to_matching_eager([begin, end, strides], _ctx)
(begin, end, strides) = _inputs_Index
ref = _ops.convert_to_tensor(ref, _dtypes.resource)
_inputs_flat = [ref, begin, end, strides, value]
_attrs = ("T", _attr_T, "Index", _attr_Index, "begin_mask", begin_mask,
"end_mask", end_mask, "ellipsis_mask", ellipsis_mask, "new_axis_mask",
new_axis_mask, "shrink_axis_mask", shrink_axis_mask)
_result = _execute.execute(b"ResourceStridedSliceAssign", 0,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_result = None
return _result
def reverse(tensor, dims, name=None):
r"""Reverses specific dimensions of a tensor.
Given a `tensor`, and a `bool` tensor `dims` representing the dimensions
of `tensor`, this operation reverses each dimension i of `tensor` where
`dims[i]` is `True`.
`tensor` can have up to 8 dimensions. The number of dimensions
of `tensor` must equal the number of elements in `dims`. In other words:
`rank(tensor) = size(dims)`
For example:
```
# tensor 't' is [[[[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]],
# [[12, 13, 14, 15],
# [16, 17, 18, 19],
# [20, 21, 22, 23]]]]
# tensor 't' shape is [1, 2, 3, 4]
# 'dims' is [False, False, False, True]
reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
[ 7, 6, 5, 4],
[ 11, 10, 9, 8]],
[[15, 14, 13, 12],
[19, 18, 17, 16],
[23, 22, 21, 20]]]]
# 'dims' is [False, True, False, False]
reverse(t, dims) ==> [[[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]]]]
# 'dims' is [False, False, True, False]
reverse(t, dims) ==> [[[[8, 9, 10, 11],
[4, 5, 6, 7],
[0, 1, 2, 3]]
[[20, 21, 22, 23],
[16, 17, 18, 19],
[12, 13, 14, 15]]]]
```
Args:
tensor: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `bool`, `half`, `float32`, `float64`, `complex64`, `complex128`, `string`.
Up to 8-D.
dims: A `Tensor` of type `bool`. 1-D. The dimensions to reverse.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Reverse",
name, _ctx._post_execution_callbacks, tensor, dims)
return _result
except _core._FallbackException:
try:
return reverse_eager_fallback(
tensor, dims, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Reverse", tensor=tensor, dims=dims, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"Reverse", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def reverse_eager_fallback(tensor, dims, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function reverse
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx)
dims = _ops.convert_to_tensor(dims, _dtypes.bool)
_inputs_flat = [tensor, dims]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"Reverse", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Reverse", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def reverse_sequence(input, seq_lengths, seq_dim, batch_dim=0, name=None):
r"""Reverses variable length slices.
This op first slices `input` along the dimension `batch_dim`, and for each
slice `i`, reverses the first `seq_lengths[i]` elements along
the dimension `seq_dim`.
The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`,
and `seq_lengths` must be a vector of length `input.dims[batch_dim]`.
The output slice `i` along dimension `batch_dim` is then given by input
slice `i`, with the first `seq_lengths[i]` slices along dimension
`seq_dim` reversed.
For example:
```
# Given this:
batch_dim = 0
seq_dim = 1
input.dims = (4, 8, ...)
seq_lengths = [7, 2, 3, 5]
# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...]
output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...]
output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...]
output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...]
# while entries past seq_lens are copied through:
output[0, 7:, :, ...] = input[0, 7:, :, ...]
output[1, 2:, :, ...] = input[1, 2:, :, ...]
output[2, 3:, :, ...] = input[2, 3:, :, ...]
output[3, 2:, :, ...] = input[3, 2:, :, ...]
```
In contrast, if:
```
# Given this:
batch_dim = 2
seq_dim = 0
input.dims = (8, ?, 4, ...)
seq_lengths = [7, 2, 3, 5]
# then slices of input are reversed on seq_dim, but only up to seq_lengths:
output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...]
output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...]
output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...]
output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...]
# while entries past seq_lens are copied through:
output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...]
output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...]
output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...]
output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...]
```
Args:
input: A `Tensor`. The input to reverse.
seq_lengths: A `Tensor`. Must be one of the following types: `int32`, `int64`.
1-D with length `input.dims(batch_dim)` and
`max(seq_lengths) <= input.dims(seq_dim)`
seq_dim: An `int`. The dimension which is partially reversed.
batch_dim: An optional `int`. Defaults to `0`.
The dimension along which reversal is performed.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ReverseSequence", name, _ctx._post_execution_callbacks, input,
seq_lengths, "seq_dim", seq_dim, "batch_dim", batch_dim)
return _result
except _core._FallbackException:
try:
return reverse_sequence_eager_fallback(
input, seq_lengths, seq_dim=seq_dim, batch_dim=batch_dim,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
seq_dim = _execute.make_int(seq_dim, "seq_dim")
if batch_dim is None:
batch_dim = 0
batch_dim = _execute.make_int(batch_dim, "batch_dim")
_, _, _op = _op_def_lib._apply_op_helper(
"ReverseSequence", input=input, seq_lengths=seq_lengths,
seq_dim=seq_dim, batch_dim=batch_dim, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("seq_dim", _op.get_attr("seq_dim"), "batch_dim",
_op.get_attr("batch_dim"), "T", _op.get_attr("T"), "Tlen",
_op.get_attr("Tlen"))
_execute.record_gradient(
"ReverseSequence", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def reverse_sequence_eager_fallback(input, seq_lengths, seq_dim, batch_dim=0, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function reverse_sequence
"""
_ctx = ctx if ctx else _context.context()
seq_dim = _execute.make_int(seq_dim, "seq_dim")
if batch_dim is None:
batch_dim = 0
batch_dim = _execute.make_int(batch_dim, "batch_dim")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tlen, (seq_lengths,) = _execute.args_to_matching_eager([seq_lengths], _ctx, _dtypes.int64)
_inputs_flat = [input, seq_lengths]
_attrs = ("seq_dim", seq_dim, "batch_dim", batch_dim, "T", _attr_T, "Tlen",
_attr_Tlen)
_result = _execute.execute(b"ReverseSequence", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ReverseSequence", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('reverse', v1=['reverse', 'manip.reverse', 'reverse_v2'])
@deprecated_endpoints('manip.reverse', 'reverse_v2')
def reverse_v2(tensor, axis, name=None):
r"""Reverses specific dimensions of a tensor.
NOTE `tf.reverse` has now changed behavior in preparation for 1.0.
`tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0.
Given a `tensor`, and a `int32` tensor `axis` representing the set of
dimensions of `tensor` to reverse. This operation reverses each dimension
`i` for which there exists `j` s.t. `axis[j] == i`.
`tensor` can have up to 8 dimensions. The number of dimensions specified
in `axis` may be 0 or more entries. If an index is specified more than
once, a InvalidArgument error is raised.
For example:
```
# tensor 't' is [[[[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]],
# [[12, 13, 14, 15],
# [16, 17, 18, 19],
# [20, 21, 22, 23]]]]
# tensor 't' shape is [1, 2, 3, 4]
# 'dims' is [3] or 'dims' is [-1]
reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
[ 7, 6, 5, 4],
[ 11, 10, 9, 8]],
[[15, 14, 13, 12],
[19, 18, 17, 16],
[23, 22, 21, 20]]]]
# 'dims' is '[1]' (or 'dims' is '[-3]')
reverse(t, dims) ==> [[[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]]]]
# 'dims' is '[2]' (or 'dims' is '[-2]')
reverse(t, dims) ==> [[[[8, 9, 10, 11],
[4, 5, 6, 7],
[0, 1, 2, 3]]
[[20, 21, 22, 23],
[16, 17, 18, 19],
[12, 13, 14, 15]]]]
```
Args:
tensor: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `bool`, `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`, `string`.
Up to 8-D.
axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
1-D. The indices of the dimensions to reverse. Must be in the range
`[-rank(tensor), rank(tensor))`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ReverseV2",
name, _ctx._post_execution_callbacks, tensor, axis)
return _result
except _core._FallbackException:
try:
return reverse_v2_eager_fallback(
tensor, axis, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
reverse_v2, tensor=tensor, axis=axis, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"ReverseV2", tensor=tensor, axis=axis, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
reverse_v2, tensor=tensor, axis=axis, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("Tidx", _op.get_attr("Tidx"), "T", _op.get_attr("T"))
_execute.record_gradient(
"ReverseV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def reverse_v2_eager_fallback(tensor, axis, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function reverse_v2
"""
_ctx = ctx if ctx else _context.context()
_attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], _ctx, _dtypes.int32)
_attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], _ctx)
_inputs_flat = [tensor, axis]
_attrs = ("Tidx", _attr_Tidx, "T", _attr_T)
_result = _execute.execute(b"ReverseV2", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ReverseV2", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('scatter_nd', v1=['scatter_nd', 'manip.scatter_nd'])
@deprecated_endpoints('manip.scatter_nd')
def scatter_nd(indices, updates, shape, name=None):
r"""Scatter `updates` into a new tensor according to `indices`.
Creates a new tensor by applying sparse `updates` to individual values or
slices within a tensor (initially zero for numeric, empty for string) of
the given `shape` according to indices. This operator is the inverse of the
`tf.gather_nd` operator which extracts values or slices from a given tensor.
This operation is similar to tensor_scatter_add, except that the tensor is
zero-initialized. Calling `tf.scatter_nd(indices, values, shape)` is identical
to `tensor_scatter_add(tf.zeros(shape, values.dtype), indices, values)`
If `indices` contains duplicates, then their updates are accumulated (summed).
**WARNING**: The order in which updates are applied is nondeterministic, so the
output will be nondeterministic if `indices` contains duplicates -- because
of some numerical approximation issues, numbers summed in different order
may yield different results.
`indices` is an integer tensor containing indices into a new tensor of shape
`shape`. The last dimension of `indices` can be at most the rank of `shape`:
indices.shape[-1] <= shape.rank
The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = shape.rank`) or slices
(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of
`shape`. `updates` is a tensor with shape
indices.shape[:-1] + shape[indices.shape[-1]:]
The simplest form of scatter is to insert individual elements in a tensor by
index. For example, say we want to insert 4 scattered elements in a rank-1
tensor with 8 elements.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd1.png" alt>
</div>
In Python, this scatter operation would look like this:
```python
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
shape = tf.constant([8])
scatter = tf.scatter_nd(indices, updates, shape)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[0, 11, 0, 10, 9, 0, 0, 12]
We can also, insert entire slices of a higher rank tensor all at once. For
example, if we wanted to insert two slices in the first dimension of a
rank-3 tensor with two matrices of new values.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd2.png" alt>
</div>
In Python, this scatter operation would look like this:
```python
indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]],
[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]]])
shape = tf.constant([4, 4, 4])
scatter = tf.scatter_nd(indices, updates, shape)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]]
Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, the index is ignored.
Args:
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Index tensor.
updates: A `Tensor`. Updates to scatter into output.
shape: A `Tensor`. Must have the same type as `indices`.
1-D. The shape of the resulting tensor.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `updates`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ScatterNd",
name, _ctx._post_execution_callbacks, indices, updates, shape)
return _result
except _core._FallbackException:
try:
return scatter_nd_eager_fallback(
indices, updates, shape, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
scatter_nd, indices=indices, updates=updates, shape=shape,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterNd", indices=indices, updates=updates, shape=shape, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
scatter_nd, indices=indices, updates=updates, shape=shape,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"))
_execute.record_gradient(
"ScatterNd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def scatter_nd_eager_fallback(indices, updates, shape, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function scatter_nd
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (updates,) = _execute.args_to_matching_eager([updates], _ctx)
_attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([indices, shape], _ctx)
(indices, shape) = _inputs_Tindices
_inputs_flat = [indices, updates, shape]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
_result = _execute.execute(b"ScatterNd", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ScatterNd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def scatter_nd_non_aliasing_add(input, indices, updates, name=None):
r"""Applies sparse addition to `input` using individual values or slices
from `updates` according to indices `indices`. The updates are non-aliasing:
`input` is only modified in-place if no other operations will use it.
Otherwise, a copy of `input` is made. This operation has a gradient with
respect to both `input` and `updates`.
`input` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`.
`indices` must be integer tensor, containing indices into `input`.
It must be shape \\([d_0, ..., d_{Q-2}, K]\\) where `0 < K <= P`.
The innermost dimension of `indices` (with length `K`) corresponds to
indices into elements (if `K = P`) or `(P-K)`-dimensional slices
(if `K < P`) along the `K`th dimension of `input`.
`updates` is `Tensor` of rank `Q-1+P-K` with shape:
$$[d_0, ..., d_{Q-2}, input.shape[K], ..., input.shape[P-1]].$$
For example, say we want to add 4 scattered elements to a rank-1 tensor to 8
elements. In Python, that addition would look like this:
input = tf.constant([1, 2, 3, 4, 5, 6, 7, 8])
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
output = tf.scatter_nd_non_aliasing_add(input, indices, updates)
with tf.Session() as sess:
print(sess.run(output))
The resulting value `output` would look like this:
[1, 13, 3, 14, 14, 6, 7, 20]
See `tf.scatter_nd` for more details about how to make updates to slices.
Args:
input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
A Tensor.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A Tensor. Must be one of the following types: `int32`, `int64`.
A tensor of indices into `input`.
updates: A `Tensor`. Must have the same type as `input`.
A Tensor. Must have the same type as ref. A tensor of updated values
to add to `input`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"ScatterNdNonAliasingAdd", name, _ctx._post_execution_callbacks,
input, indices, updates)
return _result
except _core._FallbackException:
try:
return scatter_nd_non_aliasing_add_eager_fallback(
input, indices, updates, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ScatterNdNonAliasingAdd", input=input, indices=indices,
updates=updates, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"))
_execute.record_gradient(
"ScatterNdNonAliasingAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def scatter_nd_non_aliasing_add_eager_fallback(input, indices, updates, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function scatter_nd_non_aliasing_add
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([input, updates], _ctx)
(input, updates) = _inputs_T
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
_inputs_flat = [input, indices, updates]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
_result = _execute.execute(b"ScatterNdNonAliasingAdd", 1,
inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
name=name)
_execute.record_gradient(
"ScatterNdNonAliasingAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def shape(input, out_type=_dtypes.int32, name=None):
r"""Returns the shape of a tensor.
This operation returns a 1-D integer tensor representing the shape of `input`.
For example:
```
# 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]
shape(t) ==> [2, 2, 3]
```
Args:
input: A `Tensor`.
out_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `out_type`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Shape", name,
_ctx._post_execution_callbacks, input, "out_type", out_type)
return _result
except _core._FallbackException:
try:
return shape_eager_fallback(
input, out_type=out_type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_, _, _op = _op_def_lib._apply_op_helper(
"Shape", input=input, out_type=out_type, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "out_type", _op.get_attr("out_type"))
_execute.record_gradient(
"Shape", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def shape_eager_fallback(input, out_type=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function shape
"""
_ctx = ctx if ctx else _context.context()
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T, "out_type", out_type)
_result = _execute.execute(b"Shape", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Shape", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def shape_n(input, out_type=_dtypes.int32, name=None):
r"""Returns shape of tensors.
This operation returns N 1-D integer tensors representing shape of `input[i]s`.
Args:
input: A list of at least 1 `Tensor` objects with the same type.
out_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A list with the same length as `input` of `Tensor` objects with type `out_type`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ShapeN", name,
_ctx._post_execution_callbacks, input, "out_type", out_type)
return _result
except _core._FallbackException:
try:
return shape_n_eager_fallback(
input, out_type=out_type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if not isinstance(input, (list, tuple)):
raise TypeError(
"Expected list for 'input' argument to "
"'shape_n' Op, not %r." % input)
_attr_N = len(input)
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_, _, _op = _op_def_lib._apply_op_helper(
"ShapeN", input=input, out_type=out_type, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("N", _op.get_attr("N"), "T", _op.get_attr("T"), "out_type",
_op.get_attr("out_type"))
_execute.record_gradient(
"ShapeN", _inputs_flat, _attrs, _result, name)
return _result
def shape_n_eager_fallback(input, out_type=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function shape_n
"""
_ctx = ctx if ctx else _context.context()
if not isinstance(input, (list, tuple)):
raise TypeError(
"Expected list for 'input' argument to "
"'shape_n' Op, not %r." % input)
_attr_N = len(input)
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_attr_T, input = _execute.args_to_matching_eager(list(input), _ctx)
_inputs_flat = list(input)
_attrs = ("N", _attr_N, "T", _attr_T, "out_type", out_type)
_result = _execute.execute(b"ShapeN", _attr_N, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ShapeN", _inputs_flat, _attrs, _result, name)
return _result
def size(input, out_type=_dtypes.int32, name=None):
r"""Returns the size of a tensor.
This operation returns an integer representing the number of elements in
`input`.
For example:
```
# 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]]
size(t) ==> 12
```
Args:
input: A `Tensor`.
out_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `out_type`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Size", name,
_ctx._post_execution_callbacks, input, "out_type", out_type)
return _result
except _core._FallbackException:
try:
return size_eager_fallback(
input, out_type=out_type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_, _, _op = _op_def_lib._apply_op_helper(
"Size", input=input, out_type=out_type, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "out_type", _op.get_attr("out_type"))
_execute.record_gradient(
"Size", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def size_eager_fallback(input, out_type=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function size
"""
_ctx = ctx if ctx else _context.context()
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T, "out_type", out_type)
_result = _execute.execute(b"Size", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Size", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _slice(input, begin, size, name=None):
r"""Return a slice from 'input'.
The output tensor is a tensor with dimensions described by 'size'
whose values are extracted from 'input' starting at the offsets in
'begin'.
*Requirements*:
0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n)
Args:
input: A `Tensor`.
begin: A `Tensor`. Must be one of the following types: `int32`, `int64`.
begin[i] specifies the offset into the 'i'th dimension of
'input' to slice from.
size: A `Tensor`. Must have the same type as `begin`.
size[i] specifies the number of elements of the 'i'th dimension
of 'input' to slice. If size[i] is -1, all remaining elements in dimension
i are included in the slice (i.e. this is equivalent to setting
size[i] = input.dim_size(i) - begin[i]).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Slice", name,
_ctx._post_execution_callbacks, input, begin, size)
return _result
except _core._FallbackException:
try:
return _slice_eager_fallback(
input, begin, size, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Slice", input=input, begin=begin, size=size, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Index", _op.get_attr("Index"))
_execute.record_gradient(
"Slice", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _slice_eager_fallback(input, begin, size, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function _slice
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Index, _inputs_Index = _execute.args_to_matching_eager([begin, size], _ctx)
(begin, size) = _inputs_Index
_inputs_flat = [input, begin, size]
_attrs = ("T", _attr_T, "Index", _attr_Index)
_result = _execute.execute(b"Slice", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Slice", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def snapshot(input, name=None):
r"""Returns a copy of the input tensor.
Args:
input: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Snapshot",
name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return snapshot_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Snapshot", input=input, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"Snapshot", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def snapshot_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function snapshot
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"Snapshot", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Snapshot", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def space_to_batch(input, paddings, block_size, name=None):
r"""SpaceToBatch for 4-D tensors of type T.
This is a legacy version of the more general SpaceToBatchND.
Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
More specifically, this op outputs a copy of the input tensor where values from
the `height` and `width` dimensions are moved to the `batch` dimension. After
the zero-padding, both `height` and `width` of the input must be divisible by the
block size.
Args:
input: A `Tensor`. 4-D with shape `[batch, height, width, depth]`.
paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.
2-D tensor of non-negative integers with shape `[2, 2]`. It specifies
the padding of the input with zeros across the spatial dimensions as follows:
paddings = [[pad_top, pad_bottom], [pad_left, pad_right]]
The effective spatial dimensions of the zero-padded input tensor will be:
height_pad = pad_top + height + pad_bottom
width_pad = pad_left + width + pad_right
The attr `block_size` must be greater than one. It indicates the block size.
* Non-overlapping blocks of size `block_size x block size` in the height and
width dimensions are rearranged into the batch dimension at each location.
* The batch of the output tensor is `batch * block_size * block_size`.
* Both height_pad and width_pad must be divisible by block_size.
The shape of the output will be:
[batch*block_size*block_size, height_pad/block_size, width_pad/block_size,
depth]
Some examples:
(1) For the following input of shape `[1, 2, 2, 1]` and block_size of 2:
```
x = [[[[1], [2]], [[3], [4]]]]
```
The output tensor has shape `[4, 1, 1, 1]` and value:
```
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
```
(2) For the following input of shape `[1, 2, 2, 3]` and block_size of 2:
```
x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
```
The output tensor has shape `[4, 1, 1, 3]` and value:
```
[[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
```
(3) For the following input of shape `[1, 4, 4, 1]` and block_size of 2:
```
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]],
[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
```
The output tensor has shape `[4, 2, 2, 1]` and value:
```
x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
```
(4) For the following input of shape `[2, 2, 4, 1]` and block_size of 2:
```
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]]],
[[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
```
The output tensor has shape `[8, 1, 2, 1]` and value:
```
x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
[[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
```
Among others, this operation is useful for reducing atrous convolution into
regular convolution.
block_size: An `int` that is `>= 2`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "SpaceToBatch",
name, _ctx._post_execution_callbacks, input, paddings, "block_size",
block_size)
return _result
except _core._FallbackException:
try:
return space_to_batch_eager_fallback(
input, paddings, block_size=block_size, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
block_size = _execute.make_int(block_size, "block_size")
_, _, _op = _op_def_lib._apply_op_helper(
"SpaceToBatch", input=input, paddings=paddings, block_size=block_size,
name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tpaddings", _op.get_attr("Tpaddings"),
"block_size", _op.get_attr("block_size"))
_execute.record_gradient(
"SpaceToBatch", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def space_to_batch_eager_fallback(input, paddings, block_size, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function space_to_batch
"""
_ctx = ctx if ctx else _context.context()
block_size = _execute.make_int(block_size, "block_size")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tpaddings, (paddings,) = _execute.args_to_matching_eager([paddings], _ctx, _dtypes.int32)
_inputs_flat = [input, paddings]
_attrs = ("T", _attr_T, "Tpaddings", _attr_Tpaddings, "block_size",
block_size)
_result = _execute.execute(b"SpaceToBatch", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"SpaceToBatch", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('space_to_batch_nd', v1=['space_to_batch_nd', 'manip.space_to_batch_nd'])
@deprecated_endpoints('manip.space_to_batch_nd')
def space_to_batch_nd(input, block_shape, paddings, name=None):
r"""SpaceToBatch for N-D tensors of type T.
This operation divides "spatial" dimensions `[1, ..., M]` of the input into a
grid of blocks of shape `block_shape`, and interleaves these blocks with the
"batch" dimension (0) such that in the output, the spatial dimensions
`[1, ..., M]` correspond to the position within the grid, and the batch
dimension combines both the position within a spatial block and the original
batch position. Prior to division into blocks, the spatial dimensions of the
input are optionally zero padded according to `paddings`. See below for a
precise description.
Args:
input: A `Tensor`.
N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`,
where spatial_shape has `M` dimensions.
block_shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
1-D with shape `[M]`, all values must be >= 1.
paddings: A `Tensor`. Must be one of the following types: `int32`, `int64`.
2-D with shape `[M, 2]`, all values must be >= 0.
`paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension
`i + 1`, which corresponds to spatial dimension `i`. It is required that
`block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`.
This operation is equivalent to the following steps:
1. Zero-pad the start and end of dimensions `[1, ..., M]` of the
input according to `paddings` to produce `padded` of shape `padded_shape`.
2. Reshape `padded` to `reshaped_padded` of shape:
[batch] +
[padded_shape[1] / block_shape[0],
block_shape[0],
...,
padded_shape[M] / block_shape[M-1],
block_shape[M-1]] +
remaining_shape
3. Permute dimensions of `reshaped_padded` to produce
`permuted_reshaped_padded` of shape:
block_shape +
[batch] +
[padded_shape[1] / block_shape[0],
...,
padded_shape[M] / block_shape[M-1]] +
remaining_shape
4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch
dimension, producing an output tensor of shape:
[batch * prod(block_shape)] +
[padded_shape[1] / block_shape[0],
...,
padded_shape[M] / block_shape[M-1]] +
remaining_shape
Some examples:
(1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and
`paddings = [[0, 0], [0, 0]]`:
```
x = [[[[1], [2]], [[3], [4]]]]
```
The output tensor has shape `[4, 1, 1, 1]` and value:
```
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
```
(2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and
`paddings = [[0, 0], [0, 0]]`:
```
x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
```
The output tensor has shape `[4, 1, 1, 3]` and value:
```
[[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]]
```
(3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and
`paddings = [[0, 0], [0, 0]]`:
```
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]],
[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
```
The output tensor has shape `[4, 2, 2, 1]` and value:
```
x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]]
```
(4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and
paddings = `[[0, 0], [2, 0]]`:
```
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]]],
[[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]]
```
The output tensor has shape `[8, 1, 3, 1]` and value:
```
x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
[[[0], [2], [4]]], [[[0], [10], [12]]],
[[[0], [5], [7]]], [[[0], [13], [15]]],
[[[0], [6], [8]]], [[[0], [14], [16]]]]
```
Among others, this operation is useful for reducing atrous convolution into
regular convolution.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"SpaceToBatchND", name, _ctx._post_execution_callbacks, input,
block_shape, paddings)
return _result
except _core._FallbackException:
try:
return space_to_batch_nd_eager_fallback(
input, block_shape, paddings, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
space_to_batch_nd, input=input, block_shape=block_shape,
paddings=paddings, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"SpaceToBatchND", input=input, block_shape=block_shape,
paddings=paddings, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
space_to_batch_nd, input=input, block_shape=block_shape,
paddings=paddings, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tblock_shape",
_op.get_attr("Tblock_shape"), "Tpaddings",
_op.get_attr("Tpaddings"))
_execute.record_gradient(
"SpaceToBatchND", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def space_to_batch_nd_eager_fallback(input, block_shape, paddings, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function space_to_batch_nd
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tblock_shape, (block_shape,) = _execute.args_to_matching_eager([block_shape], _ctx, _dtypes.int32)
_attr_Tpaddings, (paddings,) = _execute.args_to_matching_eager([paddings], _ctx, _dtypes.int32)
_inputs_flat = [input, block_shape, paddings]
_attrs = ("T", _attr_T, "Tblock_shape", _attr_Tblock_shape, "Tpaddings",
_attr_Tpaddings)
_result = _execute.execute(b"SpaceToBatchND", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"SpaceToBatchND", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def space_to_depth(input, block_size, data_format="NHWC", name=None):
r"""SpaceToDepth for tensors of type T.
Rearranges blocks of spatial data, into depth. More specifically,
this op outputs a copy of the input tensor where values from the `height`
and `width` dimensions are moved to the `depth` dimension.
The attr `block_size` indicates the input block size.
* Non-overlapping blocks of size `block_size x block size` are rearranged
into depth at each location.
* The depth of the output tensor is `block_size * block_size * input_depth`.
* The Y, X coordinates within each block of the input become the high order
component of the output channel index.
* The input tensor's height and width must be divisible by block_size.
The `data_format` attr specifies the layout of the input and output tensors
with the following options:
"NHWC": `[ batch, height, width, channels ]`
"NCHW": `[ batch, channels, height, width ]`
"NCHW_VECT_C":
`qint8 [ batch, channels / 4, height, width, 4 ]`
It is useful to consider the operation as transforming a 6-D Tensor.
e.g. for data_format = NHWC,
Each element in the input tensor can be specified via 6 coordinates,
ordered by decreasing memory layout significance as:
n,oY,bY,oX,bX,iC (where n=batch index, oX, oY means X or Y coordinates
within the output image, bX, bY means coordinates
within the input block, iC means input channels).
The output would be a transpose to the following layout:
n,oY,oX,bY,bX,iC
This operation is useful for resizing the activations between convolutions
(but keeping all data), e.g. instead of pooling. It is also useful for training
purely convolutional models.
For example, given an input of shape `[1, 2, 2, 1]`, data_format = "NHWC" and
block_size = 2:
```
x = [[[[1], [2]],
[[3], [4]]]]
```
This operation will output a tensor of shape `[1, 1, 1, 4]`:
```
[[[[1, 2, 3, 4]]]]
```
Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`,
the corresponding output will have a single element (i.e. width and height are
both 1) and will have a depth of 4 channels (1 * block_size * block_size).
The output element shape is `[1, 1, 4]`.
For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g.
```
x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]]
```
This operation, for block_size of 2, will return the following tensor of shape
`[1, 1, 1, 12]`
```
[[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
```
Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2:
```
x = [[[[1], [2], [5], [6]],
[[3], [4], [7], [8]],
[[9], [10], [13], [14]],
[[11], [12], [15], [16]]]]
```
the operator will return the following tensor of shape `[1 2 2 4]`:
```
x = [[[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[9, 10, 11, 12],
[13, 14, 15, 16]]]]
```
Args:
input: A `Tensor`.
block_size: An `int` that is `>= 2`. The size of the spatial block.
data_format: An optional `string` from: `"NHWC", "NCHW", "NCHW_VECT_C"`. Defaults to `"NHWC"`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "SpaceToDepth",
name, _ctx._post_execution_callbacks, input, "block_size", block_size,
"data_format", data_format)
return _result
except _core._FallbackException:
try:
return space_to_depth_eager_fallback(
input, block_size=block_size, data_format=data_format, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
block_size = _execute.make_int(block_size, "block_size")
if data_format is None:
data_format = "NHWC"
data_format = _execute.make_str(data_format, "data_format")
_, _, _op = _op_def_lib._apply_op_helper(
"SpaceToDepth", input=input, block_size=block_size,
data_format=data_format, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "block_size", _op.get_attr("block_size"),
"data_format", _op.get_attr("data_format"))
_execute.record_gradient(
"SpaceToDepth", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def space_to_depth_eager_fallback(input, block_size, data_format="NHWC", name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function space_to_depth
"""
_ctx = ctx if ctx else _context.context()
block_size = _execute.make_int(block_size, "block_size")
if data_format is None:
data_format = "NHWC"
data_format = _execute.make_str(data_format, "data_format")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T, "block_size", block_size, "data_format",
data_format)
_result = _execute.execute(b"SpaceToDepth", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"SpaceToDepth", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def split(axis, value, num_split, name=None):
r"""Splits a tensor into `num_split` tensors along one dimension.
Args:
axis: A `Tensor` of type `int32`.
0-D. The dimension along which to split. Must be in the range
`[-rank(value), rank(value))`.
value: A `Tensor`. The tensor to split.
num_split: An `int` that is `>= 1`.
The number of ways to split. Must evenly divide
`value.shape[split_dim]`.
name: A name for the operation (optional).
Returns:
A list of `num_split` `Tensor` objects with the same type as `value`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Split", name,
_ctx._post_execution_callbacks, axis, value, "num_split", num_split)
return _result
except _core._FallbackException:
try:
return split_eager_fallback(
axis, value, num_split=num_split, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
num_split = _execute.make_int(num_split, "num_split")
_, _, _op = _op_def_lib._apply_op_helper(
"Split", split_dim=axis, value=value, num_split=num_split, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num_split", _op.get_attr("num_split"), "T", _op.get_attr("T"))
_execute.record_gradient(
"Split", _inputs_flat, _attrs, _result, name)
return _result
def split_eager_fallback(axis, value, num_split, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function split
"""
_ctx = ctx if ctx else _context.context()
num_split = _execute.make_int(num_split, "num_split")
_attr_T, (value,) = _execute.args_to_matching_eager([value], _ctx)
axis = _ops.convert_to_tensor(axis, _dtypes.int32)
_inputs_flat = [axis, value]
_attrs = ("num_split", num_split, "T", _attr_T)
_result = _execute.execute(b"Split", num_split, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Split", _inputs_flat, _attrs, _result, name)
return _result
def split_v(value, size_splits, axis, num_split, name=None):
r"""Splits a tensor into `num_split` tensors along one dimension.
Args:
value: A `Tensor`. The tensor to split.
size_splits: A `Tensor`. Must be one of the following types: `int32`, `int64`.
list containing the sizes of each output tensor along the split
dimension. Must sum to the dimension of value along split_dim.
Can contain one -1 indicating that dimension is to be inferred.
axis: A `Tensor` of type `int32`.
0-D. The dimension along which to split. Must be in the range
`[-rank(value), rank(value))`.
num_split: An `int` that is `>= 1`.
name: A name for the operation (optional).
Returns:
A list of `num_split` `Tensor` objects with the same type as `value`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "SplitV", name,
_ctx._post_execution_callbacks, value, size_splits, axis, "num_split",
num_split)
return _result
except _core._FallbackException:
try:
return split_v_eager_fallback(
value, size_splits, axis, num_split=num_split, name=name,
ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
num_split = _execute.make_int(num_split, "num_split")
_, _, _op = _op_def_lib._apply_op_helper(
"SplitV", value=value, size_splits=size_splits, split_dim=axis,
num_split=num_split, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num_split", _op.get_attr("num_split"), "T", _op.get_attr("T"),
"Tlen", _op.get_attr("Tlen"))
_execute.record_gradient(
"SplitV", _inputs_flat, _attrs, _result, name)
return _result
def split_v_eager_fallback(value, size_splits, axis, num_split, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function split_v
"""
_ctx = ctx if ctx else _context.context()
num_split = _execute.make_int(num_split, "num_split")
_attr_T, (value,) = _execute.args_to_matching_eager([value], _ctx)
_attr_Tlen, (size_splits,) = _execute.args_to_matching_eager([size_splits], _ctx, _dtypes.int64)
axis = _ops.convert_to_tensor(axis, _dtypes.int32)
_inputs_flat = [value, size_splits, axis]
_attrs = ("num_split", num_split, "T", _attr_T, "Tlen", _attr_Tlen)
_result = _execute.execute(b"SplitV", num_split, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"SplitV", _inputs_flat, _attrs, _result, name)
return _result
def squeeze(input, axis=[], name=None):
r"""Removes dimensions of size 1 from the shape of a tensor.
Given a tensor `input`, this operation returns a tensor of the same type with
all dimensions of size 1 removed. If you don't want to remove all size 1
dimensions, you can remove specific size 1 dimensions by specifying
`axis`.
For example:
```
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t)) ==> [2, 3]
```
Or, to remove specific size 1 dimensions:
```
# 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1]
```
Args:
input: A `Tensor`. The `input` to squeeze.
axis: An optional list of `ints`. Defaults to `[]`.
If specified, only squeezes the dimensions listed. The dimension
index starts at 0. It is an error to squeeze a dimension that is not 1. Must
be in the range `[-rank(input), rank(input))`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Squeeze",
name, _ctx._post_execution_callbacks, input, "squeeze_dims", axis)
return _result
except _core._FallbackException:
try:
return squeeze_eager_fallback(
input, axis=axis, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if axis is None:
axis = []
if not isinstance(axis, (list, tuple)):
raise TypeError(
"Expected list for 'axis' argument to "
"'squeeze' Op, not %r." % axis)
axis = [_execute.make_int(_i, "axis") for _i in axis]
_, _, _op = _op_def_lib._apply_op_helper(
"Squeeze", input=input, squeeze_dims=axis, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "squeeze_dims",
_op.get_attr("squeeze_dims"))
_execute.record_gradient(
"Squeeze", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def squeeze_eager_fallback(input, axis=[], name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function squeeze
"""
_ctx = ctx if ctx else _context.context()
if axis is None:
axis = []
if not isinstance(axis, (list, tuple)):
raise TypeError(
"Expected list for 'axis' argument to "
"'squeeze' Op, not %r." % axis)
axis = [_execute.make_int(_i, "axis") for _i in axis]
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T, "squeeze_dims", axis)
_result = _execute.execute(b"Squeeze", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Squeeze", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('stop_gradient')
def stop_gradient(input, name=None):
r"""Stops gradient computation.
When executed in a graph, this op outputs its input tensor as-is.
When building ops to compute gradients, this op prevents the contribution of
its inputs to be taken into account. Normally, the gradient generator adds ops
to a graph to compute the derivatives of a specified 'loss' by recursively
finding out inputs that contributed to its computation. If you insert this op
in the graph it inputs are masked from the gradient generator. They are not
taken into account for computing gradients.
This is useful any time you want to compute a value with TensorFlow but need
to pretend that the value was a constant. Some examples include:
* The *EM* algorithm where the *M-step* should not involve backpropagation
through the output of the *E-step*.
* Contrastive divergence training of Boltzmann machines where, when
differentiating the energy function, the training must not backpropagate
through the graph that generated the samples from the model.
* Adversarial training, where no backprop should happen through the adversarial
example generation process.
Args:
input: A `Tensor`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "StopGradient",
name, _ctx._post_execution_callbacks, input)
return _result
except _core._FallbackException:
try:
return stop_gradient_eager_fallback(
input, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
stop_gradient, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"StopGradient", input=input, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
stop_gradient, input=input, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"StopGradient", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def stop_gradient_eager_fallback(input, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function stop_gradient
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_inputs_flat = [input]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"StopGradient", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"StopGradient", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def strided_slice(input, begin, end, strides, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None):
r"""Return a strided slice from `input`.
Note, most python users will want to use the Python `Tensor.__getitem__`
or `Variable.__getitem__` rather than this op directly.
The goal of this op is to produce a new tensor with a subset of
the elements from the `n` dimensional `input` tensor. The subset is chosen using
a sequence of `m` sparse range specifications encoded into the arguments
of this function. Note, in some cases
`m` could be equal to `n`, but this need not be the case. Each
range specification entry can be one of the following:
- An ellipsis (...). Ellipses are used to imply zero or more
dimensions of full-dimension selection and are produced using
`ellipsis_mask`. For example, `foo[...]` is the identity slice.
- A new axis. This is used to insert a new shape=1 dimension and is
produced using `new_axis_mask`. For example, `foo[:, ...]` where
`foo` is shape `(3, 4)` produces a `(1, 3, 4)` tensor.
- A range `begin:end:stride`. This is used to specify how much to choose from
a given dimension. `stride` can be any integer but 0. `begin` is an integer
which represents the index of the first value to select while `end` represents
the index of the last value to select. The number of values selected in each
dimension is `end - begin` if `stride > 0` and `begin - end` if `stride < 0`.
`begin` and `end` can be negative where `-1` is the last element, `-2` is
the second to last. `begin_mask` controls whether to replace the explicitly
given `begin` with an implicit effective value of `0` if `stride > 0` and
`-1` if `stride < 0`. `end_mask` is analogous but produces the number
required to create the largest open interval. For example, given a shape
`(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do
not assume this is equivalent to `foo[0:-1]` which has an effective `begin`
and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the
first dimension of a tensor while dropping the last two (in the original
order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`.
- A single index. This is used to keep only elements that have a given
index. For example (`foo[2, :]` on a shape `(5,6)` tensor produces a
shape `(6,)` tensor. This is encoded in `begin` and `end` and
`shrink_axis_mask`.
Each conceptual range specification is encoded in the op's argument. This
encoding is best understand by considering a non-trivial example. In
particular,
`foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as
```
begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0)
end = [2, 4, x, x, -3, x]
strides = [1, 1, x, x, -1, 1]
begin_mask = 1<<4 | 1 << 5 = 48
end_mask = 1<<5 = 32
ellipsis_mask = 1<<3 = 8
new_axis_mask = 1<<2 4
shrink_axis_mask = 1<<0
```
In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of
the slice becomes (2, 1, 5, 5, 2, 5).
Let us walk step by step through each argument specification.
1. The first argument in the example slice is turned into `begin = 1` and
`end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we
also set the appropriate bit in `shrink_axis_mask`.
2. `2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks have
zero bits contributed.
3. None is a synonym for `tf.newaxis`. This means insert a dimension of size 1
dimension in the final shape. Dummy values are contributed to begin,
end and stride, while the new_axis_mask bit is set.
4. `...` grab the full ranges from as many dimensions as needed to
fully specify a slice for every dimension of the input shape.
5. `:-3:-1` shows the use of negative indices. A negative index `i` associated
with a dimension that has shape `s` is converted to a positive index
`s + i`. So `-1` becomes `s-1` (i.e. the last element). This conversion
is done internally so begin, end and strides receive x, -3, and -1.
The appropriate begin_mask bit is set to indicate the start range is the
full range (ignoring the x).
6. `:` indicates that the entire contents of the corresponding dimension
is selected. This is equivalent to `::` or `0::1`. begin, end, and strides
receive 0, 0, and 1, respectively. The appropriate bits in `begin_mask` and
`end_mask` are also set.
