xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

aae46716baa3dadd527c4af9665b02ae64ca9df8

186

py

Python

lib/JumpScale/baselib/ayspm/__init__.py

jumpscale7/jumpscale_core7

c3115656214cab1bd32f7a1e092c0bffc84a00cd

[ "Apache-2.0" ]

null

lib/JumpScale/baselib/ayspm/__init__.py

jumpscale7/jumpscale_core7

c3115656214cab1bd32f7a1e092c0bffc84a00cd

[ "Apache-2.0" ]

4

2016-08-25T12:08:39.000Z

2018-04-12T12:36:01.000Z

lib/JumpScale/baselib/ayspm/__init__.py

jumpscale7/jumpscale_core7

c3115656214cab1bd32f7a1e092c0bffc84a00cd

[ "Apache-2.0" ]

3

2016-03-08T07:49:34.000Z

2018-10-19T13:56:43.000Z

from JumpScale import j def cb(): from .client import AYSPMClientFactory return AYSPMClientFactory() j.base.loader.makeAvailable(j, 'clients') j.clients._register('ayspm', cb)

20.666667

42

0.747312

23

186

6

0.652174

0.115942

0

0.139785

186

8

43

23.25

0.8625

0

0.064516

0

1

0.166667

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

2ab1e28b150f0549def9963e9e87de3fdd6b2579

128

py

Python

bob/ip/__init__.py

bioidiap/bob.ip.facelandmarks

055d4ebc439a05a693f186e2cd57653a4b8688db

[ "BSD-3-Clause" ]

37

2017-01-27T11:44:43.000Z

2022-03-31T16:18:08.000Z

bob/ip/__init__.py

bioidiap/bob.ip.facelandmarks

055d4ebc439a05a693f186e2cd57653a4b8688db

[ "BSD-3-Clause" ]

25

2015-07-04T17:41:40.000Z

2016-08-08T20:36:01.000Z

bob/ip/__init__.py

bioidiap/bob.ip.facelandmarks

055d4ebc439a05a693f186e2cd57653a4b8688db

[ "BSD-3-Clause" ]

7

2015-07-16T14:30:43.000Z

2019-11-27T23:44:36.000Z

# see https://docs.python.org/3/library/pkgutil.html from pkgutil import extend_path __path__ = extend_path(__path__, __name__)

32

52

0.804688

19

128

4.684211

0.736842

0.224719

0.314607

0

0.008547

0.085938

128

3

53

42.666667

0.752137

0.390625

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

2ad5c0610d72e8313f26a1bbc7b4d08f53e76745

592

py

Python

tarefa07/testar_leitura.py

PauloVictorSS/unicamp-mc102

077ca3ea6d3df40ebe205c2e874d20a934ea5541

[ "MIT" ]

null

tarefa07/testar_leitura.py

PauloVictorSS/unicamp-mc102

077ca3ea6d3df40ebe205c2e874d20a934ea5541

[ "MIT" ]

null

tarefa07/testar_leitura.py

PauloVictorSS/unicamp-mc102

077ca3ea6d3df40ebe205c2e874d20a934ea5541

[ "MIT" ]

null

from bordas import ler_imagem def testar_leitura_pbm(): largura, altura, imagem = ler_imagem("jota.pbm") assert largura == 7 assert altura == 11 matriz_esperada = [ [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 1, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0] ] assert imagem == matriz_esperada testar_leitura_pbm()

21.142857

52

0.418919

108

592

2.222222

0.175926

0.466667

0.575

0.616667

0.316667

0.275

0

0.217391

0.378378

592

27

53

21.925926

0.434783

0

0.4

0

0.013514

0

0.15

1

0.05

false

0

0.05

0

0.1

0

null

1

0

1

0

1

0

null

0

5

2ae03b8ab343cb2426d89047ddec699d024ca89e

35

py

Python

scripts/modules/process.py

snippits/snippit_ui

57fed0834cb089ce8244cc2d8cea8d6251923cfc

[ "MIT" ]

1

2017-03-23T06:41:45.000Z

scripts/modules/process.py

snippits/snippit_ui

57fed0834cb089ce8244cc2d8cea8d6251923cfc

[ "MIT" ]

5

2017-10-22T15:32:04.000Z

2017-11-15T11:30:54.000Z

scripts/modules/process.py

snippits/snippit_ui

57fed0834cb089ce8244cc2d8cea8d6251923cfc

[ "MIT" ]

null

17.5

34

0.714286

5

35

5

1

0

0.137931

0.171429

35

1

35

0.724138

0.914286

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

630d22d8b17873491bfa2c981d90b903b4e93d97

210

py

Python

mwtext/content_transformers/__init__.py

HAKSOAT/python-mwtext

f812deed7eab9a51ecc43d2940cc8fce37b66bbb

[ "MIT" ]

4

2020-05-10T17:29:18.000Z

2022-02-25T07:18:35.000Z

mwtext/content_transformers/__init__.py

HAKSOAT/python-mwtext

f812deed7eab9a51ecc43d2940cc8fce37b66bbb

[ "MIT" ]

16

2020-01-30T09:05:32.000Z

2021-03-02T21:52:26.000Z

mwtext/content_transformers/__init__.py

HAKSOAT/python-mwtext

f812deed7eab9a51ecc43d2940cc8fce37b66bbb

[ "MIT" ]

5

2020-01-30T09:06:22.000Z

2020-07-06T11:27:47.000Z

from .wikitext2words import Wikitext2Words from .wikidata2words import Wikidata2Words from .wikitext2structured import Wikitext2Structured __all__ = ("Wikitext2Words", "Wikitext2Structured", "Wikidata2Words")

35

69

0.847619

16

210

10.875

0.375

0

0.046875

0.085714

210

5

70

42

0.859375

0

0.22381

0

1

0

false

0

0.75

0

0.75

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

632c37bb9111e894272a494685c290f6c3a51c11

171

py

Python

office365/sharepoint/navigation/publishingNavigationProviderType.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

544

2016-08-04T17:10:16.000Z

2022-03-31T07:17:20.000Z

office365/sharepoint/navigation/publishingNavigationProviderType.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

438

2016-10-11T12:24:22.000Z

2022-03-31T19:30:35.000Z

office365/sharepoint/navigation/publishingNavigationProviderType.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

202

2016-08-22T19:29:40.000Z

2022-03-30T20:26:15.000Z

class PublishingNavigationProviderType: def __init__(self): pass InvalidSiteMapProvider = 0 PortalSiteMapProvider = 1 TaxonomySiteMapProvider = 2

21.375

39

0.730994

12

171

10.083333

1

0

0.022727

0.22807

171

7

40

24.428571

0.893939

0

1

0.166667

false

0.166667

0

0.833333

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

2d901318fef0e39b577ea3955b7f29ec09205587

49

py

Python

configs/sac/sac_mani_skill_state_1M_train.py

art-e-fact/ManiSkill-Learn

9742da932448a5234222cf94381ca0f861dc83fd

[ "Apache-2.0" ]

39

2021-07-29T05:59:20.000Z

2022-03-25T06:32:51.000Z

configs/sac/sac_mani_skill_state_1M_train.py

art-e-fact/ManiSkill-Learn

9742da932448a5234222cf94381ca0f861dc83fd

[ "Apache-2.0" ]

27

2021-08-04T03:37:03.000Z

2022-03-08T06:18:25.000Z

configs/sac/sac_mani_skill_state_1M_train.py

art-e-fact/ManiSkill-Learn

9742da932448a5234222cf94381ca0f861dc83fd

[ "Apache-2.0" ]

5

2021-08-24T14:21:06.000Z

2022-02-21T04:31:01.000Z

_base_ = ['../_base_/sac/sac_mani_skill_mlp.py']

24.5

48

0.714286

8

49

3.5

0.75

0

0.061224

49

1

49

0.608696

0

0.714286

0

1

0

false

0

1

0

null

0

1

0

1

null

0

5

2dcfa332c229dc1fc77fbdc5b378bf4556050086

53

py

Python

apps/trac/tests/__init__.py

tractiming/trac-gae

46c4a1fe409a45e8595210a5cf242425d40d4b41

[ "MIT" ]

3

2020-09-13T04:56:31.000Z

2021-05-26T11:46:08.000Z

apps/trac/tests/__init__.py

tractiming/trac-gae

46c4a1fe409a45e8595210a5cf242425d40d4b41

[ "MIT" ]

null

apps/trac/tests/__init__.py

tractiming/trac-gae

46c4a1fe409a45e8595210a5cf242425d40d4b41

[ "MIT" ]

1

2020-05-09T10:05:08.000Z

#from test_models import * #from test_views import *

17.666667

26

0.773585

8

53

4.875

0.625

0.410256

0

0.150943

53

2

27

26.5

0.866667

0.924528

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

5

931d15ae24ab4c903659863a1a1ce6788b6b03dd

144

py

Python

src/main.py

ingedata-net/lisa

81a1e4ef5220e8b1a4e20df2fdf9c6398ab02959

[ "MIT" ]

null

src/main.py

ingedata-net/lisa

81a1e4ef5220e8b1a4e20df2fdf9c6398ab02959

[ "MIT" ]

null

src/main.py

ingedata-net/lisa

81a1e4ef5220e8b1a4e20df2fdf9c6398ab02959

[ "MIT" ]

1

2019-04-17T18:45:56.000Z

import sys from lisa.image_converter import convert_image if __name__ == "__main__": convert_image("sample/street.jpg", "output/street.jpg")

24

57

0.777778

20

144

5.05

0.7

0.237624

0

0.104167

144

6

57

24

0.782946

0

0.289655

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

93257704f0317cd78194f5f0b5140d1e4e541a1f

20,737

py

Python

animeCards.py

braga-et-sant/betterPackOpener

c960fdf6f753cfc7c57914bb11560d031fb24905

[ "MIT" ]

1

2021-11-23T03:56:56.000Z

animeCards.py

braga-et-sant/betterPackOpener

c960fdf6f753cfc7c57914bb11560d031fb24905

[ "MIT" ]

2

2021-10-01T03:36:38.000Z

2021-11-04T19:13:14.000Z

animeCards.py

braga-et-sant/betterPackOpener

c960fdf6f753cfc7c57914bb11560d031fb24905

[ "MIT" ]

1

2021-10-03T18:37:54.000Z

animecards = [511003217, 511003218, 511003211, 511027027, 511027455, 511600382, 511600383, 511600384, 511600385, 511600386, 511600374, 511600373, 511001439, 511002592, 511001808, 511001895, 511015125, 513000157, 513000124, 511600141, 513000128, 513000019, 513000127, 513000088, 513000092, 511030047, 511018016, 513000090, 513000108, 511003204, 513000115, 511002635, 511600241, 513000082, 513000084, 513000103, 513000037, 513000125, 513000149, 513000158, 513000146, 513000150, 513000148, 513000159, 511600294, 513000153, 513000152, 513000155, 513000154, 513000156, 513000147, 513000145, 513000184, 511010536, 513000140, 513000132, 513000178, 513000165, 513000185, 513000163, 513000126, 513000099, 513000172, 511600422, 511600421, 511600420, 511600419, 511600413, 513000177, 511600412, 513000176, 511600411, 513000175, 511600410, 513000174, 513000166, 511600405, 513000171, 511600404, 513000170, 511600403, 513000169, 511600402, 513000168, 511600406, 511600407, 511600408, 511600416, 511600414, 511600417, 511600415, 511600423, 513000025, 513000097, 511050013, 511018007, 511050012, 511002532, 700000014, 511002547, 511002616, 511000308, 511013020, 511003021, 511002728, 511003056, 150000091, 511023004, 511002072, 511013016, 511001976, 810000029, 511001742, 511002267, 511000078, 511009951, 511016003, 511002507, 810000097, 511002895, 511001049, 511002239, 511002894, 511000604, 511005642, 511009322, 513000014, 511000307, 513000096, 511001516, 511024008, 511247004, 513000013, 511000368, 511600299, 511002911, 511002009, 513000121, 511002048, 511002641, 511010173, 511003019, 511009522, 511002869, 511000662, 100000510, 511009349, 100000410, 511600056, 511002846, 511002983, 511009443, 511002013, 511015134, 511004015, 511009387, 511000133, 511600067, 513000024, 511009001, 100000150, 513000004, 511015133, 511002946, 511000987, 511600127, 511000587, 511247011, 511027018, 511000475, 511023001, 511002426, 511600099, 511013001, 511600279, 511600293, 511009917, 151000033, 100000001, 513000087, 100000155, 511000997, 511018022, 511001723, 511010103, 100000244, 513000066, 511600267, 511018009, 511030013, 511018030, 511005709, 511002864, 511003205, 511009376, 511009683, 513000062, 511106005, 511000264, 511020009, 511001967, 511000293, 511002835, 511002188, 511002702, 511001025, 511002873, 511000373, 511002751, 511009022, 511002370, 511002278, 511002826, 100000009, 513000041, 511600377, 511030036, 511002625, 511000421, 810000077, 100000006, 513000040, 511000012, 511001074, 511000695, 511600369, 511009077, 100000502, 511001943, 511600268, 511600287, 511001952, 511002617, 511007019, 511009331, 511002581, 511009053, 511001282, 511002838, 511001782, 511010035, 511001942, 511009095, 511000772, 511003068, 511000765, 511002988, 511003069, 511756009, 511001700, 511002760, 511003029, 513000179, 511013017, 511001658, 511013014, 170000201, 511600012, 511000788, 511023014, 511001989, 100000365, 511001712, 700000000, 511005091, 511030014, 511002855, 511600174, 511000362, 513000095, 511002552, 700000015, 511002623, 511010508, 511000670, 511777005, 511010107, 511001999, 511001136, 511001939, 513000030, 100000069, 513000106, 511010239, 511002549, 511600096, 511002500, 511002628, 100000296, 511002483, 511001616, 511001109, 511002477, 151000016, 511002862, 700000018, 511310011, 511001956, 511050003, 511009307, 511005715, 511003005, 511027101, 511009088, 511600203, 511002223, 511600007, 511002543, 511009627, 511600023, 700000020, 511000415, 513000180, 511002001, 511019007, 151000044, 513000107, 511009564, 511009340, 511002079, 511010511, 511002768, 700000010, 511002841, 511027091, 511000613, 170000203, 511106011, 511600364, 151000005, 511000378, 511002018, 511000763, 511009705, 511600357, 511002872, 511002377, 511002487, 511030062, 511013011, 511009537, 511001291, 511002675, 170000154, 511009593, 170000204, 511002059, 511027117, 100000412, 511001744, 111302501, 511010702, 511002076, 511010039, 511600272, 511002599, 511001957, 511000614, 810000109, 170000194, 511001628, 511001795, 100000511, 511015100, 511002136, 511310001, 511018029, 511002374, 511600286, 511000754, 151000050, 100000512, 511600348, 511013031, 511600199, 511247015, 511010007, 511001663, 511600352, 511003045, 170000170, 511024000, 511002034, 511002513, 511010502, 511020000, 511002402, 511001695, 511002888, 511000372, 511000769, 511027106, 511001056, 100000093, 513000098, 511010030, 511017001, 511005063, 511002039, 511002665, 511009162, 511002200, 511247012, 511001949, 151000028, 511756002, 511600082, 511002228, 810000055, 100000247, 513000057, 511600361, 511002083, 511001997, 511000532, 511009051, 511001693, 511003023, 511003066, 511001010, 511600376, 810000102, 511009369, 511001501, 100000315, 511002480, 511002883, 511000169, 100000411, 511600062, 511003028, 511010011, 810000006, 170000202, 810000011, 511000430, 100000126, 511016004, 810000096, 511600140, 511006006, 511000212, 511027120, 511000263, 511310014, 511000277, 511002976, 511001784, 511000915, 511009543, 511001139, 511002463, 511600253, 511009180, 511600121, 511001702, 511600304, 511002514, 511000232, 511005646, 511000431, 511023006, 511004438, 511030039, 511001391, 511009071, 511009950, 511009126, 511030044, 511002125, 151000010, 511002737, 511001146, 511018024, 513000053, 513000094, 511013023, 511600064, 511002706, 511001050, 511600314, 511000276, 511004443, 511013022, 810000045, 511002518, 100003002, 511009558, 511010196, 700000001, 511030043, 511002880, 511002499, 511002467, 511002877, 511009194, 511010507, 511001785, 511016005, 511014001, 511001964, 511009065, 100000100, 513000104, 511002195, 511600042, 511001521, 511002749, 511015127, 511002990, 511001121, 511002764, 151000022, 511002528, 511002836, 511023000, 511000208, 511002830, 170000158, 511030006, 511000462, 511008010, 511015129, 511021010, 511025000, 511000623, 511003027, 511002319, 511001651, 511002652, 511023013, 511001043, 513000039, 511002601, 513000072, 511002057, 511002839, 511003004, 511000300, 511600091, 511000541, 511247005, 511002046, 511600290, 511000674, 511005008, 511009393, 511002040, 511009364, 511001415, 511009704, 511015117, 511002775, 511005633, 511009047, 511002031, 100000014, 513000051, 511002219, 100000159, 700000011, 511001116, 100001010, 511009389, 511001279, 511010514, 511002808, 511001769, 511013000, 511001454, 513000144, 100000526, 511002574, 511002002, 511247002, 511310013, 100000113, 511600083, 511002096, 511014000, 511002403, 511001565, 513000038, 511600050, 511004436, 100000488, 511247001, 511010513, 511000064, 511600139, 511002879, 511600033, 511002771, 511001253, 511009517, 511000639, 500314701, 511009542, 511000410, 511023012, 511001252, 511600381, 100000104, 511000165, 511002952, 511002545, 810000103, 511000262, 511000261, 100000482, 511005639, 511010205, 511002077, 511009442, 511002359, 511002589, 511600353, 511600125, 511600126, 511018017, 511001371, 511002610, 511010503, 511015132, 511009706, 511002353, 511002263, 511000592, 511002807, 511006005, 511002963, 511010207, 511002761, 100000050, 511600401, 513000167, 511002203, 100000129, 511003087, 513000054, 100000122, 511001363, 100000151, 511600039, 511600260, 100000110, 513000162, 511009000, 511002598, 511002515, 511600171, 511009918, 511001429, 511002821, 511015114, 511000260, 513000078, 511002670, 511010238, 511600201, 100000153, 513000186, 100000537, 511002505, 100000243, 511000183, 511050008, 511003025, 511050002, 511030045, 511004407, 511001721, 511001944, 513000026, 511002772, 100000532, 511010032, 511002981, 511019002, 511001986, 511002863, 810000078, 511000036, 513000091, 511002265, 511002816, 100000015, 513000044, 511000192, 511001595, 511010537, 511600119, 511000420, 150000077, 511000072, 513000182, 513000035, 511001517, 511001594, 100000355, 511002108, 511010025, 511019003, 511009072, 511003060, 810000022, 511009064, 511002759, 511000013, 511013033, 511247007, 511009086, 511001052, 511001202, 511002489, 511001275, 511002111, 511003057, 511009308, 511600073, 511002517, 100000154, 511600137, 511003067, 511600170, 100000275, 511002101, 511000477, 511600021, 511002379, 511600092, 511001910, 511009380, 511002485, 511016002, 511600251, 511002859, 511011004, 511001051, 511001069, 511009342, 511310010, 511001973, 511000513, 511000279, 511021009, 511002508, 151000015, 511001603, 151000051, 513000137, 511600118, 511600305, 511003081, 511002913, 511756003, 511001122, 511009425, 511010535, 511003062, 511600298, 511002896, 100000505, 511002746, 511002533, 100000282, 511000760, 511009191, 511001060, 450000001, 511600378, 511009305, 511009110, 511009416, 511600266, 511002762, 511002288, 511006003, 511002382, 511013007, 511310113, 511600322, 511600106, 100000004, 513000050, 511000743, 511600355, 700000009, 511001149, 511003089, 511002774, 511002222, 511010105, 511600197, 511000286, 100000025, 511600306, 511001369, 511002109, 151000057, 511009952, 511009540, 511002709, 511600295, 511002080, 511001743, 511000515, 100000265, 511002882, 511002776, 511002850, 511001459, 170000155, 511106012, 511002520, 511009588, 511002213, 511015116, 511000258, 513000139, 513000008, 513000009, 511010021, 511001786, 511030005, 511050004, 170000149, 100000501, 511013018, 511600051, 511002757, 511000369, 511001724, 511000749, 511018019, 511003202, 511013009, 511010139, 511001706, 511001186, 151000018, 511016006, 511002738, 511002910, 511600048, 151000029, 511001776, 511600068, 511600124, 511009351, 511010056, 511001431, 511006010, 511756001, 511600351, 511002587, 511010096, 511002777, 511002277, 511003115, 511027000, 511007001, 511013021, 511001924, 513000069, 511002446, 511001514, 511023010, 511001779, 511030068, 511002689, 511003064, 511001945, 511600359, 513000018, 513000122, 511600263, 511000552, 511002650, 511002727, 511001247, 151000052, 100000508, 511001692, 511002884, 100000257, 511001941, 511050011, 511003063, 511600001, 511000512, 511310007, 511001887, 810000051, 511600362, 511002042, 511002418, 170000210, 511002856, 511012000, 511001950, 511600215, 511600070, 511002444, 511001601, 511002371, 513000111, 100001001, 511001624, 700000012, 511023002, 511002915, 511005647, 511001732, 511002021, 511002049, 511001625, 700000028, 511001289, 511002060, 511000118, 511001974, 511009381, 513000061, 511000377, 511600204, 511000076, 513000015, 511002861, 513000022, 513000029, 511009066, 511001766, 511130000, 511600176, 511027121, 810000050, 511002741, 700000016, 511003047, 511010701, 511002544, 511001211, 151000030, 511756000, 511002773, 511001653, 511600379, 511009016, 511777001, 511024010, 511600061, 511023015, 511009013, 511009068, 511001963, 511600109, 511001040, 511600166, 810000114, 511000923, 511009583, 511009368, 511002870, 511002214, 511600004, 511002871, 511003065, 511002229, 511023007, 511000024, 513000089, 511010132, 511777008, 511010165, 511002372, 511009026, 511009062, 511010703, 511002600, 511002813, 511002309, 511010083, 511002597, 511002419, 513000017, 511600274, 511002166, 511009193, 511001290, 511030008, 511003010, 511009104, 511002710, 511010020, 511004111, 511002375, 511001822, 511106008, 511777006, 511001542, 511001374, 511600370, 511002876, 511001129, 511010192, 511600080, 511002542, 511017002, 511001661, 511018001, 511002045, 511001212, 511018012, 170000197, 511000434, 511000255, 170000153, 511015112, 511001868, 511003000, 111011901, 511001770, 151000040, 511003203, 511009073, 511027093, 511001791, 511002003, 100000529, 511002858, 511600052, 511002906, 511002716, 511021003, 511005043, 511030007, 511001128, 511001405, 511027103, 511001419, 511002227, 511009587, 511001489, 511005712, 511015108, 100000094, 344000000, 511024005, 511001160, 511017003, 100000167, 511002490, 511009545, 511600335, 513000001, 511010534, 170000157, 511600252, 511000164, 511002951, 810000098, 511002546, 100000020, 511000429, 511002590, 511310017, 511000737, 511002531, 511001699, 511009528, 511001014, 511000253, 511015124, 100000545, 511247017, 511600122, 100005002, 513000161, 511001251, 511003039, 511009567, 511001539, 511009061, 511002521, 511002756, 511003080, 511001126, 511003059, 511009311, 511020001, 511001622, 511600310, 511001701, 511015110, 511000231, 511009508, 511020002, 511001789, 511003058, 511024011, 511001416, 511030055, 511002526, 511003090, 511023005, 511005006, 511000539, 511002194, 511002673, 511000275, 513000164, 511600284, 511600311, 511009651, 511001124, 511009025, 511010505, 511000252, 511019006, 511024007, 511000480, 511004437, 511001396, 511001278, 511002358, 511600014, 511027104, 511023011, 511000251, 511004126, 810000034, 511002796, 511002854, 511009165, 511806001, 511013005, 511002486, 100000478, 511001117, 511004441, 511002755, 810000052, 511009412, 511600069, 100000086, 511002036, 511002828, 511010064, 511001981, 511002739, 100000336, 511600013, 511013028, 511002530, 100003003, 511019009, 511002276, 511027111, 511002035, 100000527, 810000075, 511000168, 511003074, 511002091, 511000657, 511002273, 511009480, 511010704, 511000419, 511001644, 511013026, 511018002, 511002416, 100000318, 511600100, 511600105, 511002642, 511001780, 511002624, 511013035, 511021006, 151000020, 511000278, 511001983, 511001593, 513000036, 511001273, 511600182, 511001455, 511002350, 513000076, 511600313, 511756006, 511008508, 511003073, 100000013, 513000047, 511001637, 511050010, 511016001, 511000073, 513000183, 511600027, 513000075, 511013029, 511001055, 511018013, 511009313, 511600026, 511002539, 511009323, 511009386, 511002092, 511600005, 511600006, 511005042, 511003208, 151000001, 513000031, 511013032, 511018028, 513000120, 511030004, 511008009, 150000085, 511009385, 511009424, 511009527, 511600380, 511806000, 511000761, 511004444, 511005701, 110000102, 511310005, 511003070, 511002945, 511002957, 511002295, 810000105, 511002818, 511002827, 511016000, 511001245, 511009192, 511000070, 511002155, 511021000, 511002221, 511001992, 511310012, 511600049, 511008509, 100000003, 513000042, 511001771, 511000474, 511002612, 511600318, 511000306, 511600110, 511002012, 511002482, 511018000, 511021008, 511000366, 511002831, 511010500, 511310003, 511000250, 511009365, 511027135, 511001426, 511000241, 511001954, 511002381, 511002286, 511002504, 511001425, 151000019, 100000002, 513000049, 511009142, 100000536, 511002260, 511001823, 511005066, 513000079, 511009109, 511001659, 511000246, 511005034, 511001777, 151000041, 511013004, 100000125, 511000589, 511001041, 511002274, 511247013, 511010034, 511005737, 511002969, 511001395, 100000056, 511600409, 513000173, 100000156, 511600138, 511002611, 511004006, 511010062, 511600424, 513000142, 100000128, 100000121, 511002064, 511002615, 511010047, 511002676, 511600280, 511001645, 511009055, 511030042, 511005637, 511009304, 511001703, 511001274, 511002829, 511010700, 511000247, 511600212, 511600367, 100000533, 100000245, 511001926, 511001522, 810000111, 511106004, 511013025, 511000821, 513000016, 511002088, 810000028, 511023008, 511027102, 511310019, 511002484, 511777003, 511002865, 511001710, 511001147, 511310009, 511247003, 511247009, 100000007, 513000003, 511020004, 511013010, 511003018, 511004004, 511600038, 511600194, 511001946, 511002664, 511600262, 151000007, 511009041, 511002502, 100000509, 511600020, 511001947, 511001597, 511003072, 100001007, 100001006, 810000057, 100000010, 513000048, 511000799, 511001632, 511001433, 511600269, 100000274, 511009214, 511010531, 511106009, 511001704, 511030022, 511019001, 511001948, 511009353, 511002387, 511002509, 511000774, 511001821, 511027094, 511013003, 511002506, 511600181, 511002954, 511002415, 511002614, 810000019, 700000013, 511000503, 511009350, 511001284, 100000483, 511002470, 511000000, 511010532, 511001951, 511000558, 511015120, 511000364, 511000077, 700000017, 511600301, 511001596, 511600104, 511009954, 511777007, 513000012, 700000029, 511002848, 511010530, 511002373, 511001283, 100000304, 511002578, 511001975, 511003088, 511130002, 513000055, 511002825, 511010053, 511006007, 511009955, 700000019, 511030003, 700000027, 511027024, 511002793, 511002989, 151000053, 511000806, 511001711, 511005644, 511002540, 511001608, 511002268, 511600308, 100000507, 100000152, 170000150, 511247010, 511600128, 151000025, 511002184, 151000026, 151000049, 511015115, 511009043, 511009735, 511009018, 511018004, 511003071, 513000002, 511009628, 511009444, 511000309, 511600366, 511247008, 511010501, 513000116, 511009002, 511001725, 511004423, 511002461, 511002878, 511000248, 513000060, 511002524, 511000807, 511000184, 511002651, 513000138, 511600235, 100000075, 100000258, 511002984, 511027110, 511002860, 100000111, 511021005, 511001430, 511001567, 511009916, 511002519, 513000063, 511000995, 511018021, 511010538, 511001445, 511021004, 511009684, 511020011, 100000530, 513000056, 511001966, 511009059, 511000548, 511001404, 170000193, 511310038, 511005711, 511015126, 511003003, 511010012, 511013024, 513000021, 511002050, 511002037, 511010504, 511010515, 511001057, 511002794, 511247020, 511001016, 511600117, 344000001, 511000508, 100000542, 511002960, 100000091, 513000071, 511002541, 511002452, 151000031, 100000506, 511600081, 511231002, 511000302, 511009553, 511600103, 511027025, 511000642, 511600169, 511021001, 511001873, 511001962, 511600115, 511009170, 100000471, 511600291, 100000042, 511001698, 511002729, 511001646, 500000051, 511001652, 511002591, 511310000, 511001277, 511600003, 511001694, 511002476, 511001435, 511000119, 511001713, 511600076, 511000218, 511000149, 511777010, 511600090, 511002352, 511002534, 100000302, 511002982, 511000675, 511012001, 511009049, 511002958, 511002525, 511001955, 511001696, 511002488, 511600296, 511009530, 511005645, 511001376, 511600302, 511003026, 140000074, 511002844, 511004447, 511310016, 511050007, 100000199, 511600130, 511005643, 511002135, 511004007, 511000230, 511000242, 511009462, 100000301, 511009014, 513000028, 511002126, 511001403, 511001961, 511021002, 511002953, 511000432, 511002750, 511002293, 511002261, 511002523, 511004341, 511001778, 511010533, 511600315, 511002501, 511000627, 151000027, 511003022, 511015121, 511002127, 511600189, 511000243, 511019008, 511013002, 100000087, 511777009, 511130001, 511002708, 511000599, 511002849, 511009441, 100000084, 511002912, 511600342, 511002378, 511002834, 511003075, 511600108, 810000002, 513000059, 511009136, 511000240, 511002845, 100000531, 511009321, 511002814, 810000026, 511600300, 511001044, 511002866, 511001599, 511001286, 511010010, 511600133, 511002220, 511001108, 170000151, 511002659, 140000131, 511756008, 511000239, 511310015, 511009572, 513000011, 511009685, 511003207, 511600347, 511002054, 810000110, 511018014, 511001600, 511002991, 511002044, 511018015, 151000024, 511002874, 110000012, 513000136, 110000010, 513000134, 110000011, 513000135, 511002081, 511106010, 511310002, 810000071, 511000433, 511004442, 511013027, 513000068, 511020007, 100000012, 513000046, 511013015, 511002351, 100000073, 511002090, 511030023, 511002726, 511247000, 511027112, 511009413, 513000052, 100001009, 511600047, 511004440, 511001993, 511002718, 511600193, 511600271, 511002425, 511600120, 511024006, 513000074, 511009384, 100100090, 511002110, 511003077, 100000008, 513000045, 511002380, 810000031, 170000167, 151000021, 511001564, 511600239, 511600025, 511002011, 511011003, 511010506, 511002058, 511009406, 511600387, 511001417, 100000233, 511000549, 511002909, 511009544, 511009531, 100000005, 513000043, 511001783, 511010519, 511001965, 511009569, 511002740, 511024001, 511001781, 511009557, 513000083, 100000124, 511002961, 511001372, 810000106, 511000234, 511002810, 511002857, 511003201, 511000629, 511002603, 511600129, 511009635, 511002833, 511600254, 511001697, 511002613, 100000127, 511015128, 511000497, 511001432, 511004439, 810000081, 511002837, 511015111, 511001392, 511030056, 511002962, 151000056, 100000061, 511600418, 511024009, 511024002, 151000043, 511002202, 810000056, 511002527, 100000120, 511002602, 151000058, 511000233, 100000303, 511002959, 511030054, 511050001, 511002112, 511010065, 511009300, 511004434, 511247006, 511010525, 511002464, 511310008, 100003004, 511018010, 511600205, 511030061, 511001982, 511247018, 511007022, 511009054, 511002732, 511247014, 100000534, 511001123, 511001998, 511020012, 511020010, 511027107, 511600077, 511000902, 513000141, 511600116, 511756007, 511002985, 100000538, 511002763, 511001598, 511002529, 151000054, 511010104, 511020008, 511010009, 511020005, 511002824, 511310006, 170000152, 513000070, 511000817, 511009074, 511002809, 511015118, 511002321, 511000074, 511009919, 511000376, 511247019, 511002701, 511001639, 511013008, 511001048, 511009089, 511000436, 511001409, 511003206, 151000003, 513000073, 511000770, 511002075, 810000017, 511777004, 810000042, 810000043, 511000075, 513000181, 511600022, 100000280, 511002881, 511025002, 511020003, 511000540, 511009402, 511002167, 511009091]

