xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

a2ec6377b6cc9303e46211e079b606a72c0cb73b

216

py

Python

app/services/category_services/list.py

brunocamposal/fast-food-simulator

6dc7f33cdebd222998fc88df9264853c741c64ca

[ "MIT" ]

2

2021-01-11T23:47:17.000Z

2021-01-13T13:16:50.000Z

app/services/category_services/list.py

brunocamposal/kitchin-kanri

6dc7f33cdebd222998fc88df9264853c741c64ca

[ "MIT" ]

7

2021-01-13T13:16:46.000Z

2021-01-21T16:07:28.000Z

app/services/category_services/list.py

brunocamposal/kitchin-kanri

6dc7f33cdebd222998fc88df9264853c741c64ca

[ "MIT" ]

null

from app.models import Category from app.serializer.category_schema import categories_schema from http import HTTPStatus def category_list(): return categories_schema.jsonify(Category.query.all()), HTTPStatus.OK

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6

a2ef2f44d915accf4336e360cd9905b8232fadb6

156

py

Python

scrapy-redis/tests/test_package_import.py

GongkunJiang/MySpider

8c088f696679b13568843af521279f9f25f40314

[ "MIT" ]

3,305

2017-07-01T09:19:10.000Z

2022-03-31T10:22:21.000Z

scrapy-redis/tests/test_package_import.py

GongkunJiang/MySpider

8c088f696679b13568843af521279f9f25f40314

[ "MIT" ]

129

2017-07-03T23:19:23.000Z

2022-03-29T18:01:29.000Z

scrapy-redis/tests/test_package_import.py

GongkunJiang/MySpider

8c088f696679b13568843af521279f9f25f40314

[ "MIT" ]

995

2017-07-02T04:09:27.000Z

2022-03-30T10:46:25.000Z

import scrapy_redis def test_package_metadata(): assert scrapy_redis.__author__ assert scrapy_redis.__email__ assert scrapy_redis.__version__

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6

a2fdad07403aa04563e846da42c76d367d5963ea

64

py

Python

torchlab/encoder/__init__.py

MarvinTeichmann/TorchLab

d837dfddf893559a259f31a1980986033665cac3

[ "MIT" ]

3

2019-08-29T00:23:28.000Z

2020-12-07T11:13:54.000Z

torchlab/encoder/__init__.py

MarvinTeichmann/TorchLab

d837dfddf893559a259f31a1980986033665cac3

[ "MIT" ]

null

torchlab/encoder/__init__.py

MarvinTeichmann/TorchLab

d837dfddf893559a259f31a1980986033665cac3

[ "MIT" ]

null

from . import resnet from . import vgg # from . import densenet

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6

0c2e4fb7c87dadb36ba2c4f4ee4d66133d257195

141

py

Python

tpc_webapp/blog/admin.py

sahilpabale/ThePunchCoders-Website

b80226e053b194882ce520e5afe8b123f0a57630

[ "MIT" ]

null

tpc_webapp/blog/admin.py

sahilpabale/ThePunchCoders-Website

b80226e053b194882ce520e5afe8b123f0a57630

[ "MIT" ]

3

2021-03-30T13:31:05.000Z

2021-09-22T19:00:31.000Z

tpc_webapp/blog/admin.py

sahilpabale/ThePunchCoders-Website

b80226e053b194882ce520e5afe8b123f0a57630

[ "MIT" ]

null

from django.contrib import admin from .models import Post, BlogComment # Register your models here. admin.site.register((Post, BlogComment))

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6

a78ce19174a232532590443629fa08a0bf25a2cf

2,644

py

Python

tests/test_time_control.py

erdc/AdhModel

2c5d49dd4cca484a6c46ded6e1f6dec25db4722c

[ "BSD-3-Clause" ]

3

2019-06-26T13:41:46.000Z

2019-10-16T02:11:29.000Z

tests/test_time_control.py

erdc/AdhModel

2c5d49dd4cca484a6c46ded6e1f6dec25db4722c

[ "BSD-3-Clause" ]

5

2019-06-26T14:29:03.000Z

2019-07-15T19:25:59.000Z

tests/test_time_control.py

erdc/AdhModel

2c5d49dd4cca484a6c46ded6e1f6dec25db4722c

[ "BSD-3-Clause" ]

2

2019-07-26T14:31:14.000Z

2019-09-03T18:06:39.000Z

import unittest from adhmodel.simulation.time_control import TimeControl class TestIo(unittest.TestCase): def test_dependency_time_step_option(self): tc = TimeControl() # test to ensure the string objects haven't changed base_list = ['Steady state solution (TC STD)', 'Time step series (SERIES DT)', 'Auto Time Step Find (TC ATF)'] curr_list = list(tc.param.time_step_option.objects) self.assertListEqual(base_list, curr_list, 'param.ObjectSelector objects have changed.') # test dependencies on TC STD tc.time_step_option = 'Steady state solution (TC STD)' self.assertLess(tc.param.max_time_step_size_time_series.precedence, 0) self.assertGreater(tc.param.steady_state_min_time_step_size.precedence, 0) self.assertGreater(tc.param.steady_state_max_time_step_size.precedence, 0) self.assertLess(tc.param.auto_time_step_find_min_time_step_size.precedence, 0) self.assertLess(tc.param.auto_time_step_find_max_time_step_size_series.precedence, 0) # test dependencies on SERIES DT tc.time_step_option = 'Time step series (SERIES DT)' self.assertGreater(tc.param.max_time_step_size_time_series.precedence, 0) self.assertLess(tc.param.steady_state_min_time_step_size.precedence, 0) self.assertLess(tc.param.steady_state_max_time_step_size.precedence, 0) self.assertLess(tc.param.auto_time_step_find_min_time_step_size.precedence, 0) self.assertLess(tc.param.auto_time_step_find_max_time_step_size_series.precedence, 0) # test dependencies on TC ATF tc.time_step_option = 'Auto Time Step Find (TC ATF)' self.assertLess(tc.param.max_time_step_size_time_series.precedence, 0) self.assertLess(tc.param.steady_state_min_time_step_size.precedence, 0) self.assertLess(tc.param.steady_state_max_time_step_size.precedence, 0) self.assertGreater(tc.param.auto_time_step_find_min_time_step_size.precedence, 0) self.assertGreater(tc.param.auto_time_step_find_max_time_step_size_series.precedence, 0) # test dependecies on TC STD tc.time_step_option = 'Steady state solution (TC STD)' self.assertLess(tc.param.max_time_step_size_time_series.precedence, 0) self.assertGreater(tc.param.steady_state_min_time_step_size.precedence, 0) self.assertGreater(tc.param.steady_state_max_time_step_size.precedence, 0) self.assertLess(tc.param.auto_time_step_find_min_time_step_size.precedence, 0) self.assertLess(tc.param.auto_time_step_find_max_time_step_size_series.precedence, 0)

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6

a79d1e4b8e52ab49fc9e107752ec59a42ab9eec0

22,321

py

Python

activatable_model/tests/tests.py

ambitioninc/django-activatable-model

e1967e44d97a03b1a6f1723aa3241bc56ab23eb7

[ "MIT" ]

16

2015-02-15T18:41:17.000Z

2021-04-13T15:53:45.000Z

activatable_model/tests/tests.py

ambitioninc/django-activatable-model

e1967e44d97a03b1a6f1723aa3241bc56ab23eb7

[ "MIT" ]

5

2015-03-30T17:40:10.000Z

2021-12-18T12:55:30.000Z

activatable_model/tests/tests.py

ambitioninc/django-activatable-model

e1967e44d97a03b1a6f1723aa3241bc56ab23eb7

[ "MIT" ]

9

2015-03-30T16:21:20.000Z

2018-10-08T14:38:33.000Z

from django.contrib.contenttypes.models import ContentType from django.core.exceptions import ValidationError from django.db import models from django.test import TestCase, TransactionTestCase from django_dynamic_fixture import G from mock import patch, MagicMock, call from activatable_model.models import BaseActivatableModel from activatable_model.signals import model_activations_changed, model_activations_updated from activatable_model.validation import get_activatable_models, validate_activatable_models from activatable_model.tests.models import ( ActivatableModel, ActivatableModelWRel, Rel, ActivatableModelWNonDefaultField, ActivatableModelWRelAndCascade, ) class BaseMockActivationsSignalHanderTest(TestCase): """ Connects a mock to the model_activations_changed signal so that it can be easily tested. """ def setUp(self): super(BaseMockActivationsSignalHanderTest, self).setUp() self.mock_model_activations_changed_handler = MagicMock() model_activations_changed.connect(self.mock_model_activations_changed_handler) self.mock_model_activations_updated_handler = MagicMock() model_activations_updated.connect(self.mock_model_activations_updated_handler) def tearDown(self): super(BaseMockActivationsSignalHanderTest, self).tearDown() model_activations_changed.disconnect(self.mock_model_activations_changed_handler) class CascadeTest(TransactionTestCase): """ Tests that cascade deletes cant happen on an activatable test model. """ def test_no_cascade(self): rel = G(Rel) G(ActivatableModelWRel, rel_field=rel) with self.assertRaises(models.ProtectedError): rel.delete() def test_allowed_cascade(self): rel = G(Rel) rel_id = rel.id G(ActivatableModelWRelAndCascade, rel_field=rel) rel.delete() self.assertEqual(ActivatableModelWRelAndCascade.objects.filter(id=rel_id).count(), 0) class ManagerQuerySetTest(BaseMockActivationsSignalHanderTest): """ Tests custom functionality in the manager and queryset for activatable models. Tests it on models that use the default is_active field and models that define their own custom activatable field. """ def test_update_no_is_active(self): G(ActivatableModel, is_active=False) G(ActivatableModel, is_active=False) ActivatableModel.objects.update(char_field='hi') self.assertEquals(ActivatableModel.objects.filter(char_field='hi', is_active=False).count(), 2) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 2) def test_update_no_is_active_custom(self): G(ActivatableModelWNonDefaultField, active=False) G(ActivatableModelWNonDefaultField, active=False) ActivatableModelWNonDefaultField.objects.update(char_field='hi') self.assertEquals(ActivatableModelWNonDefaultField.objects.filter(char_field='hi', active=False).count(), 2) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 2) def test_update_w_is_active(self): m1 = G(ActivatableModel, is_active=False) m2 = G(ActivatableModel, is_active=False) ActivatableModel.objects.filter(is_active=False).update(char_field='hi', is_active=True) self.assertEquals(ActivatableModel.objects.filter(char_field='hi', is_active=True).count(), 2) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 3) call_args = self.mock_model_activations_changed_handler.call_args self.assertEquals(call_args[1]['is_active'], True) self.assertEquals(set(call_args[1]['instance_ids']), set([m1.id, m2.id])) self.assertEquals(call_args[1]['sender'], ActivatableModel) def test_update_w_is_active_custom(self): m1 = G(ActivatableModelWNonDefaultField, active=False) m2 = G(ActivatableModelWNonDefaultField, active=False) ActivatableModelWNonDefaultField.objects.update(char_field='hi', active=True) self.assertEquals(ActivatableModelWNonDefaultField.objects.filter(char_field='hi', active=True).count(), 2) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 3) call_args = self.mock_model_activations_changed_handler.call_args self.assertEquals(call_args[1]['is_active'], True) self.assertEquals(set(call_args[1]['instance_ids']), set([m1.id, m2.id])) self.assertEquals(call_args[1]['sender'], ActivatableModelWNonDefaultField) def test_activate(self): models = [ G(ActivatableModel, is_active=False), G(ActivatableModel, is_active=True), ] ActivatableModel.objects.activate() self.assertEquals(ActivatableModel.objects.filter(is_active=True).count(), 2) static_kwargs = { 'sender': ActivatableModel, 'signal': model_activations_changed, } self.mock_model_activations_changed_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[0].id], is_active=True, **static_kwargs), ]) static_kwargs['signal'] = model_activations_updated self.mock_model_activations_updated_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[0].id, models[1].id], is_active=True, **static_kwargs), ]) def test_activate_custom(self): models = [ G(ActivatableModelWNonDefaultField, active=False), G(ActivatableModelWNonDefaultField, active=True), ] ActivatableModelWNonDefaultField.objects.activate() self.assertEquals(ActivatableModelWNonDefaultField.objects.filter(active=True).count(), 2) static_kwargs = { 'sender': ActivatableModelWNonDefaultField, 'signal': model_activations_changed, } self.mock_model_activations_changed_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[0].id], is_active=True, **static_kwargs), ]) static_kwargs['signal'] = model_activations_updated self.mock_model_activations_updated_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[0].id, models[1].id], is_active=True, **static_kwargs), ]) def test_deactivate(self): models = [ G(ActivatableModel, is_active=False), G(ActivatableModel, is_active=True), ] ActivatableModel.objects.deactivate() self.assertEquals(ActivatableModel.objects.filter(is_active=False).count(), 2) static_kwargs = { 'sender': ActivatableModel, 'signal': model_activations_changed, } self.mock_model_activations_changed_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[1].id], is_active=False, **static_kwargs), ]) static_kwargs['signal'] = model_activations_updated self.mock_model_activations_updated_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[0].id, models[1].id], is_active=False, **static_kwargs), ]) def test_deactivate_custom(self): models = [ G(ActivatableModelWNonDefaultField, active=False), G(ActivatableModelWNonDefaultField, active=True), ] ActivatableModelWNonDefaultField.objects.deactivate() self.assertEquals(ActivatableModelWNonDefaultField.objects.filter(active=False).count(), 2) static_kwargs = { 'sender': ActivatableModelWNonDefaultField, 'signal': model_activations_changed, } self.mock_model_activations_changed_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[1].id], is_active=False, **static_kwargs), ]) static_kwargs['signal'] = model_activations_updated self.mock_model_activations_updated_handler.assert_has_calls([ call(instance_ids=[models[0].id], is_active=False, **static_kwargs), call(instance_ids=[models[1].id], is_active=True, **static_kwargs), call(instance_ids=[models[0].id, models[1].id], is_active=False, **static_kwargs), ]) def test_delete_no_force(self): G(ActivatableModel, is_active=False) G(ActivatableModel, is_active=True) ActivatableModel.objects.all().delete() self.assertEquals(ActivatableModel.objects.filter(is_active=False).count(), 2) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 3) def test_delete_no_force_custom(self): G(ActivatableModelWNonDefaultField, active=False) G(ActivatableModelWNonDefaultField, active=True) ActivatableModelWNonDefaultField.objects.all().delete() self.assertEquals(ActivatableModelWNonDefaultField.objects.filter(active=False).count(), 2) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 3) def test_delete_w_force(self): G(ActivatableModel, is_active=False) G(ActivatableModel, is_active=True) ActivatableModel.objects.all().delete(force=True) self.assertFalse(ActivatableModel.objects.exists()) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 2) def test_delete_w_force_custom(self): G(ActivatableModelWNonDefaultField, active=False) G(ActivatableModelWNonDefaultField, active=True) ActivatableModelWNonDefaultField.objects.all().delete(force=True) self.assertFalse(ActivatableModelWNonDefaultField.objects.exists()) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 2) class SaveTest(BaseMockActivationsSignalHanderTest): """ Tests the custom save function in the BaseActivatableModel. """ def test_create(self): m = G(ActivatableModel, is_active=False) call_args = self.mock_model_activations_changed_handler.call_args self.assertEquals(call_args[1]['is_active'], False) self.assertEquals(call_args[1]['instance_ids'], [m.id]) self.assertEquals(call_args[1]['sender'], ActivatableModel) updated_call_args = self.mock_model_activations_updated_handler.call_args self.assertEquals(updated_call_args[1]['is_active'], False) self.assertEquals(updated_call_args[1]['instance_ids'], [m.id]) self.assertEquals(updated_call_args[1]['sender'], ActivatableModel) def test_save_not_changed(self): m = G(ActivatableModel, is_active=False) m.is_active = False m.save() self.assertEquals(self.mock_model_activations_changed_handler.call_count, 1) self.assertEquals(self.mock_model_activations_updated_handler.call_count, 2) def test_save_changed(self): m = G(ActivatableModel, is_active=False) m.is_active = True m.save() # changed self.assertEquals(self.mock_model_activations_changed_handler.call_count, 2) call_args = self.mock_model_activations_changed_handler.call_args self.assertEquals(call_args[1]['is_active'], True) self.assertEquals(call_args[1]['instance_ids'], [m.id]) self.assertEquals(call_args[1]['sender'], ActivatableModel) # updated self.assertEquals(self.mock_model_activations_updated_handler.call_count, 2) updated_call_args = self.mock_model_activations_updated_handler.call_args self.assertEquals(updated_call_args[1]['is_active'], True) self.assertEquals(updated_call_args[1]['instance_ids'], [m.id]) self.assertEquals(updated_call_args[1]['sender'], ActivatableModel) def test_save_changed_custom(self): m = G(ActivatableModelWNonDefaultField, active=False) m.active = True m.save() # changed self.assertEquals(self.mock_model_activations_changed_handler.call_count, 2) call_args = self.mock_model_activations_changed_handler.call_args self.assertEquals(call_args[1]['is_active'], True) self.assertEquals(call_args[1]['instance_ids'], [m.id]) self.assertEquals(call_args[1]['sender'], ActivatableModelWNonDefaultField) # updated self.assertEquals(self.mock_model_activations_updated_handler.call_count, 2) updated_call_args = self.mock_model_activations_updated_handler.call_args self.assertEquals(updated_call_args[1]['is_active'], True) self.assertEquals(updated_call_args[1]['instance_ids'], [m.id]) self.assertEquals(updated_call_args[1]['sender'], ActivatableModelWNonDefaultField) class SingleDeleteTest(BaseMockActivationsSignalHanderTest): """ Tests calling delete on a single model that inherits BaseActivatableModel. """ def test_delete_no_force_no_active_changed(self): m = G(ActivatableModel, is_active=False) m.delete() m = ActivatableModel.objects.get(id=m.id) self.assertFalse(m.is_active) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 1) self.assertEquals(self.mock_model_activations_updated_handler.call_count, 2) def test_delete_no_force_active_changed(self): m = G(ActivatableModel, is_active=True) m.delete() m = ActivatableModel.objects.get(id=m.id) self.assertFalse(m.is_active) self.assertEquals(self.mock_model_activations_changed_handler.call_count, 2) self.assertEquals(self.mock_model_activations_updated_handler.call_count, 2) def test_delete_force(self): m = G(ActivatableModel, is_active=False) m.delete(force=True) self.assertFalse(ActivatableModel.objects.exists()) class ValidateDbTest(TestCase): """ Tests that activatable models are validated properly upon pre_syncdb signal. """ def test_get_activatable_models(self): activatable_models = get_activatable_models() self.assertEquals( set( [ ActivatableModel, ActivatableModelWRel, ActivatableModelWRelAndCascade, ActivatableModelWNonDefaultField ] ), set(activatable_models) ) def test_all_valid_models(self): """ All models should validate fine. """ validate_activatable_models() @patch('activatable_model.validation.get_activatable_models') def test_activatable_field_is_not_boolean(self, mock_get_activatable_models): """ SET_NULL is a valid option for foreign keys in activatable models. """ # Make this an object and not an actual django model. This prevents it from always # being included when syncing the db. This is true for all other test models in this file. class NonBooleanModel(BaseActivatableModel): class Meta: abstract = True is_active = models.CharField() ctype = models.ForeignKey(ContentType, null=True, on_delete=models.SET_NULL) mock_get_activatable_models.return_value = [NonBooleanModel] with self.assertRaises(ValidationError): validate_activatable_models() @patch('activatable_model.validation.get_activatable_models') def test_activatable_field_is_not_defined(self, mock_get_activatable_models): """ SET_NULL is a valid option for foreign keys in activatable models. """ # Make this an object and not an actual django model. This prevents it from always # being included when syncing the db. This is true for all other test models in this file. class NoValidFieldModel(BaseActivatableModel): class Meta: abstract = True ACTIVATABLE_FIELD_NAME = 'active' is_active = models.BooleanField() ctype = models.ForeignKey(ContentType, null=True, on_delete=models.SET_NULL) mock_get_activatable_models.return_value = [NoValidFieldModel] with self.assertRaises(ValidationError): validate_activatable_models() @patch('activatable_model.validation.get_activatable_models') def test_foreign_key_is_null(self, mock_get_activatable_models): """ SET_NULL is a valid option for foreign keys in activatable models. """ # Make this an object and not an actual django model. This prevents it from always # being included when syncing the db. This is true for all other test models in this file. class CascadableModel(BaseActivatableModel): class Meta: abstract = True is_active = models.BooleanField(default=False) ctype = models.ForeignKey(ContentType, null=True, on_delete=models.SET_NULL) mock_get_activatable_models.return_value = [CascadableModel] validate_activatable_models() self.assertEquals(mock_get_activatable_models.call_count, 1) @patch('activatable_model.validation.get_activatable_models') def test_foreign_key_protect(self, mock_get_activatable_models): """ PROTECT is a valid option for foreign keys in activatable models. """ # Make this an object and not an actual django model. This prevents it from always # being included when syncing the db. This is true for all other test models in this file. class CascadableModel(BaseActivatableModel): class Meta: abstract = True is_active = models.BooleanField(default=False) ctype = models.ForeignKey(ContentType, null=True, on_delete=models.PROTECT) mock_get_activatable_models.return_value = [CascadableModel] validate_activatable_models() self.assertEquals(mock_get_activatable_models.call_count, 1) @patch('activatable_model.validation.get_activatable_models') def test_foreign_key_cascade(self, mock_get_activatable_models): """ The default cascade behavior is invalid for activatable models. """ class CascadableModel(BaseActivatableModel): class Meta: abstract = True is_active = models.BooleanField(default=False) ctype = models.ForeignKey(ContentType, on_delete=models.CASCADE) mock_get_activatable_models.return_value = [CascadableModel] with self.assertRaises(ValidationError): validate_activatable_models() @patch('activatable_model.validation.get_activatable_models') def test_one_to_one_is_null(self, mock_get_activatable_models): """ SET_NULL is a valid option for foreign keys in activatable models. """ # Make this an object and not an actual django model. This prevents it from always # being included when syncing the db. This is true for all other test models in this file. class CascadableModel(BaseActivatableModel): class Meta: abstract = True is_active = models.BooleanField(default=False) ctype = models.OneToOneField(ContentType, null=True, on_delete=models.SET_NULL) mock_get_activatable_models.return_value = [CascadableModel] validate_activatable_models() self.assertEquals(mock_get_activatable_models.call_count, 1) @patch('activatable_model.validation.get_activatable_models') def test_one_to_one_protect(self, mock_get_activatable_models): """ PROTECT is a valid option for foreign keys in activatable models. """ # Make this an object and not an actual django model. This prevents it from always # being included when syncing the db. This is true for all other test models in this file. class CascadableModel(BaseActivatableModel): class Meta: abstract = True is_active = models.BooleanField(default=False) ctype = models.OneToOneField(ContentType, null=True, on_delete=models.PROTECT) mock_get_activatable_models.return_value = [CascadableModel] validate_activatable_models() self.assertEquals(mock_get_activatable_models.call_count, 1) @patch('activatable_model.validation.get_activatable_models') def test_one_to_one_cascade(self, mock_get_activatable_models): """ The default cascade behavior is invalid for activatable models. """ class CascadableModel(BaseActivatableModel): class Meta: abstract = True is_active = models.BooleanField(default=False) ctype = models.OneToOneField(ContentType, on_delete=models.CASCADE) mock_get_activatable_models.return_value = [CascadableModel] with self.assertRaises(ValidationError): validate_activatable_models() class ModelUpdatedSignalTest(BaseMockActivationsSignalHanderTest): """ Tests the updated signal test """ def test_no_activatable_field_updated(self): m = G(ActivatableModel, is_active=False) m_from_db = ActivatableModel.objects.get(id=m.id) m_from_db.char_field = 'foo' m_from_db.save() self.assertEquals(self.mock_model_activations_updated_handler.call_count, 1)

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38f224c6d42f60314d43862c7f76924112fe0231

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py

Python

enthought/pyface/image_cache.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/pyface/image_cache.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/pyface/image_cache.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from pyface.image_cache import *

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py

Python

repartee/views.py

multiple1902/repartee

bfbd1b8433c086ee3ae877f08156949515c3c977

[ "Apache-2.0", "MIT" ]

1

2017-05-15T10:39:29.000Z

repartee/views.py

multiple1902/repartee

bfbd1b8433c086ee3ae877f08156949515c3c977

[ "Apache-2.0", "MIT" ]

null

repartee/views.py

multiple1902/repartee

bfbd1b8433c086ee3ae877f08156949515c3c977

[ "Apache-2.0", "MIT" ]

null

from django.shortcuts import render_to_response from django.template import loader,Context, RequestContext def homepage(request): return render_to_response("index.html", { }, context_instance = RequestContext(request))

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ac1e3a17249ed7fabec26e3144c7b5c9e12e2296

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py

Python

PythonStuff1.py

OSHI7/Learning1

fa8014066e226465bb7989fbbb82a35412c4f634

[ "MIT" ]

null

PythonStuff1.py

OSHI7/Learning1

fa8014066e226465bb7989fbbb82a35412c4f634

[ "MIT" ]

3

2020-03-24T18:02:39.000Z

2020-10-06T21:32:23.000Z

PythonStuff1.py

OSHI7/Learning1

fa8014066e226465bb7989fbbb82a35412c4f634

[ "MIT" ]

1

2017-07-31T13:15:54.000Z

import os print('hello')

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3bc1e75a9aa4aaf79ecf9267d703153fb4b64770

147

py

Python

Coding-Challenges/maxProfitWithKTransactions/max_profit_with_k_transactions.py

FergusDevelopmentLLC/Coders-Workshop

3513bd5f79eaa85b4d2a648c5f343a224842325d

[ "MIT" ]

33

2019-12-02T23:29:47.000Z

2022-03-24T02:40:36.000Z

Coding-Challenges/maxProfitWithKTransactions/max_profit_with_k_transactions.py

FergusDevelopmentLLC/Coders-Workshop

3513bd5f79eaa85b4d2a648c5f343a224842325d

[ "MIT" ]

39

2020-01-15T19:28:12.000Z

2021-11-26T05:13:29.000Z

Coding-Challenges/maxProfitWithKTransactions/max_profit_with_k_transactions.py

FergusDevelopmentLLC/Coders-Workshop

3513bd5f79eaa85b4d2a648c5f343a224842325d

[ "MIT" ]

49

2019-12-02T23:29:53.000Z

2022-03-03T01:11:37.000Z

#!/usr/bin/env python3 def max_profit_with_k_transactions(prices, k): pass print(max_profit_with_k_transactions([5, 11, 3, 50, 60, 90], 2))

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null

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1

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6

3bce665f205789716aa1b80cbfff0b1599ce98f7

1,723

py

Python

black.py

mrytty/gradu-public

537337ab3dc49be9f1f4283706b0f4dcbc8cb059

[ "MIT" ]

null

black.py

mrytty/gradu-public

537337ab3dc49be9f1f4283706b0f4dcbc8cb059

[ "MIT" ]

null

black.py

mrytty/gradu-public

537337ab3dc49be9f1f4283706b0f4dcbc8cb059

[ "MIT" ]

null

import numpy as np from scipy.stats import norm from dcf import dcf def blacklet(K, F, vol, omega=1): log_ratio = np.log(F / K) d1 = (log_ratio + 0.5 * vol**2) / vol d2 = (log_ratio - 0.5 * vol**2) / vol return F * omega * norm.cdf(omega * d1) - K * omega * norm.cdf(omega * d2) def caplet_black(bond, forward, S, T, K, sigma, method='Act360'): dcf_factor = dcf(S, T, method=method) vol = sigma * np.sqrt(S) return bond * dcf_factor * blacklet(K, S, forward, vol, omega=1) def cap_black(bonds, forwards, times, K, sigma, method='Act360'): if len(times) == 2: return caplet_black(bonds, forwards, times[0], times[1], K, sigma, method=method) else: sum = 0 for i in range(len(times) - 1): bond = bonds.pop() forward = forwards.pop() S, T = bond[i], bond[i] sum += caplet_black(bond, forward, S, T, K, sigma, method=method) return sum def floorlet_black(bond, forward, S, T, K, sigma, method='Act360'): dcf_factor = dcf(S, T, method=method) vol = sigma * np.sqrt(S) return bond * dcf_factor * blacklet(K, S, forward, vol, omega=-1) def floor_black(bonds, forwards, times, K, sigma, method='Act360'): if len(times) == 2: return floorlet_black(bonds, forwards, times[0], times[1], K, sigma, method=method) else: sum = 0 for i in range(len(times) - 1): bond = bonds.pop() forward = forwards.pop() S, T = bond[i], bond[i] sum += flooret_black(bond, forward, S, T, K, sigma, method=method) return sum

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3be24e315c01210b57e504b94f4df287c7bc40bd

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py

Python

src/pyfonycore/bootstrap/config/raw/allowed_environments_resolver.py

pyfony/core

32cb2e959590307fb845ccafec90b8264fdad4ab

[ "MIT" ]

null

src/pyfonycore/bootstrap/config/raw/allowed_environments_resolver.py

pyfony/core

32cb2e959590307fb845ccafec90b8264fdad4ab

[ "MIT" ]

null

src/pyfonycore/bootstrap/config/raw/allowed_environments_resolver.py

pyfony/core

32cb2e959590307fb845ccafec90b8264fdad4ab

[ "MIT" ]

null

def resolve(raw_config): return raw_config["allowed_environments"] if "allowed_environments" in raw_config else ["dev", "test", "prod"]

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6

ce077b174ce3b952791acabe4ad68c568a6a939e

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py

Python

src/__init__.py

Briles/gruvbox

d127fc8887ea006ead49e97eed4d89955fbb5e16

[ "MIT" ]

251

2016-03-04T04:32:10.000Z

2022-03-22T09:52:02.000Z

src/__init__.py

Briles/gruvbox

d127fc8887ea006ead49e97eed4d89955fbb5e16

[ "MIT" ]

50

2016-03-09T07:41:55.000Z

2021-01-20T11:09:56.000Z

src/__init__.py

Briles/gruvbox

d127fc8887ea006ead49e97eed4d89955fbb5e16

[ "MIT" ]

23

2016-05-21T19:57:27.000Z

2022-02-01T15:44:00.000Z

#!/usr/bin/env python # coding: utf-8 from .documentation import * from .support import * from .gruvbox import *

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6

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py

Python

vika/types/__init__.py

Borye/vika.py

7b4ac29d308e00e2bbfc37dbcaa3f6c7a4a2236f

[ "MIT" ]

39

2020-10-27T13:17:37.000Z

2022-03-17T11:04:39.000Z

vika/types/__init__.py

Borye/vika.py

7b4ac29d308e00e2bbfc37dbcaa3f6c7a4a2236f

[ "MIT" ]

9

2020-10-27T14:44:48.000Z

2022-01-19T04:46:58.000Z

vika/types/__init__.py

Borye/vika.py

7b4ac29d308e00e2bbfc37dbcaa3f6c7a4a2236f

[ "MIT" ]

8

2020-10-27T15:12:34.000Z

2022-01-19T14:23:15.000Z

from .field import * from .record import * from .view import * from .node import * from .space import * from .response import *

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py

Python

libs/yowsup/yowsup/yowsup/layers/protocol_messages/__init__.py

akshitpradhan/TomHack

837226e7b38de1140c19bc2d478eeb9e379ed1fd

[ "MIT" ]

22

2017-07-14T20:01:17.000Z

2022-03-08T14:22:39.000Z

libs/yowsup/yowsup/yowsup/layers/protocol_messages/__init__.py

akshitpradhan/TomHack

837226e7b38de1140c19bc2d478eeb9e379ed1fd

[ "MIT" ]

6

2017-07-14T21:03:50.000Z

2021-06-10T19:08:32.000Z

libs/yowsup/yowsup/yowsup/layers/protocol_messages/__init__.py

akshitpradhan/TomHack

837226e7b38de1140c19bc2d478eeb9e379ed1fd

[ "MIT" ]

13

2017-07-14T20:13:14.000Z

2020-11-12T08:06:05.000Z

from .layer import YowMessagesProtocolLayer

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6

02696a25dbbc101c97d3de57d8484432e689854f

77

py

Python

dfdt/__init__.py

zpleunis/dfdt

afd6d2a9b19c053ad5f7b6318f2e061cf7fb3964

[ "BSD-3-Clause" ]

7

2020-10-01T13:36:23.000Z

2021-12-18T02:20:33.000Z

dfdt/__init__.py

zpleunis/dfdt

afd6d2a9b19c053ad5f7b6318f2e061cf7fb3964

[ "BSD-3-Clause" ]

null

dfdt/__init__.py

zpleunis/dfdt

afd6d2a9b19c053ad5f7b6318f2e061cf7fb3964

[ "BSD-3-Clause" ]

null

from .dfdt_utils import DynamicSpectrum from .ac_mc_drift import ac_mc_drift

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0

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null

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1

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6

026e5393739ba0193181f08049127f04fc8f480d

108

py

Python

office365/teams/teamsTabConfiguration.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

null

office365/teams/teamsTabConfiguration.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

null

office365/teams/teamsTabConfiguration.py

wreiner/Office365-REST-Python-Client

476bbce4f5928a140b4f5d33475d0ac9b0783530

[ "MIT" ]

null

from office365.runtime.client_value import ClientValue class TeamsTabConfiguration(ClientValue): pass

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6

5a1803881b4e1656541c80773b9db3c8d08d9ba0

76

py

Python

utils/emote.py

Rishiraj0100/world-chat

46f0c255348e06787339c464c31577f90daea253

[ "MIT" ]

14

2021-02-16T16:01:41.000Z

2022-01-30T06:28:22.000Z

utils/emote.py

Rishiraj0100/world-chat

46f0c255348e06787339c464c31577f90daea253

[ "MIT" ]

null

utils/emote.py

Rishiraj0100/world-chat

46f0c255348e06787339c464c31577f90daea253

[ "MIT" ]

6

2021-02-16T16:01:56.000Z

2021-07-16T11:24:50.000Z

check = "<:check:773959361953267742>" xmark = "<:xmark:773959363379462184>"

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9.333333

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6

5a327e1467e7628b0f29ca76e88c9af75e3b23ec

162

py

Python

datasets/__init__.py

NoelShin/selfmask

396e0a3636b29591f505b6711be45eabe292919a

[ "MIT" ]

11

2022-03-24T02:45:33.000Z

2022-03-30T02:53:33.000Z

datasets/__init__.py

NoelShin/selfmask

396e0a3636b29591f505b6711be45eabe292919a

[ "MIT" ]