*Requirements*:
`0 != strides[i] for i in [0, m)`
`ellipsis_mask must be a power of two (only one ellipsis)`
Args:
input: A `Tensor`.
begin: A `Tensor`. Must be one of the following types: `int32`, `int64`.
`begin[k]` specifies the offset into the `k`th range specification.
The exact dimension this corresponds to will be determined by context.
Out-of-bounds values will be silently clamped. If the `k`th bit of
`begin_mask` then `begin[k]` is ignored and the full range of the
appropriate dimension is used instead. Negative values causes indexing
to start from the highest element e.g. If `foo==[1,2,3]` then `foo[-1]==3`.
end: A `Tensor`. Must have the same type as `begin`.
`end[i]` is like `begin` with the exception that `end_mask` is
used to determine full ranges.
strides: A `Tensor`. Must have the same type as `begin`.
`strides[i]` specifies the increment in the `i`th specification
after extracting a given element. Negative indices will reverse
the original order. Out or range values are
clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0`
begin_mask: An optional `int`. Defaults to `0`.
a bitmask where a bit i being 1 means to ignore the begin
value and instead use the largest interval possible. At runtime
begin[i] will be replaced with `[0, n-1)` if `stride[i] > 0` or
`[-1, n-1]` if `stride[i] < 0`
end_mask: An optional `int`. Defaults to `0`. analogous to `begin_mask`
ellipsis_mask: An optional `int`. Defaults to `0`.
a bitmask where bit `i` being 1 means the `i`th
position is actually an ellipsis. One bit at most can be 1.
If `ellipsis_mask == 0`, then an implicit ellipsis mask of `1 << (m+1)`
is provided. This means that `foo[3:5] == foo[3:5, ...]`. An ellipsis
implicitly creates as many range specifications as necessary to fully
specify the sliced range for every dimension. For example for a 4-dimensional
tensor `foo` the slice `foo[2, ..., 5:8]` implies `foo[2, :, :, 5:8]`.
new_axis_mask: An optional `int`. Defaults to `0`.
a bitmask where bit `i` being 1 means the `i`th
specification creates a new shape 1 dimension. For example
`foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.
shrink_axis_mask: An optional `int`. Defaults to `0`.
a bitmask where bit `i` implies that the `i`th
specification should shrink the dimensionality. begin and end
must imply a slice of size 1 in the dimension. For example in
python one might do `foo[:, 3, :]` which would result in
`shrink_axis_mask` being 2.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "StridedSlice",
name, _ctx._post_execution_callbacks, input, begin, end, strides,
"begin_mask", begin_mask, "end_mask", end_mask, "ellipsis_mask",
ellipsis_mask, "new_axis_mask", new_axis_mask, "shrink_axis_mask",
shrink_axis_mask)
return _result
except _core._FallbackException:
try:
return strided_slice_eager_fallback(
input, begin, end, strides, begin_mask=begin_mask,
end_mask=end_mask, ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask, shrink_axis_mask=shrink_axis_mask,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if begin_mask is None:
begin_mask = 0
begin_mask = _execute.make_int(begin_mask, "begin_mask")
if end_mask is None:
end_mask = 0
end_mask = _execute.make_int(end_mask, "end_mask")
if ellipsis_mask is None:
ellipsis_mask = 0
ellipsis_mask = _execute.make_int(ellipsis_mask, "ellipsis_mask")
if new_axis_mask is None:
new_axis_mask = 0
new_axis_mask = _execute.make_int(new_axis_mask, "new_axis_mask")
if shrink_axis_mask is None:
shrink_axis_mask = 0
shrink_axis_mask = _execute.make_int(shrink_axis_mask, "shrink_axis_mask")
_, _, _op = _op_def_lib._apply_op_helper(
"StridedSlice", input=input, begin=begin, end=end, strides=strides,
begin_mask=begin_mask, end_mask=end_mask,
ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Index", _op.get_attr("Index"),
"begin_mask", _op.get_attr("begin_mask"), "end_mask",
_op.get_attr("end_mask"), "ellipsis_mask",
_op.get_attr("ellipsis_mask"), "new_axis_mask",
_op.get_attr("new_axis_mask"), "shrink_axis_mask",
_op.get_attr("shrink_axis_mask"))
_execute.record_gradient(
"StridedSlice", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def strided_slice_eager_fallback(input, begin, end, strides, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function strided_slice
"""
_ctx = ctx if ctx else _context.context()
if begin_mask is None:
begin_mask = 0
begin_mask = _execute.make_int(begin_mask, "begin_mask")
if end_mask is None:
end_mask = 0
end_mask = _execute.make_int(end_mask, "end_mask")
if ellipsis_mask is None:
ellipsis_mask = 0
ellipsis_mask = _execute.make_int(ellipsis_mask, "ellipsis_mask")
if new_axis_mask is None:
new_axis_mask = 0
new_axis_mask = _execute.make_int(new_axis_mask, "new_axis_mask")
if shrink_axis_mask is None:
shrink_axis_mask = 0
shrink_axis_mask = _execute.make_int(shrink_axis_mask, "shrink_axis_mask")
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Index, _inputs_Index = _execute.args_to_matching_eager([begin, end, strides], _ctx)
(begin, end, strides) = _inputs_Index
_inputs_flat = [input, begin, end, strides]
_attrs = ("T", _attr_T, "Index", _attr_Index, "begin_mask", begin_mask,
"end_mask", end_mask, "ellipsis_mask", ellipsis_mask, "new_axis_mask",
new_axis_mask, "shrink_axis_mask", shrink_axis_mask)
_result = _execute.execute(b"StridedSlice", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"StridedSlice", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def strided_slice_assign(ref, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None):
r"""Assign `value` to the sliced l-value reference of `ref`.
The values of `value` are assigned to the positions in the variable
`ref` that are selected by the slice parameters. The slice parameters
`begin, `end`, `strides`, etc. work exactly as in `StridedSlice`.
NOTE this op currently does not support broadcasting and so `value`'s
shape must be exactly the shape produced by the slice of `ref`.
Args:
ref: A mutable `Tensor`.
begin: A `Tensor`. Must be one of the following types: `int32`, `int64`.
end: A `Tensor`. Must have the same type as `begin`.
strides: A `Tensor`. Must have the same type as `begin`.
value: A `Tensor`. Must have the same type as `ref`.
begin_mask: An optional `int`. Defaults to `0`.
end_mask: An optional `int`. Defaults to `0`.
ellipsis_mask: An optional `int`. Defaults to `0`.
new_axis_mask: An optional `int`. Defaults to `0`.
shrink_axis_mask: An optional `int`. Defaults to `0`.
name: A name for the operation (optional).
Returns:
A mutable `Tensor`. Has the same type as `ref`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
raise RuntimeError("strided_slice_assign op does not support eager execution. Arg 'output_ref' is a ref.")
# Add nodes to the TensorFlow graph.
if begin_mask is None:
begin_mask = 0
begin_mask = _execute.make_int(begin_mask, "begin_mask")
if end_mask is None:
end_mask = 0
end_mask = _execute.make_int(end_mask, "end_mask")
if ellipsis_mask is None:
ellipsis_mask = 0
ellipsis_mask = _execute.make_int(ellipsis_mask, "ellipsis_mask")
if new_axis_mask is None:
new_axis_mask = 0
new_axis_mask = _execute.make_int(new_axis_mask, "new_axis_mask")
if shrink_axis_mask is None:
shrink_axis_mask = 0
shrink_axis_mask = _execute.make_int(shrink_axis_mask, "shrink_axis_mask")
_, _, _op = _op_def_lib._apply_op_helper(
"StridedSliceAssign", ref=ref, begin=begin, end=end, strides=strides,
value=value, begin_mask=begin_mask,
end_mask=end_mask, ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Index", _op.get_attr("Index"),
"begin_mask", _op.get_attr("begin_mask"), "end_mask",
_op.get_attr("end_mask"), "ellipsis_mask",
_op.get_attr("ellipsis_mask"), "new_axis_mask",
_op.get_attr("new_axis_mask"), "shrink_axis_mask",
_op.get_attr("shrink_axis_mask"))
_execute.record_gradient(
"StridedSliceAssign", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def strided_slice_assign_eager_fallback(ref, begin, end, strides, value, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None, ctx=None):
raise RuntimeError("strided_slice_assign op does not support eager execution. Arg 'output_ref' is a ref.")
def strided_slice_grad(shape, begin, end, strides, dy, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None):
r"""Returns the gradient of `StridedSlice`.
Since `StridedSlice` cuts out pieces of its `input` which is size
`shape`, its gradient will have the same shape (which is passed here
as `shape`). The gradient will be zero in any element that the slice
does not select.
Arguments are the same as StridedSliceGrad with the exception that
`dy` is the input gradient to be propagated and `shape` is the
shape of `StridedSlice`'s `input`.
Args:
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
begin: A `Tensor`. Must have the same type as `shape`.
end: A `Tensor`. Must have the same type as `shape`.
strides: A `Tensor`. Must have the same type as `shape`.
dy: A `Tensor`.
begin_mask: An optional `int`. Defaults to `0`.
end_mask: An optional `int`. Defaults to `0`.
ellipsis_mask: An optional `int`. Defaults to `0`.
new_axis_mask: An optional `int`. Defaults to `0`.
shrink_axis_mask: An optional `int`. Defaults to `0`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `dy`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"StridedSliceGrad", name, _ctx._post_execution_callbacks, shape,
begin, end, strides, dy, "begin_mask", begin_mask, "end_mask",
end_mask, "ellipsis_mask", ellipsis_mask, "new_axis_mask",
new_axis_mask, "shrink_axis_mask", shrink_axis_mask)
return _result
except _core._FallbackException:
try:
return strided_slice_grad_eager_fallback(
shape, begin, end, strides, dy, begin_mask=begin_mask,
end_mask=end_mask, ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask, shrink_axis_mask=shrink_axis_mask,
name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if begin_mask is None:
begin_mask = 0
begin_mask = _execute.make_int(begin_mask, "begin_mask")
if end_mask is None:
end_mask = 0
end_mask = _execute.make_int(end_mask, "end_mask")
if ellipsis_mask is None:
ellipsis_mask = 0
ellipsis_mask = _execute.make_int(ellipsis_mask, "ellipsis_mask")
if new_axis_mask is None:
new_axis_mask = 0
new_axis_mask = _execute.make_int(new_axis_mask, "new_axis_mask")
if shrink_axis_mask is None:
shrink_axis_mask = 0
shrink_axis_mask = _execute.make_int(shrink_axis_mask, "shrink_axis_mask")
_, _, _op = _op_def_lib._apply_op_helper(
"StridedSliceGrad", shape=shape, begin=begin, end=end,
strides=strides, dy=dy, begin_mask=begin_mask,
end_mask=end_mask, ellipsis_mask=ellipsis_mask,
new_axis_mask=new_axis_mask,
shrink_axis_mask=shrink_axis_mask, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Index", _op.get_attr("Index"),
"begin_mask", _op.get_attr("begin_mask"), "end_mask",
_op.get_attr("end_mask"), "ellipsis_mask",
_op.get_attr("ellipsis_mask"), "new_axis_mask",
_op.get_attr("new_axis_mask"), "shrink_axis_mask",
_op.get_attr("shrink_axis_mask"))
_execute.record_gradient(
"StridedSliceGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def strided_slice_grad_eager_fallback(shape, begin, end, strides, dy, begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function strided_slice_grad
"""
_ctx = ctx if ctx else _context.context()
if begin_mask is None:
begin_mask = 0
begin_mask = _execute.make_int(begin_mask, "begin_mask")
if end_mask is None:
end_mask = 0
end_mask = _execute.make_int(end_mask, "end_mask")
if ellipsis_mask is None:
ellipsis_mask = 0
ellipsis_mask = _execute.make_int(ellipsis_mask, "ellipsis_mask")
if new_axis_mask is None:
new_axis_mask = 0
new_axis_mask = _execute.make_int(new_axis_mask, "new_axis_mask")
if shrink_axis_mask is None:
shrink_axis_mask = 0
shrink_axis_mask = _execute.make_int(shrink_axis_mask, "shrink_axis_mask")
_attr_T, (dy,) = _execute.args_to_matching_eager([dy], _ctx)
_attr_Index, _inputs_Index = _execute.args_to_matching_eager([shape, begin, end, strides], _ctx)
(shape, begin, end, strides) = _inputs_Index
_inputs_flat = [shape, begin, end, strides, dy]
_attrs = ("T", _attr_T, "Index", _attr_Index, "begin_mask", begin_mask,
"end_mask", end_mask, "ellipsis_mask", ellipsis_mask, "new_axis_mask",
new_axis_mask, "shrink_axis_mask", shrink_axis_mask)
_result = _execute.execute(b"StridedSliceGrad", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"StridedSliceGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('tensor_scatter_add')
def tensor_scatter_add(tensor, indices, updates, name=None):
r"""Adds sparse `updates` to an existing tensor according to `indices`.
This operation creates a new tensor by adding sparse `updates` to the passed
in `tensor`.
This operation is very similar to `tf.scatter_nd_add`, except that the updates
are added onto an existing tensor (as opposed to a variable). If the memory
for the existing tensor cannot be re-used, a copy is made and updated.
`indices` is an integer tensor containing indices into a new tensor of shape
`shape`. The last dimension of `indices` can be at most the rank of `shape`:
indices.shape[-1] <= shape.rank
The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = shape.rank`) or slices
(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of
`shape`. `updates` is a tensor with shape
indices.shape[:-1] + shape[indices.shape[-1]:]
The simplest form of tensor_scatter_add is to add individual elements to a
tensor by index. For example, say we want to add 4 elements in a rank-1
tensor with 8 elements.
In Python, this scatter add operation would look like this:
```python
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_add(tensor, indices, updates)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[1, 12, 1, 11, 10, 1, 1, 13]
We can also, insert entire slices of a higher rank tensor all at once. For
example, if we wanted to insert two slices in the first dimension of a
rank-3 tensor with two matrices of new values.
In Python, this scatter add operation would look like this:
```python
indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]],
[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4])
updated = tf.tensor_scatter_add(tensor, indices, updates)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8], [9, 9, 9, 9]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, the index is ignored.
Args:
tensor: A `Tensor`. Tensor to copy/update.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Index tensor.
updates: A `Tensor`. Must have the same type as `tensor`.
Updates to scatter into output.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"TensorScatterAdd", name, _ctx._post_execution_callbacks, tensor,
indices, updates)
return _result
except _core._FallbackException:
try:
return tensor_scatter_add_eager_fallback(
tensor, indices, updates, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
tensor_scatter_add, tensor=tensor, indices=indices,
updates=updates, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"TensorScatterAdd", tensor=tensor, indices=indices, updates=updates,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
tensor_scatter_add, tensor=tensor, indices=indices, updates=updates,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"))
_execute.record_gradient(
"TensorScatterAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def tensor_scatter_add_eager_fallback(tensor, indices, updates, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function tensor_scatter_add
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([tensor, updates], _ctx)
(tensor, updates) = _inputs_T
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
_inputs_flat = [tensor, indices, updates]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
_result = _execute.execute(b"TensorScatterAdd", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"TensorScatterAdd", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('tensor_scatter_sub')
def tensor_scatter_sub(tensor, indices, updates, name=None):
r"""Subtracts sparse `updates` from an existing tensor according to `indices`.
This operation creates a new tensor by subtracting sparse `updates` from the
passed in `tensor`.
This operation is very similar to `tf.scatter_nd_sub`, except that the updates
are subtracted from an existing tensor (as opposed to a variable). If the memory
for the existing tensor cannot be re-used, a copy is made and updated.
`indices` is an integer tensor containing indices into a new tensor of shape
`shape`. The last dimension of `indices` can be at most the rank of `shape`:
indices.shape[-1] <= shape.rank
The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = shape.rank`) or slices
(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of
`shape`. `updates` is a tensor with shape
indices.shape[:-1] + shape[indices.shape[-1]:]
The simplest form of tensor_scatter_sub is to subtract individual elements
from a tensor by index. For example, say we want to insert 4 scattered elements
in a rank-1 tensor with 8 elements.
In Python, this scatter subtract operation would look like this:
```python
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_sub(tensor, indices, updates)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[1, -10, 1, -9, -8, 1, 1, -11]
We can also, insert entire slices of a higher rank tensor all at once. For
example, if we wanted to insert two slices in the first dimension of a
rank-3 tensor with two matrices of new values.
In Python, this scatter add operation would look like this:
```python
indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]],
[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4])
updated = tf.tensor_scatter_sub(tensor, indices, updates)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[-4, -4, -4, -4], [-5, -5, -5, -5], [-6, -6, -6, -6], [-7, -7, -7, -7]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, the index is ignored.
Args:
tensor: A `Tensor`. Tensor to copy/update.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Index tensor.
updates: A `Tensor`. Must have the same type as `tensor`.
Updates to scatter into output.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"TensorScatterSub", name, _ctx._post_execution_callbacks, tensor,
indices, updates)
return _result
except _core._FallbackException:
try:
return tensor_scatter_sub_eager_fallback(
tensor, indices, updates, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
tensor_scatter_sub, tensor=tensor, indices=indices,
updates=updates, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"TensorScatterSub", tensor=tensor, indices=indices, updates=updates,
name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
tensor_scatter_sub, tensor=tensor, indices=indices, updates=updates,
name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"))
_execute.record_gradient(
"TensorScatterSub", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def tensor_scatter_sub_eager_fallback(tensor, indices, updates, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function tensor_scatter_sub
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([tensor, updates], _ctx)
(tensor, updates) = _inputs_T
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
_inputs_flat = [tensor, indices, updates]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
_result = _execute.execute(b"TensorScatterSub", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"TensorScatterSub", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('tensor_scatter_update')
def tensor_scatter_update(tensor, indices, updates, name=None):
r"""Scatter `updates` into an existing tensor according to `indices`.
This operation creates a new tensor by applying sparse `updates` to the passed
in `tensor`.
This operation is very similar to `tf.scatter_nd`, except that the updates are
scattered onto an existing tensor (as opposed to a zero-tensor). If the memory
for the existing tensor cannot be re-used, a copy is made and updated.
If `indices` contains duplicates, then their updates are accumulated (summed).
**WARNING**: The order in which updates are applied is nondeterministic, so the
output will be nondeterministic if `indices` contains duplicates -- because
of some numerical approximation issues, numbers summed in different order
may yield different results.
`indices` is an integer tensor containing indices into a new tensor of shape
`shape`. The last dimension of `indices` can be at most the rank of `shape`:
indices.shape[-1] <= shape.rank
The last dimension of `indices` corresponds to indices into elements
(if `indices.shape[-1] = shape.rank`) or slices
(if `indices.shape[-1] < shape.rank`) along dimension `indices.shape[-1]` of
`shape`. `updates` is a tensor with shape
indices.shape[:-1] + shape[indices.shape[-1]:]
The simplest form of scatter is to insert individual elements in a tensor by
index. For example, say we want to insert 4 scattered elements in a rank-1
tensor with 8 elements.
<div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="https://www.tensorflow.org/images/ScatterNd1.png" alt>
</div>
In Python, this scatter operation would look like this:
```python
indices = tf.constant([[4], [3], [1], [7]])
updates = tf.constant([9, 10, 11, 12])
tensor = tf.ones([8], dtype=tf.int32)
updated = tf.tensor_scatter_update(tensor, indices, updates)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[1, 11, 1, 10, 9, 1, 1, 12]
We can also, insert entire slices of a higher rank tensor all at once. For
example, if we wanted to insert two slices in the first dimension of a
rank-3 tensor with two matrices of new values.
In Python, this scatter operation would look like this:
```python
indices = tf.constant([[0], [2]])
updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]],
[[5, 5, 5, 5], [6, 6, 6, 6],
[7, 7, 7, 7], [8, 8, 8, 8]]])
tensor = tf.ones([4, 4, 4])
updated = tf.tensor_scatter_update(tensor, indices, updates)
with tf.Session() as sess:
print(sess.run(scatter))
```
The resulting tensor would look like this:
[[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]],
[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]]
Note that on CPU, if an out of bound index is found, an error is returned.
On GPU, if an out of bound index is found, the index is ignored.
Args:
tensor: A `Tensor`. Tensor to copy/update.
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
Index tensor.
updates: A `Tensor`. Must have the same type as `tensor`.
Updates to scatter into output.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `tensor`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"TensorScatterUpdate", name, _ctx._post_execution_callbacks, tensor,
indices, updates)
return _result
except _core._FallbackException:
try:
return tensor_scatter_update_eager_fallback(
tensor, indices, updates, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
tensor_scatter_update, tensor=tensor, indices=indices,
updates=updates, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"TensorScatterUpdate", tensor=tensor, indices=indices,
updates=updates, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
tensor_scatter_update, tensor=tensor, indices=indices,
updates=updates, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tindices", _op.get_attr("Tindices"))
_execute.record_gradient(
"TensorScatterUpdate", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def tensor_scatter_update_eager_fallback(tensor, indices, updates, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function tensor_scatter_update
"""
_ctx = ctx if ctx else _context.context()
_attr_T, _inputs_T = _execute.args_to_matching_eager([tensor, updates], _ctx)
(tensor, updates) = _inputs_T
_attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], _ctx)
_inputs_flat = [tensor, indices, updates]
_attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
_result = _execute.execute(b"TensorScatterUpdate", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"TensorScatterUpdate", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
@_dispatch.add_dispatch_list
@tf_export('tile', v1=['tile', 'manip.tile'])
@deprecated_endpoints('manip.tile')
def tile(input, multiples, name=None):
r"""Constructs a tensor by tiling a given tensor.
This operation creates a new tensor by replicating `input` `multiples` times.
The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,
and the values of `input` are replicated `multiples[i]` times along the 'i'th
dimension. For example, tiling `[a b c d]` by `[2]` produces
`[a b c d a b c d]`.
Args:
input: A `Tensor`. 1-D or higher.
multiples: A `Tensor`. Must be one of the following types: `int32`, `int64`.
1-D. Length must be the same as the number of dimensions in `input`
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Tile", name,
_ctx._post_execution_callbacks, input, multiples)
return _result
except _core._FallbackException:
try:
return tile_eager_fallback(
input, multiples, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
tile, input=input, multiples=multiples, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"Tile", input=input, multiples=multiples, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
tile, input=input, multiples=multiples, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tmultiples", _op.get_attr("Tmultiples"))
_execute.record_gradient(
"Tile", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def tile_eager_fallback(input, multiples, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function tile
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
_attr_Tmultiples, (multiples,) = _execute.args_to_matching_eager([multiples], _ctx, _dtypes.int32)
_inputs_flat = [input, multiples]
_attrs = ("T", _attr_T, "Tmultiples", _attr_Tmultiples)
_result = _execute.execute(b"Tile", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Tile", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def tile_grad(input, multiples, name=None):
r"""Returns the gradient of `Tile`.
Since `Tile` takes an input and repeats the input `multiples` times
along each dimension, `TileGrad` takes in `multiples` and aggregates
each repeated tile of `input` into `output`.
Args:
input: A `Tensor`.
multiples: A `Tensor` of type `int32`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `input`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "TileGrad",
name, _ctx._post_execution_callbacks, input, multiples)
return _result
except _core._FallbackException:
try:
return tile_grad_eager_fallback(
input, multiples, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"TileGrad", input=input, multiples=multiples, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"TileGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def tile_grad_eager_fallback(input, multiples, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function tile_grad
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (input,) = _execute.args_to_matching_eager([input], _ctx)
multiples = _ops.convert_to_tensor(multiples, _dtypes.int32)
_inputs_flat = [input, multiples]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"TileGrad", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"TileGrad", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def transpose(x, perm, name=None):
r"""Shuffle dimensions of x according to a permutation.
The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
`y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]`
Args:
x: A `Tensor`.
perm: A `Tensor`. Must be one of the following types: `int32`, `int64`.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Transpose",
name, _ctx._post_execution_callbacks, x, perm)
return _result
except _core._FallbackException:
try:
return transpose_eager_fallback(
x, perm, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Transpose", x=x, perm=perm, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Tperm", _op.get_attr("Tperm"))
_execute.record_gradient(
"Transpose", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def transpose_eager_fallback(x, perm, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function transpose
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_attr_Tperm, (perm,) = _execute.args_to_matching_eager([perm], _ctx, _dtypes.int32)
_inputs_flat = [x, perm]
_attrs = ("T", _attr_T, "Tperm", _attr_Tperm)
_result = _execute.execute(b"Transpose", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Transpose", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
_unique_outputs = ["y", "idx"]
_UniqueOutput = _collections.namedtuple(
"Unique", _unique_outputs)
def unique(x, out_idx=_dtypes.int32, name=None):
r"""Finds unique elements in a 1-D tensor.
This operation returns a tensor `y` containing all of the unique elements of `x`
sorted in the same order that they occur in `x`. This operation also returns a
tensor `idx` the same size as `x` that contains the index of each value of `x`
in the unique output `y`. In other words:
`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
For example:
```
# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx = unique(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
```
Args:
x: A `Tensor`. 1-D.
out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (y, idx).
y: A `Tensor`. Has the same type as `x`.
idx: A `Tensor` of type `out_idx`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Unique", name,
_ctx._post_execution_callbacks, x, "out_idx", out_idx)
_result = _UniqueOutput._make(_result)
return _result
except _core._FallbackException:
try:
return unique_eager_fallback(
x, out_idx=out_idx, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_, _, _op = _op_def_lib._apply_op_helper(
"Unique", x=x, out_idx=out_idx, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "out_idx", _op.get_attr("out_idx"))
_execute.record_gradient(
"Unique", _inputs_flat, _attrs, _result, name)
_result = _UniqueOutput._make(_result)
return _result
def unique_eager_fallback(x, out_idx=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function unique
"""
_ctx = ctx if ctx else _context.context()
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_inputs_flat = [x]
_attrs = ("T", _attr_T, "out_idx", out_idx)
_result = _execute.execute(b"Unique", 2, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Unique", _inputs_flat, _attrs, _result, name)
_result = _UniqueOutput._make(_result)
return _result
_unique_v2_outputs = ["y", "idx"]
_UniqueV2Output = _collections.namedtuple(
"UniqueV2", _unique_v2_outputs)
def unique_v2(x, axis, out_idx=_dtypes.int32, name=None):
r"""Finds unique elements along an axis of a tensor.
This operation either returns a tensor `y` containing unique elements
along the `axis` of a tensor. The returned unique elements is sorted
in the same order as they occur along `axis` in `x`.
This operation also returns a tensor `idx` that is the same size as
the number of the elements in `x` along the `axis` dimension. It
contains the index in the unique output `y`.
In other words, for an `1-D` tensor `x` with `axis = None:
`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
For example:
```
# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx = unique(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
```
For an `2-D` tensor `x` with `axis = 0`:
```
# tensor 'x' is [[1, 0, 0],
# [1, 0, 0],
# [2, 0, 0]]
y, idx = unique(x, axis=0)
y ==> [[1, 0, 0],
[2, 0, 0]]
idx ==> [0, 0, 1]
```
For an `2-D` tensor `x` with `axis = 1`:
```
# tensor 'x' is [[1, 0, 0],
# [1, 0, 0],
# [2, 0, 0]]
y, idx = unique(x, axis=1)
y ==> [[1, 0],
[1, 0],
[2, 0]]
idx ==> [0, 1, 1]
```
Args:
x: A `Tensor`. A `Tensor`.
axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A `Tensor` of type `int32` (default: None). The axis of the Tensor to
find the unique elements.
out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (y, idx).
y: A `Tensor`. Has the same type as `x`.
idx: A `Tensor` of type `out_idx`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "UniqueV2",
name, _ctx._post_execution_callbacks, x, axis, "out_idx", out_idx)
_result = _UniqueV2Output._make(_result)
return _result
except _core._FallbackException:
try:
return unique_v2_eager_fallback(
x, axis, out_idx=out_idx, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_, _, _op = _op_def_lib._apply_op_helper(
"UniqueV2", x=x, axis=axis, out_idx=out_idx, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Taxis", _op.get_attr("Taxis"), "out_idx",
_op.get_attr("out_idx"))
_execute.record_gradient(
"UniqueV2", _inputs_flat, _attrs, _result, name)
_result = _UniqueV2Output._make(_result)
return _result
def unique_v2_eager_fallback(x, axis, out_idx=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function unique_v2
"""
_ctx = ctx if ctx else _context.context()
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_attr_Taxis, (axis,) = _execute.args_to_matching_eager([axis], _ctx, _dtypes.int64)
_inputs_flat = [x, axis]
_attrs = ("T", _attr_T, "Taxis", _attr_Taxis, "out_idx", out_idx)
_result = _execute.execute(b"UniqueV2", 2, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"UniqueV2", _inputs_flat, _attrs, _result, name)
_result = _UniqueV2Output._make(_result)
return _result
_unique_with_counts_outputs = ["y", "idx", "count"]
_UniqueWithCountsOutput = _collections.namedtuple(
"UniqueWithCounts", _unique_with_counts_outputs)
def unique_with_counts(x, out_idx=_dtypes.int32, name=None):
r"""Finds unique elements in a 1-D tensor.
This operation returns a tensor `y` containing all of the unique elements of `x`
sorted in the same order that they occur in `x`. This operation also returns a
tensor `idx` the same size as `x` that contains the index of each value of `x`
in the unique output `y`. Finally, it returns a third tensor `count` that
contains the count of each element of `y` in `x`. In other words:
`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
For example:
```
# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]
```
Args:
x: A `Tensor`. 1-D.
out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (y, idx, count).
y: A `Tensor`. Has the same type as `x`.
idx: A `Tensor` of type `out_idx`.
count: A `Tensor` of type `out_idx`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"UniqueWithCounts", name, _ctx._post_execution_callbacks, x,
"out_idx", out_idx)
_result = _UniqueWithCountsOutput._make(_result)
return _result
except _core._FallbackException:
try:
return unique_with_counts_eager_fallback(
x, out_idx=out_idx, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_, _, _op = _op_def_lib._apply_op_helper(
"UniqueWithCounts", x=x, out_idx=out_idx, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "out_idx", _op.get_attr("out_idx"))
_execute.record_gradient(
"UniqueWithCounts", _inputs_flat, _attrs, _result, name)
_result = _UniqueWithCountsOutput._make(_result)
return _result
def unique_with_counts_eager_fallback(x, out_idx=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function unique_with_counts
"""
_ctx = ctx if ctx else _context.context()
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_inputs_flat = [x]
_attrs = ("T", _attr_T, "out_idx", out_idx)
_result = _execute.execute(b"UniqueWithCounts", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"UniqueWithCounts", _inputs_flat, _attrs, _result, name)
_result = _UniqueWithCountsOutput._make(_result)
return _result
_unique_with_counts_v2_outputs = ["y", "idx", "count"]
_UniqueWithCountsV2Output = _collections.namedtuple(
"UniqueWithCountsV2", _unique_with_counts_v2_outputs)
def unique_with_counts_v2(x, axis, out_idx=_dtypes.int32, name=None):
r"""Finds unique elements along an axis of a tensor.
This operation either returns a tensor `y` containing unique elements
along the `axis` of a tensor. The returned unique elements is sorted
in the same order as they occur along `axis` in `x`.
This operation also returns a tensor `idx` and a tensor `count`
that are the same size as the number of the elements in `x` along the
`axis` dimension. The `idx` contains the index in the unique output `y`
and the `count` contains the count in the unique output `y`.
In other words, for an `1-D` tensor `x` with `axis = None:
`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
For example:
```
# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
y, idx, count = unique_with_counts(x)
y ==> [1, 2, 4, 7, 8]
idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
count ==> [2, 1, 3, 1, 2]
```
For an `2-D` tensor `x` with `axis = 0`:
```
# tensor 'x' is [[1, 0, 0],
# [1, 0, 0],
# [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=0)
y ==> [[1, 0, 0],
[2, 0, 0]]
idx ==> [0, 0, 1]
count ==> [2, 1]
```
For an `2-D` tensor `x` with `axis = 1`:
```
# tensor 'x' is [[1, 0, 0],
# [1, 0, 0],
# [2, 0, 0]]
y, idx, count = unique_with_counts(x, axis=1)
y ==> [[1, 0],
[1, 0],
[2, 0]]
idx ==> [0, 1, 1]
count ==> [1, 2]
```
Args:
x: A `Tensor`. A `Tensor`.
axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
A `Tensor` of type `int32` (default: None). The axis of the Tensor to
find the unique elements.
out_idx: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A tuple of `Tensor` objects (y, idx, count).
y: A `Tensor`. Has the same type as `x`.
idx: A `Tensor` of type `out_idx`.
count: A `Tensor` of type `out_idx`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name,
"UniqueWithCountsV2", name, _ctx._post_execution_callbacks, x, axis,
"out_idx", out_idx)
_result = _UniqueWithCountsV2Output._make(_result)
return _result
except _core._FallbackException:
try:
return unique_with_counts_v2_eager_fallback(
x, axis, out_idx=out_idx, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_, _, _op = _op_def_lib._apply_op_helper(
"UniqueWithCountsV2", x=x, axis=axis, out_idx=out_idx, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "Taxis", _op.get_attr("Taxis"), "out_idx",
_op.get_attr("out_idx"))
_execute.record_gradient(
"UniqueWithCountsV2", _inputs_flat, _attrs, _result, name)
_result = _UniqueWithCountsV2Output._make(_result)
return _result
def unique_with_counts_v2_eager_fallback(x, axis, out_idx=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function unique_with_counts_v2
"""
_ctx = ctx if ctx else _context.context()
if out_idx is None:
out_idx = _dtypes.int32
out_idx = _execute.make_type(out_idx, "out_idx")
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_attr_Taxis, (axis,) = _execute.args_to_matching_eager([axis], _ctx, _dtypes.int64)
_inputs_flat = [x, axis]
_attrs = ("T", _attr_T, "Taxis", _attr_Taxis, "out_idx", out_idx)
_result = _execute.execute(b"UniqueWithCountsV2", 3, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"UniqueWithCountsV2", _inputs_flat, _attrs, _result, name)
_result = _UniqueWithCountsV2Output._make(_result)
return _result
def unpack(value, num, axis=0, name=None):
r"""Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
For example, given a tensor of shape `(A, B, C, D)`;
If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]`
and each tensor in `output` will have shape `(B, C, D)`. (Note that the
dimension unpacked along is gone, unlike `split`).
If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]`
and each tensor in `output` will have shape `(A, C, D)`.
Etc.
This is the opposite of `pack`.
Args:
value: A `Tensor`.
1-D or higher, with `axis` dimension size equal to `num`.
num: An `int` that is `>= 0`.
axis: An optional `int`. Defaults to `0`.
Dimension along which to unpack. Negative values wrap around, so the
valid range is `[-R, R)`.
name: A name for the operation (optional).
Returns:
A list of `num` `Tensor` objects with the same type as `value`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Unpack", name,
_ctx._post_execution_callbacks, value, "num", num, "axis", axis)
return _result
except _core._FallbackException:
try:
return unpack_eager_fallback(
value, num=num, axis=axis, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
num = _execute.make_int(num, "num")
if axis is None:
axis = 0
axis = _execute.make_int(axis, "axis")
_, _, _op = _op_def_lib._apply_op_helper(
"Unpack", value=value, num=num, axis=axis, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("num", _op.get_attr("num"), "T", _op.get_attr("T"), "axis",
_op.get_attr("axis"))
_execute.record_gradient(
"Unpack", _inputs_flat, _attrs, _result, name)
return _result
def unpack_eager_fallback(value, num, axis=0, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function unpack
"""
_ctx = ctx if ctx else _context.context()
num = _execute.make_int(num, "num")
if axis is None:
axis = 0
axis = _execute.make_int(axis, "axis")
_attr_T, (value,) = _execute.args_to_matching_eager([value], _ctx)
_inputs_flat = [value]
_attrs = ("num", num, "T", _attr_T, "axis", axis)
_result = _execute.execute(b"Unpack", num, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"Unpack", _inputs_flat, _attrs, _result, name)
return _result
@_dispatch.add_dispatch_list
@tf_export('unravel_index')
def unravel_index(indices, dims, name=None):
r"""Converts a flat index or array of flat indices into a tuple of
coordinate arrays.
@compatibility(numpy)
Equivalent to np.unravel_index
@end_compatibility
Args:
indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
An 0-D or 1-D `int` Tensor whose elements are indices into the
flattened version of an array of dimensions dims.
dims: A `Tensor`. Must have the same type as `indices`.
An 1-D `int` Tensor. The shape of the array to use for unraveling
indices.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `indices`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "UnravelIndex",
name, _ctx._post_execution_callbacks, indices, dims)
return _result
except _core._FallbackException:
try:
return unravel_index_eager_fallback(
indices, dims, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except (TypeError, ValueError):
result = _dispatch.dispatch(
unravel_index, indices=indices, dims=dims, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
try:
_, _, _op = _op_def_lib._apply_op_helper(
"UnravelIndex", indices=indices, dims=dims, name=name)
except (TypeError, ValueError):
result = _dispatch.dispatch(
unravel_index, indices=indices, dims=dims, name=name)
if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
return result
raise
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("Tidx", _op.get_attr("Tidx"))
_execute.record_gradient(
"UnravelIndex", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def unravel_index_eager_fallback(indices, dims, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function unravel_index
"""
_ctx = ctx if ctx else _context.context()
_attr_Tidx, _inputs_Tidx = _execute.args_to_matching_eager([indices, dims], _ctx, _dtypes.int32)
(indices, dims) = _inputs_Tidx
_inputs_flat = [indices, dims]
_attrs = ("Tidx", _attr_Tidx)
_result = _execute.execute(b"UnravelIndex", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"UnravelIndex", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def upper_bound(sorted_inputs, values, out_type=_dtypes.int32, name=None):
r"""Applies upper_bound(sorted_search_values, values) along each row.
Each set of rows with the same index in (sorted_inputs, values) is treated
independently. The resulting row is the equivalent of calling
`np.searchsorted(sorted_inputs, values, side='right')`.
The result is not a global index to the entire
`Tensor`, but rather just the index in the last dimension.
A 2-D example:
sorted_sequence = [[0, 3, 9, 9, 10],
[1, 2, 3, 4, 5]]
values = [[2, 4, 9],
[0, 2, 6]]
result = UpperBound(sorted_sequence, values)
result == [[1, 2, 4],
[0, 2, 5]]
Args:
sorted_inputs: A `Tensor`. 2-D Tensor where each row is ordered.
values: A `Tensor`. Must have the same type as `sorted_inputs`.
2-D Tensor with the same numbers of rows as `sorted_search_values`. Contains
the values that will be searched for in `sorted_search_values`.
out_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `out_type`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "UpperBound",
name, _ctx._post_execution_callbacks, sorted_inputs, values,
"out_type", out_type)
return _result
except _core._FallbackException:
try:
return upper_bound_eager_fallback(
sorted_inputs, values, out_type=out_type, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_, _, _op = _op_def_lib._apply_op_helper(
"UpperBound", sorted_inputs=sorted_inputs, values=values,
out_type=out_type, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"), "out_type", _op.get_attr("out_type"))
_execute.record_gradient(
"UpperBound", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def upper_bound_eager_fallback(sorted_inputs, values, out_type=_dtypes.int32, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function upper_bound
"""
_ctx = ctx if ctx else _context.context()
if out_type is None:
out_type = _dtypes.int32
out_type = _execute.make_type(out_type, "out_type")
_attr_T, _inputs_T = _execute.args_to_matching_eager([sorted_inputs, values], _ctx)
(sorted_inputs, values) = _inputs_T
_inputs_flat = [sorted_inputs, values]
_attrs = ("T", _attr_T, "out_type", out_type)
_result = _execute.execute(b"UpperBound", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"UpperBound", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def where(condition, name=None):
r"""Returns locations of nonzero / true values in a tensor.
This operation returns the coordinates of true elements in `condition`. The
coordinates are returned in a 2-D tensor where the first dimension (rows)
represents the number of true elements, and the second dimension (columns)
represents the coordinates of the true elements. Keep in mind, the shape of
the output tensor can vary depending on how many true values there are in
`condition`. Indices are output in row-major order.