20,737

0.818151

1,885

20,737

9.000531

1

0

0.899427

0.0909

20,737

1

20,737

0.00053

0

1

0

false

0

null

0

1

0

1

0

null

0

5

937fb2fb7f7f9cd4efeabf7966566b613b37d47d

94

py

Python

premade_modules/2.79/2.79b/bpy/ops/cycles.py

echantry/fake-bpy-module

004cdf198841e639b7d9a4c4db95ca1c0d3aa2c7

[ "MIT" ]

null

premade_modules/2.79/2.79b/bpy/ops/cycles.py

echantry/fake-bpy-module

004cdf198841e639b7d9a4c4db95ca1c0d3aa2c7

[ "MIT" ]

null

premade_modules/2.79/2.79b/bpy/ops/cycles.py

echantry/fake-bpy-module

004cdf198841e639b7d9a4c4db95ca1c0d3aa2c7

[ "MIT" ]

null

def use_shading_nodes(): '''Enable nodes on a material, world or lamp ''' pass

13.428571

49

0.606383

13

94

4.230769

0.923077

0

0.287234

94

6

50

15.666667

0.820896

0.43617

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

fa74422de9ed93a9cab2afd25f2936171bb31a85

203

py

Python

app/exceptions.py

ONSdigital/census-rm-print-file-service

631851867b5b73e9bd4e106a6a0ead6d38f8f78c

[ "MIT" ]

null

app/exceptions.py

ONSdigital/census-rm-print-file-service

631851867b5b73e9bd4e106a6a0ead6d38f8f78c

[ "MIT" ]

65

2019-07-03T09:58:07.000Z

2021-06-02T00:18:39.000Z

app/exceptions.py

ONSdigital/census-rm-print-file-service

631851867b5b73e9bd4e106a6a0ead6d38f8f78c

[ "MIT" ]

1

2021-04-11T07:46:13.000Z

class TemplateNotFoundError(Exception): pass class MalformedMessageError(Exception): pass class EncryptionFailedException(Exception): pass class DaemonStartupError(Exception): pass

13.533333

43

0.773399

16

203

9.8125

0.4375

0.33121

0.343949

0

0.167488

203

14

44

14.5

0.928994

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

fac21a8e95f4ab07deda7712c321d87eb91f0637

266

py

Python

raven/contrib/django/management/commands/__init__.py

ascan-io/raven-python

5b3f48c66269993a0202cfc988750e5fe66e0c00

[ "BSD-3-Clause" ]

1,108

2015-01-02T01:20:00.000Z

2022-03-09T02:22:40.000Z

raven/contrib/django/management/commands/__init__.py

nvllsvm/raven-python

c4403f21973138cd20cf9c005da4fb934836d76e

[ "BSD-3-Clause" ]

698

2015-01-04T11:12:57.000Z

2022-01-22T08:07:51.000Z

venv/lib/python3.7/site-packages/raven/contrib/django/management/commands/__init__.py

emreatadl/atadil-personal-blog

88c7be19d6a27b39fd86ff3d9c34b11443291e0e

[ "MIT" ]

486

2015-01-04T09:00:33.000Z

2022-03-09T02:37:18.000Z

""" raven.contrib.django.management.commands ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :copyright: (c) 2010-2013 by the Sentry Team, see AUTHORS for more details :license: BSD, see LICENSE for more details. """ from __future__ import absolute_import, print_function

29.555556

74

0.661654

32

266

5.3125

0.8125

0.082353

0.164706

0

0.033755

0.109023

266

8

75

33.25

0.683544

0.759399

0

1

0

true

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

fae62714ba87d3ba46c6b4c12c3a1bbc4438cb05

28

py

Python

study_bot/__main__.py

kasu77/studyadda

f29fa52a7d7dc60a92ee6c960be8dd2ba095fc32

[ "Apache-2.0" ]

null

study_bot/__main__.py

kasu77/studyadda

f29fa52a7d7dc60a92ee6c960be8dd2ba095fc32

[ "Apache-2.0" ]

null

study_bot/__main__.py

kasu77/studyadda

f29fa52a7d7dc60a92ee6c960be8dd2ba095fc32

[ "Apache-2.0" ]

null

from . import bot bot.main()

14

17

0.714286

5

28

4

0.8

0

0.142857

28

2

18

14

0.833333

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

5

fae8e18c0c4a6da523c41314bc7b0414e629cdc6

199

py

Python

persons_database/persons/models.py

Kanakala/elasticsearch

f86ab1cc57828ff2942be9d5cb9d48fbaa5718a7

[ "MIT" ]

null

persons_database/persons/models.py

Kanakala/elasticsearch

f86ab1cc57828ff2942be9d5cb9d48fbaa5718a7

[ "MIT" ]

null

persons_database/persons/models.py

Kanakala/elasticsearch

f86ab1cc57828ff2942be9d5cb9d48fbaa5718a7

[ "MIT" ]

null

from django.db import models class Restaurant(models.Model): restaurant = models.CharField(max_length=100) code = models.CharField(max_length=100) def __str__(self): return self.restaurant

18.090909

46

0.773869

27

199

5.481481

0.62963

0.216216

0.243243

0.324324

0.364865

0

0.034682

0.130653

199

10

47

19.9

0.820809

0

1

0.166667

false

0

0.166667

1

0

1

0

null

1

0

null

0

1

0

5

87c5b57bdf550c79f3f57820d0e81116b1423513

222

py

Python

hood/admin.py

hkawinzi/my-neighborhood

7c53dc14d7d030d09a9824eec4f5b3a0bd132f97

[ "Unlicense" ]

null

hood/admin.py

hkawinzi/my-neighborhood

7c53dc14d7d030d09a9824eec4f5b3a0bd132f97

[ "Unlicense" ]

null

hood/admin.py

hkawinzi/my-neighborhood

7c53dc14d7d030d09a9824eec4f5b3a0bd132f97

[ "Unlicense" ]

null

from django.contrib import admin from .models import User, Profile, Neighbourhood, Business # Register your models here. admin.register(User) admin.register(Profile) admin.register(Neighbourhood) admin.register(Business)

24.666667

58

0.81982

28

222

6.5

0.464286

0.285714

0

0.094595

222

8

59

27.75

0.905473

0.117117

0

1

0

true

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

87cda5fe2622c758448e44e6873fab6fc30a8c0e

64

py

Python

spotdl/authorize/services/__init__.py

khjxiaogu/spotify-downloader

a8dcb8d998da0769bbe210f2808d16b346453c23

[ "MIT" ]

4,698

2017-06-20T22:37:10.000Z

2022-03-28T13:38:07.000Z

spotdl/authorize/services/__init__.py

Delgan/spotify-downloader

8adf3e8d6b98269b1538dd91c9a44ed345c77545

[ "MIT" ]

690

2017-06-20T20:08:42.000Z

2022-02-26T23:36:07.000Z

spotdl/authorize/services/__init__.py

Delgan/spotify-downloader

8adf3e8d6b98269b1538dd91c9a44ed345c77545

[ "MIT" ]

741

2017-06-21T23:32:51.000Z

2022-03-07T12:11:54.000Z

from spotdl.authorize.services.spotify import AuthorizeSpotify

21.333333

62

0.875

7

64

8

1

0

0.078125

64

2

63

32

0.949153

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

87da712c5c640131677646165048a080e433a008

2,793

py

Python

Time Capsule/script.py

killua4564/2019-Crypto-CTF

f465601f3088222822d27f8135da39cd930c98bb

[ "MIT" ]

null

Time Capsule/script.py

killua4564/2019-Crypto-CTF

f465601f3088222822d27f8135da39cd930c98bb

[ "MIT" ]

null

Time Capsule/script.py

killua4564/2019-Crypto-CTF

f465601f3088222822d27f8135da39cd930c98bb

[ "MIT" ]

null

from Crypto.Util.number import * from functools import reduce c = 30263951492003430418944035844723976843761515320480688994488846431636782360488888188067655841720110193942081554547272176290791213962513701884837856823209432209367951673301622535940395295826053396595886942990258678430777333636450042181585837395671842878310404080487115827773100028876775230121509570227303374672524063165714509957850966189605469484201028704363052317830254920108664916139026741331552127849056897534960886647382429202269846392809641322613341548025760209280611758326300214885296175538901366986310471066687700879304860668964595202268317011117634615297226602309205086105573924029744405559823548638486054634428 n = 16801166465109052984956796702219479136700692152603640001472470493600002617002298302681832215942994746974878002533318970006820414971818787350153626339308150944829424332670924459749331062287393811934457789103209090873472485865328414154574392274611574654819495894137917800304580119452390318440601827273834522783696472257727329819952363099498446006266115011271978143149347765073211516486037823196033938908784720042927986421555211961923200006343296692217770693318701970436618066568854673260978968978974409802211538011638213976732286150311971354861300195440286582255769421094876667270445809991401456443444265323573485901383 t = 6039738711082505929 z = 13991757597132156574040593242062545731003627107933800388678432418251474177745394167528325524552592875014173967690166427876430087295180152485599151947856471802414472083299904768768434074446565880773029215057131908495627123103779932128807797869164409662146821626628200600678966223382354752280901657213357146668056525234446747959642220954294230018094612469738051942026463767172625588865125393400027831917763819584423585903587577154729283694206436985549513217882666427997109549686825235958909428605247221998366006018410026392446064720747424287400728961283471932279824049509228058334419865822774654587977497006575152095818 p = [15013, 583343756982313, 585503197547927, 609245815680559, 612567235432583, 634947980859229, 635224892351513, 639438000563939, 654170414254271, 654269804672441, 667954470985657, 706144068530309, 721443717105973, 737993471695639, 744872496387077, 746232585529679, 795581973851653, 815694637597057, 817224718609627, 841183196554507, 864339847436159, 873021823131881, 884236929660113, 899583643974479, 922745965897867, 942872831732189, 951697329369323, 971274523714349, 1017566110290559, 1018452110902339, 1025985735184171, 1027313536626551, 1059774237802229, 1067609726096989, 1070689247726159, 1079289330417443, 1098516592571807, 1107673252158281, 1108654254305327, 1110918654474373, 1111516996694389, 1112193819715441] phi = reduce(lambda x,y:x*y, [i-1 for i in p]) l = pow(2, pow(2, t, phi), n) m = l ^ z ^ c print(long_to_bytes(m))

199.5

722

0.93985

90

2,793

29.144444

0.822222

0.001525

0

0.927831

0.032581

2,793

14

723

199.5

0.042931

0

1

0

1

0

false

0

0.181818

0

0.181818

0.090909

0

1

null

0

1

0

1

0

null

1

0

5

87f1c33abceab487494b2800188287698ef8c0b3

188

py

Python

mneme/utils/__init__.py

GarrettMFlynn/Kinesis

07317a7556eacd49f7174bb4bd876bb73f9ce690

[ "MIT" ]

2

2020-02-26T19:08:40.000Z

2020-02-27T03:09:36.000Z

mneme/utils/__init__.py

Mousai-Neurotechnologies/Kinesis

07317a7556eacd49f7174bb4bd876bb73f9ce690

[ "MIT" ]

1

2020-02-24T08:58:00.000Z

mneme/utils/__init__.py

Mousai-Neurotechnologies/Kinesis

07317a7556eacd49f7174bb4bd876bb73f9ce690

[ "MIT" ]

null

# -*- coding:utf-8 -*- ''' :mod:'mneme.utils' provides functions for operating the Mneme pipeline ''' from . import (features,filters,plots,realtime_streams,realtime_viewer,utility_funcs)

31.333333

85

0.755319

24

188

5.791667

0.916667

0

0.005882

0.095745

188

6

85

31.333333

0.811765

0.489362

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

87f37745953b62047a0f70489b37458990cd6709

62

py

Python

lib/__init__.py

rjrivero/olmreader

88e723359423736a5637881a9ddc2495e03af0d0

[ "MIT" ]

null

lib/__init__.py

rjrivero/olmreader

88e723359423736a5637881a9ddc2495e03af0d0

[ "MIT" ]

2

2021-02-02T22:36:24.000Z

2021-08-23T20:43:54.000Z

lib/__init__.py

rjrivero/olmreader

88e723359423736a5637881a9ddc2495e03af0d0

[ "MIT" ]

null

"""Main API of lib module""" from .message import Base, Email

20.666667

32

0.709677

10

62

4.4

1

0

0.16129

62

2

33

31

0.846154

0.354839

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

356cf61659414fc86136062350017010e46899a7

173

py

Python

src/movie_service/main.py

kenesparta/movie-service-api

9b7e132e9729af516dfa9f2f9bc7613e392624f8

[ "MIT" ]

4

2021-05-03T19:37:43.000Z

2021-05-03T22:40:51.000Z

src/movie_service/main.py

kenesparta/movie-service-api

9b7e132e9729af516dfa9f2f9bc7613e392624f8

[ "MIT" ]

null

src/movie_service/main.py

kenesparta/movie-service-api

9b7e132e9729af516dfa9f2f9bc7613e392624f8

[ "MIT" ]

null

from waitress import serve import config from app import app, register_routes if __name__ == "__main__": register_routes() serve(app, listen=config.APP['LISTEN'])

19.222222

43

0.739884

23

173

5.130435

0.521739

0.237288

0

0.16185

173

8

44

21.625

0.813793

0

0.080925

0

1

0

true

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

357747ffddd999b4136081c220de4e8bb10dcdb2

58

py

Python

starfish_api/__init__.py

DEX-Company/starfish-api

44a98fbc9d53ce9f333cd004b02e64b52cd40d16

[ "Apache-2.0" ]

1

2019-03-26T19:14:55.000Z

starfish_api/__init__.py

DEX-Company/starfish-api

44a98fbc9d53ce9f333cd004b02e64b52cd40d16

[ "Apache-2.0" ]

null

starfish_api/__init__.py

DEX-Company/starfish-api

44a98fbc9d53ce9f333cd004b02e64b52cd40d16

[ "Apache-2.0" ]

null

def docs(): print('only for building documentation')

14.5

44

0.689655

7

58

5.714286

1

0

0.189655

58

3

45

19.333333

0.851064

0

0.54386

0

1

0.5

true

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

5

358020267dfee627b65dcf9f0954950de2a8c6aa

144

py

Python

python3/july/day_15_ Reverse Words in a String.py

kashyapvinay/leetcode-challenge

750b0056cb547dc5266d142a9a5048ebd50d8ae3

[ "MIT" ]

1

2020-06-01T11:35:46.000Z

python3/july/day_15_ Reverse Words in a String.py

kashyapvinay/leetcode-challenge

750b0056cb547dc5266d142a9a5048ebd50d8ae3

[ "MIT" ]

null

python3/july/day_15_ Reverse Words in a String.py

kashyapvinay/leetcode-challenge

750b0056cb547dc5266d142a9a5048ebd50d8ae3

[ "MIT" ]

null

class Solution: def reverseWords(self, s: str) -> str: return " ".join(filter(lambda x: x.strip(), reversed(s.strip().split(" "))))

36

84

0.604167

19

144

4.578947

0.789474

0

0.1875

144

3

85

48

0.74359

0

0.013889

0

1

0.333333

false

0

0.333333

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

358202e52e2e6be0e1cb8dead48c2beaea1b45a2

3,125

py

Python

usaspending_api/references/tests/test_total_budgetary_resources_v2.py

ststuck/usaspending-api

b13bd5bcba0369ff8512f61a34745626c3969391

[ "CC0-1.0" ]

217

2016-11-03T17:09:53.000Z

2022-03-10T04:17:54.000Z

usaspending_api/references/tests/test_total_budgetary_resources_v2.py

ststuck/usaspending-api

b13bd5bcba0369ff8512f61a34745626c3969391

[ "CC0-1.0" ]

622

2016-09-02T19:18:23.000Z

2022-03-29T17:11:01.000Z

usaspending_api/references/tests/test_total_budgetary_resources_v2.py

ststuck/usaspending-api

b13bd5bcba0369ff8512f61a34745626c3969391

[ "CC0-1.0" ]

93

2016-09-07T20:28:57.000Z

2022-02-25T00:25:27.000Z

import pytest from model_mommy import mommy from rest_framework import status from decimal import Decimal from usaspending_api.common.helpers.generic_helper import get_account_data_time_period_message @pytest.fixture def create_gtas_data(): mommy.make("references.GTASSF133Balances", id=1, fiscal_year=2020, fiscal_period=2, total_budgetary_resources_cpe=1) mommy.make("references.GTASSF133Balances", id=2, fiscal_year=2020, fiscal_period=2, total_budgetary_resources_cpe=2) mommy.make("references.GTASSF133Balances", id=3, fiscal_year=2020, fiscal_period=3, total_budgetary_resources_cpe=4) mommy.make("references.GTASSF133Balances", id=4, fiscal_year=2019, fiscal_period=2, total_budgetary_resources_cpe=8) @pytest.mark.django_db def test_no_params(client, create_gtas_data): resp = client.get("/api/v2/references/total_budgetary_resources/") assert resp.status_code == status.HTTP_200_OK assert resp.data == { "results": [ {"fiscal_year": 2020, "fiscal_period": 3, "total_budgetary_resources": Decimal(4)}, {"fiscal_year": 2020, "fiscal_period": 2, "total_budgetary_resources": Decimal(3)}, {"fiscal_year": 2019, "fiscal_period": 2, "total_budgetary_resources": Decimal(8)}, ], "messages": [get_account_data_time_period_message()], } @pytest.mark.django_db def test_just_fy(client, create_gtas_data): resp = client.get("/api/v2/references/total_budgetary_resources/?fiscal_year=2020") assert resp.status_code == status.HTTP_200_OK assert resp.data == { "results": [ {"fiscal_year": 2020, "fiscal_period": 3, "total_budgetary_resources": Decimal(4)}, {"fiscal_year": 2020, "fiscal_period": 2, "total_budgetary_resources": Decimal(3)}, ], "messages": [], } @pytest.mark.django_db def test_fy_and_fp(client, create_gtas_data): resp = client.get("/api/v2/references/total_budgetary_resources/?fiscal_year=2020&fiscal_period=2") assert resp.status_code == status.HTTP_200_OK assert resp.data == { "results": [{"fiscal_year": 2020, "fiscal_period": 2, "total_budgetary_resources": Decimal(3)}], "messages": [], } resp = client.get("/api/v2/references/total_budgetary_resources/?fiscal_year=2019&fiscal_period=2") assert resp.status_code == status.HTTP_200_OK assert resp.data == { "results": [{"fiscal_year": 2019, "fiscal_period": 2, "total_budgetary_resources": Decimal(8)}], "messages": [], } resp = client.get("/api/v2/references/total_budgetary_resources/?fiscal_year=2019&fiscal_period=4") assert resp.status_code == status.HTTP_200_OK assert resp.data == {"results": [], "messages": []} @pytest.mark.django_db def test_bad_params(client, create_gtas_data): resp = client.get("/api/v2/references/total_budgetary_resources/?fiscal_period=3") assert resp.status_code == status.HTTP_400_BAD_REQUEST resp = client.get("/api/v2/references/total_budgetary_resources/?fiscal_year=2015&fiscal_period=1") assert resp.status_code == status.HTTP_422_UNPROCESSABLE_ENTITY

43.402778

120

0.72288

412

3,125

5.162621

0.167476

0.118477

0.19464

0.084626

0.881053

0.809591

0.760696

0.697226

0.650212

0

0.050524

0.14496

3,125

71

121

44.014085

0.745509

0

0.421053

0

0.3232

0.24544

0

0.210526

1

0.087719

false

0

0.087719

0

0.175439

0

null

0

1

0

1

0

1

0

null

0

5

358479ae2fe17e597b1d8a4d5cdc413fb2b8b1f2

278

py

Python

app/app/mycal.py

cloudyrathor/khana-khajana-api

c9b7b0360fcf8450f4bf8f2a35a23c264a0c895c

[ "MIT" ]

1

2021-07-23T16:05:38.000Z

app/app/mycal.py

cloudyrathor/khana-khajana-api

c9b7b0360fcf8450f4bf8f2a35a23c264a0c895c

[ "MIT" ]

null

app/app/mycal.py

cloudyrathor/khana-khajana-api

c9b7b0360fcf8450f4bf8f2a35a23c264a0c895c

[ "MIT" ]

null

def addit(x,y): return x + y def subit(x,y): return x-y def multit(x,y): return x * y def divit(x,y): try: return x/y except: if y ==0: print("Denominator should not zero") else: print("It should be number")

16.352941

48

0.503597

44

278

3.181818

0.477273

0.114286

0.228571

0.192857

0.278571

0

0.005747

0.374101

278

17

49

16.352941

0.798851

0

0.164875

0

1

0.285714

false

0

0.214286

0.571429

0.142857

0

null

0

1

0

null

0

1

0

1

0

5

35899de4af44f58de51e7d9f6681f793e1e51f05

122

py

Python

irisdct/inpaint/__init__.py

DenisUllmann/IRIS

c7963843594ee4cca44134d21816c1d11d8d6203

[ "MIT" ]

null

irisdct/inpaint/__init__.py

DenisUllmann/IRIS

c7963843594ee4cca44134d21816c1d11d8d6203

[ "MIT" ]

null

irisdct/inpaint/__init__.py

DenisUllmann/IRIS

c7963843594ee4cca44134d21816c1d11d8d6203

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Tue Jun 12 20:28:52 2018 @author: Denis """ from .inpaintn import inpaintn

15.25

36

0.606557

18

122

4.111111

0.944444

0

0.138298

0.229508

122

8

37

15.25

0.648936

0.606557

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

3591e5f531bce7fc6e8f025adecbf64bad11aac5

129

py

Python

csrf_tutorial_backed/comments/admin.py

twtrubiks/CSRF-tutorial

e627faf78dd74643778b8a1a25fcc11461752e30

[ "MIT" ]

61

2017-10-09T16:07:07.000Z

2022-01-09T23:50:28.000Z

csrf_tutorial_backed/comments/admin.py

twtrubiks/CSRF-tutorial

e627faf78dd74643778b8a1a25fcc11461752e30

[ "MIT" ]

1

2020-05-17T04:16:33.000Z

2020-05-18T03:55:58.000Z

csrf_tutorial_backed/comments/admin.py

twtrubiks/CSRF-tutorial

e627faf78dd74643778b8a1a25fcc11461752e30

[ "MIT" ]

5

2017-10-11T15:29:29.000Z

2020-03-21T09:12:32.000Z

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

18.428571

35

0.813953

18

129

5.833333

0.666667

0

0.124031

129

6

36

21.5

0.929204

0.20155

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

359e5254506a110f3abce756dc9cc9a632e757e1

69

py

Python

dynalist/__init__.py

mikekocik/dynalist

d9dec7372e6231b903a270ea1eada73336c6f29f

[ "MIT" ]

10

2019-04-13T18:09:34.000Z

2021-02-23T05:31:29.000Z

dynalist/__init__.py

mikekocik/dynalist

d9dec7372e6231b903a270ea1eada73336c6f29f

[ "MIT" ]

1

2018-10-11T21:46:29.000Z

2018-10-16T04:33:38.000Z

dynalist/__init__.py

mikekocik/dynalist

d9dec7372e6231b903a270ea1eada73336c6f29f

[ "MIT" ]

5

2019-01-23T14:12:57.000Z

2020-01-04T10:21:03.000Z

from __future__ import absolute_import from .dynalist import Dynalist

34.5

38

0.884058

9

69

6.222222

0.555556

0

0.101449

69

2

39

34.5

0.903226

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ea692533c7f56c843ce017e5e1281828d096b0eb

127

py

Python

xmlutils/__init__.py

tokibito/python-xmlutils

f173b8ef01fb740d097d1875f205f714982b5829

[ "BSD-3-Clause" ]

null

xmlutils/__init__.py

tokibito/python-xmlutils

f173b8ef01fb740d097d1875f205f714982b5829

[ "BSD-3-Clause" ]

null

xmlutils/__init__.py

tokibito/python-xmlutils

f173b8ef01fb740d097d1875f205f714982b5829

[ "BSD-3-Clause" ]

null

from xmlutils.node import * from xmlutils.renderer import * __all__ = ('Node', 'dict_to_node', 'BaseRenderer', 'XMLRenderer')

25.4

65

0.740157

15

127

5.866667

0.666667

0.272727

0

0.11811

127

4

66

31.75

0.785714

0

0.307087

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

ea6e52eea032225aa28429c80428b2c9d4a02b15

67

py

Python

datasets/__init__.py

zihaoliu123/Feature-Distillation-DNN-Oriented-JPEG-Compression-Against-Adversarial-Examples

b5d523397157f6b9f561b88a79726efeec26e98f

[ "MIT" ]

8

2020-01-08T08:58:29.000Z

2021-06-17T03:40:47.000Z

datasets/__init__.py

zihaoliu123/Feature-Distillation-DNN-Oriented-JPEG-Compression-Against-Adversarial-Examples

b5d523397157f6b9f561b88a79726efeec26e98f

[ "MIT" ]

11

2019-12-16T21:53:29.000Z

2022-02-10T01:19:40.000Z

datasets/__init__.py

I2-Multimedia-Lab/Countering-Adversarial-Examples-Using-JPEG-Compression

5c80091dcf2b80d6d22af8e5e1b103218c36e889

[ "MIT" ]

3

2019-12-22T02:09:29.000Z

2021-09-14T06:59:09.000Z

from .datasets_utils import * from .imagenet import ImageNetDataset

33.5

37

0.850746

8

67

7

0.75

0

0.104478

67

2

37

33.5

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ea7d0c6c487e490cc493e37d1fc80bf1b9d720c2

87

py

Python

RLBotPack/DisasterBot/mechanic/drive_arrive_in_time/__init__.py

L0laapk3/RLBotPack

f54038475d2a57428f3784560755f96bfcf8015f

[ "MIT" ]

13

2019-05-25T20:25:51.000Z

2022-03-19T13:36:23.000Z

RLBotPack/DisasterBot/mechanic/drive_arrive_in_time/__init__.py

L0laapk3/RLBotPack

f54038475d2a57428f3784560755f96bfcf8015f

[ "MIT" ]

53

2019-06-07T13:31:59.000Z

2022-03-28T22:53:47.000Z

RLBotPack/DisasterBot/mechanic/drive_arrive_in_time/__init__.py

L0laapk3/RLBotPack

f54038475d2a57428f3784560755f96bfcf8015f

[ "MIT" ]

78

2019-06-30T08:42:13.000Z

2022-03-23T20:11:42.000Z

from .drive_arrive_in_time import DriveArriveInTime, throttle_velocity, boost_velocity

43.5

86

0.896552

11

87

6.636364

0.909091

0

0.068966

87

1

87

0.901235

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

ea8771e20b3bd80f07ce7692412b8b3bfc69e026

44

py

Python

tests/__init__.py

maxdjohnson/stkclient

fe38b01050348ab60dcf6726beb4524265cd101b

[ "MIT" ]

1

2022-02-10T05:15:30.000Z

tests/__init__.py

maxdjohnson/stkclient

fe38b01050348ab60dcf6726beb4524265cd101b

[ "MIT" ]

5

2022-03-01T12:28:24.000Z

2022-03-30T12:37:09.000Z

tests/__init__.py

maxdjohnson/stkclient

fe38b01050348ab60dcf6726beb4524265cd101b

[ "MIT" ]

null

"""Test suite for the stkclient package."""