2

2022-03-25T11:08:34.000Z

2022-03-30T14:13:26.000Z

datasets/__init__.py

NoelShin/selfmask

396e0a3636b29591f505b6711be45eabe292919a

[ "MIT" ]

1

2022-03-30T02:53:35.000Z

# saliency object detection dataset from datasets.dut_omron import DUTOMRONDataset from datasets.duts import DUTSDataset from datasets.ecssd import ECSSDDataset

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47

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0

1

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true

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0

1

0

1

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6

5a8393eb6f5fe3821855cd70263a0a338512f0ec

656

py

Python

tests/test_hacks.py

Laserlicht/deepl-translate

23332c3042065f376f3b602a281248e30c80dec8

[ "MIT" ]

1

2022-02-18T10:12:51.000Z

tests/test_hacks.py

Lain1984/deepl-translate

ca61c63ff23031291fbaf220de92018fb85a57f0

[ "MIT" ]

null

tests/test_hacks.py

Lain1984/deepl-translate

ca61c63ff23031291fbaf220de92018fb85a57f0

[ "MIT" ]

2

2020-12-09T19:00:20.000Z

2022-03-11T06:17:51.000Z

from deepl.hacks import calculate_valid_timestamp, generate_id def test_calculate_valid_timestamp(): assert 10 == calculate_valid_timestamp(timestamp=10, i_count=0) assert 11 == calculate_valid_timestamp(timestamp=10, i_count=1) assert 12 == calculate_valid_timestamp(timestamp=10, i_count=2) assert 12 == calculate_valid_timestamp(timestamp=10, i_count=3) assert 12 == calculate_valid_timestamp(timestamp=10, i_count=4) assert 15 == calculate_valid_timestamp(timestamp=10, i_count=5) assert 12 == calculate_valid_timestamp(timestamp=10, i_count=6) def test_generate_id(): assert 100_000_000 > generate_id() > 1_000_000

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1

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6

ce50f807d741b49c254ef465207f171cabe8e2b2

6,131

py

Python

src/genie/libs/parser/iosxe/tests/ShowApTagSummary/cli/equal/golden_output_expected.py

cphannan/genieparser

0b32e1ea633c532d67d89476aa5500f569cfbc6e

[ "Apache-2.0" ]

1

2020-05-26T13:06:10.000Z

src/genie/libs/parser/iosxe/tests/ShowApTagSummary/cli/equal/golden_output_expected.py

dalwar23/genieparser

a9df45d3ee23f107bfb55915068e90782f92fc99

[ "Apache-2.0" ]

null

src/genie/libs/parser/iosxe/tests/ShowApTagSummary/cli/equal/golden_output_expected.py

dalwar23/genieparser

a9df45d3ee23f107bfb55915068e90782f92fc99

[ "Apache-2.0" ]

2

2021-02-12T21:42:30.000Z

2021-02-12T21:47:51.000Z

expected_output = { "ap_name": { "b25a-13-cap10": { "ap_mac": "3c41.0fee.5094", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25b-12-cap01": { "ap_mac": "3c41.0fee.5884", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25b-11-cap01": { "ap_mac": "3c41.0fee.5d90", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-12-cap07": { "ap_mac": "3c41.0fee.5de8", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-11-cap05": { "ap_mac": "3c41.0fee.5df0", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-11-cap04": { "ap_mac": "3c41.0fee.5e5c", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-12-cap08": { "ap_mac": "3c41.0fee.5e74", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-11-cap01": { "ap_mac": "3c41.0fee.5eac", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-11-cap08": { "ap_mac": "3c41.0fee.5ef8", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-11-cap02": { "ap_mac": "3c41.0fee.5f94", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-11-cap07": { "ap_mac": "3c41.0fee.5fbc", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-12-cap02": { "ap_mac": "2c57.4518.16ac", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25b-11-cap06": { "ap_mac": "2c57.4518.2df0", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25b-11-cap08": { "ap_mac": "2c57.4518.41b0", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25b-11-cap07": { "ap_mac": "2c57.4518.432c", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-12-cap11": { "ap_mac": "2c57.4518.4330", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25b-12-cap02": { "ap_mac": "3c41.0fee.4394", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-13-cap09": { "ap_mac": "2c57.4518.564c", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25b-12-cap03": { "ap_mac": "2c57.4518.5b40", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, "b25a-12-cap10": { "ap_mac": "2c57.4518.5b48", "site_tag_name": "default-site-tag-fabric", "policy_tag_name": "PT_Fabri_B25_B25-1_fe778", "rf_tag_name": "Standard", "misconfigured": "No", "tag_source": "Static", }, }, "number_of_aps": 20, }

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6

ce5e16471e58422e7e3ce0350500eafe12061782

351

py

Python

utils/opt.py

hello-code2021/IDLMPIA

f8e303016bfc5fe7bb9978f972ad64e7b0adfe8e

[ "MIT" ]

null

utils/opt.py

hello-code2021/IDLMPIA

f8e303016bfc5fe7bb9978f972ad64e7b0adfe8e

[ "MIT" ]

null

utils/opt.py

hello-code2021/IDLMPIA

f8e303016bfc5fe7bb9978f972ad64e7b0adfe8e

[ "MIT" ]

null

epoch = 100 train_result = "/home/yetaoyu/zc/Classification/patch_train_results" train_dataset_dir = "/home/yetaoyu/zc/Classification/patch_data" test_data_dir = "/home/yetaoyu/zc/Classification/patch_data" test_result_dir = "/home/yetaoyu/zc/Classification/patch_test_results" model_weight_path = "/home/yetaoyu/zc/Classification/patch_train_results"

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ce9078ecffa5494ec28794ac6f868981192729ad

73

py

Python

examples/__init__.py

cdonovick/peak-examples

9d0799f1afafc801619a5cb15acd69603c49bb17

[ "MIT" ]

null

examples/__init__.py

cdonovick/peak-examples

9d0799f1afafc801619a5cb15acd69603c49bb17

[ "MIT" ]

null

examples/__init__.py

cdonovick/peak-examples

9d0799f1afafc801619a5cb15acd69603c49bb17

[ "MIT" ]

null

from . import condition_flags from . import fp from . import reg_overlap

18.25

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0.794521

11

73

5.090909

0.636364

0.535714

0

0.164384

73

3

30

24.333333

0.918033

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true

0

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1

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1

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null

0

1

0

1

0

1

0

6

ce9e41a98e3d71fabd2577d7712145f9919c8e0a

5,298

py

Python

financial_analysis/tests/test_set_freq.py

Kautenja/financial-analysis

96870edc4b8762bb0ed297b937b263a69221c23e

[ "MIT" ]

1

2022-02-26T01:27:23.000Z

financial_analysis/tests/test_set_freq.py

Kautenja/financial-analysis

96870edc4b8762bb0ed297b937b263a69221c23e

[ "MIT" ]

null

financial_analysis/tests/test_set_freq.py

Kautenja/financial-analysis

96870edc4b8762bb0ed297b937b263a69221c23e

[ "MIT" ]

3

2020-09-10T21:11:32.000Z

2021-12-06T09:38:18.000Z

"""Test cases for the set_freq module.""" from unittest import TestCase import numpy as np import pandas as pd from ..set_freq import set_freq # # MARK: lossless conversions # class ShouldLosslessConvertUsingGroupby(TestCase): def test(self): index = pd.to_datetime([1000, 2000, 3000], unit='ms') price = pd.Series([100., 101., 102.], index=index) dividend = pd.Series([5., 6., 7.], index=index) price, dividend = set_freq(price, dividend, '1s', groupby=True) # create the expected index and price expected_index = pd.to_datetime([1, 2, 3], unit='s') # expected values will have NaN where there were none expected_price = pd.Series([100., 101., 102.], index=expected_index) expected_dividend = pd.Series([5., 6., 7.], index=expected_index) # make assertions through the pandas testing module pd.testing.assert_series_equal(price, expected_price) pd.testing.assert_series_equal(dividend, expected_dividend) class ShouldLosslessConvertUsingAsfreq(TestCase): def test(self): index = pd.to_datetime([1000, 2000, 3000], unit='ms') price = pd.Series([100., 101., 102.], index=index) dividend = pd.Series([5., 6., 7.], index=index) price, dividend = set_freq(price, dividend, '1s', groupby=False, method=None) # create the expected index and price expected_index = pd.to_datetime([1, 2, 3], unit='s') # expected values will have NaN where there were none expected_price = pd.Series([100., 101., 102.], index=expected_index) expected_dividend = pd.Series([5., 6., 7.], index=expected_index) # make assertions through the pandas testing module pd.testing.assert_series_equal(price, expected_price) pd.testing.assert_series_equal(dividend, expected_dividend) # # MARK: Up-sampling (inserting missing values) # class ShouldUpsampleTimeScaleUsingFfillTrue(TestCase): def test(self): index = pd.to_datetime([1, 2], unit='ms') price = pd.Series([100., 101.], index=index) dividend = pd.Series([5., 6.], index=index) price, dividend = set_freq(price, dividend, '100U') # create the expected index and price expected_index = pd.to_datetime(list(range(1000, 2001, 100)), unit='us') # expected values will be forward filled to meet timescale expected_price = pd.Series(10 * [100.] + [101.], index=expected_index) expected_dividend = pd.Series(10 * [5.] + [6.], index=expected_index) # make assertions through the pandas testing module pd.testing.assert_series_equal(price, expected_price) pd.testing.assert_series_equal(dividend, expected_dividend) class ShouldNotUpsampleTimeScaleUsingFfillFalse(TestCase): def test(self): index = pd.to_datetime([1, 2], unit='ms') price = pd.Series([100., 101.], index=index) dividend = pd.Series([5., 6.], index=index) price, dividend = set_freq(price, dividend, '100U', ffill=False) # create the expected index and price expected_index = pd.to_datetime(list(range(1000, 2001, 100)), unit='us') # expected values will have NaN where there were none expected_price = pd.Series([100.] + 9 * [np.nan] + [101.], index=expected_index) expected_dividend = pd.Series([5.] + 9 * [np.nan] + [6.], index=expected_index) # make assertions through the pandas testing module pd.testing.assert_series_equal(price, expected_price) pd.testing.assert_series_equal(dividend, expected_dividend) # # MARK: Down-sampling (aggregating groups of data) # class ShouldDownsampleUsingGroupbyAndMeanValue(TestCase): def test(self): index = pd.to_datetime([1001, 1002], unit='ms') price = pd.Series([100., 101.], index=index) dividend = pd.Series([5., 6.], index=index) price, dividend = set_freq(price, dividend, freq='1s', method='mean') # create the expected index and price expected_index = pd.to_datetime([1], unit='s') # expected values will have NaN where there were none expected_price = pd.Series([100.5], index=expected_index) expected_dividend = pd.Series([5.5], index=expected_index) # make assertions through the pandas testing module pd.testing.assert_series_equal(price, expected_price) pd.testing.assert_series_equal(dividend, expected_dividend) # class ShouldDownsampleUsingAsfreq(TestCase): # def test(self): # index = pd.to_datetime([1001, 1002], unit='ms') # price = pd.Series([100., 101.], index=index) # dividend = pd.Series([5., 6.], index=index) # price, dividend = set_freq(price, dividend, freq='1s', groupby=False, method='bfill') # # create the expected index and price # expected_index = pd.to_datetime([1], unit='s') # # expected values will have NaN where there were none # expected_price = pd.Series([100.], index=expected_index) # expected_dividend = pd.Series([5.], index=expected_index) # # make assertions through the pandas testing module # pd.testing.assert_series_equal(price, expected_price) # pd.testing.assert_series_equal(dividend, expected_dividend)

46.884956

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0

null

0

1

0

null

0

6

0cac7eafb604f1065119b389c7dc00e48e4926ac

4,941

py

Python

setVolunteers.py

zoctobere/Siren

325bcbf78d721e20eecad953dfbf54cacddc0889

[ "MIT" ]

null

setVolunteers.py

zoctobere/Siren

325bcbf78d721e20eecad953dfbf54cacddc0889

[ "MIT" ]

null

setVolunteers.py

zoctobere/Siren

325bcbf78d721e20eecad953dfbf54cacddc0889

[ "MIT" ]

null

from discord.ext import commands from builtins import bot import db import config @bot.command() async def setcommentary(ctx, arg1, arg2, arg3): if ctx.channel.id != config.adminChannel: return arg1 = arg1.upper() if not db.doesRestreamExist(arg1): await ctx.send('```No restream found with Restream ID: ' + arg1 + '```') await ctx.message.delete() return if not db.isRestreamOpen(arg1): if not ctx.author.id in config.superUsers: if ctx.author.name != db.getRestreamField(arg1, 'assignedBy'): await ctx.send('```Restream ' + arg1 + ' is not open. Please check the Restream ID and try again.```') await ctx.message.delete() return if not db.doesUserExist(arg2) or not db.doesUserExist(arg3): if not db.doesUserExist(arg2): await ctx.send('```' + arg2 + ' is not in the database. Please check the spelling and try again. If this is a new restream team member, have Zoe reseed the db. Note: usernames are case sensitive.```') if not db.doesUserExist(arg3): await ctx.send('```' + arg3 + ' is not in the database. Please check the spelling and try again. If this is a new restream team member, have Zoe reseed the db. Note: usernames are case sensitive.```') await ctx.message.delete() return db.setRestreamField(arg1, 'commentary1', arg2) db.setRestreamField(arg1, 'commentary2', arg3) await ctx.send('```Commentary for Restream ' + arg1 + ' set to: ' + arg2 + ', ' + arg3 + ' by ' + ctx.author.name + '.```') await ctx.message.delete() @setcommentary.error async def clear_error(ctx, error): if ctx.channel.id != config.adminChannel: return if isinstance(error, commands.MissingRequiredArgument): await ctx.send('```Please specify the Restream ID and BOTH commentators separated by a space. Usage: .setcommentary <restreamID> <commentary1> <commentary2>```') await ctx.message.delete() @bot.command() async def settracker(ctx, arg1, arg2): if ctx.channel.id != config.adminChannel: return arg1 = arg1.upper() if not db.doesRestreamExist(arg1): await ctx.send('```No restream found with Restream ID: ' + arg1 + '```') await ctx.message.delete() return if not db.isRestreamOpen(arg1): if not ctx.author.id in config.superUsers: await ctx.send('```Restream ' + arg1 + ' is not open. Please check the Restream ID and try again.```') await ctx.message.delete() return if not db.doesUserExist(arg2): await ctx.send('```' + arg2 + ' is not in the database. Please check the spelling and try again. If this is a new restream team member, have Zoe reseed the db. Note: usernames are case sensitive.```') await ctx.message.delete() return db.setRestreamField(arg1, 'tracker', arg2) await ctx.send('```Tracker for Restream ' + arg1 + ' set to ' + arg2 + ' by ' + ctx.author.name + '.```') await ctx.message.delete() @settracker.error async def clear_error(ctx, error): if ctx.channel.id != config.adminChannel: return if isinstance(error, commands.MissingRequiredArgument): await ctx.send('```Please specify the Restream ID and tracker. Usage: .settracker <restreamID> <tracker>```') await ctx.message.delete() @bot.command() async def setrestreamer(ctx, arg1, arg2): if ctx.channel.id != config.adminChannel: return arg1 = arg1.upper() if not db.doesRestreamExist(arg1): await ctx.send('```No restream found with Restream ID: ' + arg1 + '```') await ctx.message.delete() return if not db.isRestreamOpen(arg1): if not ctx.author.id in config.superUsers: await ctx.send('```Restream ' + arg1 + ' is not open. Please check the Restream ID and try again.```') await ctx.message.delete() return if not db.doesUserExist(arg2): await ctx.send('```' + arg2 + ' is not in the database. Please check the spelling and try again. If this is a new restream team member, have Zoe reseed the db. Note: usernames are case sensitive.```') await ctx.message.delete() return db.setRestreamField(arg1, 'restreamer', arg2) await ctx.send('```Restreamer for Restream ' + arg1 + ' set to ' + arg2 + ' by ' + ctx.author.name + '.```') await ctx.message.delete() @setrestreamer.error async def clear_error(ctx, error): if ctx.channel.id != config.adminChannel: return if isinstance(error, commands.MissingRequiredArgument): await ctx.send('```Please specify the Restream ID and restreamer. Usage: .setrestreamer <restreamID> <restreamer>```') await ctx.message.delete()

39.528

213

0.626391

614

4,941

5.035831

0.143322

0.080207

0.062096

0.101876

0.799483

0.791721

0.754851

0

0.014921

0.253997

4,941

124

214

39.846774

0.823928

0

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false

0

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0

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0

null

0

1

0

1

0

null

0

6

0cc895e5b57267bc734bd131915232e7c0cc8de7

5,014

py

Python

int/tests/test_invitations.py

ryjones/aries-acapy-plugin-toolbox

70108e2264a31831dab0fe9aafc70b3c310808c5

[ "Apache-2.0" ]

1

2019-11-08T21:59:28.000Z

int/tests/test_invitations.py

ryjones/aries-acapy-plugin-toolbox

70108e2264a31831dab0fe9aafc70b3c310808c5

[ "Apache-2.0" ]

1

2019-11-15T21:30:18.000Z

int/tests/test_invitations.py

ryjones/aries-acapy-plugin-toolbox

70108e2264a31831dab0fe9aafc70b3c310808c5

[ "Apache-2.0" ]

4

2019-11-08T21:59:31.000Z

2019-11-18T21:21:22.000Z

"""Invitations tests""" import pytest from acapy_client import Client from acapy_client.api.connection import delete_connection, get_connections @pytest.fixture(autouse=True) async def clear_invitation_state(backchannel: Client, connection_id: str): """Clear invitation after each test.""" yield connections = await get_connections.asyncio(client=backchannel) for connection in connections.results: if connection.state == "invitation": await delete_connection.asyncio( client=backchannel, conn_id=connection.connection_id ) @pytest.mark.asyncio async def test_create_invitation(connection): """Test create invitation protocol""" reply = await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/create", "alias": "Invitation I sent to Alice", "label": "Bob", "group": "admin", "auto_accept": True, "multi_use": True, }, return_route="all", ) assert ( reply["@type"] == "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/invitation" ) @pytest.mark.asyncio async def test_oob_create_invitation(connection): """Test create invitation protocol""" reply = await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/oob-create", "alias": "Invitation I sent to Alice", "label": "Bob", "group": "admin", "auto_accept": True, "multi_use": True, }, return_route="all", ) assert ( reply["@type"] == "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/invitation" ) @pytest.mark.asyncio async def test_get_list(connection): """Test get list protocol""" reply = await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/get-list" }, return_route="all", ) assert ( reply["@type"] == "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/list" ) @pytest.mark.asyncio async def test_num_results(connection): """Test that the create message protocol causes new item in results list""" # Input number of messages to add to the list added_num = 2 for i in range(added_num): await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/create", "alias": "Message I sent to Alice", "label": "Bob", "group": "admin", "auto_accept": True, "multi_use": True, }, return_route="all", ) reply = await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/get-list" }, return_route="all", ) assert len(reply["results"]) == added_num print(reply["results"][0]) assert ( reply["results"][0]["invitation_type"] == "https://didcomm.org/connections/1.0/invitation" ) @pytest.mark.asyncio async def test_oob_num_results(connection): """Test that the create message protocol causes new item in results list""" # Input number of messages to add to the list added_num = 2 for i in range(added_num): await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/oob-create", "alias": "Message I sent to Alice", "label": "Bob", "group": "admin", "auto_accept": True, "multi_use": True, }, return_route="all", ) reply = await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/get-list" }, return_route="all", ) assert len(reply["results"]) == added_num print(reply["results"][0]) assert ( reply["results"][0]["invitation_type"] == "https://didcomm.org/out-of-band/1.0/invitation" ) @pytest.mark.asyncio async def test_empty_list(connection): """Test that get-list returns no results if no create messages have been sent""" reply = await connection.send_and_await_reply_async( { "@type": "https://github.com/hyperledger/aries-toolbox/tree/master/docs/admin-invitations/0.1/get-list" }, return_route="all", ) assert reply["results"] == []

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0cfb925753b1745fc256897f88dc0873a2553657

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py

Python

mfr/extensions/docx/__init__.py

yacchin1205/RDM-modular-file-renderer

5bd18175a681d21e7be7fe0238132335a1cd8ded

[ "Apache-2.0" ]

36

2015-08-31T20:24:22.000Z

2021-12-17T17:02:44.000Z

mfr/extensions/docx/__init__.py

yacchin1205/RDM-modular-file-renderer

5bd18175a681d21e7be7fe0238132335a1cd8ded

[ "Apache-2.0" ]

190

2015-01-02T06:22:01.000Z

2022-01-19T11:27:03.000Z

mfr/extensions/docx/__init__.py

yacchin1205/RDM-modular-file-renderer

5bd18175a681d21e7be7fe0238132335a1cd8ded

[ "Apache-2.0" ]

47

2015-01-27T15:45:22.000Z

2021-01-27T22:43:03.000Z

from .render import DocxRenderer # noqa

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py

Python

BingRewards/src/config.py

Stefano-Solo/bing-rewards

601eecbe9e11ba0928d3acc33d2e55a31576dbf7

[ "MIT" ]

null

BingRewards/src/config.py

Stefano-Solo/bing-rewards

601eecbe9e11ba0928d3acc33d2e55a31576dbf7

[ "MIT" ]

null

BingRewards/src/config.py

Stefano-Solo/bing-rewards

601eecbe9e11ba0928d3acc33d2e55a31576dbf7

[ "MIT" ]

null

credentials = dict( email = '', password = '', telegram_api_token = '__telegram_api_token__', telegram_userid = '__telegram_userid__' )

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0

6

0b88b303dcafb0c8f13c83989b6cba9e6d899fb7

72

py

Python

close_numerical_matches/__init__.py

shmulvad/close_numerical_matches

03bcc013eda3f79f417ded2c8e4d96af32a15401

[ "MIT" ]

1

2021-07-11T13:35:21.000Z

close_numerical_matches/__init__.py

shmulvad/close_numerical_matches

03bcc013eda3f79f417ded2c8e4d96af32a15401

[ "MIT" ]

null

close_numerical_matches/__init__.py

shmulvad/close_numerical_matches

03bcc013eda3f79f417ded2c8e4d96af32a15401

[ "MIT" ]

null

from .version import __version__ from .find_matches import find_matches

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6

0baa46c524e9807ef5dc1884c70c1f269e812b14

167

py

Python

conftest.py

mpolidori/harvest-travis

000c85b09294812d27fa5faea6ea10a60ae989c7

[ "PostgreSQL" ]

2

2017-10-02T22:25:43.000Z

2017-12-31T14:54:17.000Z

conftest.py

NCAR/ckanext-harvest

51d03fa527376eb3e73a90fd9771b82f89d97398

[ "PostgreSQL" ]

null

conftest.py

NCAR/ckanext-harvest

51d03fa527376eb3e73a90fd9771b82f89d97398

[ "PostgreSQL" ]

null

# -*- coding: utf-8 -*- pytest_plugins = [ u'ckan.tests.pytest_ckan.ckan_setup', u'ckan.tests.pytest_ckan.fixtures', u'ckanext.harvest.tests.fixtures', ]

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1

null

0

6

e7e8441db738b85aba179108724297db1901eff5

4,323

py

Python

chat-plugin/chat/test/test_predicate.py

lyoung83/chat

b21a3255db6c825a22a4ef02642fb9c3cb72d9c3

[ "Apache-2.0" ]

17

2016-04-09T09:54:43.000Z

2021-06-29T04:59:54.000Z

chat-plugin/chat/test/test_predicate.py

lyoung83/chat

b21a3255db6c825a22a4ef02642fb9c3cb72d9c3

[ "Apache-2.0" ]

210

2016-01-27T09:57:29.000Z

2020-10-29T17:19:44.000Z

chat-plugin/chat/test/test_predicate.py

lyoung83/chat

b21a3255db6c825a22a4ef02642fb9c3cb72d9c3

[ "Apache-2.0" ]

21

2016-06-22T11:48:56.000Z

2019-01-07T17:08:45.000Z

import unittest from ..query import Predicate class TestPredicate(unittest.TestCase): def test_simple_in(self): p = Predicate(_id__in=["a", "b", "c"]) self.assertListEqual( ["in", {"$type": "keypath", "$val": "_id"}, ["a", "b", "c"]] , p.to_dict()) def test_simple_not(self): p = ~Predicate(_id__in=["a", "b", "c"]) self.assertListEqual(['not', ["in", {"$type": "keypath", "$val": "_id"}, ["a", "b", "c"]]] , p.to_dict()) def test_simple_and(self): p = Predicate(_id__eq="a", deleted__eq=False) expected = ["and", ["eq", {"$type": "keypath", "$val": "_id"}, "a"], ["eq", {"$type": "keypath", "$val": "deleted"}, False] ] self.assertListEqual( expected, p.to_dict() ) p = Predicate(_id__eq="a") p = p & Predicate(deleted__eq=False) self.assertListEqual(expected, p.to_dict()) def test_simple_and_three_statements(self): p = Predicate(time__lte="2010-07-10", time__gte="2009-01-01", deleted__ne=False) expected = ["and", ["ne", {"$type": "keypath", "$val": "deleted"}, False], ["gte", {"$type": "keypath", "$val": "time"}, "2009-01-01"], ["lte", {"$type": "keypath", "$val": "time"}, "2010-07-10"]] self.assertListEqual(expected, p.to_dict()) def test_simple_or(self): p = Predicate(_id__eq="simple", gender__eq="M", op=Predicate.OR) expected = ["or", ["eq", {"$type": "keypath", "$val": "_id"}, "simple"], ["eq", {"$type": "keypath", "$val": "gender"}, "M"]] self.assertListEqual(expected, p.to_dict()) def test_simple_or_three_statement(self): p = Predicate(_id__eq="chima", gender__eq="M", type__in=["cat", "dog"] , op=Predicate.OR) expected = ["or", ["eq", {"$type": "keypath", "$val": "_id"}, "chima"], ["eq", {"$type": "keypath", "$val": "gender"}, "M"], ["in", {"$type": "keypath", "$val": "type"}, ["cat", "dog"]]] self.assertListEqual(expected, p.to_dict()) def test_compound_statement_1(self): p = Predicate(_id__eq="chima", gender__eq="M", type__eq="dog") p2 = Predicate(_id__eq="fatseng", gender__eq="F", type__eq="cat") p3 = Predicate(_id__eq="milktea", gender__eq="NA", type__eq="frog") p4 = p & p2 & p3 expected = ["and", ["eq", {"$type": "keypath", "$val": "_id"}, "chima"], ["eq", {"$type": "keypath", "$val": "gender"}, "M"], ["eq", {"$type": "keypath", "$val": "type"}, "dog"], ["eq", {"$type": "keypath", "$val": "_id"}, "fatseng"], ["eq", {"$type": "keypath", "$val": "gender"}, "F"], ["eq", {"$type": "keypath", "$val": "type"}, "cat"], ["eq", {"$type": "keypath", "$val": "_id"}, "milktea"], ["eq", {"$type": "keypath", "$val": "gender"}, "NA"], ["eq", {"$type": "keypath", "$val": "type"}, "frog"]] self.assertListEqual(expected, p4.to_dict()) def test_compound_statement_2(self): p = Predicate(_id__eq="chima", gender__eq="M", type__eq="dog") p2 = Predicate(_id__eq="fatseng", gender__eq="F", type__eq="cat") p3 = ~Predicate(_id__eq="milktea", gender__eq="NA", type__eq="frog") p4 = p | p2 | p3 expected = ["or", ["and",["eq", {"$type": "keypath", "$val": "_id"}, "chima"], ["eq", {"$type": "keypath", "$val": "gender"}, "M"], ["eq", {"$type": "keypath", "$val": "type"}, "dog"]], ["and",["eq", {"$type": "keypath", "$val": "_id"}, "fatseng"], ["eq", {"$type": "keypath", "$val": "gender"}, "F"], ["eq", {"$type": "keypath", "$val": "type"}, "cat"]], ["not", ["and", ["eq", {"$type": "keypath", "$val": "_id"}, "milktea"], ["eq", {"$type": "keypath", "$val": "gender"}, "NA"], ["eq", {"$type": "keypath", "$val": "type"}, "frog"]]]] self.assertListEqual(expected, p4.to_dict())

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6

f00da208bbb7f65efc1010ca225d6220ace0e69d

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py

Python

PyMturkGspread/__init__.py

haldunanil/PyMturkGspread

2f617930461f89323af96298948ee576f0ccea8c

[ "MIT" ]

null

PyMturkGspread/__init__.py

haldunanil/PyMturkGspread

2f617930461f89323af96298948ee576f0ccea8c

[ "MIT" ]

null

PyMturkGspread/__init__.py

haldunanil/PyMturkGspread

2f617930461f89323af96298948ee576f0ccea8c

[ "MIT" ]

null

from .mturk import GoogleForms

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0

1

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1

0

1

0

6

f05c30bc59993e3d8a80e3a05c3f976468a99996

3,987

py

Python

tests/test_product_variations.py

lotrekagency/pywoo

84034c053a4873246394203ae819190e8402c057

[ "MIT" ]

5

2019-09-11T15:39:57.000Z

2022-01-21T14:23:51.000Z

tests/test_product_variations.py

lotrekagency/pywoo

84034c053a4873246394203ae819190e8402c057

[ "MIT" ]

2

2019-10-11T15:34:28.000Z

2019-10-15T15:38:23.000Z

tests/test_product_variations.py

lotrekagency/pywoo

84034c053a4873246394203ae819190e8402c057

[ "MIT" ]

1

2021-06-17T17:27:02.000Z

import unittest from mock import patch from pywoo.pywoo import Api from pywoo.models.product_variations import ProductVariation from tests.tools import mock_request class TestProductVariation(unittest.TestCase): @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_api_post(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = api.create_product_variation(56) assert type(obj) == ProductVariation @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_api_get(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = api.get_product_variations(56) assert all(type(x) == ProductVariation for x in obj) obj = api.get_product_variations(56, 57) assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_api_put(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = api.update_product_variation(56, 57) assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_api_delete(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = api.delete_product_variation(56, 57) assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_classmethod_post(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = ProductVariation.create_product_variation(api, 56) assert type(obj) == ProductVariation @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_classmethod_get(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = ProductVariation.get_product_variations(api, 56) assert all(type(x) == ProductVariation for x in obj) obj = ProductVariation.get_product_variations(api, 56, 57) assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_classmethod_put(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = ProductVariation.edit_product_variation(api, 56, 57) assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_classmethod_delete(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = ProductVariation.delete_product_variation(api, 56, 57) assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_object_update(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = ProductVariation.get_product_variations(api, 56, 57) assert type(obj) == ProductVariation and obj.id == 57 obj = obj.update() assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_object_delete(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = api.get_product_variations(56, 57) assert type(obj) == ProductVariation and obj.id == 57 obj = obj.delete() assert type(obj) == ProductVariation and obj.id == 57 @patch('pywoo.pywoo.requests.api.request', side_effect=mock_request) def test_object_refresh(self, func): api = Api('', 'fake_consumer_key', 'fake_consumer_secret') obj = api.get_product_variations(56, 57) assert type(obj) == ProductVariation and obj.id == 57 obj.refresh() assert type(obj) == ProductVariation and obj.id == 57

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null

0

1

0

null

0

6

b2cd1abafb4efcf565e288216890e652357967bd

80

py

Python

rve/cmd/__init__.py

eupedrosa/ros-venv

7d203288ec5ab54467b1e54406f94b876ab54ece

[ "BSD-3-Clause" ]

null

rve/cmd/__init__.py

eupedrosa/ros-venv

7d203288ec5ab54467b1e54406f94b876ab54ece

[ "BSD-3-Clause" ]

null

rve/cmd/__init__.py

eupedrosa/ros-venv

7d203288ec5ab54467b1e54406f94b876ab54ece

[ "BSD-3-Clause" ]

null

from . import init from . import run from . import remove from . import status

13.333333

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null

0

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1

0

1

0

6

b2f7f5ba03af869001e57d67fd929a5d364aaa0e

9,555

py

Python

.ipynb_checkpoints/utils_continuous-checkpoint.py

hsharsh/pinn-torch

fa563b324c286ec4425529f5ea1db03e68bec2f3

[ "MIT" ]