For example:
```
# 'input' tensor is [[True, False]
# [True, False]]
# 'input' has two true values, so output has two coordinates.
# 'input' has rank of 2, so coordinates have two indices.
where(input) ==> [[0, 0],
[1, 0]]
# `condition` tensor is [[[True, False]
# [True, False]]
# [[False, True]
# [False, True]]
# [[False, False]
# [False, True]]]
# 'input' has 5 true values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
[0, 1, 0],
[1, 0, 1],
[1, 1, 1],
[2, 1, 1]]
# `condition` tensor is [[[1.5, 0.0]
# [-0.5, 0.0]]
# [[0.0, 0.25]
# [0.0, 0.75]]
# [[0.0, 0.0]
# [0.0, 0.01]]]
# 'input' has 5 nonzero values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
[0, 1, 0],
[1, 0, 1],
[1, 1, 1],
[2, 1, 1]]
# `condition` tensor is [[[1.5 + 0.0j, 0.0 + 0.0j]
# [0.0 + 0.5j, 0.0 + 0.0j]]
# [[0.0 + 0.0j, 0.25 + 1.5j]
# [0.0 + 0.0j, 0.75 + 0.0j]]
# [[0.0 + 0.0j, 0.0 + 0.0j]
# [0.0 + 0.0j, 0.01 + 0.0j]]]
# 'input' has 5 nonzero magnitude values, so output has 5 coordinates.
# 'input' has rank of 3, so coordinates have three indices.
where(input) ==> [[0, 0, 0],
[0, 1, 0],
[1, 0, 1],
[1, 1, 1],
[2, 1, 1]]
```
Args:
condition: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
name: A name for the operation (optional).
Returns:
A `Tensor` of type `int64`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "Where", name,
_ctx._post_execution_callbacks, condition)
return _result
except _core._FallbackException:
try:
return where_eager_fallback(
condition, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"Where", input=condition, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"Where", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def where_eager_fallback(condition, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function where
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (condition,) = _execute.args_to_matching_eager([condition], _ctx, _dtypes.bool)
_inputs_flat = [condition]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"Where", 1, inputs=_inputs_flat, attrs=_attrs,
ctx=_ctx, name=name)
_execute.record_gradient(
"Where", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def zeros_like(x, name=None):
r"""Returns a tensor of zeros with the same shape and type as x.
Args:
x: A `Tensor`. a tensor of type T.
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
_ctx = _context._context
if _ctx is not None and _ctx._eager_context.is_eager:
try:
_result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
_ctx._context_handle, _ctx._eager_context.device_name, "ZerosLike",
name, _ctx._post_execution_callbacks, x)
return _result
except _core._FallbackException:
try:
return zeros_like_eager_fallback(
x, name=name, ctx=_ctx)
except _core._SymbolicException:
pass # Add nodes to the TensorFlow graph.
except _core._NotOkStatusException as e:
if name is not None:
message = e.message + " name: " + name
else:
message = e.message
_six.raise_from(_core._status_to_exception(e.code, message), None)
# Add nodes to the TensorFlow graph.
_, _, _op = _op_def_lib._apply_op_helper(
"ZerosLike", x=x, name=name)
_result = _op.outputs[:]
_inputs_flat = _op.inputs
_attrs = ("T", _op.get_attr("T"))
_execute.record_gradient(
"ZerosLike", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def zeros_like_eager_fallback(x, name=None, ctx=None):
r"""This is the slowpath function for Eager mode.
This is for function zeros_like
"""
_ctx = ctx if ctx else _context.context()
_attr_T, (x,) = _execute.args_to_matching_eager([x], _ctx)
_inputs_flat = [x]
_attrs = ("T", _attr_T)
_result = _execute.execute(b"ZerosLike", 1, inputs=_inputs_flat,
attrs=_attrs, ctx=_ctx, name=name)
_execute.record_gradient(
"ZerosLike", _inputs_flat, _attrs, _result, name)
_result, = _result
return _result
def _InitOpDefLibrary(op_list_proto_bytes):
op_list = _op_def_pb2.OpList()
op_list.ParseFromString(op_list_proto_bytes)
_op_def_registry.register_op_list(op_list)
op_def_lib = _op_def_library.OpDefLibrary()
op_def_lib.add_op_list(op_list)
return op_def_lib
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now behaves the same as PlaceholderV2.\nX\n\026PlaceholderWithDefault\022\016\n\005input\"\005dtype\032\017\n\006output\"\005dtype\"\r\n\005dtype\022\004type\"\016\n\005shape\022\005shape\nL\n\017PreventGradient\022\n\n\005input\"\001T\032\013\n\006output\"\001T\"\t\n\001T\022\004type\"\025\n\007message\022\006string\032\002\022\000\n\354\001\n\025QuantizeAndDequantize\022\n\n\005input\"\001T\032\013\n\006output\"\001T\"\030\n\014signed_input\022\004bool\032\002(\001\"\023\n\010num_bits\022\003int\032\002\030\010\"\027\n\013range_given\022\004bool\032\002(\000\"\031\n\tinput_min\022\005float\032\005%\000\000\000\000\"\031\n\tinput_max\022\005float\032\005%\000\000\000\000\"\023\n\001T\022\004type:\010\n\0062\004\016\023\001\002B\'\010\026\022#Replaced by 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has been replaced with reduce_sum\nP\n\tTranspose\022\006\n\001x\"\001T\022\r\n\004perm\"\005Tperm\032\006\n\001y\"\001T\"\t\n\001T\022\004type\"\031\n\005Tperm\022\004type\032\0020\003:\006\n\0042\002\003\t\nP\n\006Unique\022\006\n\001x\"\001T\032\006\n\001y\"\001T\032\016\n\003idx\"\007out_idx\"\t\n\001T\022\004type\"\033\n\007out_idx\022\004type\032\0020\003:\006\n\0042\002\003\t\n|\n\010UniqueV2\022\006\n\001x\"\001T\022\r\n\004axis\"\005Taxis\032\006\n\001y\"\001T\032\016\n\003idx\"\007out_idx\"\t\n\001T\022\004type\"\031\n\005Taxis\022\004type\032\0020\t:\006\n\0042\002\003\t\"\033\n\007out_idx\022\004type\032\0020\003:\006\n\0042\002\003\t\nl\n\020UniqueWithCounts\022\006\n\001x\"\001T\032\006\n\001y\"\001T\032\016\n\003idx\"\007out_idx\032\020\n\005count\"\007out_idx\"\t\n\001T\022\004type\"\033\n\007out_idx\022\004type\032\0020\003:\006\n\0042\002\003\t\n\230\001\n\022UniqueWithCountsV2\022\006\n\001x\"\001T\022\r\n\004axis\"\005Taxis\032\006\n\001y\"\001T\032\016\n\003idx\"\007out_idx\032\020\n\005count\"\007out_idx\"\t\n\001T\022\004type\"\031\n\005Taxis\022\004type\032\0020\t:\006\n\0042\002\003\t\"\033\n\007out_idx\022\004type\032\0020\003:\006\n\0042\002\003\t\nP\n\006Unpack\022\n\n\005value\"\001T\032\020\n\006output\"\001T*\003num\"\014\n\003num\022\003int(\001\"\t\n\001T\022\004type\"\017\n\004axis\022\003int\032\002\030\000\nW\n\014UnravelIndex\022\017\n\007indices\"\004Tidx\022\014\n\004dims\"\004Tidx\032\016\n\006output\"\004Tidx\"\030\n\004Tidx\022\004type\032\0020\003:\006\n\0042\002\003\t\nj\n\nUpperBound\022\022\n\rsorted_inputs\"\001T\022\013\n\006values\"\001T\032\022\n\006output\"\010out_type\"\t\n\001T\022\004type\"\034\n\010out_type\022\004type\032\0020\003:\006\n\0042\002\003\t\nE\n\005Where\022\n\n\005input\"\001T\032\t\n\005index\030\t\"%\n\001T\022\004type\032\0020\n:\026\n\0242\022\001\002\003\004\005\006\010\t\013\014\r\016\021\022\023\026\027\n\n&\n\tZerosLike\022\006\n\001x\"\001T\032\006\n\001y\"\001T\"\t\n\001T\022\004type")
| [
"[email protected]"
]
| |
564d81d0051cf261ea8cf3a8060afb2cc81c2406 | 718f4a6f53da14dbd79031928900a26c4de65ccb | /optimize_NMDA_KIN2.py | bf70c8a044f4ff48aa4d86895cd5e68a6e41e55f | []
| no_license | neurosutras/CA1Sim | ff37e5ae96cc00d923bbcf333d75842c34156b5b | 9a5796e5de9b9be477d61837c164fcbccbe3c8ce | refs/heads/master | 2023-04-08T01:39:09.559475 | 2022-01-13T20:20:45 | 2022-01-13T20:20:45 | 29,497,263 | 4 | 3 | null | null | null | null | UTF-8 | Python | false | false | 7,046 | py | __author__ = 'Aaron D. Milstein'
from specify_cells import *
from plot_results import *
import scipy.optimize as optimize
import random
"""
This simulation uses scipy.optimize to iterate through NMDA_KIN mechanism parameters to fit target EPSP kinetics.
"""
#morph_filename = 'EB1-early-bifurcation.swc'
morph_filename = 'EB2-late-bifurcation.swc'
#mech_filename = '043015 pas_exp_scale kdr ka_scale ih_sig_scale - EB2'
#mech_filename = '072515 optimized basal ka_scale dend_sh_ar_nas - EB2'
mech_filename = '102915 interim dendritic excitability'
def synaptic_kinetics_error(x, plot=0):
"""
:param x: list of parameters
:param plot: int or bool: method can be called manually to compare actual to target and fit waveforms
:return: float: Error
"""
spike_times = h.Vector([equilibrate])
for i, syn in enumerate(stim_syn_list):
syn.target(syn_type).kon = x[0]
syn.target(syn_type).koff = x[1]
syn.target(syn_type).CC = x[2]
syn.target(syn_type).CO = x[3]
syn.target(syn_type).Beta = x[4]
syn.target(syn_type).Alpha = x[5]
syn.source.play(spike_times)
sim.run(v_init)
t = np.array(sim.tvec)
g = np.array(sim.rec_list[0]['vec'])
interp_t = np.arange(0, duration, 0.001)
interp_g = np.interp(interp_t, t, g)
"""
Rc = np.interp(interp_t, t, np.array(sim.rec_list[1]['vec']))
Ro = np.interp(interp_t, t, np.array(sim.rec_list[2]['vec']))
Rb = np.interp(interp_t, t, np.array(sim.rec_list[3]['vec']))
Ro_peak = np.max(Ro)
Ro_peak_loc = np.where(Ro == Ro_peak)[0][0]
Rc_max = Ro_peak + Rc[Ro_peak_loc] + Rb[Ro_peak_loc]
"""
start, end = time2index(interp_t, equilibrate, duration)
y = interp_g[start:end]
interp_t = interp_t[start:end]
interp_t -= interp_t[0]
amp = np.max(y)
t_peak = np.where(y == amp)[0][0]
y /= amp
rise_10 = np.where(y[0:t_peak] >= 0.1)[0][0]
rise_90 = np.where(y[0:t_peak] >= 0.9)[0][0]
rise_tau = interp_t[rise_90] - interp_t[rise_10]
decay_90 = np.where(y[t_peak:] <= 0.9)[0][0]
decay_10 = np.where(y[t_peak:] <= 0.1)[0]
if decay_10.any():
decay_tau = interp_t[decay_10[0]] - interp_t[decay_90]
else:
decay_tau = 1000. # large error if trace has not decayed to 10% in 1 second
result = {'rise_tau': rise_tau, 'decay_tau': decay_tau} # , 'Rc_max': Rc_max}
spike_times = h.Vector([equilibrate + i * 10. for i in range(5)])
for i, syn in enumerate(stim_syn_list):
syn.source.play(spike_times)
sim.run(v_init)
for i, syn in enumerate(stim_syn_list):
syn.source.play(h.Vector())
t = np.array(sim.tvec)
g = np.array(sim.rec_list[0]['vec'])
interp_t = np.arange(0, duration, 0.001)
interp_g = np.interp(interp_t, t, g)
start, end = time2index(interp_t, equilibrate, duration)
yf = interp_g[start:end]
interp_t = interp_t[start:end]
interp_t -= interp_t[0]
facil_amp = np.max(yf)
result['facilitation'] = facil_amp / amp
yf /= amp
Err = 0.
for target in result:
Err += ((target_val[target] - result[target])/target_range[target])**2.
print('[kon, koff, CC, CO, Beta, Alpha]: [%.3f, %.3f, %.3f, %.3f, %.3f, %.3f], Error: %.3E, Rise: %.3f, Decay: '
'%.3f, facilitation: %.2f' % (x[0], x[1], x[2], x[3], x[4], x[5], Err, rise_tau, decay_tau,
result['facilitation']))
if plot:
plt.plot(interp_t, y)
plt.plot(interp_t, yf)
plt.show()
plt.close()
return Err
equilibrate = 250. # time to steady-state
duration = 1250.
v_init = -67.
num_syns = 1
cell = CA1_Pyr(morph_filename, mech_filename, full_spines=True)
cell.zero_na()
syn_type = 'NMDA_KIN2'
sim = QuickSim(duration)
# look for a trunk bifurcation
trunk_bifurcation = [trunk for trunk in cell.trunk if len(trunk.children) > 1 and trunk.children[0].type == 'trunk' and
trunk.children[1].type == 'trunk']
# get where the thickest trunk branch gives rise to the tuft
if trunk_bifurcation: # follow the thicker trunk
trunk = max(trunk_bifurcation[0].children[:2], key=lambda node: node.sec(0.).diam)
trunk = (node for node in cell.trunk if cell.node_in_subtree(trunk, node) and 'tuft' in (child.type for child in
node.children)).next()
else:
trunk = (node for node in cell.trunk if 'tuft' in (child.type for child in node.children)).next()
tuft = (child for child in trunk.children if child.type == 'tuft').next()
trunk = trunk_bifurcation[0]
#sim.append_rec(cell, trunk, loc=1., description='trunk vm')
spine_list = []
spine_list.extend(trunk.spines)
for spine in spine_list:
syn = Synapse(cell, spine, [syn_type], stochastic=0)
local_random = random.Random()
local_random.seed(0)
stim_syn_list = [spine_list[i].synapses[0] for i in local_random.sample(range(len(spine_list)), num_syns)]
for i, syn in enumerate(stim_syn_list):
syn.target(syn_type).mg = 0.1
#syn.target(syn_type).gmax = 0.005
sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_g')
sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_Rc')
sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_Ro')
sim.append_rec(cell, syn.node, object=syn.target(syn_type), param='_ref_Rb')
#the target values and acceptable ranges
target_val = {'rise_tau': 3., 'decay_tau': 75., 'Rc_max': 0.6, 'facilitation': 1.3}
# extrapolating from Chen...Murphy and Harnett...Magee, Popescu et al.
target_range = {'rise_tau': 0.1, 'decay_tau': .5, 'Rc_max': 0.01, 'facilitation': 0.01}
#the initial guess and bounds
#x = [kon, koff, CC, CO, Beta, Alpha)
#x0 = [10., .02, 1., 0.1, 0.04, 0.09]
#x0 = [26.414, 1.903, 3.185, 5.119, 0.274, 0.0299]
#x0 = [44.35, 2.46, 10.34, 1.06, 0.40, 0.045]
x0 = [85.47, 0.68, 9.48, 2.56, 0.72, 0.078]
xmin = [10., .01, .1, .1, .01, .01]
xmax = [100., 10., 20., 20., 1., 1.]
#x1 = [1099.70, 0.07, 1.70, 14.12, 4.64, 0.19] # old NMDA_KIN2, unrealistic kon
x1 = [68.74, 1.43, 5.86, 3.32, 0.270, 0.034]
mytakestep = Normalized_Step(x0, xmin, xmax)
minimizer_kwargs = dict(method=null_minimizer)
"""
result = optimize.basinhopping(synaptic_kinetics_error, x0, niter=720, niter_success=200, disp=True, interval=20,
minimizer_kwargs=minimizer_kwargs, take_step=mytakestep)
synaptic_kinetics_error(result.x, plot=1)
polished_result = optimize.minimize(synaptic_kinetics_error, result.x, method='Nelder-Mead', options={'ftol': 1e-3,
'xtol': 1e-3, 'disp': True})
"""
polished_result = optimize.minimize(synaptic_kinetics_error, x0, method='Nelder-Mead', options={'ftol': 1e-3,
'xtol': 1e-3, 'disp': True})
synaptic_kinetics_error(polished_result.x, plot=1)
#synaptic_kinetics_error(x1, plot=1) | [
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]
| |
da748d34cb6a27059cecf0ee84bd84376e2809bf | d5ad13232e3f1ced55f6956bc4cbda87925c8085 | /cc_mcc_seq/SNVINDEL/tmp/3.1_tumor_minus_normal_exome_somatic_number/1_tumor_minus_normal_somatic.py | fb9fd44707bd9d72ef21d0edd8631473db5d86f3 | []
| no_license | arvin580/SIBS | c0ba9a8a41f59cb333517c286f7d80300b9501a2 | 0cc2378bf62359ec068336ea4de16d081d0f58a4 | refs/heads/master | 2021-01-23T21:57:35.658443 | 2015-04-09T23:11:34 | 2015-04-09T23:11:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,722 | py | def tumor_minus_normal_to_somatic(tumorFile,normalFile,oFile) :
dict_Normal=dict()
ouFile=open(oFile,'w')
inFile=open(normalFile)
for line in inFile :
line=line.strip()
fields=line.split('\t')
k='\t'.join(fields[1:-1])
dict_Normal[k]=1
inFile.close()
inFile=open(tumorFile)
for line in inFile :
line=line.strip()
fields=line.split('\t')
k='\t'.join(fields[1:-1])
if k not in dict_Normal :
ouFile.write(line+'\n')
ouFile.close()
tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC10A','sum_snp34.exome_summary.pass012.ICC10B','sum_snp.exome_summary.pass012.ICC10')
tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC4A','sum_snp34.exome_summary.pass012.ICC4B','sum_snp.exome_summary.pass012.ICC4')
tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC5A','sum_snp34.exome_summary.pass012.ICC5B','sum_snp.exome_summary.pass012.ICC5')
tumor_minus_normal_to_somatic('sum_snp.exome_summary.pass012.ICC9A','sum_snp34.exome_summary.pass012.ICC9B','sum_snp.exome_summary.pass012.ICC9')
tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC10A','sum_snp34.exome_summary.pass012.CHC10B','sum_snp.exome_summary.pass012.CHC10')
tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC5A','sum_snp34.exome_summary.pass012.CHC5B','sum_snp.exome_summary.pass012.CHC5')
tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC6A','sum_snp34.exome_summary.pass012.CHC6B','sum_snp.exome_summary.pass012.CHC6')
tumor_minus_normal_to_somatic('sum_snp2.exome_summary.pass012.CHC7A','sum_snp34.exome_summary.pass012.CHC7B','sum_snp.exome_summary.pass012.CHC7')
| [
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]
| |
d22b6020a2b3d2bfacf12fcb9cb93b0bc3d641d9 | a30362e51cb3291daf26d0c62e56c42caeec837f | /python/codeup/solved/_1068.py | 87813e822e0529ad4c300ab4f9c21997748b240f | []
| no_license | TERADA-DANTE/algorithm | 03bf52764c6fcdb93d7c8a0ed7a672834f488412 | 20bdfa1a5a6b9c378e588b17073e77a0126f7339 | refs/heads/master | 2023-04-14T21:40:11.250022 | 2023-04-12T13:00:37 | 2023-04-12T13:00:37 | 288,335,057 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 131 | py | n = int(input())
if 90 <= n:
print('A')
elif 70 <= n:
print('B')
elif 40 <= n:
print('C')
elif 0 <= n:
print('D')
| [
"[email protected]"
]
| |
b3faa68ddf38c6d15ad43fc82a48744cdae5c15b | 56f5b2ea36a2258b8ca21e2a3af9a5c7a9df3c6e | /CMGTools/H2TauTau/prod/25aug_corrMC/up/mc/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0_1377544840/HTT_24Jul_newTES_manzoni_Up_Jobs/Job_225/run_cfg.py | c1d2c055a93f6f5950d43132a49f5e864889fafd | []
| no_license | rmanzoni/HTT | 18e6b583f04c0a6ca10142d9da3dd4c850cddabc | a03b227073b2d4d8a2abe95367c014694588bf98 | refs/heads/master | 2016-09-06T05:55:52.602604 | 2014-02-20T16:35:34 | 2014-02-20T16:35:34 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,499 | py | import FWCore.ParameterSet.Config as cms
import os,sys
sys.path.append('/afs/cern.ch/user/m/manzoni/summer13/CMGTools/CMSSW_5_3_9/src/CMGTools/H2TauTau/prod/25aug_corrMC/up/mc/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0_1377544840/HTT_24Jul_newTES_manzoni_Up_Jobs')
from base_cfg import *
process.source = cms.Source("PoolSource",
noEventSort = cms.untracked.bool(True),
inputCommands = cms.untracked.vstring('keep *',
'drop cmgStructuredPFJets_cmgStructuredPFJetSel__PAT'),
duplicateCheckMode = cms.untracked.string('noDuplicateCheck'),
fileNames = cms.untracked.vstring('/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2006.root',
'/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2007.root',
'/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2008.root',
'/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_2009.root',
'/store/cmst3/user/cmgtools/CMG/DY2JetsToLL_M-50_TuneZ2Star_8TeV-madgraph/Summer12_DR53X-PU_S10_START53_V7C-v1/AODSIM/V5_B/PAT_CMG_V5_16_0/cmgTuple_201.root')
)
| [
"[email protected]"
]
| |
fc8f7fd662fe988e7f5f65c94869efdafc5af3eb | 7f0548b7191b7589712af19baebafddae1d0505f | /dojoassignments/python/django/full_stack_django/login_and_registration/apps/login_registration_app/migrations/0001_initial.py | 2e5994f927a8fa2ce9b4a5d96fd6c594f3453aa5 | []
| no_license | mtjhartley/codingdojo | dd8eab1bd61fb847e44766e89fe3db2340468102 | 65dc558d19adbe62f85ad61c32cb1c392b56567c | refs/heads/master | 2022-12-14T23:06:11.927445 | 2017-08-16T21:08:35 | 2017-08-16T21:08:35 | 92,218,728 | 1 | 5 | null | 2022-12-07T23:59:48 | 2017-05-23T20:46:03 | Python | UTF-8 | Python | false | false | 884 | py | # -*- coding: utf-8 -*-
# Generated by Django 1.10 on 2017-06-20 19:02
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='User',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(max_length=255)),
('last_name', models.CharField(max_length=255)),
('email', models.CharField(max_length=255)),
('password', models.CharField(max_length=45)),
('created_at', models.DateTimeField(auto_now_add=True)),
('updated_at', models.DateTimeField(auto_now=True)),
],
),
]
| [
"[email protected]"
]
| |
ddbc0c95647448fd2b5ee0f7983a9b7eda1fc03c | 7b054cd92eece331d2494b1ecbd6ebae76deed55 | /ecommerce/urls.py | 748d4606d02d80bf738204eb134afca866a6623b | []
| no_license | Tanmoy-Sarkar/Django-Ecommerce-Website | f23ec5a60b64f6b3f614bb9dd7aced2c694d1d75 | 24810153dcb51ae1d57c1b182b59cb40fbb8a3d2 | refs/heads/master | 2023-07-03T17:23:58.746887 | 2021-07-31T08:21:35 | 2021-07-31T08:21:35 | 389,021,036 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 948 | py | """ecommerce URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.2/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.urls import include, path
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path,include
from django.conf.urls.static import static
from django.conf import settings
urlpatterns = [
path('admin/', admin.site.urls),
path('',include('store.urls')),
]
urlpatterns += static(settings.MEDIA_URL,document_root=settings.MEDIA_ROOT) | [
"[email protected]"
]
| |
1bc2bad1c8d403cdc99de557444a6e0a0f503eb2 | fe3759747f709a41e5ff3acf78872dd6b74f772a | /samples/openapi3/client/petstore/python-experimental/petstore_api/model/animal.py | 81432c292c6459b54e18f5be8a654084c4f140d5 | [
"Apache-2.0"
]
| permissive | Januson/openapi-generator | c50e3b52765e41adba9712d745918cea39dfa490 | 5b6b4c9d4829b57716741dc35b3f1033e5483784 | refs/heads/master | 2022-10-19T04:16:38.042495 | 2022-04-23T08:42:21 | 2022-04-23T08:42:21 | 238,659,737 | 0 | 0 | Apache-2.0 | 2023-09-05T01:01:23 | 2020-02-06T10:12:38 | Java | UTF-8 | Python | false | false | 2,359 | py | # coding: utf-8
"""
OpenAPI Petstore
This spec is mainly for testing Petstore server and contains fake endpoints, models. Please do not use this for any other purpose. Special characters: \" \\ # noqa: E501
The version of the OpenAPI document: 1.0.0
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
import typing # noqa: F401
from frozendict import frozendict # noqa: F401
import decimal # noqa: F401
from datetime import date, datetime # noqa: F401
from frozendict import frozendict # noqa: F401
from petstore_api.schemas import ( # noqa: F401
AnyTypeSchema,
ComposedSchema,
DictSchema,
ListSchema,
StrSchema,
IntSchema,
Int32Schema,
Int64Schema,
Float32Schema,
Float64Schema,
NumberSchema,
UUIDSchema,
DateSchema,
DateTimeSchema,
DecimalSchema,
BoolSchema,
BinarySchema,
NoneSchema,
none_type,
Configuration,
Unset,
unset,
ComposedBase,
ListBase,
DictBase,
NoneBase,
StrBase,
IntBase,
Int32Base,
Int64Base,
Float32Base,
Float64Base,
NumberBase,
UUIDBase,
DateBase,
DateTimeBase,
BoolBase,
BinaryBase,
Schema,
_SchemaValidator,
_SchemaTypeChecker,
_SchemaEnumMaker
)
class Animal(
DictSchema
):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
_required_property_names = set((
'className',
))
className = StrSchema
color = StrSchema
@classmethod
@property
def _discriminator(cls):
return {
'className': {
'Cat': Cat,
'Dog': Dog,
}
}
def __new__(
cls,
*args: typing.Union[dict, frozendict, ],
className: className,
color: typing.Union[color, Unset] = unset,
_configuration: typing.Optional[Configuration] = None,
**kwargs: typing.Type[Schema],
) -> 'Animal':
return super().__new__(
cls,
*args,
className=className,
color=color,
_configuration=_configuration,
**kwargs,
)
from petstore_api.model.cat import Cat
from petstore_api.model.dog import Dog
| [
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]
| |
833980c8158fa0f25d3ae7485542f3655bc24ef9 | 18e48f22f88fe80ce54d12fdbf9d05a7ca5bd65a | /0x04-python-more_data_structures/3-common_elements.py | bdcdc3ae779109657e7ebde98aeb7e93558c88ae | []
| no_license | SantiagoHerreG/holbertonschool-higher_level_programming | 426c4bc9bc080a81b72d2f740c8ed2eb365023eb | ca2612ef3be92a60764d584cf39de3a2ba310f84 | refs/heads/master | 2020-07-22T19:33:48.507287 | 2020-02-14T04:34:00 | 2020-02-14T04:34:00 | 207,305,022 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 105 | py | #!/usr/bin/python3
def common_elements(set_1, set_2):
new_set = set_1 & set_2
return (new_set)
| [
"[email protected]"
]
| |
fd96964145fbc06b436ee1ecbbf561c15f201c00 | caf192dbc1ca90fee18bb4ce170d37eb14870ec5 | /Chapter-5/7. Caesar cipher.py | f827a177676fc978c4d7d8bfee8324bfba34dc4a | []
| no_license | Dfredude/PythonZelle | 858b00f5eacce841173c64b3cecd978dedbeb145 | 1923fe84df604968eebc5269f23b7c0f167d55f0 | refs/heads/main | 2023-08-30T21:45:57.070344 | 2021-10-17T01:32:57 | 2021-10-17T01:32:57 | 359,041,963 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 496 | py | def main():
#Get plaintext(p_text) and key(x) from the user
p_text = input("Enter the message you'd like encrypted.\n")
key = eval(input("What's the key? : "))
p_text = p_text.lower()
#Create string of letters
table = "abcdefghijklmnopqrstuvwxyz"
#Convert plaintext to ciphertext(c_text) using cipher loop
c_text = ""
for ch in p_text:
c_text = c_text + (table[((ord(ch)) - 97) + key % 52])
print("Your encoded message is {0}.".format(c_text))
main() | [
"[email protected]"
]
| |
80338f57e4494dc5fd84346bfab8cd6f883a4347 | b5dabe2e6da0e53498650b3c3f3f944c20f3e050 | /dolo/compiler/function_compiler_numexpr.py | e20ec37370ffaf43ad7e04c17d62a3028aaf64d8 | [
"BSD-2-Clause"
]
| permissive | christophe-gouel/dolo | 12d582ecf3289aa9168f5d825da83a6284d5a669 | d9aef6d78d19899e2669e49ee6b7ad9aacf0e35d | refs/heads/master | 2020-12-24T09:31:19.389548 | 2018-01-04T20:42:19 | 2018-01-04T20:42:19 | 6,064,096 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,105 | py | from __future__ import division
from dolo.symbolic.derivatives import DerivativesTree
from dolo.symbolic.symbolic import TSymbol
from dolo.compiler.function_compiler import compile_multiargument_function as compile_multiargument_function_regular
DerivativesTree.symbol_type = TSymbol
def compile_multiargument_function(equations, args_list, args_names, parms, fname='anonymous_function', diff=True, return_text=False, order='rows'):
return compile_multiargument_function_regular(equations, args_list, args_names, parms, fname=fname, diff=diff, return_text=return_text, use_numexpr=True, order=order)
if __name__ == '__main__':
import sympy
from pprint import pprint
[w,x,y,z,t] = vars = sympy.symbols('w, x, y, z, t')
[a,b,c,d] = parms = sympy.symbols('a, b, c, d')
[k_1,k_2] = s_sym = sympy.symbols('k_1, k_2')
[x_1,x_2] = x_sym = sympy.symbols('x_1, x_2')
args_list = [
s_sym,
x_sym
]
from sympy import exp
eqs = [
x + y*k_2 + z*exp(x_1 + t),
(y + z)**0.3,
z,
(k_1 + k_2)**0.3,
k_2**x_1
]
sdict = {s:eqs[i] for i,s in enumerate(vars) }
from dolo.misc.triangular_solver import solve_triangular_system
order = solve_triangular_system(sdict, return_order=True)
ordered_vars = [ v for v in order ]
ordered_eqs = [ eqs[vars.index(v)] for v in order ]
pprint(ordered_vars)
pprint(ordered_eqs)
import numpy
floatX = numpy.float32
s0 = numpy.array( [2,5], dtype=floatX)
x0 = numpy.array( [2,2], dtype=floatX)
p0 = numpy.array( [4,3], dtype=floatX)
N = 2000
s1 = numpy.column_stack( [s0]*N )
x1 = numpy.column_stack( [x0]*N )
p1 = numpy.array( [4,3, 6, 7], dtype=floatX )
# f = create_fun()
#
# test = f(s1,x1,p0)
# print(test)
args_names = ['s','x']
#
#
solution = solve_triangular_system(sdict)
vals = [sympy.sympify(solution[v]) for v in ordered_vars]
from dolo.compiler.compiling import compile_multiargument_function as numpy_compiler
from dolo.compiler.compiling_theano import compile_multiargument_function as theano_compiler
f_numexpr = compile_multiargument_function( vals, args_list, args_names, parms )
f_numpy = numpy_compiler( vals, args_list, args_names, parms )
f_theano = theano_compiler( vals, args_list, args_names, parms )
n_exp = 1000
import time
r = time.time()
for i in range(n_exp):
res_numexpr = f_numexpr(s1,x1,p1)
# res = numpy.row_stack(res)
s = time.time()
print('Time (numexpr) : '+ str(s-r))
r = time.time()
for i in range(n_exp):
res_theano = f_theano(s1,x1,p1)
# res = numpy.row_stack(res)
s = time.time()
print('Time (theano) : '+ str(s-r))
r = time.time()
for i in range(n_exp):
res_numpy = f_numpy(s1,x1,p1)
# res = numpy.row_stack(res)
s = time.time()
print('Time (numpy) : '+ str(s-r))
print( abs(res_numpy - res_theano).max() )
print( abs(res_numexpr - res_numpy).max() ) | [
"[email protected]"
]
| |
91dbf8f944594010b21f4e33cdd5c303b603daa0 | 50948d4cb10dcb1cc9bc0355918478fb2841322a | /azure-mgmt-network/azure/mgmt/network/v2018_02_01/models/outbound_nat_rule.py | 509f9e9922798df037d6dab645f99d2111cc92f6 | [
"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 | 2,886 | 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 .sub_resource import SubResource
class OutboundNatRule(SubResource):
"""Outbound NAT pool of the load balancer.
All required parameters must be populated in order to send to Azure.
:param id: Resource ID.
:type id: str
:param allocated_outbound_ports: The number of outbound ports to be used
for NAT.
:type allocated_outbound_ports: int
:param frontend_ip_configurations: The Frontend IP addresses of the load
balancer.
:type frontend_ip_configurations:
list[~azure.mgmt.network.v2018_02_01.models.SubResource]
:param backend_address_pool: Required. A reference to a pool of DIPs.
Outbound traffic is randomly load balanced across IPs in the backend IPs.
:type backend_address_pool:
~azure.mgmt.network.v2018_02_01.models.SubResource
:param provisioning_state: Gets the provisioning state of the PublicIP
resource. Possible values are: 'Updating', 'Deleting', and 'Failed'.
:type provisioning_state: str
:param name: The name of the resource that is unique within a resource
group. This name can be used to access the resource.
:type name: str
:param etag: A unique read-only string that changes whenever the resource
is updated.
:type etag: str
"""
_validation = {
'backend_address_pool': {'required': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'allocated_outbound_ports': {'key': 'properties.allocatedOutboundPorts', 'type': 'int'},
'frontend_ip_configurations': {'key': 'properties.frontendIPConfigurations', 'type': '[SubResource]'},
'backend_address_pool': {'key': 'properties.backendAddressPool', 'type': 'SubResource'},
'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
}
def __init__(self, **kwargs):
super(OutboundNatRule, self).__init__(**kwargs)
self.allocated_outbound_ports = kwargs.get('allocated_outbound_ports', None)
self.frontend_ip_configurations = kwargs.get('frontend_ip_configurations', None)
self.backend_address_pool = kwargs.get('backend_address_pool', None)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.name = kwargs.get('name', None)
self.etag = kwargs.get('etag', None)
| [
"[email protected]"
]
| |
bedf0931ccef770750040887a803cdba60d8d515 | de1f9d660cfb738afdb66e4a2d63a4577c07d9c6 | /test/webapi/controllers/test_wmts.py | ad52d78c1aaca58531a54c0ef0ecba42c5079c04 | [
"MIT"
]
| permissive | rabaneda/xcube | db47eb416db85df891a924063482a7943cae9d4f | 0d38ca513987184dbc4a37da1616e4076964d0f1 | refs/heads/master | 2020-11-24T00:11:17.107630 | 2020-02-11T10:11:34 | 2020-02-11T10:11:34 | 227,877,138 | 0 | 0 | MIT | 2019-12-13T16:14:51 | 2019-12-13T16:14:50 | null | UTF-8 | Python | false | false | 703 | py | import os
import unittest
from test.webapi.helpers import get_res_test_dir, new_test_service_context
from xcube.webapi.controllers.wmts import get_wmts_capabilities_xml
class WmtsControllerTest(unittest.TestCase):
def test_get_wmts_capabilities_xml(self):
self.maxDiff = None
with open(os.path.join(get_res_test_dir(), 'WMTSCapabilities.xml')) as fp:
expected_capabilities = fp.read()
ctx = new_test_service_context()
capabilities = get_wmts_capabilities_xml(ctx, 'http://bibo')
print(80 * '=')
print(capabilities)
print(80 * '=')
self.assertEqual(expected_capabilities.replace(' ', ''), capabilities.replace(' ', ''))
| [
"[email protected]"
]
| |
3be06eb873cdd1760ff6e5f63aa67790705e4936 | 70cdf0741a22c678401a306229003bf036ffe5a6 | /ocbind/interfaces/interface/routed_vlan/ipv4/state/counters/__init__.py | fc80550902dca33ef1415b13bb6c12c3d63fa5ce | []
| no_license | zsblevins/nanog81-hackathon | 5001e034339d6b0c6452ae2474f06916bcd715cf | 1b64fd207dd69837f947094fbd6d6c1cea3a1070 | refs/heads/main | 2023-03-03T09:39:28.460000 | 2021-02-15T13:41:38 | 2021-02-15T13:41:38 | 336,698,856 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 42,316 | py | # -*- coding: utf-8 -*-
from operator import attrgetter
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType
from pyangbind.lib.yangtypes import RestrictedClassType
from pyangbind.lib.yangtypes import TypedListType
from pyangbind.lib.yangtypes import YANGBool
from pyangbind.lib.yangtypes import YANGListType
from pyangbind.lib.yangtypes import YANGDynClass
from pyangbind.lib.yangtypes import ReferenceType
from pyangbind.lib.base import PybindBase
from collections import OrderedDict
from decimal import Decimal
from bitarray import bitarray
import six
# PY3 support of some PY2 keywords (needs improved)
if six.PY3:
import builtins as __builtin__
long = int
elif six.PY2:
import __builtin__
class counters(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module openconfig-interfaces - based on the path /interfaces/interface/routed-vlan/ipv4/state/counters. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: Packet and byte counters for IP transmission and
reception for the address family.
"""
__slots__ = ('_path_helper', '_extmethods', '__in_pkts','__in_octets','__in_error_pkts','__in_forwarded_pkts','__in_forwarded_octets','__in_discarded_pkts','__out_pkts','__out_octets','__out_error_pkts','__out_forwarded_pkts','__out_forwarded_octets','__out_discarded_pkts',)
_yang_name = 'counters'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
self._path_helper = False
self._extmethods = False
self.__in_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__in_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__in_error_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__in_forwarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__in_forwarded_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__in_discarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__out_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__out_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__out_error_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__out_forwarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__out_forwarded_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
self.__out_discarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return ['interfaces', 'interface', 'routed-vlan', 'ipv4', 'state', 'counters']
def _get_in_pkts(self):
"""
Getter method for in_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_pkts (oc-yang:counter64)
YANG Description: The total number of IP packets received for the specified
address family, including those received in error
"""
return self.__in_pkts
def _set_in_pkts(self, v, load=False):
"""
Setter method for in_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_in_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_in_pkts() directly.
YANG Description: The total number of IP packets received for the specified
address family, including those received in error
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """in_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__in_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_in_pkts(self):
self.__in_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_in_octets(self):
"""
Getter method for in_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_octets (oc-yang:counter64)
YANG Description: The total number of octets received in input IP packets
for the specified address family, including those received
in error.
"""
return self.__in_octets
def _set_in_octets(self, v, load=False):
"""
Setter method for in_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_octets (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_in_octets is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_in_octets() directly.
YANG Description: The total number of octets received in input IP packets
for the specified address family, including those received
in error.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """in_octets must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__in_octets = t
if hasattr(self, '_set'):
self._set()
def _unset_in_octets(self):
self.__in_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_in_error_pkts(self):
"""
Getter method for in_error_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_error_pkts (oc-yang:counter64)
YANG Description: Number of IP packets discarded due to errors for the
specified address family, including errors in the IP
header, no route found to the IP destination, invalid
address, unknown protocol, etc.
"""
return self.__in_error_pkts
def _set_in_error_pkts(self, v, load=False):
"""
Setter method for in_error_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_error_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_in_error_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_in_error_pkts() directly.
YANG Description: Number of IP packets discarded due to errors for the
specified address family, including errors in the IP
header, no route found to the IP destination, invalid
address, unknown protocol, etc.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """in_error_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__in_error_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_in_error_pkts(self):
self.__in_error_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_in_forwarded_pkts(self):
"""
Getter method for in_forwarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_forwarded_pkts (oc-yang:counter64)
YANG Description: The number of input packets for which the device was not
their final IP destination and for which the device
attempted to find a route to forward them to that final
destination.
"""
return self.__in_forwarded_pkts
def _set_in_forwarded_pkts(self, v, load=False):
"""
Setter method for in_forwarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_forwarded_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_in_forwarded_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_in_forwarded_pkts() directly.
YANG Description: The number of input packets for which the device was not
their final IP destination and for which the device
attempted to find a route to forward them to that final
destination.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """in_forwarded_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__in_forwarded_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_in_forwarded_pkts(self):
self.__in_forwarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_in_forwarded_octets(self):
"""
Getter method for in_forwarded_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_forwarded_octets (oc-yang:counter64)
YANG Description: The number of octets received in input IP packets
for the specified address family for which the device was
not their final IP destination and for which the
device attempted to find a route to forward them to that
final destination.