22

43

0.704545

6

44

5.166667

1

0

0.136364

44

1

44

0.815789

0.840909

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

576cb27e8bdc7b5987706a35df293ddb3a726ceb

85

py

Python

tests/test_properties.py

meirdev/properties

d3b24ff8d6396951c4b01f9890a3fb2a448bff1a

[ "MIT" ]

null

tests/test_properties.py

meirdev/properties

d3b24ff8d6396951c4b01f9890a3fb2a448bff1a

[ "MIT" ]

null

tests/test_properties.py

meirdev/properties

d3b24ff8d6396951c4b01f9890a3fb2a448bff1a

[ "MIT" ]

null

import properties def test_version(): assert properties.__version__ == "0.3.0"

14.166667

44

0.717647

11

85

5.090909

0.727273

0

0.042254

0.164706

85

5

45

17

0.746479

0

0.058824

0

0.333333

1

0.333333

true

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

5

57801ea4ab976ce4372b848a37ce661e42fa205a

538

py

Python

src/uvm/reg/__init__.py

rodrigomelo9/uvm-python

e3127eba2cc1519a61dc6f736d862a8dcd6fce20

[ "Apache-2.0" ]

140

2020-01-18T00:14:17.000Z

2022-03-29T10:57:24.000Z

src/uvm/reg/__init__.py

Mohsannaeem/uvm-python

1b8768a1358d133465ede9cadddae651664b1d53

[ "Apache-2.0" ]

24

2020-01-18T18:40:58.000Z

2021-03-25T17:39:07.000Z

src/uvm/reg/__init__.py

Mohsannaeem/uvm-python

1b8768a1358d133465ede9cadddae651664b1d53

[ "Apache-2.0" ]

34

2020-01-18T12:22:59.000Z

2022-02-11T07:03:11.000Z

from .uvm_mem import * from .uvm_mem_mam import * from .uvm_reg import * from .uvm_reg_adapter import * from .uvm_reg_backdoor import * from .uvm_reg_block import * from .uvm_reg_cbs import * from .uvm_reg_field import * from .uvm_reg_fifo import * from .uvm_reg_file import * from .uvm_reg_indirect import * from .uvm_reg_item import * from .uvm_reg_map import * from .uvm_reg_model import * from .uvm_reg_predictor import * from .uvm_reg_sequence import * from .uvm_vreg import * from .uvm_vreg_field import * from .sequences import *

25.619048

32

0.786245

90

538

4.333333

0.233333

0.323077

0.566667

0.574359

0

0.143123

538

20

33

26.9

0.845987

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

5

578aa427037c6daf0e78c082029187c0654ea634

16

py

Python

script.py

rshammz/myrepo

75d3b2dd8e93fd84df7170a778dd1f1085957fcb

[ "MIT" ]

null

script.py

rshammz/myrepo

75d3b2dd8e93fd84df7170a778dd1f1085957fcb

[ "MIT" ]

null

script.py

rshammz/myrepo

75d3b2dd8e93fd84df7170a778dd1f1085957fcb

[ "MIT" ]

null

# Just Checking

8

15

0.75

2

16

6

1

0

0.1875

16

1

16

0.923077

0.8125

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

578c86024a2aca7bc104eb3c126e5cfad0380b8f

112

py

Python

tests/unit/test_version.py

hunnybear/tox-via-docker

f758e374d2536929210de2342dffacd61bc22552

[ "MIT" ]

4

2020-05-16T13:50:44.000Z

2021-11-08T10:25:33.000Z

tests/unit/test_version.py

hunnybear/tox-via-docker

f758e374d2536929210de2342dffacd61bc22552

[ "MIT" ]

2

2020-02-03T19:53:47.000Z

2021-05-30T09:23:17.000Z

tests/unit/test_version.py

hunnybear/tox-via-docker

f758e374d2536929210de2342dffacd61bc22552

[ "MIT" ]

5

2019-07-11T09:06:22.000Z

2022-02-16T11:58:23.000Z

def test_version(): pkg = __import__("tox_via_docker", fromlist=["__version__"]) assert pkg.__version__

28

64

0.723214

13

112

5.076923

0.769231

0

0.142857

112

3

65

37.333333

0.6875

0

0.223214

0

0.333333

1

0.333333

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

579dfe0d9a8800dbe40d8b67dadf26904b4265b8

211

py

Python

engines/forms.py

gregorianzhang/octopus

41183daf721a6affa1bfb7c9141f627a8602d637

[ "Apache-2.0" ]

null

engines/forms.py

gregorianzhang/octopus

41183daf721a6affa1bfb7c9141f627a8602d637

[ "Apache-2.0" ]

null

engines/forms.py

gregorianzhang/octopus

41183daf721a6affa1bfb7c9141f627a8602d637

[ "Apache-2.0" ]

null

from django import forms class AddEngineForm(forms.Form): Name = forms.CharField(max_length=30) Cpus = forms.IntegerField() Memory = forms.IntegerField() Addr = forms.CharField(max_length=100)

23.444444

42

0.725118

26

211

5.807692

0.653846

0.18543

0.225166

0.304636

0

0.028571

0.170616

211

8

43

26.375

0.834286

0

1

0

false

0

0.166667

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

5

57ce8c765131f3cb3a3458da5470dfb0cbd724b8

87

py

Python

OAuth/oauthusage.py

AkashShanmugaraj/Major-Project-2021

1cb5eb0f15ade9fc9ed915c1b4c9246bab6b60fd

[ "MIT" ]

1

2021-11-23T23:25:10.000Z

OAuth/oauthusage.py

AkashShanmugaraj/Major-Project-2021

1cb5eb0f15ade9fc9ed915c1b4c9246bab6b60fd

[ "MIT" ]

1

2021-08-17T02:15:41.000Z

OAuth/oauthusage.py

AkashShanmugaraj/Major-Project-2021

1cb5eb0f15ade9fc9ed915c1b4c9246bab6b60fd

[ "MIT" ]

null

import os from subprocess import call call(['python', f"{os.getcwd()}\encrypt.py"])

12.428571

45

0.689655

13

87

4.615385

0.769231

0

0.126437

87

6

46

14.5

0.789474

0

0.348837

0.27907

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

57d2b529e24d6d23113a8f4fd217d5fb0936b268

213

py

Python

psyspy/exceptions/exceptions.py

stantontcady/psyspy

72bc0aaacea4191899a971ef46314f09d97b269f

[ "MIT" ]

1

2016-06-02T16:58:36.000Z

psyspy/exceptions/exceptions.py

stantontcady/psyspy

72bc0aaacea4191899a971ef46314f09d97b269f

[ "MIT" ]

null

psyspy/exceptions/exceptions.py

stantontcady/psyspy

72bc0aaacea4191899a971ef46314f09d97b269f

[ "MIT" ]

null

class BusError(Exception): pass class GeneratorModelError(Exception): pass class ModelError(Exception): pass class PowerLineError(Exception): pass class PowerNetworkError(Exception): pass

13.3125

37

0.741784

20

213

7.9

0.4

0.411392

0.455696

0

0.187793

213

15

38

14.2

0.913295

0

0.5

0

1

0

true

0.5

0

0.5

0

1

0

null

1

0

null

0

1

0

5

57fc4a9aa2a34eff8e7c9f908faf5082dbaa3359

53

py

Python

src/fastberry/types/__init__.py

hlop3z/fastberry

e6e42c7e5bc91942acf40a9ef27092b42d9f9b15

[ "MIT" ]

null

src/fastberry/types/__init__.py

hlop3z/fastberry

e6e42c7e5bc91942acf40a9ef27092b42d9f9b15

[ "MIT" ]

null

src/fastberry/types/__init__.py

hlop3z/fastberry

e6e42c7e5bc91942acf40a9ef27092b42d9f9b15

[ "MIT" ]

null

""" Fastberry Types """ from .model import Model

10.6

24

0.641509

6

53

5.666667

0.833333

0

0.226415

53

4

25

13.25

0.829268

0.283019

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

17d3b22ee8a18e86b4315ee640d1534f55d42dac

23

py

Python

fastapi_sqlalchemy/app/__init__.py

GrishenkovP/fastapi

7bf17b8424866b2466b73bbd90ebd2047825baef

[ "MIT" ]

null

fastapi_sqlalchemy/app/__init__.py

GrishenkovP/fastapi

7bf17b8424866b2466b73bbd90ebd2047825baef

[ "MIT" ]

null

fastapi_sqlalchemy/app/__init__.py

GrishenkovP/fastapi

7bf17b8424866b2466b73bbd90ebd2047825baef

[ "MIT" ]

null

#Для загрузки на GitHub

23

0.826087

4

23

4.75

1

0

0.130435

23

1

23

0.95

0.956522

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

17ea4cd0ee09b880703b2b959053dee7583c6ae2

26

py

Python

envs/multigrid/__init__.py

TorchPAIRED/paired

908ee00f80e72b3823c809d79e6a060b3e920912

[ "Apache-2.0" ]

44

2021-08-20T10:18:06.000Z

2022-03-29T22:26:57.000Z

envs/multigrid/__init__.py

TorchPAIRED/paired

908ee00f80e72b3823c809d79e6a060b3e920912

[ "Apache-2.0" ]

3

2021-12-22T07:01:37.000Z

2022-01-30T15:05:54.000Z

envs/multigrid/__init__.py

TorchPAIRED/paired

908ee00f80e72b3823c809d79e6a060b3e920912

[ "Apache-2.0" ]

7

2021-08-20T12:54:08.000Z

2022-02-28T23:18:21.000Z

from .adversarial import *

26

0.807692

3

26

7

1

0

0.115385

26

1

26

0.913043

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

17f72daf752e729e7396c080c9c6830a425b8a32

145

py

Python

URI/1 - INICIANTE/Python/1144 - SequenciaLogica1.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

1

2020-04-14T16:48:16.000Z

URI/1 - INICIANTE/Python/1144 - SequenciaLogica1.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

null

URI/1 - INICIANTE/Python/1144 - SequenciaLogica1.py

william-james-pj/LogicaProgramacao

629f746e34da2e829dc7ea2e489ac36bb1b1fb13

[ "MIT" ]

null

numero = int(input()) i = 1 while i <= numero: print('{} {} {}'.format(i, i*i,i*i*i)) print('{} {} {}'.format(i,i*i+1, i*i*i+1)) i+=1

24.166667

46

0.455172

28

145

2.357143

0.285714

0.272727

0.181818

0.560606

0

0.034783

0.206897

145

6

47

24.166667

0.53913

0

0.109589

0

1

0

false

0

0.333333

1

0

null

1

0

1

0

null

0

5

aa1bd20452fa51bcef47f9457241a4c9aec2150e

63

py

Python

python/testData/resolve/DocStringClass.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/resolve/DocStringClass.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

173

2018-07-05T13:59:39.000Z

2018-08-09T01:12:03.000Z

python/testData/resolve/DocStringClass.py

jnthn/intellij-community

8fa7c8a3ace62400c838e0d5926a7be106aa8557

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

class Foo: "Docstring of class Foo" pass Foo._<ref>_doc__

10.5

26

0.698413

10

63

4

0.7

0.4

0

0.206349

63

5

27

12.6

0.8

0.349206

0

0.349206

0

1

0

true

0.25

0

0.25

0

1

0

null

1

0

1

0

null

0

1

0

5

a4b665aa6f5be2b55d3c641b37ed99b22cb64712

2,924

py

Python

src/alfred3/__init__.py

mate-code/alfred

e687e9318ecefe3a49565027841ca8d910647978

[ "MIT" ]

9

2020-05-11T08:13:12.000Z

2022-01-20T11:35:14.000Z

src/alfred3/__init__.py

mate-code/alfred

e687e9318ecefe3a49565027841ca8d910647978

[ "MIT" ]

77

2019-02-22T07:34:58.000Z

2022-02-23T15:32:34.000Z

src/alfred3/__init__.py

mate-code/alfred

e687e9318ecefe3a49565027841ca8d910647978

[ "MIT" ]

6

2020-11-11T16:51:04.000Z

2022-02-21T10:29:02.000Z

# -*- coding: utf-8 -*- from ._version import __version__ from .experiment import Experiment from .quota import SessionQuota from .randomizer import ListRandomizer from .randomizer import random_condition from .section import Section from .section import RevisitSection from .section import ForwardOnlySection from .section import HideOnForwardSection from .page import Page from .page import WidePage from .page import UnlinkedDataPage from .page import AutoForwardPage from .page import AutoClosePage from .page import NoNavigationPage from .page import NoDataPage from .page import NoSavingPage from .page import PasswordPage from .element.core import Row from .element.core import Stack from .element.core import RowLayout from .element.display import VerticalSpace from .element.display import Html from .element.display import Text from .element.display import Label from .element.display import Image from .element.display import Audio from .element.display import Video from .element.display import MatPlot from .element.display import Hline from .element.display import CodeBlock from .element.display import ProgressBar from .element.display import Alert from .element.display import ButtonLabels from .element.display import BarLabels from .element.display import CountUp from .element.display import CountDown from .element.display import Card from .element.input import TextEntry from .element.input import TextArea from .element.input import MatchEntry from .element.input import RegEntry from .element.input import EmailEntry from .element.input import PasswordEntry from .element.input import NumberEntry from .element.input import RangeInput from .element.input import SingleChoice from .element.input import MultipleChoice from .element.input import SingleChoiceList # from .element.input import MultipleChoiceList from .element.input import SingleChoiceButtons from .element.input import SingleChoiceBar from .element.input import MultipleChoiceButtons from .element.input import MultipleChoiceBar from .element.input import SelectPageList from .element.action import SubmittingButtons from .element.action import SubmittingBar from .element.action import JumpButtons from .element.action import DynamicJumpButtons from .element.action import JumpList from .element.action import Button from .element.action import BackButton from .element.action import ForwardButton from .element.misc import Style from .element.misc import HideNavigation from .element.misc import JavaScript from .element.misc import WebExitEnabler from .element.misc import Value from .element.misc import Data from .element.misc import Callback from .element.misc import RepeatedCallback from .util import emoji from .util import icon from .util import is_element from .util import is_input_element from .util import is_label from .util import is_page from .util import is_section from .util import multiple_choice_numbers

32.131868

48

0.837893

381

2,924

6.393701

0.24147

0.239327

0.125616

0.167488

0.018883

0

0.000386

0.113543

2,924

91

49

32.131868

0.939429

0.022914

0

1

0

true

0.025641

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

5

a4c5e1abc653c2412fc7c4ac5ebce58ab2c64377

147

py

Python

common/common_path.py

l294265421/natural-language-image-search

71621f2208f345b922ed0f82d406526cef456d48

[ "MIT" ]

null

common/common_path.py

l294265421/natural-language-image-search

71621f2208f345b922ed0f82d406526cef456d48

[ "MIT" ]

null

common/common_path.py

l294265421/natural-language-image-search

71621f2208f345b922ed0f82d406526cef456d48

[ "MIT" ]

null

import os import sys project_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) if __name__ == '__main__': print(project_dir)

18.375

73

0.741497

22

147

4.318182

0.590909

0.189474

0.273684

0.315789

0.336842

0

0.122449

147

7

74

21

0.736434

0

0.054422

0

1

0

false

0

0.4

0

0.4

0.2

1

0

null

0

1

0

1

0

null

0

1

0

5

a4d6d802851c29eacbaea59cc26513e3bb27eb6d

114

py

Python

newsletters/admin.py

byteweaver/django-newsletters

c1fb68856dcb77b1266050a0e084a76abe90b786

[ "BSD-3-Clause" ]

2

2015-12-27T06:56:36.000Z

2016-09-26T06:55:42.000Z

newsletters/admin.py

byteweaver/django-newsletters

c1fb68856dcb77b1266050a0e084a76abe90b786

[ "BSD-3-Clause" ]

2

2015-06-30T08:16:14.000Z

2015-07-01T18:41:57.000Z

newsletters/admin.py

byteweaver/django-newsletters

c1fb68856dcb77b1266050a0e084a76abe90b786

[ "BSD-3-Clause" ]

null

from django.contrib import admin from newsletters.models import Subscription admin.site.register(Subscription)

16.285714

43

0.842105

14

114

6.857143

0.714286

0

0.105263

114

6

44

19

0.941176

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

a4ec8db240a82b5bd535c24cf51d0571b1207c26

39

py

Python

snips_nlu/nlu_engine/__init__.py

ddorian/snips-nlu

0934d386bb138ebb34764446416856cfac664e65

[ "Apache-2.0" ]

1

2021-01-03T09:23:55.000Z

snips_nlu/nlu_engine/__init__.py

ddorian/snips-nlu

0934d386bb138ebb34764446416856cfac664e65

[ "Apache-2.0" ]

null

snips_nlu/nlu_engine/__init__.py

ddorian/snips-nlu

0934d386bb138ebb34764446416856cfac664e65

[ "Apache-2.0" ]

null

from .nlu_engine import SnipsNLUEngine

19.5

38

0.871795

5

39

6.6

1

0

0.102564

39

1

39

0.942857

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

3525acead8431d588ce62d80eb7f55ab1cf38780

153

py

Python

3 while_list/3.1.py

zarina494/fisrt_git_lesson

169fc205b3a99a84f1041d578c4c120555162a66

[ "MIT" ]

null

3 while_list/3.1.py

zarina494/fisrt_git_lesson

169fc205b3a99a84f1041d578c4c120555162a66

[ "MIT" ]

null

3 while_list/3.1.py

zarina494/fisrt_git_lesson

169fc205b3a99a84f1041d578c4c120555162a66

[ "MIT" ]

null

#ishem znak 4isla number=int(input()) if number>0: # esli 4islo >0 print(1) elif number<0: #esli 4islo <0 print(-1) else: print(0) #4islo==0

17

29

0.627451

27

153

3.555556

0.518519

0.1875

0.229167

0.333333

0.479167

0

0.099174

0.20915

153

9

30

17

0.694215

0.333333

0

1

0

false

0

0.428571

1

0

null

0

1

0

1

0

null

0

1

0

5

353bace0aeac4998fcf1c899667025dc27820b7c

223

py

Python

config/__init__.py

HawkTom/EAPY

c9dabbfb233f4a135bf9560bebcf6c01ec9baf94

[ "MIT" ]

2

2020-06-14T07:20:09.000Z

2021-11-20T17:29:03.000Z

config/__init__.py

HawkTom/EAPY

c9dabbfb233f4a135bf9560bebcf6c01ec9baf94

[ "MIT" ]

null

config/__init__.py

HawkTom/EAPY

c9dabbfb233f4a135bf9560bebcf6c01ec9baf94

[ "MIT" ]

null

from .functionConfig import ContinueFunctionParameter from .algorithmConfig import AlgorithmParameter from .trialConfig import TrialParameter __all__ = ['ContinueFunctionParameter', 'AlgorithmParameter', 'TrialParameter']

37.166667

79

0.856502

16

223

11.6875

0.5625

0

0.080717

223

5

80

44.6

0.912195

0

0.255605

0.112108

0

1

0

false

0

0.75

0

0.75

0

1

0

1

null

0

1

0

null

0

1

0

1

0

5

353c7943f7be61dc3068a20fad8e786891b92ebc

195

py

Python

pycalphad/tests/test_variables.py

amkrajewski/pycalphad

313bf8042ff415abfcf979cb8a0491b8612ef96a

[ "MIT" ]

2

2021-06-16T19:46:35.000Z

2021-11-17T11:13:56.000Z

pycalphad/tests/test_variables.py

amkrajewski/pycalphad

313bf8042ff415abfcf979cb8a0491b8612ef96a

[ "MIT" ]

null

pycalphad/tests/test_variables.py

amkrajewski/pycalphad

313bf8042ff415abfcf979cb8a0491b8612ef96a

[ "MIT" ]

null

""" Test variables module. """ from pycalphad import variables as v def test_species_parse_unicode_strings(): """Species should properly parse unicode strings.""" s = v.Species(u"MG")

17.727273

56

0.712821

26

195

5.192308

0.692308

0.177778

0.281481

0

0.169231

195

10

57

19.5

0.833333

0.353846

0

0.017699

0

1

0.333333

false

0

0.333333

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1031ec7f0bbe3de2fc67a7d4c19d687b3a0dda66

10,013

py

Python

tests.py

donarb/appsync-py

1e8dc6eaf424b9693caa4a59bfd3a02ddb16756e

[ "BSD-3-Clause" ]

null

tests.py

donarb/appsync-py

1e8dc6eaf424b9693caa4a59bfd3a02ddb16756e

[ "BSD-3-Clause" ]

null

tests.py

donarb/appsync-py

1e8dc6eaf424b9693caa4a59bfd3a02ddb16756e

[ "BSD-3-Clause" ]

null

from time import sleep import unittest from server import Server from client import Client, TIMESTAMPPRIORITY from object import Object # # Test scenarios: # 1. sync from server to client: new objects and object updates # 2. sync from client to server: new objects and object updates # 3. sync from client to server to other client # 4. no unneeded syncing (e.g. client syncs update to server, and when client syncs again it receives its own update again, this should not occur) # 5. syncing of deleted objects (isdeleted=1) # 6. syncing with conflict handling: object is updated on client and on server and then syncing takes place # 7. syncing with primary key conflict: object with same PK is created both on client and on server and then syncing takes place # 8. syncing with primary key conflict: object with same PK is created client A and client B and then syncing takes place # 9. full sync, with locally created objects that are not synced yet # class ClientServerTest(unittest.TestCase): def setUp(self): self.server = Server("server") self.client1 = Client("client1", self.server) self.client1.handling = TIMESTAMPPRIORITY self.client2 = Client("client2", self.server) self.client2.handling = TIMESTAMPPRIORITY def testSyncFromServerToClient(self): ''' Add an object on the server and sync with the client ''' self.server.add_object("2014-05-10", "apples", "3") self.assertEqual(len(self.server.objects), 1) o = self.server.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.name, "apples") self.assertEqual(o.value, "3") self.assertEqual(self.server.counter, 1) # Make sure the client has no objects self.assertEqual(len(self.client1.objects), 0) # Now sync the server to the client self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) # Sync the client to the server again, should be no change self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) # Update the object on the server self.server.update_object("2014-05-10", "5") self.assertEqual(self.server.counter, 2) # Sync the client self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) # Check that the object was updated on the client o = self.client1.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.value, "5") def testSyncFromClientToServer(self): ''' Add an object on the client and sync with the server ''' self.client1.add_object("2014-05-10", "apples", "3") self.assertEqual(len(self.client1.objects), 1) o = self.client1.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.name, "apples") self.assertEqual(o.value, "3") self.assertEqual(self.client1.counter, 1) # Make sure the server has no objects self.assertEqual(len(self.server.objects), 0) # Now sync the client to the server self.client1.do_sync() # Server should now have 1 object self.assertEqual(len(self.server.objects), 1) # Sync the client to the server again, should be no change self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) self.assertEqual(len(self.server.objects), 1) # Update the object on the client self.client1.update_object("2014-05-10", "5") self.assertEqual(self.client1.counter, 2) # Sync the client self.client1.do_sync() # Check that the object was updated on the server o = self.server.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.value, "5") def testSyncFromClient1ToServerToClient2(self): ''' Add an object on client1, sync with the server, then sync with client2 ''' self.client1.add_object("2014-05-10", "apples", "3") self.assertEqual(len(self.client1.objects), 1) # Make sure the server and client2 have no objects self.assertEqual(len(self.server.objects), 0) self.assertEqual(len(self.client2.objects), 0) # Now sync the client to the server self.client1.do_sync() # Server should now have 1 object self.assertEqual(len(self.server.objects), 1) # Sync client2 to the server self.client2.do_sync() # Client2 should now have 1 object self.assertEqual(len(self.client2.objects), 1) self.assertEqual(self.client2.counter, 0) # Sync client1 to the server again, should be no change self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) # Sync client2 to the server again, should be no change self.client2.do_sync() self.assertEqual(len(self.client2.objects), 1) # Update the object on client1 self.client1.update_object("2014-05-10", "5") self.assertEqual(self.client1.counter, 2) # Sync client1 self.client1.do_sync() self.assertEqual(self.client1.counter, 2) # Sync client2 self.client2.do_sync() self.assertEqual(self.client2.counter, 0) # Check that the object on client2 has been updated from the server o = self.client2.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.value, "5") # Update the object on client2 self.client2.update_object("2014-05-10", "7") self.assertEqual(self.client2.counter, 1) # Sync client2 self.client2.do_sync() self.assertEqual(self.client2.counter, 1) # Check that the object on server has been updated from client2 o = self.server.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.value, "7") # Sync client1 self.client1.do_sync() self.assertEqual(self.client1.counter, 2) # Check that the object on client1 has been updated from the server o = self.client1.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.value, "7") def testForUnneededSync(self): ''' ''' self.client1.add_object("2014-05-10", "apples", "3") self.assertEqual(len(self.client1.objects), 1) # Sync client1 to the server self.client1.do_sync() self.assertEqual(len(self.server.objects), 1) # Sync client1 to the server again, should be no change self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) # Sync client2 to the server self.client2.do_sync() self.assertEqual(len(self.client2.objects), 1) # Sync client1 to the server again, should be no change self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) # Sync client2 to the server again, should be no change self.client2.do_sync() self.assertEqual(len(self.client2.objects), 1) # Sync client1 to the server again, should be no change self.client1.do_sync() self.assertEqual(len(self.client1.objects), 1) # Sync client2 to the server again, should be no change self.client2.do_sync() self.assertEqual(len(self.client2.objects), 1) def testDeletedItems(self): ''' Syncing of deleted items ''' self.client1.add_object("2014-05-10", "apples", "3") self.assertEqual(len(self.client1.objects), 1) o = self.client1.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.name, "apples") self.assertEqual(o.value, "3") self.assertEqual(self.client1.counter, 1) # Make sure the server has no objects self.assertEqual(len(self.server.objects), 0) # Sync the client to the server self.client1.do_sync() # Delete the object self.client1.delete_object("2014-05-10") # Sync the client to the server self.client1.do_sync() # Check that the object was marked deleted o = self.server.objects[0] self.assertTrue(o.deleted) def testSyncConflict(self): ''' Syncing with conflict handling - ojbect is updated on client and on server, then syncing takes place ''' self.client1.add_object("2014-05-10", "apples", "3") self.assertEqual(len(self.client1.objects), 1) o = self.client1.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.name, "apples") self.assertEqual(o.value, "3") self.assertEqual(self.client1.counter, 1) # Make sure the server has no objects self.assertEqual(len(self.server.objects), 0) # Sync the client to the server self.client1.do_sync() # Update the object on the client self.client1.update_object("2014-05-10", "5") # Make sure timestamplastupdate on client and server is different sleep(2) # Update the object on the server self.server.update_object("2014-05-10", "7") self.assertEqual(self.server.counter, 2) # Sync the client to the server self.client1.do_sync() o = self.client1.objects[0] self.assertEqual(o.pk, "2014-05-10") self.assertEqual(o.value, "7") if __name__ == '__main__': unittest.main()

36.410909

146

0.609408

1,293

10,013

4.683681

0.091261

0.165951

0.086196

0.10535

0.788804

0.747523

0.718791

0.693362

0.668098

0.624009

0

0.05309

0.288924

10,013

274

147

36.543796

0.797472

0.286727

0

0.775362

0

0.048316

0

0.492754

1

0.050725

false

0

0.036232

0

0.094203

0

null

0

1

0

1

0

1

0

null

0

1

0

5

1068bbb62547f69a0e91cdd8f0fb4982b1600ac2

55

py

Python

src/perimeterator/dispatcher/__init__.py

vvondra/perimeterator

6f750b5c8e6ff151472911bb45c6f11c0a6cd8ff

[ "MIT" ]

null

src/perimeterator/dispatcher/__init__.py

vvondra/perimeterator

6f750b5c8e6ff151472911bb45c6f11c0a6cd8ff

[ "MIT" ]

null

src/perimeterator/dispatcher/__init__.py

vvondra/perimeterator

6f750b5c8e6ff151472911bb45c6f11c0a6cd8ff

[ "MIT" ]

null

from perimeterator.dispatcher import csv # noqa: F401

27.5

54

0.8

7

55

6.285714

1

0

0.06383

0.145455

55

1

55

0.87234

0.181818

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

107d81f275c509228d5e4865f930cff3a5299d43

478

py

Python

pepperbot/message/pokes.py

SSmJaE/PepperBot

0f34c90fc8f6d90fd8881193992d0dde756c2dde

[ "MIT" ]

27

2021-03-26T16:17:38.000Z

2022-03-30T21:39:07.000Z

pepperbot/message/pokes.py

SSmJaE/PepperBot

0f34c90fc8f6d90fd8881193992d0dde756c2dde

[ "MIT" ]

null

pepperbot/message/pokes.py

SSmJaE/PepperBot

0f34c90fc8f6d90fd8881193992d0dde756c2dde

[ "MIT" ]

7

2021-05-27T17:28:37.000Z

2021-12-22T11:22:08.000Z

# from .MessageSegment import Poke # class PokeTypes: # 戳一戳 = Poke(1, -1) # 比心 = Poke(2, -1) # 点赞 = Poke(3, -1) # 心碎 = Poke(4, -1) # sixsixsix = Poke(5, -1) # 放大招 = Poke(6, -1) # 宝贝球 = Poke(126, 2011) # 玫瑰花 = Poke(126, 2007) # 召唤术 = Poke(126, 2006) # 让你皮 = Poke(126, 2009) # 结印 = Poke(126, 2005) # 手雷 = Poke(126, 2004) # 勾引 = Poke(126, 2003) # 抓一下 = Poke(126, 2001) # 碎屏 = Poke(126, 2002) # 敲门 = Poke(126, 2002)

22.761905

34

0.48954

70

478

3.342857

0.542857

0.299145

0.094017

0

0.253086

0.322176

478

20

35

23.9

0.469136

0.91841

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

1

0

1

0

1

0

null

0

1

0

5

52eee12eb96f9e21f72cda52a78e0d1dc77a7f4d

162

py

Python

pywizlight/__init__.py

fabaff/pywizlight

395e63846dd8bcfc99a65d50252c6a71e02590c4

[ "MIT" ]

1

2021-04-02T17:22:52.000Z

pywizlight/__init__.py

fabaff/pywizlight

395e63846dd8bcfc99a65d50252c6a71e02590c4

[ "MIT" ]

null

pywizlight/__init__.py

fabaff/pywizlight

395e63846dd8bcfc99a65d50252c6a71e02590c4

[ "MIT" ]

null

from pywizlight.bulb import PilotBuilder, PilotParser, wizlight, discovery # noqa: 401 from pywizlight.scenes import SCENES # noqa: 401 __all__ = ["wizlight"]

32.4

87

0.771605

19

162

6.368421

0.631579

0.231405

0

0.043165

0.141975

162

4

88

40.5

0.827338

0.117284

0

0.057143

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

5e135a07808c4bdbad1ef862f01e5d7745926b02

16,741

py

Python

scripts/db_test.py

duguyue100/spikefuel

e06713b62c0bc7f881dd75a5a4842723cce4aaab

[ "MIT" ]

12

2016-05-12T09:58:19.000Z

2021-04-10T02:46:21.000Z

scripts/db_test.py

colinshane/spikefuel

e06713b62c0bc7f881dd75a5a4842723cce4aaab

[ "MIT" ]

1

2019-07-08T03:50:02.000Z

2019-07-09T07:22:18.000Z

scripts/db_test.py

colinshane/spikefuel

e06713b62c0bc7f881dd75a5a4842723cce4aaab

[ "MIT" ]

