xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

4115154b2793e618bb09dcef63234d601449a08c

127

py

Python

Solutions/Habitable exoplanets.py

GuardsmanPanda/ProjectLovelace

50549114acfe98ae9511e3ec5d0e6c1335e30db9

[ "MIT" ]

null

Solutions/Habitable exoplanets.py

GuardsmanPanda/ProjectLovelace

50549114acfe98ae9511e3ec5d0e6c1335e30db9

[ "MIT" ]

null

Solutions/Habitable exoplanets.py

GuardsmanPanda/ProjectLovelace

50549114acfe98ae9511e3ec5d0e6c1335e30db9

[ "MIT" ]

null

def habitable_exoplanet(L, r): return 'too hot' if r < (L/1.11)**0.5 else 'too cold' if r > (L/0.54)**0.5 else 'just right'

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null

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5

f5c91deeb3888b319de3cd1e9d15bd34c498b056

229

py

Python

contact/views.py

asis2016/momo-ristorante-v1

d46c36d1b92212ade34d781c4e2adc91cb52cac7

[ "MIT" ]

null

contact/views.py

asis2016/momo-ristorante-v1

d46c36d1b92212ade34d781c4e2adc91cb52cac7

[ "MIT" ]

null

contact/views.py

asis2016/momo-ristorante-v1

d46c36d1b92212ade34d781c4e2adc91cb52cac7

[ "MIT" ]

null

from django.shortcuts import render from django.views.generic import TemplateView class ContactView(TemplateView): template_name = 'contact/index.html' def get_success_url(self): return reverse('contact:index')

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5

f5d1ca3568dbffb69a1334a2ae790c58b186bc45

256

py

Python

tests/test_map_dictionary_keys.py

melwell89/map-dictionary-keys

4133957f32b5e3f4b987694281cec7692ee1b0f0

[ "MIT" ]

null

tests/test_map_dictionary_keys.py

melwell89/map-dictionary-keys

4133957f32b5e3f4b987694281cec7692ee1b0f0

[ "MIT" ]

null

tests/test_map_dictionary_keys.py

melwell89/map-dictionary-keys

4133957f32b5e3f4b987694281cec7692ee1b0f0

[ "MIT" ]

null

from .test_data import input_dict, expected_output from map_dictionary_keys import map_dictionary_keys class TestMapDictionaryKeys: def test_valid_case(self): assert map_dictionary_keys(input_dict, lambda key: key.upper()) == expected_output

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null

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5

f5de34216649cfc00fd21d4ceede0ad047c01206

1,147

py

Python

tests/test_float.py

anistark/Parsenvy

280636676b80236b95e48c34fda1fb1e7343021e

[ "BSD-3-Clause" ]

39

2017-02-24T04:43:00.000Z

2021-02-24T02:13:29.000Z

tests/test_float.py

anistark/Parsenvy

280636676b80236b95e48c34fda1fb1e7343021e

[ "BSD-3-Clause" ]

41

2017-04-28T02:45:21.000Z

2021-02-25T06:50:51.000Z

tests/test_float.py

anistark/Parsenvy

280636676b80236b95e48c34fda1fb1e7343021e

[ "BSD-3-Clause" ]

11

2017-04-04T01:38:18.000Z

2021-02-24T01:58:05.000Z

import pytest import parsenvy def test_float_positive_integer(monkeypatch): monkeypatch.setenv("foo", str(float(13))) assert parsenvy.float("foo") == float(13) def test_float_positive_decimal(monkeypatch): monkeypatch.setenv("foo", str(float(13.42))) assert parsenvy.float("foo") == float(13.42) def test_float_negative_integer(monkeypatch): monkeypatch.setenv("foo", str(float(-13))) assert parsenvy.float("foo") == float(-13) def test_float_negative_decimal(monkeypatch): monkeypatch.setenv("foo", str(float(-13.42))) assert parsenvy.float("foo") == float(-13.42) def test_float_zero(monkeypatch): monkeypatch.setenv("foo", str(float(0))) assert parsenvy.float("foo") == float(0) def test_float_negative_zero(monkeypatch): monkeypatch.setenv("foo", str(float(-0))) assert parsenvy.float("foo") == float(-0) def test_float_invalid(monkeypatch): monkeypatch.setenv("foo", "bar") with pytest.raises(TypeError): parsenvy.float("foo") def test_float_empty(monkeypatch): monkeypatch.setenv("foo", "") with pytest.raises(TypeError): parsenvy.float("foo")

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1,147

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5

de477a57ca703415ab09d1bd35d0f1d6d2478d63

651

py

Python

plugins/github/komand_github/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/github/komand_github/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/github/komand_github/actions/__init__.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

# GENERATED BY KOMAND SDK - DO NOT EDIT from .add_collaborator.action import AddCollaborator from .add_issue_label.action import AddIssueLabel from .add_membership.action import AddMembership from .block_user.action import BlockUser from .close_issue.action import CloseIssue from .create.action import Create from .create_issue_comment.action import CreateIssueComment from .get_issues_by_repo.action import GetIssuesByRepo from .get_my_issues.action import GetMyIssues from .get_repo.action import GetRepo from .remove.action import Remove from .search.action import Search from .unblock_user.action import UnblockUser from .user.action import User

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5

de89de7c62cfa5032824293ea143797f51d17805

184

py

Python

datamart/materializers/parsers/csv_parser.py

juancroldan/datamart

9ec3b99f36192f812edd74ad2262bebccc22bc66

[ "MIT" ]

7

2018-10-02T01:32:23.000Z

2020-10-08T00:42:35.000Z

datamart/materializers/parsers/csv_parser.py

juancroldan/datamart

9ec3b99f36192f812edd74ad2262bebccc22bc66

[ "MIT" ]

47

2018-10-02T05:41:13.000Z

2021-02-02T21:50:31.000Z

datamart/materializers/parsers/csv_parser.py

juancroldan/datamart

9ec3b99f36192f812edd74ad2262bebccc22bc66

[ "MIT" ]

19

2018-10-01T22:27:20.000Z

2019-02-28T18:59:53.000Z

from datamart.materializers.parsers.parser_base import * class CSVParser(ParserBase): def get_all(self, url: str) -> typing.List[pd.DataFrame]: return [pd.read_csv(url)]

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5

de8a4ff49d12559c89bd27d9aa94d880d3487660

179

py

Python

app/api_v1/__init__.py

Medeirox/siots

96e0ac2ffec723d7343298daeae14f8755948763

[ "MIT" ]

null

app/api_v1/__init__.py

Medeirox/siots

96e0ac2ffec723d7343298daeae14f8755948763

[ "MIT" ]

null

app/api_v1/__init__.py

Medeirox/siots

96e0ac2ffec723d7343298daeae14f8755948763

[ "MIT" ]

null

from flask import Blueprint api_v1 = Blueprint('api_v1', __name__) from . import views from . import models models.create_table(models.Feed) models.create_table(models.Device)

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5

de8b70c0a06e176fada9e39910000eaed196af12

659

py

Python

vshare/utils/getnow.py

jeyrce/vshare

269fe05a4dc36f6fbf831ddf5057af95312b75ca

[ "Apache-2.0" ]

4

2019-11-30T06:07:14.000Z

2020-10-27T08:48:23.000Z

vshare/utils/getnow.py

jeeyshe/vshare

269fe05a4dc36f6fbf831ddf5057af95312b75ca

[ "Apache-2.0" ]

null

vshare/utils/getnow.py

jeeyshe/vshare

269fe05a4dc36f6fbf831ddf5057af95312b75ca

[ "Apache-2.0" ]

null

# coding = utf-8 # env = python3.5.2 # author = lujianxin # time = 2018-04-20 # purpose= 获得格式化当前时间 import time def now(): now_ = time.strftime('%Y-%m-%d %H:%M:%S') return now_ def date_time(): return time.strftime('%Y-%m-%d') def time_time(): return time.strftime('%H:%M:%S') def this_year(): return time.strftime('%Y') def this_month(): return time.strftime('%m') def this_day(): return time.strftime('%d') def this_hour(): return time.strftime('%H') def this_minute(): return time.strftime('%M') def this_second(): return time.strftime('%S') if __name__ == '__main__': print(now(), type(now())) pass

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5

72179d7661aca0826064ae0fb9ddcdaa2e0b0326

85

py

Python

sango/ext.py

short-greg/sango

68bcdbe8f4784fef6f7fc382ec2c4e81911c2a8a

[ "MIT" ]

null

sango/ext.py

short-greg/sango

68bcdbe8f4784fef6f7fc382ec2c4e81911c2a8a

[ "MIT" ]

null

sango/ext.py

short-greg/sango

68bcdbe8f4784fef6f7fc382ec2c4e81911c2a8a

[ "MIT" ]

1

2022-01-27T15:39:10.000Z

from ._nodes import * from ._vars import * from ._utils import * from ._ext import *

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72209e135cf101ab138efd0f1a0f2cd87a3d5c7e

151

py

Python

redux/cluster.py

devsearchcomponent/redux-python

6a026dd6ff9fcfb6631b42880f96341492ddbda9

[ "MIT" ]

1

2018-08-27T12:29:13.000Z

redux/cluster.py

xdusongwei/redux-python

6a026dd6ff9fcfb6631b42880f96341492ddbda9

[ "MIT" ]

null

redux/cluster.py

xdusongwei/redux-python

6a026dd6ff9fcfb6631b42880f96341492ddbda9

[ "MIT" ]

null

""" 一些未来打算增加的功能 redux自身把数据都集中在了state中，如果state可以序列化，很可能reducer也可以在任意网络中的进程中进行数据迁移另一方便，如果redux作为服务，所有在此运行的reducer可以作为容器承载在redux中，可能可以是热升级的机制的实现办法 """

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5

72435c87dd3c50a103223aa07ba6d57340e84d0c

5,332

py

Python

farmer/ncc/losses/losses.py

aiorhiroki/farmer

cf3bb93173efbf4a34b782be9faf40c707152ab8

[ "Apache-2.0" ]

10

2019-04-04T07:32:47.000Z

2021-01-07T00:40:50.000Z

farmer/ncc/losses/losses.py

aiorhiroki/farmer

cf3bb93173efbf4a34b782be9faf40c707152ab8

[ "Apache-2.0" ]

59

2019-04-18T05:44:31.000Z

2021-05-02T10:33:02.000Z

farmer/ncc/losses/losses.py

aiorhiroki/farmer

cf3bb93173efbf4a34b782be9faf40c707152ab8

[ "Apache-2.0" ]

4

2020-01-23T14:01:43.000Z

2021-02-11T04:16:14.000Z

import tensorflow as tf import segmentation_models from segmentation_models.base import Loss from segmentation_models.losses import CategoricalCELoss from ..losses import functional as F segmentation_models.set_framework('tf.keras') class DiceLoss(Loss): def __init__(self, beta=1, class_weights=None, flooding_level=0.): super().__init__(name='dice_loss') self.beta = beta self.class_weights = class_weights if class_weights is not None else 1 self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.dice_loss( gt=gt, pr=pr, beta=self.beta, class_weights=self.class_weights ), self.flooding_level) class JaccardLoss(Loss): def __init__(self, class_weights=None, flooding_level=0.): super().__init__(name='jaccard_loss') self.class_weights = class_weights if class_weights is not None else 1 self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.jaccard_loss( gt=gt, pr=pr, class_weights=self.class_weights ), self.flooding_level) class TverskyLoss(Loss): def __init__(self, alpha=0.45, beta=0.55, class_weights=None, flooding_level=0.): super().__init__(name='tversky_loss') self.alpha = alpha self.beta = beta self.class_weights = class_weights if class_weights is not None else 1. self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.tversky_loss( gt=gt, pr=pr, alpha=self.alpha, beta=self.beta, class_weights=self.class_weights ), self.flooding_level) class FocalTverskyLoss(Loss): def __init__(self, alpha=0.45, beta=0.55, gamma=2.5, class_weights=None, flooding_level=0.): super().__init__(name='focal_tversky_loss') self.alpha = alpha self.beta = beta self.gamma = gamma self.class_weights = class_weights if class_weights is not None else 1. self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.focal_tversky_loss( gt=gt, pr=pr, alpha=self.alpha, beta=self.beta, gamma=self.gamma, class_weights=self.class_weights ), self.flooding_level) class CategoricalFocalLoss(Loss): def __init__(self, alpha=0.25, gamma=2., class_weights=None, flooding_level=0.): super().__init__(name='categorical_focal_loss') self.alpha = alpha self.gamma = gamma self.class_weights = class_weights if class_weights is not None else 1. self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.categorical_focal_loss( gt, pr, alpha=self.alpha, gamma=self.gamma, class_weights=self.class_weights ), self.flooding_level) class LogCoshDiceLoss(Loss): def __init__(self, beta=1, class_weights=None, flooding_level=0.): super().__init__(name='log_cosh_dice_loss') self.beta = beta self.class_weights = class_weights if class_weights is not None else 1 self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.log_cosh_dice_loss( gt=gt, pr=pr, beta=self.beta, class_weights=self.class_weights ), self.flooding_level) class LogCoshTverskyLoss(Loss): def __init__(self, alpha=0.3, beta=0.7, class_weights=None, flooding_level=0.): super().__init__(name='log_cosh_tversky_loss') self.alpha = alpha self.beta = beta self.class_weights = class_weights if class_weights is not None else 1. self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.log_cosh_tversky_loss( gt=gt, pr=pr, alpha=self.alpha, beta=self.beta, class_weights=self.class_weights ), self.flooding_level) class LogCoshFocalTverskyLoss(Loss): def __init__(self, alpha=0.3, beta=0.7, gamma=1.3, class_weights=None, flooding_level=0.): super().__init__(name='log_cosh_focal_tversky_loss') self.alpha = alpha self.beta = beta self.gamma = gamma self.class_weights = class_weights if class_weights is not None else 1. self.flooding_level = flooding_level def __call__(self, gt, pr): return F.flooding(F.log_cosh_focal_tversky_loss( gt=gt, pr=pr, alpha=self.alpha, beta=self.beta, gamma=self.gamma, class_weights=self.class_weights ), self.flooding_level) class LogCoshLoss(Loss): def __init__(self, base_loss, flooding_level=0., **kwargs): super().__init__(name=f'log_cosh_{base_loss}') self.loss = getattr(F, base_loss) self.flooding_level = flooding_level self.kwargs = kwargs def __call__(self, gt, pr): x = self.loss(gt, pr, **self.kwargs) return F.flooding( tf.math.log((tf.exp(x) + tf.exp(-x)) / 2.0), self.flooding_level)

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a0d4dd994b446c9305599feaf6fb2f202da378bb

33

py

Python

main_start/config_var.py

aviskumar/speedo

758e8ac1fdeeb0b72c3a57742032ca5c79f0b2fa

[ "BSD-3-Clause" ]

null

main_start/config_var.py

aviskumar/speedo

758e8ac1fdeeb0b72c3a57742032ca5c79f0b2fa

[ "BSD-3-Clause" ]

null

main_start/config_var.py

aviskumar/speedo

758e8ac1fdeeb0b72c3a57742032ca5c79f0b2fa

[ "BSD-3-Clause" ]

3

2021-10-12T08:17:01.000Z

2021-12-21T01:17:54.000Z

from session.config_var import *

16.5

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9d09cbad278532a5ece80abaa8c83bca65292c8e

86

py

Python

data_hacking/lsh_sims/__init__.py

c4pr1c3/data_hacking

a2c746375a2b8704eb8f263f6e2b3250ad7ec0ab

[ "MIT" ]

1

2022-02-19T11:36:37.000Z

data_hacking/lsh_sims/__init__.py

c4pr1c3/data_hacking

a2c746375a2b8704eb8f263f6e2b3250ad7ec0ab

[ "MIT" ]

null

data_hacking/lsh_sims/__init__.py

c4pr1c3/data_hacking

a2c746375a2b8704eb8f263f6e2b3250ad7ec0ab

[ "MIT" ]

3

2017-09-23T01:17:54.000Z

2022-03-23T13:11:37.000Z

'''Package for the LSH (Locality Sensitive Hashing) Module''' from .lsh_sims import *

28.666667

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9d358430f00ecead9b8842c993c9f1dfce303c82

77

py

Python

Contest/ABC017/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

Contest/ABC017/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

Contest/ABC017/a/main.py

mpses/AtCoder

9c101fcc0a1394754fcf2385af54b05c30a5ae2a

[ "CC0-1.0" ]

null

#!/usr/bin/env python3 print(eval("+eval(input().replace(' ','*'))"*3) // 10)

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null

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9d36117e34cf09d2bbd19c030a83ea0ef89a9f46

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py

Python

feed/admin.py

mentix02/instagram

561f10a026fd25cb661e901c3404d050ab16620e

[ "MIT" ]

3

2021-03-31T08:46:17.000Z

2021-11-09T12:50:26.000Z

feed/admin.py

mentix02/instagram

561f10a026fd25cb661e901c3404d050ab16620e

[ "MIT" ]

null

feed/admin.py

mentix02/instagram

561f10a026fd25cb661e901c3404d050ab16620e

[ "MIT" ]

null

from django.contrib import admin from feed.models import FollowRequest admin.site.register(FollowRequest)

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108

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1

0

null

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1

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null

0

1

0

1

0

1

0

5

c232b25fc17c6e2f0c81d6d652fca4e89cfd8b4a

130

py

Python

server/schedule/admin.py

adamA113/servize

89933c3864d997188ec79ad690b37f51bca54aa3

[ "MIT" ]

null

server/schedule/admin.py

adamA113/servize

89933c3864d997188ec79ad690b37f51bca54aa3

[ "MIT" ]

null

server/schedule/admin.py

adamA113/servize

89933c3864d997188ec79ad690b37f51bca54aa3

[ "MIT" ]

2

2020-12-26T09:50:17.000Z

2020-12-26T09:52:45.000Z

from django.contrib import admin from schedule.models import Schedule # Register your models here. admin.site.register(Schedule)

21.666667

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130

5.944444

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0

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5

37

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true

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0.666667

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1

0

null

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1

0

null

0

1

0

1

0

1

0

5

c24db8227b4279b41a1e6cf364054b485c4b88f6

83

py

Python

todo/backend/todos/models/__init__.py

idle-solutions/vk-game

08aeff3fdd2a74ee1942bfe064fff988973aacdc

[ "MIT" ]

null

todo/backend/todos/models/__init__.py

idle-solutions/vk-game

08aeff3fdd2a74ee1942bfe064fff988973aacdc

[ "MIT" ]

1

2019-10-23T15:32:53.000Z

todo/backend/todos/models/__init__.py

idle-solutions/vk-game

08aeff3fdd2a74ee1942bfe064fff988973aacdc

[ "MIT" ]

null

from .character import Character from .player import Player from .todo import Todo

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83

5.666667

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0

0.144578

83

3

33

27.666667

0.957746

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

dfd22cef763145d2705b8139e9754eca5355838d

38

py

Python

tests/__init__.py

mechanicbuddy/djangito

07c08a83c57577cbf945bba461219bc0ef2a7695

[ "Apache-2.0" ]

null

tests/__init__.py

mechanicbuddy/djangito

07c08a83c57577cbf945bba461219bc0ef2a7695

[ "Apache-2.0" ]

null

tests/__init__.py

mechanicbuddy/djangito

07c08a83c57577cbf945bba461219bc0ef2a7695

[ "Apache-2.0" ]

null

"""Unit test package for djangito."""

19

37

0.684211

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38

5.2

1

0

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1

38

0.787879

0.815789

0

null

0

null

0

null

0

null

1

null

true

0

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1

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null

0

1

0

1

0

null

0

1

0

5

dfdb025fd0ebe22a13185257e8f511f2ac9dbe8b

70

py

Python

UI/__init__.py

mjbogusz/TSPGen

4916cf6276fda41b73ebdf24a7969167c63d0650

[ "MIT" ]

null

UI/__init__.py

mjbogusz/TSPGen

4916cf6276fda41b73ebdf24a7969167c63d0650

[ "MIT" ]

null

UI/__init__.py

mjbogusz/TSPGen

4916cf6276fda41b73ebdf24a7969167c63d0650

[ "MIT" ]

null

from UI.MapPainter import MapPainter from UI.TSPGenUI import TSPGenUI

23.333333

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0.857143

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70

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0.2

0

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2

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35

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1

0

true

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1

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1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

5

a03149fd4e1b34c12ec32d95d384805f87ae773c

66

py

Python

lithium/tests/__init__.py

PressLabs/lithium

a222e4021aabcbec0fd24bcecf904a0ee7ec852d

[ "Apache-2.0" ]

2

2015-03-20T10:57:14.000Z

2015-03-20T11:03:39.000Z

lithium/tests/__init__.py

PressLabs/lithium

a222e4021aabcbec0fd24bcecf904a0ee7ec852d

[ "Apache-2.0" ]

null

lithium/tests/__init__.py

PressLabs/lithium

a222e4021aabcbec0fd24bcecf904a0ee7ec852d

[ "Apache-2.0" ]

null

from .base import BaseTest from .fixtures import fixtures_wrapper

22

38

0.848485

9

66

6.111111

0.666667

0

0.121212

66

2

39

33

0.948276

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a034d68fcc0b6a57467b70d2088ec76ee3a07bdc

80

py

Python

tr.py

dpr-ankit/BeeKeepers

ef8ad12cec5e9f45e69182ef9d69fedfd7afed84

[ "CC-BY-3.0" ]

null

tr.py

dpr-ankit/BeeKeepers

ef8ad12cec5e9f45e69182ef9d69fedfd7afed84

[ "CC-BY-3.0" ]

null

tr.py

dpr-ankit/BeeKeepers

ef8ad12cec5e9f45e69182ef9d69fedfd7afed84

[ "CC-BY-3.0" ]

null

#!C:\Python27\python.exe print "Content-type: text/html" import cgi print "ytfg"

20

31

0.75

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80

4.615385

0.923077

0

0.027397

0.0875

80

4

32

20

0.794521

0.2875

0

0.473684

0

null

0

0.333333

null

0.666667

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a05323f93b6850c2f86aedb3b1a5dee16358027f

41

py

Python

Lib/site-packages/PIL/__main__.py

ashutoshsuman99/Web-Blog-D19

a01a0ccc40e8823110c01ebe4f43d9351df57295

[ "bzip2-1.0.6" ]

1,738

2017-09-21T10:59:12.000Z

2022-03-31T21:05:46.000Z

virtual/lib/python3.6/site-packages/PIL/__main__.py

kahenya-anita/Insta-Clone

4894e959c17170505e73aee6dc497aeb29d55a71

[ "MIT" ]

427

2017-09-29T22:54:36.000Z

2022-02-15T19:26:50.000Z

virtual/lib/python3.6/site-packages/PIL/__main__.py

kahenya-anita/Insta-Clone

4894e959c17170505e73aee6dc497aeb29d55a71

[ "MIT" ]

671

2017-09-21T08:04:01.000Z

2022-03-29T14:30:07.000Z

from .features import pilinfo pilinfo()

10.25

29

0.780488

5

41

6.4

0.8

0

0.146341

41

3

30

13.666667

0.914286

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

a07036b086a219eee210bb12ceb84fc268002c98

134

py

Python

omics/stats/__init__.py

choyichen/pybcb

60ba382be28bdbce466a9b24760fe44d421aa5ae

[ "MIT" ]

3

2017-05-11T02:13:03.000Z

2020-08-04T06:59:11.000Z

omics/stats/__init__.py

choyichen/pybcb

60ba382be28bdbce466a9b24760fe44d421aa5ae

[ "MIT" ]

null

omics/stats/__init__.py

choyichen/pybcb

60ba382be28bdbce466a9b24760fe44d421aa5ae

[ "MIT" ]

1

2020-07-03T06:57:51.000Z

"""Statistics functions. """ from .fisher import fisher_exact_test from .PCA import run_pca, plot_pca, plot_explained_variance_ratio

22.333333

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0.813433

19

134

5.368421

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0

0.104478

134

5

66

26.8

0.85

0.156716

0

1

0

true

0

1

0

1

0

1

0

null

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1

0

null

0

1

0

1

0

1

0

5

a098e837b2c800703a93696782f8a15118513e5f

499

py

Python

akutil/akutil/__init__.py

hokiegeek2/arkouda

1eed2df96aad212b9c6424b0d423d9375604c0ba

[ "MIT" ]

51

2021-05-15T01:35:20.000Z

2022-03-31T00:41:17.000Z

akutil/akutil/__init__.py

hokiegeek2/arkouda

1eed2df96aad212b9c6424b0d423d9375604c0ba

[ "MIT" ]

321

2021-05-12T16:02:45.000Z

2022-03-31T17:10:27.000Z

akutil/akutil/__init__.py

hokiegeek2/arkouda

1eed2df96aad212b9c6424b0d423d9375604c0ba

[ "MIT" ]

13

2021-06-03T13:44:21.000Z

2022-03-31T17:38:36.000Z

from akutil.dataframe import * from akutil.util import * from akutil.row import * from akutil.alignment import * from akutil.plotting import * from akutil.join import * from akutil.hdbscan import * from akutil.read import * from akutil.dtypes import * from akutil.segarray import * from akutil.series import * from akutil.index import * from pkg_resources import get_distribution, DistributionNotFound try: __version__ = get_distribution(__name__).version except DistributionNotFound: pass

26.263158

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0.799599

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499

6.0625

0.390625

0.309278

0.453608

0

0.138277

499

18

65

27.722222

0.902326

0

1

0

false

0.058824

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0

0.764706

0

null

1

0

null

0

1

0

1

0

5

a0a3d168004d283dd920401fb8de689a6815f286

125

py

Python

pyonepassword/op_items/__init__.py

pschelle/pyonepassword

2258c0fa851ad6a63c4f959982a66c715706b654

[ "MIT" ]

null

pyonepassword/op_items/__init__.py

pschelle/pyonepassword

2258c0fa851ad6a63c4f959982a66c715706b654

[ "MIT" ]

null

pyonepassword/op_items/__init__.py

pschelle/pyonepassword

2258c0fa851ad6a63c4f959982a66c715706b654

[ "MIT" ]

null

from ._op_item_type_registry import OPItemFactory from ._op_items_base import OPAbstractItem from .login import OPLoginItem

25

49

0.872

17

125

6

0.705882

0.117647

0

0.104

125

4

50

31.25

0.910714

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

261a0e05b6b4bda58e73e85b1b7324575ba6cb8e

137

py

Python

NUAAiCal/tests/test_main.py

NUAA-Open-Source/NUAA-iCal-Python

f71796545aa9d2f7a943f7c5d0dc2d80c6dfa4b6

[ "MIT" ]

17

2018-05-04T17:47:34.000Z

2021-07-28T11:35:17.000Z

NUAAiCal/tests/test_main.py

NUAA-Open-Source/NUAA-iCal-Python

f71796545aa9d2f7a943f7c5d0dc2d80c6dfa4b6

[ "MIT" ]

4

2018-04-27T09:16:28.000Z

2018-12-03T06:45:19.000Z

NUAAiCal/tests/test_main.py

NUAA-Open-Source/NUAA-iCal-Python

f71796545aa9d2f7a943f7c5d0dc2d80c6dfa4b6

[ "MIT" ]

5

2018-05-20T14:41:38.000Z

2019-11-13T05:01:21.000Z

# -*- coding:utf-8 -*- from __future__ import unicode_literals import pytest from NUAAiCal.main import main class TestMain: pass

12.454545

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137

5.333333

0.777778

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0.008929

0.182482

137

10

40

13.7

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0

1

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true

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null

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null

0

1

0

1

0

5

cd14a7790034b4df6492bb887c5baef80df8a334

128

py

Python

gremlinpy/__init__.py

emedgene/gremlinpy

80ccb02da3089317115190dc2b889b0d83be5e0e

[ "MIT" ]

59

2015-01-11T18:04:40.000Z

2022-03-09T13:15:52.000Z

gremlinpy/__init__.py

emedgene/gremlinpy

80ccb02da3089317115190dc2b889b0d83be5e0e

[ "MIT" ]

6

2015-12-17T14:40:19.000Z

2017-07-17T18:59:14.000Z

gremlinpy/__init__.py

emedgene/gremlinpy

80ccb02da3089317115190dc2b889b0d83be5e0e

[ "MIT" ]

7

2015-10-01T15:25:09.000Z

2017-07-28T10:02:00.000Z

from .version import __version__ from .gremlin import * from .config import * from .exception import * from .statement import *

21.333333

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0.773438

16

128

5.9375

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0.315789

0

0.15625

128

5

33

25.6

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0

1

0

true

0

1

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1

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1

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null

1

0

1

0

null

0

1

0

1

0

5

cd5700bf9e2d7e897fdf3339655a76eb522a717f

101

py

Python

codesignal/competitiveEating.py

andraantariksa/code-exercise-answer

69b7dbdc081cdb094cb110a72bc0c9242d3d344d

[ "MIT" ]

1

2019-11-06T15:17:48.000Z

codesignal/competitiveEating.py

andraantariksa/code-exercise-answer

69b7dbdc081cdb094cb110a72bc0c9242d3d344d

[ "MIT" ]

null

codesignal/competitiveEating.py

andraantariksa/code-exercise-answer

69b7dbdc081cdb094cb110a72bc0c9242d3d344d

[ "MIT" ]

1

2018-11-13T08:43:26.000Z

def competitiveEating(t, width, precision): return "{0:.{1}f}".format(t,precision).center(width)

33.666667

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0.70297

14

101

5.071429

0.785714

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0.021978

0.09901

101

2

57

50.5

0.758242

0

0.089109

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

26a1b57a92df6eb0a5edde538508491302480661

82

py

Python

ryu/ofproto/ether.py

MrCocoaCat/ryu

9e9571991a73380099b7ba7c6f37e0e587080a6a

[ "Apache-2.0" ]

null

ryu/ofproto/ether.py

MrCocoaCat/ryu

9e9571991a73380099b7ba7c6f37e0e587080a6a

[ "Apache-2.0" ]

null

ryu/ofproto/ether.py

MrCocoaCat/ryu

9e9571991a73380099b7ba7c6f37e0e587080a6a

[ "Apache-2.0" ]

null

# This module is for backward compat from ryu.lib.packet.ether_types import *

20.5

41

0.756098

13

82

4.692308

1

0

0.182927

82

3

42

27.333333

0.910448

0.414634

0

1

0

true

0

1

0

1

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1

0

null

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null

0

1

0

1

0

1

0

5

26da9f6a6c41191707a07beb0a09ce2e63b2ab46

241

py

Python

tests/test_core.py

totalhack/toolbox

f5095a824620af1fd5552cd0895fc76f2f843b6f

[ "MIT" ]

1

2019-09-09T18:53:03.000Z

tests/test_core.py

totalhack/tlbx

f5095a824620af1fd5552cd0895fc76f2f843b6f

[ "MIT" ]

null

tests/test_core.py

totalhack/tlbx

f5095a824620af1fd5552cd0895fc76f2f843b6f

[ "MIT" ]

null

import pytest from tlbx.core_utils import raiseif, raiseifnot def test_raiseif(): with pytest.raises(AssertionError): raiseif(1 != 2) def test_raiseifnot(): with pytest.raises(AssertionError): raiseifnot(1 == 2)

17.214286

47

0.692946

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241

5.655172

0.517241

0.085366

0.195122

0.365854

0

0.020942

0.207469

241

13

48

18.538462

0.837696

0

0.25

0

0.25

1

0.25

true

0

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

f82019e32d1db678c1696d6e878460496cd838a0

184

py

Python

etcaetera/formatters.py

oleiade/etcaetera

e1370cff5fc302ccdba24e7638b720d6cc43ffc0

[ "MIT" ]

4

2015-08-19T21:12:33.000Z

2022-01-25T01:13:46.000Z

etcaetera/formatters.py

bitprophet/etcaetera

f94e1a5a063744a55dfce94593ead59f32701c19

[ "MIT" ]

2

2015-08-13T12:45:43.000Z

2017-11-27T05:53:35.000Z

etcaetera/formatters.py

bitprophet/etcaetera

f94e1a5a063744a55dfce94593ead59f32701c19

[ "MIT" ]

2

2015-02-03T10:15:55.000Z

2016-10-21T14:20:10.000Z

from collections import namedtuple def uppercased(s): return s.upper() def lowercased(s): return s.lower() def environ(s): return s.strip().upper().replace(' ', '_')

13.142857

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0.646739

24

184

4.916667

0.583333

0.177966

0.20339

0

0.195652

184

13

47

14.153846

0.797297

0

0.01087

0

1

0.428571

false

0

0.142857

0.428571

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f83f4b0ddd95776833f1ac4e4389c5d80570c159

10,722

py

Python

backend/web/service/LagHandler.py

asterfusion/Tapplet-Web

d32a077810ae27d50f010e058242f04e497ef68a

[ "MIT" ]

3

2019-12-24T03:52:39.000Z

2019-12-30T11:47:53.000Z

backend/web/service/LagHandler.py

asterfusion/Tapplet-Web

d32a077810ae27d50f010e058242f04e497ef68a

[ "MIT" ]

null

backend/web/service/LagHandler.py

asterfusion/Tapplet-Web

d32a077810ae27d50f010e058242f04e497ef68a

[ "MIT" ]

6

2019-12-16T08:38:07.000Z

2020-12-02T19:37:25.000Z

#!/usr/bin/python3 from tornado.web import url,RequestHandler from tornado.web import Application, RequestHandler from tornado.ioloop import IOLoop from tornado.httpserver import HTTPServer import tornado.autoreload from tornado.concurrent import run_on_executor from concurrent.futures import ThreadPoolExecutor import json import logging import sys sys.path.append('./web/control/') sys.path.append('./web/database/') import interface_http import User import permiss import Lag import Rule import session import data_operation import Logconfig from Logconfig import Web_log import BaseHandler Open_permiss=0 class ListOutgroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_read') except : self.set_status(400,'') @tornado.gen.coroutine def get(self): self.set_header("Content-Type","application/json") Laglist=yield Lag.outlist_select(self) LagData=json.dumps(Laglist) self.write(LagData) class ListIngroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_read') except : self.set_status(400,'') @tornado.gen.coroutine def get(self): self.set_header("Content-Type","application/json") Laglist=yield Lag.inlist_select(self) LagData=json.dumps(Laglist) self.write(LagData) class InsertOutgroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) res=yield Lag.outlist_write(self,data) if(res==True): yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','创建出端口组',ip_address,json.dumps(data),200) self.write(json.dumps({"status_code":200,"res":"OK"})) self.set_status(200,'ok') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','创建出端口组',ip_address,json.dumps(data),400) self.write(json.dumps({"status_code":400,"res":"outgroupname is exsited"})) self.set_status(400,'existed') class InsertIngroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) res=yield Lag.inlist_write(self,data) if(res==True): yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','创建入端口组',ip_address,json.dumps(data),200) self.set_status(200,'OK') self.write(json.dumps("inlist insert ok")) else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','创建入端口组',ip_address,json.dumps(data)+'\nname is exsited',400) self.write(json.dumps({"status_code":400,"res":"name is exsited"})) self.set_status(400,'existed') class DeleteOutgroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) data=data["name"] res=yield Lag.outlist_delete(self,data) if(res==True): yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','删除出端口组',ip_address,json.dumps(data),200) self.write(json.dumps('OK')) self.set_status(200,'ok') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','删除出端口组',ip_address,json.dumps(data)+'\nnot exsited',400) self.write(json.dumps({"status_code":400,"res":"outgroupname is not exsited"})) self.set_status(400,'not existed') class DeleteIngroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) data=data["name"] res=yield Lag.inlist_delete(self,data) if(res==True): res_rule=Rule.rulegroup_delete('',data) if res_rule==True: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','删除入端口组',ip_address,json.dumps(data),200) self.write(json.dumps('OK')) self.set_status(200,'ok') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','删除入端口组',ip_address,json.dumps(data)+'\nrule is not exsited',400) self.write(json.dumps({"status_code":400,"res":"rule is not exsited"})) self.set_status(400,'not existed') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','删除入端口组',ip_address,json.dumps(data)+'\ningroupname is not exsited',400) self.write(json.dumps({"status_code":400,"res":"ingroupname is not exsited"})) self.set_status(400,'not existed') class UpdateIngroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip data=data_operation.ByteToJson(self.request.body) self.set_header("Content-Type","application/json") if "deduplication_cfg" not in data: data["deduplication_cfg"]='' res=yield Lag.inlist_update(self,data) if(res==True): yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新入端口组',ip_address,json.dumps(data),200) self.write(json.dumps('OK')) self.set_status(200,'ok') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新入端口组',ip_address,json.dumps(data)+'\ningroupname is not exsited',400) self.write(json.dumps({"status_code":400,"res":"ingroupname is not exsited"})) self.set_status(400,'not existed') class ReplaceOutgroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) res=Lag.outlist_update(data) if(res==True): yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新出端口组',ip_address,json.dumps(data),200) self.write(json.dumps('OK')) self.set_status(200,'ok') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新出端口组',ip_address,json.dumps(data),400) self.write(json.dumps({"status_code":400,"res":"update failed"})) self.set_status(400,'not existed') class UpdateOutgroupHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) res=Lag.outlist_put(data) if(res==True): self.write(json.dumps('OK')) self.set_status(200,'ok') else: self.write(json.dumps({"status_code":400,"res":"outgroupname is not exsited"})) self.set_status(400,'not existed') class UpdatePortHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) res=Lag.interlist_update(data["type"],data["groupname"],data["port"]) if data["type"]=='Egress': log_name='出' else: log_name='入' if(res==True): yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新'+log_name+'端口组',ip_address,json.dumps(data),200) self.write(json.dumps('OK')) self.set_status(200,'ok') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新'+log_name+'端口组',ip_address,json.dumps(data),400) self.write(json.dumps({"status_code":400,"res":"outgroupname is not exsited"})) self.set_status(400,'not existed') class DeletePortHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_write') except : self.set_status(400,'') @tornado.gen.coroutine def post(self): ip_address=self.request.remote_ip self.set_header("Content-Type","application/json") data=data_operation.ByteToJson(self.request.body) res=Lag.interlist_delete(data["type"],data["groupname"],data["port"]) if data["type"]=='Egress': log_name='出' else: log_name='入' if(res==True): yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新'+log_name+'端口组',ip_address,json.dumps(data),200) self.write(json.dumps('OK')) self.set_status(200,'ok') else: yield Logconfig.Write_Sys_Log(self,self.username,'转发策略','更新'+log_name+'端口组',ip_address,json.dumps(data),400) self.write(json.dumps({"status_code":400,"res":"outgroupname is not exsited"})) self.set_status(400,'not existed') class getDefaultRuleInterfaceHandler(BaseHandler.BaseHandler): executor = ThreadPoolExecutor(20) @tornado.gen.coroutine def prepare(self): try: super().prepare('policy_read') except : self.set_status(400,'') @tornado.gen.coroutine def get(self): self.set_header("Content-Type","application/json") data2=Lag.getDefaultInterface() data1={ "status_code": 200, "message": "success", "data": data2 } self.set_status(200,'') self.write(json.dumps(data1)) if __name__ == "__main__": app = Application( [ (r"/api/policy/ListIngroup",ListIngroupHandler), (r"/api/policy/InsertIngroup",InsertIngroupHandler), (r"/api/policy/DeleteIngroup",DeleteIngroupHandler), (r"/api/policy/UpdateIngroup",UpdateIngroupHandler), (r"/api/policy/ListOutgroup",ListOutgroupHandler), (r"/api/policy/InsertOutgroup",InsertOutgroupHandler), (r"/api/policy/DeleteOutgroup",DeleteOutgroupHandler), (r"/api/policy/UpdatePort",UpdatePortHandler), (r"/api/policy/UpdatePort",DeletePortHandler), (r"/api/policy/UpdateOutgroup",UpdateOutgroupHandler)],cookie_secret="12334") app.listen(8000) IOLoop.current().start()

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1

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null

0

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f84ee815bcdd20bca9595ad4e58c311ad4ae29bf

367

py

Python

dynamodb_meta_store/exceptions.py

sergeymazin/dynamodb-meta-store

33757240fd823f830f1d36ef6f04c2a82ee88118

[ "Apache-2.0" ]

null

dynamodb_meta_store/exceptions.py

sergeymazin/dynamodb-meta-store

33757240fd823f830f1d36ef6f04c2a82ee88118

[ "Apache-2.0" ]

null

dynamodb_meta_store/exceptions.py

sergeymazin/dynamodb-meta-store

33757240fd823f830f1d36ef6f04c2a82ee88118

[ "Apache-2.0" ]

null

class TableNotReadyException(Exception): """ Exception thrown if the table is not in ACTIVE or UPDATING state """ pass class MisconfiguredSchemaException(Exception): """ Exception thrown if the table does not match the configuration """ pass class ItemNotFound(Exception): """ Exception thrown if the item does not exist in table """ pass

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5

f85af2aa54df7f04580f0e826b8cf304ae9e57f6

85

py

Python

cube_js_client/__init__.py

downstreamimpact/CubeJsClient

6dc6c0e66e7bcd9a099105f22f1c6b1b610298fe

[ "MIT" ]

10

2019-11-12T04:37:26.000Z

2021-05-02T19:57:31.000Z

cube_js_client/__init__.py

downstreamimpact/CubeJsClient

6dc6c0e66e7bcd9a099105f22f1c6b1b610298fe

[ "MIT" ]

null

cube_js_client/__init__.py

downstreamimpact/CubeJsClient

6dc6c0e66e7bcd9a099105f22f1c6b1b610298fe

[ "MIT" ]

1

2020-04-19T03:36:18.000Z

from .exceptions import CubeError, CubeTimeoutError from .client import CubeJsClient

28.333333

51

0.858824

9

85

8.111111

0.777778

0

0.105882

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0

1

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1

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5

f8925017ca366f2941e9294288a12581434805d4

303

py

Python

tests/__init__.py

FelixKleineBoesing/FeatureSelector

b33454be39d53881b1c1b5b7b6dca8d782cabd36

[ "MIT" ]

null

tests/__init__.py

FelixKleineBoesing/FeatureSelector

b33454be39d53881b1c1b5b7b6dca8d782cabd36

[ "MIT" ]

6

2019-02-25T08:09:48.000Z

2019-02-25T08:11:55.000Z

tests/__init__.py

FelixKleineBoesing/pyFeatSel

b33454be39d53881b1c1b5b7b6dca8d782cabd36

[ "MIT" ]

null

from pyFeatSel.Models.Model import XGBoostModel, Model from pyFeatSel.FeatureSelectors.GreedySearch import GreedySearch from pyFeatSel.FeatureSelectors.CompleteFeatureSpace import CompleteFeatureSpace from pyFeatSel.Evaluator.Evaluator import EvaluatorBase, RMSE, Recall, Accuracy, FOneScore, Precision

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4

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null

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1

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1

0

1

0

5

f8a0fad66e76f163dc451b423f669802e70cbde5

134

py

Python

sqlite3_kernel/__main__.py

brownan/sqlite3-kernel

a24301d9c53765c4f23151c2f4fb8c8fba053fa0

[ "BSD-3-Clause" ]

15

2016-10-19T17:18:38.000Z

2022-03-14T00:28:44.000Z

sqlite3_kernel/__main__.py

brownan/sqlite3-kernel

a24301d9c53765c4f23151c2f4fb8c8fba053fa0

[ "BSD-3-Clause" ]

4

2016-10-24T10:28:37.000Z

2019-01-23T14:14:21.000Z

sqlite3_kernel/__main__.py

brownan/sqlite3-kernel

a24301d9c53765c4f23151c2f4fb8c8fba053fa0

[ "BSD-3-Clause" ]

13

2017-08-12T14:35:37.000Z

2020-07-07T13:30:48.000Z

from ipykernel.kernelapp import IPKernelApp from .kernel import Sqlite3Kernel IPKernelApp.launch_instance(kernel_class=Sqlite3Kernel)

33.5

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0

5

3e4ffb9c4c69ee3c927d45cf1692479b3359c012

54

py

Python

k_choice/graphical/two_choice/strategies/__init__.py

varikakasandor/dissertation-balls-into-bins

fba69dd5ffd0b4984795c9a5ec119bf8c6f47d9e

[ "Apache-2.0" ]

null

k_choice/graphical/two_choice/strategies/__init__.py

varikakasandor/dissertation-balls-into-bins

fba69dd5ffd0b4984795c9a5ec119bf8c6f47d9e

[ "Apache-2.0" ]

null

k_choice/graphical/two_choice/strategies/__init__.py

varikakasandor/dissertation-balls-into-bins

fba69dd5ffd0b4984795c9a5ec119bf8c6f47d9e

[ "Apache-2.0" ]

null

from k_choice.graphical.two_choice.strategies import *

54

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54

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0.875

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54

1

54

0.882353

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5

e40de875bc299e7947d04c32ab82a8cf017d14ce

137

py

Python

nasco_analysis/__init__.py

nanten2/NASCO-analysis

1966de41fb1dd214d067e9c26656495f34c22dea

[ "MIT" ]

1

2020-12-16T01:59:04.000Z

nasco_analysis/__init__.py

nanten2/NASCO-analysis

1966de41fb1dd214d067e9c26656495f34c22dea

[ "MIT" ]

42

2020-11-27T08:30:50.000Z

2021-04-25T06:35:08.000Z

nasco_analysis/__init__.py

nanten2/NASCO-analysis

1966de41fb1dd214d067e9c26656495f34c22dea

[ "MIT" ]

null

__version__ = "0.1.0" from . import kisa_rev from . import io from . import doppler from . import grid_convolve from . import Planet_OTF

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1

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5

e40e6572fb6634ff0805c21bed13985d775ca948

102

py

Python

codes/requirements.py

LivLilli/EPL

357f9eec1109619362c32efd8a6bb6a9eb3c2ee6

[ "MIT" ]

null

codes/requirements.py

LivLilli/EPL

357f9eec1109619362c32efd8a6bb6a9eb3c2ee6

[ "MIT" ]

null

codes/requirements.py

LivLilli/EPL

357f9eec1109619362c32efd8a6bb6a9eb3c2ee6

[ "MIT" ]

null

import pandas as pd import numpy as np import matplotlib.pyplot as plt from collections import Counter