"""
return self.__in_forwarded_octets
def _set_in_forwarded_octets(self, v, load=False):
"""
Setter method for in_forwarded_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_forwarded_octets (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_in_forwarded_octets is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_in_forwarded_octets() directly.
YANG Description: The number of octets received in input IP packets
for the specified address family for which the device was
not their final IP destination and for which the
device attempted to find a route to forward them to that
final destination.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """in_forwarded_octets must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__in_forwarded_octets = t
if hasattr(self, '_set'):
self._set()
def _unset_in_forwarded_octets(self):
self.__in_forwarded_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_in_discarded_pkts(self):
"""
Getter method for in_discarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_discarded_pkts (oc-yang:counter64)
YANG Description: The number of input IP packets for the
specified address family, for which no problems were
encountered to prevent their continued processing, but
were discarded (e.g., for lack of buffer space).
"""
return self.__in_discarded_pkts
def _set_in_discarded_pkts(self, v, load=False):
"""
Setter method for in_discarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/in_discarded_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_in_discarded_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_in_discarded_pkts() directly.
YANG Description: The number of input IP packets for the
specified address family, for which no problems were
encountered to prevent their continued processing, but
were discarded (e.g., for lack of buffer space).
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """in_discarded_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__in_discarded_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_in_discarded_pkts(self):
self.__in_discarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="in-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_out_pkts(self):
"""
Getter method for out_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_pkts (oc-yang:counter64)
YANG Description: The total number of IP packets for the
specified address family that the device supplied
to the lower layers for transmission. This includes
packets generated locally and those forwarded by the
device.
"""
return self.__out_pkts
def _set_out_pkts(self, v, load=False):
"""
Setter method for out_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_out_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_out_pkts() directly.
YANG Description: The total number of IP packets for the
specified address family that the device supplied
to the lower layers for transmission. This includes
packets generated locally and those forwarded by the
device.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """out_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__out_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_out_pkts(self):
self.__out_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_out_octets(self):
"""
Getter method for out_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_octets (oc-yang:counter64)
YANG Description: The total number of octets in IP packets for the
specified address family that the device
supplied to the lower layers for transmission. This
includes packets generated locally and those forwarded by
the device.
"""
return self.__out_octets
def _set_out_octets(self, v, load=False):
"""
Setter method for out_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_octets (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_out_octets is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_out_octets() directly.
YANG Description: The total number of octets in IP packets for the
specified address family that the device
supplied to the lower layers for transmission. This
includes packets generated locally and those forwarded by
the device.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """out_octets must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__out_octets = t
if hasattr(self, '_set'):
self._set()
def _unset_out_octets(self):
self.__out_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_out_error_pkts(self):
"""
Getter method for out_error_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_error_pkts (oc-yang:counter64)
YANG Description: Number of IP packets for the specified address family
locally generated and discarded due to errors, including
no route found to the IP destination.
"""
return self.__out_error_pkts
def _set_out_error_pkts(self, v, load=False):
"""
Setter method for out_error_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_error_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_out_error_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_out_error_pkts() directly.
YANG Description: Number of IP packets for the specified address family
locally generated and discarded due to errors, including
no route found to the IP destination.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """out_error_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__out_error_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_out_error_pkts(self):
self.__out_error_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-error-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_out_forwarded_pkts(self):
"""
Getter method for out_forwarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_forwarded_pkts (oc-yang:counter64)
YANG Description: The number of packets for which this entity was not their
final IP destination and for which it was successful in
finding a path to their final destination.
"""
return self.__out_forwarded_pkts
def _set_out_forwarded_pkts(self, v, load=False):
"""
Setter method for out_forwarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_forwarded_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_out_forwarded_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_out_forwarded_pkts() directly.
YANG Description: The number of packets for which this entity was not their
final IP destination and for which it was successful in
finding a path to their final destination.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """out_forwarded_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__out_forwarded_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_out_forwarded_pkts(self):
self.__out_forwarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_out_forwarded_octets(self):
"""
Getter method for out_forwarded_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_forwarded_octets (oc-yang:counter64)
YANG Description: The number of octets in packets for which this entity was
not their final IP destination and for which it was
successful in finding a path to their final destination.
"""
return self.__out_forwarded_octets
def _set_out_forwarded_octets(self, v, load=False):
"""
Setter method for out_forwarded_octets, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_forwarded_octets (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_out_forwarded_octets is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_out_forwarded_octets() directly.
YANG Description: The number of octets in packets for which this entity was
not their final IP destination and for which it was
successful in finding a path to their final destination.
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """out_forwarded_octets must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__out_forwarded_octets = t
if hasattr(self, '_set'):
self._set()
def _unset_out_forwarded_octets(self):
self.__out_forwarded_octets = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-forwarded-octets", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
def _get_out_discarded_pkts(self):
"""
Getter method for out_discarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_discarded_pkts (oc-yang:counter64)
YANG Description: The number of output IP packets for the
specified address family for which no problem was
encountered to prevent their transmission to their
destination, but were discarded (e.g., for lack of
buffer space).
"""
return self.__out_discarded_pkts
def _set_out_discarded_pkts(self, v, load=False):
"""
Setter method for out_discarded_pkts, mapped from YANG variable /interfaces/interface/routed_vlan/ipv4/state/counters/out_discarded_pkts (oc-yang:counter64)
If this variable is read-only (config: false) in the
source YANG file, then _set_out_discarded_pkts is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_out_discarded_pkts() directly.
YANG Description: The number of output IP packets for the
specified address family for which no problem was
encountered to prevent their transmission to their
destination, but were discarded (e.g., for lack of
buffer space).
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """out_discarded_pkts must be of a type compatible with oc-yang:counter64""",
'defined-type': "oc-yang:counter64",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)""",
})
self.__out_discarded_pkts = t
if hasattr(self, '_set'):
self._set()
def _unset_out_discarded_pkts(self):
self.__out_discarded_pkts = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..18446744073709551615']}, int_size=64), is_leaf=True, yang_name="out-discarded-pkts", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='http://openconfig.net/yang/interfaces/ip', defining_module='openconfig-if-ip', yang_type='oc-yang:counter64', is_config=False)
in_pkts = __builtin__.property(_get_in_pkts)
in_octets = __builtin__.property(_get_in_octets)
in_error_pkts = __builtin__.property(_get_in_error_pkts)
in_forwarded_pkts = __builtin__.property(_get_in_forwarded_pkts)
in_forwarded_octets = __builtin__.property(_get_in_forwarded_octets)
in_discarded_pkts = __builtin__.property(_get_in_discarded_pkts)
out_pkts = __builtin__.property(_get_out_pkts)
out_octets = __builtin__.property(_get_out_octets)
out_error_pkts = __builtin__.property(_get_out_error_pkts)
out_forwarded_pkts = __builtin__.property(_get_out_forwarded_pkts)
out_forwarded_octets = __builtin__.property(_get_out_forwarded_octets)
out_discarded_pkts = __builtin__.property(_get_out_discarded_pkts)
_pyangbind_elements = OrderedDict([('in_pkts', in_pkts), ('in_octets', in_octets), ('in_error_pkts', in_error_pkts), ('in_forwarded_pkts', in_forwarded_pkts), ('in_forwarded_octets', in_forwarded_octets), ('in_discarded_pkts', in_discarded_pkts), ('out_pkts', out_pkts), ('out_octets', out_octets), ('out_error_pkts', out_error_pkts), ('out_forwarded_pkts', out_forwarded_pkts), ('out_forwarded_octets', out_forwarded_octets), ('out_discarded_pkts', out_discarded_pkts), ])
| [
"[email protected]"
]
| |
a21dfa9182883f7045cd35880f722f3d9a36a0ab | 45e376ae66b78b17788b1d3575b334b2cb1d0b1c | /tests/terraform/checks/resource/azure/test_SynapseWorkspaceEnablesDataExfilProtection.py | 2f0a8e8e46b503edb13ed42ed956bc6d6a70830a | [
"Apache-2.0"
]
| permissive | bridgecrewio/checkov | aeb8febed2ed90e61d5755f8f9d80b125362644d | e64cbd27ffb6f09c2c9f081b45b7a821a3aa1a4d | refs/heads/main | 2023-08-31T06:57:21.990147 | 2023-08-30T23:01:47 | 2023-08-30T23:01:47 | 224,386,599 | 5,929 | 1,056 | Apache-2.0 | 2023-09-14T20:10:23 | 2019-11-27T08:55:14 | Python | UTF-8 | Python | false | false | 1,453 | py | import unittest
from pathlib import Path
from checkov.runner_filter import RunnerFilter
from checkov.terraform.checks.resource.azure.SynapseWorkspaceEnablesDataExfilProtection import check
from checkov.terraform.runner import Runner
class TestSynapseWorkspaceEnablesDataExfilProtection(unittest.TestCase):
def test(self):
# given
test_files_dir = Path(__file__).parent / "example_SynapseWorkspaceEnablesDataExfilProtection"
# when
report = Runner().run(root_folder=str(test_files_dir), runner_filter=RunnerFilter(checks=[check.id]))
# then
summary = report.get_summary()
passing_resources = {
"azurerm_synapse_workspace.pass",
}
failing_resources = {
"azurerm_synapse_workspace.fail",
"azurerm_synapse_workspace.fail2",
}
passed_check_resources = {c.resource for c in report.passed_checks}
failed_check_resources = {c.resource for c in report.failed_checks}
self.assertEqual(summary["passed"], 1)
self.assertEqual(summary["failed"], 2)
self.assertEqual(summary["skipped"], 0)
self.assertEqual(summary["parsing_errors"], 0)
self.assertEqual(summary["resource_count"], 3) # 3 unknown
self.assertEqual(passing_resources, passed_check_resources)
self.assertEqual(failing_resources, failed_check_resources)
if __name__ == "__main__":
unittest.main()
| [
"[email protected]"
]
| |
1ebd7b2c006bec2429d3ea7c144429ca6a16ab58 | 34599596e145555fde0d4264a1d222f951f49051 | /pcat2py/class/235864d6-5cc5-11e4-af55-00155d01fe08.py | 203705756d386be4768e626b13c813ce06acf1fd | [
"MIT"
]
| permissive | phnomcobra/PCAT2PY | dc2fcbee142ce442e53da08476bfe4e68619346d | 937c3b365cdc5ac69b78f59070be0a21bdb53db0 | refs/heads/master | 2021-01-11T02:23:30.669168 | 2018-02-13T17:04:03 | 2018-02-13T17:04:03 | 70,970,520 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,212 | py | #!/usr/bin/python
################################################################################
# 235864d6-5cc5-11e4-af55-00155d01fe08
#
# Justin Dierking
# [email protected]
# [email protected]
#
# 10/24/2014 Original Construction
################################################################################
class Finding:
def __init__(self):
self.output = []
self.is_compliant = False
self.uuid = "235864d6-5cc5-11e4-af55-00155d01fe08"
def check(self, cli):
# Initialize Compliance
self.is_compliant = False
# Execute command and parse capture standard output
stdout = cli.system("ls -l /etc/group")
# Split output lines
self.output = stdout.split('\n')
# Process standard output
lineNumber = 0
for line in self.output:
lineNumber += 1
if len(line.strip()) > 0:
subStrings = line.split(' ')
if subStrings[3] == "root":
self.is_compliant = True
return self.is_compliant
def fix(self, cli):
cli.system("chgrp root /etc/group")
| [
"[email protected]"
]
| |
d09e8f1f8f6ce69f17db42f0cc74904c1ba4e74e | e48375c39c0d1fc71742b1964dffdd3af0ff86c0 | /nlu/components/sentence_detectors/deep_sentence_detector/deep_sentence_detector.py | ab4ea95db84960ec483781f792af9daed7b121c3 | [
"Apache-2.0"
]
| permissive | ahmedlone127/nlu | b8da5a84f0e47640cb09616559bf8b84c259f278 | 614bc2ff94c80a7ebc34a78720ef29a1bf7080e0 | refs/heads/master | 2023-02-09T05:10:29.631583 | 2022-05-20T15:16:33 | 2022-05-20T15:16:33 | 325,437,640 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 700 | py | from sparknlp.annotator import *
class SentenceDetectorDeep:
@staticmethod
def get_default_model():
return SentenceDetectorDLModel\
.pretrained()\
.setInputCols(["document"]) \
.setOutputCol("sentence")
@staticmethod
def get_pretrained_model(name,lang, bucket=None):
return SentenceDetectorDLModel.pretrained(name,lang,bucket) \
.pretrained() \
.setInputCols(["document"]) \
.setOutputCol("sentence")
#
#
# @staticmethod
# def get_trainable_model():
# return SentenceDetectorDLApproach \
# .setInputCol("document") \
# .setOutputCol("sentence")
| [
"[email protected]"
]
| |
6312c87325c4ac19ffd4a502d580ce4c926c0ab6 | 6f269812a96d47c5670b4a7d5512f01bc7156217 | /manage.py | 18c1b3390a3f1f98873d26d42fc900e32bb82d06 | []
| no_license | kalkins/buk-django | 00a1724c19127840ac19182f003e28ed4f4f4480 | 708071d144b06ab289abdea6046437c40a81d230 | refs/heads/dev | 2022-12-13T05:51:30.664433 | 2019-02-12T03:10:04 | 2019-02-12T03:10:04 | 77,866,135 | 4 | 0 | null | 2022-12-08T01:28:31 | 2017-01-02T22:34:59 | Python | UTF-8 | Python | false | false | 801 | py | #!/usr/bin/env python
import os
import sys
if __name__ == "__main__":
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "buk.settings")
try:
from django.core.management import execute_from_command_line
except ImportError:
# The above import may fail for some other reason. Ensure that the
# issue is really that Django is missing to avoid masking other
# exceptions on Python 2.
try:
import django
except ImportError:
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?"
)
raise
execute_from_command_line(sys.argv)
| [
"[email protected]"
]
| |
70568dbd8fea74a804629bbf8c0ba8699ea10aaf | b0d7d91ccb7e388829abddb31b4aa04a2f9365cd | /archive-20200922/uncategorized/quick_palindrome_check.py | 4e1d9675666f0b9bddffa3ece524d351e0e26a37 | []
| no_license | clarkngo/python-projects | fe0e0aa02896debe82d1e9de84b1ae7d00932607 | 139a20063476f9847652b334a8495b7df1e80e27 | refs/heads/master | 2021-07-02T10:45:31.242041 | 2020-10-25T08:59:23 | 2020-10-25T08:59:23 | 188,570,684 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 365 | py |
# function which return reverse of a string
def reverse(s):
return s[::-1]
def isPalindrome(s):
# Calling reverse function
rev = reverse(s)
# Checking if both string are equal or not
if (s == rev):
return True
return False
# Driver code
s = "malayalam"
ans = isPalindrome(s)
if ans == 1:
print("Yes")
else:
print("No")
| [
"[email protected]"
]
| |
848b00dce8c68b93c85b751b4d5c57683f6980f1 | 2ed86a79d0fcd299ad4a01310954c5eddcf01edf | /homeassistant/components/airzone/coordinator.py | ba0296557a1be58bacea112719a507f82be0fb6b | [
"Apache-2.0"
]
| permissive | konnected-io/home-assistant | 037f12c87bb79e19220192eb918e49db1b1a8b3e | 2e65b77b2b5c17919939481f327963abdfdc53f0 | refs/heads/dev | 2023-05-11T08:57:41.891518 | 2023-05-07T20:03:37 | 2023-05-07T20:03:37 | 109,931,626 | 24 | 10 | Apache-2.0 | 2023-02-22T06:24:01 | 2017-11-08T05:27:21 | Python | UTF-8 | Python | false | false | 1,309 | py | """The Airzone integration."""
from __future__ import annotations
from datetime import timedelta
import logging
from typing import Any
from aioairzone.exceptions import AirzoneError
from aioairzone.localapi import AirzoneLocalApi
import async_timeout
from homeassistant.core import HomeAssistant
from homeassistant.helpers.update_coordinator import DataUpdateCoordinator, UpdateFailed
from .const import AIOAIRZONE_DEVICE_TIMEOUT_SEC, DOMAIN
SCAN_INTERVAL = timedelta(seconds=60)
_LOGGER = logging.getLogger(__name__)
class AirzoneUpdateCoordinator(DataUpdateCoordinator[dict[str, Any]]):
"""Class to manage fetching data from the Airzone device."""
def __init__(self, hass: HomeAssistant, airzone: AirzoneLocalApi) -> None:
"""Initialize."""
self.airzone = airzone
super().__init__(
hass,
_LOGGER,
name=DOMAIN,
update_interval=SCAN_INTERVAL,
)
async def _async_update_data(self) -> dict[str, Any]:
"""Update data via library."""
async with async_timeout.timeout(AIOAIRZONE_DEVICE_TIMEOUT_SEC):
try:
await self.airzone.update()
except AirzoneError as error:
raise UpdateFailed(error) from error
return self.airzone.data()
| [
"[email protected]"
]
| |
3016c687ec5ae81b1cd9d16c05eb06f58500219f | 968968aa5e81043cad5af6883f23ef077c36b65f | /load_model.py | 87518857933f46b083d4611584a50ca9100d20e9 | []
| no_license | Guya-LTD/profanity-detector | 59dbcb2e3e2fe4eba29cd49f5f028c48413f035f | ba957c42c4d14dd3c68ef2c48fce317e9db17f8f | refs/heads/main | 2023-02-11T18:26:59.205036 | 2021-01-10T06:41:25 | 2021-01-10T06:41:25 | 307,553,959 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 495 | py | import numpy as np
import joblib
def _get_profane_prob(prob):
return prob[1]
def predict(lang, texts):
vectorizer = joblib.load(lang + '/vectorizer.joblib')
model = joblib.load(lang + '/model.joblib')
return model.predict(vectorizer.transform(texts))
def predict_prob(lang, texts):
vectorizer = joblib.load(lang + '/vectorizer.joblib')
model = joblib.load(lang + '/model.joblib')
return np.apply_along_axis(_get_profane_prob, 1, model.predict_proba(vectorizer.transform(texts))) | [
"[email protected]"
]
| |
e6ea4e632b0b731721851c7db5ec5498ae307b76 | 3cb06711ab1a6e379e5778456fce5770ac994ba9 | /python/wait_functions_test_py3.py | 02cab39f268b7e1880b29bbcbcffa372099fe449 | [
"MIT"
]
| permissive | glenn-edgar/chain_flow | 7e8238c1f5e5c00f4c5906e2eb356d33c2b4696c | 750a9b126de04e46b71a58c5bd3e7500c4d26459 | refs/heads/master | 2021-01-02T22:41:30.066536 | 2017-09-05T19:34:57 | 2017-09-05T19:34:57 | 99,368,944 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,444 | py |
from py_cf_py3.chain_flow_py3 import CF_Base_Interpreter
def test_function_1( cf_handle, chainObj, parameters, event ):
print("test function 1 ",event)
def wait_test_function( cf_handle, chainObj, parameters, event ):
print("event",event)
return_value = False
if event["name"] == "INIT":
parameters.append(0)
if event["name"] == "TIME_TICK":
parameters[-1] = parameters[-1] +1
if parameters[-1] >= parameters[1]:
return_value = True
return return_value
cf = CF_Base_Interpreter()
cf.define_chain("Chain_1", False) # wait_tod
cf.insert.log("Chain 1 started")
cf.insert.wait_tod( "*","*","*",15 ) # wait for 15 seconds
cf.insert.one_step( test_function_1)
cf.insert.log("Chain 1 is reset")
cf.insert.reset( )
cf.define_chain("Chain_2",False) # wait_tod_ge wait_tod_le
cf.insert.log("Chain 2 started")
cf.insert.wait_tod_ge( "*","*","*",45 ) # wait for 15 seconds
cf.insert.check_event( test_function_1, "TIME_TICK" )
cf.insert.wait_tod_le( "*","*","*",15) # wait for 15 seconds
cf.insert.reset( )
cf.define_chain("Chain_3",False) #wait_event_count
cf.insert.log("Chain 3 started")
cf.insert.wait_event_count(count = 10)
cf.insert.one_step( test_function_1)
cf.insert.reset()
cf.define_chain("Chain_4",True) # wait_function
cf.insert.log("Chain 4 has started")
cf.insert.wait_function(wait_test_function, 10 )
cf.insert.log("Chain 4 is ended ")
cf.insert.reset()
cf.execute()
| [
"[email protected]"
]
| |
cb20e9e52b32e9c6326a763015d867ba85acb885 | 7b5f7dc5b6a0fc063aeabc9f2408dc867586c129 | /env/lib/python2.7/site-packages/sure/old.py | 59cef893c4075eff83a691a530ebb8822e678328 | []
| no_license | kingbifoe/django-calendar-reminder | 687dfa419895cfc67f5fad542179d9d4a716e75d | 3325717f53fd9825e036f21f391510f6a754aa93 | refs/heads/master | 2023-01-02T05:10:47.418059 | 2016-04-10T19:04:44 | 2016-04-10T19:04:44 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 12,705 | py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# <sure - utility belt for automated testing in python>
# Copyright (C) <2010-2013> Gabriel Falcão <[email protected]>
#
# 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/>.
from __future__ import unicode_literals
import re
import traceback
import inspect
from copy import deepcopy
from pprint import pformat
from functools import wraps
try:
from collections import Iterable
except ImportError:
Iterable = (list, dict, tuple, set)
try:
import __builtin__ as builtins
except ImportError:
import builtins
from six import string_types, text_type
from sure.core import DeepComparison
from sure.core import _get_file_name
from sure.core import _get_line_number
from sure.core import itemize_length
def is_iterable(obj):
return hasattr(obj, '__iter__') and not isinstance(obj, string_types)
def all_integers(obj):
if not is_iterable(obj):
return
for element in obj:
if not isinstance(element, int):
return
return True
def explanation(msg):
def dec(func):
@wraps(func)
def wrap(self, what):
ret = func(self, what)
assert ret, msg % (self._src, what)
return True
return wrap
return dec
class AssertionHelper(object):
def __init__(self, src,
within_range=None,
with_args=None,
with_kwargs=None,
and_kwargs=None):
self._src = src
self._attribute = None
self._eval = None
self._range = None
if all_integers(within_range):
if len(within_range) != 2:
raise TypeError(
'within_range parameter must be a tuple with 2 objects',
)
self._range = within_range
self._callable_args = []
if isinstance(with_args, (list, tuple)):
self._callable_args = list(with_args)
self._callable_kw = {}
if isinstance(with_kwargs, dict):
self._callable_kw.update(with_kwargs)
if isinstance(and_kwargs, dict):
self._callable_kw.update(and_kwargs)
@classmethod
def is_a_matcher(cls, func):
def match(self, *args, **kw):
return func(self._src, *args, **kw)
new_matcher = deepcopy(match)
new_matcher.__name__ = func.__name__
setattr(cls, func.__name__, new_matcher)
return new_matcher
def raises(self, exc, msg=None):
if not callable(self._src):
raise TypeError('%r is not callable' % self._src)
try:
self._src(*self._callable_args, **self._callable_kw)
except BaseException as e:
if isinstance(exc, string_types):
msg = exc
exc = type(e)
err = text_type(e)
if isinstance(exc, type) and issubclass(exc, BaseException):
if not isinstance(e, exc):
raise AssertionError(
'%r should raise %r, but raised %r:\nORIGINAL EXCEPTION:\n\n%s' % (
self._src, exc, e.__class__, traceback.format_exc(e)))
if isinstance(msg, string_types) and msg not in err:
raise AssertionError('''
%r raised %s, but the exception message does not
match.\n\nEXPECTED:\n%s\n\nGOT:\n%s'''.strip() % (
self._src,
type(e).__name__,
msg, err))
elif isinstance(msg, string_types) and msg not in err:
raise AssertionError(
'When calling %r the exception message does not match. ' \
'Expected: %r\n got:\n %r' % (self._src, msg, err))
else:
raise e
else:
if inspect.isbuiltin(self._src):
_src_filename = '<built-in function>'
else:
_src_filename = _get_file_name(self._src)
if inspect.isfunction(self._src):
_src_lineno = _get_line_number(self._src)
raise AssertionError(
'calling function %s(%s at line: "%d") with args %r and kwargs %r did not raise %r' % (
self._src.__name__,
_src_filename, _src_lineno,
self._callable_args,
self._callable_kw, exc))
else:
raise AssertionError(
'at %s:\ncalling %s() with args %r and kwargs %r did not raise %r' % (
_src_filename,
self._src.__name__,
self._callable_args,
self._callable_kw, exc))
return True
def deep_equals(self, dst):
deep = DeepComparison(self._src, dst)
comparison = deep.compare()
if isinstance(comparison, bool):
return comparison
raise comparison.as_assertion(self._src, dst)
def equals(self, dst):
if self._attribute and is_iterable(self._src):
msg = '%r[%d].%s should be %r, but is %r'
for index, item in enumerate(self._src):
if self._range:
if index < self._range[0] or index > self._range[1]:
continue
attribute = getattr(item, self._attribute)
error = msg % (
self._src, index, self._attribute, dst, attribute)
if attribute != dst:
raise AssertionError(error)
else:
return self.deep_equals(dst)
return True
def looks_like(self, dst):
old_src = pformat(self._src)
old_dst = pformat(dst)
self._src = re.sub(r'\s', '', self._src).lower()
dst = re.sub(r'\s', '', dst).lower()
error = '%s does not look like %s' % (old_src, old_dst)
assert self._src == dst, error
return self._src == dst
def every_one_is(self, dst):
msg = 'all members of %r should be %r, but the %dth is %r'
for index, item in enumerate(self._src):
if self._range:
if index < self._range[0] or index > self._range[1]:
continue
error = msg % (self._src, dst, index, item)
if item != dst:
raise AssertionError(error)
return True
@explanation('%r should differ to %r, but is the same thing')
def differs(self, dst):
return self._src != dst
@explanation('%r should be a instance of %r, but is not')
def is_a(self, dst):
return isinstance(self._src, dst)
def at(self, key):
assert self.has(key)
if isinstance(self._src, dict):
return AssertionHelper(self._src[key])
else:
return AssertionHelper(getattr(self._src, key))
@explanation('%r should have %r, but have not')
def has(self, that):
return that in self
def _get_that(self, that):
try:
that = int(that)
except TypeError:
that = len(that)
return that
def len_greater_than(self, that):
that = self._get_that(that)
length = len(self._src)
if length <= that:
error = 'the length of the %s should be greater then %d, but is %d' % (
type(self._src).__name__,
that,
length,
)
raise AssertionError(error)
return True
def len_greater_than_or_equals(self, that):
that = self._get_that(that)
length = len(self._src)
if length < that:
error = 'the length of %r should be greater then or equals %d, but is %d' % (
self._src,
that,
length,
)
raise AssertionError(error)
return True
def len_lower_than(self, that):
original_that = that
if isinstance(that, Iterable):
that = len(that)
else:
that = self._get_that(that)
length = len(self._src)
if length >= that:
error = 'the length of %r should be lower then %r, but is %d' % (
self._src,
original_that,
length,
)
raise AssertionError(error)
return True
def len_lower_than_or_equals(self, that):
that = self._get_that(that)
length = len(self._src)
error = 'the length of %r should be lower then or equals %d, but is %d'
if length > that:
msg = error % (
self._src,
that,
length,
)
raise AssertionError(msg)
return True
def len_is(self, that):
that = self._get_that(that)
length = len(self._src)
if length != that:
error = 'the length of %r should be %d, but is %d' % (
self._src,
that,
length,
)
raise AssertionError(error)
return True
def len_is_not(self, that):
that = self._get_that(that)
length = len(self._src)
if length == that:
error = 'the length of %r should not be %d' % (
self._src,
that,
)
raise AssertionError(error)
return True
def like(self, that):
return self.has(that)
def the_attribute(self, attr):
self._attribute = attr
return self
def in_each(self, attr):
self._eval = attr
return self
def matches(self, items):
msg = '%r[%d].%s should be %r, but is %r'
get_eval = lambda item: eval(
"%s.%s" % ('current', self._eval), {}, {'current': item},
)
if self._eval and is_iterable(self._src):
if isinstance(items, string_types):
items = [items for x in range(len(items))]
else:
if len(items) != len(self._src):
source = list(map(get_eval, self._src))
source_len = len(source)
items_len = len(items)
raise AssertionError(
'%r has %d items, but the matching list has %d: %r'
% (source, source_len, items_len, items),
)
for index, (item, other) in enumerate(zip(self._src, items)):
if self._range:
if index < self._range[0] or index > self._range[1]:
continue
value = get_eval(item)
error = msg % (self._src, index, self._eval, other, value)
if other != value:
raise AssertionError(error)
else:
return self.equals(items)
return True
@builtins.property
def is_empty(self):
try:
lst = list(self._src)
length = len(lst)
assert length == 0, \
'%r is not empty, it has %s' % (self._src,
itemize_length(self._src))
return True
except TypeError:
raise AssertionError("%r is not iterable" % self._src)
@builtins.property
def are_empty(self):
return self.is_empty
def __contains__(self, what):
if isinstance(self._src, dict):
items = self._src.keys()
if isinstance(self._src, Iterable):
items = self._src
else:
items = dir(self._src)
return what in items
def contains(self, what):
assert what in self._src, '%r should be in %r' % (what, self._src)
return True
def does_not_contain(self, what):
assert what not in self._src, \
'%r should NOT be in %r' % (what, self._src)
return True
doesnt_contain = does_not_contain
that = AssertionHelper
| [
"[email protected]"
]
| |
d82a7c81e00fa27c5ad59a4fc4811c1928d2518e | 63daf225819636397fda6ef7e52783331c27f295 | /taobao-sdk/top/api/rest/TmallProductSpecsGetRequest.py | b7150211c3b1b6c63e9ce9e9c0ee66bd56c5f336 | []
| no_license | cash2one/language-Python | e332ecfb4e9321a11407b29987ee64d44e552b15 | 8adb4f2fd2f023f9cc89b4edce1da5f71a3332ab | refs/heads/master | 2021-06-16T15:15:08.346420 | 2017-04-20T02:44:16 | 2017-04-20T02:44:16 | 112,173,361 | 1 | 0 | null | 2017-11-27T09:08:57 | 2017-11-27T09:08:57 | null | UTF-8 | Python | false | false | 356 | py | '''
Created by auto_sdk on 2014.02.28
'''
from top.api.base import RestApi
class TmallProductSpecsGetRequest(RestApi):
def __init__(self,domain='gw.api.taobao.com',port=80):
RestApi.__init__(self,domain, port)
self.cat_id = None
self.product_id = None
self.properties = None
def getapiname(self):
return 'tmall.product.specs.get'
| [
"[email protected]"
]
| |
ce502221c2081beadd2ed01aa5ddd02cf7cf7901 | 89a90707983bdd1ae253f7c59cd4b7543c9eda7e | /data_structures_and_algorithms_in_python/ch04/power_fast.py | c7f98d650facb9e5b5bb39c4db5cd09f1ee64c4c | []
| no_license | timothyshull/python_reference_code | 692a7c29608cadfd46a6cc409a000023e95b9458 | f3e2205dd070fd3210316f5f470d371950945028 | refs/heads/master | 2021-01-22T20:44:07.018811 | 2017-03-17T19:17:22 | 2017-03-17T19:17:22 | 85,346,735 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 267 | py | def power(x, n):
if n == 0:
return 1
else:
partial = power(x, n // 2) # rely on truncated division
result = partial * partial
if n % 2 == 1: # if n odd, include extra factor of x
result *= x
return result
| [
"[email protected]"
]
| |
e8fb8b6c7a2c7ba04314e431ec618dd22761941e | 612325535126eaddebc230d8c27af095c8e5cc2f | /src/build/android/pylib/utils/device_dependencies.py | c448396fbc0ab0c74370a723afeb7c9fb47be053 | [
"BSD-3-Clause"
]
| permissive | TrellixVulnTeam/proto-quic_1V94 | 1a3a03ac7a08a494b3d4e9857b24bb8f2c2cd673 | feee14d96ee95313f236e0f0e3ff7719246c84f7 | refs/heads/master | 2023-04-01T14:36:53.888576 | 2019-10-17T02:23:04 | 2019-10-17T02:23:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,315 | py | # Copyright 2016 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import os
import re
from pylib import constants
_BLACKLIST = [
re.compile(r'.*OWNERS'), # Should never be included.
re.compile(r'.*\.crx'), # Chrome extension zip files.
re.compile(r'.*\.so'), # Libraries packed into .apk.
re.compile(r'.*Mojo.*manifest\.json'), # Some source_set()s pull these in.
re.compile(r'.*\.py'), # Some test_support targets include python deps.
re.compile(r'.*\.stamp'), # Stamp files should never be included.
# Some test_support targets include python deps.
re.compile(r'.*\.mojom\.js'),
# Chrome external extensions config file.
re.compile(r'.*external_extensions\.json'),
# Exists just to test the compile, not to be run.
re.compile(r'.*jni_generator_tests'),
# v8's blobs get packaged into APKs.
re.compile(r'.*natives_blob.*\.bin'),
re.compile(r'.*snapshot_blob.*\.bin'),
]
def DevicePathComponentsFor(host_path, output_directory):
"""Returns the device path components for a given host path.
This returns the device path as a list of joinable path components,
with None as the first element to indicate that the path should be
rooted at $EXTERNAL_STORAGE.
e.g., given
'$CHROMIUM_SRC/foo/bar/baz.txt'
this would return
[None, 'foo', 'bar', 'baz.txt']
This handles a couple classes of paths differently than it otherwise would:
- All .pak files get mapped to top-level paks/
- Anything in the output directory gets mapped relative to the output
directory rather than the source directory.
e.g. given
'$CHROMIUM_SRC/out/Release/icu_fake_dir/icudtl.dat'
this would return
[None, 'icu_fake_dir', 'icudtl.dat']
Args:
host_path: The absolute path to the host file.
Returns:
A list of device path components.
"""
if host_path.startswith(output_directory):
if os.path.splitext(host_path)[1] == '.pak':
return [None, 'paks', os.path.basename(host_path)]
rel_host_path = os.path.relpath(host_path, output_directory)
else:
rel_host_path = os.path.relpath(host_path, constants.DIR_SOURCE_ROOT)
device_path_components = [None]
p = rel_host_path
while p:
p, d = os.path.split(p)
if d:
device_path_components.insert(1, d)
return device_path_components
def GetDataDependencies(runtime_deps_path):
"""Returns a list of device data dependencies.
Args:
runtime_deps_path: A str path to the .runtime_deps file.
Returns:
A list of (host_path, device_path) tuples.
"""
if not runtime_deps_path:
return []
with open(runtime_deps_path, 'r') as runtime_deps_file:
rel_host_files = [l.strip() for l in runtime_deps_file if l]
output_directory = constants.GetOutDirectory()
abs_host_files = [
os.path.abspath(os.path.join(output_directory, r))
for r in rel_host_files]
filtered_abs_host_files = [
host_file for host_file in abs_host_files
if not any(blacklist_re.match(host_file) for blacklist_re in _BLACKLIST)]
return [(f, DevicePathComponentsFor(f, output_directory))
for f in filtered_abs_host_files]
| [
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]
| |
b1dea3c4983f09b3a6dc08bf597ea9ff4f8bd617 | 163bbb4e0920dedd5941e3edfb2d8706ba75627d | /Code/CodeRecords/2158/60876/250371.py | 2410110cab9dc4be3dd9ff187554b0e5447e6868 | []
| no_license | AdamZhouSE/pythonHomework | a25c120b03a158d60aaa9fdc5fb203b1bb377a19 | ffc5606817a666aa6241cfab27364326f5c066ff | refs/heads/master | 2022-11-24T08:05:22.122011 | 2020-07-28T16:21:24 | 2020-07-28T16:21:24 | 259,576,640 | 2 | 1 | null | null | null | null | UTF-8 | Python | false | false | 537 | py | string=input()
index=0
while string[index]==' ':
index+=1
temp=""
if string[index]=='-':
temp+="-"
index+=1
while index<len(string) and string[index].isdigit():
temp+=string[index]
index+=1
if int(temp)<-2**(31):
print( -2**(31))
else:
print(temp)
elif not string[index].isdigit():
print(0)
else:
while index<len(string) and string[index].isdigit():
temp+=string[index]
index+=1
if int(temp)>2**31-1:
print(2**31-1)
else:
print(temp) | [
"[email protected]"
]
| |
745598179a1f4f9fc81a7259c54c7e70776b17c7 | 7083f3e6121a63b0c21e15d8b23c32067d7e4e5e | /scripts/debugging/test_data_utils.py | 471d21749014c77fe08ae8e78ca5ffb1b5c2ab15 | []
| no_license | nathanin/milk | e46317cf0d1b2162fd301a144f5aa3b889cf5d27 | 9afb3b01715a4f65a03b7cd45dcd121745b117f8 | refs/heads/master | 2023-02-22T05:23:06.850961 | 2019-10-26T19:08:20 | 2019-10-26T19:08:20 | 154,750,122 | 2 | 0 | null | 2023-02-15T21:36:19 | 2018-10-25T23:29:33 | Jupyter Notebook | UTF-8 | Python | false | false | 79 | py | import sys
sys.path.insert(0, '../..')