10

2016-04-09T01:58:22.000Z

2020-06-07T05:13:46.000Z

"""Testing dataset stats generation. Author: Yuhuang Hu Email : duguyue100@gmail.com """ from __future__ import print_function import os import numpy as np import cv2 import cPickle as pickle import h5py from spikefuel import dvsproc, helpers from time import gmtime, strftime option = "export-vot-bounding-boxes" data_path = os.environ["SPIKEFUEL_DATA"] stats_path = os.path.join(data_path, "sf_data") if option == "vot": # Load VOT Challenge Dataset vot_fn = "INI_VOT_30fps_20160424.hdf5" vot_path = os.path.join(data_path, vot_fn) vot_db = h5py.File(vot_path, mode="r") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] avg_num_frames = np.average(np.asarray(num_frames)) print("Average Number of Frames: %.2f" % (avg_num_frames)) tot_t = 0. tot_freq = 0. avg_freq = 0. for vidseq in vot_list: timestamps = vot_db[vidseq]["timestamps"][()] tot_t += (timestamps[-1]-timestamps[0])/1e6 event_arr = dvsproc.cal_event_count(timestamps) event_freq = dvsproc.cal_event_freq(event_arr, window=1000000) tot_freq += np.max(event_freq[:, 1]) t = float(timestamps[-1]-timestamps[0])/1e6 avg_freq += float(timestamps.shape[0])/float(t) print("Video sequence %s is processed" % (vidseq)) print("Average Recording Length: %.2f s" % (tot_t/len(vot_list))) print("Average Maximum Firing Rate: %.2f K" % (tot_freq/len(vot_list)/1e3)) print("Average Firing Rate: %.2f K" % (avg_freq/len(vot_list)/1e3)) if option == "tracking": tracking_fn = "INI_TrackingDataset_30fps_20160424.hdf5" tracking_path = os.path.join(data_path, tracking_fn) tracking_db = h5py.File(tracking_path, mode="r") tracking_stats_path = os.path.join(stats_path, "tracking_stats.pkl") f = file(tracking_stats_path, mode="r") tracking_stats = pickle.load(f) f.close() pl = tracking_stats["primary_list"] sl = tracking_stats["secondary_list"] num_videos = 0. tot_frames = 0. tot_t = 0. tot_freq = 0. avg_freq = 0. for pc in pl: # remove sequence Kalal until I got more memory if pc != "Kalal": for sc in sl[pc]: num_videos += 1 tot_frames += int(tracking_db[pc][sc].attrs["num_frames"]) timestamps = tracking_db[pc][sc]["timestamps"][()] tot_t += (timestamps[-1]-timestamps[0])/1e6 event_arr = dvsproc.cal_event_count(timestamps) event_freq = dvsproc.cal_event_freq(event_arr, window=1000000) tot_freq += np.max(event_freq[:, 1]) t = float(timestamps[-1]-timestamps[0])/1e6 avg_freq += float(timestamps.shape[0])/float(t) print("Video sequence %s is processed" % (sc)) print("Total Number of Videos: %.2f" % (num_videos)) print("Average Number of Frames: %.2f" % (tot_frames/num_videos)) print("Average Recording Length: %.2f s" % (tot_t/num_videos)) print("Average Maximum Firing Rate: %.2f K" % (tot_freq/num_videos/1e3)) print("Average Firing Rate: %.2f K" % (avg_freq/num_videos/1e3)) if option == "ucf50": ucf50_fn = "INI_UCF50_30fps_20160424.hdf5" ucf50_path = os.path.join(data_path, ucf50_fn) ucf50_db = h5py.File(ucf50_path, mode="r") ucf50_stats_path = os.path.join(stats_path, "ucf50_stats.pkl") f = file(ucf50_stats_path, mode="r") ucf50_stats = pickle.load(f) f.close() ucf50_list = ucf50_stats["ucf50_list"] num_videos = 0. tot_frames = 0. tot_t = 0. tot_freq = [] avg_freq = [] for cn in ucf50_list: for vid_name in ucf50_stats[cn]: vid_n, vid_ex = os.path.splitext(vid_name) num_videos += 1 tot_frames += int(ucf50_db[cn][vid_n].attrs["num_frames"]) timestamps = ucf50_db[cn][vid_n]["timestamps"][()] tot_t += (timestamps[-1]-timestamps[0])/1e6 event_arr = dvsproc.cal_event_count(timestamps) event_freq = dvsproc.cal_event_freq(event_arr, window=1000000) t = float(timestamps[-1]-timestamps[0])/1e6 tot_freq.append(np.max(event_freq[:, 1])) avg_freq.append(float(timestamps.shape[0])/float(t)) print("Video sequence %s is processed" % (vid_n)) average_freq = np.average(np.asarray(tot_freq)) mean_freq = np.average(np.asarray(avg_freq)) print("Total Number of Videos: %.2f" % (num_videos)) print("Average Number of Frames: %.2f" % (tot_frames/num_videos)) print("Average Recording Length: %.2f s" % (tot_t/num_videos)) print("Average Maximum Firing Rate: %.2f K" % (average_freq/1e3)) print("Average Firing Rate: %.2f K" % (mean_freq/1e3)) if option == "caltech256": caltech_fn = "INI_Caltech256_10fps_20160424.hdf5" caltech_path = os.path.join(data_path, caltech_fn) caltech_db = h5py.File(caltech_path, mode="r") caltech_stats_path = os.path.join(stats_path, "caltech256_stats.pkl") f = file(caltech_stats_path, mode="r") caltech_stats = pickle.load(f) f.close() caltech_list = caltech_stats["caltech256_list"] num_videos = 0. tot_t = 0. tot_freq = [] avg_freq = [] wrong_recordings = [] for cn in caltech_list: for img_name in caltech_stats[cn]: img_n, img_ex = os.path.splitext(img_name) num_videos += 1 timestamps = caltech_db[cn][img_n]["timestamps"][()] if timestamps.size != 0: tot_t += (timestamps[-1]-timestamps[0])/1e6 event_arr = dvsproc.cal_event_count(timestamps) event_freq = dvsproc.cal_event_freq(event_arr, window=1000000) t = float(timestamps[-1]-timestamps[0])/1e6 tot_freq.append(np.max(event_freq[:, 1])) avg_freq.append(float(timestamps.shape[0])/float(t)) else: wrong_recordings.append(img_n) print("Video sequence %s is processed" % (img_n)) average_freq = np.average(np.asarray(tot_freq)) mean_freq = np.average(np.asarray(avg_freq)) print("Total Number of Videos: %.2f" % (num_videos)) print("Average Recording Length: %.2f s" % (tot_t/num_videos)) print("Average Maximum Firing Rate: %.2f K" % (average_freq/1e3)) print("Average Firing Rate: %.2f K" % (mean_freq/1e3)) print(wrong_recordings) if option == "export-vot-bounding-boxes": vot_fn = "INI_VOT_30fps_20160610.hdf5" vot_path = os.path.join(data_path, vot_fn) vot_db = h5py.File(vot_path, mode="r") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") vot_gt_path = os.path.join(data_path, "vot-gt") if not os.path.isdir(vot_gt_path): os.mkdir(vot_gt_path) # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] for vidseq in vot_list: gt_filename = vidseq+"-groundtruth.txt" gt_savepath = os.path.join(vot_gt_path, gt_filename) header = "File is created at: " sys_time = strftime("%Y-%m-%d %H:%M:%S", gmtime()) header += sys_time+"\n" header += "Each line is a bounding box and has 9 values.\n" header += "The structure is as follows:\n" header += "[Timestamps] [X1, Y1] [X2, Y2] [X3, Y3] [X4, Y4]" gt = vot_db[vidseq]["bounding_box"][()] # gt[:, 0] -= 133332 np.savetxt(gt_savepath, gt, fmt='%.2f', delimiter=',', header=header) print("Ground Truth for %s is saved at %s" % (vidseq, gt_savepath)) if option == "export-td-bounding-boxes": tracking_fn = "INI_TrackingDataset_30fps_20160610.hdf5" tracking_path = os.path.join(data_path, tracking_fn) tracking_db = h5py.File(tracking_path, mode="r") tracking_stats_path = os.path.join(stats_path, "tracking_stats.pkl") tracking_gt_path = os.path.join(data_path, "tracking-gt") f = file(tracking_stats_path, mode="r") tracking_stats = pickle.load(f) f.close() pl = tracking_stats["primary_list"] sl = tracking_stats["secondary_list"] for pc in pl: # remove sequence Kalal until I got more memory if pc != "Kalal": for sc in sl[pc]: pc_path = os.path.join(tracking_gt_path, pc) if not os.path.isdir(pc_path): os.mkdir(pc_path) sc_path = os.path.join(pc_path, sc+"-groundtruth.txt") header = "File is created at: " sys_time = strftime("%Y-%m-%d %H:%M:%S", gmtime()) header += sys_time+"\n" header += "Each line is a bounding box and has 9 values. \n" header += "The structure is as follows:\n" header += "[Timestamps] [X1, Y1] [X2, Y2] [X3, Y3] [X4, Y4]" gt = tracking_db[pc][sc]["bounding_box"][()] np.savetxt(sc_path, gt, fmt='%.2f', delimiter=',', header=header) print("Ground Truth for %s is saved at %s" % (sc, sc_path)) if option == "calculate-tracking-event-burst-timing": tracking_fn = "INI_TrackingDataset_30fps_20160610.hdf5" td_path = os.path.join(data_path, "TrackingDataset") tracking_path = os.path.join(data_path, tracking_fn) tracking_db = h5py.File(tracking_path, mode="a") tracking_stats_path = os.path.join(stats_path, "tracking_stats.pkl") tracking_gt_path = os.path.join(data_path, "tracking-gt") f = file(tracking_stats_path, mode="r") tracking_stats = pickle.load(f) f.close() pl = tracking_stats["primary_list"] sl = tracking_stats["secondary_list"] key_idx_list = [] key_idx_ts = [] for pc in pl: # remove sequence Kalal until I got more memory if pc != "Kalal": for sc in sl[pc]: timestamps = tracking_db[pc][sc]["timestamps"][()] key_idx = dvsproc.cal_first_response(timestamps) key_idx_list.append(key_idx) key_idx_ts.append(timestamps[key_idx]-timestamps[0]) print("%s: %d" % (sc, timestamps[key_idx]-timestamps[0])) key_idx_ts = np.array(key_idx_ts) key_idx_ts = dvsproc.remove_outliers(key_idx_ts) key_idx_time = round(np.mean(key_idx_ts)) print(key_idx_time) key_idx_list_new = [] for pc in pl: # remove sequence Kalal until I got more memory if pc != "Kalal": for sc in sl[pc]: gt_path = os.path.join(td_path, pc, sc, "groundtruth.txt") gt = np.loadtxt(gt_path, dtype=np.float32, delimiter=",") gt = helpers.trans_groundtruth(gt, method="size") gt = np.reshape(gt, (gt.shape[0], 4, 2)) # load one original frame frame_path = os.path.join(td_path, pc, sc, tracking_stats[sc][0]) origin_frame = cv2.imread(frame_path) num_frames = int(tracking_db[pc][sc].attrs["num_frames"]) timestamps = tracking_db[pc][sc]["timestamps"][()] x_pos = tracking_db[pc][sc]["x_pos"][()] y_pos = tracking_db[pc][sc]["y_pos"][()] pol = tracking_db[pc][sc]["pol"][()] key_idx = dvsproc.find_nearest(timestamps, key_idx_time+timestamps[0]) key_idx_list_new.append(key_idx) print("%s: %d" % (sc, key_idx)) (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, key_idx=key_idx) frames, fs, ts = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames, fs=3) ts = np.array(ts) shift = helpers.cal_img_shift(origin_frame.shape, frames[0].shape) ratio = helpers.cal_bound_box_ratio(gt, origin_frame.shape[0], origin_frame.shape[1]) gt = helpers.cal_bound_box_position( ratio, frames[0].shape[0]-shift[1], frames[0].shape[1]-shift[0]) gt[:, :, 0] += shift[0]/2. gt[:, :, 1] += shift[1]/2. gt = np.reshape(gt, (gt.shape[0], 8)) print("[MESSAGE] Size of groundtruth: "+str(gt.shape)) gt = np.vstack((ts, gt.T)).T del tracking_db[pc][sc]["bounding_box"] tracking_db[pc][sc].create_dataset( "bounding_box", data=gt.astype(np.float32), dtype=np.float32) print("[MESSAGE] Sequence %s bounding box is saved" % (sc)) print(key_idx_list) print(key_idx_list_new) if option == "calculate-vot-event-burst-timing": vot_fn = "INI_VOT_30fps_20160424.hdf5" vot_path = os.path.join(data_path, vot_fn) vot_data_path = os.path.join(data_path, "vot2015") vot_db = h5py.File(vot_path, mode="a") vot_stats_path = os.path.join(stats_path, "vot_stats.pkl") vot_gt_path = os.path.join(data_path, "vot-gt-shifted") if not os.path.isdir(vot_gt_path): os.mkdir(vot_gt_path) # load vot stats f = file(vot_stats_path, mode="r") vot_stats = pickle.load(f) f.close() vot_list = vot_stats['vot_list'] num_frames = vot_stats['num_frames'] num_seq = len(vot_list) key_idx_list = [] key_idx_ts = [] for vidseq in vot_list: timestamps = vot_db[vidseq]["timestamps"][()] key_idx = dvsproc.cal_first_response(timestamps) key_idx_list.append(key_idx) key_idx_ts.append(timestamps[key_idx] - timestamps[0]) print("%s: %d" % (vidseq, timestamps[key_idx] - timestamps[0])) key_idx_ts = np.array(key_idx_ts) key_idx_ts = dvsproc.remove_outliers(key_idx_ts) key_idx_time = round(np.mean(key_idx_ts)) print(key_idx_time) key_idx_list_new = [] for i in xrange(num_seq): vidseq = vot_list[i] # load groundtruth gt_path = os.path.join(vot_data_path, vot_list[i]+"/groundtruth.txt") gt = np.loadtxt(gt_path, dtype=float, delimiter=",") gt = np.reshape(gt, (gt.shape[0], 4, 2)) # load a frame as reference frame_path = os.path.join(vot_data_path, vot_list[i]+"/00000001.jpg") origin_frame = cv2.imread(frame_path) print("[MESSAGE] Loading sequence %s" % (vot_list[i])) timestamps = vot_db[vidseq]["timestamps"][()] x_pos = vot_db[vidseq]["x_pos"][()] y_pos = vot_db[vidseq]["y_pos"][()] pol = vot_db[vidseq]["pol"][()] key_idx = dvsproc.find_nearest(timestamps, key_idx_time+timestamps[0]) key_idx_list_new.append(key_idx) print("%s: %d" % (vidseq, key_idx)) (timestamps, x_pos, y_pos, pol) = dvsproc.clean_up_events(timestamps, x_pos, y_pos, pol, key_idx=key_idx) frames, fs, ts = dvsproc.gen_dvs_frames(timestamps, x_pos, y_pos, pol, num_frames[i], fs=3) ts = np.array(ts) shift = helpers.cal_img_shift(origin_frame.shape, frames[0].shape) ratio = helpers.cal_bound_box_ratio(gt, origin_frame.shape[0], origin_frame.shape[1]) gt = helpers.cal_bound_box_position(ratio, frames[0].shape[0]-shift[1], frames[0].shape[1]-shift[0]) gt[:, :, 0] += shift[0]/2. gt[:, :, 1] += shift[1]/2. gt = np.reshape(gt, (gt.shape[0], 8)) print("[MESSAGE] Size of groundtruth: "+str(gt.shape)) gt = np.vstack((ts, gt.T)).T del vot_db[vidseq]["bounding_box"] vot_db[vidseq].create_dataset("bounding_box", data=gt.astype(np.float32), dtype=np.float32) print("[MESSAGE] Sequence %s bounding box is saved" % (vidseq)) print(key_idx_list) print(key_idx_list_new)

40.050239

79

0.583776

2,279

16,741

4.043879

0.101799

0.032552

0.044054

0.812391

0.770182

0.729058

0.711046

0.687066

0.667318

0

0.030672

0.283316

16,741

417

80

40.146283

0.737456

0.025387

0

0.626113

0

0.005935

0.147503

0.024788

0

1

0

false

0

0.023739

0

0.023739

0.121662

0

null

0

1

0

1

0

null

0

5

d833b30618ba4ddcb20f31d4163182c0561cfe42

235

py

Python

lib/version_control/upgdate_scripts.py

diydsp/thirtybirds3.0

8d57c73f1c6597a3a5dddaaaca07511eaa2adaf8

[ "MIT" ]

2

2020-05-13T02:53:02.000Z

2021-03-21T05:54:53.000Z

lib/version_control/upgdate_scripts.py

diydsp/thirtybirds3.0

8d57c73f1c6597a3a5dddaaaca07511eaa2adaf8

[ "MIT" ]

null

lib/version_control/upgdate_scripts.py

diydsp/thirtybirds3.0

8d57c73f1c6597a3a5dddaaaca07511eaa2adaf8

[ "MIT" ]

1

2021-05-06T18:42:41.000Z

scripts = { "0.1":[ "ls -a", "dirasdf", "echo '0.1'" ], "0.2":[ "ls -a", "dir", "echo '0.2'" ], "0.3":[ "ls -a", "dir", "echo '0.3'" ] }

13.823529

20

0.225532

25

235

2.12

0.4

0.169811

0.226415

0.377358

0.415094

0

0.107143

0.523404

235

17

21

13.823529

0.366071

0

0.411765

0

0.29661

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

dc29768e4f59933e5026b6d2e85fb23db6657d88

111

py

Python

update_campus.py

cuappdev/eatery-backend

564dbdfe790e844e41a3d2e0ed993c07cc68f2ac

[ "MIT" ]

3

2016-02-28T15:45:18.000Z

2016-02-29T21:13:54.000Z

update_campus.py

cuappdev/eatery-backend

564dbdfe790e844e41a3d2e0ed993c07cc68f2ac

[ "MIT" ]

90

2016-02-23T23:57:50.000Z

2021-09-20T04:23:23.000Z

update_campus.py

cuappdev/eatery-backend

564dbdfe790e844e41a3d2e0ed993c07cc68f2ac

[ "MIT" ]

4

2016-02-23T04:41:58.000Z

2020-12-15T22:03:15.000Z

from src.db import start_update print("Running campus and swipe data update") start_update(True, True, False)

22.2

45

0.792793

18

111

4.777778

0.777778

0.255814

0

0.126126

111

4

46

27.75

0.886598

0

0.324324

0

1

0

true

0

0.333333

0

0.333333

1

0

null

1

0

1

0

null

0

1

0

1

0

5

dc703355bf30682d225be3496f799228f55eab33

19

py

Python

fn/new.py

nultero/jetx

91b6642600f11d1cd0a4964661462811086443f5

[ "MIT" ]

null

fn/new.py

nultero/jetx

91b6642600f11d1cd0a4964661462811086443f5

[ "MIT" ]

null

fn/new.py

nultero/jetx

91b6642600f11d1cd0a4964661462811086443f5

[ "MIT" ]

null

def new(): ...

6.333333

10

0.315789

2

19

3

1

0

0.368421

19

3

11

6.333333

0.5

0

1

0.5

true

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

dc9b7be422f6e1edd02b38dc36310e004b5bbba9

96

py

Python

010-c-print.py

catherinedevlin/just-enough-python

3e272d37cbe68eed3e90fb472cfb4caf0a571881

[ "MIT" ]

5

2019-07-25T13:54:44.000Z

2021-02-05T12:16:53.000Z

010-c-print.py

catherinedevlin/just-enough-python

3e272d37cbe68eed3e90fb472cfb4caf0a571881

[ "MIT" ]

null

010-c-print.py

catherinedevlin/just-enough-python

3e272d37cbe68eed3e90fb472cfb4caf0a571881

[ "MIT" ]

null

# Print out Go Chicks! print # You'll need to add something here to # "call" the function...

13.714286

39

0.677083

16

96

4.0625

0.875

0

0.21875

96

6

40

16

0.866667

0.84375

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

dcb1ff6272bc4c6f022f9f665797888f8a4c9559

383

py

Python

Chapter06/Testing/test_example.py

pythonOsYun/Hands-On-Application-Development-with-PyCharm

4abd408413f74b179c016f279a236c1cd5e4d183

[ "MIT" ]

null

Chapter06/Testing/test_example.py

pythonOsYun/Hands-On-Application-Development-with-PyCharm

4abd408413f74b179c016f279a236c1cd5e4d183

[ "MIT" ]

null

Chapter06/Testing/test_example.py

pythonOsYun/Hands-On-Application-Development-with-PyCharm

4abd408413f74b179c016f279a236c1cd5e4d183

[ "MIT" ]

null

from unittest import TestCase class MathTest(TestCase): def test_add(self): self.assertEqual(1 + 1, 2) def test_mul(self): self.assertEqual(2 * 5, 10) def test_exp(self): self.assertEqual(2 ** 3, 9) class StringTest(TestCase): def test_stringcase(self): self.assertTrue('FOO'.isupper()) self.assertFalse('Bar'.isupper())

20.157895

41

0.629243

49

383

4.836735

0.530612

0.118143

0.240506

0.168776

0

0.034364

0.240209

383

18

42

21.277778

0.780069

0

0.015666

0

0.416667

1

0.333333

false

0

0.083333

0

0.583333

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f4b76a3d505fe3c5dd1541fee27b3bee19cd500e

94

py

Python

backend/users_auth/admin.py

mailmumailku/lala

3442abfeaa6b1260d9558720505ae8ff24cf9d45

[ "MIT" ]

7

2019-03-10T17:37:07.000Z

2021-05-14T13:28:13.000Z

backend/users_auth/admin.py

mailmumailku/lala

3442abfeaa6b1260d9558720505ae8ff24cf9d45

[ "MIT" ]

2

2019-05-22T14:54:36.000Z

2019-05-30T23:59:45.000Z

backend/users_auth/admin.py

mailmumailku/lala

3442abfeaa6b1260d9558720505ae8ff24cf9d45

[ "MIT" ]

1

2021-04-05T12:01:23.000Z

from django.contrib import admin from .models import UsersAuth admin.site.register(UsersAuth)

23.5

32

0.840426

13

94

6.076923

0.692308

0

0.095745

94

4

33

23.5

0.929412

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

f4bc9701414d384a3ac1c15862f880ff4a69a071

126

py

Python

album/admin.py

SherSingh07/albums

76fcfcfbd0d858dd761ad30298bc064083060b3a

[ "Apache-2.0" ]

null

album/admin.py

SherSingh07/albums

76fcfcfbd0d858dd761ad30298bc064083060b3a

[ "Apache-2.0" ]

null

album/admin.py

SherSingh07/albums

76fcfcfbd0d858dd761ad30298bc064083060b3a

[ "Apache-2.0" ]

null

from django.contrib import admin from album.models import Album, Photo admin.site.register(Album) admin.site.register(Photo)

21

37

0.81746

19

126

5.421053

0.526316

0.174757

0.330097

0

0.095238

126

5

38

25.2

0.903509

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

f4cace1336f55c129b5e1c223dce5cb7a35dc763

33

py

Python

moodle/__init__.py

NoahCardoza/Python3-Moodle-Web-Service-Client

e58580e032896690c46cd9b5a6cc7f7287f42bb5

[ "MIT" ]

1

2020-05-06T04:04:47.000Z

moodle/__init__.py

NoahCardoza/python-moodle-client

e58580e032896690c46cd9b5a6cc7f7287f42bb5

[ "MIT" ]

null

moodle/__init__.py

NoahCardoza/python-moodle-client

e58580e032896690c46cd9b5a6cc7f7287f42bb5

[ "MIT" ]

null

from .client import MoodleClient

16.5

32

0.848485

4

33

7

1

0

0.121212

33

1

33

0.965517

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

f4ee08e7262773840ac5d474b71bca63122cfcaa

23

py

Python

pykopyko/bots/__init__.py

skasi7/pykopyko

f4733b3b9ea46a9464abcd4820c5dbfb0ab380d9

[ "Apache-2.0" ]

null

pykopyko/bots/__init__.py

skasi7/pykopyko

f4733b3b9ea46a9464abcd4820c5dbfb0ab380d9

[ "Apache-2.0" ]

null

pykopyko/bots/__init__.py

skasi7/pykopyko

f4733b3b9ea46a9464abcd4820c5dbfb0ab380d9

[ "Apache-2.0" ]

null

__author__ = 'e022004'

11.5

22

0.73913

2

23

6.5

1

0

0.3

0.130435

23

1

23

0.35

0

0.304348

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

760fe4bf73e73b8aa962ecea4b8451cfceb95308

194

py

Python

launcher.py

Shardj/ccrawler

c917a2b25365a578007cc166864568142327133e

[ "MIT" ]

null

launcher.py

Shardj/ccrawler

c917a2b25365a578007cc166864568142327133e

[ "MIT" ]

null

launcher.py

Shardj/ccrawler

c917a2b25365a578007cc166864568142327133e

[ "MIT" ]

null

import bootstrap # Launch scraper/crawler. Unecisarrily use our previously defined projectRelativeImport function for consistancy projectRelativeImport('main', 'app') # filename, relative path

38.8

112

0.829897

20

194

8.05

0.95

0

0.108247

194

4

113

48.5

0.930636

0.690722

0

0.122807

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

523ad59cf4eb5a63bb0e671e1caeac8bae041298

391

py

Python

tests/test_paths.py

GarrettMooney/moonpy

8e44f7afa2daccac6f2b2c089f272b95e4ba2945

[ "MIT" ]

null

tests/test_paths.py

GarrettMooney/moonpy

8e44f7afa2daccac6f2b2c089f272b95e4ba2945

[ "MIT" ]

null

tests/test_paths.py

GarrettMooney/moonpy

8e44f7afa2daccac6f2b2c089f272b95e4ba2945

[ "MIT" ]

null

from pathlib import Path from moonpy.util import force_string def test_force_string_str(): assert isinstance(force_string("foobar"), str) def test_force_string_int(): assert isinstance(force_string(32), str) def test_force_string_float(): assert isinstance(force_string(32 / 3), str) def test_force_string_path(): assert isinstance(force_string(Path(__file__)), str)

20.578947

56

0.764706

56

391

4.964286

0.339286

0.356115

0.172662

0.258993

0.435252

0

0.014837

0.138107

391

18

57

21.722222

0.810089

0

0.015345

0

0.4

1

0.4

true

0

0.2

0

0.6

0

null

1

0

1

0

null

0

1

0

1

0

5

526ef6567e3e0ade7548f02a390ca2ad9076b3d4

125

py

Python

matrixio_hal/__init__.py

cmetz/python-matrixio-hal

76a38bf04c23106a7e5e685674375b121d0eef95

[ "MIT" ]

13

2018-02-07T01:23:25.000Z

2021-01-07T04:22:10.000Z

matrixio_hal/__init__.py

cmetz/python-matrixio-hal

76a38bf04c23106a7e5e685674375b121d0eef95

[ "MIT" ]

3

2018-04-03T18:53:16.000Z

2018-05-10T18:55:17.000Z

matrixio_hal/__init__.py

cmetz/python-matrixio-hal

76a38bf04c23106a7e5e685674375b121d0eef95

[ "MIT" ]

2

2018-07-13T04:36:09.000Z

2018-08-06T03:08:28.000Z

__all__ = ['sensors', 'everloop', 'GPIO'] from . import bus from . import sensors from . import everloop from . import GPIO

17.857143

41

0.704

16

125

5.25

0.4375

0.47619

0

0.176

125

6

42

20.833333

0.815534

0

0.152

0

1

0

false

0

0.8

0

0.8

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

528a85f8d1d350529b8ccbc8be42cfc000973e24

810

py

Python

PhiSpyAnalysis/correct_entries.py

linsalrob/PhispyAnalysis

e73598afc1304096b486af750c4bb306067bb5f2

[ "MIT" ]

3

2021-06-10T10:36:50.000Z

2022-01-10T12:28:04.000Z

PhiSpyAnalysis/correct_entries.py

linsalrob/PhispyAnalysis

e73598afc1304096b486af750c4bb306067bb5f2

[ "MIT" ]

null

PhiSpyAnalysis/correct_entries.py

linsalrob/PhispyAnalysis

e73598afc1304096b486af750c4bb306067bb5f2

[ "MIT" ]

1

2021-04-29T03:42:15.000Z

""" Methods to correct text in entries """ import os import sys import re def file_to_accession_name(x): regexp = re.compile('(\w+\.\d+)_([\w\.\-]+)_genomic.gbff.gz') m = regexp.match(x) if not m: sys.stderr.write(f"WARNING: Regexp did not match {x}\n") return (None, None) return list(m.groups()) def file_to_accession(x): regexp = re.compile('(\w+\.\d+)_([\w\.\-]+)_genomic.gbff.gz') m = regexp.match(x) if not m: sys.stderr.write(f"WARNING: Regexp did not match {x}\n") return None return m.groups()[0] def file_to_name(x): regexp = re.compile('(\w+\.\d+)_([\w\.\-]+)_genomic.gbff.gz') m = regexp.match(x) if not m: sys.stderr.write(f"WARNING: Regexp did not match {x}\n") return None return m.groups()[1]

24.545455

65

0.58642

128

810

3.609375

0.296875

0.077922

0.058442

0.103896

0.735931

0

0.00317

0.220988

810

32

66

25.3125

0.729002

0.041975

0

0.583333

0

0.285156

0.148438

0

1

0.125

false

0

0.125

0

0.5

0

null

0

1

0

null

0

5

5294b56b586f084d81b078c81b47f955787e0b01

136

py

Python

h5py/tests/types/__init__.py

qsnake/h5py

45e77c3798032de2f740414a9e014fbca8c0ac18

[ "BSD-3-Clause" ]

null

h5py/tests/types/__init__.py

qsnake/h5py

45e77c3798032de2f740414a9e014fbca8c0ac18

[ "BSD-3-Clause" ]

null

h5py/tests/types/__init__.py

qsnake/h5py

45e77c3798032de2f740414a9e014fbca8c0ac18

[ "BSD-3-Clause" ]

8

2018-07-05T22:16:08.000Z

2021-08-19T06:07:45.000Z

""" Type and data-conversion test package. Tests the following: 1) HDF5 to NumPy type mapping 2) Data conversion """

13.6

42

0.639706

18

136

4.833333

0.833333

0.321839

0

0.030612

0.279412

136

9

43

15.111111

0.857143

0.808824

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

1

0

1

0

1

0

null

0

1

0

5

bfdf829a157ee91c21a919e240e3a40355cfbe0b

3,706

py

Python

application.py

mima3/estat

537689ad4ebc96af34e1c66a9997241fa847d8c1

[ "MIT", "Unlicense" ]

2

2015-02-03T15:21:56.000Z

2015-09-09T12:47:12.000Z

application.py

mima3/estat

537689ad4ebc96af34e1c66a9997241fa847d8c1

[ "MIT", "Unlicense" ]

null

application.py

mima3/estat

537689ad4ebc96af34e1c66a9997241fa847d8c1

[ "MIT", "Unlicense" ]

null

# coding=utf-8 from bottle import get, post, template, request, Bottle, response, redirect, abort from json import dumps import os import json from collections import defaultdict import time import cgi import urllib import estat_db import peewee import math app = Bottle() def setup(conf): global app estat_db.connect(conf.get('database', 'path'), conf.get('database', 'mod_path'), conf.get('database', 'sep')) @app.get('/') def Home(): return 'Estat page...' @app.get('/population') def populationPage(): return template('population').replace('\n', '') def str_isfloat(str): try: float(str) return True except ValueError: return False @app.get('/json/get_population') def getPopulation(): stat_id = request.query.stat_id swlat = request.query.swlat swlng = request.query.swlng nelat = request.query.nelat nelng = request.query.nelng attrval = request.query.attr_value if (not str_isfloat(swlat) or not str_isfloat(swlng) or not str_isfloat(nelat) or not str_isfloat(nelng)): response.content_type = 'application/json;charset=utf-8' response.set_header('Access-Control-Allow-Origin', '*') response.status = 400 return json.dumps({'message':'wrong parameter type'}) maxrange = 1 if ((math.fabs(float(swlat) - float(nelat)) + math.fabs(float(swlng) - float(nelng))) > maxrange): response.content_type = 'application/json;charset=utf-8' response.set_header('Access-Control-Allow-Origin', '*') response.status = 400 return json.dumps({'message':'wrong parameter range'}) ret = estat_db.get_mesh_stat(stat_id, attrval, swlng, swlat, nelng, nelat) res = {'type': 'FeatureCollection', 'features': []} for r in ret: item = { 'type': 'Feature', 'geometry': json.loads(r['geometory']), 'properties': {'value': r['value']} } res['features'].append(item) response.content_type = 'application/json;charset=utf-8' response.set_header('Access-Control-Allow-Origin', '*') return json.dumps(res) @app.get('/json/get_mesh_stat_group_by_mesh') def getPopulationGroupByMesh(): stat_id = request.query.stat_id swlat = request.query.swlat swlng = request.query.swlng nelat = request.query.nelat nelng = request.query.nelng if (not str_isfloat(swlat) or not str_isfloat(swlng) or not str_isfloat(nelat) or not str_isfloat(nelng)): response.content_type = 'application/json;charset=utf-8' response.set_header('Access-Control-Allow-Origin', '*') response.status = 400 return json.dumps({'message':'wrong parameter type'}) maxrange = 0.7 if ((math.fabs(float(swlat) - float(nelat)) + math.fabs(float(swlng) - float(nelng))) > maxrange): response.content_type = 'application/json;charset=utf-8' response.set_header('Access-Control-Allow-Origin', '*') response.status = 400 return json.dumps({'message':'wrong parameter range'}) ret = estat_db.get_mesh_stat_group_by_mesh(stat_id, swlng, swlat, nelng, nelat) res = {'type': 'FeatureCollection', 'features': []} for r in ret: item = { 'type': 'Feature', 'geometry': json.loads(r['geometory']), 'properties': {} } for k , i in r.items(): if k == 'geometory': continue item['properties'][k] = i res['features'].append(item) response.content_type = 'application/json;charset=utf-8' response.set_header('Access-Control-Allow-Origin', '*') return json.dumps(res)