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null

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0

5

e441c18714db586b0df6c10f0f09a1ffde305b46

3,391

py

Python

enroll/migrations/0002_auto_20200620_1311.py

sudama-Inc/crud_project_python

7615c1eeafdc51b61c6e7cba217d37527307e105

[ "MIT" ]

null

enroll/migrations/0002_auto_20200620_1311.py

sudama-Inc/crud_project_python

7615c1eeafdc51b61c6e7cba217d37527307e105

[ "MIT" ]

null

enroll/migrations/0002_auto_20200620_1311.py

sudama-Inc/crud_project_python

7615c1eeafdc51b61c6e7cba217d37527307e105

[ "MIT" ]

null

# Generated by Django 3.0.7 on 2020-06-20 07:41 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('enroll', '0001_initial'), ] operations = [ migrations.RenameField( model_name='user', old_name='name', new_name='bank', ), migrations.RemoveField( model_name='user', name='email', ), migrations.RemoveField( model_name='user', name='password', ), migrations.AddField( model_name='user', name='bankcharges', field=models.IntegerField(default=0), preserve_default=False, ), migrations.AddField( model_name='user', name='brand', field=models.CharField(default=0, max_length=70), preserve_default=False, ), migrations.AddField( model_name='user', name='cheque', field=models.IntegerField(default=0), preserve_default=False, ), migrations.AddField( model_name='user', name='cltnamt', field=models.IntegerField(default=0), preserve_default=False, ), migrations.AddField( model_name='user', name='cltndate', field=models.DateField(blank=True, null=True), ), migrations.AddField( model_name='user', name='collectedby', field=models.CharField(default=0, max_length=70), preserve_default=False, ), migrations.AddField( model_name='user', name='customer', field=models.CharField(default=0, max_length=70), preserve_default=False, ), migrations.AddField( model_name='user', name='customercode', field=models.CharField(default=0, max_length=70), preserve_default=False, ), migrations.AddField( model_name='user', name='doptdate', field=models.DateField(blank=True, null=True), ), migrations.AddField( model_name='user', name='duedate', field=models.DateField(blank=True, null=True), ), migrations.AddField( model_name='user', name='invamt', field=models.IntegerField(default=0), preserve_default=False, ), migrations.AddField( model_name='user', name='invdate', field=models.DateField(blank=True, null=True), ), migrations.AddField( model_name='user', name='paymentmode', field=models.CharField(default=0, max_length=70), preserve_default=False, ), migrations.AddField( model_name='user', name='status', field=models.CharField(default='Pending', max_length=70), ), migrations.AddField( model_name='user', name='utrno', field=models.IntegerField(default=0), preserve_default=False, ), ]

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1

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null

0

5

e496fdd0a68b822cd97e3daf90327fa4452b9658

113

py

Python

reqe/__init__.py

ophlr/reqe

6c9eccc40d163bb0903ab1a9b54e062a7bccffdf

[ "Apache-2.0" ]

1

2020-08-11T10:17:59.000Z

reqe/__init__.py

ophlr/reqe

6c9eccc40d163bb0903ab1a9b54e062a7bccffdf

[ "Apache-2.0" ]

null

reqe/__init__.py

ophlr/reqe

6c9eccc40d163bb0903ab1a9b54e062a7bccffdf

[ "Apache-2.0" ]

null

from .api import request, get, head, post, patch, put, delete, options from .session import session, ReqeSession

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5

e4a30c03290163ac91c04b30652675e29a650a89

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py

Python

data_io/minibatches/__init__.py

Rekrau/PyGreentea

457d7dc5be12b15c3c7663ceaf6d74301de56e43

[ "BSD-2-Clause" ]

null

data_io/minibatches/__init__.py

Rekrau/PyGreentea

457d7dc5be12b15c3c7663ceaf6d74301de56e43

[ "BSD-2-Clause" ]

4

2016-04-22T15:39:21.000Z

2016-11-15T21:23:58.000Z

data_io/minibatches/__init__.py

Rekrau/PyGreentea

457d7dc5be12b15c3c7663ceaf6d74301de56e43

[ "BSD-2-Clause" ]

4

2017-05-12T00:17:55.000Z

2019-07-01T19:23:32.000Z

from .augmentation import augment_data_elastic, augment_data_simple

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null

0

1

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1

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5

901bbc41c1ba670fb481aff9d38e18a69297ccd7

36

py

Python

upnpavcontrol/web/__init__.py

mikedevnull/upnp-av-control

fee17c29bb713f1e4191b2ba64a7f552b4b663d8

[ "MIT" ]

2

2020-04-27T21:33:27.000Z

2022-01-12T22:17:52.000Z

upnpavcontrol/web/__init__.py

mikedevnull/upnp-av-control

fee17c29bb713f1e4191b2ba64a7f552b4b663d8

[ "MIT" ]

165

2020-04-18T23:41:58.000Z

2022-03-31T11:33:09.000Z

upnpavcontrol/web/__init__.py

mikedevnull/upnp-av-control

fee17c29bb713f1e4191b2ba64a7f552b4b663d8

[ "MIT" ]

null

from .application import app # noqa

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0.777778

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0.933333

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null

0

1

0

null

0

1

0

1

0

5

902dbaebbd6a23654edae6e48b8ec26e8a4c1948

877

py

Python

commands/parceiros.py

macoin-finance/telegram-bot

ea4a76356b0a80eb1ef0994c6be2048a0fc3c486

[ "BSD-3-Clause" ]

9

2021-07-10T04:43:30.000Z

2022-02-23T04:57:15.000Z

commands/parceiros.py

macoin-finance/telegram-bot

ea4a76356b0a80eb1ef0994c6be2048a0fc3c486

[ "BSD-3-Clause" ]

2

2021-07-12T01:56:18.000Z

2021-07-12T02:05:42.000Z

commands/parceiros.py

macoin-finance/telegram-bot

ea4a76356b0a80eb1ef0994c6be2048a0fc3c486

[ "BSD-3-Clause" ]

6

2021-07-10T04:44:32.000Z

2021-07-28T16:34:51.000Z

MESSAGE_TEXT='**Carteira de MACOIN da Associação Reconstruir Cannabis:**\t0xE14EA0C3FCF43b0f8423c23840Cf34F26F8d0cBe\n\nSite:\thttps://reconstruir.org.br\n\nInstagram:\thttps://instagram.com/reconstruircannabis\n\n\n#################################\n\nCarteira de MACOIN do Dr. Emílio Figueiredo 0xe034335EDD9966d4d0d27f99075B10308e664e49\n\nAdvogado do GROWROOM de 2009 a 2016 e atual advogado da COMUNIDADE MACOIN, o Bitcoin da maconha medicinal\nTrabalha a anos pela defesa criminal para usuários, cultivadores e pacientes, Habeas Corpus preventivo para cultivo de cannabis e consultoria para pessoas jurídicas (empresas e associações) que querem empreender com cannabis\n\nTelefone: +5521-991253403' def parceiros(update, context): context.bot.send_message(chat_id=update.effective_chat.id, text=MESSAGE_TEXT , parse_mode='markdown', disable_web_page_preview='True')

175.4

704

0.800456

113

877

6.132743

0.699115

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0

0.092269

0.085519

877

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705

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1

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0

null

0

1

0

5

902e27a64b964676f7b70c52d94df8f49a354dea

80

py

Python

ddd_seedwork/flask_utils/__init__.py

aherculano/ddd-seedwork

9b8b4ad722681190ac3c2d3e2ee5b471af02958f

[ "MIT" ]

1

2020-07-07T13:45:21.000Z

ddd_seedwork/flask_utils/__init__.py

aherculano/ddd-seedwork

9b8b4ad722681190ac3c2d3e2ee5b471af02958f

[ "MIT" ]

null

ddd_seedwork/flask_utils/__init__.py

aherculano/ddd-seedwork

9b8b4ad722681190ac3c2d3e2ee5b471af02958f

[ "MIT" ]

1

2020-07-07T13:45:56.000Z

from .ErrorHandlers import register_errors from .ApiResponse import ApiResponse

26.666667

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0.875

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80

7.666667

0.666667

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0.1

80

2

43

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0.958333

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true

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1

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

5f4e694e9a72304263e985c672f9ec4860901960

233

py

Python

3.string/1.string_array.py

Tazri/Python

f7ca625800229c8a7e20b64810d6e162ccb6b09f

[ "DOC" ]

null

3.string/1.string_array.py

Tazri/Python

f7ca625800229c8a7e20b64810d6e162ccb6b09f

[ "DOC" ]

null

3.string/1.string_array.py

Tazri/Python

f7ca625800229c8a7e20b64810d6e162ccb6b09f

[ "DOC" ]

null

name = "Md Tazri"; print("name : ",name); print("name[0] : ",name[0]); print("name[-1] : ",name[-1]); print("name[2:] : ",name[2:]); print("name[:3] : ",name[:3]); print("name[3:-2] :",name[3:-2]); print("name[::-1] : ",name[::-1]);

25.888889

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0

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233

9

34

25.888889

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1

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false

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1

0

1

0

null

0

1

0

5

5f95837c5e250fcbe9fd9790e3c0f9459a4bc21a

2,061

py

Python

tests/test_select.py

felipefrancisco/spectacles

92f7af5810e2669343dd18425b2a8cb49d7167d2

[ "MIT" ]

150

2019-10-05T18:35:36.000Z

2022-03-26T21:21:44.000Z

tests/test_select.py

felipefrancisco/spectacles

92f7af5810e2669343dd18425b2a8cb49d7167d2

[ "MIT" ]

406

2019-10-03T14:54:22.000Z

2022-03-28T04:02:31.000Z

tests/test_select.py

felipefrancisco/spectacles

92f7af5810e2669343dd18425b2a8cb49d7167d2

[ "MIT" ]

26

2019-11-08T16:21:50.000Z

2022-03-28T06:06:14.000Z

import pytest from spectacles.select import is_selected, selector_to_pattern from spectacles.exceptions import SpectaclesException def test_invalid_format_should_raise_value_error(): with pytest.raises(SpectaclesException): selector_to_pattern("model_a.explore_a") with pytest.raises(SpectaclesException): selector_to_pattern("model_a/") with pytest.raises(SpectaclesException): selector_to_pattern("explore_a") def test_empty_selector_should_raise_value_error(): with pytest.raises(ValueError): is_selected("model_a", "explore_a", [], []) def test_select_wildcard_should_match(): assert is_selected("model_a", "explore_a", ["*/*"], []) assert is_selected("model_a", "explore_a", ["model_b/explore_a", "*/*"], []) def test_select_model_wildcard_should_match(): assert is_selected("model_a", "explore_a", ["model_a/*"], []) assert is_selected("model_a", "explore_b", ["model_a/*"], []) def test_select_explore_wildcard_should_match(): assert is_selected("model_a", "explore_a", ["*/explore_a"], []) assert is_selected("model_b", "explore_a", ["*/explore_a"], []) def test_select_exact_model_and_explore_should_match(): assert is_selected("model_a", "explore_a", ["model_a/explore_a"], []) def test_select_wrong_model_should_not_match(): assert not is_selected("model_a", "explore_a", ["model_b/explore_a"], []) def test_select_wrong_explore_should_not_match(): assert not is_selected("model_a", "explore_a", ["model_a/explore_b"], []) def test_exclude_wildcard_should_not_match(): assert not is_selected("model_a", "explore_a", ["*/*"], ["*/*"]) def test_exclude_model_wildcard_should_not_match(): assert not is_selected("model_a", "explore_a", ["*/*"], ["model_a/*"]) def test_exclude_explore_wildcard_should_not_match(): assert not is_selected("model_a", "explore_a", ["*/*"], ["*/explore_a"]) def test_exclude_exact_model_and_explore_should_not_match(): assert not is_selected("model_a", "explore_a", ["*/*"], ["model_a/explore_a"])

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0.721494

278

2,061

4.841727

0.129496

0.130758

0.120357

0.156018

0.808321

0.783804

0.739227

0.644874

0.560921

0.47474

0

0.12033

2,061

62

83

33.241935

0.742416

0

0.083333

0

0.205725

0

0.361111

1

0.333333

true

0

0.083333

0

0.416667

0

null

0

1

0

null

0

1

0

5

5fb926ce3a6f3702d9bb54b2ec76e4739f73971f

2,218

py

Python

tests/test_printer.py

eracle/traceflow

88e8f41953ecf1e31984e36e89cc6566d7d4f120

[ "BSD-3-Clause" ]

68

2019-09-23T07:59:18.000Z

2022-02-25T04:39:12.000Z

tests/test_printer.py

eracle/traceflow

88e8f41953ecf1e31984e36e89cc6566d7d4f120

[ "BSD-3-Clause" ]

22

2019-09-23T07:38:50.000Z

2021-11-22T03:51:41.000Z

tests/test_printer.py

eracle/traceflow

88e8f41953ecf1e31984e36e89cc6566d7d4f120

[ "BSD-3-Clause" ]

22

2019-09-23T17:44:16.000Z

2022-01-29T14:21:49.000Z

import unittest from traceflow import printer class TestPrinter(unittest.TestCase): traces = { 1: { 1: "136.243.212.25", 2: "213.239.229.57", 3: "213.239.203.153", 4: "62.69.146.42", 5: "1.1.1.1", }, 2: { 1: "136.243.212.25", 2: "213.239.229.57", 3: "213.239.203.153", 4: "62.69.146.42", 5: "1.1.1.1", }, 3: { 1: "136.243.212.25", 2: "213.239.229.57", 3: "213.239.203.153", 4: "62.69.146.42", 5: "1.1.1.1", }, 4: { 1: "136.243.212.25", 2: "213.239.229.61", 3: "213.239.229.77", 4: "62.69.146.42", 5: "1.1.1.1", }, } def test___build_nodes(self): json_traces = printer._build_nodes(self.traces) self.assertEqual( json_traces, '{"nodes": [{"id": "136.243.212.25", "label": "136.243.212.25"}, {"id": "213.239.229.57", "label": "213.239.229.57"}, {"id": "213.239.203.153", "label": "213.239.203.153"}, {"id": "62.69.146.42", "label": "62.69.146.42"}, {"id": "1.1.1.1", "label": "1.1.1.1"}, {"id": "213.239.229.61", "label": "213.239.229.61"}, {"id": "213.239.229.77", "label": "213.239.229.77"}], "links": [{"from": "136.243.212.25", "to": "213.239.229.57"}, {"from": "213.239.229.57", "to": "213.239.203.153"}, {"from": "213.239.203.153", "to": "62.69.146.42"}, {"from": "62.69.146.42", "to": "1.1.1.1"}, {"from": "136.243.212.25", "to": "213.239.229.57"}, {"from": "213.239.229.57", "to": "213.239.203.153"}, {"from": "213.239.203.153", "to": "62.69.146.42"}, {"from": "62.69.146.42", "to": "1.1.1.1"}, {"from": "136.243.212.25", "to": "213.239.229.57"}, {"from": "213.239.229.57", "to": "213.239.203.153"}, {"from": "213.239.203.153", "to": "62.69.146.42"}, {"from": "62.69.146.42", "to": "1.1.1.1"}, {"from": "136.243.212.25", "to": "213.239.229.61"}, {"from": "213.239.229.61", "to": "213.239.229.77"}, {"from": "213.239.229.77", "to": "62.69.146.42"}, {"from": "62.69.146.42", "to": "1.1.1.1"}]}', ) if __name__ == "__main__": unittest.main()

47.191489

1,186

0.458521

368

2,218

2.720109

0.119565

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0.188811

0.125874

0.557443

0.535465

0

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0.245266

2,218

46

1,187

48.217391

0.201912

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0

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0

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false

0

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0

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1

0

1

0

1

0

1

0

null

0

5

399fd9ba9b44ee903e33ccb2a0b92edbd068c357

1,403

py

Python

kutub/migrations/0028_auto_20210916_0726.py

rbturnbull/kutub

46d88cad0fe7b3de70843daeefa7cca6d4a4a840

[ "Apache-2.0" ]

null

kutub/migrations/0028_auto_20210916_0726.py

rbturnbull/kutub

46d88cad0fe7b3de70843daeefa7cca6d4a4a840

[ "Apache-2.0" ]

null

kutub/migrations/0028_auto_20210916_0726.py

rbturnbull/kutub

46d88cad0fe7b3de70843daeefa7cca6d4a4a840

[ "Apache-2.0" ]

null

# Generated by Django 3.2.7 on 2021-09-16 07:26 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('kutub', '0027_auto_20210916_0039'), ] operations = [ migrations.AlterField( model_name='contentitem', name='end_folio', field=models.PositiveIntegerField(blank=True, default=None, help_text='The folio number where this content item ends.', null=True), ), migrations.AlterField( model_name='contentitem', name='end_folio_side', field=models.CharField(blank=True, choices=[('', 'Unknown'), ('r', 'Recto'), ('v', 'Verso')], default='', help_text='The folio side (i.e. recto or verso) where this content item ends.', max_length=1), ), migrations.AlterField( model_name='contentitem', name='start_folio', field=models.PositiveIntegerField(blank=True, default=None, help_text='The folio number where this content item begins.', null=True), ), migrations.AlterField( model_name='contentitem', name='start_folio_side', field=models.CharField(blank=True, choices=[('', 'Unknown'), ('r', 'Recto'), ('v', 'Verso')], default='', help_text='The folio side (i.e. recto or verso) where this content item begins.', max_length=1), ), ]

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0.613685

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0.800475

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0.243763

1,403

33

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null

0

1

0

1

0

null

0

5

39bd30531f8ddb33e7a0c31c2a8a15568403026f

12,005

py

Python

test/active_learning_strategies/test_strategy.py

ansunsujoe/distil

cf6cae2b88ef129d09c159aae0569978190e9f98

[ "MIT" ]

83

2021-01-06T06:50:30.000Z

2022-03-31T05:16:32.000Z

test/active_learning_strategies/test_strategy.py

ansunsujoe/distil

cf6cae2b88ef129d09c159aae0569978190e9f98

[ "MIT" ]

30

2021-02-27T06:09:47.000Z

2021-12-23T11:03:36.000Z

test/active_learning_strategies/test_strategy.py

ansunsujoe/distil

cf6cae2b88ef129d09c159aae0569978190e9f98

[ "MIT" ]

13

2021-03-05T18:26:58.000Z

2022-03-12T01:53:17.000Z

from distil.utils.models.simple_net import TwoLayerNet from distil.active_learning_strategies.strategy import Strategy from test.utils import MyLabeledDataset, MyUnlabeledDataset import unittest import torch class TestStrategy(unittest.TestCase): def setUp(self): # Create model self.input_dimension = 50 self.classes = 10 self.hidden_units = 20 mymodel = TwoLayerNet(self.input_dimension, self.classes, self.hidden_units) # Create labeled dataset self.num_labeled_points = 1000 rand_data_tensor = torch.randn((self.num_labeled_points, self.input_dimension), requires_grad=True) rand_label_tensor = torch.randint(low=0,high=self.classes,size=(self.num_labeled_points,)) rand_labeled_dataset = MyLabeledDataset(rand_data_tensor, rand_label_tensor) # Create unlabeled dataset self.num_unlabeled_points = 10000 rand_data_tensor = torch.randn((self.num_unlabeled_points, self.input_dimension), requires_grad=True) rand_unlabeled_dataset = MyUnlabeledDataset(rand_data_tensor) # Create args array device = 'cuda' if torch.cuda.is_available() else 'cpu' args = {'batch_size': 1, 'device': device, 'loss': torch.nn.functional.cross_entropy} self.strategy = Strategy(rand_labeled_dataset, rand_unlabeled_dataset, mymodel, self.classes, args) def test_update_data(self): old_unlabeled_dataset = self.strategy.unlabeled_dataset old_labeled_dataset = self.strategy.labeled_dataset # Create new labeled dataset rand_l_data_tensor = torch.randn((self.num_labeled_points, self.input_dimension), requires_grad=True) rand_label_tensor = torch.randint(low=0,high=self.classes,size=(self.num_labeled_points,)) rand_labeled_dataset = MyLabeledDataset(rand_l_data_tensor, rand_label_tensor) # Create unlabeled dataset rand_data_tensor = torch.randn((self.num_unlabeled_points, self.input_dimension), requires_grad=True) rand_unlabeled_dataset = MyUnlabeledDataset(rand_data_tensor) # Update the data self.strategy.update_data(rand_labeled_dataset, rand_unlabeled_dataset) # Make sure the tensors are different self.assertFalse(torch.equal(self.strategy.labeled_dataset.wrapped_data_tensor, old_labeled_dataset.wrapped_data_tensor)) self.assertFalse(torch.equal(self.strategy.labeled_dataset.wrapped_label_tensor, old_labeled_dataset.wrapped_label_tensor)) self.assertFalse(torch.equal(self.strategy.unlabeled_dataset.wrapped_data_tensor, old_unlabeled_dataset.wrapped_data_tensor)) # Make sure the updated datasets are the same self.assertTrue(torch.equal(self.strategy.labeled_dataset.wrapped_data_tensor, rand_l_data_tensor)) self.assertTrue(torch.equal(self.strategy.labeled_dataset.wrapped_label_tensor, rand_label_tensor)) self.assertTrue(torch.equal(self.strategy.unlabeled_dataset.wrapped_data_tensor, rand_data_tensor)) # Update works; revert back to old datasets self.strategy.update_data(old_labeled_dataset, old_unlabeled_dataset) # Make sure the tensors are the same self.assertTrue(torch.equal(self.strategy.labeled_dataset.wrapped_data_tensor, old_labeled_dataset.wrapped_data_tensor)) self.assertTrue(torch.equal(self.strategy.labeled_dataset.wrapped_label_tensor, old_labeled_dataset.wrapped_label_tensor)) self.assertTrue(torch.equal(self.strategy.unlabeled_dataset.wrapped_data_tensor, old_unlabeled_dataset.wrapped_data_tensor)) def test_update_model(self): # Create a new model with two extra hidden units old_model = self.strategy.model my_model = TwoLayerNet(self.input_dimension, self.classes, self.hidden_units + 2) self.strategy.update_model(my_model) # Make sure the models are not equal self.assertNotEqual(old_model, self.strategy.model) # Update works; revert back to old model self.strategy.update_model(old_model) # Make sure the models are equal self.assertEqual(self.strategy.model, old_model) def test_predict(self): # Predict labels for the unlabeled dataset predicted_labels = self.strategy.predict(self.strategy.unlabeled_dataset) # Ensure the same number of labels exist as the number of points self.assertEqual(len(predicted_labels), len(self.strategy.unlabeled_dataset)) # Ensure none of the predicted labels are outside the expected range for predicted_label in predicted_labels: self.assertLess(predicted_label, self.strategy.target_classes) self.assertGreaterEqual(predicted_label, 0) def test_predict_prob(self): # Predict probabilities for the unlabeled dataset predict_probs = self.strategy.predict_prob(self.strategy.unlabeled_dataset) # Ensure the same number of probability vectors and number of probabilities self.assertEqual(predict_probs.shape[0], len(self.strategy.unlabeled_dataset)) self.assertEqual(predict_probs.shape[1], self.strategy.target_classes) # Ensure probabilities sum to 1 for predicted_prob_vector in predict_probs: self.assertAlmostEqual(predicted_prob_vector.sum().item(), 1, places=6) # Ensure probabilities are geq 0, leq 1 for predicted_prob_vector in predict_probs: for predicted_prob in predicted_prob_vector: self.assertLessEqual(predicted_prob, 1) self.assertGreaterEqual(predicted_prob, 0) def test_predict_prob_dropout(self): # Predict probabilities for the unlabeled dataset predict_probs = self.strategy.predict_prob_dropout(self.strategy.unlabeled_dataset, n_drop=5) # Ensure the same number of probability vectors and number of probabilities self.assertEqual(predict_probs.shape[0], len(self.strategy.unlabeled_dataset)) self.assertEqual(predict_probs.shape[1], self.strategy.target_classes) # Ensure probabilities sum to 1 for predicted_prob_vector in predict_probs: self.assertAlmostEqual(predicted_prob_vector.sum().item(), 1, places=6) # Ensure probabilities are geq 0, leq 1 for predicted_prob_vector in predict_probs: for predicted_prob in predicted_prob_vector: self.assertLessEqual(predicted_prob, 1) self.assertGreaterEqual(predicted_prob, 0) def test_predict_prob_dropout_split(self): # Predict probabilities for the unlabeled dataset n_drop = 5 predict_probs = self.strategy.predict_prob_dropout_split(self.strategy.unlabeled_dataset, n_drop=n_drop) # Ensure the same number of probability vectors and number of probabilities and number of dropout samples self.assertEqual(predict_probs.shape[0], n_drop) self.assertEqual(predict_probs.shape[1], len(self.strategy.unlabeled_dataset)) self.assertEqual(predict_probs.shape[2], self.strategy.target_classes) # Ensure probabilities sum to 1 for predict_prob_dropout in predict_probs: for predicted_prob_vector in predict_prob_dropout: self.assertAlmostEqual(predicted_prob_vector.sum().item(), 1, places=6) # Ensure probabilities are geq 0, leq 1 for predict_prob_dropout in predict_probs: for predicted_prob_vector in predict_prob_dropout: for predicted_prob in predicted_prob_vector: self.assertLessEqual(predicted_prob, 1) self.assertGreaterEqual(predicted_prob, 0) def test_get_embedding(self): # Get a last linear layer embedding embedding = self.strategy.get_embedding(self.strategy.unlabeled_dataset) # Ensure embedding has number of points equal to the unlabeled dataset self.assertEqual(embedding.shape[0], len(self.strategy.unlabeled_dataset)) # Ensure embedding has number of features equal to the embedding of the model self.assertEqual(embedding.shape[1], self.strategy.model.get_embedding_dim()) def test_get_grad_embedding(self): # Get grad embedding (bias) bias_grad_embedding = self.strategy.get_grad_embedding(self.strategy.unlabeled_dataset, predict_labels=True, grad_embedding_type='bias') # Ensure grad embedding has correct number of points / dimension self.assertEqual(bias_grad_embedding.shape[0], len(self.strategy.unlabeled_dataset)) self.assertEqual(bias_grad_embedding.shape[1], self.strategy.target_classes) # Get grad embedding (linear) linear_grad_embedding = self.strategy.get_grad_embedding(self.strategy.unlabeled_dataset, predict_labels=True, grad_embedding_type='linear') # Ensure grad embedding has correct number of points / dimension self.assertEqual(linear_grad_embedding.shape[0], len(self.strategy.unlabeled_dataset)) self.assertEqual(linear_grad_embedding.shape[1], self.strategy.model.get_embedding_dim() * self.strategy.target_classes) # Get grad embedding (bias_linear) bias_linear_grad_embedding = self.strategy.get_grad_embedding(self.strategy.unlabeled_dataset, predict_labels=True, grad_embedding_type='bias_linear') # Ensure grad embedding has correct number of points / dimension self.assertEqual(bias_linear_grad_embedding.shape[0], len(self.strategy.unlabeled_dataset)) self.assertEqual(bias_linear_grad_embedding.shape[1], self.strategy.model.get_embedding_dim() * self.strategy.target_classes + self.strategy.target_classes) # Get grad embedding on labeled dataset (bias) bias_grad_embedding = self.strategy.get_grad_embedding(self.strategy.labeled_dataset, predict_labels=False, grad_embedding_type='bias') # Ensure grad embedding has correct number of points / dimension self.assertEqual(bias_grad_embedding.shape[0], len(self.strategy.labeled_dataset)) self.assertEqual(bias_grad_embedding.shape[1], self.strategy.target_classes) # Get grad embedding on labeled dataset (linear) linear_grad_embedding = self.strategy.get_grad_embedding(self.strategy.labeled_dataset, predict_labels=False, grad_embedding_type='linear') # Ensure grad embedding has correct number of points / dimension self.assertEqual(linear_grad_embedding.shape[0], len(self.strategy.labeled_dataset)) self.assertEqual(linear_grad_embedding.shape[1], self.strategy.model.get_embedding_dim() * self.strategy.target_classes) # Get grad embedding on labeled dataset (bias_linear) bias_linear_grad_embedding = self.strategy.get_grad_embedding(self.strategy.labeled_dataset, predict_labels=False, grad_embedding_type='bias_linear') # Ensure grad embedding has correct number of points / dimension self.assertEqual(bias_linear_grad_embedding.shape[0], len(self.strategy.labeled_dataset)) self.assertEqual(bias_linear_grad_embedding.shape[1], self.strategy.model.get_embedding_dim() * self.strategy.target_classes + self.strategy.target_classes) # Make sure that ValueError is raised on invalid grad_embedding_type with self.assertRaises(ValueError): self.strategy.get_grad_embedding(self.strategy.unlabeled_dataset, predict_labels=True, grad_embedding_type='invalid_type') if __name__ == "__main__": unittest.main()

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0

null

0

1

0

null

0

5

39e06327863d26d43ce7f4ffbfcc92d739ce8f71

2,558

py

Python

test/test_dataform_sparse.py

hirano1412/bdpy

cee6f36dcdf4f4d29fc3a6980777e1c3d7c66cbb

[ "MIT" ]

18

2018-01-22T04:18:48.000Z

2022-03-12T09:42:03.000Z

test/test_dataform_sparse.py

hirano1412/bdpy

cee6f36dcdf4f4d29fc3a6980777e1c3d7c66cbb

[ "MIT" ]

13

2018-05-01T08:31:14.000Z

2022-02-21T06:45:34.000Z

test/test_dataform_sparse.py

hirano1412/bdpy

cee6f36dcdf4f4d29fc3a6980777e1c3d7c66cbb

[ "MIT" ]

15

2019-03-04T02:43:46.000Z

2022-02-17T00:41:47.000Z

'''Tests for dataform''' from unittest import TestCase, TestLoader, TextTestRunner import numpy as np from bdpy.dataform import load_array, save_array class TestUtil(TestCase): def test_load_save_dense_array(self): # ndim = 1 data = np.random.rand(10) save_array('./tmp/test_array_dense_ndim1.mat', data, key='testdata') testdata = load_array('./tmp/test_array_dense_ndim1.mat', key='testdata') np.testing.assert_array_equal(data, testdata) # ndim = 2 data = np.random.rand(3, 2) save_array('./tmp/test_array_dense_ndim2.mat', data, key='testdata') testdata = load_array('./tmp/test_array_dense_ndim2.mat', key='testdata') np.testing.assert_array_equal(data, testdata) # ndim = 3 data = np.random.rand(4, 3, 2) save_array('./tmp/test_array_dense_ndim3.mat', data, key='testdata') testdata = load_array('./tmp/test_array_dense_ndim3.mat', key='testdata') np.testing.assert_array_equal(data, testdata) def test_load_save_sparse_array(self): # ndim = 1 data = np.random.rand(10) data[data < 0.8] = 0 save_array('./tmp/test_array_sparse_ndim1.mat', data, key='testdata', sparse=True) testdata = load_array('./tmp/test_array_sparse_ndim1.mat', key='testdata') np.testing.assert_array_equal(data, testdata) # ndim = 2 data = np.random.rand(3, 2) data[data < 0.8] = 0 save_array('./tmp/test_array_sparse_ndim2.mat', data, key='testdata', sparse=True) testdata = load_array('./tmp/test_array_sparse_ndim2.mat', key='testdata') np.testing.assert_array_equal(data, testdata) # ndim = 3 data = np.random.rand(4, 3, 2) data[data < 0.8] = 0 save_array('./tmp/test_array_sparse_ndim3.mat', data, key='testdata', sparse=True) testdata = load_array('./tmp/test_array_sparse_ndim3.mat', key='testdata') np.testing.assert_array_equal(data, testdata) def test_load_array_jl(self): data = np.array([[1, 0, 0, 0], [2, 2, 0, 0], [3, 3, 3, 0]]) testdata = load_array('data/array_jl_dense_v1.mat', key='a') np.testing.assert_array_equal(data, testdata) testdata = load_array('data/array_jl_sparse_v1.mat', key='a') np.testing.assert_array_equal(data, testdata) if __name__ == '__main__': suite = TestLoader().loadTestsFromTestCase(TestUtil) TextTestRunner(verbosity=2).run(suite)

31.975

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0

null

0

1

0

1

0

null

0

5

8426625005dc915bfb65b6d448323a0f45237104

92

py

Python

producer.py

zwcn/celery-example

f32959b156315f9cc41398c9a057fa15b9bb24a6

[ "MIT" ]

null

producer.py

zwcn/celery-example

f32959b156315f9cc41398c9a057fa15b9bb24a6

[ "MIT" ]

null

producer.py

zwcn/celery-example

f32959b156315f9cc41398c9a057fa15b9bb24a6

[ "MIT" ]

null

from tasks.add import add from tasks.minus import minus add.delay(6, 6) minus.delay(5, 5)

13.142857

29

0.73913

18

92

3.777778

0.444444

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0

0.051282

0.152174

92

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true

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1

0

1

0

null

0

1

0

1

0

5

8439ea11699528f7c728fc675790aa64fc6e6b29

1,055

py

Python

delira/data_loading/__init__.py

muizzk/delira

eb7fdfedd6bbeffa20ffad1ef6c918a4cd9abfbf

[ "BSD-2-Clause" ]

1

2019-10-03T21:00:20.000Z

delira/data_loading/__init__.py

muizzk/delira

eb7fdfedd6bbeffa20ffad1ef6c918a4cd9abfbf

[ "BSD-2-Clause" ]

null

delira/data_loading/__init__.py

muizzk/delira

eb7fdfedd6bbeffa20ffad1ef6c918a4cd9abfbf

[ "BSD-2-Clause" ]

null

from delira import get_backends as _get_backends from delira.data_loading.data_loader import BaseDataLoader from delira.data_loading.data_manager import BaseDataManager from delira.data_loading.dataset import AbstractDataset from delira.data_loading.dataset import BaseCacheDataset from delira.data_loading.dataset import BaseLazyDataset from delira.data_loading.dataset import ConcatDataset from delira.data_loading.dataset import BaseExtendCacheDataset from delira.data_loading.load_utils import default_load_fn_2d from delira.data_loading.load_utils import LoadSample from delira.data_loading.load_utils import LoadSampleLabel from delira.data_loading.sampler import LambdaSampler from delira.data_loading.sampler import RandomSampler from delira.data_loading.sampler import SequentialSampler if "TORCH" in _get_backends(): from delira.data_loading.dataset import TorchvisionClassificationDataset try: from delira.data_loading.numba_transform import NumbaTransform, \ NumbaTransformWrapper, NumbaCompose except ImportError: pass

42.2

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1,055

6.661654

0.315789

0.180587

0.23702

0.35553

0.548533

0.515801

0.121896

0

0.001047

0.094787

1,055

24

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0.926702

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true

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0.809524

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null

0

1

0

1

0

1

0

5

845e74e737e2ae4f1ea3e6b562c4ad647dc1496c

175

py

Python

User_Crawler/get_graph_by_month.py

lifei96/Medium-crawler-with-data-parser

fed1a99c0b524871d430b3090a6bd8f501654535

[ "MIT" ]

4

2018-02-03T10:57:59.000Z

2020-05-17T09:40:36.000Z

User_Crawler/get_graph_by_month.py

lifei96/Medium-crawler-with-data-parser

fed1a99c0b524871d430b3090a6bd8f501654535

[ "MIT" ]

null

User_Crawler/get_graph_by_month.py

lifei96/Medium-crawler-with-data-parser

fed1a99c0b524871d430b3090a6bd8f501654535

[ "MIT" ]

6

2017-03-02T10:30:12.000Z

2021-08-10T11:14:27.000Z

# -*- coding: utf-8 -*- from util_graph import * if __name__ == '__main__': get_graph_by_month('./data/graph/graph.dat', './data/cross-site-linking/date_username.csv')

21.875

95

0.68

25

175

4.24

0.84

0

0.006536

0.125714

175

7

96

25

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0.427632

0

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true

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1

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null

0

1

0

1

null

0

1

0

1

0

5

ffdc49e8c1fa26cc3c4c4fd91c9564bc7fd64ac6

23

py

Python

tests/helpers/__init__.py

NickleDave/conbirt

71db6c6fd68dfef1bdbdcfacd8b2a16b21b86089

[ "BSD-3-Clause" ]

null

tests/helpers/__init__.py

NickleDave/conbirt

71db6c6fd68dfef1bdbdcfacd8b2a16b21b86089

[ "BSD-3-Clause" ]

3

2018-12-16T17:57:22.000Z

2018-12-16T20:12:33.000Z

tests/helpers/__init__.py

NickleDave/conbirt

71db6c6fd68dfef1bdbdcfacd8b2a16b21b86089

[ "BSD-3-Clause" ]

null

from . import keywords

11.5

22

0.782609

3

23

6

1

0

0.173913

23

1

23

0.947368

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

ffe6f18f7c1746d0506e56c945c3e79c92849154

269

py

Python

app/FW/views/__init__.py

uncle-lu/FuckingWords

2191d657de5ac5b663ba879f2fd5acec0d856924

[ "MIT" ]

null

app/FW/views/__init__.py

uncle-lu/FuckingWords

2191d657de5ac5b663ba879f2fd5acec0d856924

[ "MIT" ]

null

app/FW/views/__init__.py

uncle-lu/FuckingWords

2191d657de5ac5b663ba879f2fd5acec0d856924

[ "MIT" ]

null

#coding:utf-8 from flask import Blueprint Words_views = Blueprint('Words_views',__name__) Units_views = Blueprint('Units_views',__name__) Create_pdf_views = Blueprint('Create_pdf_views',__name__) from FW.views import W from FW.views import U from FW.views import C

20.692308

57

0.799257

42

269

4.642857

0.404762

0.215385

0.169231

0.261538

0

0.004184

0.111524

269

13

58

20.692308

0.811715

0.04461

0

0.14786

0

1

0

false

0

0.571429

0

0.571429

0

null

1

0

1

0

null

0

1

0

1

0

5

08055b930fc392cba22bc2764162b35a16d46e82

62

py

Python

cee/__init__.py

gautierdag/cultural-evolution-engine

54ea8d374ff4345c05f03eccfb2e93161e16a050

[ "MIT" ]

4

2019-03-20T15:31:44.000Z

2020-11-28T13:40:13.000Z

cee/__init__.py

gautierdag/cultural-evolution-engine

54ea8d374ff4345c05f03eccfb2e93161e16a050

[ "MIT" ]

null

cee/__init__.py

gautierdag/cultural-evolution-engine

54ea8d374ff4345c05f03eccfb2e93161e16a050

[ "MIT" ]

1

2021-11-06T01:15:28.000Z

from .BaseAgent import BaseAgent from .BaseCEE import BaseCEE

20.666667

32

0.83871

8

62

6.5

0.5

0

0.129032

62

2

33

31

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

08194c24e31495b4fd2fe462b66e280ce62112bd

330

py

Python

genienlp/ned/__init__.py

Krish-sysadmin/genienlp

3586e4368eb0b0756a772294daedc043ce55454c

[ "BSD-3-Clause" ]

6

2019-04-30T19:47:17.000Z

2019-11-30T04:16:47.000Z

genienlp/ned/__init__.py

Krish-sysadmin/genienlp

3586e4368eb0b0756a772294daedc043ce55454c

[ "BSD-3-Clause" ]

null

genienlp/ned/__init__.py

Krish-sysadmin/genienlp

3586e4368eb0b0756a772294daedc043ce55454c

[ "BSD-3-Clause" ]

null

from .abstract import AbstractEntityDisambiguator # noqa from .bootleg import BatchBootlegEntityDisambiguator, ServingBootlegEntityDisambiguator # noqa from .main import ( # noqa EntityAndTypeOracleEntityDisambiguator, EntityOracleEntityDisambiguator, NaiveEntityDisambiguator, TypeOracleEntityDisambiguator, )

36.666667

95

0.830303

19

330

14.421053

0.684211

0.058394

0

0.130303

330

8

96

41.25

0.954704

0.042424

0

1

0

true

0

0.375

0

0.375

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

0841859e7de1857a4642a618ddecffccf3118669

52

py

Python

renconstruct/__init__.py

devorbitus/renconstruct

6aa5fae05989ede2e4cd14c632d8eb520b4cfe60

[ "MIT" ]

8

2020-04-27T00:46:15.000Z

2021-05-31T15:19:53.000Z

renconstruct/__init__.py

devorbitus/renconstruct

6aa5fae05989ede2e4cd14c632d8eb520b4cfe60

[ "MIT" ]

3

2021-06-03T15:52:57.000Z

2021-12-14T12:54:24.000Z

renconstruct/__init__.py

devorbitus/renconstruct

6aa5fae05989ede2e4cd14c632d8eb520b4cfe60

[ "MIT" ]

2

2021-05-31T15:54:44.000Z

2021-06-03T15:10:09.000Z

from .renconstruct import cli, logger # noqa: F401

26

51

0.75

7

52

5.571429

1

0

0.069767

0.173077

52

1

52

0.837209

0.192308

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

084cf057d912a22d476875da356b12d8168530ba

131

py

Python

flask_app.py

ongzhixian/zhixian.pythonanywhere.com

73ad6da5a1edb0b59c7b5ae6251d0260f23e58dd

[ "MIT" ]

null

flask_app.py

ongzhixian/zhixian.pythonanywhere.com

73ad6da5a1edb0b59c7b5ae6251d0260f23e58dd

[ "MIT" ]

null

flask_app.py

ongzhixian/zhixian.pythonanywhere.com

73ad6da5a1edb0b59c7b5ae6251d0260f23e58dd

[ "MIT" ]

null

import logging import os from forum_app import app if __name__ == '__main__': app.run(host='0.0.0.0', port=31000, debug=True)

18.714286

51

0.709924

23

131

3.652174

0.695652

0.071429

0

0.081081

0.152672

131

7

51

18.714286

0.675676

0

0.113636

0

1

0

true

0

0.6

0

0.6

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f26535526825a7073803171a5a1aa54c37de11c7

204

py

Python

humann2/maintenance/make_map_pfam_name.py

bmpbos/humann

4a8fee5596d89d805af6568d3260844f80c8f9a2

[ "MIT" ]

null

humann2/maintenance/make_map_pfam_name.py

bmpbos/humann

4a8fee5596d89d805af6568d3260844f80c8f9a2

[ "MIT" ]

null

humann2/maintenance/make_map_pfam_name.py

bmpbos/humann

4a8fee5596d89d805af6568d3260844f80c8f9a2

[ "MIT" ]

null

#!/usr/bin/env python import os url = "ftp://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.clans.tsv.gz" os.system( "curl {} | zcat | cut -f1,5 | gzip > map_pfam_name.txt.gz".format( url ) )

25.5

85

0.676471

37

204

3.648649

0.837838

0

0.011173

0.122549

204

7

86

29.142857

0.743017

0.098039

0

0.333333

0.710383

0.404372

0

1

0

false

0

0.333333

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0.333333

0

null

0

1

0

1

null

0

1

0

5

f26ae86cb226413d230642ce1be82529afb80300

93

py

Python

compipe/__init__.py

ImagineersHub/compipe

dd14c2701717d7d0901eb1139f59e7fbfeba7517

[ "MIT" ]

null

compipe/__init__.py

ImagineersHub/compipe

dd14c2701717d7d0901eb1139f59e7fbfeba7517

[ "MIT" ]

null

compipe/__init__.py

ImagineersHub/compipe

dd14c2701717d7d0901eb1139f59e7fbfeba7517

[ "MIT" ]

null

from .cmd_enroller import cmd_enroller, command_list from .cmd_wrapper import CommandWrapper

31

52

0.870968

13

93

5.923077

0.615385

0.181818

0

0.096774

93

2

53

46.5

0.916667

0

1

0

true

0

1

0

1

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1

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null

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1

0

null

0

1

0

1

0

1

0

5

f2a49c981a4031ece373adcc48393268c32b1498

197

py

Python

holobot/sdk/network/exceptions/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

1

2021-05-24T00:17:46.000Z

holobot/sdk/network/exceptions/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

41

2021-03-24T22:50:09.000Z

2021-12-17T12:15:13.000Z

holobot/sdk/network/exceptions/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

null

from .header_utils import try_get_retry_after from .http_status_error import HttpStatusError from .im_a_teapot_error import ImATeapotError from .too_many_requests_error import TooManyRequestsError

39.4

57

0.898477

28

197

5.892857

0.714286

0.2

0

0.081218

197

4

58

49.25

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0

1

0

true

0

1

0

1

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1

0

null

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1

0

null

0

1

0

1

0

1

0

5

4b42b13fedb782c2dca41aa9f58228a4ec3bfe16

18,373

py

Python

pysnmp-with-texts/IANAifType-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

8

2019-05-09T17:04:00.000Z

2021-06-09T06:50:51.000Z

pysnmp-with-texts/IANAifType-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

4

2019-05-31T16:42:59.000Z

2020-01-31T21:57:17.000Z

pysnmp-with-texts/IANAifType-MIB.py

agustinhenze/mibs.snmplabs.com

1fc5c07860542b89212f4c8ab807057d9a9206c7

[ "Apache-2.0" ]