from milk.utilities import data_utils
| [
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]
| |
4371051b460fbdb7f7e35435ddd12876a32f7a6e | 21b0b4c27193898207751c91b8b2ed168a1b1638 | /py/py_0198_ambiguous_numbers.py | ca312ac6f6f03c67908c6bd6ae8705a25e557c7b | [
"MIT"
]
| permissive | lcsm29/project-euler | 67560a4e66968f1671a3d7ecf2dda6c956893dca | fab794ece5aa7a11fc7c2177f26250f40a5b1447 | refs/heads/main | 2023-07-04T11:45:24.374841 | 2021-08-07T08:20:41 | 2021-08-07T08:20:41 | 371,808,781 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,130 | py | # Solution of;
# Project Euler Problem 198: Ambiguous Numbers
# https://projecteuler.net/problem=198
#
# A best approximation to a real number $x$ for the denominator bound $d$ is a
# rational number $\frac r s$ (in reduced form) with $s \le d$, so that any
# rational number $\frac p q$ which is closer to $x$ than $\frac r s$ has $q >
# d$. Usually the best approximation to a real number is uniquely determined
# for all denominator bounds. However, there are some exceptions, e. g. $\frac
# 9 {40}$ has the two best approximations $\frac 1 4$ and $\frac 1 5$ for the
# denominator bound $6$. We shall call a real number $x$ ambiguous, if there
# is at least one denominator bound for which $x$ possesses two best
# approximations. Clearly, an ambiguous number is necessarily rational. How
# many ambiguous numbers $x=\frac p q, 0 < x < \frac 1 {100}$, are there whose
# denominator $q$ does not exceed $10^8$?
#
# by lcsm29 http://github.com/lcsm29/project-euler
import timed
def dummy(n):
pass
if __name__ == '__main__':
n = 1000
i = 10000
prob_id = 198
timed.caller(dummy, n, i, prob_id)
| [
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]
| |
cac2318a8b307ad741c58dda75e970b204bed67a | 98c6ea9c884152e8340605a706efefbea6170be5 | /examples/data/Assignment_4/bgrtej001/piglatin.py | 8a3ae80180102da97b97c2eee4594a3e8512b2c3 | []
| no_license | MrHamdulay/csc3-capstone | 479d659e1dcd28040e83ebd9e3374d0ccc0c6817 | 6f0fa0fa1555ceb1b0fb33f25e9694e68b6a53d2 | refs/heads/master | 2021-03-12T21:55:57.781339 | 2014-09-22T02:22:22 | 2014-09-22T02:22:22 | 22,372,174 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,182 | py | #Tejasvin Bagirathi
#Assignment 4, Question 3
def toPigLatin(s):
wrdno = 1
new = ""
for i in range(len(s)):
if s[i] == " ":
wrdno+=1
string = s.split(" ")
for i in range(wrdno):
wrd = string[i]
#If word starts with vowel
if wrd[0] in "aeiou":
wrd = wrd + "way"
if wrdno == i:
new += wrd
else:
new += wrd + " "
else:
k = 0
for c in wrd[:]:
if c not in "aeiou":
k+=1
else: break
wrd = wrd[k:len(wrd)] + "a" + wrd[0:k] + "ay"
if wrdno == i:
new += wrd
else:
new += wrd + " "
return new
def toEnglish(s):
sentence=s.split()
newsentence=""
for word in range(len(sentence)):
if sentence[word][-3:]=="way":
newsentence+=sentence[word][:-3]+" "
elif sentence[word][-2:]=="ay":
nWord=sentence[word][:-2]
aPos=nWord.rfind("a")
newsentence+=nWord[aPos+1:]+nWord[:aPos]+" "
return(newsentence)
| [
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]
| |
9fb17ce7b6fb0a7b73112825f591381e23c30c80 | fe70774ff6898c5bdb0c941b4f335de576abfdb6 | /autotest/test_flopy_io.py | bb09cd2207661c1b0258d7feb56b3d6788f12990 | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
]
| permissive | robinthibaut/flopy | 35af468415d1ba6e1de119a7cb335381304fada9 | 22ef330bcfb9259fc23735d6b174d27804b624a0 | refs/heads/develop | 2023-06-30T21:43:24.101593 | 2023-06-13T19:46:03 | 2023-06-13T19:46:03 | 255,560,877 | 0 | 0 | BSD-3-Clause | 2022-10-10T12:23:38 | 2020-04-14T09:05:42 | null | UTF-8 | Python | false | false | 3,153 | py | import os
import platform
from os import getcwd
from os.path import relpath, splitdrive
from pathlib import Path
from shutil import which
import pytest
from modflow_devtools.markers import requires_exe
from modflow_devtools.misc import set_dir
from flopy.utils.flopy_io import line_parse, relpath_safe
def test_line_parse():
"""t027 test line_parse method in MNW2 Package class"""
# ensure that line_parse is working correctly
# comment handling
line = line_parse("Well-A -1 ; 2a. WELLID,NNODES")
assert line == ["Well-A", "-1"]
@requires_exe("mf6")
@pytest.mark.parametrize("scrub", [True, False])
@pytest.mark.parametrize("use_paths", [True, False])
def test_relpath_safe(function_tmpdir, scrub, use_paths):
if (
platform.system() == "Windows"
and splitdrive(function_tmpdir)[0] != splitdrive(getcwd())[0]
):
if use_paths:
assert (
Path(relpath_safe(function_tmpdir))
== function_tmpdir.absolute()
)
assert relpath_safe(Path(which("mf6"))) == str(
Path(which("mf6")).absolute()
)
else:
assert (
Path(relpath_safe(str(function_tmpdir)))
== function_tmpdir.absolute()
)
assert relpath_safe(which("mf6")) == str(
Path(which("mf6")).absolute()
)
else:
if use_paths:
assert Path(
relpath_safe(function_tmpdir, function_tmpdir.parent)
) == Path(function_tmpdir.name)
assert (
Path(
relpath_safe(
function_tmpdir, function_tmpdir.parent.parent
)
)
== Path(function_tmpdir.parent.name) / function_tmpdir.name
)
assert relpath_safe(Path(which("mf6"))) == relpath(
Path(which("mf6")), Path(getcwd())
)
else:
assert Path(
relpath_safe(str(function_tmpdir), str(function_tmpdir.parent))
) == Path(function_tmpdir.name)
assert (
Path(
relpath_safe(
str(function_tmpdir),
str(function_tmpdir.parent.parent),
)
)
== Path(function_tmpdir.parent.name) / function_tmpdir.name
)
assert relpath_safe(which("mf6")) == relpath(
which("mf6"), getcwd()
)
# test user login obfuscation
with set_dir("/"):
try:
login = os.getlogin()
if use_paths:
p = relpath_safe(Path.home(), scrub=scrub)
else:
p = relpath_safe(str(Path.home()), scrub=scrub)
if login in str(Path.home()) and scrub:
assert "***" in p
assert login not in p
except OSError:
# OSError is possible in CI, e.g. 'No such device or address'
pass
| [
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]
| |
5593fcb9ec3b1417a331b3952f8d5f7cd229fa92 | 11bb0cbe6de2a0a4e94fc0ba610f61894d5593a1 | /VBS_Zgamma/Significance/Invert_detajj/data_cards/th2_to_txt.py | 40511394bf0a015827f544aacee6e14b17f68643 | []
| no_license | AnYpku/PKU-Cluster | 0dc4a88445aeb3ca239b2d7d7f796c6a67f3f69c | f9ffbcb7988053f4618fd015c1bb656d92ff51c6 | refs/heads/master | 2022-11-01T23:46:59.442037 | 2022-10-21T06:37:43 | 2022-10-21T06:37:43 | 188,202,345 | 0 | 4 | null | null | null | null | UTF-8 | Python | false | false | 10,414 | py | #!/usr/bin/env python
from ROOT import gROOT, THStack, TH1D, TList, TFile
import sys
from math import sqrt
from numpy import sum
def merge_bin(th1):
nbins=th1.GetNbinsX()
print 'nbins', nbins
th1.SetBinContent(nbins-2,th1.GetBinContent(nbins-2)+th1.GetBinContent(nbins-1)+th1.GetBinContent(nbins))
if th1.GetBinContent(nbins-2)>0:
th1.SetBinError(nbins-2,sqrt(th1.GetBinError(nbins-2)*th1.GetBinError(nbins-2)+th1.GetBinError(nbins-1)*th1.GetBinError(nbins-1)+th1.GetBinError(nbins)*th1.GetBinError(nbins)))
else:
th1.SetBinError(nbins-2,0);
print '-----begin to transfer TH2D to txt for Higgs-combine tool----- \n'
fdir = '/home/pku/anying/cms/PKU-Cluster/Significance/Invert_detajj/root/'
fin = TFile.Open(fdir+'hist_'+sys.argv[1]+'.root')
th1_ZA_sig=fin.Get('hist_ZA-EWK'+sys.argv[1])
th1_ZA=fin.Get('hist_ZA'+sys.argv[1])
th1_non_prompt=fin.Get('hist_plj'+sys.argv[1])
th1_TTA=fin.Get('hist_TTA'+sys.argv[1])
th1_VV=fin.Get('hist_VV'+sys.argv[1])
th1_ST=fin.Get('hist_ST'+sys.argv[1])
# the bkg histo and signal histo have already contain the overflow bin in the last bin when creat the histograms
genbincontent=[]
genbinerror=[]
arr={}
f=open('/home/pku/anying/cms/PKU-Cluster/Significance/Invert_detajj/Uncer/summary_Mjj_'+sys.argv[1]+'.txt')
import re
import numpy as np
for line in f:
if not line.strip():
continue
print line
line = line.replace('[','')
line = line.replace(']','')
line = line.replace('\n','')
print line
arr_Temp = re.split(',|=',line)
print arr_Temp
name = arr_Temp[0]
arr_Temp = np.array(arr_Temp[1:])
#arr_Temp.astype(np.float)
arr_Temp = [float(x) for x in arr_Temp]
print name
arr[name]=arr_Temp
print arr
print '>>>>begin to read bin content to the txt file>>>>'
nbins=th1_ZA_sig.GetNbinsX()
print 'nbins', nbins
nbins=th1_ZA_sig.GetNbinsX()+1
print 'range in for loop 1 to', nbins
for i in range(1,nbins):
f = open('./txt/Mjj_%s_bin%i.txt'%(sys.argv[1],i),'w')
f.write('imax 1 number of channels\n')
f.write('jmax 5 number of processes-1\n')
if sys.argv[1].find("18") == -1 and sys.argv[1].find("17") == -1: #16
f.write('kmax * number of nuisance parameters (sources of systematical uncertainties)\n')
if sys.argv[1].find("16") == -1 and sys.argv[1].find("18") == -1: #17
f.write('kmax * number of nuisance parameters (sources of systematical uncertainties)\n')
if sys.argv[1].find("16") == -1 and sys.argv[1].find("17") == -1: #18
f.write('kmax * number of nuisance parameters (sources of systematical uncertainties)\n')
f.write('------------\n')
f.write('# we have just one channel, in which we observe 0 events\n')
f.write('bin bin%i\n'%(i))
# bincontent of each precess
ST_bincontent = th1_ST.GetBinContent(i) if th1_ST.GetBinContent(i)>0 else 0
TTA_bincontent = th1_TTA.GetBinContent(i) if th1_TTA.GetBinContent(i)>0 else 0
VV_bincontent = th1_VV.GetBinContent(i) if th1_VV.GetBinContent(i)>0 else 0
# WA_bincontent = th1_WA.GetBinContent(i) if th1_WA.GetBinContent(i)>0 else 0
non_prompt_bincontent = th1_non_prompt.GetBinContent(i) if th1_non_prompt.GetBinContent(i)>0 else 0
ZA_bincontent = th1_ZA.GetBinContent(i) if th1_ZA.GetBinContent(i)>0 else 0
ZA_sig_bincontent = th1_ZA_sig.GetBinContent(i) if th1_ZA_sig.GetBinContent(i)>0 else 0
# bin error
ST_binerror = th1_ST.GetBinError(i)/ST_bincontent if ST_bincontent>0 else 0
ST_binerror = ST_binerror if ST_binerror<1 else 1
ST_binerror = ST_binerror+1
TTA_binerror = th1_TTA.GetBinError(i)/TTA_bincontent if TTA_bincontent>0 else 0
TTA_binerror = TTA_binerror if TTA_binerror<1 else 1
TTA_binerror = TTA_binerror+1
VV_binerror = th1_VV.GetBinError(i)/VV_bincontent if VV_bincontent>0 else 0
VV_binerror = VV_binerror if VV_binerror<1 else 1
VV_binerror = VV_binerror+1
# WA_binerror = th1_WA.GetBinError(i)/WA_bincontent if WA_bincontent>0 else 0
# WA_binerror = WA_binerror if WA_binerror<1 else 1
# WA_binerror = WA_binerror+1
non_prompt_binerror = th1_non_prompt.GetBinError(i)/non_prompt_bincontent if non_prompt_bincontent>0 else 0
non_prompt_binerror = non_prompt_binerror if non_prompt_binerror<1 else 1
non_prompt_binerror =non_prompt_binerror+1
ZA_binerror = th1_ZA.GetBinError(i)/ZA_bincontent if ZA_bincontent>0 else 0
ZA_binerror = ZA_binerror if ZA_binerror<1 else 1
ZA_binerror = ZA_binerror+1
ZA_sig_binerror = th1_ZA_sig.GetBinError(i)/ZA_sig_bincontent if ZA_sig_bincontent>0 else 0
ZA_sig_binerror = ZA_sig_binerror if ZA_sig_binerror<1 else 1
ZA_sig_binerror = ZA_sig_binerror+1
data= ZA_sig_bincontent + ZA_bincontent+non_prompt_bincontent+TTA_bincontent+VV_bincontent+ST_bincontent
f.write('observation %0.2f\n'%(data))
f.write('------------\n')
f.write('# now we list the expected events for signal and all backgrounds in that bin\n')
f.write('# the second process line must have a positive number for backgrounds, and 0 for signal\n')
f.write('# then we list the independent sources of uncertainties, and give their effect (syst. error)\n')
f.write('# on each process and bin\n')
f.write('bin\t')
f.write('bin%i\tbin%i\tbin%i\tbin%i\tbin%i\tbin%i\n'%(i,i,i,i,i,i))
f.write('process\t')
f.write('Sig\tQCD\tnon_prompt\tTTA\tVV\tST\n')
f.write('process\t0\t1\t2\t3\t4\t5\n')
f.write('rate\t')
f.write('%0.2f\t%0.2f\t%0.2f\t%0.2f\t%0.2f\t%0.2f\n'%(ZA_sig_bincontent,ZA_bincontent, non_prompt_bincontent, TTA_bincontent, VV_bincontent, ST_bincontent))
f.write('------------\n')
f.write('lumi_%s\tlnN\t'%(sys.argv[1]))
if sys.argv[1].find("17")==-1 and sys.argv[1].find("18")==-1:
f.write('%0.3f\t%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\n'%(1.022,1.022,1.022,1.022,1.022))
if sys.argv[1].find("16")==-1 and sys.argv[1].find("18")==-1:
f.write('%0.3f\t%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\n'%(1.02,1.02,1.02,1.02,1.02))
if sys.argv[1].find("16")==-1 and sys.argv[1].find("17")==-1:
f.write('%0.3f\t%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\n'%(1.015,1.015,1.015,1.015,1.015))
f.write('VBS_Stat_bin%d_%s\tlnN\t'%(i,sys.argv[1]))
f.write('%0.2f\t-\t-\t-\t-\t-\n'%(ZA_sig_binerror))
f.write('QCD_Stat_bin%d_%s\tlnN\t'%(i,sys.argv[1]))
f.write('-\t%0.2f\t-\t-\t-\t-\n'%(ZA_binerror))
f.write('non_prompt_Stat_bin%d_%s\tlnN\t'%(i,sys.argv[1]))
f.write('-\t-\t%0.2f\t-\t-\t-\n'%(non_prompt_binerror))
f.write('TTA_Stat_bin%d_%s\tlnN\t'%(i,sys.argv[1]))
f.write('-\t-\t-\t%0.2f\t-\t-\n'%(TTA_binerror))
f.write('VV_Stat_bin%d_%s\tlnN\t'%(i,sys.argv[1]))
f.write('-\t-\t-\t-\t%0.2f\t-\n'%(VV_binerror))
f.write('ST_Stat_bin%d_%s\tlnN\t'%(i,sys.argv[1]))
f.write('-\t-\t-\t-\t-\t%0.2f\n'%(ST_binerror))
#
f.write('fake_%s\tlnN\t'%(sys.argv[1]))
if non_prompt_bincontent==0:
f.write('-\t-\t-\t-\t-\t-\n')
else:
f.write('-\t-\t%0.2f\t-\t-\t-\n'%(arr['fake'+sys.argv[1]][i-1]))
#
f.write('JES_%s\tlnN\t'%(sys.argv[1]))
f.write('%0.2f\t%0.2f\t-\t%0.2f\t%0.2f\t%0.2f\n'%(arr['jes'+sys.argv[1]+'_ZA-EWK'][i-1],arr['jes'+sys.argv[1]+'_ZA'][i-1],arr['jes'+sys.argv[1]+'_TTA'][i-1],arr['jes'+sys.argv[1]+'_VV'][i-1],arr['jes'+sys.argv[1]+'_ST'][i-1]))
#
f.write('JER_%s\tlnN\t'%(sys.argv[1]))
f.write('%0.2f\t%0.2f\t-\t%0.2f\t%0.2f\t%0.2f\n'%(arr['jer'+sys.argv[1]+'_ZA-EWK'][i-1],arr['jer'+sys.argv[1]+'_ZA'][i-1],arr['jer'+sys.argv[1]+'_TTA'][i-1],arr['jer'+sys.argv[1]+'_VV'][i-1],arr['jer'+sys.argv[1]+'_ST'][i-1]))
#
f.write('pdf_EW\tlnN\t')
f.write('%0.3f\t-\t-\t-\t-\t-\n'%(arr['Sig_pdf'][i-1]))
#
f.write('pdf_QCD\tlnN\t')
f.write('-\t%0.2f\t-\t-\t-\t-\n'%(arr['QCD_pdf'][i-1]))
#
f.write('Scale_EW\tlnN\t')
f.write('%0.2f\t-\t-\t-\t-\t-\n'%(arr['Sig_scale'][i-1]))
#
f.write('Scale_muF1\tlnN\t')
f.write('-\t%0.3f\t-\t-\t-\t-\n'%(arr['scale_muF1'][i-1]))
#
f.write('Scale_muR1\tlnN\t')
f.write('-\t%0.3f\t-\t-\t-\t-\n'%(arr['scale_muR1'][i-1]))
#
f.write('Scale_muFmuR\tlnN\t')
f.write('-\t%0.3f\t-\t-\t-\t-\n'%(arr['scale_muFmuR'][i-1]))
#
f.write('interf\tlnN\t')
f.write('%0.2f\t-\t-\t-\t-\t-\n'%(arr['interf'][i-1]))
#
f.write('mu_trigger\tlnN\t')
f.write('%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\t%0.3f\n'%(arr['muon_ZA_trigger'][i-1],arr['muon_TTA_trigger'][i-1],arr['muon_VV_trigger'][i-1],arr['muon_ST_trigger'][i-1],arr['muon_ZA-EWK_trigger'][i-1]))
#
f.write('mu_eff\tlnN\t')
f.write('%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\t%0.3f\n'%(arr['muon_ZA_all'][i-1],arr['muon_TTA_all'][i-1],arr['muon_VV_all'][i-1],arr['muon_ST_all'][i-1],arr['muon_VV_all'][i-1]))
#
f.write('ele_reco\tlnN\t')
f.write('%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\t%0.3f\n'%(arr['ele_ZA_reco'][i-1],arr['ele_TTA_reco'][i-1],arr['ele_VV_reco'][i-1],arr['ele_ST_reco'][i-1],arr['ele_ZA-EWK_reco'][i-1]))
#
f.write('ele_ID\tlnN\t')
f.write('%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\t%0.3f\n'%(arr['ele_ZA_ID'][i-1],arr['ele_TTA_ID'][i-1],arr['ele_VV_ID'][i-1],arr['ele_ST_ID'][i-1],arr['ele_ZA-EWK_ID'][i-1]))
#
f.write('photon_id\tlnN\t')
f.write('%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\t%0.3f\n'%(arr['photon_ZA_ID'][i-1],arr['photon_TTA_ID'][i-1],arr['photon_VV_ID'][i-1],arr['photon_ST_ID'][i-1],arr['photon_ZA-EWK_ID'][i-1]))
#
f.write('pileup\tlnN\t')
f.write('%0.3f\t-\t%0.3f\t%0.3f\t%0.3f\t%0.3f\n' %(arr['pileup_ZA'][i-1],arr['pileup_TTA'][i-1],arr['pileup_VV'][i-1],arr['pileup_ST'][i-1],arr['pileup_ZA-EWK'][i-1]))
#
f.write('ttgamma_xs\tlnN\t')
f.write('-\t-\t-\t1.1\t-\t-\n')
f.write('VV_xs\tlnN\t')
f.write('-\t-\t-\t-\t1.1\t-\n')
#
f.write('pileupId_eff_%s\tlnN\t'%(sys.argv[1]))
f.write('%0.2f\t%0.2f\t-\t%0.2f\t%0.2f\t%0.2f\n'%(arr['ZA-EWK_eff'][i-1],arr['ZA_eff'][i-1],arr['TTA_eff'][i-1],arr['VV_eff'][i-1],arr['ST_eff'][i-1]))
f.write('pileupId_mis_%s\tlnN\t'%(sys.argv[1]))
f.write('%0.2f\t%0.2f\t-\t%0.2f\t%0.2f\t%0.2f\n'%(arr['ZA-EWK_mis'][i-1],arr['ZA_mis'][i-1],arr['TTA_mis'][i-1],arr['VV_mis'][i-1],arr['ST_mis'][i-1]))
#
if sys.argv[1].find("18") == -1:
f.write('l1pref\tlnN\t')
f.write('%0.2f\t%0.2f\t-\t%0.2f\t%0.2f\t%0.2f\n'%(arr['l1pref_ZA'][i-1],arr['l1pref_ZA-EWK'][i-1],arr['l1pref_TTA'][i-1],arr['l1pref_VV'][i-1],arr['l1pref_ST'][i-1]))
# print 'bin ',i,' ',ZA_binerror,' ',non_prompt_binerror,' ',TTA_binerror,' ',VV_binerror,' ',ST_binerror,' ',WA_binerror,' ',ZA_sig_out_binerror
genbincontent[:]=[]
genbinerror[:]=[]
| [
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2676c8b70cc62e532379d2c46e363e54f2d94d14 | 97999ecca9e50972cc9e80df27d4768d83498dba | /credentials/migrations/0008_aboutme_resume.py | 0ca7767e117e9ddde7e97056617a2f2465605750 | []
| no_license | apatten001/portfolio | c79312d13f7a75f909e2d4d66ab6ef275b69543e | 4fdb503afccea83b849b62e3b12539e25a0b722f | refs/heads/master | 2020-04-25T05:45:20.946946 | 2019-03-07T16:53:00 | 2019-03-07T16:53:00 | 172,554,299 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 417 | py | # Generated by Django 2.1.5 on 2019-02-27 19:02
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('credentials', '0007_auto_20190128_1630'),
]
operations = [
migrations.AddField(
model_name='aboutme',
name='resume',
field=models.FileField(default='Arnold_Resume.pdf', upload_to=''),
),
]
| [
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]
| |
bf87b37a2e04bb39ba5a09c86b581bd34be15a03 | cde373aef58da4226bfadee3d1a7086d22f33414 | /Matplotlib/20-AddingMoreIndicatorData.py | 6deebcb3c6cd32cf086b49db0aff5da22174f70c | []
| no_license | ravi4all/ML_WeekEnd_Feb | 6c66c6e6845062928834986980e5c229a19da6cd | 43891ff36cfcd557861b4eebb99c44c68d24954e | refs/heads/master | 2021-01-09T06:10:34.007131 | 2017-06-12T03:57:54 | 2017-06-12T03:57:54 | 80,917,805 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 3,150 | py | import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc
from matplotlib import style
import numpy as np
import urllib
import datetime as dt
#style.use('ggplot')
style.use('fivethirtyeight')
MA1 = 10
MA2 = 30
# Will give the moving average
def moving_average(values, window):
weights = np.repeat(1.0, window)/window
smas = np.convolve(values, weights, 'valid')
return smas
def high_minus_low(highs, lows):
return highs-lows
# fmt - format
def bytespdate2num(fmt, encoding='utf-8'):
strconverter = mdates.strpdate2num(fmt)
def bytesconverter(b):
a = b.decode(encoding)
return strconverter(a)
return bytesconverter
def graph_data(stock):
fig = plt.figure()
# ax1 is a subplot
ax1 = plt.subplot2grid((6,1),(0,0), rowspan=1, colspan=1)
plt.title(stock)
ax2 = plt.subplot2grid((6,1),(1,0), rowspan=4, colspan=1)
plt.xlabel('Date')
plt.ylabel('Price')
ax3 = plt.subplot2grid((6,1),(5,0), rowspan=1, colspan=1)
stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1y/csv'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source:
split_line = line.split(',')
if len(split_line) == 6:
if 'values' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
delimiter = ',',
unpack = True,
converters={0: bytespdate2num('%Y%m%d')})
x = 0
y = len(date)
# OHLC : open high low close
ohlc = []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x += 1
ma1 = moving_average(closep, MA1)
ma2 = moving_average(closep, MA2)
start = len(date[MA2-1:])
h_l = list(map(high_minus_low, highp, lowp))
ax1.plot_date(date, h_l, '-')
candlestick_ohlc(ax2, ohlc, width=0.4, colorup='g', colordown='r')
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax2.grid(True)
bbox_props = dict(boxstyle='larrow', fc='w', ec='k', lw=1)
# to display the last price
ax2.annotate(str(closep[-1]), (date[-1], closep[-1]),
xytext = (date[-1]+4, closep[-1]), bbox=bbox_props)
ax3.plot(date[-start:], ma1[-start:])
ax3.plot(date[-start:], ma2[-start:])
#plt.legend()
plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)
plt.show()
graph_data('ebay')
| [
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]
| |
d3b5d2220dfd64a054fc44c58b941464e11c9a62 | bb2b6422476f5bd80171a31517465f9f62e15558 | /catkin_ws/build/scan_tools/laser_ortho_projector/catkin_generated/pkg.installspace.context.pc.py | a7beec3bd992862819cd8c913a124d3586a9795b | []
| no_license | Forrest-Z/MyKitAgv | ccd7b1c5fdb3a046bc5267d1827c4a08d89e74a4 | db9506ad8c8a9012fb49775e188932e28526337e | refs/heads/master | 2022-12-07T17:49:23.140713 | 2020-09-07T14:25:04 | 2020-09-07T14:25:04 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 566 | 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;nodelet;sensor_msgs;tf;pcl_ros;pcl_conversions;geometry_msgs;message_filters".replace(';', ' ')
PKG_CONFIG_LIBRARIES_WITH_PREFIX = "-llaser_ortho_projector".split(';') if "-llaser_ortho_projector" != "" else []
PROJECT_NAME = "laser_ortho_projector"
PROJECT_SPACE_DIR = "/home/nhamtung/TungNV/MyKitAgv/catkin_ws/install"
PROJECT_VERSION = "0.3.2"
| [
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]
| |
34c9d63c64f37b6a17a2adfae7b3bb9d3677a416 | 0130c8b14927097663157846adc4b146d67d2fda | /tests/common/test_run/softplus_run.py | 72090ba2620e11675993ae68cec770d88f6b7703 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference",
"Unlicense",
"BSD-3-Clause",
"NCSA",
"LLVM-exception",
"Zlib",
"BSD-2-Clause",
"MIT"
]
| permissive | Shigangli/akg | e8be3e0ee1eafe3e42b4cc4d424c28f08ef4c0bc | 3766c54e0b109541932d147a6b5643a334b82403 | refs/heads/master | 2023-09-06T05:13:40.571583 | 2021-11-23T03:44:54 | 2021-11-23T03:44:54 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,615 | py | # Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""run function for softplus"""
import numpy as np
from tests.common.tensorio import compare_tensor
from akg.utils import kernel_exec as utils
from tests.common.test_op import softplus
from tests.common.gen_random import random_gaussian
from tests.common.base import get_rtol_atol
def softplus_run(shape, dtype, attrs):
mod = utils.op_build_test(softplus.softplus, [shape], [dtype],
kernel_name="softplus", attrs=attrs)
expect, inputs, output = gen_data(dtype, shape)
output = utils.mod_launch(mod, (inputs, output), expect=expect)
rtol, atol = get_rtol_atol("softplus", dtype)
TestCase_Result = compare_tensor(
output, expect, rtol=rtol, atol=atol, equal_nan=False)
return inputs, output, expect, TestCase_Result
def gen_data(dtype, shape):
inputs = random_gaussian(shape, miu=1, sigma=0.3).astype(dtype)
expect = np.log1p(np.exp(-np.abs(inputs))) + np.maximum(inputs, 0)
output = np.full(shape, np.nan, dtype)
return expect, inputs, output
| [
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43a0cab3c9c839ec46266c935ecdf82958e35ef6 | ba3c06f9ae89479fa4987fe841ac09b5b5d71383 | /python_for_kids/book/Projects/monster6.py | a10a99ac4030b0d5d0cfab3769dc4e6741f8afab | []
| no_license | mary-tano/python-programming | 6d806e25011e770a04a0922d0b71bf38c222d026 | 829654a3274be939fa529ed94ea568c12f7f1a27 | refs/heads/master | 2021-05-17T15:30:32.710838 | 2020-04-01T13:37:18 | 2020-04-01T13:37:18 | 250,846,188 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 328 | py | # Лаборатория Франкенштейна
from monsterlab import *
# Основная программа
Frank = Monster("Фрэнки", "необычный")
Frank.show()
Albert = GMonster("Альберт", "задумчивый")
Albert.show()
Sigmund = SMonster("Зигмунд", "веселый")
Sigmund.show()
| [
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]
| |
db668ec99a3e918fab75689d177f3b571a030a86 | 8ef5a09d76a11c56963f18e6a08474a1a8bafe3c | /leet_code/7. Reverse Integer.py | 79b791271388c6874618159d647c255bde2e2e06 | []
| no_license | roiei/algo | 32c4677649c7666db148f6183fbfbf66c8b1969f | ae8bb8bf4ae4026ccaf1dce323b4098547dd35ec | refs/heads/master | 2022-04-01T19:21:27.768675 | 2022-02-19T06:15:29 | 2022-02-19T06:15:29 | 169,021,154 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 693 | py |
class Solution:
def reverse(self, x: 'int') -> 'int':
if x > float('inf') or x < float('-inf'):
return 0
sign = 1
if x < 0:
sign = -1
xstr = str(x)
if -1 == sign:
xstr = xstr[1:]
xstr = xstr[::-1]
skip_cnt = 0
for ch in xstr:
if ch != '0':
break
skip_cnt += 1
res = xstr[skip_cnt:]
if '' == res:
return 0
if -1 == sign:
res = '-' + res
return int(res)
x = 123
#x = -123
#x = 120
#x = 901000
x = 1534236469 # 0
sol = Solution()
print(sol.reverse(x))
| [
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]
| |
1d49c638c84d9cfa20e25fd85489966f882c7123 | bfda3af75d94767a5cb265bd68c17cfbf94e3ee1 | /rosalind/qrt/rosalind_qrtd_tung.py | c6d0de60e3be4d40377362c4f3b26bdba3ad70ce | []
| no_license | orenlivne/euler | d0e5b956a46eacfe423fbd6c52918beb91eea140 | 2afdd8bccdc5789c233e955b1ca626cea618eb9b | refs/heads/master | 2020-12-29T02:24:36.479708 | 2016-12-15T21:27:33 | 2016-12-15T21:27:33 | 20,263,482 | 1 | 1 | null | null | null | null | UTF-8 | Python | false | false | 4,129 | py | '''
============================================================
http://rosalind.info/problems/qrtd
Given: A list containing n taxa (n<=2000) and two unrooted
binary trees T1 and T2 on the given taxa. Both T1 and T2 are
given in Newick format.
Return: The quartet distance dq(T1,T2).
============================================================
'''
# From http://rosalind.info/problems/qrtd/solutions/.
# Need to get the rest of his libraries
import time
from rosalind import rostree
def qrtd(fp):
taxa = next(fp).split()
t1_str = next(fp)
t2_str = next(fp)
taxa_id = dict((s,i) for i, s in enumerate(taxa))
all_taxa = set(xrange(len(taxa)))
start_time = time.time()
def build_tree(t_str):
T = rostree.read_newick_str(t_str)
#T = make_unrooted_binary(T)
for node in T.walk(order=T.POSTORDER):
if node.is_leaf:
node.id = taxa_id[node.val]
node.nodes = set([node.id])
node.rest = all_taxa - node.nodes
else:
node.nodes = reduce(set.union, map(attrgetter('nodes'), node.children), set())
node.rest = all_taxa - node.nodes
# special case to walk unroot tree; the first node is also a leaf node
T.id = taxa_id[T.val]
T.nodes = set([T.id])
T.rest = all_taxa - T.nodes
return T
T1 = build_tree(t1_str)
T2 = build_tree(t2_str)
# link T2 nodes to T1. Mind the special case for root node.
id_2_T1 = dict((node.id,node) for node in T1.walk(type=T1.LEAF))
id_2_T1[T1.id] = T1
for node in T2.walk(type=T1.LEAF):
node.t1_node = id_2_T1[node.id]
T2.t1_node = id_2_T1[T2.id]
N = len(taxa)
print 'N=',N
count = 0
for i, v1 in enumerate(T1.walk(type=T1.INODE)):
if v1 is T1:
continue
if i % 10 == 0:
print 'T1 %3d %s' % (time.time() - start_time, i)
for A_node in T1.walk(exclude_node=v1):
A_node.color = 1
for B_node in v1.left.walk():
B_node.color = 2
for C_node in v1.right.walk():
C_node.color = 3
A1 = v1.rest
B1 = v1.left.nodes
C1 = v1.right.nodes
for v2 in T2.walk(order=T2.POSTORDER):
if v2 is T2:
pass
elif v2.is_leaf:
v2.a1 = 0
v2.b1 = 0
v2.c1 = 0
c = v2.t1_node.color
if c == 1: v2.a1 = 1
elif c == 2: v2.b1 = 1
else: v2.c1 = 1
else:
B = v2.left
C = v2.right
a1b2 = B.a1
a1c2 = C.a1
a1a2 = len(A1) - a1b2 - a1c2
b1b2 = B.b1
b1c2 = C.b1
b1a2 = len(B1) - b1b2 - b1c2
c1b2 = B.c1
c1c2 = C.c1
c1a2 = len(C1) - c1b2 - c1c2
# rememeber under v2, how many of them intersect with A1, B1 and C1
v2.a1 = a1b2 + a1c2
v2.b1 = b1b2 + b1c2
v2.c1 = c1b2 + c1c2
# 3x3=9 different orientation for T12 and T2,
# times in each case two ways to pair B and C from each tree
count += a1a2 * (a1a2-1) / 2 * (b1b2 * c1c2 + b1c2 * c1b2)
count += a1b2 * (a1b2-1) / 2 * (b1a2 * c1c2 + b1c2 * c1a2)
count += a1c2 * (a1c2-1) / 2 * (b1a2 * c1b2 + b1b2 * c1a2)
count += b1a2 * (b1a2-1) / 2 * (a1b2 * c1c2 + a1c2 * c1b2)
count += b1b2 * (b1b2-1) / 2 * (a1a2 * c1c2 + a1c2 * c1a2)
count += b1c2 * (b1c2-1) / 2 * (a1a2 * c1b2 + a1b2 * c1a2)
count += c1a2 * (c1a2-1) / 2 * (a1b2 * b1c2 + a1c2 * b1b2)
count += c1b2 * (c1b2-1) / 2 * (a1a2 * b1c2 + a1c2 * b1a2)
count += c1c2 * (c1c2-1) / 2 * (a1a2 * b1b2 + a1b2 * b1a2)
print N * (N - 1) * (N- 2) * (N - 3) / 12 - count
if __name__ == "__main__":
print qrtd('rosalind_qrtd_sample.dat')
#print qrtd('rosalind_qrtd.dat')
| [
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]
| |
710bcc0fb5dcc70b3aacdae1595043478681cdb2 | 02560440f9f91e583fe98d80ab11e18aa6c7a525 | /apps/usuarios/migrations/0003_usuario_correo.py | ad084ca9dc73a72604d08e401a8af1a08f618f45 | []
| no_license | eduardogpg/wamadeusV1 | a36c89176543e638486009620c5131f46743edbc | 82d93293dc6afc95a6661f727162f4055ab83a43 | refs/heads/master | 2020-12-28T01:57:47.831689 | 2015-01-08T05:14:25 | 2015-01-08T05:14:25 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 452 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('usuarios', '0002_auto_20141215_1710'),
]
operations = [
migrations.AddField(
model_name='usuario',
name='correo',
field=models.EmailField(default=' ', max_length=50),
preserve_default=False,
),
]
| [
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]
| |
58e06984a80bfb1f133e9e4eee18958f1fe78fb2 | 0f5bdddfb93154d7f3cf097ede0a8c9ac403e027 | /tests/test_rtcrtpreceiver.py | cb6865f41e23a94d28ffbaf0933d26f08ec809e6 | [
"BSD-3-Clause"
]
| permissive | pslq/aiortc | 13061885fc06eef42a05a0d8ad6eb3a3708873a3 | b27b27d3509c2a8335aadd949511b24b93530d86 | refs/heads/master | 2020-03-27T02:17:50.118454 | 2018-08-22T16:44:03 | 2018-08-22T17:58:11 | 145,780,077 | 1 | 0 | null | 2018-08-23T00:54:52 | 2018-08-23T00:54:52 | null | UTF-8 | Python | false | false | 9,343 | py | import asyncio
from unittest import TestCase
from unittest.mock import patch
from aiortc.codecs import PCMU_CODEC
from aiortc.exceptions import InvalidStateError
from aiortc.mediastreams import AudioFrame
from aiortc.rtcrtpparameters import RTCRtpCodecParameters, RTCRtpParameters
from aiortc.rtcrtpreceiver import (NackGenerator, RemoteStreamTrack,
RTCRtpReceiver, StreamStatistics)
from aiortc.rtp import RTP_SEQ_MODULO, RtcpPacket, RtpPacket
from aiortc.stats import RTCStatsReport
from .utils import dummy_dtls_transport_pair, load, run
def create_rtp_packets(count, seq=0):
packets = []
for i in range(count):
packets.append(RtpPacket(
payload_type=0,
sequence_number=(seq + i) % RTP_SEQ_MODULO,
ssrc=1234,
timestamp=i * 160))
return packets
class ClosedDtlsTransport:
state = 'closed'
class NackGeneratorTest(TestCase):
def create_generator(self):
class FakeReceiver:
def __init__(self):
self.nack = []
self.pli = []
async def _send_rtcp_nack(self, media_ssrc, lost):
self.nack.append((media_ssrc, lost))
async def _send_rtcp_pli(self, media_ssrc, lost):
self.pli.append(media_ssrc)
receiver = FakeReceiver()
return NackGenerator(receiver), receiver
def test_no_loss(self):
generator, receiver = self.create_generator()
for packet in create_rtp_packets(20, 0):
run(generator.add(packet))
self.assertEqual(receiver.nack, [])
self.assertEqual(receiver.pli, [])
self.assertEqual(generator.missing, set())
def test_with_loss(self):
generator, receiver = self.create_generator()
# receive packets: 0, <1 missing>, 2
packets = create_rtp_packets(3, 0)
missing = packets.pop(1)
for packet in packets:
run(generator.add(packet))
self.assertEqual(receiver.nack, [(1234, [1])])
self.assertEqual(receiver.pli, [])
self.assertEqual(generator.missing, set([1]))
receiver.nack.clear()
# late arrival
run(generator.add(missing))
self.assertEqual(receiver.nack, [])
self.assertEqual(receiver.pli, [])
self.assertEqual(generator.missing, set())
class StreamStatisticsTest(TestCase):
def create_counter(self):
return StreamStatistics(clockrate=8000, ssrc=0)
def test_no_loss(self):
counter = self.create_counter()
packets = create_rtp_packets(20, 0)
# receive 10 packets
for packet in packets[0:10]:
counter.add(packet)
self.assertEqual(counter.max_seq, 9)
self.assertEqual(counter.packets_received, 10)
self.assertEqual(counter.packets_lost, 0)
self.assertEqual(counter.fraction_lost, 0)
# receive 10 more packets
for packet in packets[10:20]:
counter.add(packet)
self.assertEqual(counter.max_seq, 19)
self.assertEqual(counter.packets_received, 20)
self.assertEqual(counter.packets_lost, 0)
self.assertEqual(counter.fraction_lost, 0)
def test_no_loss_cycle(self):
counter = self.create_counter()
# receive 10 packets (with sequence cycle)
for packet in create_rtp_packets(10, 65530):
counter.add(packet)
self.assertEqual(counter.max_seq, 3)
self.assertEqual(counter.packets_received, 10)
self.assertEqual(counter.packets_lost, 0)
self.assertEqual(counter.fraction_lost, 0)
def test_with_loss(self):
counter = self.create_counter()
packets = create_rtp_packets(20, 0)
packets.pop(1)
# receive 9 packets (one missing)
for packet in packets[0:9]:
counter.add(packet)
self.assertEqual(counter.max_seq, 9)
self.assertEqual(counter.packets_received, 9)
self.assertEqual(counter.packets_lost, 1)
self.assertEqual(counter.fraction_lost, 25)
# receive 10 more packets
for packet in packets[9:19]:
counter.add(packet)
self.assertEqual(counter.max_seq, 19)
self.assertEqual(counter.packets_received, 19)
self.assertEqual(counter.packets_lost, 1)
self.assertEqual(counter.fraction_lost, 0)
@patch('time.time')
def test_no_jitter(self, mock_time):
counter = self.create_counter()
packets = create_rtp_packets(3, 0)
mock_time.return_value = 1531562330.00
counter.add(packets[0])
self.assertEqual(counter._jitter_q4, 0)
self.assertEqual(counter.jitter, 0)
mock_time.return_value = 1531562330.02
counter.add(packets[1])
self.assertEqual(counter._jitter_q4, 0)
self.assertEqual(counter.jitter, 0)
mock_time.return_value = 1531562330.04
counter.add(packets[2])
self.assertEqual(counter._jitter_q4, 0)
self.assertEqual(counter.jitter, 0)
@patch('time.time')
def test_with_jitter(self, mock_time):
counter = self.create_counter()
packets = create_rtp_packets(3, 0)
mock_time.return_value = 1531562330.00
counter.add(packets[0])
self.assertEqual(counter._jitter_q4, 0)
self.assertEqual(counter.jitter, 0)
mock_time.return_value = 1531562330.03
counter.add(packets[1])
self.assertEqual(counter._jitter_q4, 80)
self.assertEqual(counter.jitter, 5)
mock_time.return_value = 1531562330.05
counter.add(packets[2])
self.assertEqual(counter._jitter_q4, 75)
self.assertEqual(counter.jitter, 4)
class RTCRtpReceiverTest(TestCase):
def test_connection_error(self):
"""
Close the underlying transport before the receiver.