1

2022-01-25T04:27:33.000Z

utils_continuous.py

hsharsh/pinn-torch

fa563b324c286ec4425529f5ea1db03e68bec2f3

[ "MIT" ]

null

utils_continuous.py

hsharsh/pinn-torch

fa563b324c286ec4425529f5ea1db03e68bec2f3

[ "MIT" ]

null

# Data processing imports import scipy.io as io import numpy as np from pyDOE import lhs # Plotting imports import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import make_axes_locatable from scipy.interpolate import griddata import matplotlib.gridspec as gridspec def load_dataset(file): data = io.loadmat(file) return data['x'], data['t'], data['usol'].T # Inference def preprocess_data_continuous_inference(file, Nu = 100, Nf = 10000): x, t, u_exact = load_dataset(file) X, T = np.meshgrid(x, t) test_X = np.hstack([X.flatten()[:,None], T.flatten()[:,None]]) test_u = u_exact.flatten()[:,None] # Sampling for initial and boundary conditions x_i = np.hstack([X[:1,:].T,T[:1,:].T]) # Initial u_i = u_exact[:1,:].T x_b1 = np.hstack([X[:,:1], T[:,:1]]) # Boundary 1 u_b1 = u_exact[:,:1] x_b2 = np.hstack([X[:,-1:], T[:,-1:]]) # Boundary 2 u_b2 = u_exact[:,-1:] train_X_u = np.vstack([x_i, x_b1, x_b2]) train_u = np.vstack([u_i, u_b1, u_b2]) # Domain bounds for Lattice Hypercube Sampling lb = test_X.min(0) ub = test_X.max(0) collocation_points = lb + (ub-lb)*lhs(2, Nf) # Samples (Nf x 2) points and scales them to be in the domain train_X_f = np.vstack([collocation_points, train_X_u]) # Restrics the boundary conditions to only Nu random points sample = np.random.choice(train_X_u.shape[0], size = Nu) train_X_u = train_X_u[sample] train_u = train_u[sample] return x, t, u_exact, X, T, lb, ub, train_X_u, train_u, train_X_f, test_X, test_u def plot_results_continuous_inference(x, t, X, T, u_exact, u_pred, train_X_u, train_X_f, train_u, test_X): u_pred = griddata(test_X, u_pred.flatten(), (X, T), method='cubic') fig = plt.figure(figsize = (10, 9.5)) ax = plt.gca() ax.axis('off') fig.patch.set_facecolor('white') ####### Row 0: u(t,x) ################## gs0 = gridspec.GridSpec(1, 2) gs0.update(top=1-0.06, bottom=1-1/3, left=0.15, right=0.85, wspace=0) ax = plt.subplot(gs0[:, :]) h = ax.imshow(u_pred.T, interpolation='nearest', cmap='rainbow', extent=[t.min(), t.max(), x.min(), x.max()], origin='lower', aspect='auto') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) fig.colorbar(h, cax=cax) ax.plot(train_X_u[:,1], train_X_u[:,0], 'kx', markersize = 4, clip_on = False) ax.plot(train_X_f[:,1], train_X_f[:,0], 'k.', markersize = 1, clip_on = False) line = np.linspace(x.min(), x.max(), 2)[:,None] ax.plot(t[25]*np.ones((2,1)), line, 'w-', linewidth = 1) ax.plot(t[50]*np.ones((2,1)), line, 'w-', linewidth = 1) ax.plot(t[75]*np.ones((2,1)), line, 'w-', linewidth = 1) ax.set_xlabel('$t$') ax.set_ylabel('$x$') # ax.legend(frameon=False, loc = 'best') ax.set_title('$u(t,x)$', fontsize = 10) ####### Row 1: u(t,x) slices ################## gs1 = gridspec.GridSpec(2, 3) gs1.update(top=1-12/30, bottom=0, left=0.1, right=0.9, wspace=0.5) ax = plt.subplot(gs1[0, 0]) ax.plot(x,u_exact[0,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[0,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.set_title('$t = 0$', fontsize = 10) ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax = plt.subplot(gs1[0, 1]) ax.plot(x,u_exact[24,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[24,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.set_title('$t = 0.25$', fontsize = 10) ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax = plt.subplot(gs1[0, 2]) ax.plot(x,u_exact[49,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[49,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax.set_title('$t = 0.50$', fontsize = 10) ax = plt.subplot(gs1[1, 0]) ax.plot(x,u_exact[74,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[74,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax.set_title('$t = 0.75$', fontsize = 10) ax = plt.subplot(gs1[1, 1]) ax.plot(x,u_exact[99,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[99,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax.set_title('$t = 1.00$', fontsize = 10) ax.legend(loc='center', bbox_to_anchor=(2.5, 0.6), ncol=5, frameon=False) plt.show() # Identification def preprocess_data_continuous_identification(file, N = 2000, noise = 0.0): x, t, u_exact = load_dataset(file) X, T = np.meshgrid(x, t) test_X = np.hstack([X.flatten()[:,None], T.flatten()[:,None]]) test_u = u_exact.flatten()[:,None] # Domain bounds for Lattice Hypercube Sampling lb = test_X.min(0) ub = test_X.max(0) # Sample N random points sample = np.random.choice(test_X.shape[0], size = N) train_X = test_X[sample] train_u = test_u[sample] train_u = train_u + noise*np.std(train_u)*np.random.randn(train_u.shape[0], train_u.shape[1]) return x, t, u_exact, X, T, lb, ub, train_X, train_u, test_X, test_u def plot_results_continuous_identification(x, t, X, T, u_exact, u_pred, train_X, train_u, test_X, lambda_1, lambda_2): u_pred = griddata(test_X, u_pred.flatten(), (X, T), method='cubic') fig = plt.figure(figsize = (10, 9.5)) ax = plt.gca() ax.axis('off') fig.patch.set_facecolor('white') ####### Row 0: u(t,x) ################## gs0 = gridspec.GridSpec(1, 2) gs0.update(top=1-0.06, bottom=1-1/3, left=0.15, right=0.85, wspace=0) ax = plt.subplot(gs0[:, :]) h = ax.imshow(u_pred.T, interpolation='nearest', cmap='rainbow', extent=[t.min(), t.max(), x.min(), x.max()], origin='lower', aspect='auto') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) fig.colorbar(h, cax=cax) ax.plot(train_X[:,1], train_X[:,0], 'k.', markersize = 2, clip_on = False) line = np.linspace(x.min(), x.max(), 2)[:,None] ax.plot(t[25]*np.ones((2,1)), line, 'w-', linewidth = 1) ax.plot(t[50]*np.ones((2,1)), line, 'w-', linewidth = 1) ax.plot(t[75]*np.ones((2,1)), line, 'w-', linewidth = 1) ax.set_xlabel('$t$') ax.set_ylabel('$x$') # ax.legend(frameon=False, loc = 'best') ax.set_title('$u(t,x)$', fontsize = 10) ####### Row 1: u(t,x) slices ################## gs1 = gridspec.GridSpec(2, 3) gs1.update(top=1-12/30, bottom=0, left=0.1, right=0.9, wspace=0.5) ax = plt.subplot(gs1[0, 0]) ax.plot(x,u_exact[0,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[0,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.set_title('$t = 0$', fontsize = 10) ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax = plt.subplot(gs1[0, 1]) ax.plot(x,u_exact[24,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[24,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.set_title('$t = 0.25$', fontsize = 10) ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax = plt.subplot(gs1[0, 2]) ax.plot(x,u_exact[49,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[49,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax.set_title('$t = 0.50$', fontsize = 10) ax = plt.subplot(gs1[1, 0]) ax.plot(x,u_exact[74,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[74,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax.set_title('$t = 0.75$', fontsize = 10) ax = plt.subplot(gs1[1, 1]) ax.plot(x,u_exact[99,:], 'b-', linewidth = 2, label = 'Exact') ax.plot(x,u_pred[99,:], 'r--', linewidth = 2, label = 'Prediction') ax.set_xlabel('$x$') ax.set_ylabel('$u(t,x)$') ax.axis('square') ax.set_xlim([-1.1,1.1]) ax.set_ylim([-1.1,1.1]) ax.set_title('$t = 1.00$', fontsize = 10) ax.legend(loc='center', bbox_to_anchor=(1.8, 0.3), ncol=5, frameon=False) # Prediction ax = plt.subplot(gs1[1, 2]) ax.axis('off') s1 = '$\begin{tabular}{ |c|c| } \hline Correct PDE & $u_t + u u_x - 0.0031831 u_{xx} = 0$ \\ \hline Identified PDE (clean data) & ' s2 = '$u_t + %.5f u u_x - %.7f u_{xx} = 0$ \\ \hline ' % (lambda_1, lambda_2) # s3 = r'Identified PDE (1\% noise) & ' # s4 = r'$u_t + %.5f u u_x - %.7f u_{xx} = 0$ \\ \hline ' % (lambda_1_value_noisy, lambda_2_value_noisy) s3 = '\end{tabular}$' s = s1+s2+s3 ax.text(-0.3,0.5,f'Correct PDE: $u_t + u u_x - 0.0031831 u_{{xx}} = 0$ \n\t\t\t$\lambda_1$: {lambda_1:.5f}, $\lambda_2$: {lambda_2:.5f}') plt.show()

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652a270a18b7168a39e6adf7657a9a7c192f2373

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Python

stubs/3.2/math.py

TimSimpsonR/mypy

5e6fd6335e0662b0477e1d678269f33e6f4194ba

[ "PSF-2.0" ]

1

2019-06-16T07:05:32.000Z

stubs/3.2/math.py

TimSimpsonR/mypy

5e6fd6335e0662b0477e1d678269f33e6f4194ba

[ "PSF-2.0" ]

null

stubs/3.2/math.py

TimSimpsonR/mypy

5e6fd6335e0662b0477e1d678269f33e6f4194ba

[ "PSF-2.0" ]

null

# Stubs for math # Ron Murawski <ron@horizonchess.com> # based on: http://docs.python.org/3.2/library/math.html from typing import overload, Tuple, Iterable # ----- variables and constants ----- e = 0.0 pi = 0.0 # ----- functions ----- def ceil(x: float) -> int: pass def copysign(x: float, y: float) -> float: pass def fabs(x: float) -> float: pass def factorial(x: int) -> int: pass def floor(x: float) -> int: pass def fmod(x: float, y: float) -> float: pass def frexp(x: float) -> Tuple[float, int]: pass def fsum(iterable: Iterable) -> float: pass def isfinite(x: float) -> bool: pass def isinf(x: float) -> bool: pass def isnan(x: float) -> bool: pass def ldexp(x: float, i: int) -> float: pass def modf(x: float) -> Tuple[float, float]: pass def trunc(x: float) -> float: pass def exp(x: float) -> float: pass def expm1(x: float) -> float: pass def log(x: float, base: float = e) -> float: pass def log1p(x: float) -> float: pass def log10(x: float) -> float: pass def pow(x: float, y: float) -> float: pass def sqrt(x: float) -> float: pass def acos(x: float) -> float: pass def asin(x: float) -> float: pass def atan(x: float) -> float: pass def atan2(y: float, x: float) -> float: pass def cos(x: float) -> float: pass def hypot(x: float, y: float) -> float: pass def sin(x: float) -> float: pass def tan(x: float) -> float: pass def degrees(x: float) -> float: pass def radians(x: float) -> float: pass def acosh(x: float) -> float: pass def asinh(x: float) -> float: pass def atanh(x: float) -> float: pass def cosh(x: float) -> float: pass def sinh(x: float) -> float: pass def tanh(x: float) -> float: pass def erf(x: object) -> float: pass def erfc(x: object) -> float: pass def gamma(x: object) -> float: pass def lgamma(x: object) -> float: pass

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6

e8f5a6f57f0912d48a5cf5d5564ecf3cd6ce96fc

22,135

py

Python

fonts/vector/romanc.py

szczys/st7789_mpy

bc854ec453d7644ce1773f7ed4d41504f37d376b

[ "MIT" ]

153

2020-02-02T11:03:14.000Z

2022-03-30T05:47:07.000Z

fonts/vector/romanc.py

skylin008/st7789_mpy

f304991fc5558be653df5f0de928494b85cbc60d

[ "MIT" ]

58

2020-04-11T23:23:02.000Z

2022-03-26T20:45:23.000Z

fonts/vector/romanc.py

skylin008/st7789_mpy

f304991fc5558be653df5f0de928494b85cbc60d

[ "MIT" ]

50

2020-02-02T11:05:23.000Z

2022-03-22T15:24:42.000Z

WIDTH = 32 HEIGHT = 32 FIRST = 0x20 LAST = 0x7f _font =\ b'\x00\x4a\x5a\x0e\x4d\x57\x52\x46\x51\x48\x52\x54\x53\x48\x52'\ b'\x46\x20\x52\x52\x48\x52\x4e\x20\x52\x52\x59\x51\x5a\x52\x5b'\ b'\x53\x5a\x52\x59\x15\x49\x5b\x4e\x46\x4d\x47\x4d\x4d\x20\x52'\ b'\x4e\x47\x4d\x4d\x20\x52\x4e\x46\x4f\x47\x4d\x4d\x20\x52\x57'\ b'\x46\x56\x47\x56\x4d\x20\x52\x57\x47\x56\x4d\x20\x52\x57\x46'\ b'\x58\x47\x56\x4d\x0b\x48\x5d\x53\x42\x4c\x62\x20\x52\x59\x42'\ b'\x52\x62\x20\x52\x4c\x4f\x5a\x4f\x20\x52\x4b\x55\x59\x55\x29'\ b'\x48\x5c\x50\x42\x50\x5f\x20\x52\x54\x42\x54\x5f\x20\x52\x58'\ b'\x49\x57\x4a\x58\x4b\x59\x4a\x59\x49\x57\x47\x54\x46\x50\x46'\ b'\x4d\x47\x4b\x49\x4b\x4b\x4c\x4d\x4d\x4e\x4f\x4f\x55\x51\x57'\ b'\x52\x59\x54\x20\x52\x4b\x4b\x4d\x4d\x4f\x4e\x55\x50\x57\x51'\ b'\x58\x52\x59\x54\x59\x58\x57\x5a\x54\x5b\x50\x5b\x4d\x5a\x4b'\ b'\x58\x4b\x57\x4c\x56\x4d\x57\x4c\x58\x1f\x46\x5e\x5b\x46\x49'\ b'\x5b\x20\x52\x4e\x46\x50\x48\x50\x4a\x4f\x4c\x4d\x4d\x4b\x4d'\ 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b'\x5a\x5b\x0b\x4d\x58\x52\x46\x52\x5b\x20\x52\x53\x46\x53\x5b'\ b'\x20\x52\x4f\x46\x53\x46\x20\x52\x4f\x5b\x56\x5b\x2b\x42\x63'\ b'\x47\x4d\x47\x5b\x20\x52\x48\x4d\x48\x5b\x20\x52\x48\x50\x4a'\ b'\x4e\x4d\x4d\x4f\x4d\x52\x4e\x53\x50\x53\x5b\x20\x52\x4f\x4d'\ b'\x51\x4e\x52\x50\x52\x5b\x20\x52\x53\x50\x55\x4e\x58\x4d\x5a'\ b'\x4d\x5d\x4e\x5e\x50\x5e\x5b\x20\x52\x5a\x4d\x5c\x4e\x5d\x50'\ b'\x5d\x5b\x20\x52\x44\x4d\x48\x4d\x20\x52\x44\x5b\x4b\x5b\x20'\ b'\x52\x4f\x5b\x56\x5b\x20\x52\x5a\x5b\x61\x5b\x1b\x47\x5d\x4c'\ b'\x4d\x4c\x5b\x20\x52\x4d\x4d\x4d\x5b\x20\x52\x4d\x50\x4f\x4e'\ b'\x52\x4d\x54\x4d\x57\x4e\x58\x50\x58\x5b\x20\x52\x54\x4d\x56'\ b'\x4e\x57\x50\x57\x5b\x20\x52\x49\x4d\x4d\x4d\x20\x52\x49\x5b'\ b'\x50\x5b\x20\x52\x54\x5b\x5b\x5b\x23\x48\x5c\x51\x4d\x4e\x4e'\ b'\x4c\x50\x4b\x53\x4b\x55\x4c\x58\x4e\x5a\x51\x5b\x53\x5b\x56'\ b'\x5a\x58\x58\x59\x55\x59\x53\x58\x50\x56\x4e\x53\x4d\x51\x4d'\ b'\x20\x52\x51\x4d\x4f\x4e\x4d\x50\x4c\x53\x4c\x55\x4d\x58\x4f'\ b'\x5a\x51\x5b\x20\x52\x53\x5b\x55\x5a\x57\x58\x58\x55\x58\x53'\ b'\x57\x50\x55\x4e\x53\x4d\x23\x47\x5c\x4c\x4d\x4c\x62\x20\x52'\ b'\x4d\x4d\x4d\x62\x20\x52\x4d\x50\x4f\x4e\x51\x4d\x53\x4d\x56'\ b'\x4e\x58\x50\x59\x53\x59\x55\x58\x58\x56\x5a\x53\x5b\x51\x5b'\ b'\x4f\x5a\x4d\x58\x20\x52\x53\x4d\x55\x4e\x57\x50\x58\x53\x58'\ b'\x55\x57\x58\x55\x5a\x53\x5b\x20\x52\x49\x4d\x4d\x4d\x20\x52'\ b'\x49\x62\x50\x62\x20\x48\x5c\x57\x4d\x57\x62\x20\x52\x58\x4d'\ b'\x58\x62\x20\x52\x57\x50\x55\x4e\x53\x4d\x51\x4d\x4e\x4e\x4c'\ b'\x50\x4b\x53\x4b\x55\x4c\x58\x4e\x5a\x51\x5b\x53\x5b\x55\x5a'\ b'\x57\x58\x20\x52\x51\x4d\x4f\x4e\x4d\x50\x4c\x53\x4c\x55\x4d'\ b'\x58\x4f\x5a\x51\x5b\x20\x52\x54\x62\x5b\x62\x16\x49\x5a\x4e'\ b'\x4d\x4e\x5b\x20\x52\x4f\x4d\x4f\x5b\x20\x52\x4f\x53\x50\x50'\ b'\x52\x4e\x54\x4d\x57\x4d\x58\x4e\x58\x4f\x57\x50\x56\x4f\x57'\ b'\x4e\x20\x52\x4b\x4d\x4f\x4d\x20\x52\x4b\x5b\x52\x5b\x1f\x4a'\ b'\x5b\x57\x4f\x58\x4d\x58\x51\x57\x4f\x56\x4e\x54\x4d\x50\x4d'\ b'\x4e\x4e\x4d\x4f\x4d\x51\x4e\x52\x50\x53\x55\x55\x57\x56\x58'\ b'\x57\x20\x52\x4d\x50\x4e\x51\x50\x52\x55\x54\x57\x55\x58\x56'\ b'\x58\x59\x57\x5a\x55\x5b\x51\x5b\x4f\x5a\x4e\x59\x4d\x57\x4d'\ b'\x5b\x4e\x59\x0f\x4b\x5a\x50\x46\x50\x57\x51\x5a\x53\x5b\x55'\ b'\x5b\x57\x5a\x58\x58\x20\x52\x51\x46\x51\x57\x52\x5a\x53\x5b'\ b'\x20\x52\x4d\x4d\x55\x4d\x1b\x47\x5d\x4c\x4d\x4c\x58\x4d\x5a'\ b'\x50\x5b\x52\x5b\x55\x5a\x57\x58\x20\x52\x4d\x4d\x4d\x58\x4e'\ b'\x5a\x50\x5b\x20\x52\x57\x4d\x57\x5b\x20\x52\x58\x4d\x58\x5b'\ b'\x20\x52\x49\x4d\x4d\x4d\x20\x52\x54\x4d\x58\x4d\x20\x52\x57'\ b'\x5b\x5b\x5b\x0e\x49\x5b\x4c\x4d\x52\x5b\x20\x52\x4d\x4d\x52'\ b'\x59\x20\x52\x58\x4d\x52\x5b\x20\x52\x4a\x4d\x50\x4d\x20\x52'\ b'\x54\x4d\x5a\x4d\x17\x46\x5e\x4a\x4d\x4e\x5b\x20\x52\x4b\x4d'\ b'\x4e\x58\x20\x52\x52\x4d\x4e\x5b\x20\x52\x52\x4d\x56\x5b\x20'\ b'\x52\x53\x4d\x56\x58\x20\x52\x5a\x4d\x56\x5b\x20\x52\x47\x4d'\ b'\x4e\x4d\x20\x52\x57\x4d\x5d\x4d\x14\x48\x5c\x4c\x4d\x57\x5b'\ b'\x20\x52\x4d\x4d\x58\x5b\x20\x52\x58\x4d\x4c\x5b\x20\x52\x4a'\ b'\x4d\x50\x4d\x20\x52\x54\x4d\x5a\x4d\x20\x52\x4a\x5b\x50\x5b'\ b'\x20\x52\x54\x5b\x5a\x5b\x15\x48\x5b\x4c\x4d\x52\x5b\x20\x52'\ b'\x4d\x4d\x52\x59\x20\x52\x58\x4d\x52\x5b\x50\x5f\x4e\x61\x4c'\ b'\x62\x4b\x62\x4a\x61\x4b\x60\x4c\x61\x20\x52\x4a\x4d\x50\x4d'\ b'\x20\x52\x54\x4d\x5a\x4d\x0f\x49\x5b\x57\x4d\x4c\x5b\x20\x52'\ b'\x58\x4d\x4d\x5b\x20\x52\x4d\x4d\x4c\x51\x4c\x4d\x58\x4d\x20'\ b'\x52\x4c\x5b\x58\x5b\x58\x57\x57\x5b\x27\x4b\x59\x54\x42\x52'\ b'\x43\x51\x44\x50\x46\x50\x48\x51\x4a\x52\x4b\x53\x4d\x53\x4f'\ b'\x51\x51\x20\x52\x52\x43\x51\x45\x51\x47\x52\x49\x53\x4a\x54'\ b'\x4c\x54\x4e\x53\x50\x4f\x52\x53\x54\x54\x56\x54\x58\x53\x5a'\ b'\x52\x5b\x51\x5d\x51\x5f\x52\x61\x20\x52\x51\x53\x53\x55\x53'\ b'\x57\x52\x59\x51\x5a\x50\x5c\x50\x5e\x51\x60\x52\x61\x54\x62'\ b'\x02\x4e\x56\x52\x42\x52\x62\x27\x4b\x59\x50\x42\x52\x43\x53'\ b'\x44\x54\x46\x54\x48\x53\x4a\x52\x4b\x51\x4d\x51\x4f\x53\x51'\ b'\x20\x52\x52\x43\x53\x45\x53\x47\x52\x49\x51\x4a\x50\x4c\x50'\ b'\x4e\x51\x50\x55\x52\x51\x54\x50\x56\x50\x58\x51\x5a\x52\x5b'\ b'\x53\x5d\x53\x5f\x52\x61\x20\x52\x53\x53\x51\x55\x51\x57\x52'\ b'\x59\x53\x5a\x54\x5c\x54\x5e\x53\x60\x52\x61\x50\x62\x17\x46'\ b'\x5e\x49\x55\x49\x53\x4a\x50\x4c\x4f\x4e\x4f\x50\x50\x54\x53'\ b'\x56\x54\x58\x54\x5a\x53\x5b\x51\x20\x52\x49\x53\x4a\x51\x4c'\ b'\x50\x4e\x50\x50\x51\x54\x54\x56\x55\x58\x55\x5a\x54\x5b\x51'\ b'\x5b\x4f\x22\x4a\x5a\x4a\x46\x4a\x5b\x4b\x5b\x4b\x46\x4c\x46'\ b'\x4c\x5b\x4d\x5b\x4d\x46\x4e\x46\x4e\x5b\x4f\x5b\x4f\x46\x50'\ b'\x46\x50\x5b\x51\x5b\x51\x46\x52\x46\x52\x5b\x53\x5b\x53\x46'\ b'\x54\x46\x54\x5b\x55\x5b\x55\x46\x56\x46\x56\x5b\x57\x5b\x57'\ b'\x46\x58\x46\x58\x5b\x59\x5b\x59\x46\x5a\x46\x5a\x5b' _index =\ b'\x00\x00\x03\x00\x22\x00\x4f\x00\x68\x00\xbd\x00\xfe\x00\x61'\ b'\x01\x6e\x01\x97\x01\xc0\x01\xd3\x01\xe0\x01\xf1\x01\xf8\x01'\ b'\x05\x02\x0c\x02\x5d\x02\x74\x02\xcf\x02\x2e\x03\x49\x03\x98'\ b'\x03\xf9\x03\x38\x04\xb7\x04\x18\x05\x31\x05\x4e\x05\x57\x05'\ b'\x64\x05\x6d\x05\xae\x05\x1f\x06\x44\x06\x9f\x06\xe0\x06\x1d'\ b'\x07\x4a\x07\x73\x07\xc4\x07\xfb\x07\x14\x08\x3d\x08\x74\x08'\ b'\x91\x08\xce\x08\xf9\x08\x52\x09\x8d\x09\x0e\x0a\x69\x0a\xae'\ b'\x0a\xcf\x0a\xfe\x0a\x1d\x0b\x4e\x0b\x79\x0b\xa2\x0b\xc3\x0b'\ b'\xdc\x0b\xe3\x0b\xfc\x0b\x0d\x0c\x14\x0c\x23\x0c\x72\x0c\xb5'\ b'\x0c\xee\x0c\x37\x0d\x76\x0d\xa3\x0d\x1c\x0e\x55\x0e\x7a\x0e'\ b'\xad\x0e\xe4\x0e\xfd\x0e\x56\x0f\x8f\x0f\xd8\x0f\x21\x10\x64'\ b'\x10\x93\x10\xd4\x10\xf5\x10\x2e\x11\x4d\x11\x7e\x11\xa9\x11'\ b'\xd6\x11\xf7\x11\x48\x12\x4f\x12\xa0\x12\xd1\x12' INDEX = memoryview(_index) FONT = memoryview(_font)

63.062678

64

0.707567

5,435

22,135

2.880957

0.027047

0.107677

0.0684

0.012262

0.461042

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0.325393

0.276408

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350

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63.242857

0.339075

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0.976945

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false

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0

1

0

1

0

1

null

1

0

6

3301c7fa1e602409a8c75b49cd17175439693d61

1,220

py

Python

api/app/customer/schemas.py

Sguerreroo/electricity-market-analysis

32576f65a056ec1c94f098273dabdd1c2090e9f6

[ "MIT" ]

null

api/app/customer/schemas.py

Sguerreroo/electricity-market-analysis

32576f65a056ec1c94f098273dabdd1c2090e9f6

[ "MIT" ]

null

api/app/customer/schemas.py

Sguerreroo/electricity-market-analysis

32576f65a056ec1c94f098273dabdd1c2090e9f6

[ "MIT" ]

null

from marshmallow import Schema, fields, validate class CustomerSchema(Schema): name = fields.Str( required=True, error_messages={"required": "Introduce tu nombre"}, validate=[ validate.Length( max=255, error="Campo demasiado largo" ) ] ) surname = fields.Str( required=True, error_messages={"required": "Introduce tus apellidos"}, validate=[ validate.Length( max=255, error="Campo demasiado largo" ) ] ) nif = fields.Str( required=True, error_messages={"required": "Introduce tu nif"}, validate=[ validate.Regexp( r"^\d{8}[a-zA-Z]$", error="Introduce un nif válido" ) ] ) email = fields.Email(error_messages={"invalid": "Introduce un email válido"}) class ProfileCustomerSchema(Schema): name = fields.Str( required=True, error_messages={"required": "Introduce tu nombre"}, validate=[ validate.Length( max=255, error="Campo demasiado largo" ) ] ) surname = fields.Str( required=True, error_messages={"required": "Introduce tus apellidos"}, validate=[ validate.Length( max=255, error="Campo demasiado largo" ) ] ) email = fields.Email(error_messages={"invalid": "Introduce un email válido"})

20.677966

78

0.662295

137

1,220

5.846715

0.284672

0.113608

0.106117

0.131086

0.817728

0.751561

0

0.013265

0.196721

1,220

59

79

20.677966

0.804082

0

0.581818

0

0.266994

0

1

0

false

0

0.018182

0

0.181818

0

null

0

1

0

null

0

6

330c84322598b728ad154dbe8a530d87c6116950

1,218

py

Python

qiling/qiling/cc/arm.py

mrTavas/owasp-fstm-auto

6e9ff36e46d885701c7419db3eca15f12063a7f3

[ "CC0-1.0" ]

2

2021-05-05T12:03:01.000Z

2021-06-04T14:27:15.000Z

qiling/qiling/cc/arm.py

mrTavas/owasp-fstm-auto

6e9ff36e46d885701c7419db3eca15f12063a7f3

[ "CC0-1.0" ]

null

qiling/qiling/cc/arm.py

mrTavas/owasp-fstm-auto

6e9ff36e46d885701c7419db3eca15f12063a7f3

[ "CC0-1.0" ]

2

2021-05-05T12:03:09.000Z

2021-06-04T14:27:21.000Z

#!/usr/bin/env python3 # # Cross Platform and Multi Architecture Advanced Binary Emulation Framework from unicorn.arm_const import UC_ARM_REG_R0, UC_ARM_REG_R1, UC_ARM_REG_R2, UC_ARM_REG_R3 from unicorn.arm64_const import ( UC_ARM64_REG_X0, UC_ARM64_REG_X1, UC_ARM64_REG_X2, UC_ARM64_REG_X3, UC_ARM64_REG_X4, UC_ARM64_REG_X5, UC_ARM64_REG_X6, UC_ARM64_REG_X7 ) from qiling import Qiling from . import QlCommonBaseCC class aarch64(QlCommonBaseCC): _argregs = (UC_ARM64_REG_X0, UC_ARM64_REG_X1, UC_ARM64_REG_X2, UC_ARM64_REG_X3, UC_ARM64_REG_X4, UC_ARM64_REG_X5, UC_ARM64_REG_X6, UC_ARM64_REG_X7) + (None, ) * 8 def __init__(self, ql: Qiling) -> None: super().__init__(ql, UC_ARM64_REG_X0) @staticmethod def getNumSlots(argbits: int) -> int: return 1 def unwind(self, nslots: int) -> int: # TODO: cleanup? return self.ql.arch.stack_pop() class aarch32(QlCommonBaseCC): _argregs = (UC_ARM_REG_R0, UC_ARM_REG_R1, UC_ARM_REG_R2, UC_ARM_REG_R3) + (None, ) * 12 def __init__(self, ql: Qiling) -> None: super().__init__(ql, UC_ARM_REG_R0) @staticmethod def getNumSlots(argbits: int) -> int: return 1 def unwind(self, nslots: int) -> int: # TODO: cleanup? return self.ql.arch.stack_pop()

29.707317

163

0.763547

208

1,218

3.990385

0.288462

0.143373

0.204819

0.036145

0.66988

0

0.068311

0.134647

1,218

40

164

30.45

0.719165

0.103448

0

0.48

0

0.025

0

1

0.24

false

0

0.16

0.72

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

683b48a041071ed728c9b592e467586fca8e65f8

5,524

py

Python

gslab_misc/gencat/tests/test_zipFile.py

AakaashRao/gslab_python

864f708ec80f4381235506489b8c117e54e16450

[ "MIT" ]

12

2017-03-03T20:48:50.000Z

2020-11-27T23:37:15.000Z

gslab_misc/gencat/tests/test_zipFile.py

AakaashRao/gslab_python

864f708ec80f4381235506489b8c117e54e16450

[ "MIT" ]

132

2017-01-11T23:32:01.000Z

2022-03-31T17:00:06.000Z

gslab_misc/gencat/tests/test_zipFile.py

AakaashRao/gslab_python

864f708ec80f4381235506489b8c117e54e16450

[ "MIT" ]

10

2017-07-22T02:35:29.000Z

2021-02-16T00:09:44.000Z

import unittest import os import shutil import zipfile import sys # Ensure the script is run from its own directory os.chdir(os.path.dirname(os.path.realpath(__file__))) sys.path.append('../../') from gencat import gencat class MockCat(gencat): def makeZipDict(self): pass def makeConcatDict(self): pass class test_zipFiles(unittest.TestCase): def setUp(self): paths = ['./test_data', './test_temp', './test_out'] for path in paths: try: os.makedirs(path) except: shutil.rmtree(path, ignore_errors = True) os.makedirs(path) for FILE in ['./test_data/file1.txt', './test_data/file2.txt']: with open(FILE, 'wb') as f: f.write('''THIS IS A TEST FILE.\n''') def test_oneFile(self): ''' Test that contentation functions for a single file. ''' testcat = MockCat('./test_data', './test_temp', './test_out') testcat.zip_dict = {} testcat.zip_dict['zip1'] = ('concat1', ) testcat.concat_dict = {} testcat.concat_dict['concat1'] = ('./test_data/file1.txt', ) testcat.zipFiles() self.assertTrue(os.path.isfile('./test_out/zip1.zip')) self.assertTrue(zipfile.is_zipfile('./test_out/zip1.zip')) with zipfile.ZipFile('./test_out/zip1.zip', 'r') as zf: zf.extractall('./test_out/') with open('./test_out/zip1/concat1.txt', 'rU') as f: text = f.read() self.assertEqual(text, '\nNEWFILE\nFILENAME: file1.txt\n\nTHIS IS A TEST FILE.\n') def test_twoFile(self): ''' Test that two text files are concatenated into one without loss of content. ''' testcat = MockCat('./test_data', './test_temp', './test_out') testcat.zip_dict = {} testcat.zip_dict['zip1'] = ('concat1', ) testcat.concat_dict = {} testcat.concat_dict['concat1'] = ('./test_data/file1.txt', ) + ('./test_data/file2.txt', ) testcat.zipFiles() self.assertTrue(os.path.isfile('./test_out/zip1.zip')) self.assertTrue(zipfile.is_zipfile('./test_out/zip1.zip')) with zipfile.ZipFile('./test_out/zip1.zip', 'r') as zf: zf.extractall('./test_out/') with open('./test_out/zip1/concat1.txt', 'rU') as f: text = f.read() test_text = '\nNEWFILE\nFILENAME: file1.txt\n\nTHIS IS A TEST FILE.' + \ '\n\nNEWFILE\nFILENAME: file2.txt\n\nTHIS IS A TEST FILE.\n' self.assertEqual(text, test_text) def test_twoZips(self): ''' Test that two files can be concatenated to different text files and stored in separate zip files. ''' testcat = MockCat('./test_data', './test_temp', './test_out') testcat.zip_dict = {} testcat.zip_dict['zip1'] = ('concat1', ) testcat.zip_dict['zip2'] = ('concat2', ) testcat.concat_dict = {} testcat.concat_dict['concat1'] = ('./test_data/file1.txt', ) testcat.concat_dict['concat2'] = ('./test_data/file2.txt', ) testcat.zipFiles() self.assertTrue(os.path.isfile('./test_out/zip1.zip')) self.assertTrue(os.path.isfile('./test_out/zip2.zip')) self.assertTrue(zipfile.is_zipfile('./test_out/zip1.zip')) self.assertTrue(zipfile.is_zipfile('./test_out/zip2.zip')) with zipfile.ZipFile('./test_out/zip1.zip', 'r') as zf: zf.extractall('./test_out/') with zipfile.ZipFile('./test_out/zip2.zip', 'r') as zf: zf.extractall('./test_out/') with open('./test_out/zip1/concat1.txt', 'rU') as f: text1 = f.read() with open('./test_out/zip2/concat2.txt', 'rU') as f: text2 = f.read() self.assertEqual(text1, '\nNEWFILE\nFILENAME: file1.txt\n\nTHIS IS A TEST FILE.\n') self.assertEqual(text2, '\nNEWFILE\nFILENAME: file2.txt\n\nTHIS IS A TEST FILE.\n') def test_twoConcatsOneZip(self): ''' Test that two files can be concatenated to different text files and stored in the same zip file. ''' testcat = MockCat('./test_data', './test_temp', './test_out') testcat.zip_dict = {} testcat.zip_dict['zip1'] = ('concat1', ) + ('concat2', ) testcat.concat_dict = {} testcat.concat_dict['concat1'] = ('./test_data/file1.txt', ) testcat.concat_dict['concat2'] = ('./test_data/file2.txt', ) testcat.zipFiles() self.assertTrue(os.path.isfile('./test_out/zip1.zip')) self.assertTrue(zipfile.is_zipfile('./test_out/zip1.zip')) with zipfile.ZipFile('./test_out/zip1.zip', 'r') as zf: zf.extractall('./test_out/') with open('./test_out/zip1/concat1.txt', 'rU') as f: text1 = f.read() with open('./test_out/zip1/concat2.txt', 'rU') as f: text2 = f.read() self.assertEqual(text1, '\nNEWFILE\nFILENAME: file1.txt\n\nTHIS IS A TEST FILE.\n') self.assertEqual(text2, '\nNEWFILE\nFILENAME: file2.txt\n\nTHIS IS A TEST FILE.\n') def tearDown(self): paths = ['./test_data', './test_temp', './test_out'] for path in paths: shutil.rmtree(path, ignore_errors = True) if __name__ == '__main__': unittest.main()