10

2019-04-30T05:51:36.000Z

2022-02-16T03:33:41.000Z

# # PySNMP MIB module IANAifType-MIB (http://snmplabs.com/pysmi) # ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/IANAifType-MIB # Produced by pysmi-0.3.4 at Wed May 1 11:03:40 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) # OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier") NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues") SingleValueConstraint, ConstraintsIntersection, ConstraintsUnion, ValueRangeConstraint, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "SingleValueConstraint", "ConstraintsIntersection", "ConstraintsUnion", "ValueRangeConstraint", "ValueSizeConstraint") ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup") Gauge32, IpAddress, Counter32, mib_2, Unsigned32, NotificationType, iso, ModuleIdentity, Counter64, ObjectIdentity, MibIdentifier, Integer32, TimeTicks, MibScalar, MibTable, MibTableRow, MibTableColumn, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "Gauge32", "IpAddress", "Counter32", "mib-2", "Unsigned32", "NotificationType", "iso", "ModuleIdentity", "Counter64", "ObjectIdentity", "MibIdentifier", "Integer32", "TimeTicks", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Bits") DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention") ianaifType = ModuleIdentity((1, 3, 6, 1, 2, 1, 30)) ianaifType.setRevisions(('2017-03-30 00:00', '2017-01-19 00:00', '2016-11-23 00:00', '2016-06-16 00:00', '2016-06-09 00:00', '2016-06-08 00:00', '2016-05-19 00:00', '2016-05-03 00:00', '2016-04-29 00:00', '2014-09-24 00:00', '2014-09-19 00:00', '2014-07-03 00:00', '2014-05-22 00:00', '2012-05-17 00:00', '2012-01-11 00:00', '2011-12-18 00:00', '2011-10-26 00:00', '2011-09-07 00:00', '2011-07-22 00:00', '2011-06-03 00:00', '2010-09-21 00:00', '2010-07-21 00:00', '2010-02-11 00:00', '2010-02-08 00:00', '2009-05-06 00:00', '2009-02-06 00:00', '2008-10-09 00:00', '2008-08-12 00:00', '2008-07-22 00:00', '2008-06-24 00:00', '2008-05-29 00:00', '2007-09-13 00:00', '2007-05-29 00:00', '2007-03-08 00:00', '2007-01-23 00:00', '2006-10-17 00:00', '2006-09-25 00:00', '2006-08-17 00:00', '2006-08-11 00:00', '2006-07-25 00:00', '2006-06-14 00:00', '2006-03-31 00:00', '2006-03-30 00:00', '2005-12-22 00:00', '2005-10-10 00:00', '2005-09-09 00:00', '2005-05-27 00:00', '2005-03-03 00:00', '2004-11-22 00:00', '2004-06-17 00:00', '2004-05-12 00:00', '2004-05-07 00:00', '2003-08-25 00:00', '2003-08-18 00:00', '2003-08-07 00:00', '2003-03-18 00:00', '2003-01-13 00:00', '2002-10-17 00:00', '2002-07-16 00:00', '2002-07-10 00:00', '2002-06-19 00:00', '2002-01-04 00:00', '2001-12-20 00:00', '2001-11-15 00:00', '2001-11-06 00:00', '2001-11-02 00:00', '2001-10-16 00:00', '2001-09-19 00:00', '2001-05-11 00:00', '2001-01-12 00:00', '2000-12-19 00:00', '2000-12-07 00:00', '2000-12-04 00:00', '2000-10-17 00:00', '2000-10-02 00:00', '2000-09-01 00:00', '2000-08-24 00:00', '2000-08-23 00:00', '2000-08-22 00:00', '2000-04-25 00:00', '2000-03-06 00:00', '1999-10-08 14:30', '1994-01-31 00:00',)) if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0): if mibBuilder.loadTexts: ianaifType.setRevisionsDescriptions(('Registration of new IANAifType 290.', 'Registration of new IANAifType 289.', 'Registration of new IANAifTypes 283-288.', 'Updated IANAtunnelType DESCRIPTION per RFC 7870', 'Registration of new IANAifType 282.', 'Updated description for tunnelType 17.', 'Updated description for tunnelType 16.', 'Registration of new IANAifType 281.', 'Registration of new tunnelTypes 16 and 17.', 'Registration of new IANAifType 280.', 'Registration of new IANAifType 279.', 'Registration of new IANAifTypes 277-278.', 'Updated contact info.', 'Registration of new IANAifType 272.', 'Registration of new IANAifTypes 266-271.', 'Registration of new IANAifTypes 263-265.', 'Registration of new IANAifType 262.', 'Registration of new IANAifTypes 260 and 261.', 'Registration of new IANAifType 259.', 'Registration of new IANAifType 258.', 'Registration of new IANAifTypes 256 and 257.', 'Registration of new IANAifType 255.', 'Registration of new IANAifType 254.', 'Registration of new IANAifTypes 252 and 253.', 'Registration of new IANAifType 251.', 'Registration of new IANAtunnelType 15.', 'Registration of new IANAifType 250.', 'Registration of new IANAifType 249.', 'Registration of new IANAifTypes 247 and 248.', 'Registration of new IANAifType 246.', 'Registration of new IANAifType 245.', 'Registration of new IANAifTypes 243 and 244.', 'Changed the description for IANAifType 228.', 'Registration of new IANAifType 242.', 'Registration of new IANAifTypes 239, 240, and 241.', 'Deprecated/Obsoleted IANAifType 230. Registration of IANAifType 238.', 'Changed the description for IANA ifType 184 and added new IANA ifType 237.', 'Changed the descriptions for IANAifTypes 20 and 21.', 'Changed the descriptions for IANAifTypes 7, 11, 62, 69, and 117.', 'Registration of new IANA ifType 236.', 'Registration of new IANA ifType 235.', 'Registration of new IANA ifType 234.', 'Registration of new IANA ifType 233.', 'Registration of new IANA ifTypes 231 and 232.', 'Registration of new IANA ifType 230.', 'Registration of new IANA ifType 229.', 'Registration of new IANA ifType 228.', 'Added the IANAtunnelType TC and deprecated IANAifType sixToFour (215) per RFC4087.', 'Registration of new IANA ifType 227 per RFC4631.', 'Registration of new IANA ifType 226.', 'Added description for IANAifType 6, and changed the descriptions for IANAifTypes 180, 181, and 182.', 'Registration of new IANAifType 225.', 'Deprecated IANAifTypes 7 and 11. Obsoleted IANAifTypes 62, 69, and 117. ethernetCsmacd (6) should be used instead of these values', 'Registration of new IANAifType 224.', 'Registration of new IANAifTypes 222 and 223.', 'Registration of new IANAifType 221.', 'Registration of new IANAifType 220.', 'Registration of new IANAifType 219.', 'Registration of new IANAifTypes 217 and 218.', 'Registration of new IANAifTypes 215 and 216.', 'Registration of new IANAifType 214.', 'Registration of new IANAifTypes 211, 212 and 213.', 'Registration of new IANAifTypes 209 and 210.', 'Registration of new IANAifTypes 207 and 208.', 'Registration of new IANAifType 206.', 'Registration of new IANAifType 205.', 'Registration of new IANAifTypes 199, 200, 201, 202, 203, and 204.', 'Registration of new IANAifType 198.', 'Registration of new IANAifType 197.', 'Registration of new IANAifTypes 195 and 196.', 'Registration of new IANAifTypes 193 and 194.', 'Registration of new IANAifTypes 191 and 192.', 'Registration of new IANAifType 190.', 'Registration of new IANAifTypes 188 and 189.', 'Registration of new IANAifType 187.', 'Registration of new IANAifTypes 184, 185, and 186.', 'Registration of new IANAifType 183.', 'Registration of new IANAifTypes 174-182.', 'Registration of new IANAifTypes 170, 171, 172 and 173.', 'Registration of new IANAifTypes 168 and 169.', 'Fixed a missing semi-colon in the IMPORT. Also cleaned up the REVISION log a bit. It is not complete, but from now on it will be maintained and kept up to date with each change to this MIB module.', 'Include new name assignments up to cnr(85). This is the first version available via the WWW at: ftp://ftp.isi.edu/mib/ianaiftype.mib', 'Initial version of this MIB as published in RFC 1573.',)) if mibBuilder.loadTexts: ianaifType.setLastUpdated('201703300000Z') if mibBuilder.loadTexts: ianaifType.setOrganization('IANA') if mibBuilder.loadTexts: ianaifType.setContactInfo(' Internet Assigned Numbers Authority Postal: ICANN 12025 Waterfront Drive, Suite 300 Los Angeles, CA 90094-2536 Tel: +1 310-301-5800 E-Mail: iana&iana.org') if mibBuilder.loadTexts: ianaifType.setDescription("This MIB module defines the IANAifType Textual Convention, and thus the enumerated values of the ifType object defined in MIB-II's ifTable.") class IANAifType(TextualConvention, Integer32): description = "This data type is used as the syntax of the ifType object in the (updated) definition of MIB-II's ifTable. The definition of this textual convention with the addition of newly assigned values is published periodically by the IANA, in either the Assigned Numbers RFC, or some derivative of it specific to Internet Network Management number assignments. (The latest arrangements can be obtained by contacting the IANA.) Requests for new values should be made to IANA via email (iana&iana.org). The relationship between the assignment of ifType values and of OIDs to particular media-specific MIBs is solely the purview of IANA and is subject to change without notice. Quite often, a media-specific MIB's OID-subtree assignment within MIB-II's 'transmission' subtree will be the same as its ifType value. However, in some circumstances this will not be the case, and implementors must not pre-assume any specific relationship between ifType values and transmission subtree OIDs." status = 'current' subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255), SingleValueConstraint(256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290)) namedValues = NamedValues(("other", 1), ("regular1822", 2), ("hdh1822", 3), ("ddnX25", 4), ("rfc877x25", 5), ("ethernetCsmacd", 6), ("iso88023Csmacd", 7), ("iso88024TokenBus", 8), ("iso88025TokenRing", 9), ("iso88026Man", 10), ("starLan", 11), ("proteon10Mbit", 12), ("proteon80Mbit", 13), ("hyperchannel", 14), ("fddi", 15), ("lapb", 16), ("sdlc", 17), ("ds1", 18), ("e1", 19), ("basicISDN", 20), ("primaryISDN", 21), ("propPointToPointSerial", 22), ("ppp", 23), ("softwareLoopback", 24), ("eon", 25), ("ethernet3Mbit", 26), ("nsip", 27), ("slip", 28), ("ultra", 29), ("ds3", 30), ("sip", 31), ("frameRelay", 32), ("rs232", 33), ("para", 34), ("arcnet", 35), ("arcnetPlus", 36), ("atm", 37), ("miox25", 38), ("sonet", 39), ("x25ple", 40), ("iso88022llc", 41), ("localTalk", 42), ("smdsDxi", 43), ("frameRelayService", 44), ("v35", 45), ("hssi", 46), ("hippi", 47), ("modem", 48), ("aal5", 49), ("sonetPath", 50), ("sonetVT", 51), ("smdsIcip", 52), ("propVirtual", 53), ("propMultiplexor", 54), ("ieee80212", 55), ("fibreChannel", 56), ("hippiInterface", 57), ("frameRelayInterconnect", 58), ("aflane8023", 59), ("aflane8025", 60), ("cctEmul", 61), ("fastEther", 62), ("isdn", 63), ("v11", 64), ("v36", 65), ("g703at64k", 66), ("g703at2mb", 67), ("qllc", 68), ("fastEtherFX", 69), ("channel", 70), ("ieee80211", 71), ("ibm370parChan", 72), ("escon", 73), ("dlsw", 74), ("isdns", 75), ("isdnu", 76), ("lapd", 77), ("ipSwitch", 78), ("rsrb", 79), ("atmLogical", 80), ("ds0", 81), ("ds0Bundle", 82), ("bsc", 83), ("async", 84), ("cnr", 85), ("iso88025Dtr", 86), ("eplrs", 87), ("arap", 88), ("propCnls", 89), ("hostPad", 90), ("termPad", 91), ("frameRelayMPI", 92), ("x213", 93), ("adsl", 94), ("radsl", 95), ("sdsl", 96), ("vdsl", 97), ("iso88025CRFPInt", 98), ("myrinet", 99), ("voiceEM", 100), ("voiceFXO", 101), ("voiceFXS", 102), ("voiceEncap", 103), ("voiceOverIp", 104), ("atmDxi", 105), ("atmFuni", 106), ("atmIma", 107), ("pppMultilinkBundle", 108), ("ipOverCdlc", 109), ("ipOverClaw", 110), ("stackToStack", 111), ("virtualIpAddress", 112), ("mpc", 113), ("ipOverAtm", 114), ("iso88025Fiber", 115), ("tdlc", 116), ("gigabitEthernet", 117), ("hdlc", 118), ("lapf", 119), ("v37", 120), ("x25mlp", 121), ("x25huntGroup", 122), ("transpHdlc", 123), ("interleave", 124), ("fast", 125), ("ip", 126), ("docsCableMaclayer", 127), ("docsCableDownstream", 128), ("docsCableUpstream", 129), ("a12MppSwitch", 130), ("tunnel", 131), ("coffee", 132), ("ces", 133), ("atmSubInterface", 134), ("l2vlan", 135), ("l3ipvlan", 136), ("l3ipxvlan", 137), ("digitalPowerline", 138), ("mediaMailOverIp", 139), ("dtm", 140), ("dcn", 141), ("ipForward", 142), ("msdsl", 143), ("ieee1394", 144), ("if-gsn", 145), ("dvbRccMacLayer", 146), ("dvbRccDownstream", 147), ("dvbRccUpstream", 148), ("atmVirtual", 149), ("mplsTunnel", 150), ("srp", 151), ("voiceOverAtm", 152), ("voiceOverFrameRelay", 153), ("idsl", 154), ("compositeLink", 155), ("ss7SigLink", 156), ("propWirelessP2P", 157), ("frForward", 158), ("rfc1483", 159), ("usb", 160), ("ieee8023adLag", 161), ("bgppolicyaccounting", 162), ("frf16MfrBundle", 163), ("h323Gatekeeper", 164), ("h323Proxy", 165), ("mpls", 166), ("mfSigLink", 167), ("hdsl2", 168), ("shdsl", 169), ("ds1FDL", 170), ("pos", 171), ("dvbAsiIn", 172), ("dvbAsiOut", 173), ("plc", 174), ("nfas", 175), ("tr008", 176), ("gr303RDT", 177), ("gr303IDT", 178), ("isup", 179), ("propDocsWirelessMaclayer", 180), ("propDocsWirelessDownstream", 181), ("propDocsWirelessUpstream", 182), ("hiperlan2", 183), ("propBWAp2Mp", 184), ("sonetOverheadChannel", 185), ("digitalWrapperOverheadChannel", 186), ("aal2", 187), ("radioMAC", 188), ("atmRadio", 189), ("imt", 190), ("mvl", 191), ("reachDSL", 192), ("frDlciEndPt", 193), ("atmVciEndPt", 194), ("opticalChannel", 195), ("opticalTransport", 196), ("propAtm", 197), ("voiceOverCable", 198), ("infiniband", 199), ("teLink", 200), ("q2931", 201), ("virtualTg", 202), ("sipTg", 203), ("sipSig", 204), ("docsCableUpstreamChannel", 205), ("econet", 206), ("pon155", 207), ("pon622", 208), ("bridge", 209), ("linegroup", 210), ("voiceEMFGD", 211), ("voiceFGDEANA", 212), ("voiceDID", 213), ("mpegTransport", 214), ("sixToFour", 215), ("gtp", 216), ("pdnEtherLoop1", 217), ("pdnEtherLoop2", 218), ("opticalChannelGroup", 219), ("homepna", 220), ("gfp", 221), ("ciscoISLvlan", 222), ("actelisMetaLOOP", 223), ("fcipLink", 224), ("rpr", 225), ("qam", 226), ("lmp", 227), ("cblVectaStar", 228), ("docsCableMCmtsDownstream", 229), ("adsl2", 230), ("macSecControlledIF", 231), ("macSecUncontrolledIF", 232), ("aviciOpticalEther", 233), ("atmbond", 234), ("voiceFGDOS", 235), ("mocaVersion1", 236), ("ieee80216WMAN", 237), ("adsl2plus", 238), ("dvbRcsMacLayer", 239), ("dvbTdm", 240), ("dvbRcsTdma", 241), ("x86Laps", 242), ("wwanPP", 243), ("wwanPP2", 244), ("voiceEBS", 245), ("ifPwType", 246), ("ilan", 247), ("pip", 248), ("aluELP", 249), ("gpon", 250), ("vdsl2", 251), ("capwapDot11Profile", 252), ("capwapDot11Bss", 253), ("capwapWtpVirtualRadio", 254), ("bits", 255)) + NamedValues(("docsCableUpstreamRfPort", 256), ("cableDownstreamRfPort", 257), ("vmwareVirtualNic", 258), ("ieee802154", 259), ("otnOdu", 260), ("otnOtu", 261), ("ifVfiType", 262), ("g9981", 263), ("g9982", 264), ("g9983", 265), ("aluEpon", 266), ("aluEponOnu", 267), ("aluEponPhysicalUni", 268), ("aluEponLogicalLink", 269), ("aluGponOnu", 270), ("aluGponPhysicalUni", 271), ("vmwareNicTeam", 272), ("docsOfdmDownstream", 277), ("docsOfdmaUpstream", 278), ("gfast", 279), ("sdci", 280), ("xboxWireless", 281), ("fastdsl", 282), ("docsCableScte55d1FwdOob", 283), ("docsCableScte55d1RetOob", 284), ("docsCableScte55d2DsOob", 285), ("docsCableScte55d2UsOob", 286), ("docsCableNdf", 287), ("docsCableNdr", 288), ("ptm", 289), ("ghn", 290)) class IANAtunnelType(TextualConvention, Integer32): description = 'The encapsulation method used by a tunnel. The value direct indicates that a packet is encapsulated directly within a normal IP header, with no intermediate header, and unicast to the remote tunnel endpoint (e.g., an RFC 2003 IP-in-IP tunnel, or an RFC 1933 IPv6-in-IPv4 tunnel). The value minimal indicates that a Minimal Forwarding Header (RFC 2004) is inserted between the outer header and the payload packet. The value UDP indicates that the payload packet is encapsulated within a normal UDP packet (e.g., RFC 1234). The values sixToFour, sixOverFour, and isatap indicates that an IPv6 packet is encapsulated directly within an IPv4 header, with no intermediate header, and unicast to the destination determined by the 6to4, 6over4, or ISATAP protocol. The remaining protocol-specific values indicate that a header of the protocol of that name is inserted between the outer header and the payload header. The IP Tunnel MIB [RFC4087] is designed to manage tunnels of any type over IPv4 and IPv6 networks; therefore, it already supports IP-in-IP tunnels. But in a DS-Lite scenario, the tunnel type is point-to-multipoint IP-in-IP tunnels. The direct(2) defined in the IP Tunnel MIB only supports point-to-point tunnels. So, it needs to define a new tunnel type for DS-Lite. The assignment policy for IANAtunnelType values is identical to the policy for assigning IANAifType values.' status = 'current' subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)) namedValues = NamedValues(("other", 1), ("direct", 2), ("gre", 3), ("minimal", 4), ("l2tp", 5), ("pptp", 6), ("l2f", 7), ("udp", 8), ("atmp", 9), ("msdp", 10), ("sixToFour", 11), ("sixOverFour", 12), ("isatap", 13), ("teredo", 14), ("ipHttps", 15), ("softwireMesh", 16), ("dsLite", 17)) mibBuilder.exportSymbols("IANAifType-MIB", IANAtunnelType=IANAtunnelType, ianaifType=ianaifType, IANAifType=IANAifType, PYSNMP_MODULE_ID=ianaifType)

510.361111

5,747

0.688837

2,608

18,373

4.85161

0.35046

0.025923

0.091362

0.06615

0.111041

0.05801

0.043942

0

0.205919

0.124585

18,373

35

5,748

524.942857

0.580763

0.017526

0

0.08

0

0.28

0.612571

0.023833

0

1

0

false

0

0.28

0

0.68

0

null

0

1

0

1

0

1

0

null

0

5

4b6ec9a96cc7fe8203910b07c32e22611f1d1e45

1,542

py

Python

tests/marltoolbox/utils/test_log.py

longtermrisk/marltoolbox

cae1ba94ccb44700b66a32e0734a0f11c9c6c7fe

[ "MIT" ]

17

2021-01-17T21:21:08.000Z

2022-01-27T00:57:30.000Z

tests/marltoolbox/utils/test_log.py

longtermrisk/marltoolbox

cae1ba94ccb44700b66a32e0734a0f11c9c6c7fe

[ "MIT" ]

5

2021-02-21T21:43:00.000Z

2021-05-04T12:27:23.000Z

tests/marltoolbox/utils/test_log.py

longtermrisk/marltoolbox

cae1ba94ccb44700b66a32e0734a0f11c9c6c7fe

[ "MIT" ]

3

2021-02-21T11:38:22.000Z

2022-03-04T12:06:19.000Z

import numpy as np from marltoolbox.utils.log import _add_entropy_to_log def test__add_entropy_to_log(): to_log = {} train_batch = {"action_dist_inputs": np.array([[0.0, 1.0]])} to_log = _add_entropy_to_log(train_batch, to_log) assert_close(to_log[f"entropy_buffer_samples_avg"], 0.00, 0.001) assert_close(to_log[f"entropy_buffer_samples_single"], 0.00, 0.001) to_log = {} train_batch = {"action_dist_inputs": np.array([[0.75, 0.25]])} to_log = _add_entropy_to_log(train_batch, to_log) assert_close(to_log[f"entropy_buffer_samples_avg"], 0.562335145, 0.001) assert_close(to_log[f"entropy_buffer_samples_single"], 0.562335145, 0.001) to_log = {} train_batch = {"action_dist_inputs": np.array([[0.62, 0.12, 0.13, 0.13]])} to_log = _add_entropy_to_log(train_batch, to_log) assert_close(to_log[f"entropy_buffer_samples_avg"], 1.081271236, 0.001) assert_close(to_log[f"entropy_buffer_samples_single"], 1.081271236, 0.001) to_log = {} train_batch = { "action_dist_inputs": np.array( [ [0.62, 0.12, 0.13, 0.13], [0.75, 0.25, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0], ] ) } to_log = _add_entropy_to_log(train_batch, to_log) assert_close(to_log[f"entropy_buffer_samples_avg"], 0.547868794, 0.001) assert_close(to_log[f"entropy_buffer_samples_single"], 0.00, 0.001) return to_log def assert_close(a, b, threshold): abs_diff = np.abs(a - b) assert abs_diff < threshold

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138

py

Python

respa_outlook/__init__.py

pnuz3n/respa

0f48eb9bec18013b27970b44b8648f03eee8dcf4

[ "MIT" ]

1

2019-12-17T10:02:17.000Z

respa_outlook/__init__.py

pnuz3n/respa

0f48eb9bec18013b27970b44b8648f03eee8dcf4

[ "MIT" ]

38

2020-01-24T11:30:53.000Z

2022-01-28T12:42:47.000Z

respa_outlook/__init__.py

digipointtku/respa

a529e0df4d3f072df7801adb5bf97a5f4abd1243

[ "MIT" ]

14

2020-02-26T08:17:34.000Z

2021-09-14T07:57:21.000Z

default_app_config = 'respa_outlook.apps.RespaOutlookConfig' __all__ = [ 'RespaOutlookConfiguration', 'RespaOutlookReservation' ]

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270

py

Python

remote_control/exceptions.py

lsapan/django-remote-control

4dc6adfaaa1d12b2ee69d3fa3745c0040de49192

[ "MIT" ]

2

2018-09-12T13:13:44.000Z

2021-09-17T05:08:01.000Z

remote_control/exceptions.py

lsapan/django-remote-control

4dc6adfaaa1d12b2ee69d3fa3745c0040de49192

[ "MIT" ]

null

remote_control/exceptions.py

lsapan/django-remote-control

4dc6adfaaa1d12b2ee69d3fa3745c0040de49192

[ "MIT" ]

null

class CommandNotRegistered(ValueError): """ An error that is raised when the requested command is not registered. """ pass class CommandNotFound(ValueError): """ An error that is raised when the requested command is not found. """ pass

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2992a93d6d093bd7981860f803e07362f30b3ff1

45

py

Python

k2pix/__init__.py

stephtdouglas/k2-pix

c206732ebc82f09b051748cac2fbb66910d22c78

[ "MIT" ]

3

2017-01-07T17:36:19.000Z

2017-11-30T01:01:05.000Z

k2pix/__init__.py

stephtdouglas/k2-pix

c206732ebc82f09b051748cac2fbb66910d22c78

[ "MIT" ]

9

2017-01-07T22:42:18.000Z

2018-01-18T15:34:23.000Z

k2pix/__init__.py

stephtdouglas/k2-pix

c206732ebc82f09b051748cac2fbb66910d22c78

[ "MIT" ]

5

2017-01-07T17:36:20.000Z

2021-12-02T02:43:39.000Z

#from . import main, figure, tpf, surveyquery

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29a54647d757070b862306e8716d8b71e38b0f81

182

py

Python

src/dataset/__init__.py

Jasonsey/BlurredImageDetection

df89079813fe8e2b66075f366d89b141af9f2501

[ "MIT" ]

5

2019-05-20T11:18:24.000Z

2020-04-09T13:27:02.000Z

src/dataset/__init__.py

Jasonsey/BlurredImageDetection

df89079813fe8e2b66075f366d89b141af9f2501

[ "MIT" ]

null

src/dataset/__init__.py

Jasonsey/BlurredImageDetection

df89079813fe8e2b66075f366d89b141af9f2501

[ "MIT" ]

2

2019-10-27T15:44:57.000Z

2021-09-26T06:10:37.000Z

# Bluerred Image Detection # # Author: Jasonsey # Email: 2627866800@qq.com # # ============================================================================= """the data set api"""

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29e38b12567519195af78c3b9df2d8dec8a0ad94

518

py

Python

winton_kafka_streams/state/_abc.py

jkramarz/winton-kafka-streams

22526da71454a8b9c7bba53e4f59f645535de602

[ "Apache-2.0" ]

null

winton_kafka_streams/state/_abc.py

jkramarz/winton-kafka-streams

22526da71454a8b9c7bba53e4f59f645535de602

[ "Apache-2.0" ]

null

winton_kafka_streams/state/_abc.py

jkramarz/winton-kafka-streams

22526da71454a8b9c7bba53e4f59f645535de602

[ "Apache-2.0" ]

1

2019-04-28T23:31:24.000Z

""" Abstract classes for implementations of state classes """ import abc import collections.abc class StoreBase(collections.abc.Iterator): """ Interface that must be implemented by all state classes """ def __init__(self, _name): self.name = _name @abc.abstractmethod def add(self, v): pass @abc.abstractmethod def empty(self): pass @abc.abstractmethod def clear(self): pass @abc.abstractmethod def __iter__(self): pass

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29f711a08c4629252bf62057f189b4500b914a24

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py

Python

app/search/__init__.py

AniaPeszek/ReclamationAndTicketSystem

42551732dcc9af42dc7401fbc13b8fdb6e3c132f

[ "MIT" ]

null

app/search/__init__.py

AniaPeszek/ReclamationAndTicketSystem

42551732dcc9af42dc7401fbc13b8fdb6e3c132f

[ "MIT" ]

null

app/search/__init__.py

AniaPeszek/ReclamationAndTicketSystem

42551732dcc9af42dc7401fbc13b8fdb6e3c132f

[ "MIT" ]

null

from flask import Blueprint bp = Blueprint("search_bp", __name__) from app.search import search

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4b08435cf591441e30ed05c29b4f4a5f376686e5

14,684

py

Python

quantsbin/derivativepricing/instruments.py

quantsbin/Quantsbin

362522653b4b8ebcf14461e3f44fe22dea465adc

[ "MIT" ]

132

2018-06-20T08:40:48.000Z

2022-03-24T11:34:22.000Z

quantsbin/derivativepricing/instruments.py

williamjiamin/Quantsbin

14a135174d9f08a70e36ed55279fbd7458e1ad48

[ "MIT" ]

5

2018-07-08T06:23:53.000Z

2021-08-08T06:30:43.000Z

quantsbin/derivativepricing/instruments.py

williamjiamin/Quantsbin

14a135174d9f08a70e36ed55279fbd7458e1ad48

[ "MIT" ]

35

2018-07-12T10:07:30.000Z

2022-03-01T04:00:17.000Z

""" developed by Quantsbin - Jun'18 """ from abc import ABCMeta, abstractmethod from datetime import datetime from .engineconfig import PricingEngine from .namesnmapper import VanillaOptionType, ExpiryType, DEFAULT_MODEL, UdlType, OBJECT_MODEL, DerivativeType class Instrument(metaclass=ABCMeta): """ Instrument - Metaclass to define financial instrument @abstract functions: payoff => defines payoff on instrument. engine => attach the instrument with the pricing model and market data. """ @abstractmethod def payoff(self): pass def engine(self, **kwargs): """ Binds pricing model class and market data to the object Args required: model: pricing model (default value set to BSM for European expiry) **kwargs: Dictionary of parameters and their corresponding value required for valuation. For arguments required and method available for each model check\ help(.derivativepricing.pricingmodels.<model name>) """ if not kwargs['model']: kwargs['model'] = DEFAULT_MODEL[self.undl][self.derivative_type][self.expiry_type] return PricingEngine(self, **kwargs) def list_models(self): return ", ".join(OBJECT_MODEL[self.undl][self.expiry_type]) class VanillaOption(Instrument): """ Parent class for all Vanilla options on different underlying. Methods: payoff(spot0) -> Calculates the payoff of the function engine(model, **kwargs) Binds the inout parameter with pricing models. To check valid models for underlying use .models() """ def __init__(self, option_type, expiry_type, strike, expiry_date, derivative_type): self.option_type = option_type or VanillaOptionType.CALL.value self.expiry_type = expiry_type or ExpiryType.EUROPEAN.value self.strike = strike self.expiry_date = datetime.strptime(expiry_date, '%Y%m%d') self.derivative_type = derivative_type or DerivativeType.VANILLA_OPTION.value @property def _option_type_flag(self): if self.option_type == VanillaOptionType.CALL.value: return 1 else: return -1 def payoff(self, spot0=None): """ Calculates the payoff of option Defines payoff of the option Payoff(Call) = max(S-K,0) Payoff(Put) = max(K-S,0) Args required: spot0: Value of underlying e.g. 110 """ return max(self._option_type_flag * (spot0 - self.strike), 0.0) class EqOption(VanillaOption): """ Defines object for vanilla options on equity with both European and American expiry type. Args required: option_type: 'Call' or 'Put' (default value is set to 'Call') expiry_type: 'European' or 'American' (default is set to 'European') strike: (Float in same unit as underlying price) e.g. 110.0 expiry_date: (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210" derivative_type: Default value as "Vanilla Option". """ def __init__(self, option_type=VanillaOptionType.CALL.value, expiry_type=ExpiryType.EUROPEAN.value, strike=None, expiry_date=None, derivative_type=None ): super().__init__(option_type, expiry_type, strike, expiry_date, derivative_type) self.undl = UdlType.STOCK.value def engine(self, model=None, spot0=None, rf_rate=0, yield_div=0, div_list=None, volatility=None, pricing_date=None, **kwargs): """ Binds pricing model class and market data to the object Args required: Core Arguments: model: pricing model (default value set to BSM for European expiry) To check available list of models use print(option_object.list_models()) fwd0: (float) current underlying price/value e.g. 110.0 rf_rate: (Float < 1) risk free continuously compounded discount rate e.g. 5% as 0.05 volatility: (Float < 1) Underlying price/value return annualized volatility. Volatility in decimal e.g. Volatility of 10% => 0.10 pricing_Date: Date on which option value need to be calculated. (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210". yield_div: (Float < 1) div yield continuously compounded (for index options) e.g. 5% as 0.05 div_list: List of tuples for discrete dividends with dates. e.g. [("20180610", 2), ("20180624", 4)] [("Date", div amount),...] Model specific arguments: MonteCarlo no_of_path = (Integer). Number of paths to be generated for simulation e.g. 10000 no_of_steps = (Integer). Number of steps(nodes) for the premium calculation e.g. 100 seed = (Integer). Used for seeding antithetic = (Boolean). A variance reduction process in Montecarlo Simulation. Default False Binomial no_of_steps = (Integer). Number of steps (nodes) for the premium calculation. Maximum value accepted is 100. This limit will be increased in future release. """ return super().engine(model=model, spot0=spot0, rf_rate=rf_rate, cnv_yield=yield_div, pv_cnv=0, div_list=div_list, volatility=volatility, pricing_date=pricing_date, **kwargs) class FutOption(VanillaOption): """ Defines object for vanilla options on futures with both European and American expiry type. Args required: option_type: 'Call' or 'Put' (default value is set to 'Call') expiry_type: 'European' or 'American' (default is set to 'European') strike: (Float in same unit as underlying price) e.g. 110.0 expiry_date: (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210". """ def __init__(self, option_type=VanillaOptionType.CALL.value, expiry_type=ExpiryType.EUROPEAN.value, strike=None, expiry_date=None, derivative_type=None ): super().__init__(option_type, expiry_type, strike, expiry_date, derivative_type) self.undl = UdlType.FUTURES.value def engine(self, model=None, fwd0=None, rf_rate=0, volatility=None, pricing_date=None, **kwargs): """ Binds pricing model class and market data to the object Args required: Core Arguments: model: pricing model (default value set to BSM for European expiry) To check available list of models use print(option_object.list_models()) fwd0: (float) current future price quote e.g. 110.0 rf_rate: (Float < 1) risk free continuously compounded discount rate e.g. 5% as 0.05 volatility: (Float < 1) Underlying price/value return annualized volatility. Volatility in decimal e.g. Volatility of 10% => 0.10 pricing_Date: Date on which option value need to be calculated. (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210". Model specific arguments: MonteCarlo no_of_path = (Integer). Number of paths to be generated for simulation e.g. 10000 no_of_steps = (Integer). Number of steps(nodes) for the premium calculation e.g. 100 seed = (Integer). Used for seeding antithetic = (Boolean). A variance reduction process in Montecarlo Simulation. Default False Binomial no_of_steps = (Integer). Number of steps (nodes) for the premium calculation. Maximum value accepted is 100. This limit will be increased in future release. """ return super().engine(model=model, spot0=fwd0, rf_rate=rf_rate, cnv_yield=rf_rate, volatility=volatility, pricing_date=pricing_date, **kwargs) class FXOption(VanillaOption): """ Defines object for vanilla options on fx rates with both European and American expiry type. Args required: option_type: 'Call' or 'Put' (default value is set to 'Call') expiry_type: 'European' or 'American' (default is set to 'European') strike: (Float in same unit as underlying price) e.g. 110.0 expiry_date: (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210". """ def __init__(self, option_type=VanillaOptionType.CALL.value, expiry_type=ExpiryType.EUROPEAN.value, strike=None, expiry_date=None, derivative_type=None ): super().__init__(option_type, expiry_type, strike, expiry_date, derivative_type) self.undl = UdlType.FX.value def engine(self, model=None, spot0=None, rf_rate_local=0, rf_rate_foreign=0, volatility=None, pricing_date=None, **kwargs): """ Binds pricing model class and market data to the object Args required: Core Arguments: model: pricing model (default value set to BSM for European expiry) To check available list of models use print(option_object.list_models()) spot0: (float) current underlying price/value e.g. 110.0 rf_rate_local: (Float < 1) risk free continuously compounded discount rate of local currency e.g. 5% as 0.05 rf_rate_foreign: (Float < 1) risk free continuously compounded discount rate of foreign currency e.g. 5% as 0.05 volatility: (Float < 1) Underlying price/value return annualized volatility. Volatility in decimal e.g. Volatility of 10% => 0.10 pricing_Date: Date on which option value need to be calculated. (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210". Model specific arguments: MonteCarlo no_of_path = (Integer). Number of paths to be generated for simulation e.g. 10000 no_of_steps = (Integer). Number of steps(nodes) for the premium calculation e.g. 100 seed = (Integer). Used for seeding antithetic = (Boolean). A variance reduction process in Montecarlo Simulation. Default False Binomial no_of_steps = (Integer). Number of steps (nodes) for the premium calculation. Maximum value accepted is 100. This limit will be increased in future release. """ return super().engine(model=model, spot0=spot0, rf_rate=rf_rate_local, cnv_yield=rf_rate_foreign, volatility=volatility, pricing_date=pricing_date, **kwargs) class ComOption(VanillaOption): """ Defines object for vanilla options on commodities with both European and American expiry type. Args required: option_type: 'Call' or 'Put' (default value is set to 'Call') expiry_type: 'European' or 'American' (default is set to 'European') strike: (Float in same unit as underlying price) e.g. 110.0 expiry_date: (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210". """ def __init__(self, option_type=VanillaOptionType.CALL.value, expiry_type=ExpiryType.EUROPEAN.value, strike=None, expiry_date=None, derivative_type=None ): super().__init__(option_type, expiry_type, strike, expiry_date, derivative_type) self.undl = UdlType.COMMODITY.value def engine(self, model=None, spot0=None, rf_rate=0, cnv_yield=0, cost_yield=0, volatility=None, pricing_date=None, **kwargs): """ Binds pricing model class and market data to the object Args required: Core Arguments: model: pricing model (default value set to BSM for European expiry) To check available list of models use print(option_object.list_models()) spot0: (float) current underlying price/value e.g. 110.0 rf_rate: (Float < 1) risk free continuously compounded discount rate e.g. 5% as 0.05 cnv_yield: (Float < 1) Convenience yield continuously compounded e.g. 4% as 0.04 cost_yield: (Float < 1) Cost yield continuously compounded e.g. 2% as 0.02 volatility: (Float < 1) Underlying price/value return annualized volatility. Volatility in decimal e.g. Volatility of 10% => 0.10 pricing_Date: Date on which option value need to be calculated. (Date in string format "YYYYMMDD") e.g. 10 Dec 2018 as "20181210". Model specific arguments: MonteCarlo no_of_path = (Integer). Number of paths to be generated for simulation e.g. 10000 no_of_steps = (Integer). Number of steps(nodes) for the premium calculation e.g. 100 seed = (Integer). Used for seeding antithetic = (Boolean). A variance reduction process in Montecarlo Simulation. Default False Binomial no_of_steps = (Integer). Number of steps (nodes) for the premium calculation. Maximum value accepted is 100. This limit will be increased in future release. """ return super().engine(model=model, spot0=spot0, rf_rate=rf_rate, cnv_yield=cnv_yield, cost_yield=cost_yield, volatility=volatility, pricing_date=pricing_date, **kwargs)

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py

Python

theme/fidashtheme/__init__.py

fidash/fiware-fidash

900e79a629b51d811e1d3eaa8ca7951138d8994c

[ "Apache-2.0" ]

null

theme/fidashtheme/__init__.py

fidash/fiware-fidash

900e79a629b51d811e1d3eaa8ca7951138d8994c

[ "Apache-2.0" ]

null

theme/fidashtheme/__init__.py

fidash/fiware-fidash

900e79a629b51d811e1d3eaa8ca7951138d8994c

[ "Apache-2.0" ]

null

parent="wirecloud.fiwarelabtheme"

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4b26702144197ceaadbfcc25feb651d26e27a0db

158

py

Python

setup.py

open-traffic-generator/otg-grpc

71a2bcaaf28ebda0e1cb202dffa6e18d67463bdb

[ "MIT" ]

3

2021-12-16T06:32:49.000Z

2022-03-17T04:12:55.000Z

setup.py

open-traffic-generator/otg-gnmi

77c33659df76a148fad9eda5950b09ed514fab30

[ "MIT" ]

2

2021-11-30T13:34:50.000Z

2022-01-25T21:40:45.000Z

setup.py

open-traffic-generator/otg-gnmi

77c33659df76a148fad9eda5950b09ed514fab30

[ "MIT" ]

null

"""Build distributions To build `python setup.py sdist --formats=gztar bdist_wheel --universal` """ import os print('Setup: Started') print('Setup: Ended')

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5

d9a828f2660c836ba19f5e2cc9c533d69287e0aa

243

py

Python

sgnlp/models/rumour_detection_twitter/__init__.py

raymondng76/sgnlp

f09eada90ef5b1ee979901e5c14413d32e758049

[ "MIT" ]

14

2021-08-02T01:52:18.000Z

2022-01-14T10:16:02.000Z

sgnlp/models/rumour_detection_twitter/__init__.py

raymondng76/sgnlp

f09eada90ef5b1ee979901e5c14413d32e758049

[ "MIT" ]

29

2021-08-02T01:53:46.000Z

2022-03-30T05:40:46.000Z

sgnlp/models/rumour_detection_twitter/__init__.py

raymondng76/sgnlp

f09eada90ef5b1ee979901e5c14413d32e758049

[ "MIT" ]

7

2021-08-02T01:54:19.000Z

2022-01-07T06:37:45.000Z

from .config import RumourDetectionTwitterConfig from .tokenization import RumourDetectionTwitterTokenizer from .modeling import RumourDetectionTwitterModel from .train import train_model from .utils import download_tokenizer_files_from_azure

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null

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0

1

0

1

0

5

d9b1c342655751dc1c795049df14a20ba83fe23d

171

py

Python

pset6/hello/hello.py

ashudva/CS50_PSets

662886dd89063be330ba0aeb9e6e74c8776b91f6

[ "MIT" ]

null

pset6/hello/hello.py

ashudva/CS50_PSets

662886dd89063be330ba0aeb9e6e74c8776b91f6

[ "MIT" ]

null

pset6/hello/hello.py

ashudva/CS50_PSets

662886dd89063be330ba0aeb9e6e74c8776b91f6

[ "MIT" ]

null

# import get_string from cs50 import get_string # prompt for name print("What is your name?") name = get_string("") # prints "hello, {your name}" print(f"hello, {name}")

19

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0.701754

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null

1

0

1

0

null

0

1

0

5

d9b2b70ed44ab128b3b6e827dfe8be147915efae

124

py

Python

restAPIapp/admin.py

shawonAlam/Article-REST-API-django

ffbc92899f014b3f656496cc2e28f33bb84c055d

[ "MIT" ]

null

restAPIapp/admin.py

shawonAlam/Article-REST-API-django

ffbc92899f014b3f656496cc2e28f33bb84c055d

[ "MIT" ]

null

restAPIapp/admin.py

shawonAlam/Article-REST-API-django

ffbc92899f014b3f656496cc2e28f33bb84c055d

[ "MIT" ]

null

from django.contrib import admin from . models import Article admin.site.register(Article) # Register your models here.

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124

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5

d9c1a66ed22c8b2de4fb310baeabf88ef07ba7d8

39

py

Python

test/conductivity/__init__.py

MSimoncelli/phono3py

b28b45a025c279833e9269e5d91330c75d3f6ae0

[ "BSD-3-Clause" ]

38

2016-04-27T04:43:25.000Z

2020-05-01T07:46:56.000Z

test/conductivity/__init__.py

MSimoncelli/phono3py

b28b45a025c279833e9269e5d91330c75d3f6ae0

[ "BSD-3-Clause" ]

36

2016-12-22T12:42:54.000Z

2020-05-02T07:31:53.000Z

test/conductivity/__init__.py

MSimoncelli/phono3py

b28b45a025c279833e9269e5d91330c75d3f6ae0

[ "BSD-3-Clause" ]

30

2016-02-11T13:33:56.000Z

2020-05-01T21:36:50.000Z

"""Tests for conductivity routines."""