"""
transport, _ = dummy_dtls_transport_pair()
receiver = RTCRtpReceiver('audio', transport)
self.assertEqual(receiver.transport, transport)
receiver._track = RemoteStreamTrack(kind='audio')
receiver._ssrc = 1234
run(receiver.receive(RTCRtpParameters(codecs=[PCMU_CODEC])))
# receive a packet to prime RTCP
packet = RtpPacket.parse(load('rtp.bin'))
run(receiver._handle_rtp_packet(packet))
# break connection
run(transport.close())
# give RTCP time to send a report
run(asyncio.sleep(2))
# shutdown
run(receiver.stop())
def test_rtp_and_rtcp(self):
transport, remote = dummy_dtls_transport_pair()
receiver = RTCRtpReceiver('audio', transport)
self.assertEqual(receiver.transport, transport)
receiver._track = RemoteStreamTrack(kind='audio')
run(receiver.receive(RTCRtpParameters(codecs=[PCMU_CODEC])))
# receive RTP
packet = RtpPacket.parse(load('rtp.bin'))
run(receiver._handle_rtp_packet(packet))
# receive RTCP SR
for packet in RtcpPacket.parse(load('rtcp_sr.bin')):
run(receiver._handle_rtcp_packet(packet))
# check stats
report = run(receiver.getStats())
self.assertTrue(isinstance(report, RTCStatsReport))
self.assertEqual(sorted(report.keys()), ['inbound-rtp', 'remote-outbound-rtp'])
# check remote track
frame = run(receiver._track.recv())
self.assertTrue(isinstance(frame, AudioFrame))
# shutdown
run(receiver.stop())
def test_rtp_empty_video_packet(self):
transport, remote = dummy_dtls_transport_pair()
receiver = RTCRtpReceiver('video', transport)
self.assertEqual(receiver.transport, transport)
receiver._track = RemoteStreamTrack(kind='video')
run(receiver.receive(RTCRtpParameters(codecs=[
RTCRtpCodecParameters(name='VP8', clockRate=90000, payloadType=100),
])))
# receive RTP with empty payload
packet = RtpPacket(payload_type=100)
run(receiver._handle_rtp_packet(packet))
# shutdown
run(receiver.stop())
def test_send_rtcp_nack(self):
transport, remote = dummy_dtls_transport_pair()
receiver = RTCRtpReceiver('video', transport)
receiver._ssrc = 1234
receiver._track = RemoteStreamTrack(kind='video')
run(receiver.receive(RTCRtpParameters(codecs=[
RTCRtpCodecParameters(name='VP8', clockRate=90000, payloadType=100),
])))
# send RTCP feedback NACK
run(receiver._send_rtcp_nack(5678, [7654]))
# shutdown
run(receiver.stop())
def test_send_rtcp_pli(self):
transport, remote = dummy_dtls_transport_pair()
receiver = RTCRtpReceiver('video', transport)
receiver._ssrc = 1234
receiver._track = RemoteStreamTrack(kind='video')
run(receiver.receive(RTCRtpParameters(codecs=[
RTCRtpCodecParameters(name='VP8', clockRate=90000, payloadType=100),
])))
# send RTCP feedback PLI
run(receiver._send_rtcp_pli(5678))
# shutdown
run(receiver.stop())
def test_invalid_dtls_transport_state(self):
dtlsTransport = ClosedDtlsTransport()
with self.assertRaises(InvalidStateError):
RTCRtpReceiver('audio', dtlsTransport)
| [
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]
| |
48067e4ceef655c896f3a35b0571079df7c10a52 | 97a4d29863d1ce96f366554fdd985c3ce580bb5d | /061.py | f14890c43a1e22228adf9d4732a5d4ba2c6c44f6 | []
| no_license | Everfighting/Python-Algorithms | 5c3a102fed3a29858f3112d657c69e077efc7e28 | 235e9b4c66602035be39a8d3b3ad9cf016aebbb9 | refs/heads/master | 2021-01-20T22:19:18.902687 | 2018-03-02T05:38:27 | 2018-03-02T05:38:27 | 61,302,323 | 2 | 0 | null | null | null | null | UTF-8 | Python | false | false | 679 | py | #!/usr/bin/python
# -*- coding: UTF-8 -*-
if __name__ == '__main__':
a = []
for i in range(10):
a.append([]) #创建十行
for j in range(10):
a[i].append(0) #每行创建i列
# 杨辉三角边界都为1
for i in range(10):
a[i][0] = 1
a[i][i] = 1
# 杨辉三角定义，下一行数值为上一行数与上一行前面数之和(除边界）
for i in range(2,10):
for j in range(1,i):
a[i][j] = a[i - 1][j-1] + a[i - 1][j]
from sys import stdout
for i in range(10):
for j in range(i + 1):
stdout.write(str(a[i][j]))
stdout.write(' ')
print | [
"[email protected]"
]
| |
e1bf319ac4b1a93b08f0dafc5fd453b9cd95d5b1 | 4e44974b9e59dfd4324d84b12b10f008117814cd | /test_autofit/integration/src/dataset/dataset.py | c3dc9773c00b8b4cc97f43fc249734b1546be650 | [
"MIT"
]
| permissive | PriyatamNayak/PyAutoFit | 2cc2608943f8c3bdbda3b268142e7307014ccaf2 | 32c0c30acd219030c86a12db82ae54e406fd7119 | refs/heads/master | 2023-03-04T07:27:41.547966 | 2021-02-11T23:21:00 | 2021-02-11T23:21:00 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,487 | py | from astropy.io import fits
import numpy as np
# The 'dataset.py' module has been extended to give the dataset a name and metadata.
class Dataset:
def __init__(self, data, noise_map, name=None):
"""A class containing the data and noise-map of a 1D line dataset.
Parameters
----------
data : np.ndarray
The array of the data, in arbitrary units.
noise_map : np.ndarray
An array describing the RMS standard deviation error in each data pixel, in arbitrary units.
"""
self.data = data
self.noise_map = noise_map
# The name of the dataset is used by the aggregator, to determine the name of the file the dataset is saved as
# and so that when using the aggregator you can know which dataset you are manipulating.
self.name = name if name is str else "dataset"
@property
def xvalues(self):
return np.arange(self.data.shape[0])
@classmethod
def from_fits(cls, data_path, noise_map_path, name=None):
"""Load the data and noise-map of a 1D line dataset from ``.fits`` files.
Parameters
----------
data_path : str
The path on your hard-disk to the ``.fits`` file of the data.
noise_map_path : str
The path on your hard-disk to the ``.fits`` file of the noise-map.
"""
data_hdu_list = fits.open(data_path)
noise_map_hdu_list = fits.open(noise_map_path)
data = np.array(data_hdu_list[0].data)
noise_map = np.array(noise_map_hdu_list[0].data)
return Dataset(data=data, noise_map=noise_map, name=name)
class MaskedDataset:
def __init__(self, dataset, mask):
"""
A masked dataset, which is an image, noise-map and mask.
Parameters
----------
dataset: im.Dataset
The dataset (the image, noise-map, etc.)
mask: msk.Mask2D
The 1D mask that is applied to the dataset.
"""
self.dataset = dataset
self.mask = mask
self.data = dataset.data * np.invert(mask)
self.noise_map = dataset.noise_map * np.invert(mask)
@property
def xvalues(self):
return np.arange(self.data.shape[0])
def signal_to_noise_map(self):
return self.data / self.noise_map
def with_left_trimmed(self, data_trim_left):
if data_trim_left is None:
return self
# Here, we use the existing masked dataset to create a trimmed dataset.
data_trimmed = self.dataset.data[data_trim_left:]
noise_map_trimmed = self.dataset.noise_map[data_trim_left:]
dataset_trimmed = Dataset(data=data_trimmed, noise_map=noise_map_trimmed)
mask_trimmed = self.mask[data_trim_left:]
return MaskedDataset(dataset=dataset_trimmed, mask=mask_trimmed)
def with_right_trimmed(self, data_trim_right):
if data_trim_right is None:
return self
# We do the same as above, but removing data to the right.
data_trimmed = self.dataset.data[:-data_trim_right]
noise_map_trimmed = self.dataset.noise_map[:-data_trim_right]
dataset_trimmed = Dataset(data=data_trimmed, noise_map=noise_map_trimmed)
mask_trimmed = self.mask[:-data_trim_right]
return MaskedDataset(dataset=dataset_trimmed, mask=mask_trimmed)
| [
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]
| |
3a59b6324f48032a8c58f34957ffbed79c1fcb08 | 72f2f37c3c33e5bc02ec6c707a7c858d7990db3a | /examples/tour_examples/driverjs_maps_tour.py | 33fb342608c1c2cd08a48da9c5a1aab3f8ac71a0 | [
"MIT"
]
| permissive | matthewxuda/SeleniumBase | 190e4917dec8c731f17fd9d6a1247f8c17086d0c | efd282a860206dad81d0d4e61a472138eb04328d | refs/heads/master | 2023-09-01T09:17:57.608760 | 2021-10-21T02:48:32 | 2021-10-21T02:48:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,129 | py | from seleniumbase import BaseCase
class MyTestClass(BaseCase):
def test_create_tour(self):
self.open("https://www.google.com/maps/@42.3591234,-71.0915634,15z")
self.wait_for_element("#searchboxinput", timeout=20)
self.wait_for_element("#minimap", timeout=20)
self.wait_for_element("#zoom", timeout=20)
# Create a website tour using the DriverJS library
# Same as: self.create_driverjs_tour()
self.create_tour(theme="driverjs")
self.add_tour_step(
"🗺️ Welcome to Google Maps 🗺️",
"html",
title="✅ SeleniumBase Tours 🌎",
)
self.add_tour_step(
"You can type a location into this Search box.", "#searchboxinput"
)
self.add_tour_step(
"Then click here to view it on the map.",
"#searchbox-searchbutton",
alignment="bottom",
)
self.add_tour_step(
"Or click here to get driving directions.",
"#searchbox-directions",
alignment="bottom",
)
self.add_tour_step(
"Use this button to get a Satellite view.",
"div.widget-minimap-shim",
alignment="right",
)
self.add_tour_step(
"Click here to zoom in.", "#widget-zoom-in", alignment="left"
)
self.add_tour_step(
"Or click here to zoom out.", "#widget-zoom-out", alignment="left"
)
self.add_tour_step(
"Use the Menu button for more options.",
".searchbox-hamburger-container",
alignment="right",
)
self.add_tour_step(
"Or click here to see more Google apps.",
'[title="Google apps"]',
alignment="left",
)
self.add_tour_step(
"Thanks for using SeleniumBase Tours",
"html",
title="🚃 End of Guided Tour 🚃",
)
self.export_tour() # The default name for exports is "my_tour.js"
self.play_tour(interval=0) # If interval > 0, autoplay after N seconds
| [
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]
| |
c216efa4d1718ae2ddfb65ef0d24f2825156d9ab | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03455/s808955934.py | 5a703a489b980a233dbc16c583c6ec0ff1dc188d | []
| 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 | 102 | py | # ABC 086
A,B= map(int,input().split())
if (A* B)%2==0:
ans ='Even'
else:
ans='Odd'
print(ans) | [
"[email protected]"
]
| |
f5e3f313c4584c8f5380fa9186122cf9b6227947 | 4cda6686b659c0bf34213a4c5faf050c4c866eea | /ExperimentSpecificCode/_2018_2019_Neuroseeker_Paper/_2019_Neuroseeker_Paper_Expanded/_33p1/NeuropixelSim/Sparce/spikesort_and_timelock_analysis.py | 841c428d900ac79f30a6bfcc8d9155a019730e56 | []
| no_license | georgedimitriadis/themeaningofbrain | da99efcf62af67bc6c2a71e504765026a4491217 | f138cf500a3ca6c8d76613c942787d9f073d67a7 | refs/heads/master | 2023-02-21T10:52:18.771691 | 2023-02-17T08:23:09 | 2023-02-17T08:23:09 | 50,346,965 | 3 | 1 | null | 2017-06-17T16:29:47 | 2016-01-25T11:42:43 | Python | UTF-8 | Python | false | false | 12,478 | py |
"""
The pipeline for spikesorting this dataset
"""
import numpy as np
from os.path import join
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import preprocessing as preproc
from BrainDataAnalysis.Spike_Sorting import positions_on_probe as spp
from spikesorting_tsne_guis import clean_kilosort_templates as clean
from spikesorting_tsne import preprocessing_kilosort_results as preproc_kilo
from ExperimentSpecificCode._2018_2019_Neuroseeker_Paper._2019_Neuroseeker_Paper_Expanded._33p1 \
import constants_33p1 as const_rat
from ExperimentSpecificCode._2018_2019_Neuroseeker_Paper._2019_Neuroseeker_Paper_Expanded \
import constants_common as const_com
import BrainDataAnalysis.neuroseeker_specific_functions as ns_funcs
from ExperimentSpecificCode._2018_Chronic_Neuroseeker_TouchingLight.Common_functions import events_sync_funcs as \
sync_funcs, firing_rates_sync_around_events_funcs as fr_funcs
from BrainDataAnalysis.Statistics import binning
import common_data_transforms as cdt
import sequence_viewer as sv
import slider as sl
# ----------------------------------------------------------------------------------------------------------------------
# <editor-fold desc = "FOLDERS NAMES"
date = 1
binary_data_filename = join(const_rat.base_save_folder, const_rat.rat_folder, const_rat.date_folders[date],
'Analysis', 'Denoised', 'Data', 'Amplifier_APs_Denoised.bin')
analysis_folder = join(const_rat.base_save_folder, const_rat.rat_folder, const_rat.date_folders[date],
'Analysis', 'NeuropixelSimulations', 'Sparce')
kilosort_folder = join(analysis_folder, 'Kilosort')
data_folder = join(const_rat.base_save_folder, const_rat.rat_folder, const_rat.date_folders[date], 'Data')
events_folder = join(data_folder, "events")
event_dataframes = ns_funcs.load_events_dataframes(events_folder, sync_funcs.event_types)
results_folder = join(analysis_folder, 'Results')
events_definitions_folder = join(results_folder, 'EventsDefinitions')
sampling_freq = const_com.SAMPLING_FREQUENCY
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# STEP 1: RUN KILOSORT ON THE DATA
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# <editor-fold desc = "STEP 2: CLEAN SPIKESORT (RIGHT AFTER KILOSORT)"
# a) Create average of templates:
# To create averages of templates use cmd (because the create_data_cubes doesn't work when called from a REPL):
# Go to where the create_data_cubes.py is (in spikesort_tsne_guis/spikesort_tsen_guis) and run the following python command
# (you can use either the raw or the denoised data to create the average)
# python E:\Software\Develop\Source\Repos\spikesorting_tsne_guis\spikesorting_tsne_guis\create_data_cubes.py
# original
# "D:\\AK_33.1\2018_04_30-11_38\Analysis\NeuropixelSimulations\Sparce\Kilosort"
# "D:\\AK_33.1\2018_04_30-11_38\Analysis\Denoised\Data\Amplifier_APs_Denoised.bin"
# 1368
# 50
# (Use single space between parameters, not Enter like here)
# (Change the folders as appropriate for where the data is)
# b) Clean:
clean.cleanup_kilosorted_data(kilosort_folder,
number_of_channels_in_binary_file=const_com.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE,
binary_data_filename=binary_data_filename,
prb_file=const_com.prb_file,
type_of_binary=const_com.BINARY_FILE_ENCODING,
order_of_binary='F',
sampling_frequency=20000,
num_of_shanks_for_vis=5)
# c) Remove some types
template_marking = np.load(join(kilosort_folder, 'template_marking.npy'))
print(len(np.argwhere(template_marking == 0)))
print(len(np.argwhere(template_marking == 1)))
print(len(np.argwhere(template_marking == 2)))
print(len(np.argwhere(template_marking == 3)))
print(len(np.argwhere(template_marking == 4)))
print(len(np.argwhere(template_marking == 5)))
print(len(np.argwhere(template_marking == 6)))
print(len(np.argwhere(template_marking == 7)))
template_marking[np.argwhere(template_marking == 5)] = 0
template_marking[np.argwhere(template_marking == 6)] = 0
np.save(join(kilosort_folder, 'template_marking.npy'), template_marking)
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# <editor-fold desc = "STEP 3: CREATE TEMPLATE INFO OF ALL THE CLEANED TEMPLATES"
# a) Create the positions of the templates on the probe (and have a look)
_ = spp.generate_probe_positions_of_templates(kilosort_folder)
bad_channel_positions = spp.get_y_spread_regions_of_bad_channel_groups(kilosort_folder, const_rat.bad_channels)
spp.view_grouped_templates_positions(kilosort_folder, const_rat.BRAIN_REGIONS, const_com.PROBE_DIMENSIONS,
const_com.POSITION_MULT)
# b) Create the template_info.df dataframe (or load it if you already have it)
# template_info = preproc_kilo.generate_template_info_after_cleaning(kilosort_folder, sampling_freq)
template_info = np.load(join(kilosort_folder, 'template_info.df'), allow_pickle=True)
# c) Make the spike info from the initial, cleaned, kilosort results
# spike_info = preproc_kilo.generate_spike_info_after_cleaning(kilosort_folder)
spike_info = np.load(join(kilosort_folder, 'spike_info_after_cleaning.df'), allow_pickle=True)
spp.view_grouped_templates_positions(kilosort_folder, const_rat.BRAIN_REGIONS, const_com.PROBE_DIMENSIONS,
const_com.POSITION_MULT, template_info=template_info)
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# <editor-fold desc = "CALCULATE SPIKING RATES">
# Make the spike rates using each frame as a binning window
# Load the pre generated DataFrames for the event CSVs
event_dataframes = ns_funcs.load_events_dataframes(events_folder, sync_funcs.event_types)
file_to_save_to = join(kilosort_folder, 'firing_rate_with_video_frame_window.npy')
template_info = pd.read_pickle(join(kilosort_folder, 'template_info.df'))
spike_info = pd.read_pickle(join(kilosort_folder, 'spike_info_after_cleaning.df'))
spike_rates = binning.spike_count_per_frame(template_info, spike_info, event_dataframes['ev_video'],
sampling_freq, file_to_save_to=file_to_save_to)
# Using the frame based spikes rates do a rolling window to average a bit more
num_of_frames_to_average = 0.25/(1/120)
spike_rates_0p25 = []
for n in np.arange(spike_rates.shape[0]):
spike_rates_0p25.append(binning.rolling_window_with_step(spike_rates[n, :], np.mean,
num_of_frames_to_average, num_of_frames_to_average))
spike_rates_0p25 = np.array(spike_rates_0p25)
np.save(join(kilosort_folder, 'firing_rate_with_0p25s_window.npy'), spike_rates_0p25)
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
# -------------------------------------------------
# <editor-fold desc="GET TIMES AND FRAMES OF SUCCESSFUL TRIALS">
video_frame_spike_rates_filename = join(kilosort_folder, 'firing_rate_with_video_frame_window.npy')
spike_rates = np.load(video_frame_spike_rates_filename)
camera_pulses, beam_breaks, sounds = \
sync_funcs.get_time_points_of_events_in_sync_file(data_folder, clean=True,
cam_ttl_pulse_period=
const_com.CAMERA_TTL_PULSES_TIMEPOINT_PERIOD)
sounds_dur = sounds[:, 1] - sounds[:, 0]
reward_sounds = sounds[sounds_dur < 4000]
# Using the trialend csv file to generate events
# succesful_trials = event_dataframes['ev_trial_end'][event_dataframes['ev_trial_end']['Result'] == 'Food']
# succesful_trials = succesful_trials['AmpTimePoints'].values
# Using the start of the reward tone to generate events
# There is a difference of 78.6 frames (+-2) between the reward tone and the csv file event (about 700ms)
succesful_trials = reward_sounds[:, 0]
# Get the average firing rates of all neurons a few seconds around the successful pokes
time_around_beam_break = 8
avg_firing_rate_around_suc_trials = fr_funcs.get_avg_firing_rates_around_events(spike_rates=spike_rates,
event_time_points=succesful_trials,
ev_video_df=event_dataframes['ev_video'],
time_around_event=time_around_beam_break)
events_random = np.random.choice(np.arange(succesful_trials.min(), succesful_trials.max(), 100),
len(succesful_trials), replace=False)
avg_firing_rate_around_random_times = fr_funcs.get_avg_firing_rates_around_events(spike_rates=spike_rates,
event_time_points=events_random,
ev_video_df=event_dataframes['ev_video'],
time_around_event=time_around_beam_break)
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
# <editor-fold desc="LOOK AT ALL THE NEURONS AROUND THE POKE EVENT">
smooth_time = 0.5
smooth_frames = smooth_time * 120
t = binning.rolling_window_with_step(avg_firing_rate_around_random_times, np.mean, smooth_frames, int(smooth_frames / 3))
tn = preproc.normalize(t, norm='l1', axis=0)
tn = np.asarray(t)
for i in np.arange(len(t)):
tn[i, :] = binning.scale(t[i], 0, 1)
y_positions = template_info['position Y'].values
position_sorted_indices = np.argsort(y_positions)
regions_pos = list(const_rat.BRAIN_REGIONS.values())
region_lines = []
for rp in regions_pos:
region_lines.append(sync_funcs.find_nearest(y_positions[position_sorted_indices] * const_com.POSITION_MULT, rp)[0])
region_lines = np.array(region_lines)
tns = tn[position_sorted_indices]
plt.imshow(np.flipud(tns), aspect='auto')
plt.hlines(y=len(t) - region_lines, xmin=0, xmax=len(tns[0])-1, linewidth=3, color='w')
plt.vlines(x=int(len(tns[0]) / 2), ymin=0, ymax=len(tns) - 1)
plt.imshow(np.flipud(tns), aspect='auto', extent=[-8, 8, len(tns), 0])
plt.hlines(y=len(t) - region_lines, xmin=-8, xmax=8, linewidth=2, color='w')
plt.vlines(x=0, ymin=0, ymax=len(tns) - 1)
i = 0
sv.graph_pane(globals(), 'i', 'tn')
time_around_beam_break = 8
index = 0
fig1 = plt.figure(1)
fig2 = plt.figure(2)
output = None
all_indices = np.arange(len(avg_firing_rate_around_suc_trials))
frames_around_beam_break = 120 *time_around_beam_break
args = [all_indices, avg_firing_rate_around_suc_trials, template_info, spike_info,
succesful_trials, frames_around_beam_break, fig1, fig2]
show_rasters_decrease = fr_funcs.show_rasters_for_live_update
sl.connect_repl_var(globals(), 'index', 'output', 'show_rasters_decrease', 'args',
slider_limits=[0, len(avg_firing_rate_around_suc_trials) - 1])
# </editor-fold>
# ----------------------------------------------------------------------------------------------------------------------
| [
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]
| |
edc117b558873902ee1d38b226f7af11cebc80c9 | 58df99d96af6a688852993e38da89b75fea1d0dc | /exps/NATS-Bench/draw-correlations.py | 6afac3b804703bc53660e618d2c2a6e820974d3e | [
"MIT"
]
| permissive | yuezhixiong/AutoDL-Projects | 0f24ed98389b70f452a79c8ef825d5e563ac5d8c | 0d3c63bdbe2d648c2119ffe8d0491f8a07cf85cb | refs/heads/master | 2023-03-22T17:15:37.013837 | 2021-03-02T05:13:51 | 2021-03-02T05:13:51 | 315,518,182 | 0 | 1 | MIT | 2021-02-26T06:36:34 | 2020-11-24T04:28:29 | Python | UTF-8 | Python | false | false | 3,860 | py | ###############################################################
# NATS-Bench (arxiv.org/pdf/2009.00437.pdf), IEEE TPAMI 2021 #
###############################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.06 #
###############################################################
# Usage: python exps/NATS-Bench/draw-correlations.py #
###############################################################
import os, gc, sys, time, scipy, torch, argparse
import numpy as np
from typing import List, Text, Dict, Any
from shutil import copyfile
from collections import defaultdict, OrderedDict
from copy import deepcopy
from pathlib import Path
import matplotlib
import seaborn as sns
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
lib_dir = (Path(__file__).parent / '..' / '..' / 'lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
from config_utils import dict2config, load_config
from nats_bench import create
from log_utils import time_string
def get_valid_test_acc(api, arch, dataset):
is_size_space = api.search_space_name == 'size'
if dataset == 'cifar10':
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
test_acc = xinfo['test-accuracy']
xinfo = api.get_more_info(arch, dataset='cifar10-valid', hp=90 if is_size_space else 200, is_random=False)
valid_acc = xinfo['valid-accuracy']
else:
xinfo = api.get_more_info(arch, dataset=dataset, hp=90 if is_size_space else 200, is_random=False)
valid_acc = xinfo['valid-accuracy']
test_acc = xinfo['test-accuracy']
return valid_acc, test_acc, 'validation = {:.2f}, test = {:.2f}\n'.format(valid_acc, test_acc)
def compute_kendalltau(vectori, vectorj):
# indexes = list(range(len(vectori)))
# rank_1 = sorted(indexes, key=lambda i: vectori[i])
# rank_2 = sorted(indexes, key=lambda i: vectorj[i])
# import pdb; pdb.set_trace()
coef, p = scipy.stats.kendalltau(vectori, vectorj)
return coef
def compute_spearmanr(vectori, vectorj):
coef, p = scipy.stats.spearmanr(vectori, vectorj)
return coef
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--save_dir', type=str, default='output/vis-nas-bench/nas-algos', help='Folder to save checkpoints and log.')
parser.add_argument('--search_space', type=str, choices=['tss', 'sss'], help='Choose the search space.')
args = parser.parse_args()
save_dir = Path(args.save_dir)
api = create(None, 'tss', fast_mode=True, verbose=False)
indexes = list(range(1, 10000, 300))
scores_1 = []
scores_2 = []
for index in indexes:
valid_acc, test_acc, _ = get_valid_test_acc(api, index, 'cifar10')
scores_1.append(valid_acc)
scores_2.append(test_acc)
correlation = compute_kendalltau(scores_1, scores_2)
print('The kendall tau correlation of {:} samples : {:}'.format(len(indexes), correlation))
correlation = compute_spearmanr(scores_1, scores_2)
print('The spearmanr correlation of {:} samples : {:}'.format(len(indexes), correlation))
# scores_1 = ['{:.2f}'.format(x) for x in scores_1]
# scores_2 = ['{:.2f}'.format(x) for x in scores_2]
# print(', '.join(scores_1))
# print(', '.join(scores_2))
dpi, width, height = 250, 1000, 1000
figsize = width / float(dpi), height / float(dpi)
LabelSize, LegendFontsize = 14, 14
fig, ax = plt.subplots(1, 1, figsize=figsize)
ax.scatter(scores_1, scores_2 , marker='^', s=0.5, c='tab:green', alpha=0.8)
save_path = '/Users/xuanyidong/Desktop/test-temp-rank.png'
fig.savefig(save_path, dpi=dpi, bbox_inches='tight', format='png')
plt.close('all')
| [
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]
| |
becb97ab51bd113a00a2a0c169559e348ee0f82c | a46b14b44c87adb0288224a0e7e31d9bed30223f | /guest_project/apps/guest_app/models.py | f6db55f42203f80a0a458a0b4a83ca4f50478693 | []
| no_license | JeffLawrence1/Python-Django-Intermediate | 0b663e5d706dc6b35ff2785ae38d7bf0f2f3b651 | d1efc3e6385286ab25bae36042987a85ae94e359 | refs/heads/master | 2020-03-09T03:42:47.348420 | 2018-04-07T21:42:04 | 2018-04-07T21:42:04 | 128,570,954 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 191 | py | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models
# Create your models here.
class User(models.Model):
name = models.CharField(max_length=255) | [
"[email protected]"
]
| |
592cca932c6d29898437e2362af88c8d578e9466 | a735cc0b04b3227720bfd97c74ef13bda5bdf571 | /python/documentation/doc/conf.py | 87be3541d59a67e9c9cc135f03e7e0690fa181a4 | [
"MIT"
]
| permissive | abstractfactory/labs | beed0aab27cd3028c67ece87ef91d18b55114eb1 | f0791fb92686456d4cef3a11f699590a949fd6a9 | refs/heads/master | 2021-01-23T20:50:07.613682 | 2014-11-18T10:30:29 | 2014-11-18T10:30:29 | 20,175,862 | 1 | 3 | null | null | null | null | UTF-8 | Python | false | false | 8,179 | py | # -*- coding: utf-8 -*-
#
# Labs documentation build configuration file, created by
# sphinx-quickstart on Tue Jun 24 15:49:19 2014.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))
# -- 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.viewcode',
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The suffix of source filenames.
source_suffix = '.rst'
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
# General information about the project.
project = u'Labs'
copyright = u'2014, Marcus Ottosson'
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.0.1'
# The full version, including alpha/beta/rc tags.
release = '0.0.1'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# -- 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 = 'default'
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# Output file base name for HTML help builder.
htmlhelp_basename = 'Labsdoc'
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
('index', 'Labs.tex', u'Labs Documentation',
u'Marcus Ottosson', 'manual'),
]
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
('index', 'labs', u'Labs Documentation',
[u'Marcus Ottosson'], 1)
]
# If true, show URL addresses after external links.
#man_show_urls = False
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
('index', 'Labs', u'Labs Documentation',
u'Marcus Ottosson', 'Labs', 'One line description of project.',
'Miscellaneous'),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
| [
"[email protected]"
]
| |
5242f6f122ece46875d63baf451df2044a5956d8 | ce083128fa87ca86c65059893aa8882d088461f5 | /python/pytest-labs/.venv/lib/python3.6/site-packages/facebook_business/adobjects/adcampaignfrequencycontrolspecs.py | d0005352b87cf20f8700d3c56dda97efc9a99ee6 | []
| no_license | marcosptf/fedora | 581a446e7f81d8ae9a260eafb92814bc486ee077 | 359db63ff1fa79696b7bc803bcfa0042bff8ab44 | refs/heads/master | 2023-04-06T14:53:40.378260 | 2023-03-26T00:47:52 | 2023-03-26T00:47:52 | 26,059,824 | 6 | 5 | null | 2022-12-08T00:43:21 | 2014-11-01T18:48:56 | null | UTF-8 | Python | false | false | 1,973 | py | # Copyright 2014 Facebook, Inc.
# You are hereby granted a non-exclusive, worldwide, royalty-free license to
# use, copy, modify, and distribute this software in source code or binary
# form for use in connection with the web services and APIs provided by
# Facebook.
# As with any software that integrates with the Facebook platform, your use
# of this software is subject to the Facebook Developer Principles and
# Policies [http://developers.facebook.com/policy/]. This copyright notice
# shall be included in all copies or substantial portions of the software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
from facebook_business.adobjects.abstractobject import AbstractObject
"""
This class is auto-generated.
For any issues or feature requests related to this class, please let us know on
github and we'll fix in our codegen framework. We'll not be able to accept
pull request for this class.
"""
class AdCampaignFrequencyControlSpecs(
AbstractObject,
):
def __init__(self, api=None):
super(AdCampaignFrequencyControlSpecs, self).__init__()
self._isAdCampaignFrequencyControlSpecs = True
self._api = api
class Field(AbstractObject.Field):
event = 'event'
interval_days = 'interval_days'
max_frequency = 'max_frequency'
_field_types = {
'event': 'string',
'interval_days': 'unsigned int',
'max_frequency': 'unsigned int',
}
@classmethod
def _get_field_enum_info(cls):
field_enum_info = {}
return field_enum_info
| [
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]
| |
824fe2517075df54beda8ff30d6a67ef447af8ae | 7a7bbdaab3cedcd9b89d9245d67fe7cc472fc288 | /1_dimention/2577.py | 057b91d168293a509f38fbb985532bb1d9aba21b | []
| no_license | rheehot/baekjun | cd213c6903e69a8e48b4942c950048c1c3e03c34 | 44792c6d125af7d9b0739c571e7918c802f73c01 | refs/heads/master | 2023-02-12T08:42:47.578842 | 2020-12-21T00:40:35 | 2020-12-21T00:40:35 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 123 | py | a = int(input())
b = int(input())
c = int(input())
mul = str(a * b * c)
for i in range(10):
print(mul.count(str(i))) | [
"[email protected]"
]
| |
254e98217498dea904c67279827063013f34b5fb | e6421de3f06af8be4234e9901d71f86b31c6c3a7 | /pdenv/bin/easy_install-3.5 | 5e6b880f3fb8150f6afd21b014f591583dfa7719 | [
"MIT"
]
| permissive | Elmartin913/PanDjango | bdb5446ee18ee297c23199cd3f9dd59cae555135 | 3b1eb52d53c87365f3d2fa5bd7ef72843ed5af32 | refs/heads/master | 2022-12-11T04:44:05.229530 | 2018-05-11T10:16:07 | 2018-05-11T10:16:07 | 128,903,323 | 0 | 0 | MIT | 2022-12-08T00:57:53 | 2018-04-10T08:54:10 | CSS | UTF-8 | Python | false | false | 276 | 5 | #!/home/elmartin913/workspace/app/PanDjango/pdenv/bin/python3
# -*- coding: utf-8 -*-
import re
import sys
from setuptools.command.easy_install import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
sys.exit(main())
| [
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]
| |
e57aebb6fb7ca69bcb5a28998f4b3016e5559651 | 47366be5cbee9d7e086291c20f97f10ab2bf74fe | /cluster/cluster_create_inequalities_subset_kdd.py | a030a3765bc70ab81a1b6e0dfd314582797a9901 | []
| no_license | nipunbatra/journal | 3d44eed05c95970606649d17402da54fc0a415ff | 94a8b88589e8f60e6f0314f8c5a374f22336b3e9 | refs/heads/master | 2021-01-09T20:40:45.844121 | 2016-07-27T15:16:29 | 2016-07-27T15:16:29 | 62,874,718 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 4,042 | py | import time
import pandas as pd
import pickle
import os
import numpy as np
SLURM_OUT = "../slurm_out"
from subprocess import Popen
import time
print "a"
out_overall = pickle.load(open('../data/input/all_regions.pkl','r'))
num_trials=25
print "b"
K = 3
for train_region in ["SanDiego"]:
if train_region=="Austin":
NUM_HOMES_MAX = 45
elif train_region=="SanDiego":
NUM_HOMES_MAX = len(out_overall['SanDiego'])
else:
NUM_HOMES_MAX = len(out_overall['Boulder'])
NUM_HOMES_MAX=20
for test_region in ["Austin"]:
if train_region!=test_region:
TRANSFORMATIONS = ["None","DD","DD-percentage","median-aggregate-percentage",
"median-aggregate",'regional','regional-percentage']
else:
TRANSFORMATIONS = ["None"]
train_df = out_overall[train_region]
test_df = out_overall[test_region]
test_df=test_df[(test_df.full_agg_available==1)&(test_df.md_available==1)]
NUM_HOMES_MIN=4
for num_homes in range(NUM_HOMES_MIN, NUM_HOMES_MAX, 2):
for transform in TRANSFORMATIONS:
#for transform in ["None","DD","DD-percentage"]:
#for transform in ["median-aggregate-percentage"]:
print transform
print "*"*40
count = 0
#for appliance in ["dw",'hvac','fridge','wm','mw','ec','wh','oven']:
for appliance in ["hvac"]:
if appliance=="hvac":
month_min, month_max = 5, 11
else:
month_min, month_max = 1, 13
count+= 1
#for appliance in ["hvac","fridge","dr","wm"]:
test_df = test_df.ix[test_df[['%s_%d' %(appliance,month) for month in range(month_min, month_max)]].dropna().index]
for test_home in test_df.index:
#for appliance in ["mw"]:
if len(test_df.ix[test_home][['%s_%d' %(appliance, m) for m in range(month_min, month_max)]].dropna())==0:
# Appliance data not present for this homes..let's save some time
continue
print appliance, test_home, count, len(test_df.index), K, transform, train_region, test_region
OFILE = "%s/%d_%s_%s_%d_%s_%s.out" % (SLURM_OUT, num_homes, train_region[0], test_region[0], test_home, appliance[0], transform[0] )
EFILE = "%s/%d_%s_%s_%d_%s_%s.err" % (SLURM_OUT, num_homes, train_region[0], test_region[0], test_home, appliance, transform )
SLURM_SCRIPT = "%d_%s_%s_%d_%s_%s.pbs" % (num_homes, train_region[0], test_region[0], test_home, appliance[:2], transform)
CMD = 'python ../new_experiments/create_inequalities_subset_kdd.py %s %s %d %s %s %d %d %d' % (train_region, test_region,
test_home, appliance,
transform, K, num_homes, num_trials)
lines = []
lines.append("#!/bin/sh\n")
lines.append('#SBATCH --time=0-05:0:00\n')
lines.append('#SBATCH --mem=16\n')
lines.append('#SBATCH -o '+'"' +OFILE+'"\n')
lines.append('#SBATCH -e '+'"' +EFILE+'"\n')
lines.append(CMD+'\n')
with open(SLURM_SCRIPT, 'w') as f:
f.writelines(lines)
command = ['sbatch', SLURM_SCRIPT]
Popen(command)
#os.remove(SLURM_SCRIPT)
print "Now sleeping.."
import time
time.sleep(40)
time.sleep(400)
time.sleep(1200)
| [
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05f02000e82ea0aa84a9665a9401fad1feec02b2 | 03587c34370995706871e45320264c2636d795f0 | /app/views/loja/AvaliacaoView.py | a391584f99994610a29e9c4c605cadf597837918 | []
| no_license | caiomarinhodev/fastdelivery | 29d1f95dc7204369806e6b99298c9aaafab5ea9f | 6ad45aa596e204b793ba47f7a0c1b918a2e0890a | refs/heads/master | 2020-03-12T03:18:04.507010 | 2018-04-20T23:49:13 | 2018-04-20T23:49:13 | 130,421,809 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,139 | py | from django.contrib import messages
from django.contrib.auth.mixins import LoginRequiredMixin
from django.shortcuts import redirect
from django.views.generic import DetailView
from app.models import Request, Avaliacao
class AvaliacaoView(LoginRequiredMixin, DetailView):
template_name = 'loja/avaliacao_cliente.html'
login_url = '/define/login/'
model = Request
context_object_name = 'pedido_obj'
def get(self, request, *args, **kwargs):
return super(AvaliacaoView, self).get(request, *args, **kwargs)
def add_avaliacao(request):
data = request.POST
pedido = Request.objects.get(id=data['pedido'])
if 'comentario' and 'nota' in data:
aval = Avaliacao(cliente=pedido.cliente, estabelecimento=pedido.estabelecimento, nota=data['nota'],
comentario=data['comentario'])
aval.save()
else:
messages.error(request, 'Insira uma nota e um comentario')
return redirect('/avaliacao/pedido/' + str(data['pedido']))
messages.success(request, 'Avaliacao Realizada com Sucesso')
return redirect('/acompanhar-pedido/' + str(data['pedido']))
| [
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368438d6bd6eb2e764a63f7c2983f6a8380944e8 | 80775c192c7084171a0371b0fe14330b8cd89f0f | /stickerizer/emojis.py | 7070fbc525378011ade618d9b44d039bdcc88f9a | [
"MIT"
]
| permissive | vanyakosmos/face2sticker | 5435ddbbc123c782a6501a78f6142e1ce88f9bc7 | 7b82eb12dd3e4c54c5033caee77f57b751f637b8 | refs/heads/master | 2021-09-13T07:40:51.156215 | 2018-04-26T17:16:24 | 2018-04-26T17:16:24 | 105,321,918 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,318 | py | import numpy as np
from emotion_clf.emotion import load_clf, vectorize, emotions
clf = load_clf('emotion_clf/clf2.pkl')
def associate_emojis(face_landmarks):
emotions_probs = predict_probabilities(face_landmarks)
emoji = map_emoji(emotions_probs)
return emoji
def predict_probabilities(face_landmarks: dict):
landmarks = []
for points in face_landmarks.values():
landmarks.extend(points)
vector = vectorize(landmarks)
data = np.array([vector])
res = clf.predict_proba(data)[0]
probs = {}
for i, e in enumerate(emotions):
probs[e] = res[i]
return probs
def map_emoji(emotions_prob: dict):
emojis = {
'😡': {
'anger': 10,
},
'😒': {
'contempt': 10,
},
'😣': {
'disgust': 10,
},
'😱': {
'fear': 10,
},
'😀': {
'happiness': 10,
},
'😢': {
'sadness': 10,
},
'😮': {
'surprise': 10,
},
}
max_s = None
result = '🌚'
for emoji, ems in emojis.items():
s = sum([ems.get(e, 1) * emotions_prob[e] for e in emotions])
if not max_s or s > max_s:
max_s = s
result = emoji
return result
| [
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| |
8f7d2e670202fe46834fd31c9e7eaf218bed9b04 | ca3d6e6683f4736792fc93352424c6e6d216ab4d | /chapter9/chapter9_app_external_api_test.py | ccdbb3f2d629abb50083ae1be6495e4b66566be2 | [
"MIT"
]
| permissive | msg4rajesh/Building-Data-Science-Applications-with-FastAPI | 11ac071583002b15bc955fc3bc72ab86d2800222 | 99b472d8295a57c5a74a63d8184ac053dc4012f2 | refs/heads/main | 2023-07-16T09:48:48.536002 | 2021-08-26T05:02:39 | 2021-08-26T05:02:39 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,297 | py | import asyncio
from typing import Any, Dict
import httpx
import pytest
from asgi_lifespan import LifespanManager
from fastapi import status
from chapter9.chapter9_app_external_api import app, external_api
class MockExternalAPI:
mock_data = {
"data": [
{
"employee_age": 61,
"employee_name": "Tiger Nixon",
"employee_salary": 320800,
"id": 1,
"profile_image": "",
}
],
"status": "success",
"message": "Success",
}
async def __call__(self) -> Dict[str, Any]:
return MockExternalAPI.mock_data
@pytest.fixture(scope="session")
def event_loop():
loop = asyncio.get_event_loop()
yield loop
loop.close()
@pytest.fixture
async def test_client():
app.dependency_overrides[external_api] = MockExternalAPI()
async with LifespanManager(app):
async with httpx.AsyncClient(app=app, base_url="http://app.io") as test_client:
yield test_client
@pytest.mark.asyncio
async def test_get_employees(test_client: httpx.AsyncClient):
response = await test_client.get("/employees")
assert response.status_code == status.HTTP_200_OK
json = response.json()
assert json == MockExternalAPI.mock_data
| [
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| |
ff7e5353de2674b363d6503c65205bd258975026 | dfff7fef4d49266db475856d4c0afef8ca672e00 | /tests/cantfit.py | 54f692c8b57158768e4561a4098cae020b3eafbe | [
"MIT"
]
| permissive | funilrys/black | 70a5a251338ab67fed0771ab6ec97cca03aa378b | b4cee97c99d5513ef81fdf2bff1809721662f87d | refs/heads/master | 2020-03-17T14:41:13.259870 | 2018-05-16T05:15:28 | 2018-05-16T05:15:28 | 133,682,656 | 1 | 0 | null | 2018-05-16T14:57:35 | 2018-05-16T14:57:35 | null | UTF-8 | Python | false | false | 2,983 | py | # long variable name
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = 0
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = 1 # with a comment
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = [
1, 2, 3
]
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function()
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function(
arg1, arg2, arg3
)
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function(
[1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3
)
# long function name
normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying()
normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying(
arg1, arg2, arg3
)
normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying(
[1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3
)
# long arguments
normal_name = normal_function_name(
"but with super long string arguments that on their own exceed the line limit so there's no way it can ever fit",
"eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs",
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it=0,
)
# output
# long variable name
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = (
0
)
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = (
1
) # with a comment
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = [
1, 2, 3
]
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = (
function()
)
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function(
arg1, arg2, arg3
)
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it = function(
[1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3
)
# long function name
normal_name = (
but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying()
)
normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying(
arg1, arg2, arg3
)
normal_name = but_the_function_name_is_now_ridiculously_long_and_it_is_still_super_annoying(
[1, 2, 3], arg1, [1, 2, 3], arg2, [1, 2, 3], arg3
)
# long arguments
normal_name = normal_function_name(
"but with super long string arguments that on their own exceed the line limit so there's no way it can ever fit",
"eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs with spam and eggs and spam with eggs",
this_is_a_ridiculously_long_name_and_nobody_in_their_right_mind_would_use_one_like_it=0,
)
| [
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]
| |
6ec673beb0c506a5c90bb8c68908c0c73c13587c | 3a74ac2e7db63069945e5bc620342b4b89b8b201 | /python/dgl/distributed/rpc_server.py | ad47de7104c4d92fc64b87b7cdc82f26fefd6a38 | [
"Apache-2.0"
]
| permissive | vishalbelsare/dgl | 5d17ba82f720d742e1274c5d48dac64eca234504 | 512a80b00d2cd35607a542eb5544fa1f1c93a6f6 | refs/heads/master | 2023-08-17T15:09:55.082014 | 2022-01-22T04:25:14 | 2022-01-22T04:25:14 | 167,955,673 | 0 | 0 | Apache-2.0 | 2022-01-23T13:57:57 | 2019-01-28T12:05:37 | Python | UTF-8 | Python | false | false | 4,476 | py | """Functions used by server."""
import time
from . import rpc
from .constants import MAX_QUEUE_SIZE
def start_server(server_id, ip_config, num_servers, num_clients, server_state, \
max_queue_size=MAX_QUEUE_SIZE, net_type='socket'):
"""Start DGL server, which will be shared with all the rpc services.