31.142857

113

0.636535

457

3,706

5.061269

0.234136

0.057069

0.044963

0.038911

0.716386

0.702118

0

0.007628

0.221802

3,706

118

114

31.40678

0.794383

0.003238

0

0.55102

0

0.207476

0.101571

0

1

0.061224

false

0

0.112245

0.020408

0.27551

0

null

0

1

0

null

0

5

bfeb0f4ec3a08212c9a97af86e6aa29d9736053a

61

py

Python

zernike_py/__init__.py

neufieldrobotics/zernike_py

2aecc9a0ec233e6cece6aa40f282bbdcd3d4c1ee

[ "MIT" ]

1

2021-06-25T14:37:32.000Z

zernike_py/__init__.py

neufieldrobotics/zernike_py

2aecc9a0ec233e6cece6aa40f282bbdcd3d4c1ee

[ "MIT" ]

null

zernike_py/__init__.py

neufieldrobotics/zernike_py

2aecc9a0ec233e6cece6aa40f282bbdcd3d4c1ee

[ "MIT" ]

1

2021-04-26T03:29:16.000Z

from zernike_py.MultiHarrisZernike import MultiHarrisZernike

30.5

60

0.918033

6

61

9.166667

0.833333

0

0.065574

61

1

61

0.964912

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

871aa0f8d9db36550fce3f15d77f866b03e83371

110

py

Python

main.py

heater40/GMHS-Bell

4afbada1461491143f7618ba6d20aea8b65df24e

[ "MIT" ]

null

main.py

heater40/GMHS-Bell

4afbada1461491143f7618ba6d20aea8b65df24e

[ "MIT" ]

null

main.py

heater40/GMHS-Bell

4afbada1461491143f7618ba6d20aea8b65df24e

[ "MIT" ]

null

#from 9f85ab77e1a11d0eebdb import * from PythonGetTimeNTPServer import get_time print(get_time.gettime_ntp())

27.5

43

0.854545

13

110

7

0.692308

0.153846

0

0.089109

0.081818

110

3

44

36.666667

0.811881

0.309091

0

1

0

true

0

0.5

0

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

871d53542f0fe40717a4e0da7130f9dfd1045280

157

py

Python

Python/CeV/Exercicios/ex97.py

WerickL/Learning

5a9a488f0422454e612439b89093d5bc11242e65

[ "MIT" ]

null

Python/CeV/Exercicios/ex97.py

WerickL/Learning

5a9a488f0422454e612439b89093d5bc11242e65

[ "MIT" ]

null

Python/CeV/Exercicios/ex97.py

WerickL/Learning

5a9a488f0422454e612439b89093d5bc11242e65

[ "MIT" ]

null

def escreva(txt): print('~' * (len(txt) + 4)) print(f' {txt}') print('~' * (len(txt) + 4)) escreva('Olá mundo') escreva('Balneário Camboriú')

17.444444

31

0.541401

20

157

4.25

0.55

0.188235

0.258824

0.329412

0.352941

0

0.016129

0.210191

157

8

32

19.625

0.669355

0

0.333333

0

0.229299

0

1

0.166667

false

0

0.166667

0.5

1

0

null

0

1

0

1

0

null

0

1

0

5

875c0d4ea917a0b21b2cf73af44d98a1ccf31879

97

py

Python

pyinstaller_hooks/hook-cairocffi.py

cromicron/pyaes256

357a844dcdd244b0990d3f605428d8833644d949

[ "MIT" ]

1

2020-11-19T13:54:06.000Z

pyinstaller_hooks/hook-cairocffi.py

The-Crocop/pyaes256

67b18257e7a662a72a69ab19e50380121f3fe70a

[ "MIT" ]

4

2020-11-14T22:06:38.000Z

2020-11-19T13:36:31.000Z

pyinstaller_hooks/hook-cairocffi.py

cromicron/pyaes256

357a844dcdd244b0990d3f605428d8833644d949

[ "MIT" ]

null

from PyInstaller.utils.hooks import collect_data_files datas = collect_data_files('cairocffi')

19.4

54

0.835052

13

97

5.923077

0.769231

0.285714

0.415584

0

0.092784

97

4

55

24.25

0.875

0

0.092784

0

1

0

false

0

0.5

0

0.5

0

1

0

null

1

0

1

0

null

0

1

0

5

876a23598cc38ef93e6f1f236227a210e061408a

876

py

Python

django_positions_2/managers.py

NyanKiyoshi/django-positions

82293094e445b2733e89bcdf6ba1c4316918fac6

[ "BSD-3-Clause" ]

null

django_positions_2/managers.py

NyanKiyoshi/django-positions

82293094e445b2733e89bcdf6ba1c4316918fac6

[ "BSD-3-Clause" ]

null

django_positions_2/managers.py

NyanKiyoshi/django-positions

82293094e445b2733e89bcdf6ba1c4316918fac6

[ "BSD-3-Clause" ]

1

2018-10-27T16:47:44.000Z

from django.db.models import Manager from django.db.models.query import QuerySet class PositionQuerySet(QuerySet): def __init__( self, model=None, query=None, using=None, position_field_name='position', hints=None): super(PositionQuerySet, self).__init__(model, query, using, hints=hints) self.position_field_name = position_field_name def _clone(self): queryset = super(PositionQuerySet, self)._clone() queryset.position_field_name = self.position_field_name return queryset class PositionManager(Manager): def __init__(self, position_field_name='position'): super(PositionManager, self).__init__() self.position_field_name = position_field_name def get_queryset(self): return PositionQuerySet(self.model, position_field_name=self.position_field_name)

32.444444

89

0.714612

101

876

5.811881

0.247525

0.221465

0.289608

0.178876

0.332198

0.269165

0.139693

0

0.199772

876

26

90

33.692308

0.837375

0

0.105263

0

0.018265

0

1

0.210526

false

0

0.105263

0.052632

0.526316

0

null

1

0

null

0

1

0

1

0

5

5e4284ce19c4ab3117c07613e6510f06832f2ef9

2,645

py

Python

timeseries/_nbdev.py

Massachute/timeseries

75b7ecddf34dc2305c439bd078428d3a086dca59

[ "Apache-2.0" ]

null

timeseries/_nbdev.py

Massachute/timeseries

75b7ecddf34dc2305c439bd078428d3a086dca59

[ "Apache-2.0" ]

null

timeseries/_nbdev.py

Massachute/timeseries

75b7ecddf34dc2305c439bd078428d3a086dca59

[ "Apache-2.0" ]

null

# AUTOGENERATED BY NBDEV! DO NOT EDIT! __all__ = ["index", "modules", "custom_doc_links", "git_url"] index = {"TSData": "80_timeseries_data.ipynb", "get_ts_items": "80_timeseries_data.ipynb", "show_timeseries": "80_timeseries_data.ipynb", "file_extract_at_filename": "80_timeseries_data.ipynb", "unzip_data": "80_timeseries_data.ipynb", "URLs_TS": "80_timeseries_data.ipynb", "get_UCR_univariate_list": "80_timeseries_data.ipynb", "get_UCR_multivariate_list": "80_timeseries_data.ipynb", "test_eq_tensor": "81_timeseries_core.ipynb", "TensorTS": "81_timeseries_core.ipynb", "ToTensorTS": "81_timeseries_core.ipynb", "TSBlock": "81_timeseries_core.ipynb", "get_min_max": "81_timeseries_core.ipynb", "get_mean_std": "81_timeseries_core.ipynb", "Standardize": "81_timeseries_core.ipynb", "Normalize": "81_timeseries_core.ipynb", "default_show_batch": "81_timeseries_core.ipynb", "lbl_dict": "82_univariate_timeseries_CAM.ipynb", "TSDataLoaders": "81_timeseries_core.ipynb", "get_n_channels": "81_timeseries_core.ipynb", "Ranger": "81_timeseries_core.ipynb", "ts_learner": "81_timeseries_core.ipynb", "CMAP": "82_univariate_timeseries_CAM.ipynb", "hooked_backward": "82_univariate_timeseries_CAM.ipynb", "hook_acts": "82_univariate_timeseries_CAM.ipynb", "cam_acts": "82_univariate_timeseries_CAM.ipynb", "cam_acts.name": "82_univariate_timeseries_CAM.ipynb", "acts_scaled": "82_univariate_timeseries_CAM.ipynb", "grad_cam_acts": "82_univariate_timeseries_CAM.ipynb", "grad_cam_acts.name": "82_univariate_timeseries_CAM.ipynb", "CAM_batch_compute": "82_univariate_timeseries_CAM.ipynb", "batchify": "82_univariate_timeseries_CAM.ipynb", "itemize": "82_univariate_timeseries_CAM.ipynb", "get_list_items": "82_univariate_timeseries_CAM.ipynb", "get_batch": "82_univariate_timeseries_CAM.ipynb", "show_cam": "82_univariate_timeseries_CAM.ipynb", "cam_batch_plot_one_fig": "82_univariate_timeseries_CAM.ipynb", "cam_batch_plot_multi_fig": "82_univariate_timeseries_CAM.ipynb", "i2o": "82_univariate_timeseries_CAM.ipynb"} modules = ["data.py", "core.py", "cam.py"] doc_url = "https://ai-fast-track.github.io/timeseries/" git_url = "https://github.com/ai-fast-track/timeseries/tree/master/" def custom_doc_links(name): return None

48.981481

75

0.671834

313

2,645

5.178914

0.277955

0.133251

0.244294

0.277606

0.500308

0.293646

0.197409

0.192474

0

0.03737

0.200756

2,645

53

76

49.90566

0.729423

0.013611

0

1

0

0.683634

0.483164

0

1

0.021739

false

0

0.021739

0

null

0

1

0

1

0

1

null

0

5

5e5b401c01a74520835734fcfb5973b53a11f6b5

212

py

Python

examples/windows_ip.py

charles-l/pyinfra

1992d98ff31d41404427dbb3cc6095a7bebd4052

[ "MIT" ]

1

2020-12-24T08:24:13.000Z

examples/windows_ip.py

charles-l/pyinfra

1992d98ff31d41404427dbb3cc6095a7bebd4052

[ "MIT" ]

null

examples/windows_ip.py

charles-l/pyinfra

1992d98ff31d41404427dbb3cc6095a7bebd4052

[ "MIT" ]

1

2021-11-12T18:36:01.000Z

from pyinfra import host # print ip address for all network entries for index in host.fact.windows_network_configuration['Index']: print(host.fact.windows_network_configuration['Index'][index]['IPAddress'])

35.333333

79

0.792453

29

212

5.655172

0.586207

0.097561

0.182927

0.268293

0.487805

0

0.103774

212

5

80

42.4

0.863158

0.188679

0

0.111765

0

1

0

false

0

0.333333

0

0.333333

1

0

null

0

1

0

1

0

null

0

1

0

5

5e7e2a6b0172399785dc99e858544ad82b01183c

95

py

Python

app/views/hello.py

graycadeau/profile

f316a29d4d08b3051e170330179fc21f4079b5ec

[ "MIT" ]

null

app/views/hello.py

graycadeau/profile

f316a29d4d08b3051e170330179fc21f4079b5ec

[ "MIT" ]

null

app/views/hello.py

graycadeau/profile

f316a29d4d08b3051e170330179fc21f4079b5ec

[ "MIT" ]

null

from app import app # test route @app.route("/hello") def hello(): return "Hello, World!"

13.571429

26

0.652632

14

95

4.428571

0.642857

0

0.189474

95

6

27

15.833333

0.805195

0.105263

0

0.228916

0

1

0.25

true

0

0.25

0.75

0

1

0

null

0

1

0

null

0

1

0

1

0

5

5eaf70270986ea56580f06e8bf82a396011c6109

36

py

Python

sisense/__init__.py

Bluemetrics/sisense

e23a8e931827c1858d79910526f1405183eecd2c

[ "MIT" ]

4

2020-10-15T14:29:22.000Z

2022-02-18T17:49:44.000Z

sisense/__init__.py

Bluemetrics/sisense

e23a8e931827c1858d79910526f1405183eecd2c

[ "MIT" ]

1

2021-03-31T14:46:34.000Z

sisense/__init__.py

Bluemetrics/sisense

e23a8e931827c1858d79910526f1405183eecd2c

[ "MIT" ]

1

2022-02-20T19:53:12.000Z

from .sisense_client import Sisense

18

35

0.861111

5

36

6

0.8

0

0.111111

36

1

36

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

0d8895a3092a00b5a56542a421da62baf3be743a

214

py

Python

cryptography/atbash_cipher/atbash_ci[her_4.py

BrianLusina/PyCharm

144dd4f6b2d254507237f46c8ee175c407fe053d

[ "Apache-2.0", "MIT" ]

null

cryptography/atbash_cipher/atbash_ci[her_4.py

BrianLusina/PyCharm

144dd4f6b2d254507237f46c8ee175c407fe053d

[ "Apache-2.0", "MIT" ]

null

cryptography/atbash_cipher/atbash_ci[her_4.py

BrianLusina/PyCharm

144dd4f6b2d254507237f46c8ee175c407fe053d

[ "Apache-2.0", "MIT" ]

null

from string import ascii_lowercase as alphabet def decode(message: str) -> str: """ Decode a message using the Atbash cipher. """ return message.translage(str.maketrans(alphabet, alphabet[::-1]))

23.777778

69

0.691589

27

214

5.444444

0.740741

0

0.00578

0.191589

214

8

70

26.75

0.843931

0.191589

0

1

0.333333

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

0dda7fe8d370bb7859fdf60498610cdd8d192271

1,083

py

Python

coco/util.py

ghosharthita/text2image

d69aee765f116fa0f1e7f12459148d1494441175

[ "MIT" ]

529

2016-02-26T14:59:36.000Z

2018-12-16T12:41:33.000Z

coco/util.py

msrocean/text2image

beafc1c1f5189c9e9021827f7dfe68eb2d9cd516

[ "MIT" ]

18

2016-03-01T02:46:14.000Z

2018-11-25T13:07:55.000Z

coco/util.py

msrocean/text2image

beafc1c1f5189c9e9021827f7dfe68eb2d9cd516

[ "MIT" ]

121

2016-03-23T20:33:34.000Z

2018-11-23T03:03:34.000Z

import numpy as np import theano def shared_normal(num_rows, num_cols, scale=0.01): '''Initialize a matrix shared variable with normally distributed elements.''' return theano.shared(np.random.normal( scale=scale, size=(num_rows, num_cols)).astype(theano.config.floatX)) def shared_normal_conv(num_filters, stack_size, num_rows, num_cols, scale=0.01): '''Initialize a matrix shared variable with normally distributed elements.''' return theano.shared(np.random.normal( scale=scale, size=(num_filters, stack_size, num_rows, num_cols)).astype(theano.config.floatX)) def shared_normal_vector(num_rows, scale=0.01): '''Initialize a vector shared variable with normally distributed elements.''' return theano.shared(np.random.normal( scale=scale, size=(num_rows)).astype(theano.config.floatX)) def shared_zeros(*shape): '''Initialize a vector shared variable with zero elements.''' return theano.shared(np.zeros(shape, dtype=theano.config.floatX)) def sigmoid(z): s = 1.0 / (1.0 + np.exp(-1.0 * z)) return s

38.678571

102

0.720222

157

1,083

4.840764

0.261147

0.055263

0.052632

0.073684

0.819737

0.772368

0.660526

0.619737

0

0.016393

0.155125

1,083

28

103

38.678571

0.814208

0.250231

0

0.1875

0

1

0.3125

false

0

0.125

0

0.75

0

null

0

1

0

1

0

null

0

1

0

1

0

5

0de36c1296779cfac65010b81544a084ac0d8a18

51

py

Python

pysoap/__init__.py

OpenShip/py-soap

3f85590d883a4e613ec83921f070596c025a903b

[ "MIT" ]

null

pysoap/__init__.py

OpenShip/py-soap

3f85590d883a4e613ec83921f070596c025a903b

[ "MIT" ]

null

pysoap/__init__.py

OpenShip/py-soap

3f85590d883a4e613ec83921f070596c025a903b

[ "MIT" ]

null

from pysoap.envelope import Header, Body, Envelope

25.5

50

0.823529

7

51

6

0.857143

0

0.117647

51

1

51

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

218441cdec465660ad4c11036cd3fc680fc88972

240,193

py

Python

models/densenet.py

Mercurialzhang/Baidu_Adversarial_Attack

f875f844b234d9ad9ac19d1607423ca6fa95d704

[ "Apache-2.0" ]

2

2020-02-07T15:35:44.000Z

2021-02-27T17:57:59.000Z

models/densenet.py

Mercurialzhang/baidu_adversarial_attack

f875f844b234d9ad9ac19d1607423ca6fa95d704

[ "Apache-2.0" ]

1

2020-06-12T07:22:39.000Z

models/densenet.py

Mercurialzhang/baidu_adversarial_attack

f875f844b234d9ad9ac19d1607423ca6fa95d704

[ "Apache-2.0" ]