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6

6841ded7f748c97b1c2bed89db300573835d7f7c

2,272

py

Python

gns3.py

packetscaper/CCIE-RS-Lab

40710a1d66a9f617488ffb3deedfa530989c2f98

[ "MIT" ]

31

2019-07-31T14:42:02.000Z

2022-03-29T16:38:37.000Z

gns3.py

packetscaper/CCIE-RS-Lab

40710a1d66a9f617488ffb3deedfa530989c2f98

[ "MIT" ]

1

2021-03-26T16:29:04.000Z

2021-07-18T14:15:09.000Z

gns3.py

packetscaper/CCIE-RS-Lab

40710a1d66a9f617488ffb3deedfa530989c2f98

[ "MIT" ]

10

2019-07-28T08:02:07.000Z

2022-02-25T10:05:19.000Z

import yaml from LabConnection import * import threading import requests,json,time,yaml class Gns3: def __init__(self): with open('yamlfiles/console.yaml') as f: o = yaml.safe_load(f) self.gns3_host = "http://"+o["gns3_host_ip"]+":3080/" def start(self,device): url = self.gns3_host+"v2/projects/" headers = {"Accept":"application/json","Content-Type":"application/json"} with open('topology.gns3') as f: json_output = json.loads(f.read()) project_id = json_output["project_id"] url = url + project_id for node in json_output['topology']['nodes']: if node['name'] == device: node_id = node['node_id'] print "starting ",device response = requests.request("POST",url+"/nodes/"+node_id+"/start") def stop(self,device): url = self.gns3_host+ "v2/projects/" headers = {"Accept":"application/json","Content-Type":"application/json"} with open('topology.gns3') as f: json_output = json.loads(f.read()) project_id = json_output["project_id"] url = url + project_id for node in json_output['topology']['nodes']: if node['name'] == device: node_id = node['node_id'] print "stopping ", device response = requests.request("POST",url+"/nodes/"+node_id+"/stop") def stop_all(self): threads = [] with open('topology.gns3') as f: json_output = json.loads(f.read()) project_id = json_output["project_id"] for node in json_output['topology']['nodes']: threads.append(threading.Thread(target=self.stop,args=(node['name'],))) for t in threads: t.start() for t in threads: t.join() def start_all(self): threads = [] with open('topology.gns3') as f: json_output = json.loads(f.read()) project_id = json_output["project_id"] for node in json_output['topology']['nodes']: threads.append(threading.Thread(target=self.start,args=(node['name'],))) for t in threads: t.start() for t in threads: t.join() def reset_lab(self): print "reseting lab" self.stop_all() self.start_all()

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null

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null

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1

0

6

686a0cb1da548004ea57c95432efe5601832a90e

27

py

Python

meiduo_mall/meiduo_mall/settings/__init__.py

linbo-boy/Meiduo-Shopping-Mall

f5a46e742f27d33a25dffc47c3fb34914c7c59b1

[ "MIT" ]

null

meiduo_mall/meiduo_mall/settings/__init__.py

linbo-boy/Meiduo-Shopping-Mall

f5a46e742f27d33a25dffc47c3fb34914c7c59b1

[ "MIT" ]

null

meiduo_mall/meiduo_mall/settings/__init__.py

linbo-boy/Meiduo-Shopping-Mall

f5a46e742f27d33a25dffc47c3fb34914c7c59b1

[ "MIT" ]

null

# 存放配置文件的目录，分为开发dev和线上prod

13.5

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0.851852

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1

0

1

0

null

0

1

0

6

d7ab6ab61d4663a28865678ca623c22ecd6b84f9

584

py

Python

tests/test.py

davidbistolas/py-simple-audio

ad8afd2086c8570ecfb29e6b404fc4a5849c1255

[ "MIT" ]

121

2015-12-01T06:18:31.000Z

2022-03-14T11:46:10.000Z

tests/test.py

davidbistolas/py-simple-audio

ad8afd2086c8570ecfb29e6b404fc4a5849c1255

[ "MIT" ]

51

2015-09-08T18:47:44.000Z

2022-01-07T14:34:44.000Z

tests/test.py

davidbistolas/py-simple-audio

ad8afd2086c8570ecfb29e6b404fc4a5849c1255

[ "MIT" ]

28

2016-03-09T17:10:58.000Z

2022-03-08T23:24:35.000Z

import simpleaudio as sa import unittest class TestSimpleaudio(unittest.TestCase): def test_num_channels(self): self.assertRaises(ValueError, sa.play_buffer, b'\0' * 16, 0, 2, 44100) self.assertRaises(ValueError, sa.play_buffer, b'\0' * 16, 3, 2, 44100) def test_bytes_per_chan(self): self.assertRaises(ValueError, sa.play_buffer, b'\0' * 16, 2, 0, 44100) self.assertRaises(ValueError, sa.play_buffer, b'\0' * 16, 2, 5, 44100) def test_sample_rate(self): self.assertRaises(ValueError, sa.play_buffer, b'\0' * 16, 2, 2, 44101)

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0.191781

584

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false

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null

0

1

0

1

0

6

0bd20c4cfdbf17e0475d21a04f34f83082a3a7fe

31

py

Python

tests/__init__.py

gitter-badger/vcspull

9584c6d40fca8e9f36970894ce620a891723d9b5

[ "MIT" ]

169

2015-01-13T14:57:28.000Z

2018-02-17T13:40:58.000Z

tests/__init__.py

gitter-badger/vcspull

9584c6d40fca8e9f36970894ce620a891723d9b5

[ "MIT" ]

198

2018-03-11T19:11:14.000Z

2022-03-26T23:01:08.000Z

tests/__init__.py

gitter-badger/vcspull

9584c6d40fca8e9f36970894ce620a891723d9b5

[ "MIT" ]

9

2015-01-05T13:37:19.000Z

2016-11-25T05:40:01.000Z

from . import fixtures # noqa

15.5

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6

040cb43b42a679bae7e1cea30ce8644b92a7c6b6

50,243

py

Python

txdav/common/datastore/podding/test/test_conduit.py

backwardn/ccs-calendarserver

13c706b985fb728b9aab42dc0fef85aae21921c3

[ "Apache-2.0" ]

462

2016-08-14T17:43:24.000Z

2022-03-17T07:38:16.000Z

txdav/common/datastore/podding/test/test_conduit.py

backwardn/ccs-calendarserver

13c706b985fb728b9aab42dc0fef85aae21921c3

[ "Apache-2.0" ]

72

2016-09-01T23:19:35.000Z

2020-02-05T02:09:26.000Z

txdav/common/datastore/podding/test/test_conduit.py

backwardn/ccs-calendarserver

13c706b985fb728b9aab42dc0fef85aae21921c3

[ "Apache-2.0" ]

171

2016-08-16T03:50:30.000Z

2022-03-26T11:49:55.000Z

## # Copyright (c) 2005-2017 Apple Inc. 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. ## from pycalendar.datetime import DateTime from pycalendar.period import Period from twext.python.clsprop import classproperty import txweb2.dav.test.util from txweb2.http_headers import MimeType from txweb2.stream import MemoryStream from twisted.internet.defer import inlineCallbacks, succeed, returnValue from twistedcaldav import caldavxml from twistedcaldav.ical import Component, normalize_iCalStr from txdav.caldav.datastore.query.filter import Filter from txdav.caldav.datastore.scheduling.cuaddress import calendarUserFromCalendarUserAddress from txdav.caldav.datastore.scheduling.freebusy import FreebusyQuery from txdav.caldav.datastore.scheduling.ischedule.localservers import ServersDB, Server from txdav.caldav.datastore.sql import ManagedAttachment, AttachmentLink from txdav.caldav.datastore.test.common import CaptureProtocol from txdav.common.datastore.podding.conduit import PoddingConduit, \ FailedCrossPodRequestError from txdav.common.datastore.podding.resource import ConduitResource from txdav.common.datastore.podding.test.util import MultiStoreConduitTest, \ FakeConduitRequest from txdav.common.datastore.sql_tables import _BIND_STATUS_ACCEPTED from txdav.common.datastore.test.util import populateCalendarsFrom, CommonCommonTests from txdav.common.icommondatastore import ObjectResourceNameAlreadyExistsError, \ ObjectResourceNameNotAllowedError from txdav.common.idirectoryservice import DirectoryRecordNotFoundError class TestConduit (CommonCommonTests, txweb2.dav.test.util.TestCase): class FakeConduit(object): def recv_fake(self, j): return succeed({ "back2u": j["echo"], "more": "bits", }) @inlineCallbacks def setUp(self): yield super(TestConduit, self).setUp() serversDB = ServersDB() serversDB.addServer(Server("A", "http://127.0.0.1", "A", True)) serversDB.addServer(Server("B", "http://127.0.0.2", "B", False)) yield self.buildStoreAndDirectory(serversDB=serversDB) self.site.resource.putChild("conduit", ConduitResource(self.site.resource, self.storeUnderTest())) yield self.populate() @inlineCallbacks def populate(self): yield populateCalendarsFrom(self.requirements, self.storeUnderTest()) self.notifierFactory.reset() @classproperty(cache=False) def requirements(cls): # @NoSelf return { "user01": { "calendar_1": { }, "inbox": { }, }, "user02": { "calendar_1": { }, "inbox": { }, }, "user03": { "calendar_1": { }, "inbox": { }, }, } @inlineCallbacks def test_validRequest(self): """ Cross-pod request fails when there is no shared secret header present. """ conduit = PoddingConduit(self.storeUnderTest()) r1, r2 = yield conduit.validRequest("user01", "puser02") self.assertTrue(r1 is not None) self.assertTrue(r2 is not None) yield self.assertFailure( conduit.validRequest("bogus01", "user02"), DirectoryRecordNotFoundError ) yield self.assertFailure( conduit.validRequest("user01", "bogus02"), DirectoryRecordNotFoundError ) yield self.assertFailure( conduit.validRequest("user01", "user02"), FailedCrossPodRequestError ) class TestConduitToConduit(MultiStoreConduitTest): class FakeConduit(PoddingConduit): @inlineCallbacks def send_fake(self, txn, ownerUID, shareeUID): _ignore_owner, sharee = yield self.validRequest(ownerUID, shareeUID) action = { "action": "fake", "echo": "bravo" } result = yield self.sendRequest(txn, sharee, action) returnValue(result) def recv_fake(self, txn, j): return succeed({ "back2u": j["echo"], "more": "bits", }) def makeConduit(self, store): """ Use our own variant. """ conduit = self.FakeConduit(store) conduit.conduitRequestClass = FakeConduitRequest return conduit @inlineCallbacks def test_fake_action(self): """ Cross-pod request works when conduit does support the action. """ store = self.theStoreUnderTest(0) response = yield store.conduit.send_fake(self.theTransactionUnderTest(0), "user01", "puser01") self.assertEqual(response, {"back2u": "bravo", "more": "bits"}) yield self.commitTransaction(0) store = self.theStoreUnderTest(1) response = yield store.conduit.send_fake(self.theTransactionUnderTest(1), "puser01", "user01") self.assertEqual(response, {"back2u": "bravo", "more": "bits"}) yield self.commitTransaction(1) class TestConduitAPI(MultiStoreConduitTest): """ Test that the conduit api works. """ nowYear = {"now": DateTime.getToday().getYear()} caldata1 = """BEGIN:VCALENDAR VERSION:2.0 CALSCALE:GREGORIAN PRODID:-//CALENDARSERVER.ORG//NONSGML Version 1//EN BEGIN:VEVENT UID:uid1 DTSTART:{now:04d}0102T140000Z DURATION:PT1H CREATED:20060102T190000Z DTSTAMP:20051222T210507Z RRULE:FREQ=WEEKLY SUMMARY:instance END:VEVENT END:VCALENDAR """.replace("\n", "\r\n").format(**nowYear) caldata1_changed = """BEGIN:VCALENDAR VERSION:2.0 CALSCALE:GREGORIAN PRODID:-//CALENDARSERVER.ORG//NONSGML Version 1//EN BEGIN:VEVENT UID:uid1 DTSTART:{now:04d}0102T150000Z DURATION:PT1H CREATED:20060102T190000Z DTSTAMP:20051222T210507Z RRULE:FREQ=WEEKLY SUMMARY:instance changed END:VEVENT END:VCALENDAR """.replace("\n", "\r\n").format(**nowYear) caldata2 = """BEGIN:VCALENDAR VERSION:2.0 CALSCALE:GREGORIAN PRODID:-//CALENDARSERVER.ORG//NONSGML Version 1//EN BEGIN:VEVENT UID:uid2 DTSTART:{now:04d}0102T160000Z DURATION:PT1H CREATED:20060102T190000Z DTSTAMP:20051222T210507Z RRULE:FREQ=WEEKLY SUMMARY:instance END:VEVENT END:VCALENDAR """.replace("\n", "\r\n").format(**nowYear) caldata3 = """BEGIN:VCALENDAR VERSION:2.0 CALSCALE:GREGORIAN PRODID:-//CALENDARSERVER.ORG//NONSGML Version 1//EN BEGIN:VEVENT UID:uid3 DTSTART:{now:04d}0102T160000Z DURATION:PT1H CREATED:20060102T190000Z DTSTAMP:20051222T210507Z RRULE:FREQ=WEEKLY SUMMARY:instance END:VEVENT END:VCALENDAR """.replace("\n", "\r\n").format(**nowYear) @inlineCallbacks def test_basic_share(self): """ Test that basic invite/uninvite works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") shared = yield calendar1.shareeView("puser01") self.assertEqual(shared.shareStatus(), _BIND_STATUS_ACCEPTED) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") self.assertTrue(shared is not None) self.assertTrue(shared.external()) yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.uninviteUIDFromShare("puser01") yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") self.assertTrue(shared is None) yield self.commitTransaction(1) @inlineCallbacks def test_countobjects(self): """ Test that action=countobjects works. """ yield self.createShare("user01", "puser01") shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") count = yield shared.countObjectResources() self.assertEqual(count, 0) yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) count = yield calendar1.countObjectResources() self.assertEqual(count, 1) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") count = yield shared.countObjectResources() self.assertEqual(count, 1) yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.remove() count = yield calendar1.countObjectResources() self.assertEqual(count, 0) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") count = yield shared.countObjectResources() self.assertEqual(count, 0) yield self.commitTransaction(1) @inlineCallbacks def test_listobjects(self): """ Test that action=listobjects works. """ yield self.createShare("user01", "puser01") shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") objects = yield shared.listObjectResources() self.assertEqual(set(objects), set()) yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield calendar1.createCalendarObjectWithName("2.ics", Component.fromString(self.caldata2)) objects = yield calendar1.listObjectResources() self.assertEqual(set(objects), set(("1.ics", "2.ics",))) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") objects = yield shared.listObjectResources() self.assertEqual(set(objects), set(("1.ics", "2.ics",))) yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.remove() objects = yield calendar1.listObjectResources() self.assertEqual(set(objects), set(("2.ics",))) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") objects = yield shared.listObjectResources() self.assertEqual(set(objects), set(("2.ics",))) yield self.commitTransaction(1) @inlineCallbacks def test_synctoken(self): """ Test that action=synctoken works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") token1_1 = yield calendar1.syncTokenRevision() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") token2_1 = yield shared.syncTokenRevision() yield self.commitTransaction(1) self.assertEqual(token1_1, token2_1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") token1_2 = yield calendar1.syncTokenRevision() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") token2_2 = yield shared.syncTokenRevision() yield self.commitTransaction(1) self.assertNotEqual(token1_1, token1_2) self.assertEqual(token1_2, token2_2) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.remove() count = yield calendar1.countObjectResources() self.assertEqual(count, 0) yield self.commitTransaction(0) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") token1_3 = yield calendar1.syncTokenRevision() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") token2_3 = yield shared.syncTokenRevision() yield self.commitTransaction(1) self.assertNotEqual(token1_1, token1_3) self.assertNotEqual(token1_2, token1_3) self.assertEqual(token1_3, token2_3) @inlineCallbacks def test_resourcenamessincerevision(self): """ Test that action=synctoken works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") token1_1 = yield calendar1.syncToken() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") token2_1 = yield shared.syncToken() yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") token1_2 = yield calendar1.syncToken() names1 = yield calendar1.resourceNamesSinceToken(token1_1) self.assertEqual(names1, ([u"1.ics"], [], [],)) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") token2_2 = yield shared.syncToken() names2 = yield shared.resourceNamesSinceToken(token2_1) self.assertEqual(names2, ([u"1.ics"], [], [],)) yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.remove() count = yield calendar1.countObjectResources() self.assertEqual(count, 0) yield self.commitTransaction(0) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") token1_3 = yield calendar1.syncToken() names1 = yield calendar1.resourceNamesSinceToken(token1_2) self.assertEqual(names1, ([], [u"1.ics"], [],)) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") token2_3 = yield shared.syncToken() names2 = yield shared.resourceNamesSinceToken(token2_2) self.assertEqual(names2, ([], [u"1.ics"], [],)) yield self.commitTransaction(1) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") names1 = yield calendar1.resourceNamesSinceToken(token1_3) self.assertEqual(names1, ([], [], [],)) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") names2 = yield shared.resourceNamesSinceToken(token2_3) self.assertEqual(names2, ([], [], [],)) yield self.commitTransaction(1) @inlineCallbacks def test_resourceuidforname(self): """ Test that action=resourceuidforname works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") uid = yield calendar1.resourceUIDForName("1.ics") self.assertEqual(uid, "uid1") uid = yield calendar1.resourceUIDForName("2.ics") self.assertTrue(uid is None) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") uid = yield shared.resourceUIDForName("1.ics") self.assertEqual(uid, "uid1") uid = yield shared.resourceUIDForName("2.ics") self.assertTrue(uid is None) yield self.commitTransaction(1) @inlineCallbacks def test_resourcenameforuid(self): """ Test that action=resourcenameforuid works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") name = yield calendar1.resourceNameForUID("uid1") self.assertEqual(name, "1.ics") name = yield calendar1.resourceNameForUID("uid2") self.assertTrue(name is None) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") name = yield shared.resourceNameForUID("uid1") self.assertEqual(name, "1.ics") name = yield shared.resourceNameForUID("uid2") self.assertTrue(name is None) yield self.commitTransaction(1) @inlineCallbacks def test_search(self): """ Test that action=resourcenameforuid works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) filter = caldavxml.Filter( caldavxml.ComponentFilter( *[caldavxml.ComponentFilter( **{"name": ("VEVENT", "VFREEBUSY", "VAVAILABILITY")} )], **{"name": "VCALENDAR"} ) ) filter = Filter(filter) calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") names = [item[0] for item in (yield calendar1.search(filter))] self.assertEqual(names, ["1.ics", ]) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") names = [item[0] for item in (yield shared.search(filter))] self.assertEqual(names, ["1.ics", ]) yield self.commitTransaction(1) @inlineCallbacks def test_loadallobjects(self): """ Test that action=loadallobjects works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") resource1 = yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) resource_id1 = resource1.id() resource2 = yield calendar1.createCalendarObjectWithName("2.ics", Component.fromString(self.caldata2)) resource_id2 = resource2.id() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resources = yield shared.objectResources() byname = dict([(obj.name(), obj) for obj in resources]) byuid = dict([(obj.uid(), obj) for obj in resources]) self.assertEqual(len(resources), 2) self.assertEqual(set([obj.name() for obj in resources]), set(("1.ics", "2.ics",))) self.assertEqual(set([obj.uid() for obj in resources]), set(("uid1", "uid2",))) self.assertEqual(set([obj.id() for obj in resources]), set((resource_id1, resource_id2,))) resource = yield shared.objectResourceWithName("1.ics") self.assertTrue(resource is byname["1.ics"]) resource = yield shared.objectResourceWithName("2.ics") self.assertTrue(resource is byname["2.ics"]) resource = yield shared.objectResourceWithName("Missing.ics") self.assertTrue(resource is None) resource = yield shared.objectResourceWithUID("uid1") self.assertTrue(resource is byuid["uid1"]) resource = yield shared.objectResourceWithUID("uid2") self.assertTrue(resource is byuid["uid2"]) resource = yield shared.objectResourceWithUID("uid-missing") self.assertTrue(resource is None) resource = yield shared.objectResourceWithID(resource_id1) self.assertTrue(resource is byname["1.ics"]) resource = yield shared.objectResourceWithID(resource_id2) self.assertTrue(resource is byname["2.ics"]) resource = yield shared.objectResourceWithID(0) self.assertTrue(resource is None) yield self.commitTransaction(1) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.remove() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resources = yield shared.objectResources() byname = dict([(obj.name(), obj) for obj in resources]) byuid = dict([(obj.uid(), obj) for obj in resources]) self.assertEqual(len(resources), 1) self.assertEqual(set([obj.name() for obj in resources]), set(("2.ics",))) self.assertEqual(set([obj.uid() for obj in resources]), set(("uid2",))) self.assertEqual(set([obj.id() for obj in resources]), set((resource_id2,))) resource = yield shared.objectResourceWithName("1.ics") self.assertTrue(resource is None) resource = yield shared.objectResourceWithName("2.ics") self.assertTrue(resource is byname["2.ics"]) resource = yield shared.objectResourceWithName("Missing.ics") self.assertTrue(resource is None) resource = yield shared.objectResourceWithUID("uid1") self.assertTrue(resource is None) resource = yield shared.objectResourceWithUID("uid2") self.assertTrue(resource is byuid["uid2"]) resource = yield shared.objectResourceWithUID("uid-missing") self.assertTrue(resource is None) resource = yield shared.objectResourceWithID(resource_id1) self.assertTrue(resource is None) resource = yield shared.objectResourceWithID(resource_id2) self.assertTrue(resource is byname["2.ics"]) resource = yield shared.objectResourceWithID(0) self.assertTrue(resource is None) yield self.commitTransaction(1) @inlineCallbacks def test_loadallobjectswithnames(self): """ Test that action=loadallobjectswithnames works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") resource1 = yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) resource_id1 = resource1.id() yield calendar1.createCalendarObjectWithName("2.ics", Component.fromString(self.caldata2)) resource3 = yield calendar1.createCalendarObjectWithName("3.ics", Component.fromString(self.caldata3)) resource_id3 = resource3.id() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resources = yield shared.objectResources() self.assertEqual(len(resources), 3) yield self.commitTransaction(1) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resources = yield shared.objectResourcesWithNames(("1.ics", "3.ics",)) byname = dict([(obj.name(), obj) for obj in resources]) byuid = dict([(obj.uid(), obj) for obj in resources]) self.assertEqual(len(resources), 2) self.assertEqual(set([obj.name() for obj in resources]), set(("1.ics", "3.ics",))) self.assertEqual(set([obj.uid() for obj in resources]), set(("uid1", "uid3",))) self.assertEqual(set([obj.id() for obj in resources]), set((resource_id1, resource_id3,))) resource = yield shared.objectResourceWithName("1.ics") self.assertTrue(resource is byname["1.ics"]) resource = yield shared.objectResourceWithName("3.ics") self.assertTrue(resource is byname["3.ics"]) resource = yield shared.objectResourceWithName("Missing.ics") self.assertTrue(resource is None) resource = yield shared.objectResourceWithUID("uid1") self.assertTrue(resource is byuid["uid1"]) resource = yield shared.objectResourceWithUID("uid3") self.assertTrue(resource is byuid["uid3"]) resource = yield shared.objectResourceWithUID("uid-missing") self.assertTrue(resource is None) resource = yield shared.objectResourceWithID(resource_id1) self.assertTrue(resource is byname["1.ics"]) resource = yield shared.objectResourceWithID(resource_id3) self.assertTrue(resource is byname["3.ics"]) resource = yield shared.objectResourceWithID(0) self.assertTrue(resource is None) yield self.commitTransaction(1) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.remove() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resources = yield shared.objectResourcesWithNames(("1.ics", "3.ics",)) byname = dict([(obj.name(), obj) for obj in resources]) byuid = dict([(obj.uid(), obj) for obj in resources]) self.assertEqual(len(resources), 1) self.assertEqual(set([obj.name() for obj in resources]), set(("3.ics",))) self.assertEqual(set([obj.uid() for obj in resources]), set(("uid3",))) self.assertEqual(set([obj.id() for obj in resources]), set((resource_id3,))) resource = yield shared.objectResourceWithName("1.ics") self.assertTrue(resource is None) resource = yield shared.objectResourceWithName("3.ics") self.assertTrue(resource is byname["3.ics"]) resource = yield shared.objectResourceWithName("Missing.ics") self.assertTrue(resource is None) resource = yield shared.objectResourceWithUID("uid1") self.assertTrue(resource is None) resource = yield shared.objectResourceWithUID("uid3") self.assertTrue(resource is byuid["uid3"]) resource = yield shared.objectResourceWithUID("uid-missing") self.assertTrue(resource is None) resource = yield shared.objectResourceWithID(resource_id1) self.assertTrue(resource is None) resource = yield shared.objectResourceWithID(resource_id3) self.assertTrue(resource is byname["3.ics"]) resource = yield shared.objectResourceWithID(0) self.assertTrue(resource is None) yield self.commitTransaction(1) @inlineCallbacks def test_objectwith(self): """ Test that action=objectwith works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") resource = yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) resource_id = resource.id() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.objectResourceWithName("1.ics") self.assertTrue(resource is not None) self.assertEqual(resource.name(), "1.ics") self.assertEqual(resource.uid(), "uid1") resource = yield shared.objectResourceWithName("2.ics") self.assertTrue(resource is None) yield self.commitTransaction(1) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.objectResourceWithUID("uid1") self.assertTrue(resource is not None) self.assertEqual(resource.name(), "1.ics") self.assertEqual(resource.uid(), "uid1") resource = yield shared.objectResourceWithUID("uid2") self.assertTrue(resource is None) yield self.commitTransaction(1) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.objectResourceWithID(resource_id) self.assertTrue(resource is not None) self.assertEqual(resource.name(), "1.ics") self.assertEqual(resource.uid(), "uid1") resource = yield shared.objectResourceWithID(0) self.assertTrue(resource is None) yield self.commitTransaction(1) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.remove() yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.objectResourceWithName("1.ics") self.assertTrue(resource is None) yield self.commitTransaction(1) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.objectResourceWithUID("uid1") self.assertTrue(resource is None) yield self.commitTransaction(1) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.objectResourceWithID(resource_id) self.assertTrue(resource is None) yield self.commitTransaction(1) @inlineCallbacks def test_create(self): """ Test that action=create works. """ yield self.createShare("user01", "puser01") shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) resource_id = resource.id() self.assertTrue(resource is not None) self.assertEqual(resource.name(), "1.ics") self.assertEqual(resource.uid(), "uid1") self.assertFalse(resource._componentChanged) yield self.commitTransaction(1) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") resource = yield shared.objectResourceWithUID("uid1") self.assertTrue(resource is not None) self.assertEqual(resource.name(), "1.ics") self.assertEqual(resource.uid(), "uid1") self.assertEqual(resource.id(), resource_id) yield self.commitTransaction(1) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") self.assertTrue(object1 is not None) self.assertEqual(object1.name(), "1.ics") self.assertEqual(object1.uid(), "uid1") self.assertEqual(object1.id(), resource_id) yield self.commitTransaction(0) @inlineCallbacks def test_create_exception(self): """ Test that action=create fails when a duplicate name is used. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") yield self.failUnlessFailure(shared.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)), ObjectResourceNameAlreadyExistsError) yield self.abortTransaction(1) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") yield self.failUnlessFailure(shared.createCalendarObjectWithName(".2.ics", Component.fromString(self.caldata2)), ObjectResourceNameNotAllowedError) yield self.abortTransaction(1) @inlineCallbacks def test_setcomponent(self): """ Test that action=setcomponent works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") ical = yield shared_object.component() self.assertTrue(isinstance(ical, Component)) self.assertEqual(normalize_iCalStr(str(ical)), normalize_iCalStr(self.caldata1)) yield self.commitTransaction(1) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") changed = yield shared_object.setComponent(Component.fromString(self.caldata1_changed)) self.assertFalse(changed) ical = yield shared_object.component() self.assertTrue(isinstance(ical, Component)) self.assertEqual(normalize_iCalStr(str(ical)), normalize_iCalStr(self.caldata1_changed)) yield self.commitTransaction(1) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") ical = yield object1.component() self.assertTrue(isinstance(ical, Component)) self.assertEqual(normalize_iCalStr(str(ical)), normalize_iCalStr(self.caldata1_changed)) yield self.commitTransaction(0) @inlineCallbacks def test_component(self): """ Test that action=component works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") ical = yield shared_object.component() self.assertTrue(isinstance(ical, Component)) self.assertEqual(normalize_iCalStr(str(ical)), normalize_iCalStr(self.caldata1)) yield self.commitTransaction(1) @inlineCallbacks def test_remove(self): """ Test that action=remove works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") yield shared_object.remove() yield self.commitTransaction(1) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") self.assertTrue(shared_object is None) yield self.commitTransaction(1) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") self.assertTrue(object1 is None) yield self.commitTransaction(0) @inlineCallbacks def test_freebusy(self): """ Test that action=component works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) fbstart = "{now:04d}0102T000000Z".format(**self.nowYear) fbend = "{now:04d}0103T000000Z".format(**self.nowYear) shared = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", name="shared-calendar") fbinfo = FreebusyQuery.FBInfo([], [], []) timerange = Period(DateTime.parseText(fbstart), DateTime.parseText(fbend)) organizer = recipient = (yield calendarUserFromCalendarUserAddress("mailto:puser01@example.com", self.theTransactionUnderTest(1))) freebusy = FreebusyQuery(organizer=organizer, recipient=recipient, timerange=timerange) matchtotal = (yield freebusy.generateFreeBusyInfo([shared, ], fbinfo)) self.assertEqual(matchtotal, 1) self.assertEqual(fbinfo[0], [Period.parseText("{now:04d}0102T140000Z/PT1H".format(**self.nowYear)), ]) self.assertEqual(len(fbinfo[1]), 0) self.assertEqual(len(fbinfo[2]), 0) yield self.commitTransaction(1) def attachmentToString(self, attachment): """ Convenience to convert an L{IAttachment} to a string. @param attachment: an L{IAttachment} provider to convert into a string. @return: a L{Deferred} that fires with the contents of the attachment. @rtype: L{Deferred} firing C{bytes} """ capture = CaptureProtocol() attachment.retrieve(capture) return capture.deferred @inlineCallbacks def test_add_attachment(self): """ Test that action=add-attachment works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") object1 = yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) resourceID = object1.id() yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") data = "Here is some text." attachment, location = yield shared_object.addAttachment(None, MimeType.fromString("text/plain"), "test.txt", MemoryStream(data)) managedID = attachment.managedID() from txdav.caldav.datastore.sql_external import ManagedAttachmentExternal self.assertTrue(isinstance(attachment, ManagedAttachmentExternal)) self.assertEqual(attachment.size(), len(data)) self.assertTrue("user01/dropbox/" in location) yield self.commitTransaction(1) cobjs = yield ManagedAttachment.referencesTo(self.theTransactionUnderTest(0), managedID) self.assertEqual(cobjs, set((resourceID,))) attachment = yield ManagedAttachment.load(self.theTransactionUnderTest(0), resourceID, managedID) self.assertEqual(attachment.name(), "test.txt") data = yield self.attachmentToString(attachment) self.assertEqual(data, "Here is some text.") yield self.commitTransaction(0) @inlineCallbacks def test_update_attachment(self): """ Test that action=update-attachment works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") resourceID = object1.id() attachment, _ignore_location = yield object1.addAttachment(None, MimeType.fromString("text/plain"), "test.txt", MemoryStream("Here is some text.")) managedID = attachment.managedID() yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") data = "Here is some more text." attachment, location = yield shared_object.updateAttachment(managedID, MimeType.fromString("text/plain"), "test.txt", MemoryStream(data)) managedID = attachment.managedID() from txdav.caldav.datastore.sql_external import ManagedAttachmentExternal self.assertTrue(isinstance(attachment, ManagedAttachmentExternal)) self.assertEqual(attachment.size(), len(data)) self.assertTrue("user01/dropbox/" in location) yield self.commitTransaction(1) cobjs = yield ManagedAttachment.referencesTo(self.theTransactionUnderTest(0), managedID) self.assertEqual(cobjs, set((resourceID,))) attachment = yield ManagedAttachment.load(self.transactionUnderTest(), resourceID, managedID) self.assertEqual(attachment.name(), "test.txt") data = yield self.attachmentToString(attachment) self.assertEqual(data, "Here is some more text.") yield self.commitTransaction(0) @inlineCallbacks def test_remove_attachment(self): """ Test that action=remove-attachment works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") resourceID = object1.id() attachment, _ignore_location = yield object1.addAttachment(None, MimeType.fromString("text/plain"), "test.txt", MemoryStream("Here is some text.")) managedID = attachment.managedID() yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") yield shared_object.removeAttachment(None, managedID) yield self.commitTransaction(1) cobjs = yield ManagedAttachment.referencesTo(self.theTransactionUnderTest(0), managedID) self.assertEqual(cobjs, set()) attachment = yield ManagedAttachment.load(self.theTransactionUnderTest(0), resourceID, managedID) self.assertTrue(attachment is None) yield self.commitTransaction(0) @inlineCallbacks def test_get_all_attachments(self): """ Test that action=get-all-attachments works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") yield object1.addAttachment(None, MimeType.fromString("text/plain"), "test.txt", MemoryStream("Here is some text.")) yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") attachments = yield shared_object.ownerHome().getAllAttachments() self.assertEqual(len(attachments), 1) self.assertTrue(isinstance(attachments[0], ManagedAttachment)) self.assertEqual(attachments[0].contentType(), MimeType.fromString("text/plain")) self.assertEqual(attachments[0].name(), "test.txt") yield self.commitTransaction(1) @inlineCallbacks def test_get_attachment_data(self): """ Test that action=get-all-attachments works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) yield self.commitTransaction(0) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") attachment, _ignore_location = yield object1.addAttachment(None, MimeType.fromString("text/plain"), "test.txt", MemoryStream("Here is some text.")) remote_id = attachment.id() yield self.commitTransaction(0) home1 = yield self.homeUnderTest(txn=self.theTransactionUnderTest(1), name="puser01") shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") attachment = yield ManagedAttachment._create(self.theTransactionUnderTest(1), None, home1.id()) attachment._contentType = MimeType.fromString("text/plain") attachment._name = "test.txt" yield shared_object.ownerHome().readAttachmentData(remote_id, attachment) yield self.commitTransaction(1) @inlineCallbacks def test_get_attachment_links(self): """ Test that action=get-attachment-links works. """ yield self.createShare("user01", "puser01") calendar1 = yield self.calendarUnderTest(txn=self.theTransactionUnderTest(0), home="user01", name="calendar") cobj1 = yield calendar1.createCalendarObjectWithName("1.ics", Component.fromString(self.caldata1)) calobjID = cobj1.id() yield self.commitTransaction(0) object1 = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(0), home="user01", calendar_name="calendar", name="1.ics") attachment, _ignore_location = yield object1.addAttachment(None, MimeType.fromString("text/plain"), "test.txt", MemoryStream("Here is some text.")) attID = attachment.id() managedID = attachment.managedID() yield self.commitTransaction(0) shared_object = yield self.calendarObjectUnderTest(txn=self.theTransactionUnderTest(1), home="puser01", calendar_name="shared-calendar", name="1.ics") links = yield shared_object.ownerHome().getAttachmentLinks() self.assertEqual(len(links), 1) self.assertTrue(isinstance(links[0], AttachmentLink)) self.assertEqual(links[0]._attachmentID, attID) self.assertEqual(links[0]._managedID, managedID) self.assertEqual(links[0]._calendarObjectID, calobjID) yield self.commitTransaction(1)