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38

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0

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true

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0

1

0

null

0

1

0

5

d9c22e8bbc313e5e33cae1e03eed3e5261fa5dff

15,309

py

Python

chainlibpy/generated/cosmos/bank/v1beta1/bank_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

chainlibpy/generated/cosmos/bank/v1beta1/bank_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

chainlibpy/generated/cosmos/bank/v1beta1/bank_pb2.py

MaCong-crypto/chainlibpy

8f91869fdf068359ebd9a3b206a7e856d8fa84f3

[ "Apache-2.0" ]

null

'Generated protocol buffer code.' from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from ....gogoproto import gogo_pb2 as gogoproto_dot_gogo__pb2 from ....cosmos_proto import cosmos_pb2 as cosmos__proto_dot_cosmos__pb2 from ....cosmos.base.v1beta1 import coin_pb2 as cosmos_dot_base_dot_v1beta1_dot_coin__pb2 DESCRIPTOR = _descriptor.FileDescriptor(name='cosmos/bank/v1beta1/bank.proto', package='cosmos.bank.v1beta1', syntax='proto3', serialized_options=b'Z)github.com/cosmos/cosmos-sdk/x/bank/types', create_key=_descriptor._internal_create_key, serialized_pb=b'\n\x1ecosmos/bank/v1beta1/bank.proto\x12\x13cosmos.bank.v1beta1\x1a\x14gogoproto/gogo.proto\x1a\x19cosmos_proto/cosmos.proto\x1a\x1ecosmos/base/v1beta1/coin.proto"\xb2\x01\n\x06Params\x12Y\n\x0csend_enabled\x18\x01 \x03(\x0b2 .cosmos.bank.v1beta1.SendEnabledB!\xf2\xde\x1f\x1dyaml:"send_enabled,omitempty"\x12G\n\x14default_send_enabled\x18\x02 \x01(\x08B)\xf2\xde\x1f%yaml:"default_send_enabled,omitempty":\x04\x98\xa0\x1f\x00"7\n\x0bSendEnabled\x12\r\n\x05denom\x18\x01 \x01(\t\x12\x0f\n\x07enabled\x18\x02 \x01(\x08:\x08\xe8\xa0\x1f\x01\x98\xa0\x1f\x00"~\n\x05Input\x12\x0f\n\x07address\x18\x01 \x01(\t\x12Z\n\x05coins\x18\x02 \x03(\x0b2\x19.cosmos.base.v1beta1.CoinB0\xc8\xde\x1f\x00\xaa\xdf\x1f(github.com/cosmos/cosmos-sdk/types.Coins:\x08\xe8\xa0\x1f\x00\x88\xa0\x1f\x00"\x7f\n\x06Output\x12\x0f\n\x07address\x18\x01 \x01(\t\x12Z\n\x05coins\x18\x02 \x03(\x0b2\x19.cosmos.base.v1beta1.CoinB0\xc8\xde\x1f\x00\xaa\xdf\x1f(github.com/cosmos/cosmos-sdk/types.Coins:\x08\xe8\xa0\x1f\x00\x88\xa0\x1f\x00"\xac\x01\n\x06Supply\x12Z\n\x05total\x18\x01 \x03(\x0b2\x19.cosmos.base.v1beta1.CoinB0\xc8\xde\x1f\x00\xaa\xdf\x1f(github.com/cosmos/cosmos-sdk/types.Coins:F\x18\x01\xe8\xa0\x1f\x01\x88\xa0\x1f\x00\xd2\xb4-8*github.com/cosmos/cosmos-sdk/x/bank/legacy/v040.SupplyI"=\n\tDenomUnit\x12\r\n\x05denom\x18\x01 \x01(\t\x12\x10\n\x08exponent\x18\x02 \x01(\r\x12\x0f\n\x07aliases\x18\x03 \x03(\t"\x91\x01\n\x08Metadata\x12\x13\n\x0bdescription\x18\x01 \x01(\t\x123\n\x0bdenom_units\x18\x02 \x03(\x0b2\x1e.cosmos.bank.v1beta1.DenomUnit\x12\x0c\n\x04base\x18\x03 \x01(\t\x12\x0f\n\x07display\x18\x04 \x01(\t\x12\x0c\n\x04name\x18\x05 \x01(\t\x12\x0e\n\x06symbol\x18\x06 \x01(\tB+Z)github.com/cosmos/cosmos-sdk/x/bank/typesb\x06proto3', dependencies=[gogoproto_dot_gogo__pb2.DESCRIPTOR, cosmos__proto_dot_cosmos__pb2.DESCRIPTOR, cosmos_dot_base_dot_v1beta1_dot_coin__pb2.DESCRIPTOR]) _PARAMS = _descriptor.Descriptor(name='Params', full_name='cosmos.bank.v1beta1.Params', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='send_enabled', full_name='cosmos.bank.v1beta1.Params.send_enabled', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xf2\xde\x1f\x1dyaml:"send_enabled,omitempty"', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='default_send_enabled', full_name='cosmos.bank.v1beta1.Params.default_send_enabled', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xf2\xde\x1f%yaml:"default_send_enabled,omitempty"', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x98\xa0\x1f\x00', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=137, serialized_end=315) _SENDENABLED = _descriptor.Descriptor(name='SendEnabled', full_name='cosmos.bank.v1beta1.SendEnabled', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='denom', full_name='cosmos.bank.v1beta1.SendEnabled.denom', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='enabled', full_name='cosmos.bank.v1beta1.SendEnabled.enabled', index=1, number=2, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\xe8\xa0\x1f\x01\x98\xa0\x1f\x00', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=317, serialized_end=372) _INPUT = _descriptor.Descriptor(name='Input', full_name='cosmos.bank.v1beta1.Input', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='address', full_name='cosmos.bank.v1beta1.Input.address', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='coins', full_name='cosmos.bank.v1beta1.Input.coins', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\xaa\xdf\x1f(github.com/cosmos/cosmos-sdk/types.Coins', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\xe8\xa0\x1f\x00\x88\xa0\x1f\x00', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=374, serialized_end=500) _OUTPUT = _descriptor.Descriptor(name='Output', full_name='cosmos.bank.v1beta1.Output', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='address', full_name='cosmos.bank.v1beta1.Output.address', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='coins', full_name='cosmos.bank.v1beta1.Output.coins', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\xaa\xdf\x1f(github.com/cosmos/cosmos-sdk/types.Coins', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\xe8\xa0\x1f\x00\x88\xa0\x1f\x00', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=502, serialized_end=629) _SUPPLY = _descriptor.Descriptor(name='Supply', full_name='cosmos.bank.v1beta1.Supply', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='total', full_name='cosmos.bank.v1beta1.Supply.total', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\xc8\xde\x1f\x00\xaa\xdf\x1f(github.com/cosmos/cosmos-sdk/types.Coins', file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=b'\x18\x01\xe8\xa0\x1f\x01\x88\xa0\x1f\x00\xd2\xb4-8*github.com/cosmos/cosmos-sdk/x/bank/legacy/v040.SupplyI', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=632, serialized_end=804) _DENOMUNIT = _descriptor.Descriptor(name='DenomUnit', full_name='cosmos.bank.v1beta1.DenomUnit', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='denom', full_name='cosmos.bank.v1beta1.DenomUnit.denom', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='exponent', full_name='cosmos.bank.v1beta1.DenomUnit.exponent', index=1, number=2, type=13, cpp_type=3, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='aliases', full_name='cosmos.bank.v1beta1.DenomUnit.aliases', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=806, serialized_end=867) _METADATA = _descriptor.Descriptor(name='Metadata', full_name='cosmos.bank.v1beta1.Metadata', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[_descriptor.FieldDescriptor(name='description', full_name='cosmos.bank.v1beta1.Metadata.description', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='denom_units', full_name='cosmos.bank.v1beta1.Metadata.denom_units', index=1, number=2, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='base', full_name='cosmos.bank.v1beta1.Metadata.base', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='display', full_name='cosmos.bank.v1beta1.Metadata.display', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='name', full_name='cosmos.bank.v1beta1.Metadata.name', index=4, number=5, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor(name='symbol', full_name='cosmos.bank.v1beta1.Metadata.symbol', index=5, number=6, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b''.decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key)], extensions=[], nested_types=[], enum_types=[], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[], serialized_start=870, serialized_end=1015) _PARAMS.fields_by_name['send_enabled'].message_type = _SENDENABLED _INPUT.fields_by_name['coins'].message_type = cosmos_dot_base_dot_v1beta1_dot_coin__pb2._COIN _OUTPUT.fields_by_name['coins'].message_type = cosmos_dot_base_dot_v1beta1_dot_coin__pb2._COIN _SUPPLY.fields_by_name['total'].message_type = cosmos_dot_base_dot_v1beta1_dot_coin__pb2._COIN _METADATA.fields_by_name['denom_units'].message_type = _DENOMUNIT DESCRIPTOR.message_types_by_name['Params'] = _PARAMS DESCRIPTOR.message_types_by_name['SendEnabled'] = _SENDENABLED DESCRIPTOR.message_types_by_name['Input'] = _INPUT DESCRIPTOR.message_types_by_name['Output'] = _OUTPUT DESCRIPTOR.message_types_by_name['Supply'] = _SUPPLY DESCRIPTOR.message_types_by_name['DenomUnit'] = _DENOMUNIT DESCRIPTOR.message_types_by_name['Metadata'] = _METADATA _sym_db.RegisterFileDescriptor(DESCRIPTOR) Params = _reflection.GeneratedProtocolMessageType('Params', (_message.Message,), {'DESCRIPTOR': _PARAMS, '__module__': 'cosmos.bank.v1beta1.bank_pb2'}) _sym_db.RegisterMessage(Params) SendEnabled = _reflection.GeneratedProtocolMessageType('SendEnabled', (_message.Message,), {'DESCRIPTOR': _SENDENABLED, '__module__': 'cosmos.bank.v1beta1.bank_pb2'}) _sym_db.RegisterMessage(SendEnabled) Input = _reflection.GeneratedProtocolMessageType('Input', (_message.Message,), {'DESCRIPTOR': _INPUT, '__module__': 'cosmos.bank.v1beta1.bank_pb2'}) _sym_db.RegisterMessage(Input) Output = _reflection.GeneratedProtocolMessageType('Output', (_message.Message,), {'DESCRIPTOR': _OUTPUT, '__module__': 'cosmos.bank.v1beta1.bank_pb2'}) _sym_db.RegisterMessage(Output) Supply = _reflection.GeneratedProtocolMessageType('Supply', (_message.Message,), {'DESCRIPTOR': _SUPPLY, '__module__': 'cosmos.bank.v1beta1.bank_pb2'}) _sym_db.RegisterMessage(Supply) DenomUnit = _reflection.GeneratedProtocolMessageType('DenomUnit', (_message.Message,), {'DESCRIPTOR': _DENOMUNIT, '__module__': 'cosmos.bank.v1beta1.bank_pb2'}) _sym_db.RegisterMessage(DenomUnit) Metadata = _reflection.GeneratedProtocolMessageType('Metadata', (_message.Message,), {'DESCRIPTOR': _METADATA, '__module__': 'cosmos.bank.v1beta1.bank_pb2'}) _sym_db.RegisterMessage(Metadata) DESCRIPTOR._options = None _PARAMS.fields_by_name['send_enabled']._options = None _PARAMS.fields_by_name['default_send_enabled']._options = None _PARAMS._options = None _SENDENABLED._options = None _INPUT.fields_by_name['coins']._options = None _INPUT._options = None _OUTPUT.fields_by_name['coins']._options = None _OUTPUT._options = None _SUPPLY.fields_by_name['total']._options = None _SUPPLY._options = None

268.578947

2,707

0.812986

2,227

15,309

5.283341

0.092052

0.041475

0.059748

0.046405

0.7684

0.741713

0.667602

0.663947

0.641849

0.600374

0

0.047531

0.042132

15,309

56

2,708

273.375

0.754842

0.002025

0

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0.090909

0.262216

0.216357

0

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false

0

0.127273

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0.127273

0

null

0

1

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1

0

1

0

1

0

null

0

5

d9f6edaaa3593333e1e0ca7149034077132efb7d

48

py

Python

viewstate/exceptions.py

TiberiuD/viewstate

7100e9aee3088c6cfc91bacc1580a9a371140407

[ "MIT" ]

null

viewstate/exceptions.py

TiberiuD/viewstate

7100e9aee3088c6cfc91bacc1580a9a371140407

[ "MIT" ]

null

viewstate/exceptions.py

TiberiuD/viewstate

7100e9aee3088c6cfc91bacc1580a9a371140407

[ "MIT" ]

null

class ViewStateException(Exception): pass

9.6

36

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null

0

1

0

null

0

1

0

5

8a099419c57fa200228fe43ac19cb9eb27f61377

41

py

Python

rockpaperscissors.py

blackstrawberry/gitkraken-demo-11

3c814a13fda2a26527150e73984bd9f43de6b804

[ "MIT" ]

null

rockpaperscissors.py

blackstrawberry/gitkraken-demo-11

3c814a13fda2a26527150e73984bd9f43de6b804

[ "MIT" ]

null

rockpaperscissors.py

blackstrawberry/gitkraken-demo-11

3c814a13fda2a26527150e73984bd9f43de6b804

[ "MIT" ]

null

# copy and paste # no time to learn haha

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1

0

null

0

1

0

5

8a0b4a3e3be6d02048841ccaa58185a26b148745

143

py

Python

scripts.py

demophoon/Google-Voice-Takeout-Analyser

622fd85b2c32371afdb84d3b29a368cb68230f78

[ "MIT" ]

null

scripts.py

demophoon/Google-Voice-Takeout-Analyser

622fd85b2c32371afdb84d3b29a368cb68230f78

[ "MIT" ]

null

scripts.py

demophoon/Google-Voice-Takeout-Analyser

622fd85b2c32371afdb84d3b29a368cb68230f78

[ "MIT" ]

null

#!/usr/bin/env python # encoding: utf-8 from munge import main from model import import_data def run_import(): main() import_data()

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8a13eec3bc00185b8c5222cea5f58a83f438d67a

167

py

Python

faker/__init__.py

svisser/faker-1

52e5018e3fcf5d2b176d2031672c56bc2140ebc9

[ "MIT" ]

1

2021-07-23T02:41:54.000Z

faker/__init__.py

svisser/faker-1

52e5018e3fcf5d2b176d2031672c56bc2140ebc9

[ "MIT" ]

null

faker/__init__.py

svisser/faker-1

52e5018e3fcf5d2b176d2031672c56bc2140ebc9

[ "MIT" ]

1

2021-05-04T04:53:57.000Z

from faker.factory import Factory from faker.generator import Generator from faker.proxy import Faker VERSION = '8.10.1' __all__ = ('Factory', 'Generator', 'Faker')

20.875

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8a27aca7cbae64e218a7a2be00aed29e1a38dfae

324

py

Python

home_web/webapp.py

tengshan2008/home_web

6702defee466184edd38848d96efc1842ce6a2f9

[ "MIT" ]

null

home_web/webapp.py

tengshan2008/home_web

6702defee466184edd38848d96efc1842ce6a2f9

[ "MIT" ]

null

home_web/webapp.py

tengshan2008/home_web

6702defee466184edd38848d96efc1842ce6a2f9

[ "MIT" ]

null

# Entry point for the application. from . import app # For application discovery by the 'flask' command. from . import views # For import side-effects of setting up routes. # Time-saver: output a URL to the VS Code terminal # so you can easily Ctrl+click to open a browser # print('http://127.0.0.1:5000/hello/VSCode')

40.5

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0.8

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324

7

73

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0.82716

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true

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5

8a337c6e479dd7d8d660b56dbd91b4d988f45459

24

py

Python

Services/LoanLiquidator/LoanBook.py

xan-crypto/CurveZero

d2e734be1dbbc4b2704adf08f627b66820f02904

[ "MIT" ]

18

2022-02-15T09:12:27.000Z

2022-03-27T14:40:13.000Z

Services/LoanLiquidator/LoanBook.py

tygakim/CurveZero

c671630efbf5e379840636e632a8dcef3ec57de2

[ "MIT" ]

2

2022-03-18T22:55:09.000Z

2022-03-21T19:40:38.000Z

Services/LoanLiquidator/LoanBook.py

tygakim/CurveZero

c671630efbf5e379840636e632a8dcef3ec57de2

[ "MIT" ]

4

2022-03-10T19:33:51.000Z

2022-03-28T15:32:31.000Z

# get and run loan book

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24

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1

24

0.944444

0.875

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null

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5

8a4f09431e4f2683304326dcb15c677eb4a58135

91

py

Python

Python Programs/Dictionary.py

Chibi-Shem/Hacktoberfest2020-Expert

324843464aec039e130e85a16e74b76d310f1497

[ "MIT" ]

77

2020-10-01T10:06:59.000Z

2021-11-08T08:57:18.000Z

Python Programs/Dictionary.py

Chibi-Shem/Hacktoberfest2020-Expert

324843464aec039e130e85a16e74b76d310f1497

[ "MIT" ]

46

2020-09-27T04:55:36.000Z

2021-05-14T18:49:06.000Z

Python Programs/Dictionary.py

Chibi-Shem/Hacktoberfest2020-Expert

324843464aec039e130e85a16e74b76d310f1497

[ "MIT" ]

327

2020-09-26T17:06:03.000Z

2021-10-09T06:04:39.000Z

dict = {'Name': 'Zara', 'Age': 7, 'Class': 'First'} print "dict['Alice']: ", dict['Alice']

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91

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null

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1

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null

0

1

0

1

0

5

8aac4c2318c59149be9eb4742320081f47ee07eb

175

py

Python

src/wl/commands/config_path.py

AlphaTechnolog/wl

09a8f883f397ba7aae80c06f61fedd1887975d3f

[ "MIT" ]

6

2021-07-13T16:34:45.000Z

2022-03-02T17:34:39.000Z

src/wl/commands/config_path.py

AlphaTechnolog/wl

09a8f883f397ba7aae80c06f61fedd1887975d3f

[ "MIT" ]

null

src/wl/commands/config_path.py

AlphaTechnolog/wl

09a8f883f397ba7aae80c06f61fedd1887975d3f

[ "MIT" ]

null

from .command import Command from ..paths import config_path class ConfigPath(Command): def run(self): print('The config path is:', str(config_path.absolute()))

21.875

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5.083333

0.666667

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175

7

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null

0

1

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5

8ab5fb1a5e8679007e7adbcf55a2b674f408609f

83

py

Python

src/poliastro/tests/test_examples.py

sundeshgupta/poliastro

0a269d43c8a082df3323d38ce73f5e1ae3262ccd

[ "MIT" ]

634

2015-05-11T08:50:42.000Z

2022-03-28T10:13:13.000Z

src/poliastro/tests/test_examples.py

sundeshgupta/poliastro

0a269d43c8a082df3323d38ce73f5e1ae3262ccd

[ "MIT" ]

1,386

2015-04-29T20:54:36.000Z

2022-03-30T13:06:34.000Z

src/poliastro/tests/test_examples.py

sundeshgupta/poliastro

0a269d43c8a082df3323d38ce73f5e1ae3262ccd

[ "MIT" ]

324

2015-04-29T20:52:43.000Z

2022-03-06T23:19:15.000Z

# This line tests all the statements so far from poliastro import examples # noqa

27.666667

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13

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1

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83

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44

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true

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null

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1

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1

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1

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5

0a0d318a4621b0b81da23883acaeda56b9fed208

443

py

Python

19_blastomatic/tests/unit_test.py

ilaydabozan/biofx_python

b7bef85dcf0b0a9e049f10a0766b9da20bf676c7

[ "MIT" ]

74

2020-12-18T16:04:31.000Z

2022-03-02T09:05:54.000Z

19_blastomatic/tests/unit_test.py

ilaydabozan/biofx_python

b7bef85dcf0b0a9e049f10a0766b9da20bf676c7

[ "MIT" ]

6

2021-06-30T19:42:04.000Z

2022-02-07T04:45:31.000Z

19_blastomatic/tests/unit_test.py

ilaydabozan/biofx_python

b7bef85dcf0b0a9e049f10a0766b9da20bf676c7

[ "MIT" ]

169

2020-11-06T19:44:36.000Z

2022-03-30T08:38:42.000Z

""" Unit tests for blastomatic """ from blastomatic import guess_delimiter # -------------------------------------------------- def test_guess_delimiter() -> None: """ Test guess_delimiter """ assert guess_delimiter('/foo/bar.csv') == ',' assert guess_delimiter('/foo/bar.txt') == '\t' assert guess_delimiter('/foo/bar.tsv') == '\t' assert guess_delimiter('/foo/bar.tab') == '\t' assert guess_delimiter('') == '\t'

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443

5.020833

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0.46473

0.414938

0.381743

0.439834

0.224066

0

0.153499

443

14

53

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0.642667

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0

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true

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0.142857

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0.285714

0

null

1

0

null

0

1

0

1

0

5

0a139a2e56f75fb8bbd427f649f65549f110a2b3

117

py

Python

dwork/mechanisms/__init__.py

kiprotect/dwork

abf2cdddf701da0e3d1987399f32f6edeed9493d

[ "BSD-3-Clause" ]

2

2020-11-17T20:05:07.000Z

2021-11-18T10:43:42.000Z

dwork/mechanisms/__init__.py

kiprotect/dwork

abf2cdddf701da0e3d1987399f32f6edeed9493d

[ "BSD-3-Clause" ]

null

dwork/mechanisms/__init__.py

kiprotect/dwork

abf2cdddf701da0e3d1987399f32f6edeed9493d

[ "BSD-3-Clause" ]

2

2020-11-17T20:05:09.000Z

2021-01-11T21:15:28.000Z

from .geometric import geometric_noise from .laplace import laplace_noise from .exponential import exponential_noise

29.25

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6.6

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5

0a183988d3d9d66efaf038d7a571ca3f4fb3bfc5

348

py

Python

exercicios/Curso_Udemy_Python/sec4_aula78.py

IgoPereiraBarros/maratona-data-science-brasil

cc07476579134a2764f00d229d415657555dcdd1

[ "MIT" ]

null

exercicios/Curso_Udemy_Python/sec4_aula78.py

IgoPereiraBarros/maratona-data-science-brasil

cc07476579134a2764f00d229d415657555dcdd1

[ "MIT" ]

null

exercicios/Curso_Udemy_Python/sec4_aula78.py

IgoPereiraBarros/maratona-data-science-brasil

cc07476579134a2764f00d229d415657555dcdd1

[ "MIT" ]

null

class Operacoes: def __init__(self, x, y): self.x = x self.y = y def soma(self): return self.x + self.y def sub(self): return self.x - self.y def mult(self): return self.x * self.y def divisao(self): return self.x / self.y def potencia(self): return self.x ** self.y def divisaoInteira(self): return self.x // self.y

13.92

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null

0

1

0

1

0

5

0a1fea93bbfc96d66e03aa5e6ee9310c3364d537

13,792

py

Python

tests/test_functions.py

geosharma/eqsig

3083022ab9e48ee422eff261560ee60846e766e2

[ "MIT" ]

15

2018-10-08T19:18:06.000Z

2022-02-05T16:03:31.000Z

tests/test_functions.py

geosharma/eqsig

3083022ab9e48ee422eff261560ee60846e766e2

[ "MIT" ]

2

2019-11-06T05:07:45.000Z

2021-04-19T09:59:25.000Z

tests/test_functions.py

geosharma/eqsig

3083022ab9e48ee422eff261560ee60846e766e2

[ "MIT" ]

8

2018-10-08T19:18:09.000Z

2022-02-03T12:08:33.000Z

import numpy as np import eqsig from eqsig import functions as fns import pytest from tests.conftest import TEST_DATA_DIR def test_determine_pseudo_cyclic_peak_only_series_with_triangle_series(): values = [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0] cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values / 2 peaks_only = eqsig.determine_pseudo_cyclic_peak_only_series(values) cum_peaks = np.sum(np.abs(peaks_only)) assert np.isclose(cum_peaks, expected_sum) def test_determine_peaks_only_delta_series_with_triangle_series(): values = [0, 1, 0, -1, 0, 1, 0, -1, 0, 1, 0] peaks_only = eqsig.determine_peaks_only_delta_series(values) cum_peaks = np.sum(np.abs(peaks_only)) cum_abs_delta_values = np.sum(np.abs(np.diff(values))) cum_diff = np.sum(peaks_only) assert np.isclose(cum_diff, 0), (cum_diff, 0) assert np.isclose(cum_abs_delta_values, 10), (cum_abs_delta_values, 10) def test_determine_pseudo_cyclic_peak_only_series_with_sine_wave(): time = np.arange(99) values = np.sin(time) values[-1] = 0 cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values / 2 peaks_only = eqsig.determine_pseudo_cyclic_peak_only_series(values) cum_peaks = np.sum(np.abs(peaks_only)) assert np.isclose(cum_peaks, expected_sum) def test_determine_peaks_only_delta_series_with_sine_wave(): time = np.arange(99) values = np.sin(time) values[-1] = 0 cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values peaks_only = eqsig.determine_peaks_only_delta_series(values) cum_peaks = np.sum(np.abs(peaks_only)) assert np.isclose(cum_peaks, expected_sum), (cum_peaks, expected_sum) def test_determine_pseudo_cyclic_peak_only_series_with_ground_motion(): record_path = TEST_DATA_DIR record_filename = 'test_motion_dt0p01.txt' rec = np.loadtxt(record_path + record_filename, skiprows=2) cum_abs_delta_values = np.sum(np.abs(np.diff(rec))) expected_sum = cum_abs_delta_values / 2 peaks_only = eqsig.determine_pseudo_cyclic_peak_only_series(rec) cum_peaks = np.sum(peaks_only) assert np.isclose(cum_peaks, expected_sum) def test_determine_peaks_only_delta_series_with_ground_motion(): record_path = TEST_DATA_DIR record_filename = 'test_motion_dt0p01.txt' rec = np.loadtxt(record_path + record_filename, skiprows=2) cum_abs_delta_values = np.sum(np.abs(np.diff(rec))) expected_sum = cum_abs_delta_values delta_peaks_only = eqsig.determine_peaks_only_delta_series(rec) cum_peaks = np.sum(np.abs(delta_peaks_only)) assert np.isclose(cum_peaks, expected_sum), (cum_peaks, expected_sum) def test_determine_pseudo_cyclic_peak_only_series_with_a_double_peak_and_offset(): values = np.array([0, 2, 1, 2, 0, 1, 0, -1, 0, 1, 0]) + 4 cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values / 2 peaks_only = eqsig.determine_pseudo_cyclic_peak_only_series(values) cum_peaks = np.sum(peaks_only) expected_series = np.array([0, 2, -1, 2, 0, 1, 0, 1, 0, 1, 0]) assert np.sum(np.abs(peaks_only - expected_series)) == 0.0 assert np.isclose(cum_peaks, expected_sum) def test_determine_peaks_only_delta_series_with_a_double_peak_and_offset(): values = np.array([0, 2, 1, 2, 0, 1, 0, -1, 0, 1, 0]) + 4 cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values delta_peaks_only = eqsig.determine_peaks_only_delta_series(values) cum_peaks = np.sum(np.abs(delta_peaks_only)) expected_series = np.array([0, 2, -1, 1, -2, 1, 0, -2, 0, 2, -1]) assert np.sum(np.abs(delta_peaks_only - expected_series)) == 0.0 assert np.isclose(cum_peaks, expected_sum) def test_determine_pseudo_cyclic_peak_only_series_with_non_zero_end(): end_value = 1. values = np.array([0, 2, -1, 2, 0, end_value]) cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values / 2 + end_value / 2 peaks_only = eqsig.determine_pseudo_cyclic_peak_only_series(values) cum_peaks = np.sum(peaks_only) assert np.isclose(cum_peaks, expected_sum) def test_determine_peaks_only_series_with_non_zero_end(): end_value = 1. values = np.array([0, 2, -1, 2, 0, end_value]) cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values delta_peaks_only = eqsig.determine_peaks_only_delta_series(values) cum_peaks = np.sum(np.abs(delta_peaks_only)) assert np.isclose(cum_peaks, expected_sum), (cum_peaks, expected_sum) def test_determine_peaks_only_series_with_nonchanging_values(): values = np.array([0, 1, 1, -3, -5, 0]) # constant then reverse cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values / 2 peaks_only = eqsig.determine_pseudo_cyclic_peak_only_series(values) cum_peaks = np.sum(peaks_only) assert np.isclose(cum_peaks, expected_sum), cum_peaks values = np.array([0, 1, 1, 3, -5, 0]) # constant the no reverse cum_abs_delta_values = np.sum(np.abs(np.diff(values))) expected_sum = cum_abs_delta_values / 2 peaks_only = eqsig.determine_pseudo_cyclic_peak_only_series(values) cum_peaks = np.sum(peaks_only) assert np.isclose(cum_peaks, expected_sum), cum_peaks def test_fa_spectrum_conversion(): record_path = TEST_DATA_DIR record_filename = 'test_motion_dt0p01.txt' dt = 0.01 values = np.loadtxt(record_path + record_filename, skiprows=2) npts = len(values) n_factor = 2 ** int(np.ceil(np.log2(npts))) fa = np.fft.fft(values, n=n_factor) points = int(n_factor / 2) fas = fa[range(points)] * dt faf = np.arange(points) / (2 * points * dt) n = 2 * len(fas) asig = eqsig.AccSignal(values, dt) fas_eqsig, faf_eqsig = fns.generate_fa_spectrum(asig) assert np.isclose(fas, fas_eqsig).all() assert np.isclose(faf, faf_eqsig).all() a = np.zeros(len(fa), dtype=complex) a[1:n // 2] = fas[1:] a[n // 2 + 1:] = np.flip(np.conj(fas[1:]), axis=0) a /= dt sig = np.fft.ifft(fa, n=n_factor) sig = sig[:len(values)] assert np.isclose(np.sum(np.abs(sig)), np.sum(np.abs(values))) asig2 = fns.fas2signal(fas_eqsig, dt, stype="signal") trimmed = asig2.values[:len(values)] assert np.isclose(np.sum(np.abs(trimmed)), np.sum(np.abs(values))) def test_get_peak_indices(): values = np.array([0, 2, 1, 2, -1, 1, 1, 0.3, -1, 0.2, 1, 0.2]) peak_indices = fns.get_peak_array_indices(values) peaks_series = np.zeros_like(values) np.put(peaks_series, peak_indices, values) expected = np.array([0, 1, 2, 3, 4, 5, 8, 10, 11]) assert np.sum(abs(peak_indices - expected)) == 0 values = np.array([2, 1, -1, 1]) peak_indices = fns.get_peak_array_indices(values) expected = np.array([0, 2, 3]) assert np.sum(abs(peak_indices - expected)) == 0 values = np.array([1, 2, -1, 1]) peak_indices = fns.get_peak_array_indices(values) expected = np.array([0, 1, 2, 3]) assert np.sum(abs(peak_indices - expected)) == 0 def test_get_zero_crossings_array_indices(): vs = np.array([0, 2, 1, 2, -1, 1, 0, 0, 1, 0.3, 0, -1, 0.2, 1, 0.2]) zci = fns.get_zero_crossings_array_indices(vs, keep_adj_zeros=True) expected = np.array([0, 4, 5, 6, 7, 10, 12]) assert np.array_equal(zci, expected) zci = fns.get_zero_crossings_array_indices(vs, keep_adj_zeros=False) expected = np.array([0, 4, 5, 6, 10, 12]) assert np.array_equal(zci, expected), zci # no zeros vs = np.array([1, 2, 1, 2, -1, 1, 1, 1, 1, 0.3, 1, -1, 0.2, 1, 0.2]) zci = fns.get_zero_crossings_array_indices(vs, keep_adj_zeros=False) expected = np.array([0, 4, 5, 11, 12]) assert np.array_equal(zci, expected), zci vs = np.array([-1, -2, 1, 2, -1, 1, 1, 1, 1, 0.3, 1, -1, 0.2, 1, 0.2]) zci = fns.get_zero_crossings_array_indices(vs, keep_adj_zeros=False) expected = np.array([0, 2, 4, 5, 11, 12]) assert np.array_equal(zci, expected), zci def test_put_array_in_2d_array(): vals = np.arange(1, 5) sfs = np.array([1, 2, 3]) expected_full = np.array([[0, 1, 2, 3, 4, 0, 0], [0, 0, 1, 2, 3, 4, 0], [0, 0, 0, 1, 2, 3, 4]]) out = fns.put_array_in_2d_array(vals, sfs) assert np.array_equal(out, expected_full), out # expected = np.array([[0, 1, 2, 3], # [0, 0, 1, 2], # [0, 0, 0, 1]]) out = fns.put_array_in_2d_array(vals, sfs, clip='end') assert np.array_equal(out, expected_full[:, :-3]), out out = fns.put_array_in_2d_array(vals, sfs, clip='start') assert np.array_equal(out, expected_full), out out = fns.put_array_in_2d_array(vals, sfs, clip='both') assert np.array_equal(out, expected_full[:, :-3]), out # neg shift vals = np.arange(4, 6) sfs = np.array([-1, 2]) expected_full = np.array([[4, 5, 0, 0, 0], [0, 0, 0, 4, 5], ]) out = fns.put_array_in_2d_array(vals, sfs, clip='none') assert np.array_equal(out, expected_full), out out = fns.put_array_in_2d_array(vals, sfs, clip='end') assert np.array_equal(out, expected_full[:, :-2]), out out = fns.put_array_in_2d_array(vals, sfs, clip='start') assert np.array_equal(out, expected_full[:, 1:]), out out = fns.put_array_in_2d_array(vals, sfs, clip='both') assert np.array_equal(out, expected_full[:, 1:-2]), out def test_join_values_w_shifts(): vals = np.arange(1, 5) sfs = np.array([1, 2, 3]) expected = np.array([[1, 3, 5, 7, 4, 0, 0], [1, 2, 4, 6, 3, 4, 0], [1, 2, 3, 5, 2, 3, 4]]) out = fns.join_values_w_shifts(vals, sfs) assert np.array_equal(out, expected), out expected = np.array([[ 1, 1, 1, 1, -4, 0, 0], [ 1, 2, 2, 2, -3, -4, 0], [ 1, 2, 3, 3, -2, -3, -4]]) def test_calc_step_fn_error(): assert min(fns.calc_step_fn_vals_error([4, 4, 4, 4, 1, 1, 1, 1])) == 0.0 assert min(fns.calc_step_fn_vals_error([4, 4, 4, 4, 1, 1, 1, 1], pow=2)) == 0.0 assert min(fns.calc_step_fn_vals_error([4, 5, 4, 4, 1, 1, 2, 1])) == 3.0 assert min(fns.calc_step_fn_vals_error([4, 5, 4, 4, 1, 1, 2, 1], pow=2)) == 1.0 def test_calc_step_fn_steps_val(): vals = [4, 4, 4, 4, 1, 1, 1, 1] ind = np.argmin(fns.calc_step_fn_vals_error(vals)) pre, post = fns.calc_step_fn_steps_vals(vals, ind) assert ind == 3 assert pre == 4 assert post == 1 vals = [4, 5, 4, 4, 1, 1, 2, 1] ind = np.argmin(fns.calc_step_fn_vals_error(vals)) pre, post = fns.calc_step_fn_steps_vals(vals, ind) assert ind == 3 assert np.isclose(pre, 4.333333) assert post == 1.25 def test_roll_av_vals(): expected = np.array([4, 4, 3, 2, 1, 1, 1, 1]) assert np.sum(fns.calc_roll_av_vals([4, 4, 4, 4, 1, 1, 1, 1], steps=3) - expected) == 0 expected = np.array([4, 4, 4, 4, 3, 2, 1, 1]) assert np.sum(fns.calc_roll_av_vals([4, 4, 4, 4, 1, 1, 1, 1], steps=3, mode='backward') - expected) == 0 expected = np.array([4, 4, 4, 3, 2, 1, 1, 1]) assert np.sum(fns.calc_roll_av_vals([4, 4, 4, 4, 1, 1, 1, 1], steps=3, mode='centre') - expected) == 0 def test_interp2d(): y = np.linspace(1, 10, 3) yf = np.linspace(0, 22, 5) f = np.arange(len(yf))[:, np.newaxis] * np.ones((len(yf), 10)) f_interp = fns.interp2d(y, yf, f) assert np.isclose(f_interp[0][0], (y[0] - yf[0]) / (yf[1] - yf[0])), (f_interp[0][0], (y[0] - yf[0]) / (yf[1] - yf[0])) assert np.isclose(f_interp[1][0], 1), (f_interp[0][0], 1) assert len(f_interp) == 3 assert len(f_interp[0]) == 10 def test_interp2d_2(): f = np.array([[0, 0, 0], # 0 [0, 1, 4], # 5 [2, 6, 2], # 10 [10, 10, 10] # 30 ]) yf = np.array([0, 1, 2, 3]) y = np.array([0.5, 1, 2.2, 2.5]) f_interp = fns.interp2d(y, yf, f) print(f_interp) assert f_interp[0][0] == 0 assert f_interp[0][1] == 0.5 assert f_interp[0][2] == 2.0 assert f_interp[1][0] == f[1][0] assert f_interp[1][1] == f[1][1] assert f_interp[1][2] == f[1][2] assert np.isclose(f_interp[2][0], f[2][0] + 8 * 0.2) assert np.isclose(f_interp[3][2], 6.) def test_interp2d_at_edge(): f = np.array([[0, 0, 0], # 0 [10, 10, 10] # 30 ]) xf = np.array([0, 3]) x = np.array([0.0, 3.0]) f_interp = fns.interp2d(x, xf, f) print(f_interp) assert f_interp[0][0] == 0 assert f_interp[1][0] == 10. def test_interp_left(): x0 = [0, 1, 5] x = [0, 2, 6] y = [1.5, 2.5, 3.5] y_new = fns.interp_left(x0, x, y) expected = np.array([1.5, 1.5, 2.5]) assert np.isclose(y_new, expected).all(), y_new x0 = [0, 2, 6] y_new = fns.interp_left(x0, x, y) expected = np.array([1.5, 2.5, 3.5]) assert np.isclose(y_new, expected).all(), y_new x0 = [-1, 2, 6] with pytest.raises(AssertionError): y_new = fns.interp_left(x0, x, y) if __name__ == '__main__': # test_interp2d() test_interp2d_at_edge() # test_put_array_in_2d_array() # test_fa_spectrum_conversion() # test_determine_peaks_only_series_with_sine_wave() # test_determine_peaks_only_series_with_triangle_series() # test_determine_peaks_only_series_with_ground_motion() # test_determine_peaks_only_series_with_a_double_peak_and_offset() # test_determine_peaks_only_series_with_nonchanging_values() # test_determine_peaks_only_series_with_non_zero_end()

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Python

text/_elisp/type/primitive.py

jedhsu/text

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[ "Apache-2.0" ]

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text/_elisp/type/primitive.py

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text/_elisp/type/primitive.py

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torch/testing/_internal/common_nn.py

pradeep90/pytorch

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torch/testing/_internal/common_nn.py

pradeep90/pytorch

9ddbce203d418fe24f8d9540bcff03faac257614

[ "Intel" ]

null

torch/testing/_internal/common_nn.py

pradeep90/pytorch

9ddbce203d418fe24f8d9540bcff03faac257614

[ "Intel" ]