null

from paddle.fluid.initializer import Constant from paddle.fluid.param_attr import ParamAttr import paddle.fluid as fluid class Densenet(): def __init__(self): pass def net(self, x2paddle_input): x2paddle_densenet161_classifier_bias = fluid.layers.create_parameter(dtype='float32', shape=[121], name='x2paddle_densenet161_classifier_bias', attr='x2paddle_densenet161_classifier_bias', default_initializer=Constant(0.0)) x2paddle_densenet161_classifier_weight = fluid.layers.create_parameter(dtype='float32', shape=[121, 2208], name='x2paddle_densenet161_classifier_weight', attr='x2paddle_densenet161_classifier_weight', default_initializer=Constant(0.0)) x2paddle_968 = fluid.layers.conv2d(x2paddle_input, num_filters=96, filter_size=[7, 7], stride=[2, 2], padding=[3, 3], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_conv0_weight', name='x2paddle_968', bias_attr=False) x2paddle_969 = fluid.layers.batch_norm(x2paddle_968, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_norm0_weight', bias_attr='x2paddle_densenet161_features_norm0_bias', moving_mean_name='x2paddle_densenet161_features_norm0_running_mean', moving_variance_name='x2paddle_densenet161_features_norm0_running_var', use_global_stats=False, name='x2paddle_969') x2paddle_970 = fluid.layers.relu(x2paddle_969, name='x2paddle_970') x2paddle_971 = fluid.layers.pool2d(x2paddle_970, pool_size=[3, 3], pool_type='max', pool_stride=[2, 2], pool_padding=[1, 1], ceil_mode=False, name='x2paddle_971', exclusive=False) x2paddle_972 = fluid.layers.concat([x2paddle_971], axis=1) x2paddle_973 = fluid.layers.batch_norm(x2paddle_972, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer1_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer1_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer1_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer1_norm1_running_var', use_global_stats=False, name='x2paddle_973') x2paddle_974 = fluid.layers.relu(x2paddle_973, name='x2paddle_974') x2paddle_975 = fluid.layers.conv2d(x2paddle_974, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer1_conv1_weight', name='x2paddle_975', bias_attr=False) x2paddle_976 = fluid.layers.batch_norm(x2paddle_975, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer1_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer1_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer1_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer1_norm2_running_var', use_global_stats=False, name='x2paddle_976') x2paddle_977 = fluid.layers.relu(x2paddle_976, name='x2paddle_977') x2paddle_978 = fluid.layers.conv2d(x2paddle_977, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer1_conv2_weight', name='x2paddle_978', bias_attr=False) x2paddle_979 = fluid.layers.concat([x2paddle_971, x2paddle_978], axis=1) x2paddle_980 = fluid.layers.batch_norm(x2paddle_979, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer2_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer2_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer2_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer2_norm1_running_var', use_global_stats=False, name='x2paddle_980') x2paddle_981 = fluid.layers.relu(x2paddle_980, name='x2paddle_981') x2paddle_982 = fluid.layers.conv2d(x2paddle_981, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer2_conv1_weight', name='x2paddle_982', bias_attr=False) x2paddle_983 = fluid.layers.batch_norm(x2paddle_982, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer2_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer2_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer2_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer2_norm2_running_var', use_global_stats=False, name='x2paddle_983') x2paddle_984 = fluid.layers.relu(x2paddle_983, name='x2paddle_984') x2paddle_985 = fluid.layers.conv2d(x2paddle_984, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer2_conv2_weight', name='x2paddle_985', bias_attr=False) x2paddle_986 = fluid.layers.concat([x2paddle_971, x2paddle_978, x2paddle_985], axis=1) x2paddle_987 = fluid.layers.batch_norm(x2paddle_986, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer3_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer3_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer3_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer3_norm1_running_var', use_global_stats=False, name='x2paddle_987') x2paddle_988 = fluid.layers.relu(x2paddle_987, name='x2paddle_988') x2paddle_989 = fluid.layers.conv2d(x2paddle_988, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer3_conv1_weight', name='x2paddle_989', bias_attr=False) x2paddle_990 = fluid.layers.batch_norm(x2paddle_989, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer3_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer3_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer3_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer3_norm2_running_var', use_global_stats=False, name='x2paddle_990') x2paddle_991 = fluid.layers.relu(x2paddle_990, name='x2paddle_991') x2paddle_992 = fluid.layers.conv2d(x2paddle_991, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer3_conv2_weight', name='x2paddle_992', bias_attr=False) x2paddle_993 = fluid.layers.concat([x2paddle_971, x2paddle_978, x2paddle_985, x2paddle_992], axis=1) x2paddle_994 = fluid.layers.batch_norm(x2paddle_993, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer4_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer4_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer4_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer4_norm1_running_var', use_global_stats=False, name='x2paddle_994') x2paddle_995 = fluid.layers.relu(x2paddle_994, name='x2paddle_995') x2paddle_996 = fluid.layers.conv2d(x2paddle_995, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer4_conv1_weight', name='x2paddle_996', bias_attr=False) x2paddle_997 = fluid.layers.batch_norm(x2paddle_996, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer4_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer4_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer4_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer4_norm2_running_var', use_global_stats=False, name='x2paddle_997') x2paddle_998 = fluid.layers.relu(x2paddle_997, name='x2paddle_998') x2paddle_999 = fluid.layers.conv2d(x2paddle_998, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer4_conv2_weight', name='x2paddle_999', bias_attr=False) x2paddle_1000 = fluid.layers.concat([x2paddle_971, x2paddle_978, x2paddle_985, x2paddle_992, x2paddle_999], axis=1) x2paddle_1001 = fluid.layers.batch_norm(x2paddle_1000, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer5_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer5_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer5_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer5_norm1_running_var', use_global_stats=False, name='x2paddle_1001') x2paddle_1002 = fluid.layers.relu(x2paddle_1001, name='x2paddle_1002') x2paddle_1003 = fluid.layers.conv2d(x2paddle_1002, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer5_conv1_weight', name='x2paddle_1003', bias_attr=False) x2paddle_1004 = fluid.layers.batch_norm(x2paddle_1003, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer5_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer5_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer5_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer5_norm2_running_var', use_global_stats=False, name='x2paddle_1004') x2paddle_1005 = fluid.layers.relu(x2paddle_1004, name='x2paddle_1005') x2paddle_1006 = fluid.layers.conv2d(x2paddle_1005, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer5_conv2_weight', name='x2paddle_1006', bias_attr=False) x2paddle_1007 = fluid.layers.concat( [x2paddle_971, x2paddle_978, x2paddle_985, x2paddle_992, x2paddle_999, x2paddle_1006], axis=1) x2paddle_1008 = fluid.layers.batch_norm(x2paddle_1007, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer6_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer6_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer6_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer6_norm1_running_var', use_global_stats=False, name='x2paddle_1008') x2paddle_1009 = fluid.layers.relu(x2paddle_1008, name='x2paddle_1009') x2paddle_1010 = fluid.layers.conv2d(x2paddle_1009, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer6_conv1_weight', name='x2paddle_1010', bias_attr=False) x2paddle_1011 = fluid.layers.batch_norm(x2paddle_1010, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock1_denselayer6_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock1_denselayer6_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock1_denselayer6_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock1_denselayer6_norm2_running_var', use_global_stats=False, name='x2paddle_1011') x2paddle_1012 = fluid.layers.relu(x2paddle_1011, name='x2paddle_1012') x2paddle_1013 = fluid.layers.conv2d(x2paddle_1012, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock1_denselayer6_conv2_weight', name='x2paddle_1013', bias_attr=False) x2paddle_1014 = fluid.layers.concat( [x2paddle_971, x2paddle_978, x2paddle_985, x2paddle_992, x2paddle_999, x2paddle_1006, x2paddle_1013], axis=1) x2paddle_1015 = fluid.layers.batch_norm(x2paddle_1014, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_transition1_norm_weight', bias_attr='x2paddle_densenet161_features_transition1_norm_bias', moving_mean_name='x2paddle_densenet161_features_transition1_norm_running_mean', moving_variance_name='x2paddle_densenet161_features_transition1_norm_running_var', use_global_stats=False, name='x2paddle_1015') x2paddle_1016 = fluid.layers.relu(x2paddle_1015, name='x2paddle_1016') x2paddle_1017 = fluid.layers.conv2d(x2paddle_1016, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_transition1_conv_weight', name='x2paddle_1017', bias_attr=False) x2paddle_1018 = fluid.layers.pad2d(x2paddle_1017, pad_value=0.0, mode='constant', paddings=[0, 0, 0, 0], name='x2paddle_1018') x2paddle_1019 = fluid.layers.pool2d(x2paddle_1018, pool_size=[2, 2], pool_type='avg', pool_stride=[2, 2], pool_padding=[0, 0], ceil_mode=False, exclusive=True, name='x2paddle_1019') x2paddle_1020 = fluid.layers.concat([x2paddle_1019], axis=1) x2paddle_1021 = fluid.layers.batch_norm(x2paddle_1020, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer1_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer1_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer1_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer1_norm1_running_var', use_global_stats=False, name='x2paddle_1021') x2paddle_1022 = fluid.layers.relu(x2paddle_1021, name='x2paddle_1022') x2paddle_1023 = fluid.layers.conv2d(x2paddle_1022, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer1_conv1_weight', name='x2paddle_1023', bias_attr=False) x2paddle_1024 = fluid.layers.batch_norm(x2paddle_1023, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer1_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer1_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer1_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer1_norm2_running_var', use_global_stats=False, name='x2paddle_1024') x2paddle_1025 = fluid.layers.relu(x2paddle_1024, name='x2paddle_1025') x2paddle_1026 = fluid.layers.conv2d(x2paddle_1025, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer1_conv2_weight', name='x2paddle_1026', bias_attr=False) x2paddle_1027 = fluid.layers.concat([x2paddle_1019, x2paddle_1026], axis=1) x2paddle_1028 = fluid.layers.batch_norm(x2paddle_1027, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer2_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer2_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer2_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer2_norm1_running_var', use_global_stats=False, name='x2paddle_1028') x2paddle_1029 = fluid.layers.relu(x2paddle_1028, name='x2paddle_1029') x2paddle_1030 = fluid.layers.conv2d(x2paddle_1029, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer2_conv1_weight', name='x2paddle_1030', bias_attr=False) x2paddle_1031 = fluid.layers.batch_norm(x2paddle_1030, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer2_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer2_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer2_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer2_norm2_running_var', use_global_stats=False, name='x2paddle_1031') x2paddle_1032 = fluid.layers.relu(x2paddle_1031, name='x2paddle_1032') x2paddle_1033 = fluid.layers.conv2d(x2paddle_1032, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer2_conv2_weight', name='x2paddle_1033', bias_attr=False) x2paddle_1034 = fluid.layers.concat([x2paddle_1019, x2paddle_1026, x2paddle_1033], axis=1) x2paddle_1035 = fluid.layers.batch_norm(x2paddle_1034, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer3_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer3_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer3_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer3_norm1_running_var', use_global_stats=False, name='x2paddle_1035') x2paddle_1036 = fluid.layers.relu(x2paddle_1035, name='x2paddle_1036') x2paddle_1037 = fluid.layers.conv2d(x2paddle_1036, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer3_conv1_weight', name='x2paddle_1037', bias_attr=False) x2paddle_1038 = fluid.layers.batch_norm(x2paddle_1037, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer3_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer3_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer3_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer3_norm2_running_var', use_global_stats=False, name='x2paddle_1038') x2paddle_1039 = fluid.layers.relu(x2paddle_1038, name='x2paddle_1039') x2paddle_1040 = fluid.layers.conv2d(x2paddle_1039, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer3_conv2_weight', name='x2paddle_1040', bias_attr=False) x2paddle_1041 = fluid.layers.concat([x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040], axis=1) x2paddle_1042 = fluid.layers.batch_norm(x2paddle_1041, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer4_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer4_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer4_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer4_norm1_running_var', use_global_stats=False, name='x2paddle_1042') x2paddle_1043 = fluid.layers.relu(x2paddle_1042, name='x2paddle_1043') x2paddle_1044 = fluid.layers.conv2d(x2paddle_1043, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer4_conv1_weight', name='x2paddle_1044', bias_attr=False) x2paddle_1045 = fluid.layers.batch_norm(x2paddle_1044, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer4_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer4_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer4_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer4_norm2_running_var', use_global_stats=False, name='x2paddle_1045') x2paddle_1046 = fluid.layers.relu(x2paddle_1045, name='x2paddle_1046') x2paddle_1047 = fluid.layers.conv2d(x2paddle_1046, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer4_conv2_weight', name='x2paddle_1047', bias_attr=False) x2paddle_1048 = fluid.layers.concat([x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047], axis=1) x2paddle_1049 = fluid.layers.batch_norm(x2paddle_1048, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer5_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer5_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer5_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer5_norm1_running_var', use_global_stats=False, name='x2paddle_1049') x2paddle_1050 = fluid.layers.relu(x2paddle_1049, name='x2paddle_1050') x2paddle_1051 = fluid.layers.conv2d(x2paddle_1050, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer5_conv1_weight', name='x2paddle_1051', bias_attr=False) x2paddle_1052 = fluid.layers.batch_norm(x2paddle_1051, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer5_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer5_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer5_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer5_norm2_running_var', use_global_stats=False, name='x2paddle_1052') x2paddle_1053 = fluid.layers.relu(x2paddle_1052, name='x2paddle_1053') x2paddle_1054 = fluid.layers.conv2d(x2paddle_1053, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer5_conv2_weight', name='x2paddle_1054', bias_attr=False) x2paddle_1055 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054], axis=1) x2paddle_1056 = fluid.layers.batch_norm(x2paddle_1055, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer6_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer6_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer6_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer6_norm1_running_var', use_global_stats=False, name='x2paddle_1056') x2paddle_1057 = fluid.layers.relu(x2paddle_1056, name='x2paddle_1057') x2paddle_1058 = fluid.layers.conv2d(x2paddle_1057, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer6_conv1_weight', name='x2paddle_1058', bias_attr=False) x2paddle_1059 = fluid.layers.batch_norm(x2paddle_1058, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer6_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer6_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer6_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer6_norm2_running_var', use_global_stats=False, name='x2paddle_1059') x2paddle_1060 = fluid.layers.relu(x2paddle_1059, name='x2paddle_1060') x2paddle_1061 = fluid.layers.conv2d(x2paddle_1060, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer6_conv2_weight', name='x2paddle_1061', bias_attr=False) x2paddle_1062 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054, x2paddle_1061], axis=1) x2paddle_1063 = fluid.layers.batch_norm(x2paddle_1062, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer7_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer7_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer7_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer7_norm1_running_var', use_global_stats=False, name='x2paddle_1063') x2paddle_1064 = fluid.layers.relu(x2paddle_1063, name='x2paddle_1064') x2paddle_1065 = fluid.layers.conv2d(x2paddle_1064, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer7_conv1_weight', name='x2paddle_1065', bias_attr=False) x2paddle_1066 = fluid.layers.batch_norm(x2paddle_1065, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer7_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer7_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer7_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer7_norm2_running_var', use_global_stats=False, name='x2paddle_1066') x2paddle_1067 = fluid.layers.relu(x2paddle_1066, name='x2paddle_1067') x2paddle_1068 = fluid.layers.conv2d(x2paddle_1067, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer7_conv2_weight', name='x2paddle_1068', bias_attr=False) x2paddle_1069 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054, x2paddle_1061, x2paddle_1068], axis=1) x2paddle_1070 = fluid.layers.batch_norm(x2paddle_1069, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer8_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer8_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer8_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer8_norm1_running_var', use_global_stats=False, name='x2paddle_1070') x2paddle_1071 = fluid.layers.relu(x2paddle_1070, name='x2paddle_1071') x2paddle_1072 = fluid.layers.conv2d(x2paddle_1071, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer8_conv1_weight', name='x2paddle_1072', bias_attr=False) x2paddle_1073 = fluid.layers.batch_norm(x2paddle_1072, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer8_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer8_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer8_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer8_norm2_running_var', use_global_stats=False, name='x2paddle_1073') x2paddle_1074 = fluid.layers.relu(x2paddle_1073, name='x2paddle_1074') x2paddle_1075 = fluid.layers.conv2d(x2paddle_1074, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer8_conv2_weight', name='x2paddle_1075', bias_attr=False) x2paddle_1076 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054, x2paddle_1061, x2paddle_1068, x2paddle_1075], axis=1) x2paddle_1077 = fluid.layers.batch_norm(x2paddle_1076, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer9_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer9_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer9_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer9_norm1_running_var', use_global_stats=False, name='x2paddle_1077') x2paddle_1078 = fluid.layers.relu(x2paddle_1077, name='x2paddle_1078') x2paddle_1079 = fluid.layers.conv2d(x2paddle_1078, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer9_conv1_weight', name='x2paddle_1079', bias_attr=False) x2paddle_1080 = fluid.layers.batch_norm(x2paddle_1079, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer9_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer9_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer9_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer9_norm2_running_var', use_global_stats=False, name='x2paddle_1080') x2paddle_1081 = fluid.layers.relu(x2paddle_1080, name='x2paddle_1081') x2paddle_1082 = fluid.layers.conv2d(x2paddle_1081, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer9_conv2_weight', name='x2paddle_1082', bias_attr=False) x2paddle_1083 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054, x2paddle_1061, x2paddle_1068, x2paddle_1075, x2paddle_1082], axis=1) x2paddle_1084 = fluid.layers.batch_norm(x2paddle_1083, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer10_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer10_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer10_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer10_norm1_running_var', use_global_stats=False, name='x2paddle_1084') x2paddle_1085 = fluid.layers.relu(x2paddle_1084, name='x2paddle_1085') x2paddle_1086 = fluid.layers.conv2d(x2paddle_1085, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer10_conv1_weight', name='x2paddle_1086', bias_attr=False) x2paddle_1087 = fluid.layers.batch_norm(x2paddle_1086, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer10_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer10_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer10_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer10_norm2_running_var', use_global_stats=False, name='x2paddle_1087') x2paddle_1088 = fluid.layers.relu(x2paddle_1087, name='x2paddle_1088') x2paddle_1089 = fluid.layers.conv2d(x2paddle_1088, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer10_conv2_weight', name='x2paddle_1089', bias_attr=False) x2paddle_1090 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054, x2paddle_1061, x2paddle_1068, x2paddle_1075, x2paddle_1082, x2paddle_1089], axis=1) x2paddle_1091 = fluid.layers.batch_norm(x2paddle_1090, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer11_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer11_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer11_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer11_norm1_running_var', use_global_stats=False, name='x2paddle_1091') x2paddle_1092 = fluid.layers.relu(x2paddle_1091, name='x2paddle_1092') x2paddle_1093 = fluid.layers.conv2d(x2paddle_1092, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer11_conv1_weight', name='x2paddle_1093', bias_attr=False) x2paddle_1094 = fluid.layers.batch_norm(x2paddle_1093, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer11_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer11_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer11_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer11_norm2_running_var', use_global_stats=False, name='x2paddle_1094') x2paddle_1095 = fluid.layers.relu(x2paddle_1094, name='x2paddle_1095') x2paddle_1096 = fluid.layers.conv2d(x2paddle_1095, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer11_conv2_weight', name='x2paddle_1096', bias_attr=False) x2paddle_1097 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054, x2paddle_1061, x2paddle_1068, x2paddle_1075, x2paddle_1082, x2paddle_1089, x2paddle_1096], axis=1) x2paddle_1098 = fluid.layers.batch_norm(x2paddle_1097, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer12_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer12_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer12_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer12_norm1_running_var', use_global_stats=False, name='x2paddle_1098') x2paddle_1099 = fluid.layers.relu(x2paddle_1098, name='x2paddle_1099') x2paddle_1100 = fluid.layers.conv2d(x2paddle_1099, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer12_conv1_weight', name='x2paddle_1100', bias_attr=False) x2paddle_1101 = fluid.layers.batch_norm(x2paddle_1100, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock2_denselayer12_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock2_denselayer12_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock2_denselayer12_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock2_denselayer12_norm2_running_var', use_global_stats=False, name='x2paddle_1101') x2paddle_1102 = fluid.layers.relu(x2paddle_1101, name='x2paddle_1102') x2paddle_1103 = fluid.layers.conv2d(x2paddle_1102, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock2_denselayer12_conv2_weight', name='x2paddle_1103', bias_attr=False) x2paddle_1104 = fluid.layers.concat( [x2paddle_1019, x2paddle_1026, x2paddle_1033, x2paddle_1040, x2paddle_1047, x2paddle_1054, x2paddle_1061, x2paddle_1068, x2paddle_1075, x2paddle_1082, x2paddle_1089, x2paddle_1096, x2paddle_1103], axis=1) x2paddle_1105 = fluid.layers.batch_norm(x2paddle_1104, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_transition2_norm_weight', bias_attr='x2paddle_densenet161_features_transition2_norm_bias', moving_mean_name='x2paddle_densenet161_features_transition2_norm_running_mean', moving_variance_name='x2paddle_densenet161_features_transition2_norm_running_var', use_global_stats=False, name='x2paddle_1105') x2paddle_1106 = fluid.layers.relu(x2paddle_1105, name='x2paddle_1106') x2paddle_1107 = fluid.layers.conv2d(x2paddle_1106, num_filters=384, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_transition2_conv_weight', name='x2paddle_1107', bias_attr=False) x2paddle_1108 = fluid.layers.pad2d(x2paddle_1107, pad_value=0.0, mode='constant', paddings=[0, 0, 0, 0], name='x2paddle_1108') x2paddle_1109 = fluid.layers.pool2d(x2paddle_1108, pool_size=[2, 2], pool_type='avg', pool_stride=[2, 2], pool_padding=[0, 0], ceil_mode=False, exclusive=True, name='x2paddle_1109') x2paddle_1110 = fluid.layers.concat([x2paddle_1109], axis=1) x2paddle_1111 = fluid.layers.batch_norm(x2paddle_1110, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer1_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer1_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer1_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer1_norm1_running_var', use_global_stats=False, name='x2paddle_1111') x2paddle_1112 = fluid.layers.relu(x2paddle_1111, name='x2paddle_1112') x2paddle_1113 = fluid.layers.conv2d(x2paddle_1112, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer1_conv1_weight', name='x2paddle_1113', bias_attr=False) x2paddle_1114 = fluid.layers.batch_norm(x2paddle_1113, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer1_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer1_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer1_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer1_norm2_running_var', use_global_stats=False, name='x2paddle_1114') x2paddle_1115 = fluid.layers.relu(x2paddle_1114, name='x2paddle_1115') x2paddle_1116 = fluid.layers.conv2d(x2paddle_1115, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer1_conv2_weight', name='x2paddle_1116', bias_attr=False) x2paddle_1117 = fluid.layers.concat([x2paddle_1109, x2paddle_1116], axis=1) x2paddle_1118 = fluid.layers.batch_norm(x2paddle_1117, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer2_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer2_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer2_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer2_norm1_running_var', use_global_stats=False, name='x2paddle_1118') x2paddle_1119 = fluid.layers.relu(x2paddle_1118, name='x2paddle_1119') x2paddle_1120 = fluid.layers.conv2d(x2paddle_1119, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer2_conv1_weight', name='x2paddle_1120', bias_attr=False) x2paddle_1121 = fluid.layers.batch_norm(x2paddle_1120, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer2_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer2_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer2_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer2_norm2_running_var', use_global_stats=False, name='x2paddle_1121') x2paddle_1122 = fluid.layers.relu(x2paddle_1121, name='x2paddle_1122') x2paddle_1123 = fluid.layers.conv2d(x2paddle_1122, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer2_conv2_weight', name='x2paddle_1123', bias_attr=False) x2paddle_1124 = fluid.layers.concat([x2paddle_1109, x2paddle_1116, x2paddle_1123], axis=1) x2paddle_1125 = fluid.layers.batch_norm(x2paddle_1124, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer3_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer3_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer3_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer3_norm1_running_var', use_global_stats=False, name='x2paddle_1125') x2paddle_1126 = fluid.layers.relu(x2paddle_1125, name='x2paddle_1126') x2paddle_1127 = fluid.layers.conv2d(x2paddle_1126, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer3_conv1_weight', name='x2paddle_1127', bias_attr=False) x2paddle_1128 = fluid.layers.batch_norm(x2paddle_1127, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer3_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer3_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer3_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer3_norm2_running_var', use_global_stats=False, name='x2paddle_1128') x2paddle_1129 = fluid.layers.relu(x2paddle_1128, name='x2paddle_1129') x2paddle_1130 = fluid.layers.conv2d(x2paddle_1129, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer3_conv2_weight', name='x2paddle_1130', bias_attr=False) x2paddle_1131 = fluid.layers.concat([x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130], axis=1) x2paddle_1132 = fluid.layers.batch_norm(x2paddle_1131, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer4_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer4_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer4_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer4_norm1_running_var', use_global_stats=False, name='x2paddle_1132') x2paddle_1133 = fluid.layers.relu(x2paddle_1132, name='x2paddle_1133') x2paddle_1134 = fluid.layers.conv2d(x2paddle_1133, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer4_conv1_weight', name='x2paddle_1134', bias_attr=False) x2paddle_1135 = fluid.layers.batch_norm(x2paddle_1134, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer4_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer4_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer4_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer4_norm2_running_var', use_global_stats=False, name='x2paddle_1135') x2paddle_1136 = fluid.layers.relu(x2paddle_1135, name='x2paddle_1136') x2paddle_1137 = fluid.layers.conv2d(x2paddle_1136, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer4_conv2_weight', name='x2paddle_1137', bias_attr=False) x2paddle_1138 = fluid.layers.concat([x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137], axis=1) x2paddle_1139 = fluid.layers.batch_norm(x2paddle_1138, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer5_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer5_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer5_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer5_norm1_running_var', use_global_stats=False, name='x2paddle_1139') x2paddle_1140 = fluid.layers.relu(x2paddle_1139, name='x2paddle_1140') x2paddle_1141 = fluid.layers.conv2d(x2paddle_1140, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer5_conv1_weight', name='x2paddle_1141', bias_attr=False) x2paddle_1142 = fluid.layers.batch_norm(x2paddle_1141, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer5_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer5_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer5_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer5_norm2_running_var', use_global_stats=False, name='x2paddle_1142') x2paddle_1143 = fluid.layers.relu(x2paddle_1142, name='x2paddle_1143') x2paddle_1144 = fluid.layers.conv2d(x2paddle_1143, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer5_conv2_weight', name='x2paddle_1144', bias_attr=False) x2paddle_1145 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144], axis=1) x2paddle_1146 = fluid.layers.batch_norm(x2paddle_1145, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer6_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer6_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer6_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer6_norm1_running_var', use_global_stats=False, name='x2paddle_1146') x2paddle_1147 = fluid.layers.relu(x2paddle_1146, name='x2paddle_1147') x2paddle_1148 = fluid.layers.conv2d(x2paddle_1147, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer6_conv1_weight', name='x2paddle_1148', bias_attr=False) x2paddle_1149 = fluid.layers.batch_norm(x2paddle_1148, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer6_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer6_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer6_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer6_norm2_running_var', use_global_stats=False, name='x2paddle_1149') x2paddle_1150 = fluid.layers.relu(x2paddle_1149, name='x2paddle_1150') x2paddle_1151 = fluid.layers.conv2d(x2paddle_1150, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer6_conv2_weight', name='x2paddle_1151', bias_attr=False) x2paddle_1152 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151], axis=1) x2paddle_1153 = fluid.layers.batch_norm(x2paddle_1152, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer7_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer7_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer7_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer7_norm1_running_var', use_global_stats=False, name='x2paddle_1153') x2paddle_1154 = fluid.layers.relu(x2paddle_1153, name='x2paddle_1154') x2paddle_1155 = fluid.layers.conv2d(x2paddle_1154, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer7_conv1_weight', name='x2paddle_1155', bias_attr=False) x2paddle_1156 = fluid.layers.batch_norm(x2paddle_1155, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer7_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer7_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer7_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer7_norm2_running_var', use_global_stats=False, name='x2paddle_1156') x2paddle_1157 = fluid.layers.relu(x2paddle_1156, name='x2paddle_1157') x2paddle_1158 = fluid.layers.conv2d(x2paddle_1157, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer7_conv2_weight', name='x2paddle_1158', bias_attr=False) x2paddle_1159 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158], axis=1) x2paddle_1160 = fluid.layers.batch_norm(x2paddle_1159, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer8_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer8_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer8_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer8_norm1_running_var', use_global_stats=False, name='x2paddle_1160') x2paddle_1161 = fluid.layers.relu(x2paddle_1160, name='x2paddle_1161') x2paddle_1162 = fluid.layers.conv2d(x2paddle_1161, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer8_conv1_weight', name='x2paddle_1162', bias_attr=False) x2paddle_1163 = fluid.layers.batch_norm(x2paddle_1162, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer8_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer8_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer8_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer8_norm2_running_var', use_global_stats=False, name='x2paddle_1163') x2paddle_1164 = fluid.layers.relu(x2paddle_1163, name='x2paddle_1164') x2paddle_1165 = fluid.layers.conv2d(x2paddle_1164, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer8_conv2_weight', name='x2paddle_1165', bias_attr=False) x2paddle_1166 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165], axis=1) x2paddle_1167 = fluid.layers.batch_norm(x2paddle_1166, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer9_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer9_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer9_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer9_norm1_running_var', use_global_stats=False, name='x2paddle_1167') x2paddle_1168 = fluid.layers.relu(x2paddle_1167, name='x2paddle_1168') x2paddle_1169 = fluid.layers.conv2d(x2paddle_1168, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer9_conv1_weight', name='x2paddle_1169', bias_attr=False) x2paddle_1170 = fluid.layers.batch_norm(x2paddle_1169, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer9_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer9_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer9_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer9_norm2_running_var', use_global_stats=False, name='x2paddle_1170') x2paddle_1171 = fluid.layers.relu(x2paddle_1170, name='x2paddle_1171') x2paddle_1172 = fluid.layers.conv2d(x2paddle_1171, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer9_conv2_weight', name='x2paddle_1172', bias_attr=False) x2paddle_1173 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172], axis=1) x2paddle_1174 = fluid.layers.batch_norm(x2paddle_1173, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer10_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer10_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer10_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer10_norm1_running_var', use_global_stats=False, name='x2paddle_1174') x2paddle_1175 = fluid.layers.relu(x2paddle_1174, name='x2paddle_1175') x2paddle_1176 = fluid.layers.conv2d(x2paddle_1175, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer10_conv1_weight', name='x2paddle_1176', bias_attr=False) x2paddle_1177 = fluid.layers.batch_norm(x2paddle_1176, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer10_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer10_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer10_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer10_norm2_running_var', use_global_stats=False, name='x2paddle_1177') x2paddle_1178 = fluid.layers.relu(x2paddle_1177, name='x2paddle_1178') x2paddle_1179 = fluid.layers.conv2d(x2paddle_1178, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer10_conv2_weight', name='x2paddle_1179', bias_attr=False) x2paddle_1180 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179], axis=1) x2paddle_1181 = fluid.layers.batch_norm(x2paddle_1180, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer11_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer11_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer11_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer11_norm1_running_var', use_global_stats=False, name='x2paddle_1181') x2paddle_1182 = fluid.layers.relu(x2paddle_1181, name='x2paddle_1182') x2paddle_1183 = fluid.layers.conv2d(x2paddle_1182, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer11_conv1_weight', name='x2paddle_1183', bias_attr=False) x2paddle_1184 = fluid.layers.batch_norm(x2paddle_1183, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer11_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer11_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer11_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer11_norm2_running_var', use_global_stats=False, name='x2paddle_1184') x2paddle_1185 = fluid.layers.relu(x2paddle_1184, name='x2paddle_1185') x2paddle_1186 = fluid.layers.conv2d(x2paddle_1185, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer11_conv2_weight', name='x2paddle_1186', bias_attr=False) x2paddle_1187 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186], axis=1) x2paddle_1188 = fluid.layers.batch_norm(x2paddle_1187, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer12_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer12_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer12_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer12_norm1_running_var', use_global_stats=False, name='x2paddle_1188') x2paddle_1189 = fluid.layers.relu(x2paddle_1188, name='x2paddle_1189') x2paddle_1190 = fluid.layers.conv2d(x2paddle_1189, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer12_conv1_weight', name='x2paddle_1190', bias_attr=False) x2paddle_1191 = fluid.layers.batch_norm(x2paddle_1190, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer12_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer12_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer12_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer12_norm2_running_var', use_global_stats=False, name='x2paddle_1191') x2paddle_1192 = fluid.layers.relu(x2paddle_1191, name='x2paddle_1192') x2paddle_1193 = fluid.layers.conv2d(x2paddle_1192, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer12_conv2_weight', name='x2paddle_1193', bias_attr=False) x2paddle_1194 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193], axis=1) x2paddle_1195 = fluid.layers.batch_norm(x2paddle_1194, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer13_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer13_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer13_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer13_norm1_running_var', use_global_stats=False, name='x2paddle_1195') x2paddle_1196 = fluid.layers.relu(x2paddle_1195, name='x2paddle_1196') x2paddle_1197 = fluid.layers.conv2d(x2paddle_1196, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer13_conv1_weight', name='x2paddle_1197', bias_attr=False) x2paddle_1198 = fluid.layers.batch_norm(x2paddle_1197, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer13_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer13_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer13_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer13_norm2_running_var', use_global_stats=False, name='x2paddle_1198') x2paddle_1199 = fluid.layers.relu(x2paddle_1198, name='x2paddle_1199') x2paddle_1200 = fluid.layers.conv2d(x2paddle_1199, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer13_conv2_weight', name='x2paddle_1200', bias_attr=False) x2paddle_1201 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200], axis=1) x2paddle_1202 = fluid.layers.batch_norm(x2paddle_1201, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer14_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer14_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer14_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer14_norm1_running_var', use_global_stats=False, name='x2paddle_1202') x2paddle_1203 = fluid.layers.relu(x2paddle_1202, name='x2paddle_1203') x2paddle_1204 = fluid.layers.conv2d(x2paddle_1203, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer14_conv1_weight', name='x2paddle_1204', bias_attr=False) x2paddle_1205 = fluid.layers.batch_norm(x2paddle_1204, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer14_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer14_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer14_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer14_norm2_running_var', use_global_stats=False, name='x2paddle_1205') x2paddle_1206 = fluid.layers.relu(x2paddle_1205, name='x2paddle_1206') x2paddle_1207 = fluid.layers.conv2d(x2paddle_1206, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer14_conv2_weight', name='x2paddle_1207', bias_attr=False) x2paddle_1208 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207], axis=1) x2paddle_1209 = fluid.layers.batch_norm(x2paddle_1208, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer15_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer15_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer15_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer15_norm1_running_var', use_global_stats=False, name='x2paddle_1209') x2paddle_1210 = fluid.layers.relu(x2paddle_1209, name='x2paddle_1210') x2paddle_1211 = fluid.layers.conv2d(x2paddle_1210, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer15_conv1_weight', name='x2paddle_1211', bias_attr=False) x2paddle_1212 = fluid.layers.batch_norm(x2paddle_1211, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer15_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer15_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer15_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer15_norm2_running_var', use_global_stats=False, name='x2paddle_1212') x2paddle_1213 = fluid.layers.relu(x2paddle_1212, name='x2paddle_1213') x2paddle_1214 = fluid.layers.conv2d(x2paddle_1213, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer15_conv2_weight', name='x2paddle_1214', bias_attr=False) x2paddle_1215 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214], axis=1) x2paddle_1216 = fluid.layers.batch_norm(x2paddle_1215, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer16_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer16_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer16_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer16_norm1_running_var', use_global_stats=False, name='x2paddle_1216') x2paddle_1217 = fluid.layers.relu(x2paddle_1216, name='x2paddle_1217') x2paddle_1218 = fluid.layers.conv2d(x2paddle_1217, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer16_conv1_weight', name='x2paddle_1218', bias_attr=False) x2paddle_1219 = fluid.layers.batch_norm(x2paddle_1218, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer16_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer16_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer16_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer16_norm2_running_var', use_global_stats=False, name='x2paddle_1219') x2paddle_1220 = fluid.layers.relu(x2paddle_1219, name='x2paddle_1220') x2paddle_1221 = fluid.layers.conv2d(x2paddle_1220, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer16_conv2_weight', name='x2paddle_1221', bias_attr=False) x2paddle_1222 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221], axis=1) x2paddle_1223 = fluid.layers.batch_norm(x2paddle_1222, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer17_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer17_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer17_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer17_norm1_running_var', use_global_stats=False, name='x2paddle_1223') x2paddle_1224 = fluid.layers.relu(x2paddle_1223, name='x2paddle_1224') x2paddle_1225 = fluid.layers.conv2d(x2paddle_1224, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer17_conv1_weight', name='x2paddle_1225', bias_attr=False) x2paddle_1226 = fluid.layers.batch_norm(x2paddle_1225, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer17_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer17_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer17_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer17_norm2_running_var', use_global_stats=False, name='x2paddle_1226') x2paddle_1227 = fluid.layers.relu(x2paddle_1226, name='x2paddle_1227') x2paddle_1228 = fluid.layers.conv2d(x2paddle_1227, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer17_conv2_weight', name='x2paddle_1228', bias_attr=False) x2paddle_1229 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228], axis=1) x2paddle_1230 = fluid.layers.batch_norm(x2paddle_1229, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer18_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer18_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer18_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer18_norm1_running_var', use_global_stats=False, name='x2paddle_1230') x2paddle_1231 = fluid.layers.relu(x2paddle_1230, name='x2paddle_1231') x2paddle_1232 = fluid.layers.conv2d(x2paddle_1231, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer18_conv1_weight', name='x2paddle_1232', bias_attr=False) x2paddle_1233 = fluid.layers.batch_norm(x2paddle_1232, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer18_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer18_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer18_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer18_norm2_running_var', use_global_stats=False, name='x2paddle_1233') x2paddle_1234 = fluid.layers.relu(x2paddle_1233, name='x2paddle_1234') x2paddle_1235 = fluid.layers.conv2d(x2paddle_1234, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer18_conv2_weight', name='x2paddle_1235', bias_attr=False) x2paddle_1236 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235], axis=1) x2paddle_1237 = fluid.layers.batch_norm(x2paddle_1236, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer19_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer19_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer19_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer19_norm1_running_var', use_global_stats=False, name='x2paddle_1237') x2paddle_1238 = fluid.layers.relu(x2paddle_1237, name='x2paddle_1238') x2paddle_1239 = fluid.layers.conv2d(x2paddle_1238, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer19_conv1_weight', name='x2paddle_1239', bias_attr=False) x2paddle_1240 = fluid.layers.batch_norm(x2paddle_1239, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer19_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer19_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer19_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer19_norm2_running_var', use_global_stats=False, name='x2paddle_1240') x2paddle_1241 = fluid.layers.relu(x2paddle_1240, name='x2paddle_1241') x2paddle_1242 = fluid.layers.conv2d(x2paddle_1241, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer19_conv2_weight', name='x2paddle_1242', bias_attr=False) x2paddle_1243 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242], axis=1) x2paddle_1244 = fluid.layers.batch_norm(x2paddle_1243, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer20_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer20_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer20_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer20_norm1_running_var', use_global_stats=False, name='x2paddle_1244') x2paddle_1245 = fluid.layers.relu(x2paddle_1244, name='x2paddle_1245') x2paddle_1246 = fluid.layers.conv2d(x2paddle_1245, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer20_conv1_weight', name='x2paddle_1246', bias_attr=False) x2paddle_1247 = fluid.layers.batch_norm(x2paddle_1246, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer20_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer20_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer20_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer20_norm2_running_var', use_global_stats=False, name='x2paddle_1247') x2paddle_1248 = fluid.layers.relu(x2paddle_1247, name='x2paddle_1248') x2paddle_1249 = fluid.layers.conv2d(x2paddle_1248, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer20_conv2_weight', name='x2paddle_1249', bias_attr=False) x2paddle_1250 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249], axis=1) x2paddle_1251 = fluid.layers.batch_norm(x2paddle_1250, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer21_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer21_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer21_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer21_norm1_running_var', use_global_stats=False, name='x2paddle_1251') x2paddle_1252 = fluid.layers.relu(x2paddle_1251, name='x2paddle_1252') x2paddle_1253 = fluid.layers.conv2d(x2paddle_1252, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer21_conv1_weight', name='x2paddle_1253', bias_attr=False) x2paddle_1254 = fluid.layers.batch_norm(x2paddle_1253, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer21_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer21_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer21_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer21_norm2_running_var', use_global_stats=False, name='x2paddle_1254') x2paddle_1255 = fluid.layers.relu(x2paddle_1254, name='x2paddle_1255') x2paddle_1256 = fluid.layers.conv2d(x2paddle_1255, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer21_conv2_weight', name='x2paddle_1256', bias_attr=False) x2paddle_1257 