This is a blocking function -- it returns only when the server shutdown.
Parameters
----------
server_id : int
Current server ID (starts from 0).
ip_config : str
Path of IP configuration file.
num_servers : int
Server count on each machine.
num_clients : int
Total number of clients that will be connected to the server.
Note that, we do not support dynamic connection for now. It means
that when all the clients connect to server, no client will can be added
to the cluster.
server_state : ServerSate object
Store in main data used by server.
max_queue_size : int
Maximal size (bytes) of server queue buffer (~20 GB on default).
Note that the 20 GB is just an upper-bound because DGL uses zero-copy and
it will not allocate 20GB memory at once.
net_type : str
Networking type. Current options are: 'socket'.
"""
assert server_id >= 0, 'server_id (%d) cannot be a negative number.' % server_id
assert num_servers > 0, 'num_servers (%d) must be a positive number.' % num_servers
assert num_clients >= 0, 'num_client (%d) cannot be a negative number.' % num_clients
assert max_queue_size > 0, 'queue_size (%d) cannot be a negative number.' % max_queue_size
assert net_type in ('socket'), 'net_type (%s) can only be \'socket\'' % net_type
# Register signal handler.
rpc.register_sig_handler()
# Register some basic services
rpc.register_service(rpc.CLIENT_REGISTER,
rpc.ClientRegisterRequest,
rpc.ClientRegisterResponse)
rpc.register_service(rpc.SHUT_DOWN_SERVER,
rpc.ShutDownRequest,
None)
rpc.register_service(rpc.GET_NUM_CLIENT,
rpc.GetNumberClientsRequest,
rpc.GetNumberClientsResponse)
rpc.register_service(rpc.CLIENT_BARRIER,
rpc.ClientBarrierRequest,
rpc.ClientBarrierResponse)
rpc.set_rank(server_id)
server_namebook = rpc.read_ip_config(ip_config, num_servers)
machine_id = server_namebook[server_id][0]
rpc.set_machine_id(machine_id)
ip_addr = server_namebook[server_id][1]
port = server_namebook[server_id][2]
rpc.create_sender(max_queue_size, net_type)
rpc.create_receiver(max_queue_size, net_type)
# wait all the senders connect to server.
# Once all the senders connect to server, server will not
# accept new sender's connection
print("Wait connections non-blockingly...")
rpc.receiver_wait(ip_addr, port, num_clients, blocking=False)
rpc.set_num_client(num_clients)
# Recv all the client's IP and assign ID to clients
addr_list = []
client_namebook = {}
for _ in range(num_clients):
# blocked until request is received
req, _ = rpc.recv_request()
assert isinstance(req, rpc.ClientRegisterRequest)
addr_list.append(req.ip_addr)
addr_list.sort()
for client_id, addr in enumerate(addr_list):
client_namebook[client_id] = addr
for client_id, addr in client_namebook.items():
client_ip, client_port = addr.split(':')
# TODO[Rhett]: server should not be blocked endlessly.
while not rpc.connect_receiver(client_ip, client_port, client_id):
time.sleep(1)
if rpc.get_rank() == 0: # server_0 send all the IDs
for client_id, _ in client_namebook.items():
register_res = rpc.ClientRegisterResponse(client_id)
rpc.send_response(client_id, register_res)
# main service loop
while True:
req, client_id = rpc.recv_request()
res = req.process_request(server_state)
if res is not None:
if isinstance(res, list):
for response in res:
target_id, res_data = response
rpc.send_response(target_id, res_data)
elif isinstance(res, str) and res == 'exit':
break # break the loop and exit server
else:
rpc.send_response(client_id, res)
| [
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| |
8a16b83756f3b6f2cc7e3bf1f7c8a50c5bc43058 | f171072a6390997ea4cf398f32ff3c7b7680af3a | /bumerang/apps/video/albums/migrations/0005_auto__add_field_videoalbum_created.py | 598fadaa544415799e09f3de533bda105c00bcce | []
| no_license | onewaterdrop/bumerang | f1ab153d033072c49de2edb976fa761bd4293bba | 0466c8b37ad1073d7ba4fc4dc00a5c6debb343a7 | refs/heads/master | 2021-01-15T23:41:23.379010 | 2014-06-23T07:57:48 | 2014-06-23T07:57:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 13,622 | py | # -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'VideoAlbum.created'
db.add_column('albums_videoalbum', 'created',
self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, null=True, blank=True),
keep_default=False)
def backwards(self, orm):
# Deleting field 'VideoAlbum.created'
db.delete_column('albums_videoalbum', 'created')
models = {
'albums.photoalbum': {
'Meta': {'object_name': 'PhotoAlbum'},
'category': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['albums.PhotoCategory']", 'null': 'True', 'blank': 'True'}),
'cover': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['photo.Photo']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}),
'published_in_archive': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'albums.photocategory': {
'Meta': {'ordering': "('sort_order', 'title')", 'object_name': 'PhotoCategory'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'sort_order': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
'albums.videoalbum': {
'Meta': {'object_name': 'VideoAlbum'},
'cover': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['video.Video']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'auth.group': {
'Meta': {'object_name': 'Group'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
'auth.permission': {
'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
'photo.photo': {
'Meta': {'ordering': "('-id',)", 'object_name': 'Photo'},
'access': ('django.db.models.fields.IntegerField', [], {'default': '1', 'null': 'True', 'blank': 'True'}),
'agency': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'album': ('django.db.models.fields.related.ForeignKey', [], {'max_length': '255', 'to': "orm['albums.PhotoAlbum']", 'null': 'True', 'blank': 'True'}),
'authors': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'city': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'country': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'festivals': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'genre': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['photo.PhotoGenre']", 'null': 'True', 'blank': 'True'}),
'icon': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'manager': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'original_file': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True'}),
'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}),
'published_in_archive': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}),
'teachers': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'thumbnail': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'views_count': ('django.db.models.fields.IntegerField', [], {'default': '0', 'null': 'True', 'blank': 'True'}),
'year': ('django.db.models.fields.IntegerField', [], {'default': '2011', 'null': 'True', 'blank': 'True'})
},
'photo.photogenre': {
'Meta': {'ordering': "('sort_order', 'title')", 'object_name': 'PhotoGenre'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'sort_order': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
'video.video': {
'Meta': {'ordering': "('-id',)", 'object_name': 'Video'},
'access': ('django.db.models.fields.IntegerField', [], {'default': '1', 'null': 'True', 'blank': 'True'}),
'agency': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'album': ('django.db.models.fields.related.ForeignKey', [], {'max_length': '255', 'to': "orm['albums.VideoAlbum']", 'null': 'True', 'blank': 'True'}),
'authors': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'category': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['video.VideoCategory']", 'null': 'True', 'blank': 'True'}),
'city': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'country': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'duration': ('django.db.models.fields.IntegerField', [], {'default': '0', 'null': 'True', 'blank': 'True'}),
'festivals': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'genre': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['video.VideoGenre']", 'null': 'True', 'blank': 'True'}),
'hq_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_in_broadcast_lists': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'manager': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'original_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}),
'published_in_archive': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'rating_score': ('django.db.models.fields.IntegerField', [], {'default': '0', 'blank': 'True'}),
'rating_votes': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0', 'blank': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '12', 'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'teachers': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'views_count': ('django.db.models.fields.IntegerField', [], {'default': '0', 'null': 'True', 'blank': 'True'}),
'year': ('django.db.models.fields.IntegerField', [], {'default': '2012', 'null': 'True', 'blank': 'True'})
},
'video.videocategory': {
'Meta': {'ordering': "('sort_order', 'title')", 'object_name': 'VideoCategory'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'sort_order': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '255'})
},
'video.videogenre': {
'Meta': {'ordering': "('sort_order', 'title')", 'object_name': 'VideoGenre'},
'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'sort_order': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '255'})
}
}
complete_apps = ['albums'] | [
"[email protected]"
]
| |
f2ed1999ab2fe5b10597c440649a3b93645b82d3 | c1e305171afcd18fdd66a46cbcf81d8dbcc3fd0c | /PyTorch/Py09_dropout.py | 447dd6e594089cf2812c287b60aa6a968b1ae24c | []
| no_license | ANRhine/PyTorch_Tutorial | 2f0d9fcc94dfec37a352b5dcb37fc66738abc37d | 378d03d2f2cfa08ff2040096218078a2e3cd659a | refs/heads/master | 2021-04-07T06:24:28.608860 | 2018-03-16T14:43:03 | 2018-03-16T14:43:03 | 125,291,327 | 1 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,116 | py | #! /usr/bin/env python
# -*- coding:utf-8 -*-
"""
-------------------------------------
File name: Py09_dropout.py
Author: Ruonan Yu
Date: 18-1-30
-------------------------------------
"""
import torch
import matplotlib.pyplot as plt
from torch.autograd import Variable
import torch.nn as nn
torch.manual_seed(1)
N_SAMPLES = 20
N_HIDDEN = 300
LR = 0.001
# fake data
# training data
x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
y = x + 0.3 * torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))
x, y = Variable(x), Variable(y)
# test data
test_x = torch.unsqueeze(torch.linspace(-1, 1, N_SAMPLES), 1)
test_y = test_x + 0.3 * torch.normal(torch.zeros(N_SAMPLES, 1), torch.ones(N_SAMPLES, 1))
test_x, test_y = Variable(test_x, volatile=True), Variable(test_y, volatile=True)
# overfitting network
net_overfitting = nn.Sequential(
nn.Linear(1, N_HIDDEN),
nn.ReLU(),
nn.Linear(N_HIDDEN, N_HIDDEN),
nn.ReLU(),
nn.Linear(N_HIDDEN, 1)
)
# dropout network
net_dropouted = nn.Sequential(
nn.Linear(1, N_HIDDEN),
nn.Dropout(0.5), # drop 50% of neuron
nn.ReLU(),
nn.Linear(N_HIDDEN, N_HIDDEN),
nn.Dropout(0.5), # drop 50% of neuron
nn.ReLU(),
nn.Linear(N_HIDDEN, 1)
)
print(net_overfitting)
print(net_dropouted)
# training
optimizer_ofit = torch.optim.Adam(net_overfitting.parameters(), lr=LR)
optimizer_drop = torch.optim.Adam(net_dropouted.parameters(), lr=LR)
loss_func = nn.MSELoss()
plt.ion()
for t in range(500):
pred_ofit = net_overfitting(x)
pred_drop = net_dropouted(x)
loss_ofit = loss_func(pred_ofit, y)
loss_drop = loss_func(pred_drop, y)
optimizer_ofit.zero_grad()
optimizer_drop.zero_grad()
loss_ofit.backward()
loss_drop.backward()
optimizer_ofit.step()
optimizer_drop.step()
if t % 10 == 0: # 每10步画一次图
# 将神经网络转换test形式，画好图之后改回训练形式
net_overfitting.eval()
net_dropouted.eval() # 因为drop网络在train的时候和test的时候参数不一样
plt.cla()
test_pred_ofit = net_overfitting(test_x)
test_pred_drop = net_dropouted(test_x)
plt.scatter(x.data.numpy(), y.data.numpy(), c='magenta', alpha=0.5, label='train')
plt.scatter(test_x.data.numpy(), test_y.data.numpy(), c='cyan', s=50, alpha=0.5, label='test')
plt.plot(test_x.data.numpy(), test_pred_ofit.data.numpy(), 'r-', lw=3, label='overfitting')
plt.plot(test_x.data.numpy(), test_pred_drop.data.numpy(), 'b--', lw=3, label='dropout(50%)')
plt.text(0, -1.2, r'$overfitting loss=%.4f$' % loss_func(test_pred_ofit, test_y).data[0],
fontdict={'size': 10, 'color': 'red'})
plt.text(0, -1.5, r'$dropout loss=%.4f$' % loss_func(test_pred_drop, test_y).data[0],
fontdict={'size': 10, 'color': 'red'})
plt.legend(loc='upper left')
plt.ylim((-2.5, 2.5))
plt.pause(0.1)
# 将两个网络改回train形式
net_overfitting.train()
net_dropouted.train()
plt.ioff()
plt.show()
| [
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]
| |
c44a67a3eaabc76d6e5635f62a79a69aa80faa77 | e5a511e346f5be8a82fe9cb2edf457aa7e82859c | /PythonNEW/Practice/StringRemoveExistingIdentitaion.py | f66992651f064d1098bc0a3e95b04ea1ee0ff896 | []
| no_license | nekapoor7/Python-and-Django | 8397561c78e599abc8755887cbed39ebef8d27dc | 8fa4d15f4fa964634ad6a89bd4d8588aa045e24f | refs/heads/master | 2022-10-10T20:23:02.673600 | 2020-06-11T09:06:42 | 2020-06-11T09:06:42 | 257,163,996 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 482 | py | """ Write a Python program to remove existing indentation from all of the lines in a given text."""
import textwrap
sample_text = '''
Python is a widely used high-level, general-purpose, interpreted,
dynamic programming language. Its design philosophy emphasizes
code readability, and its syntax allows programmers to express
concepts in fewer lines of code than possible in languages such
as C++ or Java.
'''
text = textwrap.dedent(sample_text)
print(text) | [
"[email protected]"
]
| |
9393d21961b0043d35b932fd166c21ca22c72e0c | e456cdf76c1419413931d218317d44ea4b7c3fb7 | /demo/django/pokedex/admin.py | fd91861f03cd25790e7dec41cc349aba98f35f27 | [
"MIT"
]
| permissive | Nekmo/angular-django | cbbd8bb0c6baeea6e788c5623fb98102b443f1e9 | 0464747806ce4e79571d3a72db0f04e15f0c6e5e | refs/heads/master | 2023-08-27T16:03:10.006482 | 2021-11-08T23:15:14 | 2021-11-08T23:15:14 | 298,419,330 | 14 | 6 | null | null | null | null | UTF-8 | Python | false | false | 169 | py | from django.contrib import admin
# Register your models here.
from pokedex.models import Specie
@admin.register(Specie)
class SpecieAdmin(admin.ModelAdmin):
pass
| [
"[email protected]"
]
| |
cc175ac74f032d57d8641a106ebead8e8f7f8a10 | 7c9707f0f1cb8e633ac605934f3dbd8036790868 | /projet/rpi_manager/migrations/0002_ph.py | 71da2a49b682367bb47761ea2e6341addf2a5fc5 | []
| no_license | ometeore/hydropo | 891e1abd4c1b8ccd0a3b27a043abf894b70ceb5b | 324076d4b7ddbd14e718c424eb24d129c2a2243c | refs/heads/master | 2023-06-14T08:35:55.838469 | 2021-07-04T16:28:09 | 2021-07-04T16:28:09 | 290,198,666 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 991 | py | # Generated by Django 3.1 on 2020-08-25 13:49
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
("rpi_manager", "0001_initial"),
]
operations = [
migrations.CreateModel(
name="Ph",
fields=[
(
"id",
models.AutoField(
auto_created=True,
primary_key=True,
serialize=False,
verbose_name="ID",
),
),
("date", models.DateTimeField()),
("value", models.FloatField()),
(
"rpi",
models.ForeignKey(
on_delete=django.db.models.deletion.CASCADE,
to="rpi_manager.rpi",
),
),
],
),
]
| [
"[email protected]"
]
| |
89e026a18c52f389d46597ba589fee07cc32a352 | d44d33899aaab3d2a8b693b648701d49810aca12 | /cip5-multiprofile-wave.py | e001a331576902efdc7df62b78d3e40a59f81237 | []
| no_license | izham-sugita/CIP | 208eee2e108a910abd3a137083638244b8f60303 | a0cd77531a34ad32a0cebeb6069123e89aceb0b5 | refs/heads/master | 2021-06-27T14:51:45.696969 | 2021-01-07T11:44:04 | 2021-01-07T11:44:04 | 204,810,048 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 5,796 | py | import numpy as np
import matplotlib.pyplot as plt
#Changing the default size
#fig_size = plt.rcParams["figure.figsize"]
#fig_size[0] = 20
#fig_size[1] = 16
#plt.rcParams["figure.figsize"] = fig_size
imax = 2001
imax = int( input("Enter imax ") )
length = 2.0 #-1<=x<=1
dx = length/(imax-1)
u = np.ndarray((imax),dtype=np.float64)
un = np.ndarray((imax),dtype=np.float64)
ud1 = np.zeros_like(u)
ud1n = np.zeros_like(u)
ud2 = np.zeros_like(u)
ud2n = np.zeros_like(u)
x = np.ndarray((imax),dtype=np.float64)
'''
for i in range(imax):
x[i] = i*dx
u[i] = 0.0
un[i] =0.0
if x[i] >= 4.0 and x[i] <= 6.0:
u[i] = 1.0
un[i]=1.0
'''
u[:] = 0.0
un[:] = 0.0
#multiple wave profile
for i in range(imax):
x[i] = -1.0 + i*dx
if x[i] >=-0.8 and x[i] <=-0.6:
u[i] = np.exp( -np.log(2.0)*(x[i]+0.7)**2 / 0.0009 )
un[i] = u[i]
elif x[i] >=-0.5 and x[i] <=-0.2:
u[i] = 1.0
un[i] = u[i]
elif x[i] >=0.0 and x[i] <=0.2:
u[i] = 1.0 - abs(10.0*x[i] - 1.0)
un[i] = u[i]
elif x[i] >=0.4 and x[i] <=0.6:
u[i] = np.sqrt( 1.0 - 100.0*(x[i] - 0.5)**2 )
un[i] = u[i]
#Initiate derivatives value
for i in range( 1, imax-1 ):
ud1[i] = 0.5*(u[i+1] - u[i-1])/dx
for i in range( 1, imax-1 ):
ud2[i] = 0.5*(ud1[i+1] - ud1[i-1])/dx
dt = np.float64(input("Enter dt, dx=%s\n "%dx ))
elapsed = 10.0
itermax = int( elapsed/dt )-int(elapsed/2.0) #adjusted timestep; don't know why
print("Maximum iteration: ", itermax)
c = 1.0
c = float(input("Enter c, +1.0 or -1.0 "))
alpha = c*dt/dx
eps = 1.0e-6
uexact = np.zeros_like(u)
'''
#calculating exact solution
for i in range(imax):
r1 = itermax*dt + 4.0
r2 = r1 + (6.0 - 4.0) #did this on purpose, a reminder
if x[i] >=r1 and x[i] <= r2:
uexact[i] = 1.0
'''
uexact[:] = u[:]
#matrix A
up = -np.sign(c)
A = np.array( [ [ (up*dx)**5, (up*dx)**4, (up*dx)**3],
[5.0*(up*dx)**4, 4.0*(up*dx)**3, 3.0*(up*dx)**2],
[20.0*(up*dx)**3, 12.0*(up*dx)**2, 6.0*up*dx] ] )
coef = np.array( [0.0, 0.0, 0.0] )
b = np.array( [0.0, 0.0, 0.0] )
xx = -c*dt
steps = 1
eps = 1.0e-8
phi = np.zeros_like(u)
for iter in range(itermax):
for i in range(1,imax-1):
up = -np.sign(c)
iup = i + int(up)
xx = -c*dt
b[0] = ( u[iup] - u[i] ) -0.5*ud2[i]*dx*dx - ud1[i]*up*dx
b[1] = ( ud1[iup] - ud1[i] ) - ud2[i]*up*dx
b[2] = ud2[iup] - ud2[i]
coef = np.linalg.solve(A, b)
a0 = coef[0]
a1 = coef[1]
a2 = coef[2]
a3 = ud2[i]*0.5
a4 = ud1[i]
#limiter
udif = ( u[iup] - u[i] )/dx*up
#minmod limiter
ratio = (u[i] - u[i-1]) / (u[i+1] - u[i] + eps)
phi0 = min(10.0*dx, ratio) #default is 1.0
phi[iup] = max(0.0, phi0)
#phi[iup] = 0.0
#van Leer (continuous function) #very diffusive
#ratio = (u[i] - u[i-1]) / (u[i+1] - u[i] + eps)
#phi[iup] = (ratio + abs(ratio)) / (1.0 + ratio)
#un[i] = a0*xx**5 + a1*xx**4 + a2*xx**3 + a3*xx**2 + a4*xx + u[i]
un[i] = u[i] + (1.0-phi[iup])*(a4*xx + a3*xx**2 + a2*xx**3 + a1*xx**4 + a0*xx**5) \
+ phi[iup]*(udif*xx)
ud1n[i] = (1.0 - phi[iup])*( 5.0*a0*xx**4 + 4.0*a1*xx**3 + 3.0*a2*xx**2 + 2.0*a3*xx \
+ ud1[i] ) + phi[iup]*udif
# weight 0.98, 0.01 is the least diffusive
#putting weight only on the first derivative
#un[i] = u[i] + (1.0 - phi[iup])*(a4*xx) + a3*xx**2 + a2*xx**3 + a1*xx**4 + a0*xx**5 \
# + phi[iup]*(udif*xx)
#ud1n[i] = 5.0*a0*xx**4 + 4.0*a1*xx**3 + 3.0*a2*xx**2 + 2.0*a3*xx \
# + (1.0 - phi[iup])*ud1[i] + phi[iup]*udif
#the second derivative is not affected
ud2n[i] = 20.0*a0*xx**3 + 12.0*a1*xx**2 + 6.0*a2*xx + ud2[i]
#update periodic BC
u[0] = un[imax-2]
ud1[0] = ud1n[imax-2]
ud2[0] = ud2n[imax-2]
u[imax-1] = un[imax-2]
ud1[imax-1] = ud1n[imax-2]
ud2[imax-1] = ud2n[imax-2]
for i in range(1, imax-1):
u[i] = un[i]
ud1[i] = ud1n[i]
ud2[i] = ud2n[i]
#update periodic BC
#u[imax-1] = un[imax-2]
#ud1[imax-1] = ud1n[imax-2]
#ud2[imax-1] = ud2n[imax-2]
#u[0] = un[imax-2]
#ud1[0] = ud1n[imax-2]
#ud2[0] = ud2n[imax-2]
'''
#update
u[:] = un[:]
ud1[:] = ud1n[:]
ud2[:] = ud2n[:]
'''
#if iter%steps == 0:
# num = str(iter)
# filename = "./data1D/f"+num.zfill(5)+".csv"
# fp = open(filename, "w")
# fp.write("x, u\n")
# for i in range(imax):
# str1 = str(x[i])
# str2 = str(u[i])
# comma = ","
# nextline = "\n"
# strall = str1+comma+str2+nextline
# fp.write(strall)
# fp.close()
current = iter*dt + dt
display = "t = %.4f"%(current)
phi[:] = 0.0
current = iter*dt + dt
display = "t = %.4f"%(current)
#plt.axis([0.0, 10.0, -0.5, 1.5 ] )
plt.axis([-2.0, 2.0, -0.5, 1.5 ] )
plt.title(display)
plt.ylabel("U")
plt.xlabel("x")
plt.plot(x,u,'bo-')
plt.pause(0.001)
plt.clf() #clear drawing
filename = "final.png"
#plt.axis([0.0, 10.0, -0.5, 1.5 ] )
plt.axis([-2.0, 2.0, -0.5, 1.5 ] )
plt.plot(x,u, 'bo-', x, uexact,'kv-')
plt.title(display)
plt.ylabel("U")
plt.xlabel("x")
plt.savefig(filename)
plt.show()
#plt.show(block=False)
#plt.pause(3)
#plt.close()
filename = "cip5-final.csv"
fp = open(filename, "w")
fp.write("x, u\n")
for i in range(imax):
str1 = str(x[i])
str2 = str(u[i])
comma = ","
nextline = "\n"
strall = str1+comma+str2+nextline
fp.write(strall)
fp.close()
| [
"[email protected]"
]
| |
898a057527760f01aeb95b618322cf09388c1f42 | 02e23da0431623db86c8138bda350a1d526d4185 | /Archivos Python Documentos/Graficas/.history/TRABAJO_SPT_v3_20200224230649.py | 0fe0123926d8f3bed47ddd88db91bc709c442b12 | []
| no_license | Jaamunozr/Archivos-python | d9996d3d10ff8429cd1b4c2b396016a3a5482889 | 1f0af9ba08f12ac27e111fcceed49bbcf3b39657 | refs/heads/master | 2022-08-05T14:49:45.178561 | 2022-07-13T13:44:39 | 2022-07-13T13:44:39 | 244,073,267 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 5,848 | py | import os
import pylab as pl
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
#------------------------------------------------------------------------------
os.system("clear")
fig = pl.figure()
axx = Axes3D(fig)
raiz=np.sqrt
ln=np.log
puntoX=float(0)
puntoY=float(0)
#puntoX=float(input("Seleccione la coordenada en X donde desea calcular el potencial: "))
#puntoY=float(input("Seleccione la coordenada en Y donde desea calcular el potencial: "))
print("Calculando ...")
#------------------------------------------------------------------------------
Xa = np.arange(-10, 10, 0.1) #Rango de coordenadas de X
Ya = np.arange(-10, 10, 0.1) #Rango de coordenadas de Y
l = 2 #Longitud del electrodo [m]
rho= 100 #Resistividad de terrreno [Ohm/m]
Ik=200 #Corriente de falla [A] (Total)
Rad=0.01 #Radio del electrodo [m]
Electrodos=8 #Número de electrodos
Pos1=4 #Posición 1 en Y para analisis de grafica 2D
Pos2=0 #Posición 2 en Y para analisis de grafica 2D
#------------------------------------------------------------------------------
#Posición de los electrodos
#------------------------------------------------------------------------------
P=np.array([
[-4,-4], #Electrodo A
[0,-4], #Electrodo B
[4,-4], #Electrodo C
[-4,0], #Electrodo D
[4,0], #Electrodo E
[-4,4], #Electrodo F
[0,4], #Electrodo G
[4,4] #Electrodo H
])
#------------------------------------------------------------------------------
E=Electrodos-1
ik=Ik/Electrodos
Vt=np.zeros((np.count_nonzero(Xa),np.count_nonzero(Ya)))
m=np.zeros((Electrodos,1))
V=np.zeros((Electrodos,1))
k=0
m2=np.zeros((Electrodos,1))
V2=np.zeros((Electrodos,1))
#------------------------------------------------------------------------------
#Cálculo del punto ingresado
#------------------------------------------------------------------------------
i=0
while i<=E:
m2[i][0] =round(raiz((((P[i][0])-puntoX)**2)+(((P[i][1])-puntoY)**2)),4)
o,u=((P[i][0])-puntoX),((P[i][1])-puntoY)
if ((o ==0) and (u==0)) or (m2[i][0]==0):
#print("Elementos de matriz",k,t, "x,y",P[i][0],P[i][1],"punto de eje",X,Y )
m2[i][0]=Rad
V2[i][0] =ln((l+raiz((m2[i][0])**2+l**2))/(m2[i][0]))
i += 1
Vt2=(np.sum(V2)*(rho*ik))/(2*np.pi*l)
print("El potencial en el punto (",puntoX,",",puntoY,"), es de",round(Vt2,3),"[V]")
#------------------------------------------------------------------------------
#Cálculo de la malla
#------------------------------------------------------------------------------
Vxy = [1] * (np.count_nonzero(Ya))
while k<np.count_nonzero(Ya):
Y=round(Ya[k],3)
t=0
while t<np.count_nonzero(Xa):
X=round(Xa[t],3)
i=0
while i<=E:
m[i][0] =round(raiz((((P[i][0])-X)**2)+(((P[i][1])-Y)**2)),4)
o,u=((P[i][0])-X),((P[i][1])-Y)
if ((o ==0) and (u==0)) or (m[i][0]==0):
#print("Elementos de matriz",k,t, "x,y",P[i][0],P[i][1],"punto de eje",X,Y )
m[i][0]=Rad
V[i][0] =ln((l+raiz((m[i][0])**2+l**2))/(m[i][0]))
i += 1
Vt[k][t]=np.sum(V)
if Y==Pos1:
Vxa=Vt[k]
if Y==Pos2:
Vxb=Vt[k]
if (Y==X) and ((X-Y)==0):
Vxy[k]=Vt[k][t]*(rho*ik)/(2*np.pi*l)
t +=1
k +=1
Vtt=(Vt*(rho*ik))/(2*np.pi*l)
Vxa=(Vxa*(rho*ik))/(2*np.pi*l)
Vxb=(Vxb*(rho*ik))/(2*np.pi*l)
aa=np.where(np.amax(Vtt) == Vtt)
print ("Valor máximo de tensión (GPR):",round(Vtt[::].max(),3),"[V], en posición: (",round(Xa[aa[0][0]],2),",",round(Ya[aa[1][0]],2),")")
bb=np.where(np.amin(Vtt) == Vtt)
print("Valor de Resistencia de puesta a tierra:", (round(Vtt[::].max(),3)/Ik), "[Ohm]")
#print ("Valor mínimo de tensión:",round(Vtt[::].min(),3),"[V], en posición: (",round(Xa[bb[0][0]],2),",",round(Ya[bb[1][0]],2),")")
print ("Número de elementos de Vt:",np.count_nonzero(Vtt))
#------------------------------------------------------------------------------
# GRAFICAS 3D
#------------------------------------------------------------------------------
# Configurar una figura dos veces más alta que ancha
#fig = plt.figure(figsize=plt.figaspect(0.2))
#fig = plt.figure(4,figsize=(6,4.5)) #(Ancho, alto)
#fig.suptitle('Potencial')
fig = plt.figure(figsize=plt.figaspect(2.))
fig.suptitle('A tale of 2 subplots')
# Primera imagen a imprimir
ax = fig.add_subplot(2, 2, 1, projection='3d')
X, Y = np.meshgrid(Xa, Ya)
#surf = ax.plot_surface(X, Y, Vtt, cmap = cm.get_cmap("jet"))#, antialiased=False)
surf = ax.plot_surface(X, Y, Vtt, rstride=1, cstride=1,
linewidth=0, antialiased=False)
ax.set_zlim(300, 1800)
#fig.colorbar(surf)
#------------------------------------------------------------------------------
#Graficas en 2D
#------------------------------------------------------------------------------
x1=Xa
ax = fig.add_subplot(2, 2, 2)
ax.plot(x1, Vxa, color="blue", linewidth=1.0, linestyle="-")
ax.title.set_text('Eje X1 vs V')
ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
ax.set_ylabel('Grafica 1')
ax = fig.add_subplot(2, 2, 3)
ax.plot(x1, Vxb, color="red", linewidth=1.0, linestyle="-")
ax.title.set_text('Eje X2 vs V')
ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
ax.set_ylabel('Grafica 2')
ax = fig.add_subplot(2, 2, 4)
ax.plot(x1, Vxy, color="green", linewidth=1.0, linestyle="-")
ax.title.set_text('Eje X,Y vs V')
ax.grid(color='b', alpha=0.5, linestyle='dashed', linewidth=0.5)
ax.set_ylabel('Grafica 3')
plt.pause(25)
pl.savefig('tierras.pdf')
| [
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]
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423824d04b9ff1a989d3a18f132c057b03f82f22 | 4554f8d3ab1a6267b17dad2b4d2c47b0abe8d746 | /benchmarking/remote/devices.py | c7cacd80d2eb8e3a4eb17eebb98a6ac45237cf32 | [
"Apache-2.0"
]
| permissive | jteller/FAI-PEP | 44fead3ca26f4844067d455c86ac8c5bfaf79a14 | 73b8a08815675135e9da7d68375d1218cbd04eaa | refs/heads/master | 2020-04-29T06:04:19.197966 | 2019-03-15T23:32:54 | 2019-03-15T23:32:54 | 175,904,011 | 0 | 0 | Apache-2.0 | 2019-03-15T23:30:04 | 2019-03-15T23:30:04 | null | UTF-8 | Python | false | false | 2,484 | py | #!/usr/bin/env python
##############################################################################
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
##############################################################################
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import json
import os
from utils.devices import devices as devices_dict
class Devices(object):
def __init__(self, filename=None):
if filename:
# if the user provides filename, we will load it.
assert os.path.isfile(filename), \
"Device file {} does not exist".format(filename)
with open(filename, "r") as f:
self.devices = json.load(f)
else:
# otherwise read from internal
self.devices = devices_dict
self._elaborateDevices()
def getFullNames(self, devices):
names = devices.split(",")
new_names = [self.devices[name]["name"]
if name in self.devices else name for name in names]
return ",".join(new_names)
def getAbbrs(self, abbr):
if abbr in self.devices:
device = self.devices[abbr]
if "abbr" in device:
return device["abbr"]
return None
def _elaborateDevices(self):
device_abbr = []
for name, _ in self.devices.items():
device = self.devices[name]
assert "name" in device, \
"Field name is required in devices"
assert device["name"] == name, \
"Device key ({}) and name ({})".format(name, device["name"]) + \
" do not match"
if "abbr" in device:
assert isinstance(device["abbr"], list), \
"Abbreviations for {} needs to be a list".format(name)
for abbr in device["abbr"]:
device_abbr.append((device, abbr))
for device_abbr_pair in device_abbr:
self._elaborateOneDevice(device_abbr_pair[0], device_abbr_pair[1])
def _elaborateOneDevice(self, device, abbr):
assert abbr not in self.devices, "Abbreviation " + \
"{} is already specified in the device list".format(abbr)
self.devices[abbr] = device
| [
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]
| |
431a60378e86b4b85d841143ab2f513bb7bbeeff | 1b5cc8dc487da59455dfe6749796870d51d5ab87 | /src/collective/iptvusp/tests/test_uspvideo.py | 72b74796685ac00b3964064bc7a733813671c2c5 | []
| no_license | simplesconsultoria/collective.iptvusp | eddcd726a800933127b04959bba90c63210049dc | 89b14ee4a01e19ef5cd7198c5bdf808ef555f1f0 | refs/heads/master | 2021-01-01T18:29:41.272115 | 2013-03-12T19:01:25 | 2013-03-12T19:01:25 | 6,388,881 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,806 | py | # -*- coding: utf-8 -*-
import unittest2 as unittest
from zope.component import createObject
from zope.component import queryUtility
from plone.app.testing import TEST_USER_ID
from plone.app.testing import setRoles
from plone.dexterity.interfaces import IDexterityFTI
from plone.app.dexterity.behaviors.exclfromnav import IExcludeFromNavigation
from collective.iptvusp.content import IUSPVideo
from collective.iptvusp.testing import INTEGRATION_TESTING
class CoverIntegrationTestCase(unittest.TestCase):
layer = INTEGRATION_TESTING
def setUp(self):
self.portal = self.layer['portal']
setRoles(self.portal, TEST_USER_ID, ['Manager'])
self.portal.invokeFactory('Folder', 'test-folder')
setRoles(self.portal, TEST_USER_ID, ['Member'])
self.folder = self.portal['test-folder']
self.folder.invokeFactory('iptvusp.uspvideo', 'c1',
template_layout='Layout A')
self.c1 = self.folder['c1']
def test_adding(self):
self.assertTrue(IUSPVideo.providedBy(self.c1))
def test_fti(self):
fti = queryUtility(IDexterityFTI,
name='iptvusp.uspvideo')
self.assertNotEqual(None, fti)
def test_schema(self):
fti = queryUtility(IDexterityFTI,
name='iptvusp.uspvideo')
schema = fti.lookupSchema()
self.assertEqual(IUSPVideo, schema)
def test_factory(self):
fti = queryUtility(IDexterityFTI,
name='iptvusp.uspvideo')
factory = fti.factory
new_object = createObject(factory)
self.assertTrue(IUSPVideo.providedBy(new_object))
def test_exclude_from_navigation_behavior(self):
self.assertTrue(IExcludeFromNavigation.providedBy(self.c1))
| [
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]
| |
6a28e7551bac14d5e50e838a962b64b49a7008ae | 057722b227e9f51c78bd77b622859674016f19dc | /homework4/code/p7/trysvm.py | 8783e7fd91174a983250421516e6938b0597d778 | []
| no_license | walkerning/Homework-pattern_recognition | 56508bc66d0932ad8c9899658d8229169d800551 | 843a79d1f4cc278839ade27a593ae66e603ac4ba | refs/heads/master | 2021-03-19T15:30:55.581932 | 2017-05-31T15:51:22 | 2017-05-31T15:51:22 | 84,166,823 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,740 | py | # -*- coding: utf-8 -*-
import numpy as np
from sklearn import svm
samples_w1 = np.array([[-3.0, 0.5, 2.9, -0.1, -4.0, -1.3, -3.4, -4.1, -5.1, 1.9],
[-2.9, 8.7, 2.1, 5.2, 2.2, 3.7, 6.2, 3.4, 1.6, 5.1]]).T
samples_w2 = np.array([[-2.0, -8.9, -4.2, -8.5, -6.7, -0.5, -5.3, -8.7, -7.1, -8.0],
[-8.4, 0.2, -7.7, -3.2, -4.0, -9.2, -6.7, -6.4, -9.7, -6.3]]).T
def transform_data(data):
# return 1 x1 x2 x1**2 x2**2 x1x2
return np.hstack((np.ones((data.shape[0], 1)), data, data**2, (data[:, 0] * data[:, 1])[:, np.newaxis]))
def main():
# set misclassification penalty to a large enough value
trans_samples_w1 = transform_data(samples_w1)
trans_samples_w2 = transform_data(samples_w2)
# data = np.vstack((trans_samples_w1[0, :], trans_samples_w2[0, :]))
# labels = [0, 1]
# res = svm.SVC(C=1e10, kernel="linear").fit(data, labels)
# m = np.sqrt(res.coef_[0].dot(res.coef_[0]))
# margin1 = (res.coef_.dot(trans_samples_w1[0,:]) + res.intercept_) / m
# margin2 = (res.coef_.dot(trans_samples_w2[0,:]) + res.intercept_) / m
# print "margin of w1 {}: {}; margin of w2 {}: {}".format(trans_samples_w1[0, :], margin1,
# trans_samples_w2[0, :], margin2)
for num in range(1, samples_w1.shape[0]+1):
data = np.vstack((trans_samples_w1[:num, :], trans_samples_w2[:num, :]))
labels = np.hstack((np.zeros(num), np.ones(num)))
res = svm.SVC(C=1e10, kernel="linear").fit(data, labels)
print "sample number: {}, coef: {}, b: {}, margin: {}".format(num*2, res.coef_, res.intercept_, np.sqrt(1/(res.coef_[0].dot(res.coef_[0]))))
if __name__ == "__main__":
main()
| [
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]
| |
f6d6555a8ba6236ab372c46d3874d38c6e764625 | db7660d3541c26b418ea84ca08fbf41f9ebd726b | /brax/jumpy.py | 7486fd1ac0b8ac9b44e57f3f0ed88c4c3ca5f7a4 | [
"Apache-2.0"
]
| permissive | proceduralia/brax | 8727ada08184fe9f60356d17a15ea671df0906d6 | d54dc479a32e5e99641cde921c7988d69cd5bb7b | refs/heads/main | 2023-08-27T15:39:44.618414 | 2021-11-08T21:35:42 | 2021-11-08T21:37:32 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 11,680 | py | # Copyright 2021 The Brax Authors.