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50,243

6.715064

0.080576

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0.085212

0.059678

0.810272

0.787809

0.784592

0.77297

0.751609

0.723291

0

0.030313

0.191071

50,243

1,102

159

45.592559

0.818591

0.038214

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0.089888

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null

0

1

0

null

0

6

041a41c21a479aa773799cd09b5a93ee5a8a115d

208

py

Python

Lib/ufo2ft/kernFeatureWriter.py

belluzj/ufo2ft

72cab16137c3da797b8384dd66f9fbcf65ad9978

[ "MIT" ]

null

Lib/ufo2ft/kernFeatureWriter.py

belluzj/ufo2ft

72cab16137c3da797b8384dd66f9fbcf65ad9978

[ "MIT" ]

null

Lib/ufo2ft/kernFeatureWriter.py

belluzj/ufo2ft

72cab16137c3da797b8384dd66f9fbcf65ad9978

[ "MIT" ]

null

"""This module is deprecated! It's kept here only for backward compatibility. Please import the new ufo2ft.featureWriters module. """ from ufo2ft.featureWriters.kernFeatureWriter import * # pragma: no cover

41.6

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0.793269

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6.111111

0.851852

0.242424

0

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0.129808

208

4

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0.900552

0.692308

0

1

0

true

0

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1

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null

0

1

0

1

0

1

0

6

9bdd9739f584772f354f12dda8704ca96ca7e7ac

7,380

py

Python

im2mesh/onet_multi_layers_predict/models/feature_extractor.py

bezirganyan/Occup-R2N2

9adf6d0a9cc6f884fc17c80b24e72060dbacf3c1

[ "MIT" ]

2

2020-06-03T20:54:26.000Z

2021-09-15T06:57:57.000Z

im2mesh/onet_multi_layers_predict/models/feature_extractor.py

bezirganyan/Occup-R2N2

9adf6d0a9cc6f884fc17c80b24e72060dbacf3c1

[ "MIT" ]

null

im2mesh/onet_multi_layers_predict/models/feature_extractor.py

bezirganyan/Occup-R2N2

9adf6d0a9cc6f884fc17c80b24e72060dbacf3c1

[ "MIT" ]

2

2020-06-14T12:27:22.000Z

2020-10-27T16:55:04.000Z

import torch.nn as nn import torch import torch.nn.functional as F from im2mesh.onet_multi_layers_predict.models import resnet from im2mesh.common import normalize_imagenet import im2mesh.common as common def kmax_pooling(x, dim, k): index = x.topk(k, dim=dim)[1].sort(dim=dim)[0] return x.gather(dim, index) class Resnet18_Full(nn.Module): r''' ResNet-18 encoder network for image input. Args: c_dim (int): output dimension of the latent embedding normalize (bool): whether the input images should be normalized use_linear (bool): whether a final linear layer should be used ''' def __init__(self, c_dim, normalize=True, batch_norm=False, pretrained=True, pretrained_path=None): super().__init__() self.normalize = normalize self.features = resnet.resnet18(pretrained=pretrained, pretrained_path=pretrained_path) if c_dim != 512: self.fc3 = nn.Linear(512, c_dim) self.fc2 = nn.Linear(512, c_dim) self.fc1 = nn.Linear(512, c_dim) else: self.fc3 = nn.Sequential() self.fc2 = nn.Sequential() self.fc1 = nn.Sequential() self.batch_norm = batch_norm if self.batch_norm: print('Using layer norm in encdoer') self.f1_bn = nn.BatchNorm1d(c_dim) self.f2_bn = nn.BatchNorm1d(c_dim) self.f3_bn = nn.BatchNorm1d(c_dim) self.f1_fc = nn.Linear(1568,512) self.f2_fc = nn.Linear(1568,512) def forward(self, x): if self.normalize: x = normalize_imagenet(x) f3,f2,f1 = self.features(x) # f3: 512 f2: 256 * 14 * 14 f1: 128 * 28 * 28 # full kmax pooling f1 = f1.detach() f1 = kmax_pooling(f1,1,2) f1 = f1.view(f1.size(0), -1) f1 = self.f1_fc(f1) f2 = f2.detach() f2 = kmax_pooling(f2,1,8) f2 = f2.view(f2.size(0), -1) f2 = self.f2_fc(f2) f3 = self.fc3(f3) f2 = self.fc2(f2) f1 = self.fc1(f1) if self.batch_norm: f3 = self.f3_bn(f3) f2 = self.f2_bn(f2) f1 = self.f1_bn(f1) return f3, f2, f1 class Resnet18_Local(nn.Module): r''' ResNet-18 encoder network for image input. Args: c_dim (int): output dimension of the latent embedding normalize (bool): whether the input images should be normalized use_linear (bool): whether a final linear layer should be used ''' def __init__(self, c_dim, feature_map_dim=64 ,normalize=True, batch_norm=False, pretrained=True, pretrained_path=None): super().__init__() self.normalize = normalize self.features = resnet.resnet18(pretrained=pretrained, pretrained_path=pretrained_path) if c_dim != 512: self.fc3 = nn.Linear(512, c_dim) else: self.fc3 = nn.Sequential() self.feature_map_dim = feature_map_dim self.batch_norm = batch_norm if self.batch_norm: print('Using layer norm in encdoer') self.f1_bn = nn.BatchNorm1d(feature_map_dim) self.f2_bn = nn.BatchNorm1d(feature_map_dim) self.f3_bn = nn.BatchNorm1d(c_dim) self.f2_conv = nn.Conv1d(256, self.feature_map_dim ,1) self.f1_conv = nn.Conv1d(128, self.feature_map_dim ,1) def forward(self, x, pts, world_mat, camera_mat): f3, f2, f1 = self.encode_first_step(x) f3, f2, f1 = self.encode_second_step(f3, f2, f1, pts, world_mat, camera_mat) return f3, f2, f1 def encode_first_step(self, x): if self.normalize: x = normalize_imagenet(x) f3,f2,f1 = self.features(x) return f3, f2, f1 def encode_second_step(self, f3, f2, f1, pts, world_mat, camera_mat): pts = common.transform_points(pts, world_mat) points_img = common.project_to_camera(pts, camera_mat) points_img = points_img.unsqueeze(1) f2 = f2.detach() f2 = F.relu(f2) f2 = F.grid_sample(f2, points_img) f2 = f2.squeeze(2) f2 = self.f2_conv(f2) f1 = f1.detach() f1 = F.relu(f1) f1 = F.grid_sample(f1, points_img) f1 = f1.squeeze(2) f1 = self.f1_conv(f1) f3 = self.fc3(f3) if self.batch_norm: f3 = self.f3_bn(f3) f2 = self.f2_bn(f2) f1 = self.f1_bn(f1) f2 = f2.transpose(1, 2) f1 = f1.transpose(1, 2) # f2 : batch * n_pts * fmap_dim # f1 : batch * n_pts * fmap_dim return f3, f2, f1 class Resnet18_Local_1(nn.Module): r''' ResNet-18 encoder network for image input. Args: c_dim (int): output dimension of the latent embedding normalize (bool): whether the input images should be normalized use_linear (bool): whether a final linear layer should be used ''' def __init__(self, c_dim, feature_map_dim=64 ,normalize=True, batch_norm=False, pretrained=True, pretrained_path=None): super().__init__() self.normalize = normalize self.features = resnet.resnet18(pretrained=pretrained, pretrained_path=pretrained_path) if c_dim != 512: self.fc3 = nn.Linear(512, c_dim) else: self.fc3 = nn.Sequential() self.feature_map_dim = feature_map_dim self.batch_norm = batch_norm if self.batch_norm: print('Using layer norm in encdoer') self.f1_bn = nn.BatchNorm1d(feature_map_dim) self.f2_bn = nn.BatchNorm1d(feature_map_dim) self.f3_bn = nn.BatchNorm1d(c_dim) self.f2_conv = nn.Conv1d(256+128, self.feature_map_dim ,1) self.f1_conv = nn.Conv1d(64+64+3, self.feature_map_dim ,1) def forward(self, x, pts, world_mat, camera_mat): f3, fs2, fs1 = self.encode_first_step(x) f3, f2, f1 = self.encode_second_step(f3, fs2, fs1, pts, world_mat, camera_mat) return f3, f2, f1 def encode_first_step(self, x): if self.normalize: x = normalize_imagenet(x) f3,fs2,fs1 = self.features.calc_feature_maps(x) return f3, fs2, fs1 def encode_second_step(self, f3, fs2, fs1, pts, world_mat, camera_mat): pts = common.transform_points(pts, world_mat) points_img = common.project_to_camera(pts, camera_mat) points_img = points_img.unsqueeze(1) fs2_sampled = [] for f2 in fs2: f2 = f2.detach() f2 = F.relu(f2) f2 = F.grid_sample(f2, points_img) f2 = f2.squeeze(2) fs2_sampled.append(f2) fs2 = torch.cat(fs2_sampled, dim=1) fs1_sampled = [] for f1 in fs1: f1 = f1.detach() f1 = F.relu(f1) f1 = F.grid_sample(f1, points_img) f1 = f1.squeeze(2) fs1_sampled.append(f1) fs1 = torch.cat(fs1_sampled, dim=1) fs2 = self.f2_conv(fs2) fs1 = self.f1_conv(fs1) f3 = self.fc3(f3) if self.batch_norm: f3 = self.f3_bn(f3) fs2 = self.f2_bn(fs2) fs1 = self.f1_bn(fs1) fs2 = fs2.transpose(1, 2) fs1 = fs1.transpose(1, 2) # f2 : batch * n_pts * fmap_dim # f1 : batch * n_pts * fmap_dim return f3, fs2, fs1

32.654867

123

0.59336

1,062

7,380

3.925612

0.12806

0.01823

0.043656

0.021588

0.80355

0.794435

0.769729

0.754857

0.749101

0.741425

0

0.068772

0.300542

7,380

225

124

32.8

0.738861

0.123035

0

0.610063

0

0.012686

0

1

0.069182

false

0

0.037736

0

0.176101

0.018868

0

null

0

1

0

null

0

6

50062d3eb0b7a6ce853267189edef5492f1a8e9e

17,597

py

Python

misago/misago/threads/tests/test_thread_editreply_api.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

2

2021-03-06T21:06:13.000Z

2021-03-09T15:05:12.000Z

misago/misago/threads/tests/test_thread_editreply_api.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

null

misago/misago/threads/tests/test_thread_editreply_api.py

vascoalramos/misago-deployment

20226072138403108046c0afad9d99eb4163cedc

[ "MIT" ]

null

from datetime import timedelta from django.test.client import BOUNDARY, MULTIPART_CONTENT, encode_multipart from django.urls import reverse from django.utils import timezone from .. import test from ...acl.test import patch_user_acl from ...categories.models import Category from ...users.test import AuthenticatedUserTestCase from ..models import Post, Thread from ..test import patch_category_acl class EditReplyTests(AuthenticatedUserTestCase): def setUp(self): super().setUp() self.category = Category.objects.get(slug="first-category") self.thread = test.post_thread(category=self.category) self.post = test.reply_thread(self.thread, poster=self.user) self.api_link = reverse( "misago:api:thread-post-detail", kwargs={"thread_pk": self.thread.pk, "pk": self.post.pk}, ) def put(self, url, data=None): content = encode_multipart(BOUNDARY, data or {}) return self.client.put(url, content, content_type=MULTIPART_CONTENT) def test_cant_edit_reply_as_guest(self): """user has to be authenticated to be able to edit reply""" self.logout_user() response = self.put(self.api_link) self.assertEqual(response.status_code, 403) def test_thread_visibility(self): """thread's visibility is validated""" with patch_category_acl({"can_see": False}): response = self.put(self.api_link) self.assertEqual(response.status_code, 404) with patch_category_acl({"can_browse": False}): response = self.put(self.api_link) self.assertEqual(response.status_code, 404) with patch_category_acl({"can_see_all_threads": False}): response = self.put(self.api_link) self.assertEqual(response.status_code, 404) @patch_category_acl({"can_edit_posts": 0}) def test_cant_edit_reply(self): """permission to edit reply is validated""" response = self.put(self.api_link) self.assertEqual(response.status_code, 403) self.assertEqual( response.json(), {"detail": "You can't edit posts in this category."} ) @patch_category_acl({"can_edit_posts": 1}) def test_cant_edit_other_user_reply(self): """permission to edit reply by other users is validated""" self.post.poster = None self.post.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 403) self.assertEqual( response.json(), {"detail": "You can't edit other users posts in this category."}, ) @patch_category_acl({"can_edit_posts": 1, "post_edit_time": 1}) def test_edit_too_old(self): """permission to edit reply within timelimit is validated""" self.post.posted_on = timezone.now() - timedelta(minutes=5) self.post.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 403) self.assertEqual( response.json(), {"detail": "You can't edit posts that are older than 1 minute."}, ) @patch_category_acl({"can_edit_posts": 1, "can_close_threads": False}) def test_closed_category_no_permission(self): """permssion to edit reply in closed category is validated""" self.category.is_closed = True self.category.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 403) self.assertEqual( response.json(), {"detail": "This category is closed. You can't edit posts in it."}, ) @patch_category_acl({"can_edit_posts": 1, "can_close_threads": True}) def test_closed_category(self): """permssion to edit reply in closed category is validated""" self.category.is_closed = True self.category.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 400) @patch_category_acl({"can_edit_posts": 1, "can_close_threads": False}) def test_closed_thread_no_permission(self): """permssion to edit reply in closed thread is validated""" self.thread.is_closed = True self.thread.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 403) self.assertEqual( response.json(), {"detail": "This thread is closed. You can't edit posts in it."}, ) @patch_category_acl({"can_edit_posts": 1, "can_close_threads": True}) def test_closed_thread(self): """permssion to edit reply in closed thread is validated""" self.thread.is_closed = True self.thread.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 400) @patch_category_acl({"can_edit_posts": 1, "can_protect_posts": False}) def test_protected_post_no_permission(self): """permssion to edit protected post is validated""" self.post.is_protected = True self.post.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 403) self.assertEqual( response.json(), {"detail": "This post is protected. You can't edit it."} ) @patch_category_acl({"can_edit_posts": 1, "can_protect_posts": True}) def test_protected_post_no(self): """permssion to edit protected post is validated""" self.post.is_protected = True self.post.save() response = self.put(self.api_link) self.assertEqual(response.status_code, 400) @patch_category_acl({"can_edit_posts": 1}) def test_empty_data(self): """no data sent handling has no showstoppers""" response = self.put(self.api_link, data={}) self.assertEqual(response.status_code, 400) self.assertEqual(response.json(), {"post": ["You have to enter a message."]}) @patch_category_acl({"can_edit_posts": 1}) def test_invalid_data(self): """api errors for invalid request data""" response = self.client.put( self.api_link, "false", content_type="application/json" ) self.assertEqual(response.status_code, 400) self.assertEqual( response.json(), { "non_field_errors": [ "Invalid data. Expected a dictionary, but got bool." ] }, ) @patch_category_acl({"can_edit_posts": 1}) def test_edit_event(self): """events can't be edited""" self.post.is_event = True self.post.save() response = self.put(self.api_link, data={}) self.assertEqual(response.status_code, 403) self.assertEqual(response.json(), {"detail": "Events can't be edited."}) @patch_category_acl({"can_edit_posts": 1}) def test_post_is_validated(self): """post is validated""" response = self.put(self.api_link, data={"post": "a"}) self.assertEqual(response.status_code, 400) self.assertEqual( response.json(), { "post": [ "Posted message should be at least 5 characters long (it has 1)." ] }, ) @patch_category_acl({"can_edit_posts": 1}) def test_edit_reply_no_change(self): """endpoint isn't bumping edits count if no change was made to post's body""" self.assertEqual(self.post.edits_record.count(), 0) response = self.put(self.api_link, data={"post": self.post.original}) self.assertEqual(response.status_code, 200) response = self.client.get(self.thread.get_absolute_url()) self.assertContains(response, self.post.parsed) post = self.thread.post_set.order_by("id").last() self.assertEqual(post.edits, 0) self.assertEqual(post.original, self.post.original) self.assertIsNone(post.last_editor_id, self.user.id) self.assertIsNone(post.last_editor_name, self.user.username) self.assertIsNone(post.last_editor_slug, self.user.slug) self.assertEqual(self.post.edits_record.count(), 0) @patch_category_acl({"can_edit_posts": 1}) def test_edit_reply(self): """endpoint updates reply""" self.assertEqual(self.post.edits_record.count(), 0) response = self.put(self.api_link, data={"post": "This is test edit!"}) self.assertEqual(response.status_code, 200) response = self.client.get(self.thread.get_absolute_url()) self.assertContains(response, "<p>This is test edit!</p>") self.assertEqual(self.user.audittrail_set.count(), 1) post = self.thread.post_set.order_by("id").last() self.assertEqual(post.edits, 1) self.assertEqual(post.original, "This is test edit!") self.assertEqual(post.last_editor_id, self.user.id) self.assertEqual(post.last_editor_name, self.user.username) self.assertEqual(post.last_editor_slug, self.user.slug) self.assertEqual(self.post.edits_record.count(), 1) post_edit = post.edits_record.last() self.assertEqual(post_edit.edited_from, self.post.original) self.assertEqual(post_edit.edited_to, post.original) self.assertEqual(post_edit.editor_id, self.user.id) self.assertEqual(post_edit.editor_name, self.user.username) self.assertEqual(post_edit.editor_slug, self.user.slug) @patch_category_acl({"can_edit_posts": 2, "can_hide_threads": 1}) def test_edit_first_post_hidden(self): """endpoint updates hidden thread's first post""" self.thread.is_hidden = True self.thread.save() self.thread.first_post.is_hidden = True self.thread.first_post.save() api_link = reverse( "misago:api:thread-post-detail", kwargs={"thread_pk": self.thread.pk, "pk": self.thread.first_post.pk}, ) response = self.put(api_link, data={"post": "This is test edit!"}) self.assertEqual(response.status_code, 200) @patch_category_acl({"can_edit_posts": 1, "can_protect_posts": True}) def test_protect_post(self): """can protect post""" response = self.put( self.api_link, data={"post": "Lorem ipsum dolor met!", "protect": 1} ) self.assertEqual(response.status_code, 200) post = self.user.post_set.order_by("id").last() self.assertTrue(post.is_protected) @patch_category_acl({"can_edit_posts": 1, "can_protect_posts": False}) def test_protect_post_no_permission(self): """cant protect post without permission""" response = self.put( self.api_link, data={"post": "Lorem ipsum dolor met!", "protect": 1} ) self.assertEqual(response.status_code, 200) post = self.user.post_set.order_by("id").last() self.assertFalse(post.is_protected) @patch_category_acl({"can_edit_posts": 1}) def test_post_unicode(self): """unicode characters can be posted""" response = self.put( self.api_link, data={"post": "Chrzążczyżewoszyce, powiat Łękółody."} ) self.assertEqual(response.status_code, 200) @patch_category_acl({"can_edit_posts": 1}) def test_reply_category_moderation_queue(self): """edit sends reply to queue due to category setup""" self.category.require_edits_approval = True self.category.save() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) post = self.user.post_set.all()[:1][0] self.assertTrue(post.is_unapproved) @patch_category_acl({"can_edit_posts": 1}) @patch_user_acl({"can_approve_content": True}) def test_reply_category_moderation_queue_bypass(self): """bypass moderation queue due to user's acl""" self.category.require_edits_approval = True self.category.save() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) post = self.user.post_set.all()[:1][0] self.assertFalse(post.is_unapproved) @patch_category_acl({"can_edit_posts": 1, "require_edits_approval": True}) def test_reply_user_moderation_queue(self): """edit sends reply to queue due to user acl""" response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) post = self.user.post_set.all()[:1][0] self.assertTrue(post.is_unapproved) @patch_category_acl({"can_edit_posts": 1, "require_edits_approval": True}) @patch_user_acl({"can_approve_content": True}) def test_reply_user_moderation_queue_bypass(self): """bypass moderation queue due to user's acl""" response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) post = self.user.post_set.all()[:1][0] self.assertFalse(post.is_unapproved) @patch_category_acl( { "can_edit_posts": 1, "require_threads_approval": True, "require_replies_approval": True, } ) def test_reply_omit_other_moderation_queues(self): """other queues are omitted""" self.category.require_threads_approval = True self.category.require_replies_approval = True self.category.save() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) post = self.user.post_set.all()[:1][0] self.assertFalse(post.is_unapproved) def setUpFirstReplyTest(self): self.post = self.thread.first_post self.post.poster = self.user self.post.save() self.api_link = reverse( "misago:api:thread-post-detail", kwargs={"thread_pk": self.thread.pk, "pk": self.post.pk}, ) @patch_category_acl({"can_edit_posts": 1}) def test_first_reply_category_moderation_queue(self): """edit sends thread to queue due to category setup""" self.setUpFirstReplyTest() self.category.require_edits_approval = True self.category.save() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) thread = Thread.objects.get(pk=self.thread.pk) self.assertTrue(thread.is_unapproved) self.assertTrue(thread.has_unapproved_posts) post = Post.objects.get(pk=self.post.pk) self.assertTrue(post.is_unapproved) @patch_category_acl({"can_edit_posts": 1}) @patch_user_acl({"can_approve_content": True}) def test_first_reply_category_moderation_queue_bypass(self): """bypass moderation queue due to user's acl""" self.setUpFirstReplyTest() self.category.require_edits_approval = True self.category.save() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) thread = Thread.objects.get(pk=self.thread.pk) self.assertFalse(thread.is_unapproved) self.assertFalse(thread.has_unapproved_posts) post = Post.objects.get(pk=self.post.pk) self.assertFalse(post.is_unapproved) @patch_category_acl({"can_edit_posts": 1, "require_edits_approval": True}) def test_first_reply_user_moderation_queue(self): """edit sends thread to queue due to user acl""" self.setUpFirstReplyTest() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) thread = Thread.objects.get(pk=self.thread.pk) self.assertTrue(thread.is_unapproved) self.assertTrue(thread.has_unapproved_posts) post = Post.objects.get(pk=self.post.pk) self.assertTrue(post.is_unapproved) @patch_category_acl({"can_edit_posts": 1, "require_edits_approval": True}) @patch_user_acl({"can_approve_content": True}) def test_first_reply_user_moderation_queue_bypass(self): """bypass moderation queue due to user's acl""" self.setUpFirstReplyTest() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) thread = Thread.objects.get(pk=self.thread.pk) self.assertFalse(thread.is_unapproved) self.assertFalse(thread.has_unapproved_posts) post = Post.objects.get(pk=self.post.pk) self.assertFalse(post.is_unapproved) @patch_category_acl( { "can_edit_posts": 1, "require_threads_approval": True, "require_replies_approval": True, } ) def test_first_reply_omit_other_moderation_queues(self): """other queues are omitted""" self.setUpFirstReplyTest() self.category.require_threads_approval = True self.category.require_replies_approval = True self.category.save() response = self.put(self.api_link, data={"post": "Lorem ipsum dolor met!"}) self.assertEqual(response.status_code, 200) thread = Thread.objects.get(pk=self.thread.pk) self.assertFalse(thread.is_unapproved) self.assertFalse(thread.has_unapproved_posts) post = Post.objects.get(pk=self.post.pk) self.assertFalse(post.is_unapproved)

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0.813983

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0.759433

0.753432

0.722066

0.711246

0

0.01124

0.226459

17,597

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50521f699f2cd7deae7492a6a14bf8db3ecbdf8e

34,732

py

Python

deepsleep/trainer.py

HTJR/deepsleepnet

d4906b4875547a45175eaba8bdde280b7b1496f1

[ "Apache-2.0" ]

266

2017-06-24T03:27:52.000Z

2022-03-28T14:21:03.000Z

deepsleep/trainer.py

HOCHAN-LEE/deepsleepnet

24dedefbff5f3ab9cd7e8d20808afb866261302d

[ "Apache-2.0" ]

43

2017-07-13T13:03:02.000Z

2022-01-07T06:49:45.000Z

deepsleep/trainer.py

HOCHAN-LEE/deepsleepnet

24dedefbff5f3ab9cd7e8d20808afb866261302d

[ "Apache-2.0" ]