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from abc import abstractmethod import math import tempfile import unittest from copy import deepcopy from functools import reduce from itertools import product from operator import mul from math import pi import torch import torch.cuda import torch.nn as nn import torch.nn.functional as F from torch.nn import _reduction as _Reduction from torch.testing._internal.common_utils import TestCase, to_gpu, freeze_rng_state, is_iterable, \ TEST_WITH_ROCM, gradcheck, gradgradcheck from torch.testing._internal.common_cuda import TEST_CUDA from torch.autograd.gradcheck import _get_numerical_jacobian, _iter_tensors from torch.autograd import Variable from torch.types import _TensorOrTensors import torch.backends.cudnn from typing import Dict, Callable, Tuple, List, Sequence, Union, Any TemporaryFile = tempfile.TemporaryFile PRECISION = 1e-5 def get_reduction(m): result = getattr(m, 'reduction', None) if result is None: result = _Reduction.legacy_get_string(getattr(m, 'sizeAverage', None), True, emit_warning=False) assert result is not None return result def get_weight(m): result = getattr(m, 'weight', None) if result is not None: return result return getattr(m, 'weights', None) # NOTE [How to check NN module / functional API parity between Python and C++ frontends] # # The way to check API parity is to add parity tests for the NN module / functional of interest. # Here are the detailed steps: # # For NN module: # 1. Make sure you already have a test dict with the module configuration you want to test. # 2. Add `cpp_constructor_args` entry to the test dict, with its value exactly matching # the Python module constructor arguments. For example, if in the test dict we pass # `(10, 8)` to `torch.nn.Linear` constructor, then we should pass `torch::nn::LinearOptions(10, 8)` # as the corresponding C++ constructor argument to `torch::nn::Linear`. # 3. If in the process of performing the above step you referenced any variables # in the `cpp_constructor_args` entry, you must add `cpp_var_map` entry # to the test dict to make sure that those variables are populated with the right Python values. # For example, if the Python constructor call is # `torch.nn.FractionalMaxPool2d(2, output_ratio=0.5, _random_samples=random_samples)`, # the corresponding C++ constructor argument is # `torch::nn::FractionalMaxPool2dOptions(2).output_ratio(0.5)._random_samples(random_samples)`, # and the `cpp_var_map` entry must be # `{'random_samples': random_samples}` in order to populate the C++ variable `random_samples` # used in the C++ constructor argument with the Python tensor value `random_samples`. # # For NN functional: # 1. Make sure you already have a test dict with the functional configuration you want to test. # 2. If the test dict's `constructor` entry looks like `wrap_functional(F.some_functional_name, ...)`, # then you must add `cpp_options_args` entry to the test dict, with its value exactly matching the Python # functional optional arguments. For example, if the test dict's `constructor` entry is # `wrap_functional(F.interpolate, size=12, scale_factor=None, mode='nearest')`, # then the `cpp_options_args` entry should be # "F::InterpolateFuncOptions().size(std::vector<int64_t>({12})).scale_factor(c10::nullopt).mode(torch::kNearest)". # 3. Otherwise, if the test dict's `constructor` entry looks like # `wrap_functional(lambda i: F.some_functional_name(...))`, # then you must add `cpp_function_call` entry to the test dict, with its value exactly matching the Python # functional function call. For example, if the test dict's `constructor` entry is # `wrap_functional(lambda i: F.poisson_nll_loss(i, t.type_as(i), reduction='none'))`, # then the `cpp_function_call` entry should be # "F::poisson_nll_loss(i, t.to(i.options()), F::PoissonNLLLossFuncOptions().reduction(torch::kNone))". # 4. If in the process of performing the above two steps you referenced any variables # in the `cpp_options_args` or `cpp_function_call` entry, you must # add `cpp_var_map` entry to the test dict to make sure that those variables # are populated with the right Python values. For example, if the test dict's `constructor` entry is # `wrap_functional(lambda i: F.poisson_nll_loss(i, t.type_as(i), reduction='none'))`, # then the `cpp_function_call` entry should be # "F::poisson_nll_loss(i, t.to(i.options()), F::PoissonNLLLossFuncOptions().reduction(torch::kNone))". # Notice that there are two variables `i` and `t` that need to have their values provided, # and the way to do so is to add a `cpp_var_map` entry: `cpp_var_map={'i': '_get_input()', 't': t}`. # (Note that for `i`, since we want it to take the Python input value, we pass '_get_input()' string as value # and the C++ parity test mechanism will populate `i` with the Python input value correctly.) # # There are also a few optional flags in the test dict to control the C++ parity test behavior: # # - `test_cpp_api_parity`: if `False`, skips the C++ parity test for this test dict. Default: True. # - `has_parity`: if `False`, expects this test dict to fail the C++ parity test. Default: True. module_tests = [ dict( module_name='Linear', constructor_args=(10, 8), cpp_constructor_args='torch::nn::LinearOptions(10, 8)', input_size=(4, 10), reference_fn=lambda i, p, _: torch.mm(i, p[0].t()) + p[1].view(1, -1).expand(4, 8), with_tf32=True, tf32_precision=0.005, ), dict( module_name='Linear', constructor_args=(10, 8, False), cpp_constructor_args='torch::nn::LinearOptions(10, 8).bias(false)', input_size=(4, 10), desc='no_bias', reference_fn=lambda i, p, _: torch.mm(i, p[0].t()), with_tf32=True, tf32_precision=0.005, ), dict( module_name='Threshold', constructor_args=(2., 1.), cpp_constructor_args='torch::nn::ThresholdOptions(2., 1.)', input_size=(2, 3, 4, 5), check_inplace=True, desc='threshold_value' ), dict( module_name='Threshold', constructor_args=(2., 10.), cpp_constructor_args='torch::nn::ThresholdOptions(2., 10.)', input_size=(2, 3, 4, 5), desc='large_value' ), dict( module_name='ReLU', input_size=(2, 3, 4, 5), check_inplace=True, ), dict( module_name='ReLU6', input_size=(2, 3, 4, 5), check_inplace=True, ), dict( module_name='RReLU', input_size=(1, 2, 2), test_cuda=False, ), dict( module_name='RReLU', constructor_args=(0.1, 0.9), cpp_constructor_args='torch::nn::RReLUOptions().lower(0.1).upper(0.9)', input_size=(4, 4, 5), desc='with_up_down', test_cuda=False, ), dict( module_name='Hardtanh', input_size=(3, 2, 5), reference_fn=lambda i, *_: i.clamp(-1, 1), ), dict( module_name='Sigmoid', input_size=(2, 3, 4, 5), ), dict( module_name='Tanh', input_size=(2, 3, 4, 5), ), dict( module_name='Flatten', input_size=(2, 3, 4, 5), reference_fn=lambda i, *_: torch.flatten(i, 1) ), dict( module_name='Softmax', constructor_args=(1,), cpp_constructor_args='torch::nn::SoftmaxOptions(1)', input_size=(10, 20), reference_fn=lambda i, *_: torch.exp(i).div(torch.exp(i).sum(1, True).expand(10, 20)), ), dict( module_name='Softmax2d', input_size=(1, 3, 10, 20), reference_fn=lambda i, *_: torch.exp(i).div(torch.exp(i).sum(1, False)), ), dict( module_name='LogSoftmax', constructor_args=(1,), cpp_constructor_args='torch::nn::LogSoftmaxOptions(1)', input_size=(10, 20), reference_fn=lambda i, *_: torch.exp(i).div_(torch.exp(i).sum(1, True).expand(10, 20)).log_(), ), dict( module_name='LogSoftmax', constructor_args=(1,), cpp_constructor_args='torch::nn::LogSoftmaxOptions(1)', input_size=(1, 3, 10, 20), reference_fn=lambda i, *_: torch.exp(i).div_(torch.exp(i).sum(1, False)).log_(), desc='multiparam', ), dict( module_name='ELU', constructor_args=(2.,), cpp_constructor_args='torch::nn::ELUOptions().alpha(2.)', input_size=(3, 2, 5), reference_fn=lambda x, *_: torch.where(x >= 0, x, 2 * (x.exp() - 1)), ), # TODO: reference function dict( module_name='Hardshrink', constructor_args=(2.,), cpp_constructor_args='torch::nn::HardshrinkOptions(2.)', input_size=(4, 3, 2, 4), ), dict( module_name='LeakyReLU', input_size=(3, 2, 5), check_inplace=True ), dict( module_name='LeakyReLU', constructor_args=(0.5,), cpp_constructor_args='torch::nn::LeakyReLUOptions().negative_slope(0.5)', input_size=(3, 2, 5), check_inplace=True, desc='with_negval' ), dict( module_name='LeakyReLU', constructor_args=(0.0,), cpp_constructor_args='torch::nn::LeakyReLUOptions().negative_slope(0.0)', input_fn=lambda: torch.randn(10, 10), check_inplace=True, desc='with_zero_negval' ), dict( module_name='LogSigmoid', input_size=(2, 3, 4), reference_fn=lambda i, *_: i.sigmoid().log(), ), dict( module_name='Softplus', input_size=(10, 20), reference_fn=lambda i, *_: torch.log(1 + torch.exp(i)), ), dict( module_name='Softplus', constructor_args=(2,), cpp_constructor_args='torch::nn::SoftplusOptions().beta(2)', input_size=(10, 20), reference_fn=lambda i, *_: 1. / 2. * torch.log(1 + torch.exp(2 * i)), desc='beta', ), dict( module_name='Softplus', constructor_args=(2, -100), cpp_constructor_args='torch::nn::SoftplusOptions().beta(2).threshold(-100)', input_size=(10, 20), reference_fn=( lambda i, *_: ((i * 2) > -100).type_as(i) * i + ((i * 2) <= -100).type_as(i) * 1. / 2. * torch.log(1 + torch.exp(2 * i)) ), desc='beta_threshold', ), dict( module_name='Softshrink', input_size=(3, 2, 5), ), dict( module_name='Softshrink', constructor_args=(1,), cpp_constructor_args='torch::nn::SoftshrinkOptions(1)', input_size=(3, 2, 5), desc='lambda', ), dict( module_name='CrossMapLRN2d', constructor_args=(5, 5e-3, 1e-3, 2), cpp_constructor_args='torch::nn::CrossMapLRN2dOptions(5).alpha(5e-3).beta(1e-3).k(2)', input_size=(2, 3, 6, 6), check_gradgrad=False, # TODO(#50743): Figure out the error. "RuntimeError: Unrecognized tensor type ID: Batched" check_batched_grad=False, ), dict( module_name='PReLU', input_size=(2, 3, 4), reference_fn=lambda i, p, _: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='1d', ), dict( module_name='PReLU', constructor_args=(3,), cpp_constructor_args='torch::nn::PReLUOptions().num_parameters(3)', input_size=(2, 3, 4), desc='1d_multiparam', reference_fn=lambda i, p, _: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], ), dict( module_name='PReLU', input_size=(2, 3, 4, 5), desc='2d', reference_fn=lambda i, p, _: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], ), dict( module_name='PReLU', constructor_args=(3,), cpp_constructor_args='torch::nn::PReLUOptions().num_parameters(3)', input_size=(2, 3, 4, 5), desc='2d_multiparam', reference_fn=lambda i, p, _: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], ), dict( module_name='PReLU', input_size=(2, 3, 4, 5, 6), reference_fn=lambda i, p, _: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='3d', ), dict( module_name='PReLU', constructor_args=(3,), cpp_constructor_args='torch::nn::PReLUOptions().num_parameters(3)', input_size=(2, 3, 4, 5, 6), desc='3d_multiparam', reference_fn=lambda i, p, _: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], ), dict( module_name='Softsign', input_size=(3, 2, 5), reference_fn=lambda i, *_: i.div(1 + torch.abs(i)), ), dict( module_name='Softmin', constructor_args=(1,), cpp_constructor_args='torch::nn::SoftminOptions(1)', input_size=(10, 20), ), dict( module_name='Softmin', constructor_args=(1,), cpp_constructor_args='torch::nn::SoftminOptions(1)', input_size=(2, 3, 5, 10), desc='multidim', ), dict( module_name='Tanhshrink', input_size=(2, 3, 4, 5), ), ] # Generates rand tensor with non-equal values. This ensures that duplicate # values won't be causing test failure for modules like MaxPooling. # size should be small, otherwise randperm fails / long overflows. def _rand_tensor_non_equal(*size): total = reduce(mul, size, 1) return torch.randperm(total).view(*size).double() def wrap_functional(fn, **kwargs): class FunctionalModule(nn.Module): def forward(self, *args): return fn(*args, **kwargs) return FunctionalModule def poissonnllloss_no_reduce_test(): t = torch.randn(10, 10) return dict( fullname='PoissonNLLLoss_no_reduce', constructor=wrap_functional( lambda i: F.poisson_nll_loss(i, t.type_as(i), reduction='none')), cpp_function_call='F::poisson_nll_loss(' 'i, t.to(i.options()), F::PoissonNLLLossFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.rand(10, 10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: i.exp() - t.mul(i), pickle=False) def bceloss_no_reduce_test(): t = Variable(torch.randn(15, 10).gt(0).double()) return dict( fullname='BCELoss_no_reduce', constructor=wrap_functional( lambda i: F.binary_cross_entropy(i, t.type_as(i), reduction='none')), cpp_function_call='F::binary_cross_entropy(' 'i, t.to(i.options()), F::BinaryCrossEntropyFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.rand(15, 10).clamp_(2.8e-2, 1 - 2.8e-2), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: -(t * i.log() + (1 - t) * (1 - i).log()), pickle=False, precision=7e-4) def bceloss_no_reduce_scalar_test(): t = torch.randn(()).gt(0).double() return dict( fullname='BCELoss_no_reduce_scalar', constructor=wrap_functional( lambda i: F.binary_cross_entropy(i, t.type_as(i), reduction='none')), cpp_function_call='F::binary_cross_entropy(' 'i, t.to(i.options()), F::BinaryCrossEntropyFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.rand(()).clamp_(2.8e-2, 1 - 2.8e-2), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: -(t * i.log() + (1 - t) * (1 - i).log()), pickle=False) def bceloss_weights_no_reduce_test(): t = Variable(torch.randn(15, 10).gt(0).double()) weights = torch.rand(10) return dict( fullname='BCELoss_weights_no_reduce', constructor=wrap_functional( lambda i: F.binary_cross_entropy(i, t.type_as(i), weight=weights.type_as(i), reduction='none')), cpp_function_call='F::binary_cross_entropy(' 'i, t.to(i.options()), ' 'F::BinaryCrossEntropyFuncOptions().weight(weights.to(i.options())).reduction(torch::kNone))', input_fn=lambda: torch.rand(15, 10).clamp_(2.8e-2, 1 - 2.8e-2), cpp_var_map={'i': '_get_input()', 't': t, 'weights': weights}, reference_fn=lambda i, p, m: -(t * i.log() + (1 - t) * (1 - i).log()) * weights, pickle=False, precision=3e-4 ) def bceloss_weights_no_reduce_scalar_test(): t = torch.randn(()).double() weights = torch.rand(()) return dict( fullname='BCELoss_weights_no_reduce_scalar', constructor=wrap_functional( lambda i: F.binary_cross_entropy(i, t.type_as(i), weight=weights.type_as(i), reduction='none')), cpp_function_call='''F::binary_cross_entropy( i, t.to(i.options()), F::BinaryCrossEntropyFuncOptions().weight(weights.to(i.options())).reduction(torch::kNone))''', cpp_var_map={'i': '_get_input()', 't': t, 'weights': weights}, input_fn=lambda: torch.rand(()).clamp_(2.8e-2, 1 - 2.8e-2), reference_fn=lambda i, *_: -(t * i.log() + (1 - t) * (1 - i).log()) * weights, pickle=False ) def bce_with_logistic_legacy_enum_test(): t = Variable(torch.randn(15, 10).gt(0).double()) sigmoid = nn.Sigmoid() return dict( fullname='BCEWithLogitsLoss_legacy_enum', constructor=wrap_functional( lambda i: F.binary_cross_entropy_with_logits(i, t.type_as(i), reduce=False)), cpp_function_call='''F::binary_cross_entropy_with_logits( i, t.to(i.options()), F::BinaryCrossEntropyWithLogitsFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.rand(15, 10).clamp_(2.8e-2, 1 - 2.8e-2), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: -(t * sigmoid(i).log() + (1 - t) * (1 - sigmoid(i)).log()), check_gradgrad=False, pickle=False, ) def bce_with_logistic_no_reduce_test(): t = Variable(torch.randn(15, 10).gt(0).double()) sigmoid = nn.Sigmoid() return dict( fullname='BCEWithLogitsLoss_no_reduce', constructor=wrap_functional( lambda i: F.binary_cross_entropy_with_logits(i, t.type_as(i), reduction='none')), cpp_function_call='''F::binary_cross_entropy_with_logits( i, t.to(i.options()), F::BinaryCrossEntropyWithLogitsFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.rand(15, 10).clamp_(2.8e-2, 1 - 2.8e-2), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: -(t * sigmoid(i).log() + (1 - t) * (1 - sigmoid(i)).log()), check_gradgrad=False, pickle=False, ) def bce_with_logistic_no_reduce_scalar_test(): t = torch.randn(()).gt(0).double() sigmoid = nn.Sigmoid() return dict( fullname='BCEWithLogitsLoss_no_reduce_scalar', constructor=wrap_functional( lambda i: F.binary_cross_entropy_with_logits(i, t.type_as(i), reduction='none')), cpp_function_call='''F::binary_cross_entropy_with_logits( i, t.to(i.options()), F::BinaryCrossEntropyWithLogitsFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.rand(()).clamp_(2.8e-2, 1 - 2.8e-2), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: -(t * sigmoid(i).log() + (1 - t) * (1 - sigmoid(i)).log()), check_gradgrad=False, pickle=False ) def kldivloss_with_target_no_reduce_test(): i = torch.rand(10, 10).log() return dict( fullname='KLDivLoss_with_target_no_reduce', constructor=wrap_functional( lambda t: F.kl_div(i.type_as(t), t, reduction='none')), cpp_function_call='F::kl_div(i.to(t.options()), t, F::KLDivFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.rand(10, 10), cpp_var_map={'i': i, 't': '_get_input()'}, reference_fn=lambda t, *_: loss_reference_fns['KLDivLoss'](i.type_as(t), t, reduction='none'), pickle=False) def kldivloss_no_reduce_test(): t = torch.randn(10, 10) return dict( fullname='KLDivLoss_no_reduce', constructor=wrap_functional( lambda i: F.kl_div(i, t.type_as(i), reduction='none')), cpp_function_call='F::kl_div(i, t.to(i.options()), F::KLDivFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.rand(10, 10).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['KLDivLoss'](i, t.type_as(i), reduction='none'), pickle=False, ) def kldivloss_no_reduce_scalar_test(): t = torch.randn(()) return dict( fullname='KLDivLoss_no_reduce_scalar', constructor=wrap_functional( lambda i: F.kl_div(i, t.type_as(i), reduction='none')), cpp_function_call='F::kl_div(i, t.to(i.options()), F::KLDivFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.rand(()).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['KLDivLoss'](i, t.type_as(i), reduction='none'), pickle=False) def kldivloss_with_log_target_no_reduce_test(): i = torch.rand(10, 10).log() return dict( fullname='KLDivLoss_with_log_target_no_reduce', constructor=wrap_functional( lambda t: F.kl_div(i.type_as(t), t, reduction='none', log_target=True)), cpp_function_call='F::kl_div(i.to(t.options()), t, F::KLDivFuncOptions().reduction(torch::kNone).log_target(true))', input_fn=lambda: torch.rand(10, 10), cpp_var_map={'i': i, 't': '_get_input()'}, reference_fn=lambda t, *_: loss_reference_fns['KLDivLoss_log_target'](i.type_as(t), t, reduction='none'), pickle=False) def kldivloss_no_reduce_log_target_test(): t = torch.randn(10, 10) return dict( fullname='KLDivLoss_no_reduce_log_target', constructor=wrap_functional( lambda i: F.kl_div(i, t.type_as(i), reduction='none', log_target=True)), cpp_function_call='F::kl_div(i, t.to(i.options()), F::KLDivFuncOptions().reduction(torch::kNone).log_target(true))', input_fn=lambda: torch.rand(10, 10).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['KLDivLoss_log_target'](i, t.type_as(i), reduction='none'), pickle=False, ) def kldivloss_no_reduce_scalar_log_target_test(): t = torch.randn(()) return dict( fullname='KLDivLoss_no_reduce_scalar_log_target', constructor=wrap_functional( lambda i: F.kl_div(i, t.type_as(i), reduction='none', log_target=True)), cpp_function_call='F::kl_div(i, t.to(i.options()), F::KLDivFuncOptions().reduction(torch::kNone).log_target(true))', input_fn=lambda: torch.rand(()).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['KLDivLoss_log_target'](i, t.type_as(i), reduction='none'), pickle=False) def l1loss_no_reduce_test(): t = torch.randn(2, 3, 4) return dict( fullname='L1Loss_no_reduce', constructor=wrap_functional( lambda i: F.l1_loss(i, t.type_as(i), reduction='none')), cpp_function_call='F::l1_loss(i, t.to(i.options()), F::L1LossFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.randn(2, 3, 4), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: (i - t.type_as(i)).abs(), pickle=False) def l1loss_no_reduce_complex_test(): t = torch.randn(2, 3, 4, dtype=torch.cdouble) return dict( fullname='L1Loss_no_reduce_complex', constructor=wrap_functional( lambda i: F.l1_loss(i, t.type_as(i), reduction='none')), cpp_function_call='F::l1_loss(i, t.to(i.options()), F::L1LossFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.randn(2, 3, 4, dtype=torch.cdouble), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: (i - t.type_as(i)).abs(), pickle=False) def l1loss_no_reduce_scalar_test(): t = torch.randn(()) return dict( fullname='L1Loss_no_reduce_scalar', constructor=wrap_functional( lambda i: F.l1_loss(i, t.type_as(i), reduction='none')), cpp_function_call='F::l1_loss(i, t.to(i.options()), F::L1LossFuncOptions().reduction(torch::kNone))', input_fn=lambda: torch.randn(()), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: (i - t.type_as(i)).abs(), pickle=False) def mseloss_no_reduce_test(): input_size = (2, 3, 4, 5) target = torch.randn(*input_size) return dict( fullname='MSELoss_no_reduce', constructor=wrap_functional( lambda i: F.mse_loss(i, target.type_as(i), reduction='none')), cpp_function_call='F::mse_loss(i, target.to(i.options()), F::MSELossFuncOptions().reduction(torch::kNone))', input_size=input_size, cpp_var_map={'i': '_get_input()', 'target': target}, reference_fn=lambda i, *_: (i - target).pow(2), pickle=False) def mseloss_no_reduce_scalar_test(): input_size = () target = torch.randn(input_size) return dict( fullname='MSELoss_no_reduce_scalar', constructor=wrap_functional( lambda i: F.mse_loss(i, target.type_as(i), reduction='none')), cpp_function_call='F::mse_loss(i, target.to(i.options()), F::MSELossFuncOptions().reduction(torch::kNone))', input_size=input_size, cpp_var_map={'i': '_get_input()', 'target': target}, reference_fn=lambda i, *_: (i - target).pow(2), pickle=False) def nllloss_no_reduce_test(): t = Variable(torch.empty(15).uniform_().mul(10).floor().long()) kwargs = {'reduction': 'none'} return dict( fullname='NLLLoss_no_reduce', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), reduction=kwargs['reduction'])), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.rand(15, 10).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['NLLLoss'](i, t.type_as(i).long(), **kwargs), pickle=False) def nllloss_no_reduce_ignore_index_test(): t = Variable(torch.empty(15).uniform_().mul(10).floor().long()) kwargs: Dict[str, Union[int, str]] = {'ignore_index': 2, 'reduction': 'none'} return dict( fullname='NLLLoss_no_reduce_ignore_index', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), ignore_index=int(kwargs['ignore_index']), reduction=str(kwargs['reduction']))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().ignore_index(2).reduction(torch::kNone))''', input_fn=lambda: torch.rand(15, 10).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['NLLLoss'](i, t.type_as(i).long(), **kwargs), pickle=False) def nllloss_no_reduce_weights_test(): t = Variable(torch.empty(15).uniform_().mul(10).floor().long()) weight = torch.rand(10) def kwargs(i): return {'weight': weight.type_as(i), 'reduction': 'none'} return dict( fullname='NLLLoss_no_reduce_weights', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), **kwargs(i))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().weight(weight.to(i.options())).reduction(torch::kNone))''', input_fn=lambda: torch.rand(15, 10).add(1e-2).log(), cpp_var_map={'i': '_get_input()', 't': t, 'weight': weight}, reference_fn=lambda i, *_: loss_reference_fns['NLLLoss'](i, t.type_as(i).long(), **kwargs(i)), pickle=False) def nllloss_no_reduce_weights_ignore_index_test(): t = Variable(torch.empty(15).uniform_().mul(10).floor().long()) weight = torch.rand(10) def kwargs(i): return {'weight': weight.type_as(i), 'reduction': 'none', 'ignore_index': 2} return dict( fullname='NLLLoss_no_reduce_weights_ignore_index', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), **kwargs(i.data))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().weight(weight.to(i.options())).reduction(torch::kNone).ignore_index(2))''', input_fn=lambda: torch.rand(15, 10).add(1e-2).log(), cpp_var_map={'i': '_get_input()', 't': t, 'weight': weight}, reference_fn=lambda i, *_: loss_reference_fns['NLLLoss'](i, t.type_as(i).long(), **kwargs(i)), pickle=False) def nllloss_no_reduce_weights_ignore_index_neg_test(): t = Variable(torch.empty(15).uniform_().mul(10).floor().long()) weight = torch.rand(10) def kwargs(i): return {'weight': weight.type_as(i), 'reduction': 'none', 'ignore_index': -1} return dict( fullname='NLLLoss_no_reduce_weights_ignore_index_neg', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), **kwargs(i))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().weight(weight.to(i.options())).reduction(torch::kNone).ignore_index(-1))''', input=torch.rand(15, 10).add(1e-2).log(), cpp_var_map={'i': '_get_input()', 't': t, 'weight': weight}, reference_fn=lambda i, *_: loss_reference_fns['NLLLoss'](i, t.type_as(i).long(), **kwargs(i)), pickle=False) def nllloss2d_no_reduce_test(): t = Variable(torch.rand(2, 5, 5).mul(3).floor().long()) kwargs = {'reduction': 'none'} return dict( fullname='NLLLoss2d_no_reduce', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), reduction=kwargs['reduction'])), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.rand(2, 3, 5, 5).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['NLLLossNd'](i, t.type_as(i).long(), **kwargs), pickle=False) def nllloss2d_no_reduce_ignore_index_test(): t = Variable(torch.rand(2, 5, 5).mul(3).floor().long()) kwargs: Dict[str, Union[int, str]] = {'ignore_index': 1, 'reduction': 'none'} return dict( fullname='NLLLoss2d_no_reduce_ignore_index', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), ignore_index=int(kwargs['ignore_index']), reduction=str(kwargs['reduction']))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().ignore_index(1).reduction(torch::kNone))''', input_fn=lambda: torch.rand(2, 3, 5, 5).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['NLLLossNd'](i, t.type_as(i).long(), **kwargs), pickle=False) def nllloss2d_no_reduce_weights_test(): t = Variable(torch.rand(2, 5, 5).mul(3).floor().long()) weight = torch.rand(3) def kwargs(i): return {'weight': weight.type_as(i), 'reduction': 'none'} return dict( fullname='NLLLoss2d_no_reduce_weights', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), **kwargs(i))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().weight(weight.to(i.options())).reduction(torch::kNone))''', input_fn=lambda: torch.rand(2, 3, 5, 5).log(), cpp_var_map={'i': '_get_input()', 't': t, 'weight': weight}, reference_fn=lambda i, *_: loss_reference_fns['NLLLossNd'](i, t.type_as(i).long(), **kwargs(i)), pickle=False) def nlllossNd_no_reduce_test(): t = Variable(torch.rand(2, 5, 5, 2, 2).mul(3).floor().long()) kwargs = {'reduction': 'none'} return dict( fullname='NLLLossNd_no_reduce', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), reduction=kwargs['reduction'])), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.rand(2, 3, 5, 5, 2, 2).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['NLLLossNd'](i, t.type_as(i).long(), **kwargs), pickle=False) def nlllossNd_no_reduce_ignore_index_test(): t = Variable(torch.rand(2, 5, 5, 2, 2).mul(3).floor().long()) kwargs: Dict[str, Union[int, str]] = {'ignore_index': 1, 'reduction': 'none'} return dict( fullname='NLLLossNd_no_reduce_ignore_index', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), ignore_index=int(kwargs['ignore_index']), reduction=str(kwargs['reduction']))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().ignore_index(1).reduction(torch::kNone))''', input_fn=lambda: torch.rand(2, 3, 5, 5, 2, 2).log(), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['NLLLossNd'](i, t.type_as(i).long(), **kwargs), pickle=False) def nlllossNd_no_reduce_weights_test(): t = Variable(torch.rand(2, 5, 5, 2, 2).mul(3).floor().long()) weight = torch.rand(3) def kwargs(i): return {'weight': weight.type_as(i), 'reduction': 'none'} return dict( fullname='NLLLossNd_no_reduce_weights', constructor=wrap_functional( lambda i: F.nll_loss(i, t.type_as(i).long(), **kwargs(i))), cpp_function_call='''F::nll_loss( i, t.to(i.options()).to(torch::kLong), F::NLLLossFuncOptions().weight(weight.to(i.options())).reduction(torch::kNone))''', input_fn=lambda: torch.rand(2, 3, 5, 5, 2, 2).log(), cpp_var_map={'i': '_get_input()', 't': t, 'weight': weight}, reference_fn=lambda i, *_: loss_reference_fns['NLLLossNd'](i, t.type_as(i).long(), **kwargs(i)), pickle=False) def smoothl1loss_no_reduce_test(): t = torch.randn(2, 3, 4) return dict( fullname='SmoothL1Loss_no_reduce', constructor=wrap_functional( lambda i: F.smooth_l1_loss(i, t.type_as(i), reduction='none')), cpp_function_call='''F::smooth_l1_loss( i, t.to(i.options()), F::SmoothL1LossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(2, 3, 4), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['SmoothL1Loss'](i, t.type_as(i), reduction='none'), pickle=False) def smoothl1loss_no_reduce_scalar_test(): t = torch.randn(()) return dict( fullname='SmoothL1Loss_no_reduce_scalar', constructor=wrap_functional( lambda i: F.smooth_l1_loss(i, t.type_as(i), reduction='none')), cpp_function_call='''F::smooth_l1_loss( i, t.to(i.options()), F::SmoothL1LossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(()), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['SmoothL1Loss'](i, t.type_as(i), reduction='none'), pickle=False) def smoothl1loss_beta_test(): t = torch.randn(2, 3, 4) return dict( fullname='SmoothL1Loss_beta', constructor=wrap_functional( lambda i: F.smooth_l1_loss(i, t.type_as(i), reduction='none', beta=0.5)), cpp_function_call='''F::smooth_l1_loss( i, t.to(i.options()), F::SmoothL1LossFuncOptions().reduction(torch::kNone), 0.5)''', input_fn=lambda: torch.randn(2, 3, 4), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['SmoothL1Loss'](i, t.type_as(i), reduction='none', beta=0.5), pickle=False) def smoothl1loss_zero_beta_test(): t = torch.randn(2, 3, 4) return dict( fullname='SmoothL1Loss_zero_beta', constructor=wrap_functional( lambda i: F.smooth_l1_loss(i, t.type_as(i), reduction='none', beta=0)), cpp_function_call='''F::smooth_l1_loss( i, t.to(i.options()), F::SmoothL1LossFuncOptions().reduction(torch::kNone), 0)''', input_fn=lambda: torch.randn(2, 3, 4), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['SmoothL1Loss'](i, t.type_as(i), reduction='none', beta=0), pickle=False) def huberloss_delta_test(): t = torch.randn(2, 3, 4) return dict( fullname='HuberLoss_delta', constructor=wrap_functional( lambda i: F.huber_loss(i, t.type_as(i), reduction='none', delta=0.5)), cpp_function_call='''F::huber_loss( i, t.to(i.options()), F::HuberLossFuncOptions().reduction(torch::kNone).delta(0.5))''', input_fn=lambda: torch.randn(2, 3, 4), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['HuberLoss'](i, t.type_as(i), reduction='none', delta=0.5), pickle=False) def multilabelmarginloss_0d_no_reduce_test(): t = torch.zeros(()).long() return dict( fullname='MultiLabelMarginLoss_0d_no_reduce', constructor=wrap_functional( lambda i: F.multilabel_margin_loss(i, t.type_as(i).long(), reduction='none')), cpp_function_call='''F::multilabel_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultilabelMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(()), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiLabelMarginLoss'](i, t.data.type_as(i).long(), reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multilabelmarginloss_1d_no_reduce_test(): t = Variable(torch.rand(10).mul(10).floor().long()) return dict( fullname='MultiLabelMarginLoss_1d_no_reduce', constructor=wrap_functional( lambda i: F.multilabel_margin_loss(i, t.type_as(i).long(), reduction='none')), cpp_function_call='''F::multilabel_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultilabelMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiLabelMarginLoss'](i, t.data.type_as(i).long(), reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multilabelmarginloss_index_neg_test(): t = Variable(torch.clamp(torch.rand(5, 10).add(-.5).mul(20).floor().long(), min=-1)) return dict( fullname='MultiLabelMarginLoss_index_neg', constructor=wrap_functional( lambda i: F.multilabel_margin_loss(i, t.type_as(i).long(), reduction='none')), cpp_function_call='''F::multilabel_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultilabelMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiLabelMarginLoss'](i, t.data.type_as(i).long(), reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multilabelmarginloss_no_reduce_test(): t = Variable(torch.rand(5, 10).mul(10).floor().long()) return dict( fullname='MultiLabelMarginLoss_no_reduce', constructor=wrap_functional( lambda i: F.multilabel_margin_loss(i, t.type_as(i).long(), reduction='none')), cpp_function_call='''F::multilabel_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultilabelMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiLabelMarginLoss'](i, t.data.type_as(i).long(), reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def hingeembeddingloss_no_reduce_test(): t = Variable(torch.randn(10).gt(0).double().mul_(2).sub(1)) return dict( fullname='HingeEmbeddingLoss_no_reduce', constructor=wrap_functional( lambda i: F.hinge_embedding_loss(i, t.type_as(i), reduction='none')), cpp_function_call='''F::hinge_embedding_loss( i, t.to(i.options()), F::HingeEmbeddingLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['HingeEmbeddingLoss'](i, t.type_as(i), reduction='none'), check_sum_reduction=True, pickle=False) def hingeembeddingloss_margin_no_reduce_test(): t = Variable(torch.randn(10).gt(0).double().mul_(2).sub(1)) return dict( fullname='HingeEmbeddingLoss_margin_no_reduce', constructor=wrap_functional( lambda i: F.hinge_embedding_loss(i, t.type_as(i), margin=0.5, reduction='none')), cpp_function_call='''F::hinge_embedding_loss( i, t.to(i.options()), F::HingeEmbeddingLossFuncOptions().margin(0.5).reduction(torch::kNone))''', input_fn=lambda: torch.randn(10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['HingeEmbeddingLoss'](i, t.type_as(i), margin=0.5, reduction='none'), check_sum_reduction=True, pickle=False) def softmarginloss_no_reduce_test(): t = torch.randn(5, 5) return dict( fullname='SoftMarginLoss_no_reduce', constructor=wrap_functional( lambda i: F.soft_margin_loss(i, t.type_as(i), reduction='none')), cpp_function_call='''F::soft_margin_loss( i, t.to(i.options()), F::SoftMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 5), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['SoftMarginLoss'](i, t.type_as(i), reduction='none'), pickle=False) def multilabelsoftmarginloss_no_reduce_test(): t = torch.rand(5, 10).mul(2).floor() return dict( fullname='MultiLabelSoftMarginLoss_no_reduce', constructor=wrap_functional( lambda i: F.multilabel_soft_margin_loss(i, t.type_as(i), reduction='none')), cpp_function_call='''F::multilabel_soft_margin_loss( i, t.to(i.options()), F::MultilabelSoftMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: (-(t * i.sigmoid().log() + (1 - t) * (-i).sigmoid().log())).sum(dim=1) / i.size(1), check_gradgrad=False, pickle=False) def multilabelsoftmarginloss_weights_no_reduce_test(): t = torch.rand(5, 10).mul(2).floor() weights = torch.rand(10) return dict( fullname='MultiLabelSoftMarginLoss_weights_no_reduce', constructor=wrap_functional( lambda i: F.multilabel_soft_margin_loss(i, t.type_as(i), weight=weights.type_as(i), reduction='none')), cpp_function_call='''F::multilabel_soft_margin_loss( i, t.to(i.options()), F::MultilabelSoftMarginLossFuncOptions().weight(weights.to(i.options())).reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10), cpp_var_map={'i': '_get_input()', 't': t, 'weights': weights}, reference_fn=lambda i, *_: (-(t * i.sigmoid().log() + (1 - t) * (-i).sigmoid().log()) * weights).sum(dim=1) / i.size(1), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multimarginloss_no_reduce_test(): t = torch.rand(5).mul(8).floor().long() return dict( fullname='MultiMarginLoss_no_reduce', constructor=wrap_functional( lambda i: F.multi_margin_loss(i, t.type_as(i).long(), reduction='none')), cpp_function_call='''F::multi_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultiMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiMarginLoss'](i, t.data.type_as(i).long(), reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multimarginloss_1d_no_reduce_test(): t = torch.rand(1).mul(8).floor().long() return dict( fullname='MultiMarginLoss_1d_no_reduce', constructor=wrap_functional( lambda i: F.multi_margin_loss(i, t.type_as(i).long(), reduction='none')), cpp_function_call='''F::multi_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultiMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiMarginLoss'](i, t.data.type_as(i).long(), reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multimarginloss_1d_input_0d_target_no_reduce_test(): t = torch.rand(()).mul(8).floor().long() return dict( fullname='multimarginloss_1d_input_0d_target_no_reduce', constructor=wrap_functional( lambda i: F.multi_margin_loss(i, t.type_as(i).long(), reduction='none')), cpp_function_call='''F::multi_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultiMarginLossFuncOptions().reduction(torch::kNone))''', input_fn=lambda: torch.randn(10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiMarginLoss'](i, t.data.type_as(i).long(), reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multimarginloss_p_no_reduce_test(): t = torch.rand(5).mul(8).floor().long() return dict( fullname='MultiMarginLoss_p_no_reduce', constructor=wrap_functional( lambda i: F.multi_margin_loss(i, t.type_as(i).long(), p=2, reduction='none')), cpp_function_call='''F::multi_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultiMarginLossFuncOptions().p(2).reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10).clamp_(1e-2, 1 - 1e-2), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiMarginLoss'](i, t.data.type_as(i).long(), p=2, reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multimarginloss_margin_no_reduce_test(): t = torch.rand(5).mul(8).floor().long() return dict( fullname='MultiMarginLoss_margin_no_reduce', constructor=wrap_functional( lambda i: F.multi_margin_loss(i, t.type_as(i).long(), margin=0.5, reduction='none')), cpp_function_call='''F::multi_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultiMarginLossFuncOptions().margin(0.5).reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10), cpp_var_map={'i': '_get_input()', 't': t}, reference_fn=lambda i, *_: loss_reference_fns['MultiMarginLoss'](i, t.data.type_as(i).long(), margin=0.5, reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def multimarginloss_weights_no_reduce_test(): t = torch.rand(5).mul(8).floor().long() weights = torch.rand(10) return dict( fullname='MultiMarginLoss_weights_no_reduce', constructor=wrap_functional( lambda i: F.multi_margin_loss(i, t.type_as(i).long(), weight=weights.type_as(i), reduction='none')), cpp_function_call='''F::multi_margin_loss( i, t.to(i.options()).to(torch::kLong), F::MultiMarginLossFuncOptions().weight(weights.to(i.options())).reduction(torch::kNone))''', input_fn=lambda: torch.randn(5, 10), cpp_var_map={'i': '_get_input()', 't': t, 'weights': weights}, reference_fn=lambda i, *_: loss_reference_fns['MultiMarginLoss'](i, t.data.type_as(i).long(), weight=weights, reduction='none'), check_sum_reduction=True, check_gradgrad=False, pickle=False) def fractional_max_pool2d_test(test_case): random_samples = torch.empty((1, 3, 2), dtype=torch.double).uniform_() if test_case == 'ratio': return dict( constructor=lambda: nn.FractionalMaxPool2d( 2, output_ratio=0.5, _random_samples=random_samples), cpp_constructor_args='''torch::nn::FractionalMaxPool2dOptions(2) .output_ratio(0.5) ._random_samples(random_samples)''', input_size=(1, 3, 5, 7), cpp_var_map={'random_samples': random_samples}, fullname='FractionalMaxPool2d_ratio') elif test_case == 'size': return dict( constructor=lambda: nn.FractionalMaxPool2d((2, 3), output_size=( 4, 3), _random_samples=random_samples), cpp_constructor_args='''torch::nn::FractionalMaxPool2dOptions({2, 3}) .output_size(std::vector<int64_t>({4, 3})) ._random_samples(random_samples)''', input_size=(1, 3, 7, 6), cpp_var_map={'random_samples': random_samples}, fullname='FractionalMaxPool2d_size') def fractional_max_pool3d_test(test_case): random_samples = torch.empty((2, 4, 3), dtype=torch.double).uniform_() if test_case == 'ratio': return dict( constructor=lambda: nn.FractionalMaxPool3d( 2, output_ratio=0.5, _random_samples=random_samples), cpp_constructor_args='''torch::nn::FractionalMaxPool3dOptions(2) .output_ratio(0.5) ._random_samples(random_samples)''', input_size=(2, 4, 5, 5, 5), cpp_var_map={'random_samples': random_samples}, fullname='FractionalMaxPool3d_ratio') elif test_case == 'size': return dict( constructor=lambda: nn.FractionalMaxPool3d((2, 2, 2), output_size=( 4, 4, 4), _random_samples=random_samples), cpp_constructor_args='''torch::nn::FractionalMaxPool3dOptions({2, 2, 2}) .output_size(std::vector<int64_t>({4, 4, 4})) ._random_samples(random_samples)''', input_size=(2, 4, 7, 7, 7), cpp_var_map={'random_samples': random_samples}, fullname='FractionalMaxPool3d_size') elif test_case == 'asymsize': return dict( constructor=lambda: nn.FractionalMaxPool3d((4, 2, 3), output_size=( 10, 3, 2), _random_samples=random_samples), cpp_constructor_args='''torch::nn::FractionalMaxPool3dOptions({4, 2, 3}) .output_size(std::vector<int64_t>({10, 3, 2})) ._random_samples(random_samples)''', input_size=(2, 4, 16, 7, 5), cpp_var_map={'random_samples': random_samples}, fullname='FractionalMaxPool3d_asymsize') def single_batch_reference_fn(input, parameters, module): """Reference function for modules supporting no batch dimensions. The module is passed the input and target in batched form with a single item. The output is squeezed to compare with the no-batch input. """ single_batch_input = input.unsqueeze(0) with freeze_rng_state(): return module(single_batch_input).squeeze(0) new_module_tests = [ poissonnllloss_no_reduce_test(), bceloss_no_reduce_test(), bceloss_weights_no_reduce_test(), bce_with_logistic_legacy_enum_test(), bce_with_logistic_no_reduce_test(), bceloss_no_reduce_scalar_test(), bceloss_weights_no_reduce_scalar_test(), bce_with_logistic_no_reduce_scalar_test(), kldivloss_with_target_no_reduce_test(), kldivloss_no_reduce_test(), kldivloss_no_reduce_scalar_test(), kldivloss_with_log_target_no_reduce_test(), kldivloss_no_reduce_log_target_test(), kldivloss_no_reduce_scalar_log_target_test(), l1loss_no_reduce_test(), l1loss_no_reduce_complex_test(), l1loss_no_reduce_scalar_test(), mseloss_no_reduce_test(), mseloss_no_reduce_scalar_test(), nllloss_no_reduce_test(), nllloss_no_reduce_ignore_index_test(), nllloss_no_reduce_weights_test(), nllloss_no_reduce_weights_ignore_index_test(), nllloss_no_reduce_weights_ignore_index_neg_test(), nllloss2d_no_reduce_test(), nllloss2d_no_reduce_weights_test(), nllloss2d_no_reduce_ignore_index_test(), nlllossNd_no_reduce_test(), nlllossNd_no_reduce_weights_test(), nlllossNd_no_reduce_ignore_index_test(), smoothl1loss_no_reduce_test(), smoothl1loss_no_reduce_scalar_test(), smoothl1loss_beta_test(), smoothl1loss_zero_beta_test(), huberloss_delta_test(), multilabelmarginloss_0d_no_reduce_test(), multilabelmarginloss_1d_no_reduce_test(), multilabelmarginloss_index_neg_test(), multilabelmarginloss_no_reduce_test(), hingeembeddingloss_no_reduce_test(), hingeembeddingloss_margin_no_reduce_test(), softmarginloss_no_reduce_test(), multilabelsoftmarginloss_no_reduce_test(), multilabelsoftmarginloss_weights_no_reduce_test(), multimarginloss_no_reduce_test(), multimarginloss_1d_no_reduce_test(), multimarginloss_1d_input_0d_target_no_reduce_test(), multimarginloss_p_no_reduce_test(), multimarginloss_margin_no_reduce_test(), multimarginloss_weights_no_reduce_test(), fractional_max_pool2d_test('ratio'), fractional_max_pool2d_test('size'), fractional_max_pool3d_test('ratio'), fractional_max_pool3d_test('size'), fractional_max_pool3d_test('asymsize'), dict( module_name='BatchNorm1d', constructor_args=(10,), cpp_constructor_args='torch::nn::BatchNorm1dOptions(10)', input_size=(4, 10), cudnn=True, check_eval=True, desc='affine', ), dict( module_name='BatchNorm1d', constructor_args=(5,), cpp_constructor_args='torch::nn::BatchNorm1dOptions(5)', input_size=(4, 5, 3), cudnn=True, check_eval=True, desc='3d_input', ), dict( module_name='BatchNorm1d', constructor_args=(10, 1e-3, None), cpp_constructor_args='torch::nn::BatchNorm1dOptions(10).eps(1e-3).momentum(c10::nullopt)', input_size=(4, 10), cudnn=True, check_eval=True, desc='affine_simple_average', ), dict( module_name='BatchNorm1d', constructor_args=(10, 1e-3, 0.3, False), cpp_constructor_args='torch::nn::BatchNorm1dOptions(10).eps(1e-3).momentum(0.3).affine(false)', input_size=(4, 10), cudnn=True, check_eval=True, desc='not_affine', ), dict( module_name='BatchNorm1d', constructor_args=(10, 1e-3, 0.3, True, False), cpp_constructor_args='''torch::nn::BatchNorm1dOptions(10) .eps(1e-3).momentum(0.3).affine(true).track_running_stats(false)''', input_size=(4, 10), cudnn=True, check_eval=True, desc='not_tracking_stats', ), dict( module_name='BatchNorm1d', constructor_args=(5, 1e-3, 0.3, False), cpp_constructor_args='torch::nn::BatchNorm1dOptions(5).eps(1e-3).momentum(0.3).affine(false)', input_size=(4, 5, 3), cudnn=True, check_eval=True, desc='3d_input_not_affine', ), dict( module_name='BatchNorm1d', constructor_args=(5, 1e-3, 0.3, False), cpp_constructor_args='torch::nn::BatchNorm1dOptions(5).eps(1e-3).momentum(0.3).affine(false)', input_size=(0, 5, 9), cudnn=True, check_eval=True, desc='zero_batch', ), dict( module_name='BatchNorm2d', constructor_args=(3,), cpp_constructor_args='torch::nn::BatchNorm2dOptions(3)', input_size=(2, 3, 6, 6), cudnn=True, check_eval=True, ), dict( module_name='BatchNorm2d', constructor_args=(3, 1e-3, None), cpp_constructor_args='torch::nn::BatchNorm2dOptions(3).eps(1e-3).momentum(c10::nullopt)', input_size=(2, 3, 6, 6), cudnn=True, check_eval=True, desc='2d_simple_average', ), dict( module_name='BatchNorm2d', constructor_args=(3, 1e-3, 0.8), cpp_constructor_args='torch::nn::BatchNorm2dOptions(3).eps(1e-3).momentum(0.8)', input_size=(2, 3, 6, 6), cudnn=True, check_eval=True, desc='momentum', ), dict( module_name='BatchNorm2d', constructor_args=(3, 1e-3, 0.8, False), cpp_constructor_args='torch::nn::BatchNorm2dOptions(3).eps(1e-3).momentum(0.8).affine(false)', input_size=(2, 3, 6, 6), cudnn=True, check_eval=True, desc='not_affine', ), dict( module_name='BatchNorm2d', constructor_args=(3, 1e-3, 0.8, True, False), cpp_constructor_args='''torch::nn::BatchNorm2dOptions(3) .eps(1e-3).momentum(0.8).affine(true).track_running_stats(false)''', input_size=(2, 3, 6, 6), cudnn=True, check_eval=True, desc='not_tracking_stats', ), dict( module_name='BatchNorm2d', constructor_args=(5, 1e-3, 0.3, False), cpp_constructor_args='torch::nn::BatchNorm2dOptions(5).eps(1e-3).momentum(0.3).affine(false)', input_size=(0, 5, 2, 2), cudnn=True, check_eval=True, desc='zero_batch', ), dict( module_name='BatchNorm3d', constructor_args=(3,), cpp_constructor_args='torch::nn::BatchNorm3dOptions(3)', input_size=(2, 3, 4, 4, 4), cudnn=True, check_eval=True, ), dict( module_name='BatchNorm3d', constructor_args=(3, 1e-3, None), cpp_constructor_args='torch::nn::BatchNorm3dOptions(3).eps(1e-3).momentum(c10::nullopt)', input_size=(2, 3, 4, 4, 4), cudnn=True, check_eval=True, desc='3d_simple_average', ), dict( module_name='BatchNorm3d', constructor_args=(3, 1e-3, 0.7), cpp_constructor_args='torch::nn::BatchNorm3dOptions(3).eps(1e-3).momentum(0.7)', input_size=(2, 3, 4, 4, 4), cudnn=True, check_eval=True, desc='momentum', ), dict( module_name='BatchNorm3d', constructor_args=(3, 1e-3, 0.7, False), cpp_constructor_args='torch::nn::BatchNorm3dOptions(3).eps(1e-3).momentum(0.7).affine(false)', input_size=(2, 3, 4, 4, 4), cudnn=True, check_eval=True, desc='not_affine', ), dict( module_name='BatchNorm3d', constructor_args=(3, 1e-3, 0.7, True, False), cpp_constructor_args='''torch::nn::BatchNorm3dOptions(3) .eps(1e-3).momentum(0.7).affine(true).track_running_stats(false)''', input_size=(2, 3, 4, 4, 4), cudnn=True, check_eval=True, desc='not_tracking_stats', ), dict( module_name='BatchNorm3d', constructor_args=(5, 1e-3, 0.3, False), cpp_constructor_args='torch::nn::BatchNorm3dOptions(5).eps(1e-3).momentum(0.3).affine(false)', input_size=(0, 5, 2, 2, 2), cudnn=True, check_eval=True, desc='zero_batch', ), dict( module_name='InstanceNorm1d', constructor_args=(3, 1e-3, 0.3), cpp_constructor_args='torch::nn::InstanceNorm1dOptions(3).eps(1e-3).momentum(0.3)', input_size=(4, 3, 15), cudnn=True, check_eval=True, ), dict( module_name='InstanceNorm1d', constructor_args=(3, 1e-3, 0.3, False, True), cpp_constructor_args='''torch::nn::InstanceNorm1dOptions(3) .eps(1e-3).momentum(0.3).affine(false).track_running_stats(true)''', input_size=(4, 3, 15), cudnn=True, check_eval=True, desc='tracking_stats', ), dict( module_name='InstanceNorm2d', constructor_args=(3, 1e-3, 0.3), cpp_constructor_args='torch::nn::InstanceNorm2dOptions(3).eps(1e-3).momentum(0.3)', input_size=(2, 3, 6, 6), cudnn=True, check_eval=True, ), dict( module_name='InstanceNorm2d', constructor_args=(3, 1e-3, 0.3, False, True), cpp_constructor_args='''torch::nn::InstanceNorm2dOptions(3) .eps(1e-3).momentum(0.3).affine(false).track_running_stats(true)''', input_size=(2, 3, 6, 6), cudnn=True, check_eval=True, desc='tracking_stats', ), dict( module_name='InstanceNorm3d', constructor_args=(3, 1e-3, 0.3), cpp_constructor_args='torch::nn::InstanceNorm3dOptions(3).eps(1e-3).momentum(0.3)', input_size=(2, 3, 4, 4, 4), cudnn=True, check_eval=True, ), dict( module_name='InstanceNorm3d', constructor_args=(3, 1e-3, 0.3, False, True), cpp_constructor_args='''torch::nn::InstanceNorm3dOptions(3) .eps(1e-3).momentum(0.3).affine(false).track_running_stats(true)''', input_size=(2, 3, 4, 4, 4), cudnn=True, check_eval=True, desc='tracking_stats', ), dict( module_name='LayerNorm', constructor_args=([5], 1e-3), cpp_constructor_args='torch::nn::LayerNormOptions({5}).eps(1e-3)', input_size=(4, 5, 5), cudnn=True, check_eval=True, desc='1d_elementwise_affine', ), dict( module_name='LayerNorm', constructor_args=([5], 1e-3, False), cpp_constructor_args='torch::nn::LayerNormOptions({5}).eps(1e-3).elementwise_affine(false)', input_size=(4, 5, 5), cudnn=True, check_eval=True, desc='1d_no_elementwise_affine', ), dict( module_name='LayerNorm', constructor_args=([2, 2, 5], 1e-3), cpp_constructor_args='torch::nn::LayerNormOptions({2, 2, 5}).eps(1e-3)', input_size=(4, 2, 2, 5), cudnn=True, check_eval=True, desc='3d_elementwise_affine', ), dict( module_name='LayerNorm', constructor_args=([2, 2, 5], 1e-3, False), cpp_constructor_args='torch::nn::LayerNormOptions({2, 2, 5}).eps(1e-3).elementwise_affine(false)', input_size=(4, 2, 2, 5), cudnn=True, check_eval=True, desc='3d_no_elementwise_affine', ), dict( module_name='LayerNorm', constructor_args=([56, 56, 56], 1e-5, False), cpp_constructor_args='torch::nn::LayerNormOptions({56, 56, 56}).eps(1e-5).elementwise_affine(false)', input_size=(4, 56, 56, 56), cudnn=True, check_eval=True, gradcheck_fast_mode=True, desc='3d_no_affine_large_feature', ), dict( module_name='LayerNorm', constructor_args=([5], 1e-3), cpp_constructor_args='torch::nn::LayerNormOptions({5}).eps(1e-3)', input_size=(0, 5), cudnn=True, check_eval=True, desc='1d_empty_elementwise_affine', ), dict( module_name='GroupNorm', constructor_args=(3, 6, 1e-3), cpp_constructor_args='torch::nn::GroupNormOptions(3, 6).eps(1e-3)', input_size=(4, 6, 5), cudnn=True, check_eval=True, check_bfloat16=True, desc='1d_affine', ), dict( module_name='GroupNorm', constructor_args=(3, 12, 1e-3), cpp_constructor_args='torch::nn::GroupNormOptions(3, 12).eps(1e-3)', input_size=(4, 12), cudnn=True, check_eval=True, check_bfloat16=True, desc='1d_affine_GN', ), dict( module_name='GroupNorm', constructor_args=(1, 6, 1e-3), cpp_constructor_args='torch::nn::GroupNormOptions(1, 6).eps(1e-3)', input_size=(150, 6), cudnn=True, check_eval=True, desc='1d_affine_large_batch', # For large batch_size check_bfloat16=True, test_cpu=False, ), dict( module_name='GroupNorm', constructor_args=(5, 5, 1e-3, False), cpp_constructor_args='torch::nn::GroupNormOptions(5, 5).eps(1e-3).affine(false)', input_size=(4, 5, 5), cudnn=True, check_eval=True, check_bfloat16=True, desc='1d_no_affine_IN', # this setting is equivalent with InstanceNormi ), dict( module_name='GroupNorm', constructor_args=(1, 10, 1e-3, False), cpp_constructor_args='torch::nn::GroupNormOptions(1, 10).eps(1e-3).affine(false)', input_size=(4, 10), cudnn=True, check_eval=True, check_bfloat16=True, desc='1d_no_affine_LN', # this setting is equivalent with LayerNorm ), dict( module_name='GroupNorm', constructor_args=(3, 6, 1e-3), cpp_constructor_args='torch::nn::GroupNormOptions(3, 6).eps(1e-3)', input_size=(4, 6, 2, 3), cudnn=True, check_eval=True, check_bfloat16=True, desc='2d_affine', ), dict( module_name='GroupNorm', constructor_args=(3, 6, 1e-3), cpp_constructor_args='torch::nn::GroupNormOptions(3, 6).eps(1e-3)', input_size=(4, 6, 28, 28), cudnn=True, check_eval=True, check_bfloat16=True, desc='2d_affine_large_feature', test_cpu=False, ), dict( module_name='GroupNorm', constructor_args=(3, 51, 1e-5, False), cpp_constructor_args='torch::nn::GroupNormOptions(3, 51).eps(1e-5).affine(false)', input_size=(2, 51, 28, 28), cudnn=True, check_eval=True, check_bfloat16=True, desc='2d_no_affine_large_feature', test_cpu=False, ), dict( module_name='GroupNorm', constructor_args=(3, 3, 1e-3, False), cpp_constructor_args='torch::nn::GroupNormOptions(3, 3).eps(1e-3).affine(false)', input_size=(4, 3, 2, 3), cudnn=True, check_eval=True, check_bfloat16=True, desc='2d_no_affine_IN', # this setting is equivalent with InstanceNorm ), dict( module_name='GroupNorm', constructor_args=(1, 3, 1e-3, False), cpp_constructor_args='torch::nn::GroupNormOptions(1, 3).eps(1e-3).affine(false)', input_size=(4, 3, 2, 3), cudnn=True, check_eval=True, check_bfloat16=True, desc='2d_no_affine_LN', # this setting is equivalent with LayerNorm ), dict( module_name='Conv1d', constructor_args=(4, 5, 3), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3)', input_size=(2, 4, 10), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv1d', constructor_args=(4, 5, 3, 2), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3).stride(2)', input_size=(2, 4, 10), cudnn=True, desc='stride', with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv1d', constructor_args=(4, 5, 3, 1, 1), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3).stride(1).padding(1)', input_size=(2, 4, 10), cudnn=True, desc='pad1', with_tf32=True, tf32_precision=0.01, ), dict( module_name='Conv1d', constructor_args=(4, 5, 5, 1, 2), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 5).stride(1).padding(2)', input_size=(2, 4, 10), cudnn=True, desc='pad2', with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv1d', constructor_args=(4, 4, 3, 1, 1), cpp_constructor_args='torch::nn::Conv1dOptions(4, 4, 3).stride(1).padding(1)', input_size=(1, 4, 1), cudnn=True, desc='pad1size1', with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv1d', constructor_args=(4, 4, 5, 1, 2), cpp_constructor_args='torch::nn::Conv1dOptions(4, 4, 5).stride(1).padding(2)', input_size=(1, 4, 1), cudnn=True, desc='pad2size1', with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv1d', constructor_args=(4, 5, 3), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3)', input_size=(0, 4, 10), cudnn=True, desc='zero_batch', with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv1d_dilated', constructor=lambda: nn.Conv1d(4, 5, kernel_size=3, dilation=2), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3).dilation(2)', input_size=(2, 4, 10), with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv1d_groups', constructor=lambda: nn.Conv1d(4, 6, kernel_size=3, groups=2), cpp_constructor_args='torch::nn::Conv1dOptions(4, 6, 3).groups(2)', input_size=(2, 4, 6), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv1d_pad_valid', constructor=lambda: nn.Conv1d(4, 5, 3, padding="valid"), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3).padding(torch::kValid)', input_size=(2, 4, 10), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv1d_pad_same', constructor=lambda: nn.Conv1d(4, 5, 3, padding="same"), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3).padding(torch::kSame)', input_size=(2, 4, 10), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv1d_pad_same2', constructor=lambda: nn.Conv1d(4, 5, 4, padding="same"), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 4).padding(torch::kSame)', input_size=(2, 4, 10), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv1d_pad_same_dilated', constructor=lambda: nn.Conv1d(4, 5, 4, padding="same", dilation=2), cpp_constructor_args='torch::nn::Conv1dOptions(4, 5, 3).padding(torch::kSame).dilation(2)', input_size=(2, 4, 10), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='ConvTranspose1d', constructor=lambda: nn.ConvTranspose1d(3, 4, kernel_size=3, stride=(3,), padding=1, output_padding=(1,)), cpp_constructor_args='torch::nn::ConvTranspose1dOptions(3, 4, 3).stride(3).padding(1).output_padding(1)', cudnn=True, input_size=(1, 3, 7), with_tf32=True, tf32_precision=0.005, ), dict( module_name='ConvTranspose1d', constructor_args=(3, 4, 3, 2, 1, 1, 1, False), cpp_constructor_args='''torch::nn::ConvTranspose1dOptions(3, 4, 