#
# 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.
# pylint:disable=redefined-builtin
"""Numpy backend for JAX that is called for non-jit/non-jax arrays."""
from typing import Any, Callable, List, Optional, Sequence, Tuple, TypeVar, Union
import jax
from jax import core
from jax import numpy as jnp
import numpy as onp
ndarray = Union[onp.ndarray, jnp.ndarray] # pylint:disable=invalid-name
tree_map = jax.tree_map # works great with jax or numpy as-is
pi = onp.pi
inf = onp.inf
float32 = onp.float32
int32 = onp.int32
def _in_jit() -> bool:
"""Returns true if currently inside a jax.jit call."""
return core.cur_sublevel().level > 0
def _which_np(*args):
"""Returns np or jnp depending on args."""
for a in args:
if isinstance(a, jnp.ndarray):
return jnp
return onp
F = TypeVar('F', bound=Callable)
def vmap(fun: F, include: Optional[Sequence[bool]] = None) -> F:
"""Creates a function which maps ``fun`` over argument axes."""
if _in_jit():
in_axes = 0
if include:
in_axes = [0 if inc else None for inc in include]
return jax.vmap(fun, in_axes=in_axes)
def _batched(*args):
args_flat, args_treedef = jax.tree_flatten(args)
vargs, vargs_idx = [], []
rets = []
if include:
for i, (inc, arg) in enumerate(zip(include, args_flat)):
if inc:
vargs.append(arg)
vargs_idx.append(i)
else:
vargs, vargs_idx = list(args_flat), list(range(len(args_flat)))
for zvargs in zip(*vargs):
for varg, idx in zip(zvargs, vargs_idx):
args_flat[idx] = varg
args_unflat = jax.tree_unflatten(args_treedef, args_flat)
rets.append(fun(*args_unflat))
return jax.tree_map(lambda *x: onp.stack(x), *rets)
return _batched
Carry = TypeVar('Carry')
X = TypeVar('X')
Y = TypeVar('Y')
def scan(f: Callable[[Carry, X], Tuple[Carry, Y]],
init: Carry,
xs: X,
length: Optional[int] = None,
reverse: bool = False,
unroll: int = 1) -> Tuple[Carry, Y]:
"""Scan a function over leading array axes while carrying along state."""
if _in_jit():
return jax.lax.scan(f, init, xs, length, reverse, unroll)
else:
xs_flat, xs_tree = jax.tree_flatten(xs)
carry = init
ys = []
maybe_reversed = reversed if reverse else lambda x: x
for i in maybe_reversed(range(length)):
xs_slice = [x[i] for x in xs_flat]
carry, y = f(carry, jax.tree_unflatten(xs_tree, xs_slice))
ys.append(y)
stacked_y = jax.tree_map(lambda *y: onp.vstack(y), *maybe_reversed(ys))
return carry, stacked_y
def take(tree: Any, i: Union[ndarray, Sequence[int]], axis: int = 0) -> Any:
"""Returns tree sliced by i."""
np = _which_np(i)
if isinstance(i, list) or isinstance(i, tuple):
i = np.array(i, dtype=int)
return jax.tree_map(lambda x: np.take(x, i, axis=axis, mode='clip'), tree)
def norm(x: ndarray,
axis: Optional[Union[Tuple[int, ...], int]] = None) -> ndarray:
"""Returns the array norm."""
return _which_np(x, axis).linalg.norm(x, axis=axis)
def index_update(x: ndarray, idx: ndarray, y: ndarray) -> ndarray:
"""Pure equivalent of x[idx] = y."""
if _which_np(x) is jnp:
return x.at[idx].set(y)
x = onp.copy(x)
x[idx] = y
return x
def safe_norm(x: ndarray,
axis: Optional[Union[Tuple[int, ...], int]] = None) -> ndarray:
"""Calculates a linalg.norm(x) that's safe for gradients at x=0.
Avoids a poorly defined gradient for jnp.linal.norm(0) see
https://github.com/google/jax/issues/3058 for details
Args:
x: A jnp.array
axis: The axis along which to compute the norm
Returns:
Norm of the array x.
"""
np = _which_np(x)
if np is jnp:
is_zero = jnp.allclose(x, 0.)
# temporarily swap x with ones if is_zero, then swap back
x = jnp.where(is_zero, jnp.ones_like(x), x)
n = jnp.linalg.norm(x, axis=axis)
n = jnp.where(is_zero, 0., n)
else:
n = onp.linalg.norm(x, axis=axis)
return n
def any(a: ndarray, axis: Optional[int] = None) -> ndarray:
"""Test whether any array element along a given axis evaluates to True."""
return _which_np(a).any(a, axis=axis)
def all(a: ndarray, axis: Optional[int] = None) -> ndarray:
"""Test whether all array elements along a given axis evaluate to True."""
return _which_np(a).all(a, axis=axis)
def mean(a: ndarray, axis: Optional[int] = None) -> ndarray:
"""Compute the arithmetic mean along the specified axis."""
return _which_np(a).mean(a, axis=axis)
def arange(start: int, stop: int) -> ndarray:
"""Return evenly spaced values within a given interval."""
return _which_np().arange(start, stop)
def dot(x: ndarray, y: ndarray) -> ndarray:
"""Returns dot product of two arrays."""
return _which_np(x, y).dot(x, y)
def outer(a: ndarray, b: ndarray) -> ndarray:
"""Compute the outer product of two vectors."""
return _which_np(a, b).outer(a, b)
def matmul(x1: ndarray, x2: ndarray) -> ndarray:
"""Matrix product of two arrays."""
return _which_np(x1, x2).matmul(x1, x2)
def inv(a: ndarray) -> ndarray:
"""Compute the (multiplicative) inverse of a matrix."""
return _which_np(a).linalg.inv(a)
def square(x: ndarray) -> ndarray:
"""Return the element-wise square of the input."""
return _which_np(x).square(x)
def repeat(a: ndarray, repeats: Union[int, ndarray]) -> ndarray:
"""Repeat elements of an array."""
return _which_np(a, repeats).repeat(a, repeats=repeats)
def floor(x: ndarray) -> ndarray:
"""Returns the floor of the input, element-wise.."""
return _which_np(x).floor(x)
def cross(x: ndarray, y: ndarray) -> ndarray:
"""Returns cross product of two arrays."""
return _which_np(x, y).cross(x, y)
def sin(angle: ndarray) -> ndarray:
"""Returns trigonometric sine, element-wise."""
return _which_np(angle).sin(angle)
def cos(angle: ndarray) -> ndarray:
"""Returns trigonometric cosine, element-wise."""
return _which_np(angle).cos(angle)
def arctan2(x1: ndarray, x2: ndarray) -> ndarray:
"""Returns element-wise arc tangent of x1/x2 choosing the quadrant correctly."""
return _which_np(x1, x2).arctan2(x1, x2)
def arccos(x: ndarray) -> ndarray:
"""Trigonometric inverse cosine, element-wise."""
return _which_np(x).arccos(x)
def logical_not(x: ndarray) -> ndarray:
"""Returns the truth value of NOT x element-wise."""
return _which_np(x).logical_not(x)
def multiply(x1: ndarray, x2: ndarray) -> ndarray:
"""Multiply arguments element-wise."""
return _which_np(x1, x2).multiply(x1, x2)
def minimum(x1: ndarray, x2: ndarray) -> ndarray:
"""Element-wise minimum of array elements."""
return _which_np(x1, x2).minimum(x1, x2)
def amin(x: ndarray) -> ndarray:
"""Returns the minimum along a given axis."""
return _which_np(x).amin(x)
def exp(x: ndarray) -> ndarray:
"""Returns the exponential of all elements in the input array."""
return _which_np(x).exp(x)
def sign(x: ndarray) -> ndarray:
"""Returns an element-wise indication of the sign of a number."""
return _which_np(x).sign(x)
def sum(a: ndarray, axis: Optional[int] = None):
"""Returns sum of array elements over a given axis."""
return _which_np(a).sum(a, axis=axis)
def random_prngkey(seed: int) -> ndarray:
"""Returns a PRNG key given a seed."""
if _which_np() is jnp:
return jax.random.PRNGKey(seed)
else:
rng = onp.random.default_rng(seed)
return rng.integers(low=0, high=2**32, dtype='uint32', size=2)
def random_uniform(rng: ndarray,
shape: Tuple[int, ...] = (),
low: Optional[float] = 0.0,
high: Optional[float] = 1.0) -> ndarray:
"""Sample uniform random values in [low, high) with given shape/dtype."""
if _which_np(rng) is jnp:
return jax.random.uniform(rng, shape=shape, minval=low, maxval=high)
else:
return onp.random.default_rng(rng).uniform(size=shape, low=low, high=high)
def random_split(rng: ndarray, num: int = 2) -> ndarray:
"""Splits a PRNG key into num new keys by adding a leading axis."""
if _which_np(rng) is jnp:
return jax.random.split(rng, num=num)
else:
rng = onp.random.default_rng(rng)
return rng.integers(low=0, high=2**32, dtype='uint32', size=(num, 2))
def segment_sum(data: ndarray,
segment_ids: ndarray,
num_segments: Optional[int] = None) -> ndarray:
"""Computes the sum within segments of an array."""
if _which_np(data, segment_ids) is jnp:
s = jax.ops.segment_sum(data, segment_ids, num_segments)
else:
if num_segments is None:
num_segments = onp.amax(segment_ids) + 1
s = onp.zeros((num_segments,) + data.shape[1:])
onp.add.at(s, segment_ids, data)
return s
def top_k(operand: ndarray, k: int) -> ndarray:
"""Returns top k values and their indices along the last axis of operand."""
if _which_np(operand) is jnp:
return jax.lax.top_k(operand, k)
else:
ind = onp.argpartition(operand, -k)[-k:]
return operand[ind], ind
def stack(x: List[ndarray], axis=0) -> ndarray:
"""Join a sequence of arrays along a new axis."""
return _which_np(*x).stack(x, axis=axis)
def concatenate(x: Sequence[ndarray], axis=0) -> ndarray:
"""Join a sequence of arrays along an existing axis."""
return _which_np(*x).concatenate(x, axis=axis)
def sqrt(x: ndarray) -> ndarray:
"""Returns the non-negative square-root of an array, element-wise."""
return _which_np(x).sqrt(x)
def where(condition: ndarray, x: ndarray, y: ndarray) -> ndarray:
"""Return elements chosen from `x` or `y` depending on `condition`."""
return _which_np(condition, x, y).where(condition, x, y)
def diag(v: ndarray, k: int = 0) -> ndarray:
"""Extract a diagonal or construct a diagonal array."""
return _which_np(v).diag(v, k)
def clip(a: ndarray, a_min: ndarray, a_max: ndarray) -> ndarray:
"""Clip (limit) the values in an array."""
return _which_np(a, a_min, a_max).clip(a, a_min, a_max)
def eye(n: int) -> ndarray:
"""Return a 2-D array with ones on the diagonal and zeros elsewhere."""
return _which_np().eye(n)
def zeros(shape, dtype=float) -> ndarray:
"""Return a new array of given shape and type, filled with zeros."""
return _which_np().zeros(shape, dtype=dtype)
def zeros_like(a: ndarray) -> ndarray:
"""Return an array of zeros with the same shape and type as a given array."""
return _which_np(a).zeros_like(a)
def ones(shape, dtype=float) -> ndarray:
"""Return a new array of given shape and type, filled with ones."""
return _which_np().ones(shape, dtype=dtype)
def ones_like(a: ndarray) -> ndarray:
"""Return an array of ones with the same shape and type as a given array."""
return _which_np(a).ones_like(a)
def reshape(a: ndarray, newshape: Union[Tuple[int, ...], int]) -> ndarray:
"""Gives a new shape to an array without changing its data."""
return _which_np(a).reshape(a, newshape)
def array(object: Any, dtype=None) -> ndarray:
"""Creates an array given a list."""
try:
np = _which_np(*object)
except TypeError:
np = _which_np(object) # object is not iterable (e.g. primitive type)
return np.array(object, dtype)
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d2f6e5faa8e1f124af00e0502dca3ad30670785e | b5fabc6c6de064690f8d4ee423001cf9365a3d9f | /flash/image/segmentation/model.py | 9296db60cbcff1e6220d5aee051ddb36549a8b1f | [
"Apache-2.0"
]
| permissive | dmarx/lightning-flash | 021dfd76bde6e30309f14feb5853020b0babe90d | 4cda031c1f9c8d8754fd36b5720d2a5a7d866765 | refs/heads/master | 2023-09-06T06:24:29.856354 | 2021-11-24T23:38:14 | 2021-11-24T23:38:14 | 422,352,910 | 0 | 1 | null | null | null | null | UTF-8 | Python | false | false | 7,182 | py | # Copyright The PyTorch Lightning 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.
from typing import Any, Dict, List, Optional, Union
import torch
from torch import nn
from torch.nn import functional as F
from torchmetrics import IoU
from flash.core.classification import ClassificationTask
from flash.core.data.io.input import DataKeys
from flash.core.data.io.output_transform import OutputTransform
from flash.core.registry import FlashRegistry
from flash.core.utilities.imports import _KORNIA_AVAILABLE
from flash.core.utilities.isinstance import _isinstance
from flash.core.utilities.types import (
LOSS_FN_TYPE,
LR_SCHEDULER_TYPE,
METRICS_TYPE,
OPTIMIZER_TYPE,
OUTPUT_TRANSFORM_TYPE,
OUTPUT_TYPE,
)
from flash.image.segmentation.backbones import SEMANTIC_SEGMENTATION_BACKBONES
from flash.image.segmentation.heads import SEMANTIC_SEGMENTATION_HEADS
from flash.image.segmentation.output import SegmentationLabels
if _KORNIA_AVAILABLE:
import kornia as K
class SemanticSegmentationOutputTransform(OutputTransform):
def per_sample_transform(self, sample: Any) -> Any:
resize = K.geometry.Resize(sample[DataKeys.METADATA]["size"][-2:], interpolation="bilinear")
sample[DataKeys.PREDS] = resize(sample[DataKeys.PREDS])
sample[DataKeys.INPUT] = resize(sample[DataKeys.INPUT])
return super().per_sample_transform(sample)
class SemanticSegmentation(ClassificationTask):
"""``SemanticSegmentation`` is a :class:`~flash.Task` for semantic segmentation of images. For more details, see
:ref:`semantic_segmentation`.
Args:
num_classes: Number of classes to classify.
backbone: A string or model to use to compute image features.
backbone_kwargs: Additional arguments for the backbone configuration.
head: A string or (model, num_features) tuple to use to compute image features.
head_kwargs: Additional arguments for the head configuration.
pretrained: Use a pretrained backbone.
loss_fn: Loss function for training.
optimizer: Optimizer to use for training.
lr_scheduler: The LR scheduler to use during training.
metrics: Metrics to compute for training and evaluation. Can either be an metric from the `torchmetrics`
package, a custom metric inherenting from `torchmetrics.Metric`, a callable function or a list/dict
containing a combination of the aforementioned. In all cases, each metric needs to have the signature
`metric(preds,target)` and return a single scalar tensor. Defaults to :class:`torchmetrics.IOU`.
learning_rate: Learning rate to use for training.
multi_label: Whether the targets are multi-label or not.
output: The :class:`~flash.core.data.io.output.Output` to use when formatting prediction outputs.
output_transform: :class:`~flash.core.data.io.output_transform.OutputTransform` use for post processing samples.
"""
output_transform_cls = SemanticSegmentationOutputTransform
backbones: FlashRegistry = SEMANTIC_SEGMENTATION_BACKBONES
heads: FlashRegistry = SEMANTIC_SEGMENTATION_HEADS
required_extras: str = "image"
def __init__(
self,
num_classes: int,
backbone: Union[str, nn.Module] = "resnet50",
backbone_kwargs: Optional[Dict] = None,
head: str = "fpn",
head_kwargs: Optional[Dict] = None,
pretrained: Union[bool, str] = True,
loss_fn: LOSS_FN_TYPE = None,
optimizer: OPTIMIZER_TYPE = "Adam",
lr_scheduler: LR_SCHEDULER_TYPE = None,
metrics: METRICS_TYPE = None,
learning_rate: float = 1e-3,
multi_label: bool = False,
output: OUTPUT_TYPE = None,
output_transform: OUTPUT_TRANSFORM_TYPE = None,
) -> None:
if metrics is None:
metrics = IoU(num_classes=num_classes)
if loss_fn is None:
loss_fn = F.cross_entropy
# TODO: need to check for multi_label
if multi_label:
raise NotImplementedError("Multi-label not supported yet.")
super().__init__(
model=None,
loss_fn=loss_fn,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
metrics=metrics,
learning_rate=learning_rate,
output=output or SegmentationLabels(),
output_transform=output_transform or self.output_transform_cls(),
)
self.save_hyperparameters()
if not backbone_kwargs:
backbone_kwargs = {}
if not head_kwargs:
head_kwargs = {}
if isinstance(backbone, nn.Module):
self.backbone = backbone
else:
self.backbone = self.backbones.get(backbone)(**backbone_kwargs)
self.head: nn.Module = self.heads.get(head)(
backbone=self.backbone, num_classes=num_classes, pretrained=pretrained, **head_kwargs
)
self.backbone = self.head.encoder
def training_step(self, batch: Any, batch_idx: int) -> Any:
batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET])
return super().training_step(batch, batch_idx)
def validation_step(self, batch: Any, batch_idx: int) -> Any:
batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET])
return super().validation_step(batch, batch_idx)
def test_step(self, batch: Any, batch_idx: int) -> Any:
batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET])
return super().test_step(batch, batch_idx)
def predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int = 0) -> Any:
batch_input = batch[DataKeys.INPUT]
batch[DataKeys.PREDS] = super().predict_step(batch_input, batch_idx, dataloader_idx=dataloader_idx)
return batch
def forward(self, x) -> torch.Tensor:
res = self.head(x)
# some frameworks like torchvision return a dict.
# In particular, torchvision segmentation models return the output logits
# in the key `out`.
if _isinstance(res, Dict[str, torch.Tensor]):
res = res["out"]
return res
@classmethod
def available_pretrained_weights(cls, backbone: str):
result = cls.backbones.get(backbone, with_metadata=True)
pretrained_weights = None
if "weights_paths" in result["metadata"]:
pretrained_weights = list(result["metadata"]["weights_paths"])
return pretrained_weights
@staticmethod
def _ci_benchmark_fn(history: List[Dict[str, Any]]):
"""This function is used only for debugging usage with CI."""
assert history[-1]["val_iou"] > 0.2
| [
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| |
bf2bb21fe32c046e31ac269a94e444f91dc0217b | ca7aa979e7059467e158830b76673f5b77a0f5a3 | /Python_codes/p03626/s873858203.py | 5c64e4b78d0537220084f2cc138a77120c711579 | []
| 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 | 440 | py | MOD = 1000000007
n = int(input())
s1 = input()
s2 = input()
if s1[0] == s2[0]:
ans = 3
i = 1
prev = 1
else:
ans = 6
i = 2
prev = 2
while i<n:
if s1[i] == s2[i]:
i += 1
if prev == 1:
ans *= 2
else:
prev = 1
else:
i += 2
if prev == 1:
ans *= 2
prev = 2
else:
ans *= 3
ans %= MOD
print(ans) | [
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]
| |
deb150440060e0d6c968a2ccf2812970012b495a | 27c4e774f053594473da202c1c45dcbf237465be | /Scorm.py | 566403fdd0e407806057a4daa6e2727586ed572a | []
| no_license | Gamboua/zope-migration | 34e6b27962859352fe08a4277a8215b36b01889c | 7a83ed67c5ea561bfa8aa300728390b7220f3633 | refs/heads/master | 2020-12-25T14:49:22.173420 | 2017-10-19T20:47:50 | 2017-10-19T20:47:50 | 67,830,154 | 0 | 1 | null | 2016-10-20T21:42:09 | 2016-09-09T20:20:57 | PHP | UTF-8 | Python | false | false | 1,910 | py | import paramiko
import os
from scp import SCPClient
from config import *
from Command import Command
import random, string
class Scorm:
def __init__(self, scorm, course):
self.course = course
self.scp = None
self.type = 'scorm'
self.section = 0
self.folder = self.get_if_exists('folder', scorm)
self.title = self.get_if_exists('title', scorm)
def get_if_exists(self, parameter, json):
return json.get(parameter) if parameter in json else None
def scorm_add(self):
self.scorm_import_folder()
zip_name = self.scorm_zip()
Command.command_execute(Command.activity_create_command(
options=self.get_scorm_options(zip_name), type=self.type, id=self.course.id
))
def get_scorm_options(self, name):
params = []
if self.section is not None:
params.append('--section %s' % self.section)
if self.title:
params.append('--name "%s"' % self.title)
params.append('--filepath /tmp/%s.zip' % name)
return ' '.join(params)
def scorm_zip(self):
name = ''.join(random.choice(string.ascii_letters) for x in range(8))
os.chdir(self.folder)
os.system('zip -r /tmp/%s *' % name)
os.chdir(os.path.dirname(os.path.abspath(__file__)))
return name
def scorm_import_folder(self):
client = paramiko.SSHClient()
client.load_system_host_keys()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
client.connect(REMOTE_SCORM_SERVER, REMOTE_SCORM_PORT, REMOTE_SCORM_USER)
scp = SCPClient(client.get_transport())
if not os.path.isdir('/opt/zope298/courses'):
os.makedirs('/opt/zope298/courses')
scp.get(
self.folder,
'/opt/zope298/courses/',
recursive=True
)
scp.close()
| [
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| |
4a5b7c6844ca194b50ed70323648cba57b6e0b8d | c6e5bbafd810d23e0ee46d69026cba35339d1dbd | /accounts/managers.py | 42d3aae755fba1358031994ccd3a06d4ca8dcdd1 | []
| no_license | mfonism/django-inqueerstigate | 9c8b729848bf3df9fb9ec991ec47391b69ad7b66 | af5420bf8adf6aa89533cd1462d9eeed6e8c88db | refs/heads/main | 2023-05-26T12:59:55.774989 | 2021-06-07T11:46:48 | 2021-06-07T11:46:48 | 323,681,513 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,179 | py | from django.contrib.auth.models import BaseUserManager
class UserManager(BaseUserManager):
use_in_migrations = True
def _create_user(self, email, password, **extra_fields):
if not email:
raise ValueError("The given email must be set")
email = self.normalize_email(email)
user = self.model(email=email, **extra_fields)
user.set_password(password)
user.save(using=self._db)
return user
def create_user(self, email, password=None, **extra_fields):
extra_fields.setdefault("is_staff", False)
extra_fields.setdefault("is_superuser", False)
return self._create_user(email, password, **extra_fields)
def create_superuser(self, email, password, **extra_fields):
extra_fields.setdefault("is_staff", True)
extra_fields.setdefault("is_superuser", True)
if extra_fields.get("is_staff") is not True:
raise ValueError("Superuser must have is_staff=True.")
if extra_fields.get("is_superuser") is not True:
raise ValueError("Superuser must have is_superuser=True.")
return self._create_user(email, password, **extra_fields)
| [
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| |
847e83de22c9dbcb04f87362a0d956c786584799 | caace044baf7a6f2b0bda65ae361eed06bddfc3c | /dailyQuestion/2020/2020-06/06-01/python/solution_items.py | 1f7df5de69c465f7a57e918ca5eee350c02c2603 | [
"Apache-2.0"
]
| permissive | russellgao/algorithm | fd6126e89c40d7d351c53bbd5fde690c9be899ef | ad5e724d20a8492b8eba03fc0f24e4ff5964b3ea | refs/heads/master | 2023-03-28T03:00:02.370660 | 2021-03-28T10:56:38 | 2021-03-28T10:56:38 | 259,038,372 | 3 | 0 | null | null | null | null | UTF-8 | Python | false | false | 1,550 | py |
# Definition for singly-linked list.
class ListNode:
def __init__(self, x):
self.val = x
self.next = None
# 迭代
def sortList(head: ListNode) -> ListNode:
head_len = 0
invc = 1
h = head
while h :
head_len += 1
h = h.next
result = ListNode(0)
result.next = head
while invc <= head_len :
pre = result
h = result.next
while h :
h1 ,i = h , invc
while i and h :
i -= 1
h = h.next
if i :
break
h2, i = h, invc
while i and h :
i -= 1
h = h.next
c1, c2 = invc, invc-i
while c1 and c2 :
if h1.val > h2.val :
pre.next = h2
h2 = h2.next
c2 -= 1
else :
pre.next = h1
h1 = h1.next
c1 -= 1
pre = pre.next
pre.next = h1 if c1 else h2
while c1 > 0 or c2 > 0 :
pre = pre.next
c1 -= 1
c2 -= 1
pre.next = h
invc <<= 1
return result.next
if __name__ == "__main__" :
node = ListNode(4)
node.next = ListNode(2)
node.next.next = ListNode(1)
node.next.next.next = ListNode(3)
node.next.next.next.next = ListNode(5)
result = sortList(node)
while result :
print(result.val)
result = result.next
print()
| [
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| |
db0454c4c301f4b509ebb198c08bac7e87c6a3bd | d19d16ddc922b0915aff982568c5c71ee58fb8b9 | /dataset/utils.py | f13a627e795ae92c6dca77770e719e98d0542e2e | []
| no_license | zhaoyuzhi/HSGAN | 036a6fec722d564f9b203f6032bf47039c1eadd4 | f974761ec4a65ef58283ae4ccba618b97e79c4bc | refs/heads/main | 2023-08-03T10:06:05.195187 | 2023-07-27T14:21:54 | 2023-07-27T14:21:54 | 337,642,689 | 6 | 0 | null | null | null | null | UTF-8 | Python | false | false | 2,688 | py | import os
import numpy as np
# ----------------------------------------
# PATH processing
# ----------------------------------------
def check_path(path):
if not os.path.exists(path):
os.makedirs(path)
def get_files(path):
# read a folder, return the complete path
ret = []
for root, dirs, files in os.walk(path):
for filespath in files:
ret.append(os.path.join(root, filespath))
return ret
def get_jpgs(path):
# read a folder, return the image name
ret = []
for root, dirs, files in os.walk(path):
for filespath in files:
ret.append(filespath)
return ret
def get_mats(path):
# read a folder, return the image name
ret = []
for root, dirs, files in os.walk(path):
for filespath in files:
if filespath[-3:] == 'mat':
ret.append(os.path.join(root, filespath))
return ret
def get_mats_name(path):
# read a folder, return the image name
ret = []
for root, dirs, files in os.walk(path):
for filespath in files:
if filespath[-3:] == 'mat':
ret.append(filespath.split('.')[0])
return ret
def get_bmps(path):
# read a folder, return the image name
ret = []
for root, dirs, files in os.walk(path):
for filespath in files:
if filespath[-3:] == 'bmp':
ret.append(os.path.join(root, filespath))
return ret
def get_pairs_name(path):
# read a folder, return the image name
ret = []
for root, dirs, files in os.walk(path):
for filespath in files:
if filespath[-3:] == 'mat':
ret.append(filespath.split('.')[0])
return ret
# ----------------------------------------
# PATH processing
# ----------------------------------------
def text_readlines(filename):
# Try to read a txt file and return a list.Return [] if there was a mistake.
try:
file = open(filename, 'r')
except IOError:
error = []
return error
content = file.readlines()
# This for loop deletes the EOF (like \n)
for i in range(len(content)):
content[i] = content[i][:len(content[i])-1]
file.close()
return content
def text_save(content, filename, mode = 'a'):
# save a list to a txt
# Try to save a list variable in txt file.
file = open(filename, mode)
for i in range(len(content)):
file.write(str(content[i]) + '\n')
file.close()
def savetxt(name, loss_log):
np_loss_log = np.array(loss_log)
np.savetxt(name, np_loss_log)
| [
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971930662e9f48b55e5e7268f17b00a473b909c6 | 4fb5b869f6690b73e32a2d8624f5fc8954540b42 | /pypiplot/examples.py | b73f61adfb29a23b32d768d116b50680a0502255 | [
"MIT"
]
| permissive | erdogant/pypiplot | cc8eb15f9b6855cba270256591ba8b1ec4ae41f6 | 2016cca3d0b4022cda1806c2c4b8c4eb2d31ee19 | refs/heads/master | 2023-04-16T03:26:26.935072 | 2023-02-21T23:46:01 | 2023-02-21T23:46:01 | 293,334,020 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 3,271 | py | import pypiplot
# print(pypiplot.__version__)
# print(dir(Pypiplot))
from pypiplot import Pypiplot
# %% Update all libraries to date.
pp = Pypiplot(username='erdogant', repo_type=['owner', 'fork'])
pp.update()
results = pp.stats()
pp.plot_year(vmin=700)
pp.plot()
pp.plot_year()
# %% Top 10 best repos
pp = Pypiplot(username='erdogant', savepath='D://REPOS/pypiplot/repo_data/')
# Get download statistics
pp.stats()
# Get top 10
repo=pp.results['data'].sum().sort_values()[-10:].index.values
# Get stats for the top10
pp.stats(repo=repo)
# Plot
pp.plot()
#
pp.plot_year()
#
pp.plot_cal()
#
path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap_full.html'
pp.plot_heatmap(vmin=10, vmax=2000, cmap='interpolateOranges', path=path)
# %% Plot
# Init
pp = Pypiplot(username='erdogant', savepath='D://REPOS/pypiplot/repo_data/')
# Get download statistics
results = pp.stats()
# Store svg on github.io
# path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap.html'
path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap.html'
path = 'C://temp/pypi_heatmap.html'
pp.plot_year(path=path, vmin=700)
# Store all repo info in github.io
pp.plot(legend=False)
# %% D3blocks
pp = Pypiplot(username='d3blocks')
pp.update(repo=['d3blocks'])
pp.stats(repo='d3blocks')
pp.plot()
# %%
pp = Pypiplot(username='erdogant')
pp.stats(repo='distfit')
pp.plot_year()
pp.plot(vmin=25)
# %% Update single repo
pp.update(repo=['bnlearn'])
pp.update(repo='bnlearn')
results = pp.stats(repo=['distfit','pca', 'bnlearn'])
pp.plot(legend=True)
# %% Get some stats
results = pp.stats(repo=['df2onehot','pca','bnlearn','ismember','thompson'])
pp.plot(legend=True)
# %%
pp = Pypiplot(username='erdogant')
pp.stats(repo='distfit')
pp.plot_year()
pp.plot(vmin=25)
pp.stats(repo='worldmap')
pp.plot_year()
pp.stats(repo='hnet')
pp.plot_year()
pp.stats(repo='ismember')
pp.plot_year()
pp.stats(repo='flameplot')
pp.plot_year()
pp.stats(repo='pca')
pp.plot_year()
pp.stats()
pp.stats(repo=['df2onehot','clustimage','bnlearn','distfit','pypickle','clusteval','findpeaks', 'kaplanmeier','pca','colourmap'])
pp.results['data'].rolling(window=30).mean().plot(figsize=(15,10))
plt.grid(True)
plt.xlabel('Time')
plt.ylabel('Average nr. download based on a rolling window of 30 days')
# pp.results['data'].cumsum().plot()
pp.plot_year(vmin=100)
pp.plot(vmin=25)
pp.results['data'].cumsum().plot()
# %% Plot bnlearn
results = pp.stats(repo='bnlearn')
pp.plot_year()
# %%
pp.update()
results = pp.stats()
pp.plot_year(vmin=700)
pp.plot(vmin=25)
# %% Plot
# Init
pp = Pypiplot(username='erdogant', savepath='D://REPOS/pypiplot/repo_data/')
# Get download statistics
results = pp.stats()
# Store svg on github.io
path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap.html'
path = 'C://temp/pypi_heatmap.html'
pp.plot_year(path=path, vmin=700)
# Store all repo info in github.io
path = 'D://REPOS/erdogant.github.io/docs/imagesc/pypi/pypi_heatmap_repos.html'
pp.plot(path=path, vmin=100)
# %%
from pypiplot import Pypiplot
# results = pp.stats()
pp.stats(repo=['df2onehot','clustimage','bnlearn','distfit','pypickle','clusteval','findpeaks', 'kaplanmeier','colourmap'])
pp.plot_cal(method='mean', vmin=100)
pp.plot(method='mean')
# %%
| [
"[email protected]"
]
| |
a60ce595e94bd01b6f46c0cb382957eebfd7ab07 | 576cc83449e10fd3f98281970c46016ea7a5aea2 | /Tensorflow/CNN/莫烦python02.py | 2e08d2c51048bcd31c14f4a4a131722ae38111f1 | []
| no_license | HotView/PycharmProjects | 215ab9edd341e3293daebcf86d97537f8cd28d75 | 61393fe5ba781a8c1216a5cbe7e0d06149a10190 | refs/heads/master | 2020-06-02T07:41:53.608742 | 2019-11-13T08:31:57 | 2019-11-13T08:31:57 | 191,085,178 | 3 | 2 | null | null | null | null | UTF-8 | Python | false | false | 1,519 | py | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("data",one_hot=True)
def add_layer(inputs,in_size,out_size,activaion_function = None):
Weights = tf.Variable(tf.random_normal([in_size,out_size]))
biases = tf.Variable(tf.zeros([1,out_size])+0.1)
Wx_plus_b = tf.matmul(inputs,Weights)+biases
if activaion_function is None:
outputs = Wx_plus_b
else:
outputs =activaion_function(Wx_plus_b)
return outputs
def compute_accuracy(v_xs,v_ys):
global prediction
y_pre = sess.run(prediction,feed_dict={xs:v_xs})
correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
return result
xs = tf.placeholder(tf.float32,[None,784])
ys = tf.placeholder(tf.float32,[None,10])
# add output layer
prediction = add_layer(xs,784,10,activaion_function=tf.nn.softmax)
# error
crosss_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(crosss_entropy)
sess =tf.Session()
sess.run(tf.initialize_all_variables())
for i in range(5000):
batch_xs,batch_ys = mnist.train.next_batch(100)
sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
if i%50==0:
print(compute_accuracy(mnist.test.images,mnist.test.labels)) | [
"[email protected]"
]
| |
6567d0f8b19425ebfd1cd990c73c0e2498f971f2 | 41294ab88364fbb40ee67fcc643a91cc355c25d5 | /solution/accounting.py | 368251986f18af4b2806c42760073666909b3c70 | []
| no_license | tessajules/underpaid-customers-HB-homework | 96e542cc736d03b1476c88c43cd931081b03926d | ec3526debea68ecbf7aed25d041baf26110e40b2 | refs/heads/master | 2021-05-28T22:11:25.106565 | 2015-04-10T02:56:36 | 2015-04-10T02:56:36 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 969 | py | MELON_COST = 1.00
def melon_payment_calculator(payment_data):
"""Calculate cost of melons and determine who has underpaid."""
payment_data = open(payment_data)
for line in payment_data:
order = line.split('|')
customer_name = order[1]
customer_first = customer_name.split(" ")[0]
customer_melons = float(order[2])
customer_paid = float(order[3])
customer_expected = customer_melons * MELON_COST
if customer_expected < customer_paid:
print customer_name, "paid %.2f, expected %.2f" % (
customer_paid, customer_expected)
print customer_first, "has overpaid for their melons."
elif customer_expected > customer_paid:
print customer_name, "paid %.2f, expected %.2f" % (
customer_paid, customer_expected)
print customer_first, "has underpaid for their melons."
melon_payment_calculator("customer-orders.txt") | [
"[email protected]"
]
| |
ea8bb3f37fef6e37cd9f9274f22db69548ed5b99 | 1a59a9076c1e9f1eb98e24ff41a4c1c95e2b353e | /xcp2k/classes/_program_run_info36.py | df87e8835f3ba808b0a2fb5f2bbb04a979030521 | []
| no_license | Roolthasiva/xcp2k | 66b2f30ebeae1a946b81f71d22f97ea4076e11dc | fc3b5885503c6f6dc549efeb4f89f61c8b6b8242 | refs/heads/master | 2022-12-23T06:03:14.033521 | 2020-10-07T08:01:48 | 2020-10-07T08:01:48 | null | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 695 | py | from xcp2k.inputsection import InputSection
from xcp2k.classes._each343 import _each343
class _program_run_info36(InputSection):
def __init__(self):
InputSection.__init__(self)
self.Section_parameters = None
self.Add_last = None
self.Common_iteration_levels = None
self.Filename = None
self.Log_print_key = None
self.EACH = _each343()
self._name = "PROGRAM_RUN_INFO"
self._keywords = {'Add_last': 'ADD_LAST', 'Common_iteration_levels': 'COMMON_ITERATION_LEVELS', 'Filename': 'FILENAME', 'Log_print_key': 'LOG_PRINT_KEY'}
self._subsections = {'EACH': 'EACH'}
self._attributes = ['Section_parameters']
| [
"[email protected]"
]
| |
acc4aef5d2a6eb365488380fe43780058d19a3d6 | 9e988c0dfbea15cd23a3de860cb0c88c3dcdbd97 | /sdBs/AllRun/pg_1623+386/sdB_PG_1623+386_lc.py | 79bf55d3e02753efc2d185b0d3025a46f7a7b55a | []
| no_license | tboudreaux/SummerSTScICode | 73b2e5839b10c0bf733808f4316d34be91c5a3bd | 4dd1ffbb09e0a599257d21872f9d62b5420028b0 | refs/heads/master | 2021-01-20T18:07:44.723496 | 2016-08-08T16:49:53 | 2016-08-08T16:49:53 | 65,221,159 | 0 | 0 | null | null | null | null | UTF-8 | Python | false | false | 346 | py | from gPhoton.gAperture import gAperture
def main():
gAperture(band="NUV", skypos=[246.351292,38.505214], stepsz=30., csvfile="/data2/fleming/GPHOTON_OUTPU/LIGHTCURVES/sdBs/sdB_PG_1623+386 /sdB_PG_1623+386_lc.csv", maxgap=1000., overwrite=True, radius=0.00555556, annulus=[0.005972227,0.0103888972], verbose=3)
if __name__ == "__main__":
main()
| [
"[email protected]"
]
|
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