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256], axis=1) x2paddle_1258 = fluid.layers.batch_norm(x2paddle_1257, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer22_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer22_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer22_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer22_norm1_running_var', use_global_stats=False, name='x2paddle_1258') x2paddle_1259 = fluid.layers.relu(x2paddle_1258, name='x2paddle_1259') x2paddle_1260 = fluid.layers.conv2d(x2paddle_1259, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer22_conv1_weight', name='x2paddle_1260', bias_attr=False) x2paddle_1261 = fluid.layers.batch_norm(x2paddle_1260, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer22_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer22_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer22_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer22_norm2_running_var', use_global_stats=False, name='x2paddle_1261') x2paddle_1262 = fluid.layers.relu(x2paddle_1261, name='x2paddle_1262') x2paddle_1263 = fluid.layers.conv2d(x2paddle_1262, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer22_conv2_weight', name='x2paddle_1263', bias_attr=False) x2paddle_1264 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263], axis=1) x2paddle_1265 = fluid.layers.batch_norm(x2paddle_1264, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer23_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer23_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer23_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer23_norm1_running_var', use_global_stats=False, name='x2paddle_1265') x2paddle_1266 = fluid.layers.relu(x2paddle_1265, name='x2paddle_1266') x2paddle_1267 = fluid.layers.conv2d(x2paddle_1266, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer23_conv1_weight', name='x2paddle_1267', bias_attr=False) x2paddle_1268 = fluid.layers.batch_norm(x2paddle_1267, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer23_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer23_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer23_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer23_norm2_running_var', use_global_stats=False, name='x2paddle_1268') x2paddle_1269 = fluid.layers.relu(x2paddle_1268, name='x2paddle_1269') x2paddle_1270 = fluid.layers.conv2d(x2paddle_1269, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer23_conv2_weight', name='x2paddle_1270', bias_attr=False) x2paddle_1271 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270], axis=1) x2paddle_1272 = fluid.layers.batch_norm(x2paddle_1271, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer24_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer24_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer24_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer24_norm1_running_var', use_global_stats=False, name='x2paddle_1272') x2paddle_1273 = fluid.layers.relu(x2paddle_1272, name='x2paddle_1273') x2paddle_1274 = fluid.layers.conv2d(x2paddle_1273, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer24_conv1_weight', name='x2paddle_1274', bias_attr=False) x2paddle_1275 = fluid.layers.batch_norm(x2paddle_1274, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer24_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer24_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer24_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer24_norm2_running_var', use_global_stats=False, name='x2paddle_1275') x2paddle_1276 = fluid.layers.relu(x2paddle_1275, name='x2paddle_1276') x2paddle_1277 = fluid.layers.conv2d(x2paddle_1276, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer24_conv2_weight', name='x2paddle_1277', bias_attr=False) x2paddle_1278 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277], axis=1) x2paddle_1279 = fluid.layers.batch_norm(x2paddle_1278, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer25_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer25_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer25_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer25_norm1_running_var', use_global_stats=False, name='x2paddle_1279') x2paddle_1280 = fluid.layers.relu(x2paddle_1279, name='x2paddle_1280') x2paddle_1281 = fluid.layers.conv2d(x2paddle_1280, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer25_conv1_weight', name='x2paddle_1281', bias_attr=False) x2paddle_1282 = fluid.layers.batch_norm(x2paddle_1281, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer25_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer25_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer25_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer25_norm2_running_var', use_global_stats=False, name='x2paddle_1282') x2paddle_1283 = fluid.layers.relu(x2paddle_1282, name='x2paddle_1283') x2paddle_1284 = fluid.layers.conv2d(x2paddle_1283, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer25_conv2_weight', name='x2paddle_1284', bias_attr=False) x2paddle_1285 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284], axis=1) x2paddle_1286 = fluid.layers.batch_norm(x2paddle_1285, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer26_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer26_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer26_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer26_norm1_running_var', use_global_stats=False, name='x2paddle_1286') x2paddle_1287 = fluid.layers.relu(x2paddle_1286, name='x2paddle_1287') x2paddle_1288 = fluid.layers.conv2d(x2paddle_1287, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer26_conv1_weight', name='x2paddle_1288', bias_attr=False) x2paddle_1289 = fluid.layers.batch_norm(x2paddle_1288, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer26_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer26_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer26_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer26_norm2_running_var', use_global_stats=False, name='x2paddle_1289') x2paddle_1290 = fluid.layers.relu(x2paddle_1289, name='x2paddle_1290') x2paddle_1291 = fluid.layers.conv2d(x2paddle_1290, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer26_conv2_weight', name='x2paddle_1291', bias_attr=False) x2paddle_1292 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291], axis=1) x2paddle_1293 = fluid.layers.batch_norm(x2paddle_1292, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer27_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer27_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer27_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer27_norm1_running_var', use_global_stats=False, name='x2paddle_1293') x2paddle_1294 = fluid.layers.relu(x2paddle_1293, name='x2paddle_1294') x2paddle_1295 = fluid.layers.conv2d(x2paddle_1294, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer27_conv1_weight', name='x2paddle_1295', bias_attr=False) x2paddle_1296 = fluid.layers.batch_norm(x2paddle_1295, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer27_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer27_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer27_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer27_norm2_running_var', use_global_stats=False, name='x2paddle_1296') x2paddle_1297 = fluid.layers.relu(x2paddle_1296, name='x2paddle_1297') x2paddle_1298 = fluid.layers.conv2d(x2paddle_1297, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer27_conv2_weight', name='x2paddle_1298', bias_attr=False) x2paddle_1299 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298], axis=1) x2paddle_1300 = fluid.layers.batch_norm(x2paddle_1299, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer28_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer28_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer28_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer28_norm1_running_var', use_global_stats=False, name='x2paddle_1300') x2paddle_1301 = fluid.layers.relu(x2paddle_1300, name='x2paddle_1301') x2paddle_1302 = fluid.layers.conv2d(x2paddle_1301, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer28_conv1_weight', name='x2paddle_1302', bias_attr=False) x2paddle_1303 = fluid.layers.batch_norm(x2paddle_1302, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer28_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer28_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer28_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer28_norm2_running_var', use_global_stats=False, name='x2paddle_1303') x2paddle_1304 = fluid.layers.relu(x2paddle_1303, name='x2paddle_1304') x2paddle_1305 = fluid.layers.conv2d(x2paddle_1304, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer28_conv2_weight', name='x2paddle_1305', bias_attr=False) x2paddle_1306 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305], axis=1) x2paddle_1307 = fluid.layers.batch_norm(x2paddle_1306, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer29_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer29_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer29_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer29_norm1_running_var', use_global_stats=False, name='x2paddle_1307') x2paddle_1308 = fluid.layers.relu(x2paddle_1307, name='x2paddle_1308') x2paddle_1309 = fluid.layers.conv2d(x2paddle_1308, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer29_conv1_weight', name='x2paddle_1309', bias_attr=False) x2paddle_1310 = fluid.layers.batch_norm(x2paddle_1309, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer29_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer29_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer29_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer29_norm2_running_var', use_global_stats=False, name='x2paddle_1310') x2paddle_1311 = fluid.layers.relu(x2paddle_1310, name='x2paddle_1311') x2paddle_1312 = fluid.layers.conv2d(x2paddle_1311, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer29_conv2_weight', name='x2paddle_1312', bias_attr=False) x2paddle_1313 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312], axis=1) x2paddle_1314 = fluid.layers.batch_norm(x2paddle_1313, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer30_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer30_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer30_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer30_norm1_running_var', use_global_stats=False, name='x2paddle_1314') x2paddle_1315 = fluid.layers.relu(x2paddle_1314, name='x2paddle_1315') x2paddle_1316 = fluid.layers.conv2d(x2paddle_1315, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer30_conv1_weight', name='x2paddle_1316', bias_attr=False) x2paddle_1317 = fluid.layers.batch_norm(x2paddle_1316, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer30_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer30_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer30_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer30_norm2_running_var', use_global_stats=False, name='x2paddle_1317') x2paddle_1318 = fluid.layers.relu(x2paddle_1317, name='x2paddle_1318') x2paddle_1319 = fluid.layers.conv2d(x2paddle_1318, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer30_conv2_weight', name='x2paddle_1319', bias_attr=False) x2paddle_1320 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312, x2paddle_1319], axis=1) x2paddle_1321 = fluid.layers.batch_norm(x2paddle_1320, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer31_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer31_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer31_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer31_norm1_running_var', use_global_stats=False, name='x2paddle_1321') x2paddle_1322 = fluid.layers.relu(x2paddle_1321, name='x2paddle_1322') x2paddle_1323 = fluid.layers.conv2d(x2paddle_1322, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer31_conv1_weight', name='x2paddle_1323', bias_attr=False) x2paddle_1324 = fluid.layers.batch_norm(x2paddle_1323, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer31_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer31_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer31_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer31_norm2_running_var', use_global_stats=False, name='x2paddle_1324') x2paddle_1325 = fluid.layers.relu(x2paddle_1324, name='x2paddle_1325') x2paddle_1326 = fluid.layers.conv2d(x2paddle_1325, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer31_conv2_weight', name='x2paddle_1326', bias_attr=False) x2paddle_1327 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312, x2paddle_1319, x2paddle_1326], axis=1) x2paddle_1328 = fluid.layers.batch_norm(x2paddle_1327, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer32_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer32_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer32_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer32_norm1_running_var', use_global_stats=False, name='x2paddle_1328') x2paddle_1329 = fluid.layers.relu(x2paddle_1328, name='x2paddle_1329') x2paddle_1330 = fluid.layers.conv2d(x2paddle_1329, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer32_conv1_weight', name='x2paddle_1330', bias_attr=False) x2paddle_1331 = fluid.layers.batch_norm(x2paddle_1330, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer32_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer32_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer32_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer32_norm2_running_var', use_global_stats=False, name='x2paddle_1331') x2paddle_1332 = fluid.layers.relu(x2paddle_1331, name='x2paddle_1332') x2paddle_1333 = fluid.layers.conv2d(x2paddle_1332, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer32_conv2_weight', name='x2paddle_1333', bias_attr=False) x2paddle_1334 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312, x2paddle_1319, x2paddle_1326, x2paddle_1333], axis=1) x2paddle_1335 = fluid.layers.batch_norm(x2paddle_1334, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer33_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer33_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer33_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer33_norm1_running_var', use_global_stats=False, name='x2paddle_1335') x2paddle_1336 = fluid.layers.relu(x2paddle_1335, name='x2paddle_1336') x2paddle_1337 = fluid.layers.conv2d(x2paddle_1336, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer33_conv1_weight', name='x2paddle_1337', bias_attr=False) x2paddle_1338 = fluid.layers.batch_norm(x2paddle_1337, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer33_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer33_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer33_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer33_norm2_running_var', use_global_stats=False, name='x2paddle_1338') x2paddle_1339 = fluid.layers.relu(x2paddle_1338, name='x2paddle_1339') x2paddle_1340 = fluid.layers.conv2d(x2paddle_1339, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer33_conv2_weight', name='x2paddle_1340', bias_attr=False) x2paddle_1341 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312, x2paddle_1319, x2paddle_1326, x2paddle_1333, x2paddle_1340], axis=1) x2paddle_1342 = fluid.layers.batch_norm(x2paddle_1341, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer34_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer34_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer34_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer34_norm1_running_var', use_global_stats=False, name='x2paddle_1342') x2paddle_1343 = fluid.layers.relu(x2paddle_1342, name='x2paddle_1343') x2paddle_1344 = fluid.layers.conv2d(x2paddle_1343, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer34_conv1_weight', name='x2paddle_1344', bias_attr=False) x2paddle_1345 = fluid.layers.batch_norm(x2paddle_1344, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer34_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer34_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer34_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer34_norm2_running_var', use_global_stats=False, name='x2paddle_1345') x2paddle_1346 = fluid.layers.relu(x2paddle_1345, name='x2paddle_1346') x2paddle_1347 = fluid.layers.conv2d(x2paddle_1346, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer34_conv2_weight', name='x2paddle_1347', bias_attr=False) x2paddle_1348 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312, x2paddle_1319, x2paddle_1326, x2paddle_1333, x2paddle_1340, x2paddle_1347], axis=1) x2paddle_1349 = fluid.layers.batch_norm(x2paddle_1348, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer35_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer35_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer35_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer35_norm1_running_var', use_global_stats=False, name='x2paddle_1349') x2paddle_1350 = fluid.layers.relu(x2paddle_1349, name='x2paddle_1350') x2paddle_1351 = fluid.layers.conv2d(x2paddle_1350, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer35_conv1_weight', name='x2paddle_1351', bias_attr=False) x2paddle_1352 = fluid.layers.batch_norm(x2paddle_1351, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer35_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer35_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer35_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer35_norm2_running_var', use_global_stats=False, name='x2paddle_1352') x2paddle_1353 = fluid.layers.relu(x2paddle_1352, name='x2paddle_1353') x2paddle_1354 = fluid.layers.conv2d(x2paddle_1353, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer35_conv2_weight', name='x2paddle_1354', bias_attr=False) x2paddle_1355 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312, x2paddle_1319, x2paddle_1326, x2paddle_1333, x2paddle_1340, x2paddle_1347, x2paddle_1354], axis=1) x2paddle_1356 = fluid.layers.batch_norm(x2paddle_1355, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer36_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer36_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer36_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer36_norm1_running_var', use_global_stats=False, name='x2paddle_1356') x2paddle_1357 = fluid.layers.relu(x2paddle_1356, name='x2paddle_1357') x2paddle_1358 = fluid.layers.conv2d(x2paddle_1357, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer36_conv1_weight', name='x2paddle_1358', bias_attr=False) x2paddle_1359 = fluid.layers.batch_norm(x2paddle_1358, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock3_denselayer36_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock3_denselayer36_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock3_denselayer36_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock3_denselayer36_norm2_running_var', use_global_stats=False, name='x2paddle_1359') x2paddle_1360 = fluid.layers.relu(x2paddle_1359, name='x2paddle_1360') x2paddle_1361 = fluid.layers.conv2d(x2paddle_1360, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock3_denselayer36_conv2_weight', name='x2paddle_1361', bias_attr=False) x2paddle_1362 = fluid.layers.concat( [x2paddle_1109, x2paddle_1116, x2paddle_1123, x2paddle_1130, x2paddle_1137, x2paddle_1144, x2paddle_1151, x2paddle_1158, x2paddle_1165, x2paddle_1172, x2paddle_1179, x2paddle_1186, x2paddle_1193, x2paddle_1200, x2paddle_1207, x2paddle_1214, x2paddle_1221, x2paddle_1228, x2paddle_1235, x2paddle_1242, x2paddle_1249, x2paddle_1256, x2paddle_1263, x2paddle_1270, x2paddle_1277, x2paddle_1284, x2paddle_1291, x2paddle_1298, x2paddle_1305, x2paddle_1312, x2paddle_1319, x2paddle_1326, x2paddle_1333, x2paddle_1340, x2paddle_1347, x2paddle_1354, x2paddle_1361], axis=1) x2paddle_1363 = fluid.layers.batch_norm(x2paddle_1362, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_transition3_norm_weight', bias_attr='x2paddle_densenet161_features_transition3_norm_bias', moving_mean_name='x2paddle_densenet161_features_transition3_norm_running_mean', moving_variance_name='x2paddle_densenet161_features_transition3_norm_running_var', use_global_stats=False, name='x2paddle_1363') x2paddle_1364 = fluid.layers.relu(x2paddle_1363, name='x2paddle_1364') x2paddle_1365 = fluid.layers.conv2d(x2paddle_1364, num_filters=1056, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_transition3_conv_weight', name='x2paddle_1365', bias_attr=False) x2paddle_1366 = fluid.layers.pad2d(x2paddle_1365, pad_value=0.0, mode='constant', paddings=[0, 0, 0, 0], name='x2paddle_1366') x2paddle_1367 = fluid.layers.pool2d(x2paddle_1366, pool_size=[2, 2], pool_type='avg', pool_stride=[2, 2], pool_padding=[0, 0], ceil_mode=False, exclusive=True, name='x2paddle_1367') x2paddle_1368 = fluid.layers.concat([x2paddle_1367], axis=1) x2paddle_1369 = fluid.layers.batch_norm(x2paddle_1368, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer1_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer1_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer1_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer1_norm1_running_var', use_global_stats=False, name='x2paddle_1369') x2paddle_1370 = fluid.layers.relu(x2paddle_1369, name='x2paddle_1370') x2paddle_1371 = fluid.layers.conv2d(x2paddle_1370, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer1_conv1_weight', name='x2paddle_1371', bias_attr=False) x2paddle_1372 = fluid.layers.batch_norm(x2paddle_1371, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer1_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer1_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer1_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer1_norm2_running_var', use_global_stats=False, name='x2paddle_1372') x2paddle_1373 = fluid.layers.relu(x2paddle_1372, name='x2paddle_1373') x2paddle_1374 = fluid.layers.conv2d(x2paddle_1373, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer1_conv2_weight', name='x2paddle_1374', bias_attr=False) x2paddle_1375 = fluid.layers.concat([x2paddle_1367, x2paddle_1374], axis=1) x2paddle_1376 = fluid.layers.batch_norm(x2paddle_1375, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer2_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer2_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer2_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer2_norm1_running_var', use_global_stats=False, name='x2paddle_1376') x2paddle_1377 = fluid.layers.relu(x2paddle_1376, name='x2paddle_1377') x2paddle_1378 = fluid.layers.conv2d(x2paddle_1377, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer2_conv1_weight', name='x2paddle_1378', bias_attr=False) x2paddle_1379 = fluid.layers.batch_norm(x2paddle_1378, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer2_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer2_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer2_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer2_norm2_running_var', use_global_stats=False, name='x2paddle_1379') x2paddle_1380 = fluid.layers.relu(x2paddle_1379, name='x2paddle_1380') x2paddle_1381 = fluid.layers.conv2d(x2paddle_1380, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer2_conv2_weight', name='x2paddle_1381', bias_attr=False) x2paddle_1382 = fluid.layers.concat([x2paddle_1367, x2paddle_1374, x2paddle_1381], axis=1) x2paddle_1383 = fluid.layers.batch_norm(x2paddle_1382, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer3_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer3_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer3_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer3_norm1_running_var', use_global_stats=False, name='x2paddle_1383') x2paddle_1384 = fluid.layers.relu(x2paddle_1383, name='x2paddle_1384') x2paddle_1385 = fluid.layers.conv2d(x2paddle_1384, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer3_conv1_weight', name='x2paddle_1385', bias_attr=False) x2paddle_1386 = fluid.layers.batch_norm(x2paddle_1385, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer3_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer3_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer3_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer3_norm2_running_var', use_global_stats=False, name='x2paddle_1386') x2paddle_1387 = fluid.layers.relu(x2paddle_1386, name='x2paddle_1387') x2paddle_1388 = fluid.layers.conv2d(x2paddle_1387, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer3_conv2_weight', name='x2paddle_1388', bias_attr=False) x2paddle_1389 = fluid.layers.concat([x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388], axis=1) x2paddle_1390 = fluid.layers.batch_norm(x2paddle_1389, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer4_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer4_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer4_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer4_norm1_running_var', use_global_stats=False, name='x2paddle_1390') x2paddle_1391 = fluid.layers.relu(x2paddle_1390, name='x2paddle_1391') x2paddle_1392 = fluid.layers.conv2d(x2paddle_1391, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer4_conv1_weight', name='x2paddle_1392', bias_attr=False) x2paddle_1393 = fluid.layers.batch_norm(x2paddle_1392, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer4_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer4_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer4_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer4_norm2_running_var', use_global_stats=False, name='x2paddle_1393') x2paddle_1394 = fluid.layers.relu(x2paddle_1393, name='x2paddle_1394') x2paddle_1395 = fluid.layers.conv2d(x2paddle_1394, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer4_conv2_weight', name='x2paddle_1395', bias_attr=False) x2paddle_1396 = fluid.layers.concat([x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395], axis=1) x2paddle_1397 = fluid.layers.batch_norm(x2paddle_1396, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer5_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer5_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer5_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer5_norm1_running_var', use_global_stats=False, name='x2paddle_1397') x2paddle_1398 = fluid.layers.relu(x2paddle_1397, name='x2paddle_1398') x2paddle_1399 = fluid.layers.conv2d(x2paddle_1398, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer5_conv1_weight', name='x2paddle_1399', bias_attr=False) x2paddle_1400 = fluid.layers.batch_norm(x2paddle_1399, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer5_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer5_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer5_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer5_norm2_running_var', use_global_stats=False, name='x2paddle_1400') x2paddle_1401 = fluid.layers.relu(x2paddle_1400, name='x2paddle_1401') x2paddle_1402 = fluid.layers.conv2d(x2paddle_1401, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer5_conv2_weight', name='x2paddle_1402', bias_attr=False) x2paddle_1403 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402], axis=1) x2paddle_1404 = fluid.layers.batch_norm(x2paddle_1403, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer6_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer6_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer6_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer6_norm1_running_var', use_global_stats=False, name='x2paddle_1404') x2paddle_1405 = fluid.layers.relu(x2paddle_1404, name='x2paddle_1405') x2paddle_1406 = fluid.layers.conv2d(x2paddle_1405, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer6_conv1_weight', name='x2paddle_1406', bias_attr=False) x2paddle_1407 = fluid.layers.batch_norm(x2paddle_1406, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer6_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer6_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer6_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer6_norm2_running_var', use_global_stats=False, name='x2paddle_1407') x2paddle_1408 = fluid.layers.relu(x2paddle_1407, name='x2paddle_1408') x2paddle_1409 = fluid.layers.conv2d(x2paddle_1408, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer6_conv2_weight', name='x2paddle_1409', bias_attr=False) x2paddle_1410 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409], axis=1) x2paddle_1411 = fluid.layers.batch_norm(x2paddle_1410, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer7_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer7_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer7_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer7_norm1_running_var', use_global_stats=False, name='x2paddle_1411') x2paddle_1412 = fluid.layers.relu(x2paddle_1411, name='x2paddle_1412') x2paddle_1413 = fluid.layers.conv2d(x2paddle_1412, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer7_conv1_weight', name='x2paddle_1413', bias_attr=False) x2paddle_1414 = fluid.layers.batch_norm(x2paddle_1413, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer7_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer7_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer7_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer7_norm2_running_var', use_global_stats=False, name='x2paddle_1414') x2paddle_1415 = fluid.layers.relu(x2paddle_1414, name='x2paddle_1415') x2paddle_1416 = fluid.layers.conv2d(x2paddle_1415, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer7_conv2_weight', name='x2paddle_1416', bias_attr=False) x2paddle_1417 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416], axis=1) x2paddle_1418 = fluid.layers.batch_norm(x2paddle_1417, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer8_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer8_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer8_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer8_norm1_running_var', use_global_stats=False, name='x2paddle_1418') x2paddle_1419 = fluid.layers.relu(x2paddle_1418, name='x2paddle_1419') x2paddle_1420 = fluid.layers.conv2d(x2paddle_1419, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer8_conv1_weight', name='x2paddle_1420', bias_attr=False) x2paddle_1421 = fluid.layers.batch_norm(x2paddle_1420, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer8_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer8_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer8_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer8_norm2_running_var', use_global_stats=False, name='x2paddle_1421') x2paddle_1422 = fluid.layers.relu(x2paddle_1421, name='x2paddle_1422') x2paddle_1423 = fluid.layers.conv2d(x2paddle_1422, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer8_conv2_weight', name='x2paddle_1423', bias_attr=False) x2paddle_1424 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423], axis=1) x2paddle_1425 = fluid.layers.batch_norm(x2paddle_1424, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer9_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer9_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer9_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer9_norm1_running_var', use_global_stats=False, name='x2paddle_1425') x2paddle_1426 = fluid.layers.relu(x2paddle_1425, name='x2paddle_1426') x2paddle_1427 = fluid.layers.conv2d(x2paddle_1426, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer9_conv1_weight', name='x2paddle_1427', bias_attr=False) x2paddle_1428 = fluid.layers.batch_norm(x2paddle_1427, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer9_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer9_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer9_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer9_norm2_running_var', use_global_stats=False, name='x2paddle_1428') x2paddle_1429 = fluid.layers.relu(x2paddle_1428, name='x2paddle_1429') x2paddle_1430 = fluid.layers.conv2d(x2paddle_1429, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer9_conv2_weight', name='x2paddle_1430', bias_attr=False) x2paddle_1431 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430], axis=1) x2paddle_1432 = fluid.layers.batch_norm(x2paddle_1431, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer10_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer10_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer10_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer10_norm1_running_var', use_global_stats=False, name='x2paddle_1432') x2paddle_1433 = fluid.layers.relu(x2paddle_1432, name='x2paddle_1433') x2paddle_1434 = fluid.layers.conv2d(x2paddle_1433, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer10_conv1_weight', name='x2paddle_1434', bias_attr=False) x2paddle_1435 = fluid.layers.batch_norm(x2paddle_1434, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer10_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer10_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer10_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer10_norm2_running_var', use_global_stats=False, name='x2paddle_1435') x2paddle_1436 = fluid.layers.relu(x2paddle_1435, name='x2paddle_1436') x2paddle_1437 = fluid.layers.conv2d(x2paddle_1436, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer10_conv2_weight', name='x2paddle_1437', bias_attr=False) x2paddle_1438 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437], axis=1) x2paddle_1439 = fluid.layers.batch_norm(x2paddle_1438, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer11_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer11_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer11_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer11_norm1_running_var', use_global_stats=False, name='x2paddle_1439') x2paddle_1440 = fluid.layers.relu(x2paddle_1439, name='x2paddle_1440') x2paddle_1441 = fluid.layers.conv2d(x2paddle_1440, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer11_conv1_weight', name='x2paddle_1441', bias_attr=False) x2paddle_1442 = fluid.layers.batch_norm(x2paddle_1441, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer11_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer11_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer11_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer11_norm2_running_var', use_global_stats=False, name='x2paddle_1442') x2paddle_1443 = fluid.layers.relu(x2paddle_1442, name='x2paddle_1443') x2paddle_1444 = fluid.layers.conv2d(x2paddle_1443, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer11_conv2_weight', name='x2paddle_1444', bias_attr=False) x2paddle_1445 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444], axis=1) x2paddle_1446 = fluid.layers.batch_norm(x2paddle_1445, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer12_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer12_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer12_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer12_norm1_running_var', use_global_stats=False, name='x2paddle_1446') x2paddle_1447 = fluid.layers.relu(x2paddle_1446, name='x2paddle_1447') x2paddle_1448 = fluid.layers.conv2d(x2paddle_1447, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer12_conv1_weight', name='x2paddle_1448', bias_attr=False) x2paddle_1449 = fluid.layers.batch_norm(x2paddle_1448, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer12_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer12_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer12_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer12_norm2_running_var', use_global_stats=False, name='x2paddle_1449') x2paddle_1450 = fluid.layers.relu(x2paddle_1449, name='x2paddle_1450') x2paddle_1451 = fluid.layers.conv2d(x2paddle_1450, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer12_conv2_weight', name='x2paddle_1451', bias_attr=False) x2paddle_1452 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451], axis=1) x2paddle_1453 = fluid.layers.batch_norm(x2paddle_1452, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer13_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer13_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer13_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer13_norm1_running_var', use_global_stats=False, name='x2paddle_1453') x2paddle_1454 = fluid.layers.relu(x2paddle_1453, name='x2paddle_1454') x2paddle_1455 = fluid.layers.conv2d(x2paddle_1454, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer13_conv1_weight', name='x2paddle_1455', bias_attr=False) x2paddle_1456 = fluid.layers.batch_norm(x2paddle_1455, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer13_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer13_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer13_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer13_norm2_running_var', use_global_stats=False, name='x2paddle_1456') x2paddle_1457 = fluid.layers.relu(x2paddle_1456, name='x2paddle_1457') x2paddle_1458 = fluid.layers.conv2d(x2paddle_1457, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer13_conv2_weight', name='x2paddle_1458', bias_attr=False) x2paddle_1459 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458], axis=1) x2paddle_1460 = fluid.layers.batch_norm(x2paddle_1459, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer14_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer14_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer14_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer14_norm1_running_var', use_global_stats=False, name='x2paddle_1460') x2paddle_1461 = fluid.layers.relu(x2paddle_1460, name='x2paddle_1461') x2paddle_1462 = fluid.layers.conv2d(x2paddle_1461, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer14_conv1_weight', name='x2paddle_1462', bias_attr=False) x2paddle_1463 = fluid.layers.batch_norm(x2paddle_1462, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer14_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer14_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer14_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer14_norm2_running_var', use_global_stats=False, name='x2paddle_1463') x2paddle_1464 = fluid.layers.relu(x2paddle_1463, name='x2paddle_1464') x2paddle_1465 = fluid.layers.conv2d(x2paddle_1464, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer14_conv2_weight', name='x2paddle_1465', bias_attr=False) x2paddle_1466 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465], axis=1) x2paddle_1467 = fluid.layers.batch_norm(x2paddle_1466, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer15_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer15_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer15_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer15_norm1_running_var', use_global_stats=False, name='x2paddle_1467') x2paddle_1468 = fluid.layers.relu(x2paddle_1467, name='x2paddle_1468') x2paddle_1469 = fluid.layers.conv2d(x2paddle_1468, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer15_conv1_weight', name='x2paddle_1469', bias_attr=False) x2paddle_1470 = fluid.layers.batch_norm(x2paddle_1469, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer15_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer15_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer15_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer15_norm2_running_var', use_global_stats=False, name='x2paddle_1470') x2paddle_1471 = fluid.layers.relu(x2paddle_1470, name='x2paddle_1471') x2paddle_1472 = fluid.layers.conv2d(x2paddle_1471, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer15_conv2_weight', name='x2paddle_1472', bias_attr=False) x2paddle_1473 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472], axis=1) x2paddle_1474 = fluid.layers.batch_norm(x2paddle_1473, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer16_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer16_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer16_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer16_norm1_running_var', use_global_stats=False, name='x2paddle_1474') x2paddle_1475 = fluid.layers.relu(x2paddle_1474, name='x2paddle_1475') x2paddle_1476 = fluid.layers.conv2d(x2paddle_1475, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer16_conv1_weight', name='x2paddle_1476', bias_attr=False) x2paddle_1477 = fluid.layers.batch_norm(x2paddle_1476, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer16_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer16_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer16_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer16_norm2_running_var', use_global_stats=False, name='x2paddle_1477') x2paddle_1478 = fluid.layers.relu(x2paddle_1477, name='x2paddle_1478') x2paddle_1479 = fluid.layers.conv2d(x2paddle_1478, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer16_conv2_weight', name='x2paddle_1479', bias_attr=False) x2paddle_1480 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479], axis=1) x2paddle_1481 = fluid.layers.batch_norm(x2paddle_1480, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer17_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer17_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer17_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer17_norm1_running_var', use_global_stats=False, name='x2paddle_1481') x2paddle_1482 = fluid.layers.relu(x2paddle_1481, name='x2paddle_1482') x2paddle_1483 = fluid.layers.conv2d(x2paddle_1482, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer17_conv1_weight', name='x2paddle_1483', bias_attr=False) x2paddle_1484 = fluid.layers.batch_norm(x2paddle_1483, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer17_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer17_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer17_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer17_norm2_running_var', use_global_stats=False, name='x2paddle_1484') x2paddle_1485 = fluid.layers.relu(x2paddle_1484, name='x2paddle_1485') x2paddle_1486 = fluid.layers.conv2d(x2paddle_1485, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer17_conv2_weight', name='x2paddle_1486', bias_attr=False) x2paddle_1487 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486], axis=1) x2paddle_1488 = fluid.layers.batch_norm(x2paddle_1487, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer18_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer18_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer18_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer18_norm1_running_var', use_global_stats=False, name='x2paddle_1488') x2paddle_1489 = fluid.layers.relu(x2paddle_1488, name='x2paddle_1489') x2paddle_1490 = fluid.layers.conv2d(x2paddle_1489, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer18_conv1_weight', name='x2paddle_1490', bias_attr=False) x2paddle_1491 = fluid.layers.batch_norm(x2paddle_1490, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer18_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer18_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer18_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer18_norm2_running_var', use_global_stats=False, name='x2paddle_1491') x2paddle_1492 = fluid.layers.relu(x2paddle_1491, name='x2paddle_1492') x2paddle_1493 = fluid.layers.conv2d(x2paddle_1492, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer18_conv2_weight', name='x2paddle_1493', bias_attr=False) x2paddle_1494 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486, x2paddle_1493], axis=1) x2paddle_1495 = fluid.layers.batch_norm(x2paddle_1494, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer19_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer19_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer19_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer19_norm1_running_var', use_global_stats=False, name='x2paddle_1495') x2paddle_1496 = fluid.layers.relu(x2paddle_1495, name='x2paddle_1496') x2paddle_1497 = fluid.layers.conv2d(x2paddle_1496, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer19_conv1_weight', name='x2paddle_1497', bias_attr=False) x2paddle_1498 = fluid.layers.batch_norm(x2paddle_1497, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer19_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer19_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer19_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer19_norm2_running_var', use_global_stats=False, name='x2paddle_1498') x2paddle_1499 = fluid.layers.relu(x2paddle_1498, name='x2paddle_1499') x2paddle_1500 = fluid.layers.conv2d(x2paddle_1499, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer19_conv2_weight', name='x2paddle_1500', bias_attr=False) x2paddle_1501 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486, x2paddle_1493, x2paddle_1500], axis=1) x2paddle_1502 = fluid.layers.batch_norm(x2paddle_1501, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer20_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer20_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer20_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer20_norm1_running_var', use_global_stats=False, name='x2paddle_1502') x2paddle_1503 = fluid.layers.relu(x2paddle_1502, name='x2paddle_1503') x2paddle_1504 = fluid.layers.conv2d(x2paddle_1503, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer20_conv1_weight', name='x2paddle_1504', bias_attr=False) x2paddle_1505 = fluid.layers.batch_norm(x2paddle_1504, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer20_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer20_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer20_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer20_norm2_running_var', use_global_stats=False, name='x2paddle_1505') x2paddle_1506 = fluid.layers.relu(x2paddle_1505, name='x2paddle_1506') x2paddle_1507 = fluid.layers.conv2d(x2paddle_1506, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer20_conv2_weight', name='x2paddle_1507', bias_attr=False) x2paddle_1508 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486, x2paddle_1493, x2paddle_1500, x2paddle_1507], axis=1) x2paddle_1509 = fluid.layers.batch_norm(x2paddle_1508, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer21_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer21_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer21_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer21_norm1_running_var', use_global_stats=False, name='x2paddle_1509') x2paddle_1510 = fluid.layers.relu(x2paddle_1509, name='x2paddle_1510') x2paddle_1511 = fluid.layers.conv2d(x2paddle_1510, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer21_conv1_weight', name='x2paddle_1511', bias_attr=False) x2paddle_1512 = fluid.layers.batch_norm(x2paddle_1511, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer21_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer21_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer21_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer21_norm2_running_var', use_global_stats=False, name='x2paddle_1512') x2paddle_1513 = fluid.layers.relu(x2paddle_1512, name='x2paddle_1513') x2paddle_1514 = fluid.layers.conv2d(x2paddle_1513, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer21_conv2_weight', name='x2paddle_1514', bias_attr=False) x2paddle_1515 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486, x2paddle_1493, x2paddle_1500, x2paddle_1507, x2paddle_1514], axis=1) x2paddle_1516 = fluid.layers.batch_norm(x2paddle_1515, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer22_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer22_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer22_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer22_norm1_running_var', use_global_stats=False, name='x2paddle_1516') x2paddle_1517 = fluid.layers.relu(x2paddle_1516, name='x2paddle_1517') x2paddle_1518 = fluid.layers.conv2d(x2paddle_1517, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer22_conv1_weight', name='x2paddle_1518', bias_attr=False) x2paddle_1519 = fluid.layers.batch_norm(x2paddle_1518, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer22_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer22_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer22_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer22_norm2_running_var', use_global_stats=False, name='x2paddle_1519') x2paddle_1520 = fluid.layers.relu(x2paddle_1519, name='x2paddle_1520') x2paddle_1521 = fluid.layers.conv2d(x2paddle_1520, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer22_conv2_weight', name='x2paddle_1521', bias_attr=False) x2paddle_1522 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486, x2paddle_1493, x2paddle_1500, x2paddle_1507, x2paddle_1514, x2paddle_1521], axis=1) x2paddle_1523 = fluid.layers.batch_norm(x2paddle_1522, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer23_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer23_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer23_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer23_norm1_running_var', use_global_stats=False, name='x2paddle_1523') x2paddle_1524 = fluid.layers.relu(x2paddle_1523, name='x2paddle_1524') x2paddle_1525 = fluid.layers.conv2d(x2paddle_1524, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer23_conv1_weight', name='x2paddle_1525', bias_attr=False) x2paddle_1526 = fluid.layers.batch_norm(x2paddle_1525, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer23_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer23_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer23_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer23_norm2_running_var', use_global_stats=False, name='x2paddle_1526') x2paddle_1527 = fluid.layers.relu(x2paddle_1526, name='x2paddle_1527') x2paddle_1528 = fluid.layers.conv2d(x2paddle_1527, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer23_conv2_weight', name='x2paddle_1528', bias_attr=False) x2paddle_1529 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486, x2paddle_1493, x2paddle_1500, x2paddle_1507, x2paddle_1514, x2paddle_1521, x2paddle_1528], axis=1) x2paddle_1530 = fluid.layers.batch_norm(x2paddle_1529, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer24_norm1_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer24_norm1_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer24_norm1_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer24_norm1_running_var', use_global_stats=False, name='x2paddle_1530') x2paddle_1531 = fluid.layers.relu(x2paddle_1530, name='x2paddle_1531') x2paddle_1532 = fluid.layers.conv2d(x2paddle_1531, num_filters=192, filter_size=[1, 1], stride=[1, 1], padding=[0, 0], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer24_conv1_weight', name='x2paddle_1532', bias_attr=False) x2paddle_1533 = fluid.layers.batch_norm(x2paddle_1532, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_denseblock4_denselayer24_norm2_weight', bias_attr='x2paddle_densenet161_features_denseblock4_denselayer24_norm2_bias', moving_mean_name='x2paddle_densenet161_features_denseblock4_denselayer24_norm2_running_mean', moving_variance_name='x2paddle_densenet161_features_denseblock4_denselayer24_norm2_running_var', use_global_stats=False, name='x2paddle_1533') x2paddle_1534 = fluid.layers.relu(x2paddle_1533, name='x2paddle_1534') x2paddle_1535 = fluid.layers.conv2d(x2paddle_1534, num_filters=48, filter_size=[3, 3], stride=[1, 1], padding=[1, 1], dilation=[1, 1], groups=1, param_attr='x2paddle_densenet161_features_denseblock4_denselayer24_conv2_weight', name='x2paddle_1535', bias_attr=False) x2paddle_1536 = fluid.layers.concat( [x2paddle_1367, x2paddle_1374, x2paddle_1381, x2paddle_1388, x2paddle_1395, x2paddle_1402, x2paddle_1409, x2paddle_1416, x2paddle_1423, x2paddle_1430, x2paddle_1437, x2paddle_1444, x2paddle_1451, x2paddle_1458, x2paddle_1465, x2paddle_1472, x2paddle_1479, x2paddle_1486, x2paddle_1493, x2paddle_1500, x2paddle_1507, x2paddle_1514, x2paddle_1521, x2paddle_1528, x2paddle_1535], axis=1) x2paddle_1537 = fluid.layers.batch_norm(x2paddle_1536, momentum=0.8999999761581421, epsilon=9.999999747378752e-06, data_layout='NCHW', is_test=True, param_attr='x2paddle_densenet161_features_norm5_weight', bias_attr='x2paddle_densenet161_features_norm5_bias', moving_mean_name='x2paddle_densenet161_features_norm5_running_mean', moving_variance_name='x2paddle_densenet161_features_norm5_running_var', use_global_stats=False, name='x2paddle_1537') x2paddle_1538 = fluid.layers.relu(x2paddle_1537, name='x2paddle_1538') x2paddle_1539 = fluid.layers.pool2d(x2paddle_1538, pool_type='avg', global_pooling=True, name='x2paddle_1539') x2paddle_1540 = fluid.layers.flatten(x2paddle_1539, axis=1, name='x2paddle_1540') x2paddle_output_mm = fluid.layers.matmul(x=x2paddle_1540, y=x2paddle_densenet161_classifier_weight, transpose_x=False, transpose_y=True, alpha=1.0, name='x2paddle_output_mm') x2paddle_output = fluid.layers.elementwise_add(x=x2paddle_output_mm, y=x2paddle_densenet161_classifier_bias, name='x2paddle_output') return x2paddle_output def densenet(): return Densenet()