3) .stride(2).padding(1).output_padding(1).groups(1).bias(false)''', input_size=(1, 3, 6), cudnn=True, desc='no_bias', with_tf32=True, tf32_precision=0.005, ), dict( module_name='ConvTranspose1d', constructor_args=(3, 4, 3, 2, 1, 1, 1, True, 2), cpp_constructor_args='''torch::nn::ConvTranspose1dOptions(3, 4, 3) .stride(2).padding(1).output_padding(1).groups(1).bias(true).dilation(2)''', input_size=(1, 3, 6), cudnn=True, desc='dilated', with_tf32=True, tf32_precision=0.005, ), dict( fullname='ConvTranspose1d_groups', constructor=lambda: nn.ConvTranspose1d(4, 6, 3, stride=(3,), padding=1, output_padding=(1,), groups=2), cpp_constructor_args='''torch::nn::ConvTranspose1dOptions(4, 6, 3) .stride(3).padding(1).output_padding(1).groups(2)''', cudnn=True, input_size=(2, 4, 7), with_tf32=True, tf32_precision=0.005, ), dict( module_name='MaxPool1d', constructor_args=(4,), cpp_constructor_args='torch::nn::MaxPool1dOptions(4)', input_size=(2, 10, 4), ), dict( module_name='MaxPool1d', constructor_args=(4, 4), cpp_constructor_args='torch::nn::MaxPool1dOptions(4).stride(4)', input_size=(2, 10, 4), desc='stride', ), dict( module_name='Conv2d', constructor_args=(3, 4, (3, 2)), cpp_constructor_args='torch::nn::Conv2dOptions(3, 4, {3, 2})', input_size=(2, 3, 7, 5), cudnn=True, check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv2d', constructor_args=(3, 4, (3, 3), (2, 2)), cpp_constructor_args='torch::nn::Conv2dOptions(3, 4, {3, 3}).stride({2, 2})', input_size=(2, 3, 6, 6), cudnn=True, desc='strided', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv2d', constructor_args=(3, 4, (3, 3), (2, 2), (1, 1)), cpp_constructor_args='torch::nn::Conv2dOptions(3, 4, {3, 3}).stride({2, 2}).padding({1, 1})', input_size=(2, 3, 6, 6), cudnn=True, desc='padding', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv2d', constructor_args=(3, 2, (3, 3), (2, 2), (1, 1), (2, 2)), cpp_constructor_args='torch::nn::Conv2dOptions(3, 2, {3, 3}).stride({2, 2}).padding({1, 1}).dilation({2, 2})', input_size=(2, 3, 8, 8), cudnn=True, desc='dilated', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( module_name='Conv2d', constructor_args=(3, 4, (3, 2), 1, 0, 1, 1, False), cpp_constructor_args='''torch::nn::Conv2dOptions(3, 4, {3, 2}) .stride(1).padding(0).dilation(1).groups(1).bias(false)''', input_size=(2, 3, 6, 5), cudnn=True, desc='no_bias', check_with_long_tensor=True, with_tf32=True, ), dict( module_name='Conv2d', constructor_args=(3, 4, (3, 2)), cpp_constructor_args='torch::nn::Conv2dOptions(3, 4, {3, 2})', input_size=(0, 3, 7, 5), cudnn=True, desc='zero_batch', check_with_long_tensor=True, with_tf32=True, ), dict( fullname='Conv2d_groups', constructor=lambda: nn.Conv2d(4, 6, (3, 2), groups=2), cpp_constructor_args='torch::nn::Conv2dOptions(4, 6, {3, 2}).groups(2)', input_size=(2, 4, 6, 5), cudnn=True, check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv2d_groups_thnn', constructor=lambda: nn.Conv2d(4, 6, (3, 2), groups=2), cpp_constructor_args='torch::nn::Conv2dOptions(4, 6, {3, 2}).groups(2)', input_size=(2, 4, 6, 5), check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv2d_pad_valid', constructor=lambda: nn.Conv2d(2, 4, (3, 4), padding="valid"), cpp_constructor_args='torch::nn::Conv2dOptions(2, 4, {3, 4}).padding(torch::kValid)', input_size=(2, 2, 6, 5), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv2d_pad_same', constructor=lambda: nn.Conv2d(2, 4, (3, 4), padding="same"), cpp_constructor_args='torch::nn::Conv2dOptions(2, 4, {3, 4}).padding(torch::kSame)', input_size=(2, 2, 6, 5), cudnn=True, with_tf32=True, tf32_precision=0.01, ), dict( fullname='Conv2d_pad_same_dilated', constructor=lambda: nn.Conv2d(2, 4, (3, 4), padding="same", dilation=2), cpp_constructor_args='torch::nn::Conv2dOptions(2, 4, {3, 4}).padding(torch::kSame).dilation(2)', input_size=(2, 2, 6, 5), cudnn=True, with_tf32=True, tf32_precision=0.005, ), dict( module_name='ConvTranspose2d', constructor_args=(3, 4, 3, (3, 2), 1, (1, 1)), cpp_constructor_args='''torch::nn::ConvTranspose2dOptions(3, 4, 3) .stride({3, 2}).padding(1).output_padding({1, 1})''', cudnn=True, input_size=(1, 3, 7, 6), check_with_long_tensor=True, with_tf32=True, tf32_precision=0.01, ), dict( module_name='ConvTranspose2d', constructor_args=(3, 4, 3, (2, 3), 1, (1, 1), 1, False, (2, 2)), cpp_constructor_args='''torch::nn::ConvTranspose2dOptions(3, 4, 3) .stride({2, 3}) .padding(1) .output_padding({1, 1}) .groups(1) .bias(false) .dilation({2, 2})''', input_size=(1, 3, 6, 7), cudnn=True, desc='dilated', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( module_name='ConvTranspose2d', constructor_args=(3, 4, 3, (2, 3), 1, (1, 1), 1, False), cpp_constructor_args='''torch::nn::ConvTranspose2dOptions(3, 4, 3) .stride({2, 3}).padding(1).output_padding({1, 1}).groups(1).bias(false)''', input_size=(1, 3, 6, 7), cudnn=True, desc='no_bias', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='ConvTranspose2d_groups', constructor=lambda: nn.ConvTranspose2d(2, 4, (2, 3), groups=2), cpp_constructor_args='torch::nn::ConvTranspose2dOptions(2, 4, {2, 3}).groups(2)', input_size=(1, 2, 4, 5), cudnn=True, check_with_long_tensor=True, with_tf32=True, tf32_precision=0.01, ), dict( fullname='Conv2d_depthwise', constructor=lambda: nn.Conv2d(4, 4, (3, 3), groups=4), cpp_constructor_args='torch::nn::Conv2dOptions(4, 4, {3, 3}).groups(4)', input_size=(2, 4, 6, 6), with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv2d_depthwise_with_multiplier', constructor=lambda: nn.Conv2d(4, 8, (3, 3), groups=4), cpp_constructor_args='torch::nn::Conv2dOptions(4, 8, {3, 3}).groups(4)', input_size=(2, 4, 6, 6), with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv2d_depthwise_strided', constructor=lambda: nn.Conv2d(4, 4, (3, 3), stride=(2, 2), groups=4), cpp_constructor_args='torch::nn::Conv2dOptions(4, 4, {3, 3}).stride({2, 2}).groups(4)', input_size=(2, 4, 6, 6), with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv2d_depthwise_padded', constructor=lambda: nn.Conv2d(4, 4, (3, 3), padding=(1, 1), groups=4), cpp_constructor_args='torch::nn::Conv2dOptions(4, 4, {3, 3}).padding({1, 1}).groups(4)', input_size=(2, 4, 6, 6), with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv2d_depthwise_dilated', constructor=lambda: nn.Conv2d(4, 4, (2, 2), dilation=(2, 2), groups=4), cpp_constructor_args='torch::nn::Conv2dOptions(4, 4, {2, 2}).dilation({2, 2}).groups(4)', input_size=(2, 4, 5, 5), with_tf32=True, tf32_precision=0.005, ), dict( module_name='MaxPool2d', constructor_args=((3, 3), (2, 2), (1, 1)), cpp_constructor_args='torch::nn::MaxPool2dOptions({3, 3}).stride({2, 2}).padding({1, 1})', input_size=(3, 7, 7), desc='3d_input' ), dict( module_name='MaxPool2d', constructor_args=((3, 3), (2, 2), (1, 1)), cpp_constructor_args='torch::nn::MaxPool2dOptions({3, 3}).stride({2, 2}).padding({1, 1})', input_size=(1, 3, 7, 7), check_with_channels_last=True, desc='4d_input' ), dict( module_name='AvgPool1d', constructor_args=(2,), cpp_constructor_args='torch::nn::AvgPool1dOptions(2)', input_size=(2, 3, 6), ), dict( module_name='AvgPool1d', constructor_args=((2,), (2,)), cpp_constructor_args='torch::nn::AvgPool1dOptions(2).stride(2)', input_size=(2, 3, 6), desc='stride', ), dict( module_name='AvgPool1d', constructor_args=(2, 2, 1), cpp_constructor_args='torch::nn::AvgPool1dOptions(2).stride(2).padding(1)', input_size=(2, 3, 6), desc='stride_pad', ), dict( module_name='AvgPool1d', constructor_args=(2,), cpp_constructor_args='torch::nn::AvgPool1dOptions(2)', input_size=(3, 6), reference_fn=single_batch_reference_fn, desc='no_batch_dim', ), dict( module_name='AvgPool2d', constructor_args=((2, 2),), cpp_constructor_args='torch::nn::AvgPool2dOptions({2, 2})', input_size=(2, 3, 6, 6), ), dict( module_name='AvgPool2d', constructor_args=((2, 2),), cpp_constructor_args='torch::nn::AvgPool2dOptions({2, 2})', input_size=(3, 6, 6), reference_fn=single_batch_reference_fn, desc='no_batch_dim' ), dict( module_name='AvgPool2d', constructor_args=((2, 2), (2, 2)), cpp_constructor_args='torch::nn::AvgPool2dOptions({2, 2}).stride({2, 2})', input_size=(2, 3, 6, 6), desc='stride', ), dict( module_name='AvgPool2d', constructor_args=((2, 2), (2, 2), (1, 1)), cpp_constructor_args='torch::nn::AvgPool2dOptions({2, 2}).stride({2, 2}).padding({1, 1})', input_size=(2, 3, 6, 6), desc='stride_pad', ), dict( fullname='AvgPool2d_divisor', constructor=lambda: nn.AvgPool2d((2, 2), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool2dOptions({2, 2}).divisor_override(1)', input_size=(2, 3, 6, 6), check_with_long_tensor=True, ), dict( fullname='AvgPool2d_divisor_stride', constructor=lambda: nn.AvgPool2d((2, 2), (2, 2), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool2dOptions({2, 2}).stride({2, 2}).divisor_override(1)', input_size=(2, 3, 6, 6), check_with_long_tensor=True, ), dict( fullname='AvgPool2d_divisor_stride_pad', constructor=lambda: nn.AvgPool2d((2, 2), (2, 2), (1, 1), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool2dOptions({2, 2}).stride({2, 2}).padding({1, 1}).divisor_override(1)', input_size=(2, 3, 6, 6), check_with_long_tensor=True, ), dict( module_name='LPPool2d', constructor_args=(2, 2, 2), cpp_constructor_args='torch::nn::LPPool2dOptions(2, 2).stride(2)', input_size=(1, 3, 7, 7), ), dict( module_name='LPPool2d', constructor_args=(1.5, 2), cpp_constructor_args='torch::nn::LPPool2dOptions(1.5, 2)', input_fn=lambda: torch.rand(1, 3, 7, 7), desc='norm', ), dict( module_name='LPPool1d', constructor_args=(1.5, 2), cpp_constructor_args='torch::nn::LPPool1dOptions(1.5, 2)', input_fn=lambda: torch.rand(1, 3, 7), desc='norm', ), dict( module_name='LPPool1d', constructor_args=(2, 2, 3), cpp_constructor_args='torch::nn::LPPool1dOptions(2, 2).stride(3)', input_size=(1, 3, 7), ), dict( module_name='LocalResponseNorm', constructor_args=(3, ), cpp_constructor_args='torch::nn::LocalResponseNormOptions(3)', input_size=(1, 5, 7), desc='1d', ), dict( module_name='LocalResponseNorm', constructor_args=(2, ), cpp_constructor_args='torch::nn::LocalResponseNormOptions(2)', input_size=(1, 5, 7, 7), desc='2d_uneven_pad', ), dict( module_name='LocalResponseNorm', constructor_args=(1, 1., 0.5, 2.), cpp_constructor_args='torch::nn::LocalResponseNormOptions(1).alpha(1.).beta(0.5).k(2.)', input_size=(1, 5, 7, 7, 7), desc='3d_custom_params', ), dict( module_name='ReflectionPad1d', constructor_args=((1, 2),), cpp_constructor_args='torch::nn::ReflectionPad1dOptions({1, 2})', input_size=(2, 3, 8), ), dict( module_name='ReflectionPad1d', constructor_args=((1, 2),), cpp_constructor_args='torch::nn::ReflectionPad1dOptions({1, 2})', input_size=(3, 8), reference_fn=single_batch_reference_fn, desc='batch', ), dict( module_name='ReflectionPad1d', constructor_args=((1, 2),), cpp_constructor_args='torch::nn::ReflectionPad1dOptions({1, 2})', input_fn=lambda: torch.rand(2, 3, 8, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ReflectionPad2d', constructor_args=((1, 2, 3, 4),), cpp_constructor_args='torch::nn::ReflectionPad2dOptions({1, 2, 3, 4})', input_size=(2, 3, 8, 8), ), dict( module_name='ReflectionPad2d', constructor_args=((1, 2, 3, 4),), cpp_constructor_args='torch::nn::ReflectionPad2dOptions({1, 2, 3, 4})', input_fn=lambda: torch.rand(2, 3, 8, 8, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ReflectionPad3d', constructor_args=((1, 2, 0, 2, 1, 2),), cpp_constructor_args='torch::nn::ReflectionPad3dOptions({1, 2, 0, 2, 1, 2})', input_size=(2, 3, 8, 8, 8), ), dict( module_name='ReflectionPad3d', constructor_args=((1, 2, 0, 2, 1, 2),), cpp_constructor_args='torch::nn::ReflectionPad3dOptions({1, 2, 0, 2, 1, 2})', input_fn=lambda: torch.rand(2, 3, 8, 8, 8, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ReplicationPad1d', constructor_args=((1, 2),), cpp_constructor_args='torch::nn::ReplicationPad1dOptions({1, 2})', input_size=(2, 3, 4), ), dict( module_name='ReplicationPad1d', constructor_args=((1, 2),), cpp_constructor_args='torch::nn::ReplicationPad1dOptions({1, 2})', input_size=(3, 4), reference_fn=single_batch_reference_fn, desc='batch', ), dict( module_name='ReplicationPad1d', constructor_args=((1, 2),), cpp_constructor_args='torch::nn::ReplicationPad1dOptions({1, 2})', input_fn=lambda: torch.rand(2, 3, 4, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ReplicationPad2d', constructor_args=((1, 2, 3, 4),), cpp_constructor_args='torch::nn::ReplicationPad2dOptions({1, 2, 3, 4})', input_size=(2, 3, 4, 4), ), dict( module_name='ReplicationPad2d', constructor_args=((1, 2, 3, 4),), cpp_constructor_args='torch::nn::ReplicationPad2dOptions({1, 2, 3, 4})', input_fn=lambda: torch.rand(2, 3, 4, 4, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ZeroPad2d', constructor_args=((1, 2, 3, 4),), cpp_constructor_args='torch::nn::ZeroPad2dOptions({1, 2, 3, 4})', input_size=(2, 3, 4, 4), ), dict( module_name='ZeroPad2d', constructor_args=((1, 2, 3, 4),), cpp_constructor_args='torch::nn::ZeroPad2dOptions({1, 2, 3, 4})', input_fn=lambda: torch.rand(2, 3, 4, 4, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ZeroPad2d', constructor_args=((-1, -1, -1, -2),), cpp_constructor_args='torch::nn::ZeroPad2dOptions({-1, -1, -1, -2})', input_size=(2, 3, 4, 4), desc='negative_dims' ), dict( module_name='ConstantPad1d', constructor_args=((1, 2), 2.), cpp_constructor_args='torch::nn::ConstantPad1dOptions({1, 2}, 2.)', input_size=(2, 3, 4), ), dict( module_name='ConstantPad1d', constructor_args=((1, 2), 2.), cpp_constructor_args='torch::nn::ConstantPad1dOptions({1, 2}, 2.)', input_size=(3, 4), reference_fn=single_batch_reference_fn, desc='batch', ), dict( module_name='ConstantPad1d', constructor_args=((1, 2), 2.), cpp_constructor_args='torch::nn::ConstantPad1dOptions({1, 2}, 2.)', input_fn=lambda: torch.rand(2, 3, 4, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ConstantPad2d', constructor_args=((1, 2, 3, 4), 2.), cpp_constructor_args='torch::nn::ConstantPad2dOptions({1, 2, 3, 4}, 2.)', input_size=(2, 3, 4, 4), ), dict( module_name='ConstantPad2d', constructor_args=((1, 2, 3, 4), 2.), cpp_constructor_args='torch::nn::ConstantPad2dOptions({1, 2, 3, 4}, 2.)', input_size=(3, 4, 4), reference_fn=single_batch_reference_fn, desc='no_batch_dim' ), dict( module_name='ConstantPad2d', constructor_args=((1, 2, 3, 4), 2.), cpp_constructor_args='torch::nn::ConstantPad2dOptions({1, 2, 3, 4}, 2.)', input_fn=lambda: torch.rand(2, 3, 4, 4, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='ConstantPad3d', constructor_args=((1, 2, 3, 4, 1, 0), 2.), cpp_constructor_args='torch::nn::ConstantPad3dOptions({1, 2, 3, 4, 1, 0}, 2.)', input_size=(2, 3, 4, 4, 5), ), dict( module_name='ConstantPad3d', constructor_args=((1, 2, 3, 4, 1, 0), 2.), cpp_constructor_args='torch::nn::ConstantPad3dOptions({1, 2, 3, 4, 1, 0}, 2.)', input_size=(3, 4, 4, 5), reference_fn=single_batch_reference_fn, desc='no_batch_dim' ), dict( module_name='ConstantPad3d', constructor_args=((1, 2, 3, 4, 1, 0), 2.), cpp_constructor_args='torch::nn::ConstantPad3dOptions({1, 2, 3, 4, 1, 0}, 2.)', input_fn=lambda: torch.rand(2, 3, 4, 4, 5, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='Conv3d', constructor_args=(2, 3, (2, 3, 2)), cpp_constructor_args='torch::nn::Conv3dOptions(2, 3, {2, 3, 2})', input_size=(1, 2, 4, 5, 4), cudnn=True, check_with_long_tensor=True, with_tf32=True, tf32_precision=0.05, ), dict( module_name='Conv3d', constructor_args=(2, 3, (2, 3, 4), 1, 0, 1, 1, False), cpp_constructor_args='''torch::nn::Conv3dOptions(2, 3, {2, 3, 4}) .stride(1).padding(0).dilation(1).groups(1).bias(false)''', input_size=(1, 2, 3, 4, 5), cudnn=True, desc='no_bias', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.05, ), dict( module_name='Conv3d', constructor_args=(2, 3, (1, 1, 1), 1, 0, 1, 1, False), cpp_constructor_args='''torch::nn::Conv3dOptions(2, 3, {2, 3, 4}) .stride(1).padding(0).dilation(1).groups(1).bias(false)''', input_size=(1, 2, 3, 4, 5), cudnn=True, desc='1x1x1_no_bias', check_with_long_tensor=False, with_tf32=True, tf32_precision=0.05, ), dict( module_name='Conv3d', constructor_args=(3, 4, 2, 2), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, 2).stride(2)', input_size=(2, 3, 5, 5, 5), cudnn=True, desc='stride', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.05, ), dict( module_name='Conv3d', constructor_args=(3, 4, 2, 2, 1), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, 2).stride(2).padding(1)', input_size=(2, 3, 5, 5, 5), cudnn=True, desc='stride_padding', check_with_long_tensor=True, with_tf32=True, tf32_precision=0.05, ), dict( module_name='Conv3d', constructor_args=(3, 4, (2, 3, 4)), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, {2, 3, 4})', input_size=(0, 3, 3, 4, 5), cudnn=True, check_with_long_tensor=True, desc='zero_batch', with_tf32=True, ), dict( fullname='Conv3d_groups', constructor=lambda: nn.Conv3d(2, 4, kernel_size=3, groups=2), cpp_constructor_args='torch::nn::Conv3dOptions(2, 4, 3).groups(2)', input_size=(1, 2, 4, 5, 4), cudnn=True, check_with_long_tensor=True, with_tf32=True, tf32_precision=0.005, ), dict( fullname='Conv3d_dilated', constructor=lambda: nn.Conv3d(3, 4, kernel_size=2, dilation=2), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, 2).dilation(2)', input_size=(2, 3, 5, 5, 5), with_tf32=True, tf32_precision=0.05, ), dict( fullname='Conv3d_dilated_strided', constructor=lambda: nn.Conv3d(3, 4, kernel_size=2, dilation=2, stride=2), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, 2).dilation(2).stride(2)', input_size=(2, 3, 5, 5, 5), with_tf32=True, tf32_precision=0.05 ), dict( fullname='Conv3d_pad_valid', constructor=lambda: nn.Conv3d(3, 4, (2, 3, 4), padding="valid"), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, {2, 3, 4}).padding(torch::kValid)', input_size=(2, 3, 6, 5, 4), cudnn=True, with_tf32=True, tf32_precision=0.05, ), dict( fullname='Conv3d_pad_same', constructor=lambda: nn.Conv3d(3, 4, (2, 3, 4), padding="same"), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, {2, 3, 4}).padding(torch::kSame)', input_size=(2, 3, 6, 5, 4), cudnn=True, with_tf32=True, tf32_precision=0.05, ), dict( fullname='Conv3d_pad_same_dilated', constructor=lambda: nn.Conv3d(3, 4, (2, 3, 4), padding="same", dilation=2), cpp_constructor_args='torch::nn::Conv3dOptions(3, 4, {2, 3, 4}).padding(torch::kSame).dilation(2)', input_size=(2, 3, 6, 5, 4), cudnn=True, with_tf32=True, tf32_precision=0.05, ), dict( module_name='ConvTranspose3d', constructor_args=(2, 3, (2, 3, 2)), cpp_constructor_args='torch::nn::ConvTranspose3dOptions(2, 3, {2, 3, 2})', cudnn=True, input_size=(1, 2, 4, 5, 4), with_tf32=True, tf32_precision=0.05 ), dict( module_name='ConvTranspose3d', constructor_args=(2, 3, (2, 3, 2), 1, 0, 0, 1, True, (2, 2, 2)), cpp_constructor_args='''torch::nn::ConvTranspose3dOptions(2, 3, {2, 3, 2}) .stride(1).padding(0).output_padding(0).groups(1).bias(true).dilation({2, 2, 2})''', cudnn=True, input_size=(1, 2, 4, 5, 4), desc='dilated', with_tf32=True, tf32_precision=0.05 ), dict( module_name='MaxPool3d', constructor_args=((2, 2, 2),), cpp_constructor_args='torch::nn::MaxPool3dOptions({2, 2, 2})', input_size=(2, 3, 5, 5, 5), ), dict( module_name='MaxPool3d', constructor_args=(2, (2, 2, 2)), cpp_constructor_args='torch::nn::MaxPool3dOptions(2).stride({2, 2, 2})', input_size=(2, 3, 5, 5, 5), desc='stride', ), dict( module_name='MaxPool3d', constructor_args=(2, 2, (1, 1, 1)), cpp_constructor_args='torch::nn::MaxPool3dOptions(2).stride(2).padding({1, 1, 1})', input_size=(2, 3, 5, 5, 5), desc='stride_padding', ), dict( module_name='AvgPool3d', constructor_args=((2, 2, 2),), cpp_constructor_args='torch::nn::AvgPool3dOptions({2, 2, 2})', input_size=(2, 3, 4, 4, 4), ), dict( module_name='AvgPool3d', constructor_args=((2, 2, 2),), cpp_constructor_args='torch::nn::AvgPool3dOptions({2, 2, 2})', input_size=(3, 4, 4, 4), desc='no_batch_dim', ), dict( module_name='AvgPool3d', constructor_args=(2, (2, 2, 2)), cpp_constructor_args='torch::nn::AvgPool3dOptions(2).stride({2, 2, 2})', input_size=(2, 3, 5, 5, 5), desc='stride', ), dict( module_name='AvgPool3d', constructor_args=(2, 2, (1, 1, 1)), cpp_constructor_args='torch::nn::AvgPool3dOptions(2).stride(2).padding({1, 1, 1})', input_size=(2, 3, 5, 5, 5), desc='stride_pad', ), dict( module_name='AvgPool3d', constructor_args=(4, 2, (1, 2, 1)), cpp_constructor_args='torch::nn::AvgPool3dOptions(4).stride(2).padding({1, 2, 1})', input_size=(2, 3, 5, 5, 5), desc='stride_pad_gpu_fixedkw_output', ), dict( module_name='AvgPool3d', constructor_args=((2, 4, 8), 1, (1, 1, 2)), cpp_constructor_args='torch::nn::AvgPool3dOptions({2, 4, 8}).stride(1).padding({1, 1, 2})', input_size=(2, 3, 2, 4, 8), desc='stride_pad_gpu_general_output', ), dict( module_name='AvgPool3d', constructor_args=(3, 1, 0), cpp_constructor_args='torch::nn::AvgPool3dOptions(3).stride(1).padding(0)', input_size=(2, 3, 4, 4, 4), desc='stride1_pad0_gpu_input', ), dict( module_name='AvgPool3d', constructor_args=(2, 2, (1, 1, 1)), cpp_constructor_args='torch::nn::AvgPool3dOptions(2).stride(2).padding({1, 1, 1})', input_size=(2, 3, 4, 4, 4), desc='stride_pad_gpu_input_nooverlap', ), dict( fullname='AvgPool3d_divisor', constructor=lambda: nn.AvgPool3d((2, 2, 2), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool3dOptions({2, 2, 2}).divisor_override(1)', input_size=(2, 3, 4, 4, 4), check_with_long_tensor=True, ), dict( fullname='AvgPool3d_divisor_stride', constructor=lambda: nn.AvgPool3d(2, (2, 2, 2), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool3dOptions(2).stride({2, 2, 2}).divisor_override(1)', input_size=(2, 3, 5, 5, 5), check_with_long_tensor=True, ), dict( fullname='AvgPool3d_divisor_stride_pad', constructor=lambda: nn.AvgPool3d(2, 2, (1, 1, 1), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool3dOptions(2).stride(2).padding({1, 1, 1}).divisor_override(1)', input_size=(2, 3, 5, 5, 5), check_with_long_tensor=True, ), dict( fullname='AvgPool3d_divisor_stride_pad_gpu_fixedkw_output', constructor=lambda: nn.AvgPool3d(4, 2, (1, 2, 1), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool3dOptions(4).stride(2).padding({1, 2, 1}).divisor_override(1)', input_size=(2, 3, 5, 5, 5), check_with_long_tensor=True, ), dict( fullname='AvgPool3d_divisor_stride_pad_gpu_general_output', constructor=lambda: nn.AvgPool3d((2, 4, 8), 1, (1, 1, 2), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool3dOptions({2, 4, 8}).stride(1).padding({1, 1, 2}).divisor_override(1)', input_size=(2, 3, 2, 4, 8), check_with_long_tensor=True, ), dict( fullname='AvgPool3d_divisor_stride1_pad0_gpu_input', constructor=lambda: nn.AvgPool3d(3, 1, 0, divisor_override=1), cpp_constructor_args='torch::nn::AvgPool3dOptions(3).stride(1).padding(0).divisor_override(1)', input_size=(2, 3, 4, 4, 4), check_with_long_tensor=True, ), dict( fullname='AvgPool3d_divisor_stride_pad_gpu_input_nooverlap', constructor=lambda: nn.AvgPool3d(2, 2, (1, 1, 1), divisor_override=1), cpp_constructor_args='torch::nn::AvgPool3dOptions(2).stride(2).padding({1, 1, 1}).divisor_override(1)', input_size=(2, 3, 4, 4, 4), check_with_long_tensor=True, ), dict( module_name='ReplicationPad3d', constructor_args=((1, 2, 3, 3, 2, 1),), cpp_constructor_args='torch::nn::ReplicationPad3dOptions({1, 2, 3, 3, 2, 1})', input_size=(2, 3, 2, 2, 2), ), dict( module_name='ReplicationPad3d', constructor_args=((1, 2, 3, 3, 2, 1),), cpp_constructor_args='torch::nn::ReplicationPad3dOptions({1, 2, 3, 3, 2, 1})', input_fn=lambda: torch.rand(2, 3, 2, 2, 2, dtype=torch.complex128, requires_grad=True), skip_half=True, desc='complex' ), dict( module_name='Embedding', constructor_args=(4, 3), cpp_constructor_args='torch::nn::EmbeddingOptions(4, 3)', input_fn=lambda: torch.empty(2, 3, dtype=torch.long).random_(4), check_gradgrad=False, ), dict( module_name='EmbeddingBag', constructor_args=(4, 3), cpp_constructor_args='torch::nn::EmbeddingBagOptions(4, 3)', input_fn=lambda: torch.empty(2, 3, dtype=torch.long).random_(4), check_gradgrad=False, desc='mean', ), dict( module_name='EmbeddingBag', constructor_args=(4, 3, None, 2., False, 'sum'), cpp_constructor_args='''torch::nn::EmbeddingBagOptions(4, 3) .max_norm(c10::nullopt).norm_type(2.).scale_grad_by_freq(false).mode(torch::kSum)''', input_fn=lambda: torch.empty(2, 3, dtype=torch.long).random_(4), check_gradgrad=False, desc='sum', ), dict( module_name='EmbeddingBag', constructor_args=(4, 3, None, 2., False, 'max'), cpp_constructor_args='''torch::nn::EmbeddingBagOptions(4, 3) .max_norm(c10::nullopt).norm_type(2.).scale_grad_by_freq(false).mode(torch::kMax)''', input_fn=lambda: torch.empty(2, 3, dtype=torch.long).random_(4), check_gradgrad=False, desc='max', ), dict( fullname='EmbeddingBag_mean_padding_idx', constructor=lambda: nn.EmbeddingBag(4, 3, padding_idx=1), cpp_constructor_args='torch::nn::EmbeddingBagOptions(4, 3).padding_idx(1)', input_fn=lambda: torch.stack([torch.randperm(3), torch.randperm(3)]), check_gradgrad=False, ), dict( fullname='EmbeddingBag_sum_padding_idx', constructor=lambda: nn.EmbeddingBag(4, 3, None, 2., False, 'sum', padding_idx=1), cpp_constructor_args='''torch::nn::EmbeddingBagOptions(4, 3) .max_norm(c10::nullopt).norm_type(2.).scale_grad_by_freq(false).mode(torch::kSum).padding_idx(1)''', input_fn=lambda: torch.stack([torch.randperm(3), torch.randperm(3)]), check_gradgrad=False, ), dict( fullname='EmbeddingBag_max_padding_idx', constructor=lambda: nn.EmbeddingBag(4, 3, None, 2., False, 'max', padding_idx=1), cpp_constructor_args='''torch::nn::EmbeddingBagOptions(4, 3) .max_norm(c10::nullopt).norm_type(2.).scale_grad_by_freq(false).mode(torch::kMax).padding_idx(1)''', input_fn=lambda: torch.stack([torch.randperm(3), torch.randperm(3)]), check_gradgrad=False, ), dict( fullname='EmbeddingBag_sparse', constructor=lambda: nn.EmbeddingBag(4, 3, sparse=True), cpp_constructor_args='torch::nn::EmbeddingBagOptions(4, 3).sparse(true)', input_fn=lambda: torch.randperm(2).repeat(1, 2), check_gradgrad=False, has_sparse_gradients=True, ), dict( constructor=lambda: nn.Embedding(4, 3, sparse=True), cpp_constructor_args='torch::nn::EmbeddingOptions(4, 3).sparse(true)', input_fn=lambda: torch.randperm(2).repeat(1, 2), fullname='Embedding_sparse', check_gradgrad=False, has_sparse_gradients=True, ), dict( module_name='PixelShuffle', constructor_args=(3,), cpp_constructor_args='torch::nn::PixelShuffleOptions(3)', input_size=(1, 9, 4, 4), ), dict( module_name='PixelUnshuffle', constructor_args=(3,), cpp_constructor_args='torch::nn::PixelUnshuffleOptions(3)', input_size=(1, 1, 12, 12), ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12})).scale_factor(c10::nullopt).mode(torch::kNearest)''', input_size=(1, 2, 4), fullname='interpolate_nearest_1d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12})).scale_factor(c10::nullopt).mode(torch::kNearest)''', input_size=(0, 2, 4), fullname='interpolate_nearest_1d_zero_dim', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(12, ), scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12})).scale_factor(c10::nullopt).mode(torch::kNearest)''', input_size=(1, 2, 3), fullname='interpolate_nearest_tuple_1d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=4., mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt).scale_factor(std::vector<double>({4.})).mode(torch::kNearest)''', input_size=(1, 2, 4), fullname='interpolate_nearest_scale_1d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='linear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12})) .scale_factor(c10::nullopt) .mode(torch::kLinear) .align_corners(false)''', input_size=(1, 2, 4), fullname='interpolate_linear_1d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(4, ), scale_factor=None, mode='linear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({4})) .scale_factor(c10::nullopt) .mode(torch::kLinear) .align_corners(false)''', input_size=(1, 2, 3), fullname='interpolate_linear_tuple_1d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=4., mode='linear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({4.})) .mode(torch::kLinear) .align_corners(false)''', input_size=(1, 2, 4), fullname='interpolate_linear_scale_1d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='linear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12})) .scale_factor(c10::nullopt) .mode(torch::kLinear) .align_corners(false)''', input_size=(0, 2, 4), fullname='interpolate_linear_1d_zero_dim', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='linear', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12})) .scale_factor(c10::nullopt) .mode(torch::kLinear) .align_corners(true)''', input_size=(1, 2, 4), fullname='interpolate_linear_1d_align_corners', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=4., mode='linear', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({4.})) .mode(torch::kLinear) .align_corners(true)''', input_size=(1, 2, 4), fullname='interpolate_linear_scale_1d_align_corners', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=2, scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({2, 2})) .scale_factor(c10::nullopt) .mode(torch::kNearest)''', input_size=(1, 128, 1, 1), fullname='interpolate_nearest_2d_launch_configs', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12})) .scale_factor(c10::nullopt) .mode(torch::kNearest)''', input_size=(1, 2, 4, 4), fullname='interpolate_nearest_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(12, 16), scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 16})) .scale_factor(c10::nullopt) .mode(torch::kNearest)''', input_size=(1, 2, 3, 4), fullname='interpolate_nearest_tuple_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=4., mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({4., 4.})) .mode(torch::kNearest)''', input_size=(1, 2, 4, 4), fullname='interpolate_nearest_scale_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12})) .scale_factor(c10::nullopt) .mode(torch::kNearest)''', input_size=(0, 2, 4, 4), fullname='interpolate_nearest_2d_zero_dim', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='bilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12})) .scale_factor(c10::nullopt) .mode(torch::kBilinear) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bilinear_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='bilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12})) .scale_factor(c10::nullopt) .mode(torch::kBilinear) .align_corners(false)''', input_size=(0, 2, 4, 4), fullname='interpolate_bilinear_2d_zero_dim', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(4, 6), scale_factor=None, mode='bilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({4, 6})) .scale_factor(c10::nullopt) .mode(torch::kBilinear) .align_corners(false)''', input_size=(1, 2, 2, 3), fullname='interpolate_bilinear_tuple_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=4., mode='bilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({4., 4.})) .mode(torch::kBilinear) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bilinear_scale_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=(2., 2.), mode='bilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({2., 2.})) .mode(torch::kBilinear) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bilinear_scale_tuple_shared_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=(2., 1.), mode='bilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({2., 1.})) .mode(torch::kBilinear) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bilinear_scale_tuple_skewed_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(4, 6), scale_factor=None, mode='bilinear', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({4, 6})) .scale_factor(c10::nullopt) .mode(torch::kBilinear) .align_corners(true)''', input_size=(1, 2, 4, 4), fullname='interpolate_bilinear_tuple_2d_align_corners', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=(2., 1.), mode='bilinear', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({2., 1.})) .mode(torch::kBilinear) .align_corners(true)''', input_size=(1, 2, 4, 4), fullname='interpolate_bilinear_scale_tuple_skewed_2d_align_corners', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='bicubic', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12})) .scale_factor(c10::nullopt) .mode(torch::kBicubic) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bicubic_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='bicubic', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12})) .scale_factor(c10::nullopt) .mode(torch::kBicubic) .align_corners(false)''', input_size=(0, 2, 4, 4), fullname='interpolate_bicubic_2d_zero_dim', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(4, 6), scale_factor=None, mode='bicubic', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({4, 6})) .scale_factor(c10::nullopt) .mode(torch::kBicubic) .align_corners(false)''', input_size=(1, 2, 2, 3), fullname='interpolate_bicubic_tuple_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=4., mode='bicubic', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({4., 4.})) .mode(torch::kBicubic) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bicubic_scale_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=(2., 2.), mode='bicubic', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({2., 2.})) .mode(torch::kBicubic) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bicubic_scale_tuple_shared_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=(2., 1.), mode='bicubic', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({2., 1.})) .mode(torch::kBicubic) .align_corners(false)''', input_size=(1, 2, 4, 4), fullname='interpolate_bicubic_scale_tuple_skewed_2d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(4, 6), scale_factor=None, mode='bicubic', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({4, 6})) .scale_factor(c10::nullopt) .mode(torch::kBicubic) .align_corners(true)''', input_size=(1, 2, 4, 4), fullname='interpolate_bicubic_tuple_2d_align_corners', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=(2., 1.), mode='bicubic', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({2., 1.})) .mode(torch::kBicubic) .align_corners(true)''', input_size=(1, 2, 4, 4), fullname='interpolate_bicubic_scale_tuple_skewed_2d_align_corners', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12, 12})) .scale_factor(c10::nullopt) .mode(torch::kNearest)''', input_size=(1, 2, 4, 4, 4), fullname='interpolate_nearest_3d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12, 12})) .scale_factor(c10::nullopt) .mode(torch::kNearest)''', input_size=(0, 2, 4, 4, 4), fullname='interpolate_nearest_3d_zero_dim', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(12, 16, 16), scale_factor=None, mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 16, 16})) .scale_factor(c10::nullopt) .mode(torch::kNearest)''', input_size=(1, 2, 3, 4, 4), fullname='interpolate_nearest_tuple_3d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=4., mode='nearest'), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({4., 4., 4.})) .mode(torch::kNearest)''', input_size=(1, 2, 4, 4, 4), fullname='interpolate_nearest_scale_3d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='trilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12, 12})) .scale_factor(c10::nullopt) .mode(torch::kTrilinear) .align_corners(false)''', input_size=(1, 2, 4, 4, 4), fullname='interpolate_trilinear_3d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=12, scale_factor=None, mode='trilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({12, 12, 12})) .scale_factor(c10::nullopt) .mode(torch::kTrilinear) .align_corners(false)''', input_size=(0, 2, 4, 4, 4), fullname='interpolate_trilinear_3d_zero_dim', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(4, 6, 6), scale_factor=None, mode='trilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({4, 6, 6})) .scale_factor(c10::nullopt) .mode(torch::kTrilinear) .align_corners(false)''', input_size=(1, 2, 2, 3, 3), fullname='interpolate_trilinear_tuple_3d', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=3., mode='trilinear', align_corners=False), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({3., 3., 3.})) .mode(torch::kTrilinear) .align_corners(false)''', input_size=(1, 2, 3, 4, 5), fullname='interpolate_trilinear_scale_3d', # See https://github.com/pytorch/pytorch/issues/5006 precision=3e-4, pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=(4, 6, 6), scale_factor=None, mode='trilinear', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(std::vector<int64_t>({4, 6, 6})) .scale_factor(c10::nullopt) .mode(torch::kTrilinear) .align_corners(true)''', input_size=(1, 2, 2, 3, 3), fullname='interpolate_trilinear_tuple_3d_align_corners', pickle=False, ), dict( constructor=wrap_functional(F.interpolate, size=None, scale_factor=3., mode='trilinear', align_corners=True), cpp_options_args='''F::InterpolateFuncOptions() .size(c10::nullopt) .scale_factor(std::vector<double>({3., 3., 3.})) .mode(torch::kTrilinear) .align_corners(true)''', input_size=(1, 2, 3, 4, 4), fullname='interpolate_trilinear_scale_3d_align_corners', # See https://github.com/pytorch/pytorch/issues/5006 precision=3e-4, pickle=False, ), dict( module_name='AdaptiveMaxPool1d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveMaxPool1dOptions(3)', input_fn=lambda: _rand_tensor_non_equal(1, 3, 5), ), dict( module_name='AdaptiveMaxPool1d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveMaxPool1dOptions(3)', input_fn=lambda: _rand_tensor_non_equal(3, 5), desc='no_batch_dim', ), dict( module_name='AdaptiveMaxPool2d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveMaxPool2dOptions(3)', input_fn=lambda: _rand_tensor_non_equal(1, 3, 5, 6), desc='single', ), dict( module_name='AdaptiveMaxPool2d', constructor_args=((3, 4),), cpp_constructor_args='torch::nn::AdaptiveMaxPool2dOptions({3, 4})', input_fn=lambda: _rand_tensor_non_equal(1, 3, 5, 6), desc='tuple', ), dict( module_name='AdaptiveMaxPool2d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveMaxPool2dOptions(3)', input_fn=lambda: _rand_tensor_non_equal(3, 5, 6), reference_fn=single_batch_reference_fn, desc='no_batch_dim', ), dict( module_name='AdaptiveMaxPool2d', constructor_args=((3, None),), cpp_constructor_args='torch::nn::AdaptiveMaxPool2dOptions({3, c10::nullopt})', input_fn=lambda: _rand_tensor_non_equal(1, 3, 5, 6), desc='tuple_none', ), dict( module_name='AdaptiveMaxPool3d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveMaxPool3dOptions(3)', input_fn=lambda: _rand_tensor_non_equal(2, 3, 5, 6, 7), desc='single', ), dict( module_name='AdaptiveMaxPool3d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveMaxPool3dOptions(3)', input_fn=lambda: _rand_tensor_non_equal(3, 5, 6, 7), reference_fn=single_batch_reference_fn, desc='no_batch_dim', ), dict( module_name='AdaptiveMaxPool3d', constructor_args=((3, 4, 5),), cpp_constructor_args='torch::nn::AdaptiveMaxPool3dOptions({3, 4, 5})', input_fn=lambda: _rand_tensor_non_equal(2, 3, 5, 6, 7), desc='tuple', ), dict( module_name='AdaptiveMaxPool3d', constructor_args=((3, None, 5),), cpp_constructor_args='torch::nn::AdaptiveMaxPool3dOptions({3, c10::nullopt, 5})', input_fn=lambda: _rand_tensor_non_equal(2, 3, 5, 6, 7), desc='tuple_none', ), dict( module_name='AdaptiveMaxPool3d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveMaxPool3dOptions(3)', input_fn=lambda: _rand_tensor_non_equal(2, 3, 12, 9, 3), desc='single_nonatomic', ), dict( module_name='AdaptiveMaxPool3d', constructor_args=((3, 4, 5),), cpp_constructor_args='torch::nn::AdaptiveMaxPool3dOptions({3, 4, 5})', input_fn=lambda: _rand_tensor_non_equal(2, 3, 6, 4, 10), desc='tuple_nonatomic', ), dict( module_name='AdaptiveAvgPool1d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveAvgPool1dOptions(3)', input_fn=lambda: torch.rand(1, 3, 5), ), dict( module_name='AdaptiveAvgPool1d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveAvgPool1dOptions(3)', input_fn=lambda: torch.rand(3, 5), reference_fn=single_batch_reference_fn, desc='no_batch_dim', ), dict( module_name='AdaptiveAvgPool1d', constructor_args=(1,), cpp_constructor_args='torch::nn::AdaptiveAvgPool1dOptions(1)', input_fn=lambda: torch.rand(1, 3, 5), desc='one_output', ), dict( module_name='AdaptiveAvgPool2d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveAvgPool2dOptions(3)', input_fn=lambda: torch.rand(1, 3, 5, 6), desc='single', ), dict( module_name='AdaptiveAvgPool2d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveAvgPool2dOptions(3)', input_fn=lambda: torch.rand(3, 5, 6), reference_fn=single_batch_reference_fn, desc='no_batch_dim', ), dict( module_name='AdaptiveAvgPool2d', constructor_args=(1,), cpp_constructor_args='torch::nn::AdaptiveAvgPool2dOptions(1)', input_fn=lambda: torch.rand(1, 3, 5, 6), desc='single_1x1output', ), dict( module_name='AdaptiveAvgPool2d', constructor_args=((3, 4),), cpp_constructor_args='torch::nn::AdaptiveAvgPool2dOptions({3, 4})', input_fn=lambda: torch.rand(1, 3, 5, 6), desc='tuple', ), dict( module_name='AdaptiveAvgPool2d', constructor_args=((3, None),), cpp_constructor_args='torch::nn::AdaptiveAvgPool2dOptions({3, c10::nullopt})', input_fn=lambda: torch.rand(1, 3, 5, 6), desc='tuple_none', ), dict( module_name='AdaptiveAvgPool3d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveAvgPool3dOptions(3)', input_fn=lambda: torch.rand(2, 3, 5, 2, 7), desc='single', ), dict( module_name='AdaptiveAvgPool3d', constructor_args=(3,), cpp_constructor_args='torch::nn::AdaptiveAvgPool3dOptions(3)', input_fn=lambda: torch.rand(3, 5, 2, 7), reference_fn=single_batch_reference_fn, desc='no_batch_dim', ), dict( module_name='AdaptiveAvgPool3d', constructor_args=((3, 4, 5),), cpp_constructor_args='torch::nn::AdaptiveAvgPool3dOptions({3, 4, 5})', input_fn=lambda: torch.rand(2, 3, 5, 3, 7), desc='tuple', ), dict( module_name='AdaptiveAvgPool3d', constructor_args=((None, 4, 5),), cpp_constructor_args='torch::nn::AdaptiveAvgPool3dOptions({c10::nullopt, 4, 5})', input_fn=lambda: torch.rand(2, 3, 5, 3, 7), desc='tuple_none', ), dict( module_name='AdaptiveAvgPool3d', constructor_args=((3, 2, 2),), cpp_constructor_args='torch::nn::AdaptiveAvgPool3dOptions({3, 2, 2})', input_fn=lambda: torch.rand(1, 1, 3, 2, 6), desc='last_dim', ), dict( module_name='SELU', input_size=(3, 2, 5), check_inplace=True ), dict( module_name='SELU', input_size=(), check_inplace=True, desc='scalar' ), dict( module_name='CELU', input_size=(3, 2, 5), constructor_args=(2.,), cpp_constructor_args='torch::nn::CELUOptions().alpha(2.)', check_inplace=True, reference_fn=lambda x, *_: torch.where(x >= 0, x, 2. * ((.5 * x).exp() - 1)), ), dict( module_name='CELU', input_size=(), constructor_args=(2.,), cpp_constructor_args='torch::nn::CELUOptions().alpha(2.)', check_inplace=True, reference_fn=lambda x, *_: torch.where(x >= 0, x, 2. * ((.5 * x).exp() - 1)), desc='scalar' ), dict( module_name='GLU', input_size=(5, 6), ), dict( module_name='GLU', constructor_args=(1,), cpp_constructor_args='torch::nn::GLUOptions(1)', input_size=(5, 6, 7), desc='dim', ), dict( module_name='GELU', input_size=(), desc='scalar', reference_fn=lambda x, *_: x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))), ), dict( module_name='GELU', input_size=(3, 2, 5), reference_fn=lambda x, *_: x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))), ), dict( module_name='SiLU', input_size=(), desc='scalar', reference_fn=lambda x, *_: x * torch.sigmoid(x), ), dict( module_name='SiLU', input_size=(5, 6, 7), reference_fn=lambda x, *_: x * torch.sigmoid(x), ), dict( module_name='Mish', input_size=(), desc='scalar', reference_fn=lambda x, *_: x * torch.tanh(F.softplus(x)), ), dict( module_name='Mish', input_size=(5, 6, 7), reference_fn=lambda x, *_: x * torch.tanh(F.softplus(x)), ), dict( constructor=wrap_functional(F.softmax, dim=-1), cpp_options_args='F::SoftmaxFuncOptions(-1)', input_size=(2, 128), # trigger the last-dim algo in CUDA fullname='softmax_lastdim', pickle=False, ), dict( constructor=wrap_functional(F.softmax, dim=1, dtype=torch.float64), cpp_options_args='F::SoftmaxFuncOptions(1).dtype(torch::kFloat64)', input_size=(2, 128), fullname='softmax_lastdim_dtype', pickle=False, test_cuda=False ), dict( constructor=wrap_functional(F.softmax, dim=1), cpp_options_args='F::SoftmaxFuncOptions(1)', input_size=(2, 128, 2, 2), # trigger special case of spatial CUDA algo fullname='softmax_spatial_special', pickle=False, ), dict( constructor=wrap_functional(F.softmax, dim=1), cpp_options_args='F::SoftmaxFuncOptions(1)', input_size=(2, 2, 4, 4), # regular spatial algorithm fullname='softmax_spatial', pickle=False, ), dict( constructor=wrap_functional(F.softmax, dim=1, dtype=torch.float64), cpp_options_args='F::SoftmaxFuncOptions(1).dtype(torch::kFloat64)', input_size=(2, 2, 4, 4), # regular spatial algorithm fullname='softmax_spatial_dtype', pickle=False, test_cuda=False ), dict( constructor=wrap_functional(F.softmax, dim=0), cpp_options_args='F::SoftmaxFuncOptions(0)', input_size=(2, 3, 4, 5), fullname='softmax_functional_dim0', test_cuda=False, pickle=False, ), dict( constructor=wrap_functional(F.softmax, dim=3), cpp_options_args='F::SoftmaxFuncOptions(3)', input_size=(2, 3, 4, 5), fullname='softmax_functional_dim3', test_cuda=False, pickle=False, ), dict( constructor=wrap_functional(F.softmax, dim=-1), cpp_options_args='F::SoftmaxFuncOptions(-1)', input_size=(), fullname='softmax_functional_scalar', test_cuda=False, pickle=False, ), dict( constructor=wrap_functional(F.log_softmax, dim=-1), cpp_options_args='F::LogSoftmaxFuncOptions(-1)', input_size=(2, 128), # trigger the last-dim algo in CUDA fullname='log_softmax_lastdim', pickle=False, ), dict( constructor=wrap_functional(F.log_softmax, dim=1), cpp_options_args='F::LogSoftmaxFuncOptions(1)', input_size=(2, 128, 2, 2), # trigger special case of spatial CUDA algo fullname='log_softmax_spatial_special', pickle=False, ), dict( constructor=wrap_functional(F.log_softmax, dim=1), cpp_options_args='F::LogSoftmaxFuncOptions(1)', input_size=(2, 2, 4, 4), # regular spatial algorithm fullname='log_softmax_spatial', pickle=False, ), dict( constructor=wrap_functional(F.log_softmax, dim=0), cpp_options_args='F::LogSoftmaxFuncOptions(0)', input_size=(2, 3, 4, 5), fullname='log_softmax_dim0', pickle=False, ), dict( constructor=wrap_functional(F.log_softmax, dim=3), cpp_options_args='F::LogSoftmaxFuncOptions(3)', input_size=(2, 3, 4, 5), fullname='log_softmax_dim3', pickle=False, ), dict( constructor=wrap_functional(F.log_softmax, dim=0), cpp_options_args='F::LogSoftmaxFuncOptions(0)', input_size=(), fullname='log_softmax_scalar', pickle=False, ), dict( fullname='Unfold', constructor=lambda: nn.Unfold((2, 2), (1, 1), (0, 0), (1, 1)), cpp_constructor_args='torch::nn::UnfoldOptions({2, 2}).dilation({1, 1}).padding({0, 0}).stride({1, 1})', input_size=(2, 4, 3, 3), check_gradgrad=False, test_cuda=True, ), dict( fullname='Fold', constructor=lambda: nn.Fold((3, 3), (2, 2), (1, 1), (0, 0), (1, 1)), cpp_constructor_args='torch::nn::FoldOptions({3, 3}, {2, 2}).dilation({1, 1}).padding({0, 0}).stride({1, 1})', input_size=(2, 16, 4), check_gradgrad=False, test_cuda=True, ), dict( fullname='Unfold_int_input', constructor=lambda: nn.Unfold(2, 1, 0, 1), cpp_constructor_args='torch::nn::UnfoldOptions(2).dilation(1).padding(0).stride(1)', input_size=(2, 4, 3, 3), check_gradgrad=False, test_cuda=True, ), dict( fullname='Fold_int_input', constructor=lambda: nn.Fold(3, 2, 1, 0, 1), cpp_constructor_args='torch::nn::FoldOptions(3, 2).dilation(1).padding(0).stride(1)', input_size=(2, 16, 4), check_gradgrad=False, test_cuda=True, ), dict( module_name='Threshold', constructor_args=(2., 1.), cpp_constructor_args='torch::nn::ThresholdOptions(2., 1.)', input_size=(), check_inplace=True, desc='threshold_value_scalar' ), dict( module_name='ReLU', input_size=(), check_inplace=True, desc='scalar' ), dict( module_name='ReLU6', input_size=(), check_inplace=True, desc='scalar' ), dict( module_name='RReLU', constructor_args=(0.1, 0.9), cpp_constructor_args='torch::nn::RReLUOptions().lower(0.1).upper(0.9)', input_size=(), desc='with_up_down_scalar', test_cuda=False, ), dict( module_name='Hardtanh', input_size=(), reference_fn=lambda i, *_: i.clamp(-1, 1), desc='scalar' ), dict( module_name='Sigmoid', input_size=(), desc='scalar', ), dict( module_name='Tanh', input_size=(), desc='scalar', ), dict( module_name='Softmax', constructor_args=(0,), cpp_constructor_args='torch::nn::SoftmaxOptions(0)', input_size=(), reference_fn=lambda i, *_: torch.exp(i).div(torch.exp(i).sum(0, True)), desc='scalar', ), dict( module_name='LogSoftmax', constructor_args=(0,), cpp_constructor_args='torch::nn::LogSoftmaxOptions(0)', input_size=(), reference_fn=lambda i, *_: torch.exp(i).div_(torch.exp(i).sum(0, False)).log_(), desc='multiparam_scalar', ), dict( module_name='ELU', constructor_args=(2.,), cpp_constructor_args='torch::nn::ELUOptions().alpha(2.)', input_size=(), desc='scalar', ), dict( module_name='Hardshrink', constructor_args=(2.,), cpp_constructor_args='torch::nn::HardshrinkOptions(2.)', input_size=(), desc='scalar', ), dict( module_name='LeakyReLU', constructor_args=(0.5,), cpp_constructor_args='torch::nn::LeakyReLUOptions().negative_slope(0.5)', input_size=(), check_inplace=True, desc='with_negval_scalar' ), dict( module_name='LogSigmoid', input_size=(), reference_fn=lambda i, *_: i.sigmoid().log(), desc='scalar' ), dict( module_name='Softplus', constructor_args=(2, -100), cpp_constructor_args='torch::nn::SoftplusOptions().beta(2).threshold(-100)', input_size=(), reference_fn=( lambda i, *_: ((i * 2) > -100).type_as(i) * i + ((i * 2) <= -100).type_as(i) * 1.0 / 2.0 * torch.log(1 + torch.exp(2 * i)) ), desc='beta_threshold_scalar', ), dict( module_name='Softshrink', constructor_args=(1,), cpp_constructor_args='torch::nn::SoftshrinkOptions(1)', input_size=(), desc='lambda_scalar', ), dict( module_name='PReLU', input_size=(), reference_fn=lambda i, p, _: torch.clamp(i, min=0) + torch.clamp(i, max=0) * p[0][0], desc='scalar', ), dict( module_name='Softsign', input_size=(), reference_fn=lambda i, *_: i.div(1 + torch.abs(i)), desc='scalar', ), dict( module_name='Softmin', constructor_args=(0,), cpp_constructor_args='torch::nn::SoftminOptions(0)', input_size=(), desc='scalar', ), dict( module_name='Tanhshrink', input_size=(), desc='scalar', ), dict( fullname='Padding12_1dcircular', constructor=wrap_functional(F.pad, pad=(1, 2), mode='circular'), cpp_options_args='F::PadFuncOptions({1, 2}).mode(torch::kCircular)', input_fn=lambda: torch.arange(6, out=torch.DoubleTensor()).reshape([1, 2, 3]), reference_fn=lambda i, *_: padding1d_circular(i, (1, 2)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding31_1dcircular', constructor=wrap_functional(F.pad, pad=(3, 1), mode='circular'), cpp_options_args='F::PadFuncOptions({3, 1}).mode(torch::kCircular)', input_fn=lambda: torch.arange(6, out=torch.DoubleTensor()).reshape([1, 2, 3]), reference_fn=lambda i, *_: padding1d_circular(i, (3, 1)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding33_1dcircular', constructor=wrap_functional(F.pad, pad=(3, 3), mode='circular'), cpp_options_args='F::PadFuncOptions({3, 3}).mode(torch::kCircular)', input_fn=lambda: torch.arange(6, out=torch.DoubleTensor()).reshape([1, 2, 3]), reference_fn=lambda i, *_: padding1d_circular(i, (3, 3)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding1221_2dcircular', constructor=wrap_functional(F.pad, pad=(1, 2, 2, 1), mode='circular'), cpp_options_args='F::PadFuncOptions({1, 2, 2, 1}).mode(torch::kCircular)', input_fn=lambda: torch.arange(6, out=torch.DoubleTensor()).reshape([1, 1, 2, 3]), reference_fn=lambda i, *_: padding2d_circular(i, (1, 2, 2, 1)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding2322_2dcircular', constructor=wrap_functional(F.pad, pad=(2, 3, 2, 2), mode='circular'), cpp_options_args='F::PadFuncOptions({2, 3, 2, 2}).mode(torch::kCircular)', input_fn=lambda: torch.arange(6, out=torch.DoubleTensor()).reshape([1, 1, 2, 3]), reference_fn=lambda i, *_: padding2d_circular(i, (2, 3, 2, 2)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding3331_2dcircular', constructor=wrap_functional(F.pad, pad=(3, 3, 3, 1), mode='circular'), cpp_options_args='F::PadFuncOptions({3, 3, 3, 1}).mode(torch::kCircular)', input_fn=lambda: torch.arange(9, out=torch.DoubleTensor()).reshape([1, 1, 3, 3]), reference_fn=lambda i, *_: padding2d_circular(i, (3, 3, 3, 1)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding122112_3dcircular', constructor=wrap_functional(F.pad, pad=(1, 2, 2, 1, 1, 2), mode='circular'), cpp_options_args='F::PadFuncOptions({1, 2, 2, 1, 1, 2}).mode(torch::kCircular)', input_fn=lambda: torch.arange(12, out=torch.DoubleTensor()).reshape([1, 1, 2, 2, 3]), reference_fn=lambda i, *_: padding3d_circular(i, (1, 2, 2, 1, 1, 2)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding322112_3dcircular', constructor=wrap_functional(F.pad, pad=(3, 2, 2, 1, 1, 2), mode='circular'), cpp_options_args='F::PadFuncOptions({3, 2, 2, 1, 1, 2}).mode(torch::kCircular)', input_fn=lambda: torch.arange(12, out=torch.DoubleTensor()).reshape([1, 1, 2, 2, 3]), reference_fn=lambda i, *_: padding3d_circular(i, (3, 2, 2, 1, 1, 2)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( fullname='Padding332122_3dcircular', constructor=wrap_functional(F.pad, pad=(3, 3, 2, 1, 2, 2), mode='circular'), cpp_options_args='F::PadFuncOptions({3, 3, 2, 1, 2, 2}).mode(torch::kCircular)', input_fn=lambda: torch.arange(12, out=torch.DoubleTensor()).reshape([1, 1, 2, 2, 3]), reference_fn=lambda i, *_: padding3d_circular(i, (3, 3, 2, 1, 2, 2)), skip_double=TEST_WITH_ROCM, pickle=False, ), dict( module_name='TransformerEncoderLayer', constructor_args=(4, 2, 16, 0.0), cpp_constructor_args='''torch::nn::TransformerEncoderLayerOptions(4, 2) .dim_feedforward(16) .dropout(0.0)''', input_size=(2, 3, 4), desc='relu_activation', with_tf32=True, tf32_precision=0.1, # TODO(#50743): figure out the error # RuntimeError: The size of tensor a (6) must match the size of tensor b (4) # at non-singleton dimension 2 check_batched_grad=False, ), dict( module_name='TransformerEncoderLayer', constructor_args=(4, 2, 8, 0.0, 'gelu'), cpp_constructor_args='''torch::nn::TransformerEncoderLayerOptions(4, 2) .dim_feedforward(8) .dropout(0.0) .activation(torch::kGELU)''', input_size=(2, 3, 4), check_gradgrad=False, desc='gelu_activation', with_tf32=True, tf32_precision=0.05, ), dict( module_name='TransformerDecoderLayer', constructor_args=(4, 2, 8, 0.0), cpp_constructor_args='''torch::nn::TransformerDecoderLayerOptions(4, 2) .dim_feedforward(8) .dropout(0.0)''', input_fn=lambda: (torch.rand(3, 3, 4), torch.rand(2, 3, 4)), check_gradgrad=False, desc='relu_activation', with_tf32=True, tf32_precision=0.05, ), dict( module_name='TransformerDecoderLayer', constructor_args=(4, 2, 8, 0.0, 'gelu'), cpp_constructor_args='''torch::nn::TransformerDecoderLayerOptions(4, 2) .dim_feedforward(8) .dropout(0.0) .activation(torch::kGELU)''', input_fn=lambda: (torch.rand(3, 3, 4), torch.rand(2, 3, 4)), check_gradgrad=False, desc='gelu_activation', with_tf32=True, tf32_precision=0.05, ), dict( module_name='Transformer', constructor_args=(4, 2, 2, 2, 8, 0.0, "relu"), cpp_constructor_args='''torch::nn::TransformerOptions() .d_model(4) .nhead(2) .num_encoder_layers(2) .num_decoder_layers(2) .dim_feedforward(8) .dropout(0.0) .activation(torch::kReLU)''', input_fn=lambda:(torch.rand(3, 3, 4), torch.rand(2, 3, 4), torch.rand(3, 3)), check_gradgrad=False, desc='multilayer_coder', with_tf32=True, tf32_precision=0.01, ), dict( module_name='Linear', constructor_args=(3, 5), cpp_constructor_args='torch::nn::LinearOptions(3, 5)', input_fn=lambda: torch.rand(3), reference_fn=lambda i, p, _: torch.mm(i.view(1, -1), p[0].t()).view(-1) + p[1], desc="no_batch_dim", with_tf32=True, tf32_precision=0.005, ), ] # add conv padding mode tests: for padding_mode, cpp_padding_mode in zip( ['reflect', 'circular', 'replicate', 'zeros'], ['torch::kReflect', 'torch::kCircular', 'torch::kReplicate', 'torch::kZeros']): # conv signature: # in_channels, out_channels, kernel_size, stride=1, # padding=0, dilation=1, groups=1, # bias=True, padding_mode='zeros' for d in (1, 2, 3): if d == 3 and padding_mode == 'reflect': # FIXME: remove after implementing reflection pad 3d # https://github.com/pytorch/pytorch/issues/27655 continue padding = tuple(range(1, d + 1)) cpp_padding = '{' + ', '.join(map(str, padding)) + '}' input_size = (2, 2) + (4,) * d output_size = (2, 3) + tuple(p + 1 for p in padding) # simplified from `(4 + 2 * p - 3) // 2 + 1` new_module_tests.append( dict( module_name='Conv{}d'.format(d), constructor_args=(2, 3, 3, 2, padding, 1, 1, True, padding_mode), cpp_constructor_args='''torch::nn::Conv{}dOptions(2, 3, 3) .stride(2) .padding({}) .dilation(1) .groups(1) .bias(true) .padding_mode({})'''.format(d, cpp_padding, cpp_padding_mode), input_size=input_size, output_size=output_size, cudnn=True, desc='{}_stride2_pad2'.format(padding_mode), with_tf32=True, tf32_precision=0.05 ), ) # Check that non linear activations work with no batch dimensions non_linear_activations_no_batch = [ 'ELU', 'Hardshrink', 'Hardsigmoid', 'Hardtanh', 'Hardswish', 'LeakyReLU', 'LogSigmoid', 'PReLU', 'ReLU', 'ReLU6', 'RReLU', 'SELU', 'CELU', 'GELU', 'Sigmoid', 'SiLU', 'Mish', 'Softplus', 'Softshrink', 'Softsign', 'Tanh', 'Tanhshrink', 'Threshold' ] non_linear_activations_extra_info: Dict[str, dict] = { 'CELU': {'constructor_args': (2.,)}, 'Threshold': {'constructor_args': (2., 1.)