136

2017-07-09T11:45:51.000Z

2022-03-24T19:45:30.000Z

import itertools import os import re import time from datetime import datetime import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from sklearn.metrics import confusion_matrix, f1_score from deepsleep.data_loader import NonSeqDataLoader, SeqDataLoader from deepsleep.model import DeepFeatureNet, DeepSleepNet from deepsleep.optimize import adam, adam_clipping_list_lr from deepsleep.utils import iterate_minibatches, iterate_batch_seq_minibatches # from tensorlayer.db import TensorDB # from tensorlayer.db import JobStatus # db = TensorDB(ip='146.169.33.34', port=27020, db_name='DeepSleepNet', user_name='tensorlayer', password='Tensorlayer123', studyID='1') class Trainer(object): def __init__( self, interval_plot_filter=50, interval_save_model=100, interval_print_cm=10 ): self.interval_plot_filter = interval_plot_filter self.interval_save_model = interval_save_model self.interval_print_cm = interval_print_cm def print_performance(self, sess, output_dir, network_name, n_train_examples, n_valid_examples, train_cm, valid_cm, epoch, n_epochs, train_duration, train_loss, train_acc, train_f1, valid_duration, valid_loss, valid_acc, valid_f1): # Get regularization loss train_reg_loss = tf.add_n(tf.compat.v1.get_collection("losses", scope=network_name + "\/")) train_reg_loss_value = sess.run(train_reg_loss) valid_reg_loss_value = train_reg_loss_value # Print performance if ((epoch + 1) % self.interval_print_cm == 0) or ((epoch + 1) == n_epochs): print(" ") print("[{}] epoch {}:".format( datetime.now(), epoch+1 )) print(( "train ({:.3f} sec): n={}, loss={:.3f} ({:.3f}), acc={:.3f}, " "f1={:.3f}".format( train_duration, n_train_examples, train_loss, train_reg_loss_value, train_acc, train_f1 ) )) print(train_cm) print(( "valid ({:.3f} sec): n={}, loss={:.3f} ({:.3f}), acc={:.3f}, " "f1={:.3f}".format( valid_duration, n_valid_examples, valid_loss, valid_reg_loss_value, valid_acc, valid_f1 ) )) print(valid_cm) print(" ") else: print(( "epoch {}: " "train ({:.2f} sec): n={}, loss={:.3f} ({:.3f}), " "acc={:.3f}, f1={:.3f} | " "valid ({:.2f} sec): n={}, loss={:.3f} ({:.3f}), " "acc={:.3f}, f1={:.3f}".format( epoch+1, train_duration, n_train_examples, train_loss, train_reg_loss_value, train_acc, train_f1, valid_duration, n_valid_examples, valid_loss, valid_reg_loss_value, valid_acc, valid_f1 ) )) def print_network(self, network): print("inputs ({}): {}".format( network.inputs.name, network.inputs.get_shape() )) print("targets ({}): {}".format( network.targets.name, network.targets.get_shape() )) for name, act in network.activations: print("{} ({}): {}".format(name, act.name, act.get_shape())) print(" ") def plot_filters(self, sess, epoch, reg_exp, output_dir, n_viz_filters): conv_weight = re.compile(reg_exp) for v in tf.compat.v1.trainable_variables(): value = sess.run(v) if conv_weight.match(v.name): weights = np.squeeze(value) # Only plot conv that has one channel if len(weights.shape) > 2: continue weights = weights.T plt.figure(figsize=(18, 10)) plt.title(v.name) for w_idx in range(n_viz_filters): plt.subplot(4, 4, w_idx+1) plt.plot(weights[w_idx]) plt.axis("tight") plt.savefig(os.path.join( output_dir, "{}_{}.png".format( v.name.replace("/", "_").replace(":0", ""), epoch+1 ) )) plt.close("all") class DeepFeatureNetTrainer(Trainer): def __init__( self, data_dir, output_dir, n_folds, fold_idx, batch_size, input_dims, n_classes, interval_plot_filter=50, interval_save_model=100, interval_print_cm=10 ): super(self.__class__, self).__init__( interval_plot_filter=interval_plot_filter, interval_save_model=interval_save_model, interval_print_cm=interval_print_cm ) self.data_dir = data_dir self.output_dir = output_dir self.n_folds = n_folds self.fold_idx = fold_idx self.batch_size = batch_size self.input_dims = input_dims self.n_classes = n_classes def _run_epoch(self, sess, network, inputs, targets, train_op, is_train): start_time = time.time() y = [] y_true = [] total_loss, n_batches = 0.0, 0 is_shuffle = True if is_train else False for x_batch, y_batch in iterate_minibatches(inputs, targets, self.batch_size, shuffle=is_shuffle): feed_dict = { network.input_var: x_batch, network.target_var: y_batch } # # MONITORING # if n_batches == 0: # print "BEFORE UPDATE [is_train={}]".format(is_train) # for n, v in network.monitor_vars[:2]: # val = sess.run(v, feed_dict=feed_dict) # val = np.transpose(val, axes=(3, 0, 1, 2)).reshape((64, -1)) # mean_val = np.mean(val, axis=1) # var_val = np.var(val, axis=1) # print "{}: {}\nmean_shape={}, mean_val={}\nvar_shape={}, var_val={}".format( # n, val.shape, mean_val.shape, mean_val[:5], var_val.shape, var_val[:5] # ) _, loss_value, y_pred = sess.run( [train_op, network.loss_op, network.pred_op], feed_dict=feed_dict ) # # MONITORING # if n_batches == 0: # print "AFTER UPDATE [is_train={}]".format(is_train) # for n, v in network.monitor_vars[:2]: # val = sess.run(v, feed_dict=feed_dict) # val = np.transpose(val, axes=(3, 0, 1, 2)).reshape((64, -1)) # mean_val = np.mean(val, axis=1) # var_val = np.var(val, axis=1) # print "{}: {}\nmean_shape={}, mean_val={}\nvar_shape={}, var_val={}".format( # n, val.shape, mean_val.shape, mean_val[:5], var_val.shape, var_val[:5] # ) total_loss += loss_value n_batches += 1 y.append(y_pred) y_true.append(y_batch) # Check the loss value assert not np.isnan(loss_value), \ "Model diverged with loss = NaN" duration = time.time() - start_time total_loss /= n_batches total_y_pred = np.hstack(y) total_y_true = np.hstack(y_true) return total_y_true, total_y_pred, total_loss, duration def train(self, n_epochs, resume): with tf.Graph().as_default(), tf.compat.v1.Session() as sess: # Build training and validation networks train_net = DeepFeatureNet( batch_size=self.batch_size, input_dims=self.input_dims, n_classes=self.n_classes, is_train=True, reuse_params=False, use_dropout=True ) valid_net = DeepFeatureNet( batch_size=self.batch_size, input_dims=self.input_dims, n_classes=self.n_classes, is_train=False, reuse_params=True, use_dropout=True ) # Initialize parameters train_net.init_ops() valid_net.init_ops() print("Network (layers={})".format(len(train_net.activations))) print("inputs ({}): {}".format( train_net.input_var.name, train_net.input_var.get_shape() )) print("targets ({}): {}".format( train_net.target_var.name, train_net.target_var.get_shape() )) for name, act in train_net.activations: print("{} ({}): {}".format(name, act.name, act.get_shape())) print(" ") # Define optimization operations train_op, grads_and_vars_op = adam( loss=train_net.loss_op, lr=1e-4, train_vars=tf.compat.v1.trainable_variables() ) # Make subdirectory for pretraining output_dir = os.path.join(self.output_dir, "fold{}".format(self.fold_idx), train_net.name) if not os.path.exists(output_dir): os.makedirs(output_dir) # Global step for resume training with tf.compat.v1.variable_scope(train_net.name) as scope: global_step = tf.Variable(0, name="global_step", trainable=False) # print "Trainable Variables:" # for v in tf.compat.v1.trainable_variables(): # print v.name, v.get_shape() # print " " # print "All Variables:" # for v in tf.compat.v1.global_variables(): # print v.name, v.get_shape() # print " " # Create a saver saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables(), max_to_keep=0) # Initialize variables in the graph sess.run(tf.compat.v1.global_variables_initializer()) # Add the graph structure into the Tensorboard writer train_summary_wrt = tf.compat.v1.summary.FileWriter( os.path.join(output_dir, "train_summary"), sess.graph ) # Resume the training if applicable if resume: if os.path.exists(output_dir): if os.path.isfile(os.path.join(output_dir, "checkpoint")): # Restore the last checkpoint saver.restore(sess, tf.train.latest_checkpoint(output_dir)) print("Model restored") print("[{}] Resume pre-training ...\n".format(datetime.now())) else: print("[{}] Start pre-training ...\n".format(datetime.now())) else: print("[{}] Start pre-training ...\n".format(datetime.now())) # Load data if sess.run(global_step) < n_epochs: data_loader = NonSeqDataLoader( data_dir=self.data_dir, n_folds=self.n_folds, fold_idx=self.fold_idx ) x_train, y_train, x_valid, y_valid = data_loader.load_train_data() # Performance history all_train_loss = np.zeros(n_epochs) all_train_acc = np.zeros(n_epochs) all_train_f1 = np.zeros(n_epochs) all_valid_loss = np.zeros(n_epochs) all_valid_acc = np.zeros(n_epochs) all_valid_f1 = np.zeros(n_epochs) # Loop each epoch for epoch in range(sess.run(global_step), n_epochs): # # MONITORING # print "BEFORE TRAINING" # monitor_vars = [ # "deepfeaturenet/l1_conv/bn/moving_mean:0", # "deepfeaturenet/l1_conv/bn/moving_variance:0" # ] # for n in monitor_vars: # v = tf.compat.v1.get_default_graph().get_tensor_by_name(n) # val = sess.run(v) # print "{}: {}, {}".format(n, val.shape, val[:5]) # Update parameters and compute loss of training set y_true_train, y_pred_train, train_loss, train_duration = \ self._run_epoch( sess=sess, network=train_net, inputs=x_train, targets=y_train, train_op=train_op, is_train=True ) n_train_examples = len(y_true_train) train_cm = confusion_matrix(y_true_train, y_pred_train) train_acc = np.mean(y_true_train == y_pred_train) train_f1 = f1_score(y_true_train, y_pred_train, average="macro") # # MONITORING # print "AFTER TRAINING and BEFORE VALID" # for n in monitor_vars: # v = tf.compat.v1.get_default_graph().get_tensor_by_name(n) # val = sess.run(v) # print "{}: {}, {}".format(n, val.shape, val[:5]) # Evaluate the model on the validation set y_true_val, y_pred_val, valid_loss, valid_duration = \ self._run_epoch( sess=sess, network=valid_net, inputs=x_valid, targets=y_valid, train_op=tf.no_op(), is_train=False ) n_valid_examples = len(y_true_val) valid_cm = confusion_matrix(y_true_val, y_pred_val) valid_acc = np.mean(y_true_val == y_pred_val) valid_f1 = f1_score(y_true_val, y_pred_val, average="macro") # db.train_log(args={ # "n_folds": self.n_folds, # "fold_idx": self.fold_idx, # "epoch": epoch, # "train_step": "pretraining", # "datetime": datetime.utcnow(), # "model": train_net.name, # "n_train_examples": n_train_examples, # "n_valid_examples": n_valid_examples, # "train_loss": train_loss, # "train_acc": train_acc, # "train_f1": train_f1, # "train_duration": train_duration, # "valid_loss": valid_loss, # "valid_acc": valid_acc, # "valid_f1": valid_f1, # "valid_duration": valid_duration, # }) all_train_loss[epoch] = train_loss all_train_acc[epoch] = train_acc all_train_f1[epoch] = train_f1 all_valid_loss[epoch] = valid_loss all_valid_acc[epoch] = valid_acc all_valid_f1[epoch] = valid_f1 # Report performance self.print_performance( sess, output_dir, train_net.name, n_train_examples, n_valid_examples, train_cm, valid_cm, epoch, n_epochs, train_duration, train_loss, train_acc, train_f1, valid_duration, valid_loss, valid_acc, valid_f1 ) # Save performance history np.savez( os.path.join(output_dir, "perf_fold{}.npz".format(self.fold_idx)), train_loss=all_train_loss, valid_loss=all_valid_loss, train_acc=all_train_acc, valid_acc=all_valid_acc, train_f1=all_train_f1, valid_f1=all_valid_f1, y_true_val=np.asarray(y_true_val), y_pred_val=np.asarray(y_pred_val) ) # Visualize weights from convolutional layers if ((epoch + 1) % self.interval_plot_filter == 0) or ((epoch + 1) == n_epochs): self.plot_filters(sess, epoch, train_net.name + "(_[0-9])?\/l[0-9]+_conv\/(weights)", output_dir, 16) self.plot_filters(sess, epoch, train_net.name + "(_[0-9])?/l[0-9]+_conv\/conv1d\/(weights)", output_dir, 16) # Save checkpoint sess.run(tf.compat.v1.assign(global_step, epoch+1)) if ((epoch + 1) % self.interval_save_model == 0) or ((epoch + 1) == n_epochs): start_time = time.time() save_path = os.path.join( output_dir, "model_fold{}.ckpt".format(self.fold_idx) ) saver.save(sess, save_path, global_step=global_step) duration = time.time() - start_time print("Saved model checkpoint ({:.3f} sec)".format(duration)) # Save paramaters if ((epoch + 1) % self.interval_save_model == 0) or ((epoch + 1) == n_epochs): start_time = time.time() save_dict = {} for v in tf.compat.v1.global_variables(): save_dict[v.name] = sess.run(v) np.savez( os.path.join( output_dir, "params_fold{}.npz".format(self.fold_idx)), **save_dict ) duration = time.time() - start_time print("Saved trained parameters ({:.3f} sec)".format(duration)) print("Finish pre-training") return os.path.join(output_dir, "params_fold{}.npz".format(self.fold_idx)) class DeepSleepNetTrainer(Trainer): def __init__( self, data_dir, output_dir, n_folds, fold_idx, batch_size, input_dims, n_classes, seq_length, n_rnn_layers, return_last, interval_plot_filter=50, interval_save_model=100, interval_print_cm=10 ): super(self.__class__, self).__init__( interval_plot_filter=interval_plot_filter, interval_save_model=interval_save_model, interval_print_cm=interval_print_cm ) self.data_dir = data_dir self.output_dir = output_dir self.n_folds = n_folds self.fold_idx = fold_idx self.batch_size = batch_size self.input_dims = input_dims self.n_classes = n_classes self.seq_length = seq_length self.n_rnn_layers = n_rnn_layers self.return_last = return_last def _run_epoch(self, sess, network, inputs, targets, train_op, is_train): start_time = time.time() y = [] y_true = [] total_loss, n_batches = 0.0, 0 for sub_idx, each_data in enumerate(zip(inputs, targets)): each_x, each_y = each_data # # Initialize state of LSTM - Unidirectional LSTM # state = sess.run(network.initial_state) # Initialize state of LSTM - Bidirectional LSTM fw_state = sess.run(network.fw_initial_state) bw_state = sess.run(network.bw_initial_state) for x_batch, y_batch in iterate_batch_seq_minibatches(inputs=each_x, targets=each_y, batch_size=self.batch_size, seq_length=self.seq_length): feed_dict = { network.input_var: x_batch, network.target_var: y_batch } # Unidirectional LSTM # for i, (c, h) in enumerate(network.initial_state): # feed_dict[c] = state[i].c # feed_dict[h] = state[i].h # _, loss_value, y_pred, state = sess.run( # [train_op, network.loss_op, network.pred_op, network.final_state], # feed_dict=feed_dict # ) for i, (c, h) in enumerate(network.fw_initial_state): feed_dict[c] = fw_state[i].c feed_dict[h] = fw_state[i].h for i, (c, h) in enumerate(network.bw_initial_state): feed_dict[c] = bw_state[i].c feed_dict[h] = bw_state[i].h _, loss_value, y_pred, fw_state, bw_state = sess.run( [train_op, network.loss_op, network.pred_op, network.fw_final_state, network.bw_final_state], feed_dict=feed_dict ) total_loss += loss_value n_batches += 1 y.append(y_pred) y_true.append(y_batch) # Check the loss value assert not np.isnan(loss_value), \ "Model diverged with loss = NaN" duration = time.time() - start_time total_loss /= n_batches total_y_pred = np.hstack(y) total_y_true = np.hstack(y_true) return total_y_true, total_y_pred, total_loss, duration def finetune(self, pretrained_model_path, n_epochs, resume): pretrained_model_name = "deepfeaturenet" with tf.Graph().as_default(), tf.compat.v1.Session() as sess: # Build training and validation networks train_net = DeepSleepNet( batch_size=self.batch_size, input_dims=self.input_dims, n_classes=self.n_classes, seq_length=self.seq_length, n_rnn_layers=self.n_rnn_layers, return_last=self.return_last, is_train=True, reuse_params=False, use_dropout_feature=True, use_dropout_sequence=True ) valid_net = DeepSleepNet( batch_size=self.batch_size, input_dims=self.input_dims, n_classes=self.n_classes, seq_length=self.seq_length, n_rnn_layers=self.n_rnn_layers, return_last=self.return_last, is_train=False, reuse_params=True, use_dropout_feature=True, use_dropout_sequence=True ) # Initialize parameters train_net.init_ops() valid_net.init_ops() print("Network (layers={})".format(len(train_net.activations))) print("inputs ({}): {}".format( train_net.input_var.name, train_net.input_var.get_shape() )) print("targets ({}): {}".format( train_net.target_var.name, train_net.target_var.get_shape() )) for name, act in train_net.activations: print("{} ({}): {}".format(name, act.name, act.get_shape())) print(" ") # Get list of all pretrained parameters with np.load(pretrained_model_path) as f: pretrain_params = list(f.keys()) # Remove the network-name-prefix for i in range(len(pretrain_params)): pretrain_params[i] = pretrain_params[i].replace(pretrained_model_name, "network") # Get trainable variables of the pretrained, and new ones train_vars1 = [v for v in tf.compat.v1.trainable_variables() if v.name.replace(train_net.name, "network") in pretrain_params] train_vars2 = list(set(tf.compat.v1.trainable_variables()) - set(train_vars1)) # Optimizer that use different learning rates for each part of the network train_op, grads_and_vars_op = adam_clipping_list_lr( loss=train_net.loss_op, list_lrs=[1e-6, 1e-4], list_train_vars=[train_vars1, train_vars2], clip_value=10.0 ) # Make subdirectory for pretraining output_dir = os.path.join(self.output_dir, "fold{}".format(self.fold_idx), train_net.name) if not os.path.exists(output_dir): os.makedirs(output_dir) # Global step for resume training with tf.compat.v1.variable_scope(train_net.name) as scope: global_step = tf.Variable(0, name="global_step", trainable=False) # print "Pretrained parameters:" # for v in train_vars1: # print v.name # print " " # print "Optimizing parameters:" # for v in train_vars2: # print v.name # print " " # print "Trainable Variables:" # for v in tf.compat.v1.trainable_variables(): # print v.name, v.get_shape() # print " " # print "All Variables:" # for v in tf.compat.v1.global_variables(): # print v.name, v.get_shape() # print " " # Create a saver saver = tf.compat.v1.train.Saver(tf.compat.v1.global_variables(), max_to_keep=0) # Initialize variables in the graph sess.run(tf.compat.v1.global_variables_initializer()) # Add the graph structure into the Tensorboard writer train_summary_wrt = tf.compat.v1.summary.FileWriter( os.path.join(output_dir, "train_summary"), sess.graph ) # Resume the training if applicable load_pretrain = False if resume: if os.path.exists(output_dir): if os.path.isfile(os.path.join(output_dir, "checkpoint")): # Restore the last checkpoint saver.restore(sess, tf.train.latest_checkpoint(output_dir)) print("Model restored") print("[{}] Resume fine-tuning ...\n".format(datetime.now())) else: load_pretrain = True else: load_pretrain = True if load_pretrain: # Load pre-trained model print("Loading pre-trained parameters to the model ...") print(" | --> {} from {}".format(pretrained_model_name, pretrained_model_path)) with np.load(pretrained_model_path) as f: for k, v in f.items(): if "Adam" in k or "softmax" in k or "power" in k or "global_step" in k: continue prev_k = k k = k.replace(pretrained_model_name, train_net.name) tmp_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name(k) sess.run( tf.compat.v1.assign( tmp_tensor, v ) ) print("assigned {}: {} to {}: {}".format( prev_k, v.shape, k, tmp_tensor.get_shape() )) print(" ") print("[{}] Start fine-tuning ...\n".format(datetime.now())) # Load data if sess.run(global_step) < n_epochs: data_loader = SeqDataLoader( data_dir=self.data_dir, n_folds=self.n_folds, fold_idx=self.fold_idx ) x_train, y_train, x_valid, y_valid = data_loader.load_train_data() # Performance history all_train_loss = np.zeros(n_epochs) all_train_acc = np.zeros(n_epochs) all_train_f1 = np.zeros(n_epochs) all_valid_loss = np.zeros(n_epochs) all_valid_acc = np.zeros(n_epochs) all_valid_f1 = np.zeros(n_epochs) # Loop each epoch for epoch in range(sess.run(global_step), n_epochs): # Update parameters and compute loss of training set y_true_train, y_pred_train, train_loss, train_duration = \ self._run_epoch( sess=sess, network=train_net, inputs=x_train, targets=y_train, train_op=train_op, is_train=True ) n_train_examples = len(y_true_train) train_cm = confusion_matrix(y_true_train, y_pred_train) train_acc = np.mean(y_true_train == y_pred_train) train_f1 = f1_score(y_true_train, y_pred_train, average="macro") # Evaluate the model on the validation set y_true_val, y_pred_val, valid_loss, valid_duration = \ self._run_epoch( sess=sess, network=valid_net, inputs=x_valid, targets=y_valid, train_op=tf.no_op(), is_train=False ) n_valid_examples = len(y_true_val) valid_cm = confusion_matrix(y_true_val, y_pred_val) valid_acc = np.mean(y_true_val == y_pred_val) valid_f1 = f1_score(y_true_val, y_pred_val, average="macro") all_train_loss[epoch] = train_loss all_train_acc[epoch] = train_acc all_train_f1[epoch] = train_f1 all_valid_loss[epoch] = valid_loss all_valid_acc[epoch] = valid_acc all_valid_f1[epoch] = valid_f1 # db.train_log(args={ # "n_folds": self.n_folds, # "fold_idx": self.fold_idx, # "epoch": epoch, # "train_step": "finetuning", # "datetime": datetime.utcnow(), # "model": train_net.name, # "n_train_examples": n_train_examples, # "n_valid_examples": n_valid_examples, # "train_loss": train_loss, # "train_acc": train_acc, # "train_f1": train_f1, # "train_duration": train_duration, # "valid_loss": valid_loss, # "valid_acc": valid_acc, # "valid_f1": valid_f1, # "valid_duration": valid_duration, # }) # Report performance self.print_performance( sess, output_dir, train_net.name, n_train_examples, n_valid_examples, train_cm, valid_cm, epoch, n_epochs, train_duration, train_loss, train_acc, train_f1, valid_duration, valid_loss, valid_acc, valid_f1 ) # Save performance history np.savez( os.path.join(output_dir, "perf_fold{}.npz".format(self.fold_idx)), train_loss=all_train_loss, valid_loss=all_valid_loss, train_acc=all_train_acc, valid_acc=all_valid_acc, train_f1=all_train_f1, valid_f1=all_valid_f1, y_true_val=np.asarray(y_true_val), y_pred_val=np.asarray(y_pred_val) ) # Visualize weights from convolutional layers if ((epoch + 1) % self.interval_plot_filter == 0) or ((epoch + 1) == n_epochs): self.plot_filters(sess, epoch, train_net.name + "(_[0-9])?\/l[0-9]+_conv\/(weights)", output_dir, 16) self.plot_filters(sess, epoch, train_net.name + "(_[0-9])?/l[0-9]+_conv\/conv1d\/(weights)", output_dir, 16) # Save checkpoint sess.run(tf.compat.v1.assign(global_step, epoch+1)) if ((epoch + 1) % self.interval_save_model == 0) or ((epoch + 1) == n_epochs): start_time = time.time() save_path = os.path.join( output_dir, "model_fold{}.ckpt".format(self.fold_idx) ) saver.save(sess, save_path, global_step=global_step) duration = time.time() - start_time print("Saved model checkpoint ({:.3f} sec)".format(duration)) # Save paramaters if ((epoch + 1) % self.interval_save_model == 0) or ((epoch + 1) == n_epochs): start_time = time.time() save_dict = {} for v in tf.compat.v1.global_variables(): save_dict[v.name] = sess.run(v) np.savez( os.path.join( output_dir, "params_fold{}.npz".format(self.fold_idx)), **save_dict ) duration = time.time() - start_time print("Saved trained parameters ({:.3f} sec)".format(duration)) print("Finish fine-tuning") return os.path.join(output_dir, "params_fold{}.npz".format(self.fold_idx))

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3,759

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4.288641

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0.017989

0.012902

0.803548

0.773959

0.752311

0.73916

0.719682

0

0.01286

0.406714

34,732

809

138

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0.091075

0

null

0

1

0

null

0

6

5059e219845b4a249306d11e0aeb71b92919c849

101

py

Python

tests/test_util.py

eikendev/dudendas

b03074deac55e4fb2eed105d2685a19c21651b2e

[ "MIT" ]

null

tests/test_util.py

eikendev/dudendas

b03074deac55e4fb2eed105d2685a19c21651b2e

[ "MIT" ]

null

tests/test_util.py

eikendev/dudendas

b03074deac55e4fb2eed105d2685a19c21651b2e

[ "MIT" ]

null

from dudendas.util import * def test_textify(): assert textify(" foo bar (3a) ") == "foo bar"

16.833333

50

0.633663

14

101

4.5

0.785714

0.190476

0

0.012658

0.217822

101

5

51

20.2

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0

0.217822

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

acf4b1f9aea318929865cb4e4d405e43d405a471

19

py

Python

rpiwepd/lib/__init__.py

genwch/rpiwepd

27ced8ef1255f17b475b231f6aefc1e4f8ab6a27

[ "MIT" ]

null

rpiwepd/lib/__init__.py

genwch/rpiwepd

27ced8ef1255f17b475b231f6aefc1e4f8ab6a27

[ "MIT" ]

null

rpiwepd/lib/__init__.py

genwch/rpiwepd

27ced8ef1255f17b475b231f6aefc1e4f8ab6a27

[ "MIT" ]

null

from .epd import *

9.5

18

0.684211

3

19

4.333333

1

0

0.210526

19

1

19

0.866667

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4a078c75b2e45c2072b55df47666e43db044972b

348

py

Python

lazythumbs/tests/__init__.py

caktus/lazythumbs

006ac42f9f4ac600d4c85d0929f4e2c755d4f853

[ "MIT" ]

1

2017-07-24T22:06:25.000Z

lazythumbs/tests/__init__.py

caktus/lazythumbs

006ac42f9f4ac600d4c85d0929f4e2c755d4f853

[ "MIT" ]

null

lazythumbs/tests/__init__.py

caktus/lazythumbs

006ac42f9f4ac600d4c85d0929f4e2c755d4f853

[ "MIT" ]

null

from lazythumbs.tests.test_server import RenderTest, GetViewTest from lazythumbs.tests.test_templatetag import LazythumbSyntaxTest, LazythumbGeometryCompileTest, LazythumbRenderTest from lazythumbs.tests.test_templatetag import ImgAttrsRenderTest from lazythumbs.tests.test_util import TestGeometry, TestComputeIMG, TestGetImgAttrs, TestGetFormat

69.6

116

0.893678

34

348

9.029412

0.529412

0.18241

0.247557

0.299674

0.260586

0

0.066092

348

4

117

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1

0

true

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1

0

1

0

null

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1

0

1

0

null

0

1

0

1

0

1

0

6

4a140e53ca915217379ec66d0341919746bb8c0d

58

py

Python

src/apps/trainings/viewsets/__init__.py

sanderland/katago-server

6414fab080d007c05068a06ff4f25907b92848bd

[ "MIT" ]

27

2020-05-03T11:01:27.000Z

2022-03-17T05:33:10.000Z

src/apps/trainings/viewsets/__init__.py

sanderland/katago-server

6414fab080d007c05068a06ff4f25907b92848bd

[ "MIT" ]

54

2020-05-09T01:18:41.000Z

2022-01-22T10:31:15.000Z

src/apps/trainings/viewsets/__init__.py

sanderland/katago-server

6414fab080d007c05068a06ff4f25907b92848bd

[ "MIT" ]

9

2020-09-29T11:31:32.000Z

2022-03-09T01:37:50.000Z

from .network import NetworkViewSet, NetworkViewSetForElo

29

57

0.87931

5

58

10.2

1

0

0.086207

58

1

58

0.962264

0

1

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true

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1

0

1

0

1

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0

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0

null

0

1

0

1

0

1

0

6

4a22fcc3c8ad1a3e760e61dd35132bec7ac943e6

6,735

py

Python

src/pymap3d/ned.py

ryanpavlick/pymap3d

968f6837b1550503461f884d8ce2e1b10c0db1f4

[ "BSD-2-Clause" ]

116

2020-02-23T02:04:18.000Z

2022-03-29T00:19:37.000Z

src/pymap3d/ned.py

ryanpavlick/pymap3d

968f6837b1550503461f884d8ce2e1b10c0db1f4

[ "BSD-2-Clause" ]

19

2020-03-02T08:13:46.000Z

2022-03-30T17:50:00.000Z

src/pymap3d/ned.py

ryanpavlick/pymap3d

968f6837b1550503461f884d8ce2e1b10c0db1f4

[ "BSD-2-Clause" ]

28

2020-02-24T11:56:03.000Z

2022-03-29T02:29:37.000Z

""" Transforms involving NED North East Down """ from __future__ import annotations import typing from .enu import geodetic2enu, aer2enu, enu2aer from .ecef import ecef2geodetic, ecef2enuv, ecef2enu, enu2ecef from .ellipsoid import Ellipsoid if typing.TYPE_CHECKING: from numpy import ndarray def aer2ned( az: ndarray, elev: ndarray, slantRange: ndarray, deg: bool = True ) -> tuple[ndarray, ndarray, ndarray]: """ converts azimuth, elevation, range to target from observer to North, East, Down Parameters ----------- az : float azimuth elev : float elevation slantRange : float slant range [meters] deg : bool, optional degrees input/output (False: radians in/out) Results ------- n : float North NED coordinate (meters) e : float East NED coordinate (meters) d : float Down NED coordinate (meters) """ e, n, u = aer2enu(az, elev, slantRange, deg=deg) return n, e, -u def ned2aer( n: ndarray, e: ndarray, d: ndarray, deg: bool = True ) -> tuple[ndarray, ndarray, ndarray]: """ converts North, East, Down to azimuth, elevation, range Parameters ---------- n : float North NED coordinate (meters) e : float East NED coordinate (meters) d : float Down NED coordinate (meters) deg : bool, optional degrees input/output (False: radians in/out) Results ------- az : float azimuth elev : float elevation slantRange : float slant range [meters] """ return enu2aer(e, n, -d, deg=deg) def ned2geodetic( n: ndarray, e: ndarray, d: ndarray, lat0: ndarray, lon0: ndarray, h0: ndarray, ell: Ellipsoid = None, deg: bool = True, ) -> tuple[ndarray, ndarray, ndarray]: """ Converts North, East, Down to target latitude, longitude, altitude Parameters ---------- n : float North NED coordinate (meters) e : float East NED coordinate (meters) d : float Down NED coordinate (meters) lat0 : float Observer geodetic latitude lon0 : float Observer geodetic longitude h0 : float observer altitude above geodetic ellipsoid (meters) ell : Ellipsoid, optional reference ellipsoid deg : bool, optional degrees input/output (False: radians in/out) Results ------- lat : float target geodetic latitude lon : float target geodetic longitude h : float target altitude above geodetic ellipsoid (meters) """ x, y, z = enu2ecef(e, n, -d, lat0, lon0, h0, ell, deg=deg) return ecef2geodetic(x, y, z, ell, deg=deg) def ned2ecef( n: ndarray, e: ndarray, d: ndarray, lat0: ndarray, lon0: ndarray, h0: ndarray, ell: Ellipsoid = None, deg: bool = True, ) -> tuple[ndarray, ndarray, ndarray]: """ North, East, Down to target ECEF coordinates Parameters ---------- n : float North NED coordinate (meters) e : float East NED coordinate (meters) d : float Down NED coordinate (meters) lat0 : float Observer geodetic latitude lon0 : float Observer geodetic longitude h0 : float observer altitude above geodetic ellipsoid (meters) ell : Ellipsoid, optional reference ellipsoid deg : bool, optional degrees input/output (False: radians in/out) Results ------- x : float ECEF x coordinate (meters) y : float ECEF y coordinate (meters) z : float ECEF z coordinate (meters) """ return enu2ecef(e, n, -d, lat0, lon0, h0, ell, deg=deg) def ecef2ned( x: ndarray, y: ndarray, z: ndarray, lat0: ndarray, lon0: ndarray, h0: ndarray, ell: Ellipsoid = None, deg: bool = True, ) -> tuple[ndarray, ndarray, ndarray]: """ Convert ECEF x,y,z to North, East, Down Parameters ---------- x : float ECEF x coordinate (meters) y : float ECEF y coordinate (meters) z : float ECEF z coordinate (meters) lat0 : float Observer geodetic latitude lon0 : float Observer geodetic longitude h0 : float observer altitude above geodetic ellipsoid (meters) ell : Ellipsoid, optional reference ellipsoid deg : bool, optional degrees input/output (False: radians in/out) Results ------- n : float North NED coordinate (meters) e : float East NED coordinate (meters) d : float Down NED coordinate (meters) """ e, n, u = ecef2enu(x, y, z, lat0, lon0, h0, ell, deg=deg) return n, e, -u def geodetic2ned( lat: ndarray, lon: ndarray, h: ndarray, lat0: ndarray, lon0: ndarray, h0: ndarray, ell: Ellipsoid = None, deg: bool = True, ) -> tuple[ndarray, ndarray, ndarray]: """ convert latitude, longitude, altitude of target to North, East, Down from observer Parameters ---------- lat : float target geodetic latitude lon : float target geodetic longitude h : float target altitude above geodetic ellipsoid (meters) lat0 : float Observer geodetic latitude lon0 : float Observer geodetic longitude h0 : float observer altitude above geodetic ellipsoid (meters) ell : Ellipsoid, optional reference ellipsoid deg : bool, optional degrees input/output (False: radians in/out) Results ------- n : float North NED coordinate (meters) e : float East NED coordinate (meters) d : float Down NED coordinate (meters) """ e, n, u = geodetic2enu(lat, lon, h, lat0, lon0, h0, ell, deg=deg) return n, e, -u def ecef2nedv( x: float, y: float, z: float, lat0: float, lon0: float, deg: bool = True ) -> tuple[float, float, float]: """ for VECTOR between two points Parameters ---------- x : float ECEF x coordinate (meters) y : float ECEF y coordinate (meters) z : float ECEF z coordinate (meters) lat0 : float Observer geodetic latitude lon0 : float Observer geodetic longitude deg : bool, optional degrees input/output (False: radians in/out) Results ------- (Vector) n : float North NED coordinate (meters) e : float East NED coordinate (meters) d : float Down NED coordinate (meters) """ e, n, u = ecef2enuv(x, y, z, lat0, lon0, deg=deg) return n, e, -u

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null

0

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null

0

6

c5928fd8bf3c19beebe27419c9d9fea66df3cb49

3,262

py

Python

GrowingNeuralGasPlotter.py

lucasolip/UnsupervisedArchitectures

fd724cdbb6a0fba8274edba1f4de49a74ab315bd

[ "MIT" ]

null

GrowingNeuralGasPlotter.py

lucasolip/UnsupervisedArchitectures

fd724cdbb6a0fba8274edba1f4de49a74ab315bd

[ "MIT" ]

null

GrowingNeuralGasPlotter.py

lucasolip/UnsupervisedArchitectures

fd724cdbb6a0fba8274edba1f4de49a74ab315bd

[ "MIT" ]

null

import matplotlib.pyplot as plt import tensorflow as tf class GrowingNeuralGasPlotter(object): @staticmethod def plotGraphConnectedComponent(pathFigure, nameFigure, A, N, X, edges): if len(X[0]) == 3: figure = plt.figure() axis = figure.add_subplot(projection='3d') axis.scatter(X[:, 0], X[:, 1], X[:, 2]) x = [A[index][0].numpy() for index in tf.range(A.shape[0])] y = [A[index][1].numpy() for index in tf.range(A.shape[0])] z = [A[index][2].numpy() for index in tf.range(A.shape[0])] graphZero = axis.scatter(x, y, z) for edge in edges: axis.plot(edge[:, 0], edge[:, 1], edge[:, 2], 'r-') elif len(X[0]) == 2: figure = plt.figure() axis = figure.add_subplot(projection='3d') axis.scatter(X[:, 0], X[:, 1]) x = [A[index][0].numpy() for index in tf.range(A.shape[0])] y = [A[index][1].numpy() for index in tf.range(A.shape[0])] graphZero = axis.scatter(x, y) for edge in edges: axis.plot(edge[:, 0], edge[:, 1], 'r-') # matplotlib.pyplot.show() figure.savefig(pathFigure + '//' + nameFigure + '.png', transparent=False, dpi=80, bbox_inches="tight") plt.close(figure) @staticmethod def plotNetworkStructure2D(A, X, edges, title="", save=False, pathFigure=".//", nameFigure="networkStructure2D"): fig = plt.figure() ax = fig.add_subplot() ax.scatter(X[:, 0], X[:, 1]) ax.scatter(A[:, 0], A[:, 1], c='r') for edge in edges: ax.plot(edge[:, 0], edge[:, 1], c='r') ax.set_title(title) if save: fig.savefig(pathFigure + '//' + nameFigure + '.png', transparent=False, dpi=80, bbox_inches="tight") @staticmethod def plotNetworkStructure3D(A, X, edges, title="", save=False, pathFigure=".//", nameFigure="networkStructure2D"): fig = plt.figure() ax = fig.add_subplot(projection='3d') ax.scatter(X[:, 0], X[:, 1], X[:, 2]) ax.scatter(A[:, 0], A[:, 1], A[:, 2], 'r') for edge in edges: ax.plot(edge[:, 0], edge[:, 1], edge[:, 2], 'r-') ax.set_title(title) if save: fig.savefig(pathFigure + '//' + nameFigure + '.png', transparent=False, dpi=80, bbox_inches="tight") @staticmethod def plotClusters2D(growingNeuralGas, X, title=""): fig = plt.figure() ax = fig.add_subplot() clusters = [0 for i in range(X.shape[0])] for i in range(X.shape[0]): clusters[i] = growingNeuralGas.predict(X[i]) ax.scatter(X[:, 0], X[:, 1], c=clusters) ax.set_title(title) @staticmethod def plotClusters3D(growingNeuralGas, X, title=""): fig = plt.figure() ax = fig.add_subplot(projection='3d') clusters = [0 for i in range(X.shape[0])] for i in range(X.shape[0]): clusters[i] = growingNeuralGas.predict(X[i]) ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=clusters) ax.set_title(title) @staticmethod def show(): plt.show()

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0.031858

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0.810619

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0.710324

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0.29767

3,262

82

119

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0.711043

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0

null

0

1

0

null

0

6

c59e8e61f9141dc03f11a055fa4a295a8b6650b8

146

py

Python

build_gpcr/management/commands/build_endogenous_ligands.py

pszgaspar/protwis

4989a67175ef3c95047d795c843cf6b9cf4141fa

[ "Apache-2.0" ]

21

2016-01-20T09:33:14.000Z

2021-12-20T19:19:45.000Z

build_gpcr/management/commands/build_endogenous_ligands.py

pszgaspar/protwis

4989a67175ef3c95047d795c843cf6b9cf4141fa

[ "Apache-2.0" ]

75

2016-02-26T16:29:58.000Z

2022-03-21T12:35:13.000Z

build_gpcr/management/commands/build_endogenous_ligands.py

pszgaspar/protwis

4989a67175ef3c95047d795c843cf6b9cf4141fa

[ "Apache-2.0" ]

77

2016-01-22T08:44:26.000Z

2022-02-01T15:54:56.000Z

from build.management.commands.build_endogenous_ligands import Command as BuildEndogenousLigands class Command(BuildEndogenousLigands): pass