106.799911

144

0.588985

22,140

240,193

5.924661

0.032791

0.074742

0.165493

0.113912

0.812498

0.753957

0.7517

0.748247

0.674961

0.576236

0

0.172818

0.343332

240,193

2,249

145

106.799911

0.658822

0

0.202763

0

0.25723

0.227733

0

1

0.001337

false

0.000446

0.001337

0.000446

0.004011

0

null

0

1

0

1

0

null

0

5

21a6c48e4f2aedd63e073217816108eeec0c6f45

69

py

Python

savecode/threeyears/idownclient/cmdmanagement/__init__.py

Octoberr/swm0920

8f05a6b91fc205960edd57f9076facec04f49a1a

[ "Apache-2.0" ]

2

2019-05-19T11:54:26.000Z

2019-05-19T12:03:49.000Z

savecode/threeyears/idownclient/cmdmanagement/__init__.py

Octoberr/swm0920

8f05a6b91fc205960edd57f9076facec04f49a1a

[ "Apache-2.0" ]

1

2020-11-27T07:55:15.000Z

savecode/threeyears/idownclient/cmdmanagement/__init__.py

Octoberr/swm0920

8f05a6b91fc205960edd57f9076facec04f49a1a

[ "Apache-2.0" ]

2

2021-09-06T18:06:12.000Z

2021-12-31T07:44:43.000Z

from .cmdprocess import CmdProcess from .cmdmanager import CmdManager

34.5

34

0.869565

8

69

7.5

0.5

0

0.101449

69

2

35

34.5

0.967742

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

21f778d6fe30c3d273892092c48410c7ef2f5855

162

py

Python

src/__init__.py

ItJustWorksTM/pySMCE

7e00fd3ec5e76a48a8278f3384219fc4c6a0316a

[ "Apache-2.0" ]

null

src/__init__.py

ItJustWorksTM/pySMCE

7e00fd3ec5e76a48a8278f3384219fc4c6a0316a

[ "Apache-2.0" ]

null

src/__init__.py

ItJustWorksTM/pySMCE

7e00fd3ec5e76a48a8278f3384219fc4c6a0316a

[ "Apache-2.0" ]

null

import os.path as path resources_archive_path = path.realpath(path.dirname(path.abspath(__file__)) + '/SMCE_Resources.zip') __all__ = ['resources_archive_path']

32.4

100

0.790123

22

162

5.227273

0.590909

0.278261

0.347826

0

0.080247

162

4

101

40.5

0.771812

0

0.253086

0.135802

0

1

0

false

0

0.333333

0

0.333333

0

1

0

null

1

0

1

0

null

0

1

0

5

df37bf953016348d96251427e335c9734ee8851c

4,744

py

Python

core/topology/constraint/base.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

84

2017-10-22T11:01:39.000Z

2022-02-27T03:43:48.000Z

core/topology/constraint/base.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

22

2017-12-11T07:21:56.000Z

2021-09-23T02:53:50.000Z

core/topology/constraint/base.py

prorevizor/noc

37e44b8afc64318b10699c06a1138eee9e7d6a4e

[ "BSD-3-Clause" ]

23

2017-12-06T06:59:52.000Z

2022-02-24T00:02:25.000Z

# ---------------------------------------------------------------------- # BaseConstraint class # ---------------------------------------------------------------------- # Copyright (C) 2007-2020 The NOC Project # See LICENSE for details # ---------------------------------------------------------------------- # NOC modules from noc.sa.models.managedobject import ManagedObject from noc.inv.models.interface import Interface from noc.inv.models.link import Link class BaseConstraint(object): def __init__(self) -> None: pass def is_valid_neighbor(self, current: ManagedObject, neighbor: ManagedObject) -> bool: """ Check if neighbor is valid neighbor for the path :param current: Current Managed Object :param neighbor: Neighbor Managed Object :return: True if path can be continued via neighbors """ return True def is_valid_link(self, link: Link) -> bool: """ Check if link is valid on the path :param link: Link instance :return: """ return True def is_valid_interface(self, interface: Interface) -> bool: """ Check if interface is valid interface on the path :param interface: :return: """ return True def is_valid_egress(self, interface: Interface) -> bool: """ Check if egress interface is valid interface on the path :param interface: Interface instance :return: True if path can be continued across the interface """ return self.is_valid_interface(interface) def is_valid_ingress(self, interface: Interface) -> bool: """ Check if ingress interface is valid interface on the path :param interface: Interface instance :return: True if path can be continued across the interface """ return self.is_valid_interface(interface) def __neg__(self: "BaseConstraint") -> "BaseConstraint": return NotConstraint(self) def __and__(self, other: "BaseConstraint") -> "BaseConstraint": return AndConstraint(self, other) def __or__(self, other: "BaseConstraint") -> "BaseConstraint": return OrConstraint(self, other) class AndConstraint(BaseConstraint): def __init__(self, left: BaseConstraint, right: BaseConstraint) -> None: super().__init__() self.left = left self.right = right def is_valid_neighbor(self, current: ManagedObject, neighbor: ManagedObject) -> bool: return self.left.is_valid_neighbor(current, neighbor) and self.right.is_valid_neighbor( current, neighbor ) def is_valid_interface(self, interface: Interface) -> bool: return self.left.is_valid_interface(interface) and self.right.is_valid_interface(interface) def is_valid_ingress(self, interface: Interface) -> bool: return self.left.is_valid_ingress(interface) and self.right.is_valid_ingress(interface) def is_valid_egress(self, interface: Interface) -> bool: return self.left.is_valid_egress(interface) and self.right.is_valid_egress(interface) class OrConstraint(BaseConstraint): def __init__(self, left: BaseConstraint, right: BaseConstraint) -> None: super().__init__() self.left = left self.right = right def is_valid_neighbor(self, current: ManagedObject, neighbor: ManagedObject) -> bool: return self.left.is_valid_neighbor(current, neighbor) or self.right.is_valid_neighbor( current, neighbor ) def is_valid_interface(self, interface: Interface) -> bool: return self.left.is_valid_interface(interface) or self.right.is_valid_interface(interface) def is_valid_ingress(self, interface: Interface) -> bool: return self.left.is_valid_ingress(interface) or self.right.is_valid_ingress(interface) def is_valid_egress(self, interface: Interface) -> bool: return self.left.is_valid_egress(interface) or self.right.is_valid_egress(interface) class NotConstraint(BaseConstraint): def __init__(self, constraint: BaseConstraint) -> None: super().__init__() self.constraint = constraint def is_valid_neighbor(self, current: ManagedObject, neighbor: ManagedObject) -> bool: return not self.constraint.is_valid_neighbor(current, neighbor) def is_valid_interface(self, interface: Interface) -> bool: return not self.constraint.is_valid_interface(interface) def is_valid_ingress(self, interface: Interface) -> bool: return not self.constraint.is_valid_ingress(interface) def is_valid_egress(self, interface: Interface) -> bool: return not self.constraint.is_valid_egress(interface)

36.21374

99

0.659992

536

4,744

5.621269

0.115672

0.102224

0.056422

0.103551

0.77929

0.732161

0.709592

0.670096

0.641221

0.62529

0

0.002141

0.212268

4,744

130

100

36.492308

0.804121

0.200885

0

0.5

0

0.023457

0

1

0.375

false

0.015625

0.046875

0.234375

0.796875

0

null

0

1

0

1

0

null

0

1

0

1

0

5

df7116af629986f5aa5a77050d28d4d8605338fb

229

py

Python

kosh/transformers/__init__.py

tanimislam/kosh

aba17fd5393090e9fbfb3c6b3e7ab0f4a301ab26

[ "MIT" ]

null

kosh/transformers/__init__.py

tanimislam/kosh

aba17fd5393090e9fbfb3c6b3e7ab0f4a301ab26

[ "MIT" ]

null

kosh/transformers/__init__.py

tanimislam/kosh

aba17fd5393090e9fbfb3c6b3e7ab0f4a301ab26

[ "MIT" ]

null

from .core import KoshTransformer, get_path, kosh_cache_dir # noqa from .npy import KoshSimpleNpCache # noqa try: from .skl import StandardScaler, KMeans, DBSCAN, Splitter # noqa except NameError: # no skl... pass

28.625

69

0.729258

29

229

5.655172

0.758621

0

0.196507

229

7

70

32.714286

0.891304

0.104803

0

1

0

true

0.166667

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

df77ae622a91f7163ec25d6e7833b62f72e25496

158

py

Python

yc216/841.py

c-yan/yukicoder

cdbbd65402177225dd989df7fe01f67908484a69

[ "MIT" ]

null

yc216/841.py

c-yan/yukicoder

cdbbd65402177225dd989df7fe01f67908484a69

[ "MIT" ]

null

yc216/841.py

c-yan/yukicoder

cdbbd65402177225dd989df7fe01f67908484a69

[ "MIT" ]

null

S1, S2 = input().split() if S1 in ['Sat', 'Sun']: if S2 in ['Sat', 'Sun']: print('8/33') else: print('8/32') else: print('8/31')

15.8

28

0.443038

25

158

2.8

0.56

0.257143

0.228571

0

0.118182

0.303797

158

9

29

17.555556

0.518182

0

0.25

0

0.151899

0

1

0

true

0

0.375

1

0

null

1

0

1

0

null

0

1

0

5

df92ae890490ad041fbdcc0c8b6ce23131f970f7

18,267

py

Python

indy_client/test/cli/test_send_node_validation.py

ArtObr/indy-node

f3491c42eba1a1b45df98f0e4dabe749d281ae33

[ "Apache-2.0" ]

1

2018-07-05T19:34:29.000Z

indy_client/test/cli/test_send_node_validation.py

ArtObr/indy-node

f3491c42eba1a1b45df98f0e4dabe749d281ae33

[ "Apache-2.0" ]

null

indy_client/test/cli/test_send_node_validation.py

ArtObr/indy-node

f3491c42eba1a1b45df98f0e4dabe749d281ae33

[ "Apache-2.0" ]

1

2021-06-06T15:48:30.000Z

import pytest from plenum.common.signer_did import DidSigner from stp_core.crypto.util import randomSeed from plenum.common.constants import NODE_IP, NODE_PORT, CLIENT_IP, CLIENT_PORT, \ ALIAS, SERVICES, VALIDATOR from plenum.common.signer_simple import SimpleSigner from plenum.common.util import cryptonymToHex, randomString from indy_client.test.cli.conftest import newStewardCli as getNewStewardCli, \ newStewardVals as getNewStewardVals, newNodeVals as getNewNodeVals from indy_client.test.cli.constants import NODE_REQUEST_COMPLETED, NODE_REQUEST_FAILED, INVALID_SYNTAX from indy_client.test.cli.helper import addAgent NYM_ADDED = "Nym {remote} added" @pytest.yield_fixture(scope="function") def cliWithRandomName(CliBuilder): yield from CliBuilder(randomString(6)) @pytest.fixture(scope="function") def newStewardVals(): return getNewStewardVals() @pytest.fixture(scope="function") def newNodeVals(): return getNewNodeVals() @pytest.fixture(scope="function") def newStewardCli(be, do, poolNodesStarted, trusteeCli, cliWithRandomName, newStewardVals): return getNewStewardCli(be, do, poolNodesStarted, trusteeCli, cliWithRandomName, newStewardVals) def ensurePoolIsOperable(be, do, cli): randomNymMapper = { 'remote': DidSigner(seed=randomSeed()).identifier } addAgent(be, do, cli, randomNymMapper) def testSendNodeSucceedsIfServicesIsArrayWithValidatorValueOnly( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][SERVICES] = [VALIDATOR] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_COMPLETED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeSucceedsIfServicesIsEmptyArray( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][SERVICES] = [] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_COMPLETED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfDestIsSmallDecimalNumber( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeIdr'] = 42 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfDestIsShortReadableName( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeIdr'] = 'TheNewNode' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfDestIsHexKey( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeIdr'] = cryptonymToHex( newNodeVals['newNodeIdr']).decode() be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip(reason='SOV-1096') def testSendNodeHasInvalidSyntaxIfDestIsEmpty( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeIdr'] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=INVALID_SYNTAX, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip(reason='SOV-1096') def testSendNodeHasInvalidSyntaxIfDestIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): be(newStewardCli) do('send NODE data={newNodeData}', mapper=newNodeVals, expect=INVALID_SYNTAX, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodeIpContainsLeadingSpace( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_IP] = ' 122.62.52.13' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodeIpContainsTrailingSpace( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_IP] = '122.62.52.13 ' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodeIpHasWrongFormat( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_IP] = '122.62.52' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfSomeNodeIpComponentsAreNegative( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_IP] = '122.-1.52.13' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfSomeNodeIpComponentsAreHigherThanUpperBound( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_IP] = '122.62.256.13' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodeIpIsEmpty( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_IP] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodeIpIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): del newNodeVals['newNodeData'][NODE_IP] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodePortIsNegative( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_PORT] = -1 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodePortIsHigherThanUpperBound( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_PORT] = 65536 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodePortIsFloat( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_PORT] = 5555.5 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodePortHasWrongFormat( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_PORT] = 'ninety' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodePortIsEmpty( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][NODE_PORT] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfNodePortIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): del newNodeVals['newNodeData'][NODE_PORT] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientIpContainsLeadingSpace( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_IP] = ' 122.62.52.13' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientIpContainsTrailingSpace( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_IP] = '122.62.52.13 ' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientIpHasWrongFormat( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_IP] = '122.62.52' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfSomeClientIpComponentsAreNegative( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_IP] = '122.-1.52.13' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfSomeClientIpComponentsAreHigherThanUpperBound( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_IP] = '122.62.256.13' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientIpIsEmpty( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_IP] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientIpIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): del newNodeVals['newNodeData'][CLIENT_IP] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientPortIsNegative( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_PORT] = -1 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientPortIsHigherThanUpperBound( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_PORT] = 65536 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientPortIsFloat( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_PORT] = 5555.5 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientPortHasWrongFormat( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_PORT] = 'ninety' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientPortIsEmpty( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][CLIENT_PORT] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfClientPortIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): del newNodeVals['newNodeData'][CLIENT_PORT] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfAliasIsEmpty( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][ALIAS] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfAliasIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): del newNodeVals['newNodeData'][ALIAS] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfServicesContainsUnknownValue( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][SERVICES] = [VALIDATOR, 'DECIDER'] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfServicesIsValidatorValue( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][SERVICES] = VALIDATOR # just string, not array be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfServicesIsEmptyString( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'][SERVICES] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfDataContainsUnknownField( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData']['extra'] = 42 be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeFailsIfDataIsEmptyJson( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'] = {} be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip(reason='INDY-68') def testSendNodeFailsIfDataIsBrokenJson( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'] = "{'node_ip': '10.0.0.105', 'node_port': 9701" be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip(reason='INDY-68') def testSendNodeFailsIfDataIsNotJson( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'] = 'not_json' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_FAILED, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip(reason='SOV-1096') def testSendNodeHasInvalidSyntaxIfDataIsEmptyString( be, do, poolNodesStarted, newStewardCli, newNodeVals): newNodeVals['newNodeData'] = '' be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=INVALID_SYNTAX, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip(reason='SOV-1096') def testSendNodeHasInvalidSyntaxIfDataIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): be(newStewardCli) do('send NODE dest={newNodeIdr}', mapper=newNodeVals, expect=INVALID_SYNTAX, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip(reason='SOV-1096') def testSendNodeHasInvalidSyntaxIfUnknownParameterIsPassed( be, do, poolNodesStarted, newStewardCli, newNodeVals): be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData} extra=42', mapper=newNodeVals, expect=INVALID_SYNTAX, within=8) ensurePoolIsOperable(be, do, newStewardCli) def testSendNodeHasInvalidSyntaxIfAllParametersAreMissed( be, do, poolNodesStarted, newStewardCli): be(newStewardCli) do('send NODE', expect=INVALID_SYNTAX, within=8) ensurePoolIsOperable(be, do, newStewardCli) @pytest.mark.skip('INDY-88') def testSendNodeSucceedsIfServicesIsMissed( be, do, poolNodesStarted, newStewardCli, newNodeVals): del newNodeVals['newNodeData'][SERVICES] be(newStewardCli) do('send NODE dest={newNodeIdr} data={newNodeData}', mapper=newNodeVals, expect=NODE_REQUEST_COMPLETED, within=8) ensurePoolIsOperable(be, do, newStewardCli)

29.995074

102

0.737176

1,730

18,267

7.706358

0.087283

0.029403

0.073507

0.116337

0.780753

0.767627

0.758476

0.725773

0.718722

0.623612

0

0.012921

0.156895

18,267

608

103

30.044408

0.852737

0.001204

0

0.667582

0

0.158855

0

1

0.142857

false

0.002747

0.024725

0.008242

0.175824

0

null

0

1

0

null

0

5

10fa29a45cab563f5d1958fb875180081c962445

63

py

Python

product1/product.py

Sim4n6/PyPorject-repoTemplate

b171705fff879e7a3557b6c5af3720fc6129f552

[ "MIT" ]

null

product1/product.py

Sim4n6/PyPorject-repoTemplate

b171705fff879e7a3557b6c5af3720fc6129f552

[ "MIT" ]

null

product1/product.py

Sim4n6/PyPorject-repoTemplate

b171705fff879e7a3557b6c5af3720fc6129f552

[ "MIT" ]

null

def inc(x): return x + 1 def sub(a, b): return a - b

9

16

0.492063

13

63

2.384615

0.615385

0.129032

0

0.025

0.365079

63

6

17

10.5

0.75

0

1

0.5

false

0

0.5

1

0

1

0

null

0

1

0

null

0

1

0

1

0

5

803d2f6d9aef5ed667098dcbb1534a689569dbe1

152

py

Python

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/platform/platform_architecture.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/platform/platform_architecture.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

WEEKS/CD_Sata-Structures/_MISC/misc-examples/python3-book-examples/platform/platform_architecture.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

null

# """ """ # end_pymotw_header import platform print("interpreter:", platform.architecture()) print("/bin/ls :", platform.architecture("/bin/ls"))

13.818182

55

0.671053

16

152

6.25

0.625

0.4

0

0.118421

152

10

56

15.2

0.746269

0.111842

0

0.248

0

1

0

true

0

0.333333

0

0.333333

0.666667

1

0

null

1

0

null

0

1

0

1

0

1

0

5

8054eb6bf1a8a7b475f241197b9d175fb8c90e82

87

py

Python

tests/test_cli/commands/foo/__init__.py

matyasrichter/prisma-client-py

1b320ba32ff8c70c7481396e2ecf5a3fcbf4b5c7

[ "Apache-2.0" ]

518

2021-08-28T01:57:06.000Z

2022-03-30T15:44:45.000Z

tests/test_cli/commands/foo/__init__.py

matyasrichter/prisma-client-py

1b320ba32ff8c70c7481396e2ecf5a3fcbf4b5c7

[ "Apache-2.0" ]

288

2021-08-28T04:15:27.000Z

2022-03-29T16:54:51.000Z

tests/test_cli/commands/foo/__init__.py

matyasrichter/prisma-client-py

1b320ba32ff8c70c7481396e2ecf5a3fcbf4b5c7

[ "Apache-2.0" ]

19

2021-11-16T15:16:19.000Z

2022-03-14T09:59:34.000Z

import click @click.command('foo') def cli() -> None: """foo command""" pass

10.875

21

0.574713

11

87

4.545455

0.727273

0

0.229885

87

7

22

12.428571

0.746269

0.126437

0

0.042857

0

1

0.25

true

0.25

0

0.5

0

1

0

null

0

1

0

null

0

1

0

5

33a83f60a6b914755c7465931fb79e470dea7674

81

py

Python

src/fate_of_dice/system/alien/__init__.py

bonczeq/FateOfDice

ce1704ac490f55bc600c0963958d4175104e85e5

[ "MIT" ]

null

src/fate_of_dice/system/alien/__init__.py

bonczeq/FateOfDice

ce1704ac490f55bc600c0963958d4175104e85e5

[ "MIT" ]

null

src/fate_of_dice/system/alien/__init__.py

bonczeq/FateOfDice

ce1704ac490f55bc600c0963958d4175104e85e5

[ "MIT" ]

null

from .action_check import check_action, ActionCheckResult, ActionCheckResultType

40.5

80

0.888889

8

81

8.75

0.75

0

0.074074

81

1

81

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

33b061c9a275b7f86e4260db77488997a35fb37a

27

py

Python

pdftowrite/__init__.py

kosmospredanie/pdftowrite

2e25ee29696e8a93e0e00ab50b10b2b7d6d63d3a

[ "MIT" ]

4

2021-08-29T06:32:27.000Z

2021-11-25T10:18:55.000Z

pdftowrite/__init__.py

kosmospredanie/pdftowrite

2e25ee29696e8a93e0e00ab50b10b2b7d6d63d3a

[ "MIT" ]

1

2021-07-15T13:45:57.000Z

2021-12-30T08:51:24.000Z

pdftowrite/__init__.py

kosmospredanie/pdftowrite

2e25ee29696e8a93e0e00ab50b10b2b7d6d63d3a

[ "MIT" ]

null

__version__ = '2021.05.03'

13.5

26

0.703704

4

27

3.75

1

0

0.333333

0.111111

27

1

27

0.291667

0

0.37037

0

1

0

false

0

1

0

null

0

1

0

1

0

1

0

null

0

5

33c9c145bb8fe75d846c58278664c6cd73a2fa52

99

py

Python

plotly/validators/surface/colorbar/title/__init__.py

mprostock/plotly.py

3471c3dfbf783927c203c676422260586514b341

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/surface/colorbar/title/__init__.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/surface/colorbar/title/__init__.py

Vesauza/plotly.py

e53e626d59495d440341751f60aeff73ff365c28

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

from ._text import TextValidator from ._side import SideValidator from ._font import FontValidator

24.75

32

0.848485

12

99

6.75

0.666667

0

0.121212

99

3

33

0.931034

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1d46e19b4079e4f8952b816e7b1e694482bf6609

51

py

Python

dace/transformation/__init__.py

tobiasholenstein/dace

38fb56d12b59aa8dfe8bb1ff0068e29c5c75efc9

[ "BSD-3-Clause" ]

1

2021-07-26T07:58:06.000Z

dace/transformation/__init__.py

tobiasholenstein/dace

38fb56d12b59aa8dfe8bb1ff0068e29c5c75efc9

[ "BSD-3-Clause" ]

null

dace/transformation/__init__.py

tobiasholenstein/dace

38fb56d12b59aa8dfe8bb1ff0068e29c5c75efc9

[ "BSD-3-Clause" ]

1

2021-03-04T13:01:48.000Z

from .transformation import strict_transformations

25.5

50

0.901961

5

51

9

1

0

0.078431

51

1

51

0.957447

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

1d86f44f352babf666de8a655307d788828f70fd

105

py

Python

app/main.py

macio-matheus/diagnosis-disease-based-symptoms

9e4e2c8a80e204ff4008f1e828c53f3f827f95b2

[ "MIT" ]

3

2019-01-09T01:38:37.000Z

2020-01-23T19:02:28.000Z

app/main.py

macio-matheus/diagnosis-disease-based-symptoms

9e4e2c8a80e204ff4008f1e828c53f3f827f95b2

[ "MIT" ]

null

app/main.py

macio-matheus/diagnosis-disease-based-symptoms

9e4e2c8a80e204ff4008f1e828c53f3f827f95b2

[ "MIT" ]

null

# pylint: skip-file from app import app import views app.run(port=5000, host='0.0.0.0', threaded=False)

17.5

50

0.72381

20

105

3.8

0.7

0.078947

0

0.086957

0.12381

105

5

51

21

0.73913

0.161905

0

0.081395

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

d53c51f516de02e8f410566fb5c4bc7369a37596

131

py

Python

python-tutorial/py/summary/00. first.py

U-Jhin-s-Python-Tutorial/Python-Tutorial

bdbf3095296d0c36ec9ea1572976dfee61612738

[ "Apache-2.0" ]

1

2021-01-15T02:45:59.000Z

python-tutorial/py/summary/00. first.py

U-Jhin-s-Python-Tutorial/Python-Tutorial

bdbf3095296d0c36ec9ea1572976dfee61612738

[ "Apache-2.0" ]

null

python-tutorial/py/summary/00. first.py

U-Jhin-s-Python-Tutorial/Python-Tutorial

bdbf3095296d0c36ec9ea1572976dfee61612738

[ "Apache-2.0" ]

null

# python's comment statement is '#' # such as; # comment: Hello, my friends print('My first Python') # [result] # My first Python

16.375

35

0.679389

19

131

4.684211

0.684211

0.157303

0.292135

0

0.183206

131

7

36

18.714286

0.831776

0.709924

0

0.483871

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

d564790fe347bf46e48cb964bd5a4b2abeaffa44

127

py

Python

smartsim/_core/__init__.py

billschereriii/SmartSim

7ef4cffeba23fe19b931bdae819f4de99bb112a3

[ "BSD-2-Clause" ]

1

2022-01-19T21:18:59.000Z

smartsim/_core/__init__.py

billschereriii/SmartSim

7ef4cffeba23fe19b931bdae819f4de99bb112a3

[ "BSD-2-Clause" ]

null

smartsim/_core/__init__.py

billschereriii/SmartSim

7ef4cffeba23fe19b931bdae819f4de99bb112a3

[ "BSD-2-Clause" ]

null

from .control import Controller, Manifest from .generation import Generator __all__ = ["Controller", "Manifest", "Generator"]

25.4

49

0.771654

13

127

7.230769

0.615385

0.382979

0

0.11811

127

4

50

31.75

0.839286

0

0.212598

0

1

0

false

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5