}, 'Hardsigmoid': {'check_gradgrad': False, 'check_jit': False}, 'Hardswish': {'check_gradgrad': False, 'check_jit': False}, # For RRelu, test that compare CPU and GPU results fail because RNG # is different between CPU and GPU 'RReLU': {'test_cuda': False}, } for non_linear_activation in non_linear_activations_no_batch: activation_test_info = dict( module_name=non_linear_activation, input_size=(3,), reference_fn=single_batch_reference_fn, desc='no_batch_dim', test_cpp_api_parity=False, ) extra_info = non_linear_activations_extra_info.get(non_linear_activation, {}) activation_test_info.update(extra_info) new_module_tests.append(activation_test_info) def kldivloss_reference(input, target, reduction='mean'): safe_target = target * (target > 0).type_as(target) safe_target_log = (safe_target + (target <= 0).type_as(target)).log() result = safe_target * (safe_target_log - input) if reduction == 'mean': return result.mean() elif reduction == 'sum': return result.sum() elif reduction == 'batchmean' and result.dim() != 0: return result.sum() / result.size(0) return result def kldivloss_log_target_reference(input, target, reduction='mean'): result = torch.exp(target) * (target - input) if reduction == 'mean': return result.mean() elif reduction == 'sum': return result.sum() elif reduction == 'batchmean' and result.dim() != 0: return result.sum() / result.size(0) return result def nlllossNd_reference(input, target, weight=None, ignore_index=-100, reduction='mean'): assert input.dim() >= 3 N = input.size(0) C = input.size(1) out_size = (N,) + input.size()[2:] output = torch.zeros(out_size).type_as(input) if weight is None: weight = torch.ones(C).type_as(input) total_weight = 0 for tup in product(*[range(size) for size in out_size]): t_nx = target[tup] norm = 0. if ignore_index == t_nx else weight[t_nx].item() input_index = list(tup) input_index.insert(1, t_nx) output[tup] = -input[tuple(input_index)] * norm total_weight += norm if reduction == 'mean': return output.sum() / total_weight elif reduction == 'sum': return output.sum() return output def cross_entropy_loss_reference(input, target, weight=None, ignore_index=-100, reduction='mean'): return nlllossNd_reference( torch.log_softmax(input, 1), target, weight, ignore_index=ignore_index, reduction=reduction) def nllloss_reference(input, target, weight=None, ignore_index=-100, reduction='mean'): def nll_loss_helper(input, target, weight, ignore_index): if target == ignore_index: return (0, 0) norm = 1 if weight is None else weight[target] result = -input[target] * norm return (result, norm) losses_and_weights = [nll_loss_helper(i, t, weight, ignore_index) for i, t in zip(input, target)] losses, weights = zip(*losses_and_weights) losses_tensor = input.new_tensor(losses) if reduction == 'mean': return sum(losses_tensor) / sum(weights) elif reduction == 'sum': return sum(losses_tensor) else: return losses_tensor def smoothl1loss_reference(input, target, reduction='mean', beta=1.0): abs_diff = (input - target).abs() ge_beta_mask = (abs_diff >= beta).type_as(abs_diff) lt_beta_mask = (abs_diff < beta).type_as(abs_diff) # when beta <= 0 we should just use l1_loss if beta == 0: output = abs_diff else: output = ge_beta_mask * (abs_diff - 0.5 * beta) + lt_beta_mask * 0.5 * (abs_diff ** 2) / beta if reduction == 'mean': return output.mean() elif reduction == 'sum': return output.sum() return output def huberloss_reference(input, target, reduction='mean', delta=1.0): abs_diff = (input - target).abs() ge_delta_mask = (abs_diff >= delta) lt_delta_mask = (abs_diff < delta) output = ge_delta_mask * delta * (abs_diff - 0.5 * delta) + lt_delta_mask * 0.5 * (abs_diff ** 2) if reduction == 'mean': return output.mean() elif reduction == 'sum': return output.sum() return output def _multilabelmarginloss_reference(input, target): targets = [] for target_index in target: if target_index < 0: break targets.append(target_index) sum = 0 for target_index in targets: for i in range(0, len(input)): if i not in targets: sum += max(0, 1 - input[target_index] + input[i]) return sum def multilabelmarginloss_reference(input, target, reduction='mean'): # make everything 2-dimensional input_dim = input.dim() if input.dim() < 2: assert target.dim() < 2 input = input.unsqueeze(0) if input.dim() == 1 else input.unsqueeze(0).unsqueeze(0) target = target.unsqueeze(0) if target.dim() == 1 else target.unsqueeze(0).unsqueeze(0) n = input.size(0) dim = input.size(1) output = input.new(n).zero_() for i in range(0, n): output[i] = _multilabelmarginloss_reference(input[i], target[i]) if reduction == 'mean': return output.mean() / dim elif reduction == 'sum': return output.sum() / dim elif input_dim < 2: # we know we have (1, C) X (1, C) -> (1,), so squeeze will get us # back to correct dimensionality return output.squeeze() / dim else: return output / dim def hingeembeddingloss_reference(input, target, margin=1.0, reduction='mean'): margin_clamp = (margin - input).clamp(min=0).type_as(input) output = torch.where(target == 1, input, margin_clamp) if reduction == 'mean': return output.mean() elif reduction == 'sum': return output.sum() return output def softmarginloss_reference(input, target, reduction='mean'): output = (1 + (-input * target).exp()).log() if reduction == 'mean': return output.mean() elif reduction == 'sum': return output.sum() return output def _multimarginloss_reference(input, target_idx, p, margin, weight): if weight is None: weight = input.new(len(input)).fill_(1) output = 0 for i in range(0, len(input)): if i != target_idx: output += max(0, weight[target_idx] * (margin - input[target_idx] + input[i]) ** p) return output def multimarginloss_reference(input, target, p=1, margin=1, weight=None, reduction='mean'): if input.dim() < 2: input = input.unsqueeze(0) if input.dim() == 1 else input.unsqueeze(0).unsqueeze(0) target_dim = target.dim() if target.dim() == 0: target = target.unsqueeze(0) n = input.size(0) dim = input.size(1) output = input.new(n) for x in range(0, n): output[x] = _multimarginloss_reference(input[x], target[x], p, margin, weight) if reduction == 'mean': return output.mean() / dim elif reduction == 'sum': return output.sum() / dim elif target_dim == 0: return output.squeeze(0) / dim return output / dim def cosineembeddingloss_reference(input1, input2, target, margin=0, reduction='mean'): def _cos(a, b): cos = a.new(a.size(0)) for i in range(0, a.size(0)): cos[i] = (a[i] * b[i]).sum() / ((((a[i] * a[i]).sum() + 1e-12) * ((b[i] * b[i]).sum() + 1e-12)) ** 0.5) return cos output = torch.where(target == 1, 1 - _cos(input1, input2), (_cos(input1, input2) - margin).clamp(min=0)) if reduction == 'mean': return output.mean() elif reduction == 'sum': return output.sum() return output def tripletmarginloss_reference(anchor, positive, negative, margin=1.0, p=2, eps=1e-6, swap=False, reduction='mean'): d_p = torch.pairwise_distance(anchor, positive, p, eps) d_n = torch.pairwise_distance(anchor, negative, p, eps) if swap: d_s = torch.pairwise_distance(positive, negative, p, eps) d_n = torch.min(d_n, d_s) output = torch.clamp(margin + d_p - d_n, min=0.0) if reduction == 'mean': return output.mean() elif reduction == 'sum': return output.sum() return output def marginrankingloss_reference(input1, input2, target, margin=0, reduction='mean'): output = (-target * (input1 - input2) + margin).clamp(min=0) if reduction == 'mean': return output.mean() elif reduction == 'sum': return output.sum() return output # this directly follows Graves et al's paper, in contrast to the production implementation, it does not use log-space def ctcloss_reference(log_probs, targets, input_lengths, target_lengths, blank=0, reduction='mean'): input_lengths = torch.as_tensor(input_lengths, dtype=torch.long) target_lengths = torch.as_tensor(target_lengths, dtype=torch.long) dt = log_probs.dtype log_probs = log_probs.double() # we need the accuracy as we are not in logspace targets = targets.long() cum_target_lengths = target_lengths.cumsum(0) losses = [] for i in range(log_probs.size(1)): input_length = input_lengths[i].item() target_length = target_lengths[i].item() cum_target_length = cum_target_lengths[i].item() targets_prime = targets.new_full((2 * target_length + 1,), blank) if targets.dim() == 2: targets_prime[1::2] = targets[i, :target_length] else: targets_prime[1::2] = targets[cum_target_length - target_length:cum_target_length] probs = log_probs[:input_length, i].exp() alpha = log_probs.new_zeros((target_length * 2 + 1,)) alpha[0] = probs[0, blank] alpha[1] = probs[0, targets_prime[1]] mask_third = (targets_prime[:-2] != targets_prime[2:]) for t in range(1, input_length): alpha_next = alpha.clone() alpha_next[1:] += alpha[:-1] alpha_next[2:] += torch.where(mask_third, alpha[:-2], alpha.new_zeros(1)) alpha = probs[t, targets_prime] * alpha_next losses.append(-alpha[-2:].sum().log()[None]) output = torch.cat(losses, 0) if reduction == 'mean': return (output / target_lengths.to(dtype=output.dtype, device=output.device)).mean() elif reduction == 'sum': return output.sum() output = output.to(dt) return output def padding1d_circular(input, pad): r""" input: [[[0., 1., 2.], [3., 4., 5.]]] pad: (1, 2) output: [[[2., 0., 1., 2., 0., 1.], [5., 3., 4., 5., 3., 4.]]] """ return torch.cat([input[:, :, -pad[0]:], input, input[:, :, 0:pad[1]]], dim=2) def padding2d_circular(input, pad): r"""input: [[[[0., 1., 2], [3., 4., 5.]]]] pad: (1, 2, 2, 1) output: [[[[2., 0., 1., 2., 0., 1.], [5., 3., 4., 5., 3., 4.], [2., 0., 1., 2., 0., 1.], [5., 3., 4., 5., 3., 4.], [2., 0., 1., 2., 0., 1.]]]] """ input = torch.cat([input[:, :, -pad[2]:], input, input[:, :, 0:pad[3]]], dim=2) return torch.cat([input[:, :, :, -pad[0]:], input, input[:, :, :, 0:pad[1]]], dim=3) def padding3d_circular(input, pad): r"""input: [[[[[ 0., 1., 2.], [ 3., 4., 5.]], [[ 6., 7., 8.], [ 9., 10., 11.]]]]] pad: (1, 2, 2, 1, 1, 2) output: [[[[[ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.]], [[ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.]], [[ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.]], [[ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.], [ 5., 3., 4., 5., 3., 4.], [ 2., 0., 1., 2., 0., 1.]], [[ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.], [11., 9., 10., 11., 9., 10.], [ 8., 6., 7., 8., 6., 7.]]]]] """ input = torch.cat([input[:, :, -pad[4]:], input, input[:, :, 0:pad[5]]], dim=2) input = torch.cat([input[:, :, :, -pad[2]:], input, input[:, :, :, 0:pad[3]]], dim=3) return torch.cat([input[:, :, :, :, -pad[0]:], input, input[:, :, :, :, 0:pad[1]]], dim=4) loss_reference_fns: Dict['str', Callable] = { 'KLDivLoss': kldivloss_reference, 'KLDivLoss_log_target': kldivloss_log_target_reference, 'NLLLoss': nllloss_reference, 'NLLLossNd': nlllossNd_reference, 'SmoothL1Loss': smoothl1loss_reference, 'HuberLoss': huberloss_reference, 'MultiLabelMarginLoss': multilabelmarginloss_reference, 'HingeEmbeddingLoss': hingeembeddingloss_reference, 'SoftMarginLoss': softmarginloss_reference, 'MultiMarginLoss': multimarginloss_reference, 'CosineEmbeddingLoss': cosineembeddingloss_reference, 'TripletMarginLoss': tripletmarginloss_reference, 'MarginRankingLoss': marginrankingloss_reference, 'CTCLoss': ctcloss_reference, 'CrossEntropyLoss': cross_entropy_loss_reference } criterion_tests = [ dict( module_name='L1Loss', input_size=(2, 3, 4), target_fn=lambda: torch.randn((2, 3, 4), requires_grad=True), reference_fn=lambda i, t, _: 1. / i.numel() * sum((a - b).abs().sum() for a, b in zip(i, t)), check_complex=True, ), dict( module_name='NLLLoss', input_fn=lambda: torch.rand(15, 10).log(), target_fn=lambda: torch.empty(15).uniform_().mul(10).floor().long(), reference_fn=lambda i, t, m: nllloss_reference(i, t, reduction=get_reduction(m)), check_sum_reduction=True, check_bfloat16=True, ), dict( module_name='NLLLoss', constructor_args=(None, None, 2), cpp_constructor_args='torch::nn::NLLLossOptions().weight({}).ignore_index(2)', input_fn=lambda: torch.rand(15, 10).log(), target_fn=lambda: torch.empty(15).uniform_().mul(10).floor().long(), reference_fn=lambda i, t, _: nllloss_reference(i, t, ignore_index=2), desc='ignore_index', check_bfloat16=True, ), dict( module_name='NLLLoss', constructor_args_fn=lambda: (torch.rand(10),), cpp_constructor_args='torch::nn::NLLLossOptions().weight(torch::rand(10))', input_fn=lambda: torch.rand(15, 10).add(1e-2).log(), target_fn=lambda: torch.empty(15).uniform_().mul(10).floor().long(), reference_fn=lambda i, t, m: nllloss_reference(i, t, weight=get_weight(m)), desc='weights', check_bfloat16=True, ), dict( module_name='NLLLoss', constructor_args_fn=lambda: (torch.rand(10), None, 2), cpp_constructor_args='torch::nn::NLLLossOptions().weight(torch::rand(10)).ignore_index(2)', input_fn=lambda: torch.rand(15, 10).add(1e-2).log(), target_fn=lambda: torch.empty(15).uniform_().mul(10).floor().long(), reference_fn=lambda i, t, m: nllloss_reference(i, t, weight=get_weight(m), ignore_index=2), desc='weights_ignore_index', check_bfloat16=True, ), dict( module_name='NLLLoss', constructor_args_fn=lambda: (torch.rand(10), None, -1), cpp_constructor_args='torch::nn::NLLLossOptions().weight(torch::rand(10)).ignore_index(-1)', input_fn=lambda: torch.rand(15, 10).add(1e-2).log(), target_fn=lambda: torch.empty(15).uniform_().mul(10 + 1).floor().long() - 1, reference_fn=lambda i, t, m: nllloss_reference(i, t, weight=get_weight(m), ignore_index=-1), desc='weights_ignore_index_neg', check_bfloat16=True, ), dict( module_name='KLDivLoss', input_fn=lambda: torch.rand(10, 10).log(), target_fn=lambda: torch.rand(10, 10), reference_fn=lambda i, t, m: kldivloss_reference(i, t, get_reduction(m)), check_sum_reduction=True, ), dict( module_name='KLDivLoss', input_fn=lambda: torch.rand(10, 10).log(), target_fn=lambda: torch.rand(10, 10), reference_fn=lambda i, t, m: kldivloss_log_target_reference(i, t.log(), get_reduction(m)), check_sum_reduction=True, desc='log_target', ), dict( module_name='MSELoss', input_size=(2, 3, 4, 5), target_fn=lambda: torch.randn((2, 3, 4, 5), requires_grad=True), reference_fn=lambda i, t, m: ((i - t).abs().pow(2).sum() / (i.numel() if get_reduction(m) == 'mean' else 1)), check_sum_reduction=True, ), dict( module_name='BCELoss', input_fn=lambda: torch.rand(15, 10).clamp_(1e-2, 1 - 1e-2), target_fn=lambda: torch.randn(15, 10).gt(0).double(), reference_fn=lambda i, t, m: -(t * i.log() + (1 - t) * (1 - i).log()).sum() / (i.numel() if get_reduction(m) else 1), check_bfloat16=True, ), dict( module_name='BCELoss', constructor_args_fn=lambda: (torch.rand(10),), cpp_constructor_args='torch::nn::BCELossOptions().weight(torch::rand(10))', input_fn=lambda: torch.rand(15, 10).clamp_(1e-2, 1 - 1e-2), target_fn=lambda: torch.randn(15, 10).gt(0).double(), reference_fn=lambda i, t, m: -((t * i.log() + (1 - t) * (1 - i).log()) * get_weight(m)).sum() / (i.numel() if get_reduction(m) else 1), desc='weights', check_bfloat16=True, ), dict( module_name='CrossEntropyLoss', input_size=(15, 10), target_fn=lambda: torch.empty(15).uniform_().mul(10).floor().long(), ), dict( module_name='CrossEntropyLoss', constructor_args_fn=lambda: (torch.rand(10),), cpp_constructor_args='torch::nn::CrossEntropyLossOptions().weight(torch::rand(10))', input_size=(15, 10), target_fn=lambda: torch.empty(15).uniform_().mul(10).floor().long(), desc='weights', ), dict( module_name='HingeEmbeddingLoss', input_size=(10,), target_fn=lambda: torch.randn(10).gt(0).double().mul_(2).sub(1), reference_fn=lambda i, t, m: hingeembeddingloss_reference(i, t, reduction=get_reduction(m)), check_sum_reduction=True, ), dict( module_name='HingeEmbeddingLoss', constructor_args=(0.5,), cpp_constructor_args='torch::nn::HingeEmbeddingLossOptions().margin(0.5)', input_size=(10,), target_fn=lambda: torch.randn(10).gt(0).double().mul_(2).sub(1), reference_fn=lambda i, t, m: hingeembeddingloss_reference(i, t, margin=0.5, reduction=get_reduction(m)), desc='margin', check_sum_reduction=True, ), dict( module_name='MultiLabelMarginLoss', input_size=(10,), target_fn=lambda: torch.rand(10).mul(10).floor().long(), reference_fn=lambda i, t, m: multilabelmarginloss_reference(i, t, reduction=get_reduction(m)), desc="1d", check_sum_reduction=True, check_gradgrad=False, check_bfloat16=True, ), dict( module_name='MultiLabelMarginLoss', input_size=(5, 10), target_fn=lambda: torch.rand(5, 10).mul(10).floor().long(), reference_fn=lambda i, t, m: multilabelmarginloss_reference(i, t, reduction=get_reduction(m)), check_sum_reduction=True, check_gradgrad=False, check_bfloat16=True, ), dict( module_name='MultiLabelSoftMarginLoss', input_size=(5, 10), target_fn=lambda: torch.rand(5, 10).mul(2).floor(), reference_fn=lambda i, t, m: -(t * i.sigmoid().log() + (1 - t) * (-i).sigmoid().log()).sum() / i.numel(), check_gradgrad=False, ), dict( module_name='MultiMarginLoss', input_size=(5, 10), target_fn=lambda: torch.rand(5).mul(8).floor().long(), reference_fn=lambda i, t, m: multimarginloss_reference(i, t, reduction=get_reduction(m)), check_sum_reduction=True, check_gradgrad=False, ), dict( module_name='MultiMarginLoss', input_size=(10,), target_fn=lambda: torch.rand(1).mul(8).floor().long(), reference_fn=lambda i, t, m: multimarginloss_reference(i, t, reduction=get_reduction(m)), desc='1d', check_sum_reduction=True, check_gradgrad=False, ), dict( module_name='MultiMarginLoss', constructor_args=(2,), cpp_constructor_args='torch::nn::MultiMarginLossOptions().p(2)', input_fn=lambda: torch.rand(5, 10).clamp_(1e-2, 1 - 1e-2), target_fn=lambda: torch.rand(5).mul(8).floor().long(), reference_fn=lambda i, t, m: multimarginloss_reference(i, t, p=2, reduction=get_reduction(m)), desc='p', check_sum_reduction=True, check_gradgrad=False, ), dict( module_name='MultiMarginLoss', constructor_args=(1, 0.5), cpp_constructor_args='torch::nn::MultiMarginLossOptions().p(1).margin(0.5)', legacy_constructor_args=(1, None, 0.5), input_size=(5, 10), target_fn=lambda: torch.rand(5).mul(8).floor().long(), reference_fn=lambda i, t, m: multimarginloss_reference(i, t, margin=0.5, reduction=get_reduction(m)), desc='margin', check_sum_reduction=True, check_gradgrad=False, ), dict( module_name='MultiMarginLoss', constructor_args=(1, 1., torch.rand(10).double()), cpp_constructor_args='torch::nn::MultiMarginLossOptions().p(1).margin(1.).weight(torch::rand(10))', legacy_constructor_args=(1, torch.rand(10).double()), input_size=(5, 10), target_fn=lambda: torch.rand(5).mul(8).floor().long(), reference_fn=lambda i, t, m: multimarginloss_reference(i, t, weight=get_weight(m), reduction=get_reduction(m)), desc='weights', check_sum_reduction=True, check_gradgrad=False, ), dict( module_name='SmoothL1Loss', input_size=(5, 10), target_fn=lambda: torch.randn((5, 10), requires_grad=True), check_sum_reduction=True, reference_fn=lambda i, t, m, b=1.0: smoothl1loss_reference(i, t, reduction=get_reduction(m), beta=b), ), dict( module_name='HuberLoss', input_size=(5, 10), target_fn=lambda: torch.randn((5, 10), requires_grad=True), check_sum_reduction=True, check_half=True, check_bfloat16=True, reference_fn=lambda i, t, m: huberloss_reference(i, t, reduction=get_reduction(m)), ), dict( module_name='SoftMarginLoss', input_size=(5, 5), target_fn=lambda: torch.randn(5, 5).sign(), reference_fn=lambda i, t, m: softmarginloss_reference(i, t, reduction=get_reduction(m)), check_sum_reduction=True, ), dict( module_name='CosineEmbeddingLoss', input_fn=lambda: (torch.rand(15, 10), torch.rand(15, 10)), target_fn=lambda: torch.randn(15).sign(), reference_fn=lambda i, t, m: cosineembeddingloss_reference(i[0], i[1], t, reduction=get_reduction(m)), check_sum_reduction=True, ), dict( module_name='CosineEmbeddingLoss', constructor_args=(0.7,), cpp_constructor_args='torch::nn::CosineEmbeddingLossOptions().margin(0.7)', input_fn=lambda: (torch.rand(15, 10), torch.rand(15, 10)), target_fn=lambda: torch.randn(15).sign(), reference_fn=lambda i, t, m: cosineembeddingloss_reference(i[0], i[1], t, margin=0.7, reduction=get_reduction(m)), desc='margin', check_sum_reduction=True, ), dict( module_name='MarginRankingLoss', input_fn=lambda: (torch.randn(50).mul(10), torch.randn(50).mul(10)), target_fn=lambda: torch.randn(50).sign(), reference_fn=lambda i, t, m: marginrankingloss_reference(i[0], i[1], t, reduction=get_reduction(m)), check_sum_reduction=True, ), dict( module_name='MarginRankingLoss', constructor_args=(0.5,), cpp_constructor_args='torch::nn::MarginRankingLossOptions().margin(0.5)', input_fn=lambda: (torch.randn(50).mul(10), torch.randn(50).mul(10)), target_fn=lambda: torch.randn(50).sign(), reference_fn=lambda i, t, m: marginrankingloss_reference(i[0], i[1], t, margin=0.5, reduction=get_reduction(m)), desc='margin', check_sum_reduction=True, ), dict( module_name='BCEWithLogitsLoss', input_fn=lambda: torch.rand(15, 10).clamp_(1e-2, 1 - 1e-2), target_fn=lambda: torch.randn(15, 10).gt(0).double(), ), dict( module_name='BCEWithLogitsLoss', constructor_args=(torch.rand(10),), cpp_constructor_args='torch::nn::BCEWithLogitsLossOptions().weight(torch::rand(10))', input_fn=lambda: torch.rand(15, 10).clamp_(1e-2, 1 - 1e-2), target_fn=lambda: torch.randn(15, 10).gt(0).double(), desc='weights', ), dict( module_name='BCEWithLogitsLoss', constructor_args=(torch.rand(()),), cpp_constructor_args='torch::nn::BCEWithLogitsLossOptions().weight(torch::rand({}))', input_fn=lambda: torch.rand(()).clamp_(1e-2, 1 - 1e-2), target_fn=lambda: torch.randn(()).gt(0).double(), desc='scalar_weights' ), dict( module_name='NLLLoss', input_size=(2, 3, 5, 5), target_fn=lambda: torch.rand(2, 5, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['NLLLossNd'](i, t, reduction=get_reduction(m)), check_sum_reduction=True, desc='2d', check_bfloat16=True, ), dict( module_name='NLLLoss', constructor_args_fn=lambda: (torch.rand(3),), cpp_constructor_args='torch::nn::NLLLossOptions().weight(torch::rand(3))', input_size=(2, 3, 5, 5), target=torch.rand(2, 5, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['NLLLossNd'](i, t, weight=get_weight(m)), desc='2d_weights', check_bfloat16=True, ), dict( module_name='NLLLoss', constructor_args=(None, None, 1), cpp_constructor_args='torch::nn::NLLLossOptions().weight({}).ignore_index(1)', input_size=(2, 3, 5, 5), target_fn=lambda: torch.rand(2, 5, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['NLLLossNd'](i, t, ignore_index=1), desc='2d_ignore_index', check_bfloat16=True, ), dict( module_name='NLLLoss', input_size=(2, 3, 5, 5, 2, 2), target_fn=lambda: torch.rand(2, 5, 5, 2, 2).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['NLLLossNd'](i, t, reduction=get_reduction(m)), check_sum_reduction=True, desc='higher_dim', check_bfloat16=True, ), dict( module_name='NLLLoss', input_size=(2, 3, 5), target_fn=lambda: torch.rand(2, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['NLLLossNd'](i, t, reduction=get_reduction(m)), check_sum_reduction=True, desc='dim_is_3', check_bfloat16=True, ), dict( module_name='CrossEntropyLoss', input_size=(2, 3, 5, 5), target_fn=lambda: torch.rand(2, 5, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['CrossEntropyLoss'](i, t, reduction=get_reduction(m)), check_sum_reduction=True, desc='2d', check_bfloat16=False, ), dict( module_name='CrossEntropyLoss', constructor_args_fn=lambda: (torch.rand(3),), cpp_constructor_args='torch::nn::CrossEntropyLossOptions().weight(torch::rand(3))', input_size=(2, 3, 5, 5), target=torch.rand(2, 5, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['CrossEntropyLoss'](i, t, weight=get_weight(m)), desc='2d_weights', check_bfloat16=False, ), dict( module_name='CrossEntropyLoss', constructor_args=(None, None, 1), cpp_constructor_args='torch::nn::CrossEntropyLossOptions().weight({}).ignore_index(1)', input_size=(2, 3, 5, 5), target_fn=lambda: torch.rand(2, 5, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['CrossEntropyLoss'](i, t, ignore_index=1), desc='2d_ignore_index', check_bfloat16=False, ), dict( module_name='CrossEntropyLoss', input_size=(2, 3, 5, 5, 2, 2), target_fn=lambda: torch.rand(2, 5, 5, 2, 2).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['CrossEntropyLoss'](i, t, reduction=get_reduction(m)), check_sum_reduction=True, desc='higher_dim', check_bfloat16=False, ), dict( module_name='CrossEntropyLoss', input_size=(2, 3, 5), target_fn=lambda: torch.rand(2, 5).mul(3).floor().long(), reference_fn=lambda i, t, m: loss_reference_fns['CrossEntropyLoss'](i, t, reduction=get_reduction(m)), check_sum_reduction=True, desc='dim_is_3', check_bfloat16=False, ), dict( module_name='PoissonNLLLoss', # Default is log_input=True, full=False input_size=(2, 3, 4, 5), target_fn=lambda: torch.randn(2, 3, 4, 5).floor_().abs_(), reference_fn=lambda i, t, _: (i.exp() - t.mul(i)).mean(), desc='no_full_loss', ), dict( module_name='PoissonNLLLoss', constructor_args=(False, False), # log_input=False, full=False cpp_constructor_args='torch::nn::PoissonNLLLossOptions().log_input(false).full(false)', input_fn=lambda: torch.randn(2, 3, 4, 5).abs_().add_(0.001), target_fn=lambda: torch.randn(2, 3, 4, 5).floor_().abs_(), reference_fn=lambda i, t, _: (i - t.mul((i + 1e-8).log())).mean(), desc='no_full_loss_no_log_input', ), dict( module_name='PoissonNLLLoss', constructor_args=(True, True), # log_input=True, full=True cpp_constructor_args='torch::nn::PoissonNLLLossOptions().log_input(true).full(true)', input_size=(2, 3, 4, 5), target_fn=lambda: torch.randn(2, 3, 4, 5).floor_().abs_(), reference_fn=lambda i, t, _: (i.exp() - t.mul(i) + (t.mul(t.log()) - t + 0.5 * (2. * pi * t).log()).masked_fill(t <= 1, 0)).mean(), desc='full_loss', ), dict( module_name='PoissonNLLLoss', constructor_args=(False, True), # log_input=False, full=True cpp_constructor_args='torch::nn::PoissonNLLLossOptions().log_input(false).full(true)', input_fn=lambda: torch.randn(2, 3, 4, 5).abs_().add_(0.001), target_fn=lambda: torch.randn(2, 3, 4, 5).floor_().abs_(), reference_fn=lambda i, t, _: ( i - t.mul((i + 1e-8).log()) + (t.mul(t.log()) - t + 0.5 * (2. * pi * t).log()).masked_fill(t <= 1, 0) ).mean(), desc='full_loss_no_log_input', ), dict( module_name='L1Loss', input_size=(), target_fn=lambda: torch.randn((), requires_grad=True), reference_fn=lambda i, t, _: 1. / i.numel() * (i - t).abs().sum(), desc='scalar', check_complex=True, ), dict( module_name='KLDivLoss', input_fn=lambda: torch.rand(()).log(), target_fn=lambda: torch.rand(()), reference_fn=lambda i, t, m: kldivloss_reference(i, t, get_reduction(m)), check_sum_reduction=True, desc='scalar', ), dict( module_name='KLDivLoss', input_fn=lambda: torch.rand(()).log(), target_fn=lambda: torch.rand(()), reference_fn=lambda i, t, m: kldivloss_log_target_reference(i, t.log(), get_reduction(m)), check_sum_reduction=True, desc='scalar_log_target', ), dict( module_name='MSELoss', input_size=(), target_fn=lambda: torch.randn((), requires_grad=True), reference_fn=lambda i, t, m: ((i - t).abs().pow(2).sum() / (i.numel() if get_reduction(m) == 'mean' else 1)), check_sum_reduction=True, desc='scalar', check_bfloat16=True, ), dict( module_name='MSELoss', input_fn=lambda: torch.ones(5, 68, 64, 64, dtype=torch.float) / 10, target_fn=lambda: torch.zeros(5, 68, 64, 64, dtype=torch.float), reference_fn=lambda i, t, m: ((i - t).abs().pow(2).sum() / (i.numel() if get_reduction(m) == 'mean' else 1)), check_forward_only=True, desc='prec', check_bfloat16=True, ), dict( module_name='BCELoss', constructor_args_fn=lambda: (torch.rand(()),), cpp_constructor_args='torch::nn::BCELossOptions().weight(torch::rand({}))', input_fn=lambda: torch.rand(()).clamp_(1e-2, 1 - 1e-2), target_fn=lambda: torch.rand(()).gt(0).double(), reference_fn=lambda i, t, m: -((t * i.log() + (1 - t) * (1 - i).log()) * get_weight(m)).sum() / (i.numel() if get_reduction(m) == 'mean' else 1), desc='scalar_weights', check_bfloat16=True, ), dict( module_name='HingeEmbeddingLoss', constructor_args=(0.5,), cpp_constructor_args='torch::nn::HingeEmbeddingLossOptions().margin(0.5)', input_size=(), target_fn=lambda: torch.randn(()).gt(0).double().mul_(2).sub(1), desc='scalar_margin', check_sum_reduction=True, ), dict( module_name='SmoothL1Loss', input_size=(), target_fn=lambda: torch.randn((), requires_grad=True), check_sum_reduction=True, reference_fn=lambda i, t, m, b=1.0: smoothl1loss_reference(i, t, reduction=get_reduction(m), beta=b), desc='scalar', ), dict( module_name='MultiLabelSoftMarginLoss', constructor_args=(torch.rand(10),), cpp_constructor_args='torch::nn::MultiLabelSoftMarginLossOptions().weight(torch::rand(10))', input_fn=lambda: torch.randn(5, 10), target_fn=lambda: torch.rand(5, 10).mul(2).floor(), reference_fn=lambda i, t, m: -((t * i.sigmoid().log() + (1 - t) * (-i).sigmoid().log()) * get_weight(m)).sum() / (i.numel() if get_reduction(m) == 'mean' else i.size(1) if get_reduction(m) == 'sum' else 1), desc='weights', check_sum_reduction=True, check_gradgrad=False, ), dict( module_name='CTCLoss', constructor_args=(14,), # blank=14 extra_args=([50, 50, 50], [30, 25, 20]), # input_lengths, target_lengths input_fn=lambda: torch.randn(50, 3, 15).log_softmax(2), target_fn=lambda: torch.randint(0, 14, (3, 30), dtype=torch.long), reference_fn=lambda i, t, il, tl, m: ctcloss_reference(i, t, il, tl, blank=14, reduction=get_reduction(m)), desc='lengths_intlists', check_forward_only=True, check_sum_reduction=True, check_gradgrad=False, check_half=False, # `CTCLoss` in C++ frontend doesn't accept integer list for `input_lengths` or `target_lengths` test_cpp_api_parity=False, check_jit=False, ), dict( module_name='CTCLoss', constructor_args=(14,), # blank=14 cpp_constructor_args='torch::nn::CTCLossOptions().blank(14)', extra_args=(torch.tensor([50, 50, 50]), torch.tensor([30, 25, 20])), # input_lengths, target_lengths input_fn=lambda: torch.randn(50, 3, 15).log_softmax(2), target_fn=lambda: torch.randint(0, 14, (3, 30), dtype=torch.long), reference_fn=lambda i, t, il, tl, m: ctcloss_reference(i, t, il, tl, blank=14, reduction=get_reduction(m)), desc='lengths_tensors', check_forward_only=True, check_sum_reduction=True, check_gradgrad=False, check_half=False, ), # Test is flaky # See https://github.com/pytorch/pytorch/issues/29380. # dict( # module_name='CTCLoss', # desc='1d_target', # constructor_args=(14,), # blank=14 # extra_args=([50, 50, 50], [30, 25, 20]), # input_lengths, target_lengths # input_fn=lambda: torch.randn(50, 3, 15).log_softmax(2), # target_fn=lambda: torch.randint(0, 14, (3, 30), dtype=torch.long), # reference_fn=lambda i, t, il, tl, m: # ctcloss_reference(i, t, il, tl, blank=14, reduction=get_reduction(m)), # check_sum_reduction=True, # check_gradgrad=False, # check_half=False, # ), dict( module_name='CTCLoss', desc='2d_int_target_lengths_intlists', constructor_args=(0,), # blank=0 extra_args=([50, 50, 50], [30, 25, 20]), # input_lengths, target_lengths input_fn=lambda: torch.randn(50, 3, 15).log_softmax(2), target_fn=lambda: torch.randint(1, 15, (3, 30), dtype=torch.int), reference_fn=lambda i, t, il, tl, m: ctcloss_reference(i, t, il, tl, blank=0, reduction=get_reduction(m)), check_forward_only=True, check_sum_reduction=True, check_gradgrad=False, check_half=False, # `CTCLoss` in C++ frontend doesn't accept integer list for `input_lengths` or `target_lengths` test_cpp_api_parity=False, check_jit=False, ), dict( module_name='CTCLoss', desc='2d_int_target_lengths_tensors', constructor_args=(0,), # blank=0 cpp_constructor_args='torch::nn::CTCLossOptions().blank(0)', extra_args=(torch.tensor([50, 50, 50]), torch.tensor([30, 25, 20])), # input_lengths, target_lengths input_fn=lambda: torch.randn(50, 3, 15).log_softmax(2), target_fn=lambda: torch.randint(1, 15, (3, 30), dtype=torch.int), reference_fn=lambda i, t, il, tl, m: ctcloss_reference(i, t, il, tl, blank=0, reduction=get_reduction(m)), check_forward_only=True, check_sum_reduction=True, check_gradgrad=False, check_half=False, ), dict( module_name='CTCLoss', desc='2d_lengths_tensors', constructor_args=(0,), # blank=0 cpp_constructor_args='torch::nn::CTCLossOptions().blank(0)', extra_args=(torch.tensor([50, 50, 50]), torch.tensor([30, 25, 20])), # input_lengths, target_lengths input_fn=lambda: torch.randn(50, 3, 15).log_softmax(2), target_fn=lambda: torch.randint(1, 15, (3, 30), dtype=torch.int), reference_fn=lambda i, t, il, tl, m: ctcloss_reference(i, t, il, tl, blank=0, reduction=get_reduction(m)), check_forward_only=True, check_sum_reduction=True, check_gradgrad=False, check_half=False, ), ] def single_batch_reference_criterion_fn(*args): """Reference function for criterion supporting no batch dimensions. The criterion is passed the input and target in batched form with a single item. The output is squeezed to compare with the no-batch input. """ criterion = args[-1] single_batch_input_args = [input.unsqueeze(0) for input in args[:-1]] output = criterion(*single_batch_input_args) reduction = get_reduction(criterion) if reduction == 'none': return output.squeeze(0) # reduction is 'sum' or 'mean' which results in a scalar return output # Check that regression criterion work with no batch dimensions regression_criterion_no_batch = [ 'L1Loss', 'MSELoss', 'PoissonNLLLoss', 'KLDivLoss', 'HuberLoss', 'SmoothL1Loss' ] reductions = ['none', 'mean', 'sum'] for regression_criterion, reduction in product(regression_criterion_no_batch, reductions): regression_test_info = dict( fullname="{}_no_batch_dim_{}".format(regression_criterion, reduction), constructor=lambda *args: getattr(nn, regression_criterion)(reduction=reduction), input_size=(3, ), target_fn=lambda: torch.randn(3), reference_fn=single_batch_reference_criterion_fn, test_cpp_api_parity=False, ) criterion_tests.append(regression_test_info) class NNTestCase(TestCase): # _forward is defined in classes inheriting from NNTestCase @abstractmethod def _forward(self, *args, **kwargs): raise NotImplementedError @abstractmethod def _get_parameters(self, module: nn.Module) -> Tuple[List[nn.Parameter], List[nn.Parameter]]: raise NotImplementedError @abstractmethod def _zero_grad_parameters(self, module: nn.Module) -> None: raise NotImplementedError @abstractmethod def _backward(self, module: nn.Module, input: _TensorOrTensors, output: torch.Tensor, grad_output: Union[torch.Tensor, Sequence[torch.Tensor]], create_graph: bool = False): raise NotImplementedError def _jacobian(self, input, num_out): if isinstance(input, tuple): return tuple(self._jacobian(elem, num_out) for elem in input) elif isinstance(input, list): return [self._jacobian(elem, num_out) for elem in input] else: return torch.zeros(input.nelement(), num_out) def _flatten_tensors(self, x): if isinstance(x, torch.Tensor): if x.is_sparse: return x.to_dense().view(-1) else: return x.view(-1) else: return tuple(self._flatten_tensors(a) for a in x) def _zero_grad_input(self, input): if isinstance(input, torch.Tensor): if input.requires_grad and input.grad is not None: input.grad.zero_() input.grad.detach_() else: for i in input: self._zero_grad_input(i) def _analytical_jacobian(self, module, input: _TensorOrTensors, jacobian_input=True, jacobian_parameters=True): output = self._forward(module, input) output_size = output.nelement() if jacobian_input: jacobian_inp = self._jacobian(input, output_size) flat_jacobian_input = list(_iter_tensors(jacobian_inp)) if jacobian_parameters: num_param = sum(p.numel() for p in self._get_parameters(module)[0]) jacobian_param = torch.zeros(num_param, output_size) for i in range(output_size): param, d_param = self._get_parameters(module) # make non grad zeros d_param = [torch.zeros_like(p) if d is None else d for (p, d) in zip(param, d_param)] d_out = torch.zeros_like(output) flat_d_out = d_out.view(-1) flat_d_out[i] = 1 if jacobian_parameters: self._zero_grad_parameters(module) # Tensors will accumulate gradient from multiple steps if jacobian_input: self._zero_grad_input(input) d_input = self._backward(module, input, output, d_out) if jacobian_input: for jacobian_x, d_x in zip(flat_jacobian_input, _iter_tensors(d_input)): jacobian_x[:, i] = d_x.contiguous().view(-1) if jacobian_parameters: jacobian_param[:, i] = torch.cat(self._flatten_tensors(d_param), 0) res: Tuple[torch.Tensor, ...] = tuple() if jacobian_input: res += jacobian_inp, if jacobian_parameters: res += jacobian_param, return res def _numerical_jacobian(self, module, input: _TensorOrTensors, jacobian_input=True, jacobian_parameters=True): def fw(*input): return self._forward(module, input).detach() res: Tuple[torch.Tensor, ...] = tuple() if jacobian_input: res += _get_numerical_jacobian(fw, input, eps=1e-6), if jacobian_parameters: param, _ = self._get_parameters(module) to_cat = [] for p in param: jacobian = _get_numerical_jacobian(fw, input, target=p, eps=1e-6) # get_numerical_jacobian returns a list of tuples but we require a tensor to_cat.append(jacobian[0][0]) res += (torch.cat(to_cat, 0),) return res def check_jacobian(self, module, input: _TensorOrTensors, jacobian_input=True): jacobian_parameters = bool(self._get_parameters(module)[0]) analytical = self._analytical_jacobian(module, input, jacobian_input, jacobian_parameters) numerical = self._numerical_jacobian(module, input, jacobian_input, jacobian_parameters) analytical_t = list(_iter_tensors(analytical)) numerical_t = list(_iter_tensors(numerical)) differences = [] for a, n in zip(analytical_t, numerical_t): if a.numel() != 0: differences.append(a.add(n, alpha=-1).abs().max()) # TODO: compare structure (ensure analytic jacobian has correct shape) if len(differences) > 0: self.assertLessEqual(max(differences), PRECISION) # type: ignore[type-var] class TestBase(object): _required_arg_names = {'constructor_args', 'input', 'extra_args'} def __init__(self, constructor, desc='', reference_fn=None, fullname=None, **kwargs): self.desc = desc self.fullname = fullname self.constructor = constructor self.reference_fn = reference_fn for name in self._required_arg_names: if name not in kwargs and name + '_fn' not in kwargs and name + '_size' not in kwargs: if name in {'constructor_args', 'extra_args'}: kwargs[name] = tuple() else: raise ValueError("{}: Specify {} by a value, a function to generate it, or it's size!" .format(self.get_name(), name)) self._extra_kwargs = kwargs self._arg_cache = {} def get_name(self): if self.fullname is not None: return 'test_' + self.fullname test_name = 'test_' + self.constructor.__name__ if self.desc: test_name += '_' + self.desc return test_name def _unpack(self, value): if isinstance(value, torch.Tensor): return value elif is_iterable(value): return type(value)(self._unpack(v) for v in value) else: return value @property def constructor_args(self): return self._get_arg('constructor_args', True) @property def extra_args(self): return self._get_arg('extra_args', True) def _get_arg(self, name, unpack): assert name in self._required_arg_names if name not in self._arg_cache: fn_name = name + '_fn' size_name = name + '_size' if name in self._extra_kwargs: self._arg_cache[name] = self._extra_kwargs[name] elif fn_name in self._extra_kwargs: self._arg_cache[name] = self._extra_kwargs[fn_name]() else: assert size_name in self._extra_kwargs, \ "Missing `{}`, `{}` or `{}` for {}".format(name, size_name, fn_name, self.get_name()) def map_tensor_sizes(sizes): if isinstance(sizes, list): return [map_tensor_sizes(s) for s in sizes] elif isinstance(sizes, torch.Tensor): return sizes.double() else: return torch.randn(sizes) self._arg_cache[name] = map_tensor_sizes(self._extra_kwargs[size_name]) return self._unpack(self._arg_cache[name]) if unpack else self._arg_cache[name] def _get_input(self, unpack=True): return self._get_arg('input', unpack) def __call__(self, test_case): raise NotImplementedError class ModuleTest(TestBase): @abstractmethod def _do_test(self, test_case: Any, module: nn.Module, input: Any) -> Any: raise NotImplementedError def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.jacobian_input = kwargs.get('jacobian_input', True) self.should_test_cuda = kwargs.get('test_cuda', True) self.should_test_pickle = kwargs.get('pickle', True) self.check_gradgrad = kwargs.get('check_gradgrad', True) self.FIXME_no_cuda_gradgrad_comparison = \ kwargs.get('FIXME_no_cuda_gradgrad_comparison', False) self.precision = kwargs.get('precision', 2e-4) self.check_forward_only = kwargs.get('check_forward_only', False) def __call__(self, test_case): module = self.constructor(*self.constructor_args) input = self._get_input() if self.reference_fn is not None: out = test_case._forward(module, input) ref_input = deepcopy(input) ref_module = deepcopy(module) expected_out = self.reference_fn(ref_input, test_case._get_parameters(module)[0], ref_module) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(out, expected_out) if self.check_forward_only: return self.test_noncontig(test_case, module, input) if self.should_test_pickle: # TODO: do this with in-memory files as soon as torch.save will support it with tempfile.TemporaryFile() as f: test_case._forward(module, input) torch.save(module, f) f.seek(0) module_copy = torch.load(f) test_case.assertEqual(test_case._forward(module, input), test_case._forward(module_copy, input)) self._do_test(test_case, module, input) def noncontiguize(self, obj): if isinstance(obj, list): return [self.noncontiguize(o) for o in obj] elif isinstance(obj, tuple): return tuple(self.noncontiguize(o) for o in obj) tensor = obj ndim = tensor.dim() # Always making only the last dimension noncontiguous is easy to hide # bugs because .view(-1) will still work. So try to find a dim with size # > 1 and make that non-contiguous, i.e., stack + select on the # dimension directly after that. dim = ndim for d in range(ndim): if tensor.size(d) > 1: dim = d + 1 break noncontig = torch.stack([torch.empty_like(tensor), tensor], dim).select(dim, 1).detach() assert noncontig.numel() == 1 or noncontig.numel() == 0 or not noncontig.is_contiguous() noncontig.requires_grad = tensor.requires_grad return noncontig def test_noncontig(self, test_case, module, input): # check no scalars, can't make non-contig if isinstance(input, torch.Tensor) and input.dim() == 0: return if any(i.dim() == 0 for i in input if isinstance(i, torch.Tensor)): return test_case._zero_grad_parameters(module) test_case._zero_grad_input(input) with freeze_rng_state(): output = test_case._forward(module, input) grad_output = output.new(output.shape).normal_() output = output.clone() d_input = deepcopy(test_case._backward(module, input, output, grad_output)) d_param = deepcopy(test_case._get_parameters(module)[1]) nc_input = self.noncontiguize(input) nc_grad_output = self.noncontiguize(grad_output) for contig_i, contig_g in product((True, False), repeat=2): i = input if contig_i else nc_input # Some ops, e.g., nn.Flatten, return gradient that shares # storage with the grad_output. Hence we copy here. go = deepcopy(grad_output if contig_g else nc_grad_output) test_case._zero_grad_parameters(module) test_case._zero_grad_input(i) with freeze_rng_state(): out = test_case._forward(module, i) grad = test_case._backward(module, i, out, go) test_case.assertEqual(out, output) test_case.assertEqual(grad, d_input, atol=1e-4, rtol=0) test_case.assertEqual(test_case._get_parameters(module)[1], d_param) def test_cuda(self, test_case): if not TEST_CUDA or not self.should_test_cuda: raise unittest.SkipTest('Excluded from CUDA tests') cpu_input = self._get_input() type_map = {torch.double: torch.float} cpu_input_tuple = cpu_input if isinstance(cpu_input, tuple) else (cpu_input,) gpu_input_tuple = to_gpu(cpu_input_tuple, type_map=type_map) cpu_module = self.constructor(*self.constructor_args) gpu_module = self.constructor(*self.constructor_args).float().cuda() cpu_param = test_case._get_parameters(cpu_module) gpu_param = test_case._get_parameters(gpu_module) for cpu_p, gpu_p in zip(cpu_param[0], gpu_param[0]): gpu_p.data.copy_(cpu_p) test_case._zero_grad_input(cpu_input_tuple) test_case._zero_grad_input(gpu_input_tuple) test_case._zero_grad_parameters(cpu_module) test_case._zero_grad_parameters(gpu_module) cpu_output = test_case._forward(cpu_module, cpu_input_tuple) gpu_output = test_case._forward(gpu_module, gpu_input_tuple) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(cpu_output, gpu_output, atol=self.precision, rtol=0) # Run backwards on CPU and GPU and compare results for _ in range(5): cpu_gradOutput = cpu_output.clone().normal_() gpu_gradOutput = cpu_gradOutput.type_as(gpu_output) cpu_gradInput = test_case._backward(cpu_module, cpu_input_tuple, cpu_output, cpu_gradOutput) gpu_gradInput = test_case._backward(gpu_module, gpu_input_tuple, gpu_output, gpu_gradOutput) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(cpu_gradInput, gpu_gradInput, atol=self.precision, rtol=0) for cpu_d_p, gpu_d_p in zip(cpu_param[1], gpu_param[1]): test_case.assertEqual(cpu_d_p, gpu_d_p, atol=self.precision, rtol=0) # Run double-backwards on CPU and GPU and compare results if self.check_gradgrad and not self.FIXME_no_cuda_gradgrad_comparison: cpu_output = cpu_module(*cpu_input_tuple) gpu_output = gpu_module(*gpu_input_tuple) cpu_gradOutput = torch.randn_like(cpu_output, requires_grad=True) gpu_gradOutput = cpu_gradOutput.type_as(gpu_output).detach() gpu_gradOutput.requires_grad = True cpu_gradInputs = torch.autograd.grad( cpu_output, cpu_input_tuple + tuple(cpu_module.parameters()), cpu_gradOutput, create_graph=True) gpu_gradInputs = torch.autograd.grad( gpu_output, gpu_input_tuple + tuple(gpu_module.parameters()), gpu_gradOutput, create_graph=True) for cpu_d_i, gpu_d_i in zip(cpu_gradInputs, gpu_gradInputs): # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(cpu_d_i, gpu_d_i, atol=self.precision, rtol=0) # We mix output into the second backwards computation so that # torch.autograd.grad doesn't complain that some inputs # are unreachable (which can happen if you differentiate # only on the gradient. cpu_gg = torch.autograd.grad( cpu_output.sum() + sum(x.sum() for x in cpu_gradInputs), cpu_input_tuple + (cpu_gradOutput,) + tuple(cpu_module.parameters()), retain_graph=True) gpu_gg = torch.autograd.grad( gpu_output.sum() + sum(x.sum() for x in gpu_gradInputs), gpu_input_tuple + (gpu_gradOutput,) + tuple(gpu_module.parameters()), retain_graph=True) # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(cpu_gradInput, gpu_gradInput, atol=self.precision, rtol=0) for cpu_d_p, gpu_d_p in zip(cpu_gg, gpu_gg): # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(cpu_d_p, gpu_d_p, atol=self.precision, rtol=0) self.test_noncontig(test_case, gpu_module, gpu_input_tuple) class InputVariableMixin(object): def _get_input(self): input = TestBase._get_input(self, False) # type: ignore[arg-type] def map_variables(i): if isinstance(i, torch.Tensor): if i.is_floating_point() or i.is_complex(): i.requires_grad = True return i else: return type(i)(map_variables(elem) for elem in i) return map_variables(input) class NewModuleTest(InputVariableMixin, ModuleTest): # type: ignore[misc] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.cudnn = kwargs.get('cudnn', False) self.check_inplace = kwargs.get('check_inplace', False) self.check_gradgrad = kwargs.get('check_gradgrad', True) self.skip_double = kwargs.get('skip_double', False) self.skip_half = kwargs.get('skip_half', False) self.with_tf32 = kwargs.get('with_tf32', False) self.tf32_precision = kwargs.get('tf32_precision', 0.001) self.test_cpu = kwargs.get('test_cpu', True) self.has_sparse_gradients = kwargs.get('has_sparse_gradients', False) self.check_batched_grad = kwargs.get('check_batched_grad', True) self.gradcheck_fast_mode = kwargs.get('gradcheck_fast_mode', None) def _check_gradients(self, test_case, module, input_tuple): params = tuple(x for x in module.parameters()) num_inputs = len(input_tuple) def fn_to_gradcheck(*inputs_and_params, **kwargs): assert not kwargs return test_case._forward(module, inputs_and_params[:num_inputs]) # gradcheck doesn't support operators that take in dense inputs but # return sparse parameters. This only happens in the case of nn.Embedding # and nn.EmbeddingBag. Instead, we call `self.check_jacobian`, which # is a slightly different version of gradcheck that can handle this. if self.has_sparse_gradients: assert num_inputs == 1 test_input_jacobian = torch.is_floating_point(input_tuple[0]) test_case.check_jacobian(module, input_tuple[0], test_input_jacobian) else: test_case.assertTrue(gradcheck(fn_to_gradcheck, input_tuple + params, check_batched_grad=self.check_batched_grad, fast_mode=self.gradcheck_fast_mode)) if self.check_gradgrad: test_case.assertTrue(gradgradcheck(fn_to_gradcheck, input_tuple + params, check_batched_grad=self.check_batched_grad, fast_mode=self.gradcheck_fast_mode)) def _do_test(self, test_case, module, input): num_threads = torch.get_num_threads() torch.set_num_threads(1) input_tuple = input if isinstance(input, tuple) else (input,) self._check_gradients(test_case, module, input_tuple) # check if module can be printed module.__repr__() if self.check_inplace: # check if the inplace variant of the module gives the same result # as the out-of-place # check_inplace doesn't support multiple input tensors, since we don't have any modules # that modify the inputs in-place and that accept more than one input assert len(input_tuple) == 1 input = input_tuple[0] module_ip = self.constructor(*self.constructor_args, inplace=True) input_version = input._version with freeze_rng_state(): output = module(input) test_case.assertEqual(input._version, input_version) input_ip = deepcopy(input) input_ip_clone = input_ip.clone() with freeze_rng_state(): output_ip = module_ip(input_ip_clone) test_case.assertNotEqual(input_ip_clone._version, input_version) test_case.assertEqual(output, output_ip) grad = output.data.clone().normal_() if input.grad is not None: with torch.no_grad(): input.grad.zero_() if input_ip.grad is not None: with torch.no_grad(): input_ip.grad.zero_() output.backward(grad) output_ip.backward(grad) test_case.assertEqual(input.grad, input_ip.grad) def assert_module_parameters_are(tensor_type, device_id=None): for p in module.parameters(): test_case.assertIsInstance(p, tensor_type) if device_id is not None: test_case.assertEqual(p.get_device(), device_id) if all(isinstance(t, torch.LongTensor) for t in input_tuple) and TEST_CUDA: # check that cuda() moves module parameters to correct GPU device, # and that float() casts parameters correctly input_tuple = tuple(t.cuda() for t in input_tuple) module.float().cuda() module(*input_tuple) assert_module_parameters_are(torch.cuda.FloatTensor, 0) # type: ignore[attr-defined] if torch.cuda.device_count() > 1: input_tuple = tuple(t.cuda(1) for t in input_tuple) module.cuda(1) with torch.cuda.device(1): module(*input_tuple) assert_module_parameters_are(torch.cuda.FloatTensor, 1) # type: ignore[attr-defined] else: # check that float()/double() casters work correctly def to_type(tensor, real, complex): if tensor.is_complex(): return tensor.to(complex) elif tensor.is_floating_point(): return tensor.to(real) else: return tensor def to_half(x): # TODO: torch.complex32 when properly supported return to_type(x, torch.float16, None) def to_single(x): return to_type(x, torch.float32, torch.complex64) def to_double(x): return to_type(x, torch.float64, torch.complex128) # to float input_tuple = tuple(to_single(t) for t in input_tuple) module.float() module(*input_tuple) assert_module_parameters_are(torch.FloatTensor) # and back to double input_tuple = tuple(to_double(t) for t in input_tuple) module.double() module(*input_tuple) assert_module_parameters_are(torch.DoubleTensor) if TEST_CUDA and self.should_test_cuda: # check that cuda() moves module parameters to correct GPU device, # and that float() casts parameters correctly # to GPU0 input_tuple = tuple(to_single(t).cuda() for t in input_tuple) module.float().cuda() module(*input_tuple) assert_module_parameters_are(torch.cuda.FloatTensor, 0) # type: ignore[attr-defined] # to CPU input_tuple = tuple(t.cpu() for t in input_tuple) module.cpu() module(*input_tuple) assert_module_parameters_are(torch.FloatTensor) # back to GPU0 input_tuple = tuple(t.cuda() for t in input_tuple) module.cuda() module(*input_tuple) assert_module_parameters_are(torch.cuda.FloatTensor, 0) # type: ignore[attr-defined] # test that forwards of module runs correctly without cuDNN if self.cudnn: with torch.backends.cudnn.flags(enabled=False): module(*input_tuple) assert_module_parameters_are(torch.cuda.FloatTensor, 0) # type: ignore[attr-defined] if torch.cuda.device_count() >= 2: # test cross-GPU transfer works # to GPU1 input_tuple = tuple(t.cuda(1) for t in input_tuple) module.cuda(1) with torch.cuda.device(1): module(*input_tuple) assert_module_parameters_are(torch.cuda.FloatTensor, 1) # type: ignore[attr-defined] if not self.skip_double: # test double() input_tuple = tuple(to_double(t).cuda() for t in input_tuple) module.double().cuda() module(*input_tuple) assert_module_parameters_are(torch.cuda.DoubleTensor, 0) # type: ignore[attr-defined] # test half() if not self.skip_half: input_tuple = tuple(to_half(t).cuda() for t in input_tuple) module.half().cuda() module(*input_tuple) assert_module_parameters_are(torch.cuda.HalfTensor, 0) # type: ignore[attr-defined] torch.set_num_threads(num_threads) def _get_target(self): return self._get_arg('target', False) @property def constructor_args(self): return self._get_arg('constructor_args', False) class CriterionTest(InputVariableMixin, TestBase): # type: ignore[misc] # TODO: check that criterions don't ignore grad_output _required_arg_names = TestBase._required_arg_names.union({'target'}) def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.should_test_cuda = kwargs.get('test_cuda', True) self.check_forward_only = kwargs.get('check_forward_only', False) self.check_gradgrad = kwargs.get('check_gradgrad', True) self.check_half = kwargs.get('check_half', True) self.check_bfloat16 = kwargs.get('check_bfloat16', False) self.check_complex = kwargs.get('check_complex', False) self.test_cpu = kwargs.get('test_cpu', True) self.with_tf32 = kwargs.get('with_tf32', True) self.tf32_precision = kwargs.get('tf32_precision', 0.001) self.check_batched_grad = kwargs.get('check_batched_grad', True) def __call__(self, test_case): module = self.constructor(*self.constructor_args) input = self._get_input() # Check that these methods don't raise errors module.__repr__() str(module) target = self._get_target() if self.reference_fn is not None: out = test_case._forward_criterion(module, input, target, extra_args=self.extra_args) ref_args = (deepcopy(input), deepcopy(target)) + self.extra_args + (module,) expected_out = self.reference_fn(*ref_args) test_case.assertEqual(out, expected_out) if self.check_forward_only: return params = tuple(x for x in module.parameters()) if not isinstance(input, tuple): inputs = (input,) + params + (target,) def apply_fn(input, target, *params): return module(input, target) else: inputs = input + params + (target,) def apply_fn(input1, input2, target, *params): # type: ignore[misc] return module(input1, input2, target) gradcheck(apply_fn, inputs, check_batched_grad=self.check_batched_grad) if self.check_gradgrad: gradgradcheck(apply_fn, inputs, check_batched_grad=self.check_batched_grad) def test_cuda(self, test_case, dtype, extra_args=None): def convert_dtype(obj, dtype, requires_grad=False): if isinstance(obj, torch.Tensor): return obj.detach().to(dtype=dtype).requires_grad_(requires_grad) elif isinstance(obj, tuple): return tuple(convert_dtype(o, dtype, requires_grad) for o in obj) else: return obj if not TEST_CUDA or not self.should_test_cuda: raise unittest.SkipTest('Excluded from CUDA tests') cpu_input = self._get_input() cpu_target = self._get_target() cpu_module = self.constructor(*self.constructor_args) gpu_module = self.constructor(*self.constructor_args) # Convert input, target and module parameters to dtype cpu_input = convert_dtype(cpu_input, dtype, True) if cpu_target.is_floating_point() or cpu_target.is_complex(): cpu_target = convert_dtype(cpu_target, dtype) cpu_module.type(dtype) gpu_module.type(dtype) # GPU setup gpu_input = to_gpu(cpu_input) gpu_target = to_gpu(cpu_target) gpu_module.cuda() # torch.HalfTensor doesn't support most operations, converting back to default if dtype in {torch.half, torch.bfloat16}: cpu_input = self._get_input() cpu_target = self._get_target() # Loss modules with weights require consistent input/module weight types cpu_module = self.constructor(*self.constructor_args) cpu_output = test_case._forward_criterion(cpu_module, cpu_input, cpu_target, extra_args=extra_args) gpu_output = test_case._forward_criterion(gpu_module, gpu_input, gpu_target, extra_args=extra_args) # dtype used to be able to be None, so set precision in this way instead of a precision map # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(cpu_output, gpu_output, atol=1e-1 if dtype in {torch.half, torch.bfloat16} else 4e-4, rtol=0) cpu_gradInput = test_case._backward_criterion(cpu_module, cpu_input, cpu_output, cpu_target, extra_args=extra_args) gpu_gradInput = test_case._backward_criterion(gpu_module, gpu_input, gpu_output, gpu_target, extra_args=extra_args) # dtype used to be able to be None, so set precision in this way instead of a precision map # TODO(#38095): Replace assertEqualIgnoreType. See issue #38095 test_case.assertEqualIgnoreType(cpu_gradInput, gpu_gradInput, atol=1e-1 if dtype in {torch.half, torch.bfloat16} else 4e-4, rtol=0) def _get_target(self): return self._get_arg('target', False) @property def constructor_args(self): return self._get_arg('constructor_args', False) @property def extra_args(self): return self._get_arg('extra_args', False)