29.2

96

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15

146

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0

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146

5

97

29.2

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true

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0.666667

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null

0

1

0

null

0

1

0

1

0

6

c5a64c18b42a86afdc12e1669501e0e9abbc3754

11,909

py

Python

Prob_WE/wasserstein/operators.py

albpurpura/PE4IR

54c5d471181cdb64225ecd738577b9f1f94c8d24

[ "Apache-2.0" ]

null

Prob_WE/wasserstein/operators.py

albpurpura/PE4IR

54c5d471181cdb64225ecd738577b9f1f94c8d24

[ "Apache-2.0" ]

null

Prob_WE/wasserstein/operators.py

albpurpura/PE4IR

54c5d471181cdb64225ecd738577b9f1f94c8d24

[ "Apache-2.0" ]

null

""" Author: Marco Maggipinto Copyright: (C) 2019-2020 <http://www.dei.unipd.it/ Department of Information Engineering> (DEI), <http://www.unipd.it/ University of Padua>, Italy License: <http://www.apache.org/licenses/LICENSE-2.0 Apache License, Version 2.0> """ import time import torch class BuresProduct: def __init__(self, e=1E-8, num_iters=20, reg=2.0): self.e = e self.num_iters = num_iters self.reg = reg def __call__(self, a, b, L_A, L_B): bures = BuresMetric.apply dim = a.shape[1] prod = a.view(-1, 1, dim).matmul(b.view(-1, dim, 1)).squeeze(dim=2) return prod + bures(L_A, L_B, self.e, self.num_iters, self.reg) class BuresProductNormalized: def __init__(self, e=1E-8, num_iters=20, reg=2.0): self.e = e self.num_iters = num_iters self.reg = reg def __call__(self, a, b, L_A, L_B): bures = BuresMetricNormalized.apply dim = a.shape[1] prod = a.view(-1, 1, dim).matmul(b.view(-1, dim, 1)).squeeze(dim=2) / (a.norm(dim=1) * b.norm(dim=1)).view(-1, 1) return prod + bures(L_A, L_B, self.e, self.num_iters, self.reg) class DistanceW2: def __init__(self, e=1E-8, num_iters=20, reg=2.0): self.e = e self.num_iters = num_iters self.reg = reg def __call__(self, a, b, L_A, L_B): bures = BuresMetric2.apply dim = a.shape[1] diff = a - b dist = torch.norm(diff, dim=1, keepdim=True) return dist ** 2 # + 1/(dim+2)*bures(L_A, L_B, self.e, self.num_iters, self.reg) class BuresProductNormalizedModule(torch.nn.Module): def __init__(self, e=1E-8, num_iters=20, reg=2.0): super().__init__() self.e = e self.num_iters = num_iters self.reg = reg def forward(self, a, b, L_A, L_B): bures = BuresMetricNormalized.apply dim = a.shape[1] prod = a.view(-1, 1, dim).matmul(b.view(-1, dim, 1)).squeeze(dim=2) / (a.norm(dim=1) * b.norm(dim=1)).view(-1, 1) return prod + bures(L_A, L_B, self.e, self.num_iters, self.reg) class BuresMetric(torch.autograd.Function): @staticmethod def forward(ctx, L_A, L_B, e=1E-8, num_iters=20, reg=2.0): device = L_A.device batch_size = L_A.shape[0] dim = L_A.shape[1] if L_A.shape[2] != dim: raise Exception("Matrix must be square") A = L_A.matmul(L_A.permute((0, 2, 1))) + e * torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to( device) B = L_B.matmul(L_B.permute((0, 2, 1))) + e * torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to( device) Y1, Z1 = matrix_square_root(A, num_iters, reg) supp = Y1.matmul(B).matmul(Y1) Y2, Z2 = matrix_square_root(supp, num_iters, reg) T_AB = Z1.matmul(Y2).matmul(Z1) T_BA = Y1.matmul(Z2).matmul(Y1) output = torch.zeros((batch_size, 1)).to(device) for i in range(batch_size): output[i, 0] = torch.trace(Y2[i, :, :]) ctx.save_for_backward(L_A, L_B, T_AB, T_BA) return output @staticmethod def backward(ctx, grad_output): L_A, L_B, T_AB, T_BA = ctx.saved_tensors device = L_A.device batch_size = L_A.shape[0] dim = L_A.shape[1] I = torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to(device) # grad_L_A = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * (I - T_AB).matmul(L_A) # grad_L_B = grad_output.expand(-1, dim*dim).view(-1, dim,dim) * (I - T_BA).matmul(L_B) grad_L_A = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * T_AB.matmul(L_A) grad_L_B = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * T_BA.matmul(L_B) return grad_L_A, grad_L_B, None, None, None class BuresMetricNormalized(torch.autograd.Function): @staticmethod def forward(ctx, L_A, L_B, e=1E-8, num_iters=20, reg=2.0): device = L_A.device batch_size = L_A.shape[0] dim = L_A.shape[1] if L_A.shape[2] != dim: raise Exception("Matrix must be square") A = L_A.matmul(L_A.permute((0, 2, 1))) + e * torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to( device) B = L_B.matmul(L_B.permute((0, 2, 1))) + e * torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to( device) Y1, Z1 = matrix_square_root(A, num_iters, reg) supp = Y1.matmul(B).matmul(Y1) Y2, Z2 = matrix_square_root(supp, num_iters, reg) T_AB = Z1.matmul(Y2).matmul(Z1) T_BA = Y1.matmul(Z2).matmul(Y1) output = torch.zeros((batch_size, 1)).to(device) trA = torch.zeros((batch_size, 1)).to(device) trB = torch.zeros((batch_size, 1)).to(device) for i in range(batch_size): trA[i, 0] = torch.trace(A[i, :, :]) trB[i, 0] = torch.trace(B[i, :, :]) output[i, 0] = torch.trace(Y2[i, :, :]) / torch.sqrt(trA[i] * trB[i]) ctx.save_for_backward(L_A, L_B, T_AB, T_BA, trA, trB, output) return output @staticmethod def backward(ctx, grad_output): L_A, L_B, T_AB, T_BA, trA, trB, output = ctx.saved_tensors device = L_A.device batch_size = L_A.shape[0] dim = L_A.shape[1] I = torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to(device) # grad_L_A = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * (I - T_AB).matmul(L_A)Normalized # grad_L_B = grad_output.expand(-1, dim*dim).view(-1, dim,dim) * (I - T_BA).matmul(L_B) grad_L_A = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * T_AB.matmul(L_A) / (trA * trB).sqrt().view( batch_size, 1, 1) + \ (output * trB / ((trA * trB) ** 3).sqrt()).view(-1, 1, 1) * L_A grad_L_B = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * T_BA.matmul(L_B) / (trA * trB).sqrt().view( batch_size, 1, 1) + \ (output * trA / ((trA * trB) ** 3).sqrt()).view(-1, 1, 1) * L_B return grad_L_A, grad_L_B, None, None, None class BuresMetric2(torch.autograd.Function): @staticmethod def forward(ctx, L_A, L_B, e=1E-8, num_iters=20, reg=2.0): device = L_A.device batch_size = L_A.shape[0] dim = L_A.shape[1] if L_A.shape[2] != dim: raise Exception("Matrix must be square") A = L_A.matmul(L_A.permute((0, 2, 1))) + e * torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to( device) B = L_B.matmul(L_B.permute((0, 2, 1))) + e * torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to( device) Y1, Z1 = matrix_square_root(A, num_iters, reg) supp = Y1.matmul(B).matmul(Y1) Y2, Z2 = matrix_square_root(supp, num_iters, reg) T_AB = Z1.matmul(Y2).matmul(Z1) T_BA = Y1.matmul(Z2).matmul(Y1) output = torch.zeros((batch_size, 1)).to(device) for i in range(batch_size): output[i, 0] = torch.trace(A[i, :, :] + B[i, :, :] - 2 * Y2[i, :, :]) ctx.save_for_backward(L_A, L_B, T_AB, T_BA) return output @staticmethod def backward(ctx, grad_output): L_A, L_B, T_AB, T_BA = ctx.saved_tensors device = L_A.device batch_size = L_A.shape[0] dim = L_A.shape[1] I = torch.eye(dim).view((1, dim, dim)).repeat(batch_size, 1, 1).to(device) grad_L_A = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * (I - T_AB).matmul(L_A) grad_L_B = grad_output.expand(-1, dim * dim).view(-1, dim, dim) * (I - T_BA).matmul(L_B) # grad_L_A = grad_output.expand(-1, dim*dim).view(-1, dim,dim) * T_AB.matmul(L_A) # grad_L_B = grad_output.expand(-1, dim*dim).view(-1, dim,dim) * T_BA.matmul(L_B) return grad_L_A, grad_L_B, None, None, None class RelTol: def __init__(self, dim): self.old_param = torch.zeros((1, dim)) def __call__(self, param): param = param.detach() device = param.device tol = (param - self.old_param.to(device)).norm() self.old_param = param.data.clone() return tol / param.norm() def centroid(means, L, metric, doc_lengths, tol=1E-3, lrd=0.9999): device = means.device dim = means.shape[1] t = RelTol(dim ** 2) n_docs = len(doc_lengths) Lc = torch.randn((n_docs, dim, dim), requires_grad=True, device=device) mc = torch.randn((n_docs, dim), requires_grad=True, device=device) tl = float('Inf') optimizer = torch.optim.SGD(list((Lc, mc)), lr=0.1) ind = get_indeces(doc_lengths) # lc_exp = Lc[ind, :, :] while tl > tol: ind = get_indeces(doc_lengths) # start = time.time() optimizer.zero_grad() # lc_exp = Lc.index_select(0, ind.to(Lc.device)) # mc_exp = mc.index_select(0, ind.to(Lc.device)) loss = -metric(mc[ind, :], means, Lc[ind, :, :], L).mean() # loss = -metric(mc_exp, means, lc_exp, L).mean() loss.backward() optimizer.step() for param_group in optimizer.param_groups: param_group['lr'] = param_group['lr'] * lrd # tl = t(Lc.view(n_docs, -1)) tl = t(Lc.data.view(n_docs, -1)) # print('time for one cycle: %2.4s' % (time.time() - start)) return mc, Lc def centroid_alt(w, q, metric, doc_lengths, tol=1E-3, lrd=0.9999): device = w.device dim = 50 # dim = w.shape[1] t = RelTol(dim ** 2) n_docs = len(doc_lengths) Lc = torch.randn((n_docs, dim, dim), requires_grad=True, device=device) mc = torch.randn((n_docs, dim), requires_grad=True, device=device) tl = float('Inf') optimizer = torch.optim.SGD(list((Lc, mc)), lr=0.1) # ind = get_indeces(doc_lengths) # lc_exp = Lc[ind, :, :] while tl > tol: m, v = (w[q, 0:dim].view(-1, dim), w[q, dim:].view((-1, dim, dim))) ind = get_indeces(doc_lengths) # start = time.time() optimizer.zero_grad() # lc_exp = Lc.index_select(0, ind.to(Lc.device)) # mc_exp = mc.index_select(0, ind.to(Lc.device)) loss = -metric(mc[ind, :], m, Lc[ind, :, :], v).mean() # loss = -metric(mc_exp, means, lc_exp, L).mean() loss.backward() optimizer.step() for param_group in optimizer.param_groups: param_group['lr'] = param_group['lr'] * lrd # tl = t(Lc.view(n_docs, -1)) tl = t(Lc.data.view(n_docs, -1)) # print('time for one cycle: %2.4s' % (time.time() - start)) return mc, Lc def get_indeces(doc_lenghts): n_docs = len(doc_lenghts) l = [] for i in range(n_docs): l.append(torch.ones(doc_lenghts[i]) * i) return torch.cat(l).long() def matrix_square_root(A, num_iters=20, reg=2.0): A = A.detach() device = A.device batch_size = A.shape[0] dim = A.shape[1] if A.shape[2] != dim: raise Exception("Matrix must be square") normA = reg * frobenius(A) Y = A.view(batch_size, -1).div(normA) Y = Y.view(batch_size, dim, dim) I = torch.eye(dim).reshape(1, dim, dim).repeat(batch_size, 1, 1).to(device) Z = torch.eye(dim).reshape(1, dim, dim).repeat(batch_size, 1, 1).to(device) for i in range(num_iters): T = 0.5 * (3.0 * I - torch.matmul(Z, Y)) Y = torch.matmul(Y, T) Z = torch.matmul(T, Z) sqrtA = Y.view(batch_size, -1) * torch.sqrt(normA) sqrtA = sqrtA.view(batch_size, dim, dim) sqrtAinv = Z.view(batch_size, -1).div(torch.sqrt(normA)) sqrtAinv = sqrtAinv.view(batch_size, dim, dim) return sqrtA, sqrtAinv def frobenius(A): batch_size = A.shape[0] return torch.norm(A.view(batch_size, -1), dim=1).view(batch_size, 1)

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a869172e2eed90c5a66784f962ea72e355055a89

14,012

py

Python

tests/schema/mysql/test_mysql_schema.py

mandarvaze/orm

35b3858caafab91c8690fc325a9472c04de4d00b

[ "MIT" ]

null

tests/schema/mysql/test_mysql_schema.py

mandarvaze/orm

35b3858caafab91c8690fc325a9472c04de4d00b

[ "MIT" ]

null

tests/schema/mysql/test_mysql_schema.py

mandarvaze/orm

35b3858caafab91c8690fc325a9472c04de4d00b

[ "MIT" ]

null

from src.masonite.orm.grammar.mysql_grammar import MySQLGrammar from src.masonite.orm.blueprint.Blueprint import Blueprint from src.masonite.orm.grammar.GrammarFactory import GrammarFactory from src.masonite.orm.schema.Schema import Schema import unittest, inspect class BaseTestCreateGrammar: def setUp(self): self.schema = Schema.on("mysql") def test_can_compile_column(self): with self.schema.create("users") as blueprint: blueprint.string("name") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_column_constraint(self): with self.schema.create("users") as blueprint: blueprint.string("name").unique() sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_multiple_columns(self): with self.schema.create("users") as blueprint: blueprint.string("name").nullable() blueprint.integer("age") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_not_null(self): with self.schema.create("users") as blueprint: blueprint.string("name") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_primary_key(self): with self.schema.create("users") as blueprint: blueprint.increments("id") blueprint.string("name") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_primary_key(self): with self.schema.create("users") as blueprint: blueprint.increments("id") blueprint.string("name") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_multiple_constraints(self): with self.schema.create("users") as blueprint: blueprint.increments("id") blueprint.string("name").unique() sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_enum(self): with self.schema.create("users") as blueprint: blueprint.enum("age", [1, 2, 3]).nullable() sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_column_exists(self): to_sql = self.schema.has_column("users", "email", query_only=True) sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(to_sql, sql) def test_drop_table(self): to_sql = self.schema.drop_table("users", query_only=True) sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(to_sql, sql) def test_drop_table_if_exists(self): to_sql = self.schema.drop_table_if_exists("users", query_only=True) sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(to_sql, sql) def test_drop_column(self): with self.schema.table("users") as blueprint: blueprint.drop_column("name") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_large_blueprint(self): with self.schema.create("users") as blueprint: blueprint.string("name") blueprint.string("email") blueprint.string("password") blueprint.integer("age").nullable() blueprint.enum("type", ["Open", "Closed"]) blueprint.datetime("pick_up") blueprint.binary("profile") blueprint.boolean("of_age") blueprint.char("first_initial", length=4) blueprint.date("birthday") blueprint.decimal("credit", 17, 6) blueprint.text("description") blueprint.unsigned("bank").nullable() sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_timestamps_columns_with_default(self): with self.schema.create("users") as blueprint: blueprint.timestamps() sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_timestamp_column_without_default(self): with self.schema.create("users") as blueprint: blueprint.timestamp("logged_at") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_timestamps_columns_mixed_defaults_and_not_default(self): with self.schema.create("users") as blueprint: blueprint.timestamps() blueprint.timestamp("logged_at") blueprint.timestamp("expirated_at") sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_timestamp_nullable_columns(self): with self.schema.create("users") as blueprint: blueprint.timestamp("logged_at") blueprint.timestamp("expirated_at").nullable() sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) def test_can_compile_timestamps_columns_with_default_of_now(self): with self.schema.create("users") as blueprint: blueprint.timestamp("logged_at", now=True) sql = getattr( self, inspect.currentframe().f_code.co_name.replace("test_", "") )() self.assertEqual(blueprint.to_sql(), sql) class TestMySQLCreateGrammar(BaseTestCreateGrammar, unittest.TestCase): def setUp(self): self.schema = Schema.on("mysql") def can_compile_column(self): """ with self.schema.create('users') as blueprint: blueprint.string('name') """ return "CREATE TABLE `users` (`name` VARCHAR(255) NOT NULL)" def can_compile_column_constraint(self): """ with self.schema.create('users') as blueprint: blueprint.string('name').unique() """ return "CREATE TABLE `users` (`name` VARCHAR(255) NOT NULL, CONSTRAINT name_unique UNIQUE (name))" def can_compile_multiple_columns(self): """ with self.schema.create('users') as blueprint: blueprint.string('name').nullable() blueprint.integer('age') """ return ( "CREATE TABLE `users` (" "`name` VARCHAR(255), " "`age` INT(11) NOT NULL" ")" ) def can_compile_not_null(self): """ with self.schema.create('users') as blueprint: blueprint.string('name') """ return "CREATE TABLE `users` (" "`name` VARCHAR(255) NOT NULL" ")" def can_compile_primary_key(self): """ with self.schema.create('users') as blueprint: blueprint.increments('id') blueprint.string('name') """ return ( "CREATE TABLE `users` (" "`id` INT AUTO_INCREMENT PRIMARY KEY NOT NULL, " "`name` VARCHAR(255) NOT NULL" ")" ) def can_compile_primary_key(self): """ with self.schema.create('users') as blueprint: blueprint.increments('id') blueprint.string('name') """ return ( "CREATE TABLE `users` (" "`id` INT AUTO_INCREMENT PRIMARY KEY NOT NULL, " "`name` VARCHAR(255) NOT NULL" ")" ) def can_compile_multiple_constraints(self): """ with self.schema.create('users') as blueprint: blueprint.increments('id') blueprint.string('name').unique() """ return ( "CREATE TABLE `users` (" "`id` INT AUTO_INCREMENT PRIMARY KEY NOT NULL, " "`name` VARCHAR(255) NOT NULL, " "CONSTRAINT name_unique UNIQUE (name)" ")" ) def can_compile_enum(self): """ with self.schema.create('users') as blueprint: blueprint.enum('age', [1,2,3]).nullable() """ return "CREATE TABLE `users` (" "`age` ENUM('1','2','3')" ")" def column_exists(self): """ self.schema.has_column('users', 'email', query_only=True) """ return "SHOW COLUMNS FROM `users` LIKE 'email'" def drop_table(self): """ to_sql = self.schema.drop_table('users', query_only=True) """ return "DROP TABLE `users`" def drop_table_if_exists(self): """ to_sql = self.schema.drop_table_if_exists('users', query_only=True) """ return "DROP TABLE IF EXISTS `users`" def drop_column(self): """ with self.schema.table('users') as blueprint: blueprint.drop_column('name') """ return "ALTER TABLE `users` " "DROP COLUMN `name`" def can_compile_large_blueprint(self): """ with self.schema.create('users') as blueprint: blueprint.string('name') blueprint.string('email') blueprint.string('password') blueprint.integer('age').nullable() blueprint.enum('type', ['Open', 'Closed']) blueprint.datetime('pick_up') blueprint.binary('profile') blueprint.boolean('of_age') blueprint.char('first_initial', length=4) blueprint.date('birthday') blueprint.decimal('credit', 17,6) blueprint.text('description') blueprint.unsigned('bank').nullable() """ return ( "CREATE TABLE `users` (" "`name` VARCHAR(255) NOT NULL, " "`email` VARCHAR(255) NOT NULL, " "`password` VARCHAR(255) NOT NULL, " "`age` INT(11), " "`type` ENUM('Open','Closed') NOT NULL, " "`pick_up` DATETIME NOT NULL, " "`profile` LONGBLOB NOT NULL, " "`of_age` BOOLEAN NOT NULL, " "`first_initial` CHAR(4) NOT NULL, " "`birthday` DATE NOT NULL, " "`credit` DECIMAL(17, 6) NOT NULL, " "`description` TEXT NOT NULL, " "`bank` INT UNSIGNED" ")" ) def test_default_string_length(self): with self.schema.table("users") as blueprint: blueprint.string("name") self.assertEqual(str(blueprint._columns[0].length), "255") return "ALTER TABLE `users` " "ADD `name` VARCHAR(255) NOT NULL" self.assertEqual(blueprint.to_sql(), sql) Schema.set_default_string_length("191") with self.schema.table("users") as blueprint: blueprint.string("name") self.assertEqual(str(blueprint._columns[0].length), "191") return "ALTER TABLE `users` " "ADD `name` VARCHAR(191) NOT NULL" self.assertEqual(blueprint.to_sql(), sql) def can_compile_timestamps_columns_with_default(self): """ with self.schema.create('users') as blueprint: blueprint.timestamps() """ return ( "CREATE TABLE `users` (" "`created_at` TIMESTAMP DEFAULT CURRENT_TIMESTAMP, " "`updated_at` TIMESTAMP DEFAULT CURRENT_TIMESTAMP" ")" ) def can_compile_timestamps_columns_with_default_of_now(self): """ with self.schema.create('users') as blueprint: blueprint.timestamp('logged_at', now=True) """ return "CREATE TABLE `users` (" "`logged_at` TIMESTAMP DEFAULT NOW()" ")" def can_compile_timestamp_column_without_default(self): """ with self.schema.create('users') as blueprint: blueprint.timestamp('logged_at') """ return "CREATE TABLE `users` (" "`logged_at` TIMESTAMP NOT NULL" ")" def can_compile_timestamps_columns_mixed_defaults_and_not_default(self): """ with self.schema.create('users') as blueprint: blueprint.timestamps() blueprint.timestamp('logged_at') blueprint.timestamp('expirated_at') """ return ( "CREATE TABLE `users` (" "`created_at` TIMESTAMP DEFAULT CURRENT_TIMESTAMP, " "`updated_at` TIMESTAMP DEFAULT CURRENT_TIMESTAMP, " "`logged_at` TIMESTAMP NOT NULL, " "`expirated_at` TIMESTAMP NOT NULL" ")" ) def can_compile_timestamp_nullable_columns(self): """ with self.schema.create('users') as blueprint: blueprint.timestamp('logged_at') blueprint.timestamp('expirated_at').nullable() """ return ( "CREATE TABLE `users` (" "`logged_at` TIMESTAMP NOT NULL, " "`expirated_at` TIMESTAMP" ")" )

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0

null

0

1

0

null

0

6

a89a53e13ecb73b4d2bf5fea96f5746f2db7b3f1

162

py

Python

app/config/views.py

maro99/yapen

0de7aa9d4b152aadd18511be6e536e89645452d9

[ "MIT" ]

1

2019-04-28T12:21:51.000Z

app/config/views.py

maro99/yapen

0de7aa9d4b152aadd18511be6e536e89645452d9

[ "MIT" ]

5

2018-07-30T05:44:44.000Z

2020-06-05T18:56:41.000Z

app/config/views.py

maro99/yapen

0de7aa9d4b152aadd18511be6e536e89645452d9

[ "MIT" ]

5

2018-07-23T05:21:41.000Z

2018-08-08T05:00:42.000Z

from django.http import HttpResponse from django.shortcuts import render, redirect def index(request): return render(request, 'pensions/pensions_list.html')

27

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21

162

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null

0

1

0

1

0

6

a8b92df4236143df4b3c01c4d048c78f795db432

127

py

Python

t.py

vansin/pix2code

caa3d2bcba9944ca9e9439f7551c8440c0087b8a

[ "Apache-2.0" ]

null

t.py

vansin/pix2code

caa3d2bcba9944ca9e9439f7551c8440c0087b8a

[ "Apache-2.0" ]

null

t.py

vansin/pix2code

caa3d2bcba9944ca9e9439f7551c8440c0087b8a

[ "Apache-2.0" ]

null

import tensorflow as tf gpu_device_name = tf.test.gpu_device_name() #print(gpu_device_name) print(tf.test.is_gpu_available())

21.166667

43

0.811024

22

127

4.318182

0.5

0.284211

0.410526

0.378947

0

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127

6

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false

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null

1

0

1

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null

0

1

0

6

a8cbab364f4bef461d1cad7e23b10052916535a2

45

py

Python

src/flask_pyoidc/__init__.py

infohash/Flask-pyoidc

7d50c3e4bb5298044752e07aea8e2d2d00d29b1b

[ "Apache-2.0" ]

64

2017-01-31T09:08:15.000Z

2021-12-21T21:05:45.000Z

src/flask_pyoidc/__init__.py

infohash/Flask-pyoidc

7d50c3e4bb5298044752e07aea8e2d2d00d29b1b

[ "Apache-2.0" ]

99

2017-02-08T22:38:54.000Z

2022-03-31T22:03:27.000Z

src/flask_pyoidc/__init__.py

infohash/Flask-pyoidc

7d50c3e4bb5298044752e07aea8e2d2d00d29b1b

[ "Apache-2.0" ]

33

2017-02-09T18:19:51.000Z

2021-12-24T17:48:52.000Z

from .flask_pyoidc import OIDCAuthentication

22.5

44

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1

0

6

7633fdcafcc2291c659fc2d7c33f8c232d071a76

102

py

Python

simulations/ecg/p.py

sensomatrix/sensocore

f2e94b7dae6ab3e95785c4c1b363e49aab23ddab

[ "MIT" ]

2

2019-04-02T00:17:57.000Z

2019-08-20T05:21:46.000Z

simulations/ecg/p.py

sensomatrix/sensocore

f2e94b7dae6ab3e95785c4c1b363e49aab23ddab

[ "MIT" ]

13

2019-04-01T00:37:01.000Z

2020-10-04T00:50:01.000Z

simulations/ecg/p.py

sensomatrix/sensocore

f2e94b7dae6ab3e95785c4c1b363e49aab23ddab

[ "MIT" ]

null

import math def p(M_P,t_P,W_P,t): return M_P * math.exp(-1 * ((t - t_P)/(math.sqrt(2) * W_P)) ** 2)

20.4

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0

1

0

null

0

1

0

1

0

6

4f50e42e960f390840fab47ebb82d938368fc417

1,688

py

Python

tests/test_gke.py

andrewpsp/astrobase

7adaa7c23bab3dbfdd90e18b5a10ed114b20bedd

[ "Apache-2.0" ]

34

2021-04-12T02:56:07.000Z

2022-03-24T21:56:58.000Z

tests/test_gke.py

andrewpsp/astrobase

7adaa7c23bab3dbfdd90e18b5a10ed114b20bedd

[ "Apache-2.0" ]

2

2021-12-29T04:07:24.000Z

2022-03-16T06:05:05.000Z

tests/test_gke.py

andrewpsp/astrobase

7adaa7c23bab3dbfdd90e18b5a10ed114b20bedd

[ "Apache-2.0" ]

1

2021-05-30T03:59:07.000Z

from unittest import mock from tests.factories import ClusterFactory cluster_examples = ClusterFactory() def test_create_cluster(client): with mock.patch( "astrobase.apis.gke.GKEApi.make_create_request" ) as mock_gke_api_request: mock_gke_api_request.return_value = {"name": "astrobase-gke-api"} response = client.post("/gke", json=cluster_examples.gke_example()) assert response.status_code == 200 assert response.json().get("name") == "astrobase-gke-api" def test_get_clusters(client): with mock.patch( "astrobase.apis.gke.GKEApi.make_get_request" ) as mock_gke_api_request: mock_gke_api_request.return_value = {"name": "astrobase-gke-api"} response = client.get( "/gke?project_id=test&location=us-central1", json=cluster_examples.gke_example(), ) assert response.status_code == 200 assert response.json().get("name") == "astrobase-gke-api" def test_describe_cluster(client): with mock.patch( "astrobase.apis.gke.GKEApi.make_describe_request" ) as mock_gke_api_request: mock_gke_api_request.return_value = {"name": "astrobase-gke-api"} response = client.get( "/gke/astrobase-gke-api?project_id=test&location=us-central1" ) assert response.status_code == 200 assert response.json().get("name") == "astrobase-gke-api" def test_delete_clister(client): with mock.patch("astrobase.apis.gke.GKEApi.make_delete_request"): response = client.delete( "/gke/astrobase-gke-api?project_id=test&location=us-central1" ) assert response.status_code == 200

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0

6

8c23ca0439fd9a2cd9b86c5ac82d687887c84d5e

145

py

Python

client_wishlist/wishlist/admin.py

EVolpert/client_whishlist

b2da64a53e978bc77bc4fb9a8c9b9dc4af66c5b1

[ "CC0-1.0" ]

null

client_wishlist/wishlist/admin.py

EVolpert/client_whishlist

b2da64a53e978bc77bc4fb9a8c9b9dc4af66c5b1

[ "CC0-1.0" ]

5

2021-03-30T14:20:02.000Z

2021-09-22T19:29:15.000Z

client_wishlist/wishlist/admin.py

EVolpert/client_whishlist

b2da64a53e978bc77bc4fb9a8c9b9dc4af66c5b1

[ "CC0-1.0" ]

1

2020-08-18T16:35:12.000Z

from django.contrib import admin from wishlist.models import Wishlist @admin.register(Wishlist) class WishlistAdmin(admin.ModelAdmin): pass

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1

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6

8b22e4d1c248a5fd419cb4dc89a783f169ce6d7c

10,955

py

Python

tests/api/test_communities.py

LEv145/python-twitch-client

f1f45cf8afb1da1335b8f29023d4db5855f009bf

[ "MIT" ]

171

2017-02-25T19:22:22.000Z

2022-02-13T22:23:14.000Z

tests/api/test_communities.py

LEv145/python-twitch-client

f1f45cf8afb1da1335b8f29023d4db5855f009bf

[ "MIT" ]

55

2017-03-13T01:41:50.000Z

2022-02-11T20:38:54.000Z

tests/api/test_communities.py

LEv145/python-twitch-client

f1f45cf8afb1da1335b8f29023d4db5855f009bf

[ "MIT" ]

68

2017-02-25T20:07:46.000Z

2022-02-04T16:48:47.000Z

import json import pytest import responses from twitch.client import TwitchClient from twitch.constants import BASE_URL from twitch.exceptions import TwitchAttributeException from twitch.resources import Community, User example_community = { "_id": "e9f17055-810f-4736-ba40-fba4ac541caa", "name": "DallasTesterCommunity", } example_user = { "_id": "44322889", "name": "dallas", } @responses.activate def test_get_by_name(): responses.add( responses.GET, "{}communities".format(BASE_URL), body=json.dumps(example_community), status=200, content_type="application/json", ) client = TwitchClient("client id") community = client.communities.get_by_name("spongebob") assert len(responses.calls) == 1 assert isinstance(community, Community) assert community.id == example_community["_id"] assert community.name == example_community["name"] @responses.activate def test_get_by_id(): community_id = "abcd" responses.add( responses.GET, "{}communities/{}".format(BASE_URL, community_id), body=json.dumps(example_community), status=200, content_type="application/json", ) client = TwitchClient("client id") community = client.communities.get_by_id(community_id) assert len(responses.calls) == 1 assert isinstance(community, Community) assert community.id == example_community["_id"] assert community.name == example_community["name"] @responses.activate def test_update(): community_id = "abcd" responses.add( responses.PUT, "{}communities/{}".format(BASE_URL, community_id), body=json.dumps(example_community), status=204, content_type="application/json", ) client = TwitchClient("client id") client.communities.update(community_id) assert len(responses.calls) == 1 @responses.activate def test_get_top(): response = {"_cursor": "MTA=", "_total": 100, "communities": [example_community]} responses.add( responses.GET, "{}communities/top".format(BASE_URL), body=json.dumps(response), status=200, content_type="application/json", ) client = TwitchClient("client id") communities = client.communities.get_top() assert len(responses.calls) == 1 assert len(communities) == 1 community = communities[0] assert isinstance(community, Community) assert community.id == example_community["_id"] assert community.name == example_community["name"] @responses.activate @pytest.mark.parametrize("param,value", [("limit", 101)]) def test_get_top_raises_if_wrong_params_are_passed_in(param, value): client = TwitchClient("client id") kwargs = {param: value} with pytest.raises(TwitchAttributeException): client.communities.get_top(**kwargs) @responses.activate def test_get_banned_users(): community_id = "abcd" response = {"_cursor": "", "banned_users": [example_user]} responses.add( responses.GET, "{}communities/{}/bans".format(BASE_URL, community_id), body=json.dumps(response), status=200, content_type="application/json", ) client = TwitchClient("client id", "oauth token") users = client.communities.get_banned_users(community_id) assert len(responses.calls) == 1 assert len(users) == 1 user = users[0] assert isinstance(user, User) assert user.id == example_user["_id"] assert user.name == example_user["name"] @responses.activate @pytest.mark.parametrize("param,value", [("limit", 101)]) def test_get_banned_users_raises_if_wrong_params_are_passed_in(param, value): client = TwitchClient("client id", "oauth token") kwargs = {param: value} with pytest.raises(TwitchAttributeException): client.communities.get_banned_users("1234", **kwargs) @responses.activate def test_ban_user(): community_id = "abcd" user_id = 1234 responses.add( responses.PUT, "{}communities/{}/bans/{}".format(BASE_URL, community_id, user_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.ban_user(community_id, user_id) assert len(responses.calls) == 1 @responses.activate def test_unban_user(): community_id = "abcd" user_id = 1234 responses.add( responses.DELETE, "{}communities/{}/bans/{}".format(BASE_URL, community_id, user_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.unban_user(community_id, user_id) assert len(responses.calls) == 1 @responses.activate def test_create_avatar_image(): community_id = "abcd" responses.add( responses.POST, "{}communities/{}/images/avatar".format(BASE_URL, community_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.create_avatar_image(community_id, "imagecontent") assert len(responses.calls) == 1 @responses.activate def test_delete_avatar_image(): community_id = "abcd" responses.add( responses.DELETE, "{}communities/{}/images/avatar".format(BASE_URL, community_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.delete_avatar_image(community_id) assert len(responses.calls) == 1 @responses.activate def test_create_cover_image(): community_id = "abcd" responses.add( responses.POST, "{}communities/{}/images/cover".format(BASE_URL, community_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.create_cover_image(community_id, "imagecontent") assert len(responses.calls) == 1 @responses.activate def test_delete_cover_image(): community_id = "abcd" responses.add( responses.DELETE, "{}communities/{}/images/cover".format(BASE_URL, community_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.delete_cover_image(community_id) assert len(responses.calls) == 1 @responses.activate def test_get_moderators(): community_id = "abcd" response = {"moderators": [example_user]} responses.add( responses.GET, "{}communities/{}/moderators".format(BASE_URL, community_id), body=json.dumps(response), status=200, content_type="application/json", ) client = TwitchClient("client id", "oauth token") moderators = client.communities.get_moderators(community_id) assert len(responses.calls) == 1 assert len(moderators) == 1 user = moderators[0] assert isinstance(user, User) assert user.id == example_user["_id"] assert user.name == example_user["name"] @responses.activate def test_add_moderator(): community_id = "abcd" user_id = 12345 responses.add( responses.PUT, "{}communities/{}/moderators/{}".format(BASE_URL, community_id, user_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.add_moderator(community_id, user_id) assert len(responses.calls) == 1 @responses.activate def test_delete_moderator(): community_id = "abcd" user_id = 12345 responses.add( responses.DELETE, "{}communities/{}/moderators/{}".format(BASE_URL, community_id, user_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.delete_moderator(community_id, user_id) assert len(responses.calls) == 1 @responses.activate def test_get_permissions(): community_id = "abcd" response = {"ban": True, "timeout": True, "edit": True} responses.add( responses.GET, "{}communities/{}/permissions".format(BASE_URL, community_id), body=json.dumps(response), status=200, content_type="application/json", ) client = TwitchClient("client id", "oauth token") permissions = client.communities.get_permissions(community_id) assert len(responses.calls) == 1 assert isinstance(permissions, dict) assert permissions["ban"] is True @responses.activate def test_report_violation(): community_id = "abcd" responses.add( responses.POST, "{}communities/{}/report_channel".format(BASE_URL, community_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.report_violation(community_id, 12345) assert len(responses.calls) == 1 @responses.activate def test_get_timed_out_users(): community_id = "abcd" response = {"_cursor": "", "timed_out_users": [example_user]} responses.add( responses.GET, "{}communities/{}/timeouts".format(BASE_URL, community_id), body=json.dumps(response), status=200, content_type="application/json", ) client = TwitchClient("client id", "oauth token") users = client.communities.get_timed_out_users(community_id) assert len(responses.calls) == 1 assert len(users) == 1 user = users[0] assert isinstance(user, User) assert user.id == example_user["_id"] assert user.name == example_user["name"] @responses.activate @pytest.mark.parametrize("param,value", [("limit", 101)]) def test_get_timed_out_users_raises_if_wrong_params_are_passed_in(param, value): client = TwitchClient("client id", "oauth token") kwargs = {param: value} with pytest.raises(TwitchAttributeException): client.communities.get_timed_out_users("1234", **kwargs) @responses.activate def test_add_timed_out_user(): community_id = "abcd" user_id = 12345 responses.add( responses.PUT, "{}communities/{}/timeouts/{}".format(BASE_URL, community_id, user_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.add_timed_out_user(community_id, user_id, 5) assert len(responses.calls) == 1 @responses.activate def test_delete_timed_out_user(): community_id = "abcd" user_id = 12345 responses.add( responses.DELETE, "{}communities/{}/timeouts/{}".format(BASE_URL, community_id, user_id), status=204, content_type="application/json", ) client = TwitchClient("client id", "oauth token") client.communities.delete_timed_out_user(community_id, user_id) assert len(responses.calls) == 1