39.907892

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0.588211

28,399

227,036

4.473327

0.035846

0.061163

0.03229

0.049064

0.827075

0.788221

0.759355

0.725988

0.690274

0.65175

0

0.041203

0.263681

227,036

5,688

133

39.914909

0.718725

0.053414

0

0.68547

0

0.01809

0.221244

0.127193

0.000195

0

0.000527

0.008559

1

0.026065

false

0

0.004085

0.065357

0

null

0

1

0

1

0

null

0

5

0a6a974a8615f792f525f72d49e7171fa68fa8ff

207

py

Python

conveyancer_ui/views/utils.py

LandRegistry/digital-street-conveyancer-ui

54667ffe25dc816591eeb1c347b42f9beee21b03

[ "MIT" ]

null

conveyancer_ui/views/utils.py

LandRegistry/digital-street-conveyancer-ui

54667ffe25dc816591eeb1c347b42f9beee21b03

[ "MIT" ]

null

conveyancer_ui/views/utils.py

LandRegistry/digital-street-conveyancer-ui

54667ffe25dc816591eeb1c347b42f9beee21b03

[ "MIT" ]

4

2019-04-26T06:37:56.000Z

2021-04-11T05:22:23.000Z

def suffix(d): return 'th' if 11 <= d <= 13 else {1: 'st', 2: 'nd', 3: 'rd'}.get(d % 10, 'th') def custom_strftime(format, t): return t.strftime(format).replace('{S}', str(t.day) + suffix(t.day))

25.875

83

0.565217

37

207

3.135135

0.675676

0.241379

0

0.053571

0.188406

207

7

84

29.571429

0.636905

0

0.063107

0

1

0.5

false

0

0.5

1

0

null

1

0

1

0

null

0

1

0

1

0

5

0a6c10ecbc8790e8fcece489276ec037cfe2cca8

116

py

Python

example-generation.py

szymanskir/TAIO

0a818cd2d9073d5cca322fcdc2a11afe425e73ef

[ "MIT" ]

null

example-generation.py

szymanskir/TAIO

0a818cd2d9073d5cca322fcdc2a11afe425e73ef

[ "MIT" ]

null

example-generation.py

szymanskir/TAIO

0a818cd2d9073d5cca322fcdc2a11afe425e73ef

[ "MIT" ]

null

import networkx as nx from src.utils import save_graph G1 = nx.Graph() G1.add_edge(0, 1) save_graph(G1, 'g1.txt')

14.5

32

0.724138

23

116

3.521739

0.652174

0.259259

0.271605

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0.060606

0.146552

116

7

33

16.571429

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0.4

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null

1

0

1

0

null

0

1

0

5

0a6e1ad622e11beb33d63f94b9179fe97b220680

4,232

py

Python

migrations/versions/eee104ca831d_.py

Kbaek11/Drug-Education-Project

57500813179f901e601d3e809e05ca24115ee7d5

[ "MIT" ]

null

migrations/versions/eee104ca831d_.py

Kbaek11/Drug-Education-Project

57500813179f901e601d3e809e05ca24115ee7d5

[ "MIT" ]

null

migrations/versions/eee104ca831d_.py

Kbaek11/Drug-Education-Project

57500813179f901e601d3e809e05ca24115ee7d5

[ "MIT" ]

null

"""empty message Revision ID: eee104ca831d Revises: Create Date: 2018-04-23 22:06:22.684637 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = 'eee104ca831d' down_revision = None branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.create_table('users', sa.Column('userId', sa.String(length=40), nullable=False), sa.Column('team', sa.String(length=80), nullable=True), sa.PrimaryKeyConstraint('userId') ) op.create_table('calendar', sa.Column('id', sa.Integer(), nullable=False), sa.Column('userId', sa.String(length=40), nullable=True), sa.Column('answeredDate', sa.DateTime(), nullable=False), sa.Column('day1a', sa.String(length=80), nullable=True), sa.Column('day1b', sa.String(length=80), nullable=True), sa.Column('day1c', sa.String(length=80), nullable=True), sa.Column('day2a', sa.String(length=80), nullable=True), sa.Column('day2b', sa.String(length=80), nullable=True), sa.Column('day2c', sa.String(length=80), nullable=True), sa.Column('day3a', sa.String(length=80), nullable=True), sa.Column('day3b', sa.String(length=80), nullable=True), sa.Column('day3c', sa.String(length=80), nullable=True), sa.Column('day4a', sa.String(length=80), nullable=True), sa.Column('day4b', sa.String(length=80), nullable=True), sa.Column('day4c', sa.String(length=80), nullable=True), sa.Column('day5a', sa.String(length=80), nullable=True), sa.Column('day5b', sa.String(length=80), nullable=True), sa.Column('day5c', sa.String(length=80), nullable=True), sa.Column('day6a', sa.String(length=80), nullable=True), sa.Column('day6b', sa.String(length=80), nullable=True), sa.Column('day6c', sa.String(length=80), nullable=True), sa.Column('day7a', sa.String(length=80), nullable=True), sa.Column('day7b', sa.String(length=80), nullable=True), sa.Column('day7c', sa.String(length=80), nullable=True), sa.Column('day8a', sa.String(length=80), nullable=True), sa.Column('day8b', sa.String(length=80), nullable=True), sa.Column('day8c', sa.String(length=80), nullable=True), sa.Column('day9a', sa.String(length=80), nullable=True), sa.Column('day9b', sa.String(length=80), nullable=True), sa.Column('day9c', sa.String(length=80), nullable=True), sa.Column('day10a', sa.String(length=80), nullable=True), sa.Column('day10b', sa.String(length=80), nullable=True), sa.Column('day10c', sa.String(length=80), nullable=True), sa.Column('day11a', sa.String(length=80), nullable=True), sa.Column('day11b', sa.String(length=80), nullable=True), sa.Column('day11c', sa.String(length=80), nullable=True), sa.Column('day12a', sa.String(length=80), nullable=True), sa.Column('day12b', sa.String(length=80), nullable=True), sa.Column('day12c', sa.String(length=80), nullable=True), sa.Column('day13a', sa.String(length=80), nullable=True), sa.Column('day13b', sa.String(length=80), nullable=True), sa.Column('day13c', sa.String(length=80), nullable=True), sa.Column('day14a', sa.String(length=80), nullable=True), sa.Column('day14b', sa.String(length=80), nullable=True), sa.Column('day14c', sa.String(length=80), nullable=True), sa.Column('q1', sa.String(length=80), nullable=True), sa.Column('q2', sa.String(length=80), nullable=True), sa.Column('q3', sa.String(length=80), nullable=True), sa.Column('q4', sa.String(length=80), nullable=True), sa.Column('q5', sa.String(length=80), nullable=True), sa.Column('q6', sa.String(length=80), nullable=True), sa.Column('q7', sa.String(length=80), nullable=True), sa.Column('q8', sa.String(length=80), nullable=True), sa.Column('q9', sa.String(length=80), nullable=True), sa.Column('q10', sa.String(length=80), nullable=True), sa.ForeignKeyConstraint(['userId'], ['users.userId'], ), sa.PrimaryKeyConstraint('id') ) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_table('calendar') op.drop_table('users') # ### end Alembic commands ###

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6a59fc41fe9acfa4b268a431ce6c579b99481536

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Python

tests/test_reactions.py

DleanJeans/dpytest

36713995b8cbccfbb597a53c499c1c8bb7fab8ff

[ "MIT" ]

71

2019-04-23T09:28:30.000Z

2022-02-07T15:32:07.000Z

tests/test_reactions.py

DleanJeans/dpytest

36713995b8cbccfbb597a53c499c1c8bb7fab8ff

[ "MIT" ]

61

2019-05-04T09:35:32.000Z

2022-03-19T16:37:20.000Z

tests/test_reactions.py

DleanJeans/dpytest

36713995b8cbccfbb597a53c499c1c8bb7fab8ff

[ "MIT" ]

29

2019-04-12T12:24:49.000Z

2022-01-20T19:09:30.000Z

import pytest import discord.ext.test as dpytest @pytest.mark.asyncio async def test_add_reaction(bot): g = bot.guilds[0] c = g.text_channels[0] message = await c.send("Test Message") await message.add_reaction("😂") # This is d.py/discord's fault, the message object from send isn't the same as the one in the state message = await c.fetch_message(message.id) assert len(message.reactions) == 1 @pytest.mark.asyncio async def test_remove_reaction(bot): g = bot.guilds[0] c = g.text_channels[0] message = await c.send("Test Message") await message.add_reaction("😂") # Assumes the test above passed await message.remove_reaction("😂", g.me) message = await c.fetch_message(message.id) assert len(message.reactions) == 0 @pytest.mark.asyncio async def test_user_add_reaction(bot): g = bot.guilds[0] c = g.text_channels[0] m = g.members[0] message = await c.send("Test Message") await dpytest.add_reaction(m, message, "😂") # Assumes the above tests pass message = await c.fetch_message(message.id) react = message.reactions[0] assert react.emoji == "😂" assert react.me is False @pytest.mark.asyncio async def test_user_remove_reaction(bot): g = bot.guilds[0] c = g.text_channels[0] m = g.members[0] message = await c.send("Test Message") await message.add_reaction("😂") await dpytest.add_reaction(m, message, "😂") await dpytest.remove_reaction(m, message, "😂") # Assumes the above tests pass message = await c.fetch_message(message.id) react = message.reactions[0] assert react.emoji == "😂" assert react.count == 1 assert react.me is True

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6a86304b826be5cd24e22821a8ab54ebcf716d66

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py

Python

electrum_gui/common/provider/__init__.py

BixinKey/electrum

f5de4e74e313b9b569f13ba6ab9142a38bf095f2

[ "MIT" ]

12

2020-11-12T08:53:05.000Z

2021-07-06T17:30:39.000Z

electrum_gui/common/provider/__init__.py

liyanhrxy/electrum

107608ef201ff1d20d2f6091c257b1ceff9b7362

[ "MIT" ]

209

2020-09-23T06:58:18.000Z

2021-11-18T11:25:41.000Z

electrum_gui/common/provider/__init__.py

liyanhrxy/electrum

107608ef201ff1d20d2f6091c257b1ceff9b7362

[ "MIT" ]

19

2020-10-13T11:42:26.000Z

2022-02-06T01:26:34.000Z

from electrum_gui.common.provider import manager as provider_manager

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6ab4271b474ac04fbcd7b2b60bc201e377143cbc

109

py

Python

alastria_identity/types/config_parser.py

alastria/alastria-identity-lib-py

63ec9d9e60d267c3900d2a827b5d4adb7d265acb

[ "MIT" ]

null

alastria_identity/types/config_parser.py

alastria/alastria-identity-lib-py

63ec9d9e60d267c3900d2a827b5d4adb7d265acb

[ "MIT" ]

2

2020-12-01T08:50:25.000Z

2020-12-16T15:10:33.000Z

alastria_identity/types/config_parser.py

alastria/alastria-identity-lib-py

63ec9d9e60d267c3900d2a827b5d4adb7d265acb

[ "MIT" ]

2

2020-10-21T11:22:40.000Z

2021-04-17T15:36:56.000Z

from abc import ABC, abstractmethod class ConfigParser(ABC): @abstractmethod def parse(self): pass

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