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null

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null

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6

8c6672e903fbd2db5155939f1b1597017c2c194e

40

py

Python

pyhcl/lib/__init__.py

raybdzhou/PyChip-py-hcl

08edc6ad4d2978eb417482f6f92678f8f9a1e3c7

[ "MIT" ]

1

2021-12-10T14:02:54.000Z

pyhcl/lib/__init__.py

raybdzhou/PyChip-py-hcl

08edc6ad4d2978eb417482f6f92678f8f9a1e3c7

[ "MIT" ]

null

pyhcl/lib/__init__.py

raybdzhou/PyChip-py-hcl

08edc6ad4d2978eb417482f6f92678f8f9a1e3c7

[ "MIT" ]

1

2022-03-04T03:36:01.000Z

from .fifo.fifo import BubbleFifoFactory

40

0.875

5

40

7

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40

1

40

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1

0

1

0

6

8cef041bcd91a43c1443fa376c88bd6bfb52ff6a

23

py

Python

pixel_table/modes/rain/__init__.py

Spooner/pixel-table

87ac04adbb74702bee3dcaa5c6bded7786cf73e7

[ "MIT" ]

null

pixel_table/modes/rain/__init__.py

Spooner/pixel-table

87ac04adbb74702bee3dcaa5c6bded7786cf73e7

[ "MIT" ]

null

pixel_table/modes/rain/__init__.py

Spooner/pixel-table

87ac04adbb74702bee3dcaa5c6bded7786cf73e7

[ "MIT" ]

null

from .rain import Rain

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null

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1

0

1

0

1

0

6

50f2010022ed389ce74c9ce90b8243696999822e

17,354

py

Python

tests/gold_tests/pluginTest/stek_share/stek_share.test.py

cmcfarlen/trafficserver

2aa1d3106398eb082e5a454212b0273c63d5f69d

[ "Apache-2.0" ]

null

tests/gold_tests/pluginTest/stek_share/stek_share.test.py

cmcfarlen/trafficserver

2aa1d3106398eb082e5a454212b0273c63d5f69d

[ "Apache-2.0" ]

null

tests/gold_tests/pluginTest/stek_share/stek_share.test.py

cmcfarlen/trafficserver

2aa1d3106398eb082e5a454212b0273c63d5f69d

[ "Apache-2.0" ]

null

''' ''' # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import re Test.Summary = 'Test the STEK Share plugin' Test.testName = "stek_share" Test.SkipUnless(Condition.PluginExists('stek_share.so')) server = Test.MakeOriginServer('server') ts1 = Test.MakeATSProcess("ts1", select_ports=True, enable_tls=True) ts2 = Test.MakeATSProcess("ts2", select_ports=True, enable_tls=True) ts3 = Test.MakeATSProcess("ts3", select_ports=True, enable_tls=True) ts4 = Test.MakeATSProcess("ts4", select_ports=True, enable_tls=True) ts5 = Test.MakeATSProcess("ts5", select_ports=True, enable_tls=True) Test.Setup.Copy('ssl/self_signed.crt') Test.Setup.Copy('ssl/self_signed.key') Test.Setup.Copy('server_list.yaml') cert_path = os.path.join(Test.RunDirectory, 'self_signed.crt') key_path = os.path.join(Test.RunDirectory, 'self_signed.key') server_list_path = os.path.join(Test.RunDirectory, 'server_list.yaml') request_header1 = { 'headers': 'GET / HTTP/1.1\r\nHost: www.example.com\r\n\r\n', 'timestamp': '1469733493.993', 'body': '' } response_header1 = { 'headers': 'HTTP/1.1 200 OK\r\nConnection: close\r\n\r\n', 'timestamp': '1469733493.993', 'body': 'curl test' } server.addResponse('sessionlog.json', request_header1, response_header1) stek_share_conf_path_1 = os.path.join(ts1.Variables.CONFIGDIR, 'stek_share_conf.yaml') stek_share_conf_path_2 = os.path.join(ts2.Variables.CONFIGDIR, 'stek_share_conf.yaml') stek_share_conf_path_3 = os.path.join(ts3.Variables.CONFIGDIR, 'stek_share_conf.yaml') stek_share_conf_path_4 = os.path.join(ts4.Variables.CONFIGDIR, 'stek_share_conf.yaml') stek_share_conf_path_5 = os.path.join(ts5.Variables.CONFIGDIR, 'stek_share_conf.yaml') ts1.Disk.File(stek_share_conf_path_1, id="stek_share_conf_1", typename="ats:config") ts2.Disk.File(stek_share_conf_path_2, id="stek_share_conf_2", typename="ats:config") ts3.Disk.File(stek_share_conf_path_3, id="stek_share_conf_3", typename="ats:config") ts4.Disk.File(stek_share_conf_path_4, id="stek_share_conf_4", typename="ats:config") ts5.Disk.File(stek_share_conf_path_5, id="stek_share_conf_5", typename="ats:config") ts1.Disk.stek_share_conf_1.AddLines([ 'server_id: 1', 'address: 127.0.0.1', 'port: 10001', 'asio_thread_pool_size: 4', 'heart_beat_interval: 100', 'election_timeout_lower_bound: 200', 'election_timeout_upper_bound: 400', 'reserved_log_items: 5', 'snapshot_distance: 5', 'client_req_timeout: 3000', # this is in milliseconds 'key_update_interval: 3600', # this is in seconds 'server_list_file: {0}'.format(server_list_path), 'root_cert_file: {0}'.format(cert_path), 'server_cert_file: {0}'.format(cert_path), 'server_key_file: {0}'.format(key_path), 'cert_verify_str: /C=US/ST=IL/O=Yahoo/OU=Edge/CN=stek-share' ]) ts2.Disk.stek_share_conf_2.AddLines([ 'server_id: 2', 'address: 127.0.0.1', 'port: 10002', 'asio_thread_pool_size: 4', 'heart_beat_interval: 100', 'election_timeout_lower_bound: 200', 'election_timeout_upper_bound: 400', 'reserved_log_items: 5', 'snapshot_distance: 5', 'client_req_timeout: 3000', # this is in milliseconds 'key_update_interval: 3600', # this is in seconds 'server_list_file: {0}'.format(server_list_path), 'root_cert_file: {0}'.format(cert_path), 'server_cert_file: {0}'.format(cert_path), 'server_key_file: {0}'.format(key_path), 'cert_verify_str: /C=US/ST=IL/O=Yahoo/OU=Edge/CN=stek-share' ]) ts3.Disk.stek_share_conf_3.AddLines([ 'server_id: 3', 'address: 127.0.0.1', 'port: 10003', 'asio_thread_pool_size: 4', 'heart_beat_interval: 100', 'election_timeout_lower_bound: 200', 'election_timeout_upper_bound: 400', 'reserved_log_items: 5', 'snapshot_distance: 5', 'client_req_timeout: 3000', # this is in milliseconds 'key_update_interval: 3600', # this is in seconds 'server_list_file: {0}'.format(server_list_path), 'root_cert_file: {0}'.format(cert_path), 'server_cert_file: {0}'.format(cert_path), 'server_key_file: {0}'.format(key_path), 'cert_verify_str: /C=US/ST=IL/O=Yahoo/OU=Edge/CN=stek-share' ]) ts4.Disk.stek_share_conf_4.AddLines([ 'server_id: 4', 'address: 127.0.0.1', 'port: 10004', 'asio_thread_pool_size: 4', 'heart_beat_interval: 100', 'election_timeout_lower_bound: 200', 'election_timeout_upper_bound: 400', 'reserved_log_items: 5', 'snapshot_distance: 5', 'client_req_timeout: 3000', # this is in milliseconds 'key_update_interval: 3600', # this is in seconds 'server_list_file: {0}'.format(server_list_path), 'root_cert_file: {0}'.format(cert_path), 'server_cert_file: {0}'.format(cert_path), 'server_key_file: {0}'.format(key_path), 'cert_verify_str: /C=US/ST=IL/O=Yahoo/OU=Edge/CN=stek-share' ]) ts5.Disk.stek_share_conf_5.AddLines([ 'server_id: 5', 'address: 127.0.0.1', 'port: 10005', 'asio_thread_pool_size: 4', 'heart_beat_interval: 100', 'election_timeout_lower_bound: 200', 'election_timeout_upper_bound: 400', 'reserved_log_items: 5', 'snapshot_distance: 5', 'client_req_timeout: 3000', # this is in milliseconds 'key_update_interval: 3600', # this is in seconds 'server_list_file: {0}'.format(server_list_path), 'root_cert_file: {0}'.format(cert_path), 'server_cert_file: {0}'.format(cert_path), 'server_key_file: {0}'.format(key_path), 'cert_verify_str: /C=US/ST=IL/O=Yahoo/OU=Edge/CN=stek-share' ]) ts1.Disk.records_config.update({'proxy.config.diags.debug.enabled': 1, 'proxy.config.diags.debug.tags': 'stek_share', 'proxy.config.exec_thread.autoconfig': 0, 'proxy.config.exec_thread.limit': 4, 'proxy.config.ssl.server.cert.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.server.private_key.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.session_cache': 2, 'proxy.config.ssl.session_cache.size': 1024, 'proxy.config.ssl.session_cache.timeout': 7200, 'proxy.config.ssl.session_cache.num_buckets': 16, 'proxy.config.ssl.server.session_ticket.enable': 1, 'proxy.config.ssl.server.cipher_suite': 'ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-DSS-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA256:DHE-RSA-AES128-SHA256:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA:DHE-DSS-AES256-SHA:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!MD5:!PSK:!aECDH:!EDH-DSS-DES-CBC3-SHA:!EDH-RSA-DES-CBC3-SHA:!KRB5-DES-CBC3-SHA'}) ts1.Disk.plugin_config.AddLine('stek_share.so {0}'.format(stek_share_conf_path_1)) ts1.Disk.ssl_multicert_config.AddLine('dest_ip=* ssl_cert_name=self_signed.crt ssl_key_name=self_signed.key') ts1.Disk.remap_config.AddLine('map / http://127.0.0.1:{0}'.format(server.Variables.Port)) ts2.Disk.records_config.update({'proxy.config.diags.debug.enabled': 1, 'proxy.config.diags.debug.tags': 'stek_share', 'proxy.config.exec_thread.autoconfig': 0, 'proxy.config.exec_thread.limit': 4, 'proxy.config.ssl.server.cert.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.server.private_key.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.session_cache': 2, 'proxy.config.ssl.session_cache.size': 1024, 'proxy.config.ssl.session_cache.timeout': 7200, 'proxy.config.ssl.session_cache.num_buckets': 16, 'proxy.config.ssl.server.session_ticket.enable': 1, 'proxy.config.ssl.server.cipher_suite': 'ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-DSS-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA256:DHE-RSA-AES128-SHA256:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA:DHE-DSS-AES256-SHA:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!MD5:!PSK:!aECDH:!EDH-DSS-DES-CBC3-SHA:!EDH-RSA-DES-CBC3-SHA:!KRB5-DES-CBC3-SHA'}) ts2.Disk.plugin_config.AddLine('stek_share.so {0}'.format(stek_share_conf_path_2)) ts2.Disk.ssl_multicert_config.AddLine('dest_ip=* ssl_cert_name=self_signed.crt ssl_key_name=self_signed.key') ts2.Disk.remap_config.AddLine('map / http://127.0.0.1:{0}'.format(server.Variables.Port)) ts3.Disk.records_config.update({'proxy.config.diags.debug.enabled': 1, 'proxy.config.diags.debug.tags': 'stek_share', 'proxy.config.exec_thread.autoconfig': 0, 'proxy.config.exec_thread.limit': 4, 'proxy.config.ssl.server.cert.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.server.private_key.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.session_cache': 2, 'proxy.config.ssl.session_cache.size': 1024, 'proxy.config.ssl.session_cache.timeout': 7200, 'proxy.config.ssl.session_cache.num_buckets': 16, 'proxy.config.ssl.server.session_ticket.enable': 1, 'proxy.config.ssl.server.cipher_suite': 'ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-DSS-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA256:DHE-RSA-AES128-SHA256:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA:DHE-DSS-AES256-SHA:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!MD5:!PSK:!aECDH:!EDH-DSS-DES-CBC3-SHA:!EDH-RSA-DES-CBC3-SHA:!KRB5-DES-CBC3-SHA'}) ts3.Disk.plugin_config.AddLine('stek_share.so {0}'.format(stek_share_conf_path_3)) ts3.Disk.ssl_multicert_config.AddLine('dest_ip=* ssl_cert_name=self_signed.crt ssl_key_name=self_signed.key') ts3.Disk.remap_config.AddLine('map / http://127.0.0.1:{0}'.format(server.Variables.Port)) ts4.Disk.records_config.update({'proxy.config.diags.debug.enabled': 1, 'proxy.config.diags.debug.tags': 'stek_share', 'proxy.config.exec_thread.autoconfig': 0, 'proxy.config.exec_thread.limit': 4, 'proxy.config.ssl.server.cert.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.server.private_key.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.session_cache': 2, 'proxy.config.ssl.session_cache.size': 1024, 'proxy.config.ssl.session_cache.timeout': 7200, 'proxy.config.ssl.session_cache.num_buckets': 16, 'proxy.config.ssl.server.session_ticket.enable': 1, 'proxy.config.ssl.server.cipher_suite': 'ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-DSS-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA256:DHE-RSA-AES128-SHA256:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA:DHE-DSS-AES256-SHA:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!MD5:!PSK:!aECDH:!EDH-DSS-DES-CBC3-SHA:!EDH-RSA-DES-CBC3-SHA:!KRB5-DES-CBC3-SHA'}) ts4.Disk.plugin_config.AddLine('stek_share.so {0}'.format(stek_share_conf_path_4)) ts4.Disk.ssl_multicert_config.AddLine('dest_ip=* ssl_cert_name=self_signed.crt ssl_key_name=self_signed.key') ts4.Disk.remap_config.AddLine('map / http://127.0.0.1:{0}'.format(server.Variables.Port)) ts5.Disk.records_config.update({'proxy.config.diags.debug.enabled': 1, 'proxy.config.diags.debug.tags': 'stek_share', 'proxy.config.exec_thread.autoconfig': 0, 'proxy.config.exec_thread.limit': 4, 'proxy.config.ssl.server.cert.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.server.private_key.path': '{0}'.format(Test.RunDirectory), 'proxy.config.ssl.session_cache': 2, 'proxy.config.ssl.session_cache.size': 1024, 'proxy.config.ssl.session_cache.timeout': 7200, 'proxy.config.ssl.session_cache.num_buckets': 16, 'proxy.config.ssl.server.session_ticket.enable': 1, 'proxy.config.ssl.server.cipher_suite': 'ECDHE-ECDSA-AES256-GCM-SHA384:ECDHE-RSA-AES256-GCM-SHA384:ECDHE-ECDSA-AES128-GCM-SHA256:ECDHE-RSA-AES128-GCM-SHA256:DHE-RSA-AES256-GCM-SHA384:DHE-DSS-AES256-GCM-SHA384:DHE-RSA-AES128-GCM-SHA256:DHE-DSS-AES128-GCM-SHA256:ECDHE-ECDSA-AES256-SHA384:ECDHE-RSA-AES256-SHA384:ECDHE-ECDSA-AES256-SHA:ECDHE-RSA-AES256-SHA:ECDHE-ECDSA-AES128-SHA256:ECDHE-RSA-AES128-SHA256:ECDHE-ECDSA-AES128-SHA:ECDHE-RSA-AES128-SHA:DHE-RSA-AES256-SHA256:DHE-DSS-AES256-SHA256:DHE-RSA-AES128-SHA256:DHE-DSS-AES128-SHA256:DHE-RSA-AES256-SHA:DHE-DSS-AES256-SHA:DHE-RSA-AES128-SHA:DHE-DSS-AES128-SHA:!aNULL:!eNULL:!EXPORT:!DES:!RC4:!MD5:!PSK:!aECDH:!EDH-DSS-DES-CBC3-SHA:!EDH-RSA-DES-CBC3-SHA:!KRB5-DES-CBC3-SHA'}) ts5.Disk.plugin_config.AddLine('stek_share.so {0}'.format(stek_share_conf_path_5)) ts5.Disk.ssl_multicert_config.AddLine('dest_ip=* ssl_cert_name=self_signed.crt ssl_key_name=self_signed.key') ts5.Disk.remap_config.AddLine('map / http://127.0.0.1:{0}'.format(server.Variables.Port)) def check_session(ev, test): retval = False f = open(test.GetContent(ev), 'r') err = "Session ids match" if not f: err = "Failed to open {0}".format(openssl_output) return (retval, "Check that session ids match", err) content = f.read() match = re.findall('Session-ID: ([0-9A-F]+)', content) if match: if all(i == j for i, j in zip(match, match[1:])): err = "{0} reused successfully {1} times".format(match[0], len(match) - 1) retval = True else: err = "Session is not being reused as expected" else: err = "Didn't find session id" return (retval, "Check that session ids match", err) tr1 = Test.AddTestRun('Basic Curl test, and give it enough time for all ATS to start up and sync STEK') tr1.Processes.Default.Command = 'sleep 10 && curl https://127.0.0.1:{0} -k'.format(ts1.Variables.ssl_port) tr1.Processes.Default.ReturnCode = 0 tr1.Processes.Default.StartBefore(server) tr1.Processes.Default.StartBefore(ts1) tr1.Processes.Default.StartBefore(ts2) tr1.Processes.Default.StartBefore(ts3) tr1.Processes.Default.StartBefore(ts4) tr1.Processes.Default.StartBefore(ts5) tr1.Processes.Default.Streams.All = Testers.ContainsExpression('curl test', 'Making sure the basics still work') ts1.Streams.All = Testers.ContainsExpression('Generate initial STEK succeeded', 'should succeed') ts2.Streams.All = Testers.ContainsExpression('Generate initial STEK succeeded', 'should succeed') ts3.Streams.All = Testers.ContainsExpression('Generate initial STEK succeeded', 'should succeed') ts4.Streams.All = Testers.ContainsExpression('Generate initial STEK succeeded', 'should succeed') ts5.Streams.All = Testers.ContainsExpression('Generate initial STEK succeeded', 'should succeed') tr1.StillRunningAfter = server tr1.StillRunningAfter += ts1 tr1.StillRunningAfter += ts2 tr1.StillRunningAfter += ts3 tr1.StillRunningAfter += ts4 tr1.StillRunningAfter += ts5 tr2 = Test.AddTestRun("TLSv1.2 Session Ticket") tr2.Command = \ 'echo -e "GET / HTTP/1.1\r\n" | openssl s_client -tls1_2 -connect 127.0.0.1:{0} -sess_out {5} && ' \ 'echo -e "GET / HTTP/1.1\r\n" | openssl s_client -tls1_2 -connect 127.0.0.1:{0} -sess_in {5} && ' \ 'echo -e "GET / HTTP/1.1\r\n" | openssl s_client -tls1_2 -connect 127.0.0.1:{1} -sess_in {5} && ' \ 'echo -e "GET / HTTP/1.1\r\n" | openssl s_client -tls1_2 -connect 127.0.0.1:{2} -sess_in {5} && ' \ 'echo -e "GET / HTTP/1.1\r\n" | openssl s_client -tls1_2 -connect 127.0.0.1:{3} -sess_in {5} && ' \ 'echo -e "GET / HTTP/1.1\r\n" | openssl s_client -tls1_2 -connect 127.0.0.1:{4} -sess_in {5}' \ .format( ts1.Variables.ssl_port, ts2.Variables.ssl_port, ts3.Variables.ssl_port, ts4.Variables.ssl_port, ts5.Variables.ssl_port, os.path.join(Test.RunDirectory, 'sess.dat') ) tr2.ReturnCode = 0 tr2.Processes.Default.Streams.All.Content = Testers.Lambda(check_session)

68.054902

719

0.737006

2,755

17,354

4.48167

0.115789

0.053454

0.045355

0.032397

0.776788

0.766664

0.728841

0.711185

0

0.075361

0.09773

17,354

254

720

68.322835

0.713182

0.056471

0

0.409524

0

0.085714

0.604345

0.39492

0

1

0.004762

false

0

0.009524

0

0.02381

0

null

0

1

0

1

0

null

0

6

0f9903717282ace68a922c0e9fff62361a606a72

5,637

py

Python

graphtheory/algorithms/topsort.py

gitter-badger/graphs-dict

2be1a5b140feb050eec799d6cadf6de5eef01745

[ "BSD-3-Clause" ]

36

2015-09-20T20:55:39.000Z

2021-09-20T05:49:03.000Z

graphtheory/algorithms/topsort.py

gitter-badger/graphs-dict

2be1a5b140feb050eec799d6cadf6de5eef01745

[ "BSD-3-Clause" ]

6

2016-03-25T21:41:46.000Z

2020-02-12T03:18:59.000Z

graphtheory/algorithms/topsort.py

gitter-badger/graphs-dict

2be1a5b140feb050eec799d6cadf6de5eef01745

[ "BSD-3-Clause" ]

9

2016-09-12T07:57:27.000Z

2022-03-21T16:15:39.000Z

#!/usr/bin/python try: from Queue import Queue except ImportError: # Python 3 from queue import Queue xrange = range #from graphtheory.traversing.dfs import DFSWithRecursion as SimpleDFS from graphtheory.traversing.dfs import SimpleDFS class TopologicalSortDFS: """Topological sorting of nodes from a dag using DFS. Attributes ---------- graph : input directed acyclic graph sorted_nodes : list of sorted nodes Notes ----- Based on: Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C., 2009, Introduction to Algorithms, third edition, The MIT Press, Cambridge, London. https://en.wikipedia.org/wiki/Topological_sorting """ def __init__(self, graph): """The algorithm initialization.""" if not graph.is_directed(): raise ValueError("the graph is not directed") self.graph = graph self.sorted_nodes = [] def run(self): """Executable pseudocode.""" algorithm = SimpleDFS(self.graph) algorithm.run(post_action=lambda node: self.sorted_nodes.append(node)) self.sorted_nodes.reverse() class TopologicalSortQueue: """Topological sorting of nodes from a dag (Kahn's algorithm). Attributes ---------- graph : input directed acyclic graph sorted_nodes : list of sorted nodes Notes ----- Based on: Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C., 2009, Introduction to Algorithms, third edition, The MIT Press, Cambridge, London. https://en.wikipedia.org/wiki/Topological_sorting """ def __init__(self, graph): """The algorithm initialization.""" if not graph.is_directed(): raise ValueError("the graph is not directed") self.graph = graph self.sorted_nodes = list() def run(self): """Executable pseudocode.""" Q = Queue() # queue or stack or set # Calculate indegree of nodes. inedges = dict((node, 0) for node in self.graph.iternodes()) for edge in self.graph.iteredges(): inedges[edge.target] += 1 for node in self.graph.iternodes(): if inedges[node] == 0: Q.put(node) while not Q.empty(): node = Q.get() self.sorted_nodes.append(node) # Remove all outedges. for edge in self.graph.iteroutedges(node): inedges[edge.target] -= 1 if inedges[edge.target] == 0: Q.put(edge.target) class TopologicalSortSet: """Topological sorting of nodes from a dag (Kahn's algorithm). Attributes ---------- graph : input directed acyclic graph sorted_nodes : list of sorted nodes Notes ----- Based on: Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C., 2009, Introduction to Algorithms, third edition, The MIT Press, Cambridge, London. https://en.wikipedia.org/wiki/Topological_sorting """ def __init__(self, graph): """The algorithm initialization.""" if not graph.is_directed(): raise ValueError("the graph is not directed") self.graph = graph self.sorted_nodes = [] def run(self): """Executable pseudocode.""" Q = set() # queue or stack or set # Calculate indegree of nodes. inedges = dict((node, 0) for node in self.graph.iternodes()) for edge in self.graph.iteredges(): inedges[edge.target] += 1 for node in self.graph.iternodes(): if inedges[node] == 0: Q.add(node) while Q: node = Q.pop() self.sorted_nodes.append(node) # Remove all outedges. for edge in self.graph.iteroutedges(node): inedges[edge.target] -= 1 if inedges[edge.target] == 0: Q.add(edge.target) class TopologicalSortList: """Topological sorting of nodes from a dag (Kahn's algorithm). Attributes ---------- graph : input directed acyclic graph sorted_nodes : list of sorted nodes Notes ----- Based on: Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C., 2009, Introduction to Algorithms, third edition, The MIT Press, Cambridge, London. https://en.wikipedia.org/wiki/Topological_sorting """ def __init__(self, graph): """The algorithm initialization.""" if not graph.is_directed(): raise ValueError("the graph is not directed") self.graph = graph self.sorted_nodes = [None] * self.graph.v() def run(self): """Executable pseudocode.""" # Calculate indegree of nodes. inedges = dict((node, 0) for node in self.graph.iternodes()) for edge in self.graph.iteredges(): inedges[edge.target] += 1 qstart = 0 # first to get qend = 0 # first free place for node in self.graph.iternodes(): if inedges[node] == 0: self.sorted_nodes[qend] = node qend += 1 for step in xrange(self.graph.v()): source = self.sorted_nodes[qstart] qstart += 1 # Remove all outedges. for edge in self.graph.iteroutedges(source): inedges[edge.target] -= 1 if inedges[edge.target] == 0: self.sorted_nodes[qend] = edge.target qend += 1 # EOF

30.144385

78

0.570694

652

5,637

4.872699

0.190184

0.065156

0.041549

0.024551

0.830028

0.779352

0.769279

0.758892

0.732137

0

0.00963

0.318432

5,637

186

79

30.306452

0.817283

0.367571

0

0.597561

0

0.03095

0

1

0.097561

false

0

0.04878

0

0.195122

0

null

0

1

0

null

0

6

ba01e35a3224231d896e89ab978be9af3db5660b

25

py

Python

S1E1/Sample Code/my_first_program.py

SMPParthaS/Python-For-Beginners

d3d8d08c405a9358d64ae23d00d574654532b96a

[ "MIT" ]

null

S1E1/Sample Code/my_first_program.py

SMPParthaS/Python-For-Beginners

d3d8d08c405a9358d64ae23d00d574654532b96a

[ "MIT" ]

null

S1E1/Sample Code/my_first_program.py

SMPParthaS/Python-For-Beginners

d3d8d08c405a9358d64ae23d00d574654532b96a

[ "MIT" ]

2

2020-11-09T19:02:47.000Z

2020-12-09T19:48:05.000Z

print("This is the way!")

25

0.68

5

25

3.4

1

0

0.12

25

1

25

0.772727

0

0.615385

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

e849c439b1a082baae91c909db599ebf6e20023c

72,480

py

Python

tests/test_elmo_ner.py

K-Mike/deep_ner

ffe1bcd64f7e38066866daa0cdd943300ba9ed4e

[ "Apache-2.0" ]

null

tests/test_elmo_ner.py

K-Mike/deep_ner

ffe1bcd64f7e38066866daa0cdd943300ba9ed4e

[ "Apache-2.0" ]

null

tests/test_elmo_ner.py

K-Mike/deep_ner

ffe1bcd64f7e38066866daa0cdd943300ba9ed4e

[ "Apache-2.0" ]

null

import copy import gc import os import pickle import re import sys import tempfile import unittest import numpy as np from sklearn.exceptions import NotFittedError try: from deep_ner.elmo_ner import ELMo_NER from deep_ner.utils import load_dataset from deep_ner.quality import calculate_prediction_quality except: sys.path.append(os.path.join(os.path.dirname(__file__), '..')) from deep_ner.elmo_ner import ELMo_NER from deep_ner.utils import load_dataset from deep_ner.quality import calculate_prediction_quality class TestELMoNER(unittest.TestCase): @classmethod def setUpClass(cls): cls.ELMO_HUB_MODULE = 'http://files.deeppavlov.ai/deeppavlov_data/elmo_ru-news_wmt11-16_1.5M_steps.tar.gz' def tearDown(self): if hasattr(self, 'ner'): del self.ner if hasattr(self, 'another_ner'): del self.another_ner if hasattr(self, 'temp_file_name'): if os.path.isfile(self.temp_file_name): os.remove(self.temp_file_name) def test_creation(self): self.ner = ELMo_NER(elmo_hub_module_handle=self.ELMO_HUB_MODULE) self.assertIsInstance(self.ner, ELMo_NER) self.assertTrue(hasattr(self.ner, 'batch_size')) self.assertTrue(hasattr(self.ner, 'lr')) self.assertTrue(hasattr(self.ner, 'l2_reg')) self.assertTrue(hasattr(self.ner, 'elmo_hub_module_handle')) self.assertTrue(hasattr(self.ner, 'finetune_elmo')) self.assertTrue(hasattr(self.ner, 'max_epochs')) self.assertTrue(hasattr(self.ner, 'patience')) self.assertTrue(hasattr(self.ner, 'random_seed')) self.assertTrue(hasattr(self.ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.ner, 'max_seq_length')) self.assertTrue(hasattr(self.ner, 'validation_fraction')) self.assertTrue(hasattr(self.ner, 'verbose')) self.assertIsInstance(self.ner.batch_size, int) self.assertIsInstance(self.ner.lr, float) self.assertIsInstance(self.ner.l2_reg, float) self.assertIsInstance(self.ner.finetune_elmo, bool) self.assertIsInstance(self.ner.max_epochs, int) self.assertIsInstance(self.ner.patience, int) self.assertIsNone(self.ner.random_seed) self.assertIsInstance(self.ner.gpu_memory_frac, float) self.assertIsInstance(self.ner.max_seq_length, int) self.assertIsInstance(self.ner.validation_fraction, float) self.assertIsInstance(self.ner.verbose, bool) def test_check_params_positive(self): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) self.assertTrue(True) def test_check_params_negative001(self): true_err_msg = re.escape('`elmo_hub_module_handle` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative002(self): true_err_msg = re.escape('`elmo_hub_module_handle` is wrong! Expected `{0}`, got `{1}`.'.format( type('abc'), type(123))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=1, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative003(self): true_err_msg = re.escape('`batch_size` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative004(self): true_err_msg = re.escape('`batch_size` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size='32', max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative005(self): true_err_msg = re.escape('`batch_size` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=-3, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative006(self): true_err_msg = re.escape('`max_epochs` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative007(self): true_err_msg = re.escape('`max_epochs` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs='10', patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative008(self): true_err_msg = re.escape('`max_epochs` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=-3, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative009(self): true_err_msg = re.escape('`patience` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative010(self): true_err_msg = re.escape('`patience` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience='3', gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative011(self): true_err_msg = re.escape('`patience` is wrong! Expected a positive integer value, but -3 is not positive.') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=-3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative012(self): true_err_msg = re.escape('`max_seq_length` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative013(self): true_err_msg = re.escape('`max_seq_length` is wrong! Expected `{0}`, got `{1}`.'.format( type(3), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length='512', lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative014(self): true_err_msg = re.escape('`max_seq_length` is wrong! Expected a positive integer value, but -3 is not ' 'positive.') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=-3, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative015(self): true_err_msg = re.escape('`validation_fraction` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative016(self): true_err_msg = re.escape('`validation_fraction` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction='0.1', max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative017(self): true_err_msg = '`validation_fraction` is wrong! Expected a positive floating-point value less than 1.0, but ' \ '{0} is not positive.'.format(-0.1) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=-0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative018(self): true_err_msg = '`validation_fraction` is wrong! Expected a positive floating-point value less than 1.0, but ' \ '{0} is not less than 1.0.'.format(1.1) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=1.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative019(self): true_err_msg = re.escape('`gpu_memory_frac` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, verbose=False, random_seed=42 ) def test_check_params_negative020(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac='1.0', verbose=False, random_seed=42 ) def test_check_params_negative021(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected a floating-point value in the (0.0, 1.0], ' 'but {0} is not proper.'.format(-1.0)) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=-1.0, verbose=False, random_seed=42 ) def test_check_params_negative022(self): true_err_msg = re.escape('`gpu_memory_frac` is wrong! Expected a floating-point value in the (0.0, 1.0], ' 'but {0} is not proper.'.format(1.3)) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.3, verbose=False, random_seed=42 ) def test_check_params_negative023(self): true_err_msg = re.escape('`lr` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative024(self): true_err_msg = re.escape('`lr` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr='1e-3', l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative025(self): true_err_msg = re.escape('`lr` is wrong! Expected a positive floating-point value, but {0} is not ' 'positive.'.format(0.0)) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=0.0, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative026(self): true_err_msg = re.escape('`lr` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative027(self): true_err_msg = re.escape('`lr` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr='1e-3', l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative028(self): true_err_msg = re.escape('`lr` is wrong! Expected a positive floating-point value, but {0} is not ' 'positive.'.format(0.0)) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=0.0, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative029(self): true_err_msg = re.escape('`l2_reg` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative030(self): true_err_msg = re.escape('`l2_reg` is wrong! Expected `{0}`, got `{1}`.'.format( type(3.5), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg='1e-4', validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative031(self): true_err_msg = re.escape('`l2_reg` is wrong! Expected a non-negative floating-point value, but {0} is ' 'negative.'.format(-2.0)) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=-2.0, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative032(self): true_err_msg = re.escape('`finetune_elmo` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative033(self): true_err_msg = re.escape('`finetune_elmo` is wrong! Expected `{0}`, got `{1}`.'.format( type(True), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo='True', batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose=False, random_seed=42 ) def test_check_params_negative034(self): true_err_msg = re.escape('`verbose` is not specified!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, random_seed=42 ) def test_check_params_negative035(self): true_err_msg = re.escape('`verbose` is wrong! Expected `{0}`, got `{1}`.'.format( type(True), type('3'))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_params( elmo_hub_module_handle=self.ELMO_HUB_MODULE, finetune_elmo=True, batch_size=32, max_seq_length=512, lr=1e-3, l2_reg=1e-4, validation_fraction=0.1, max_epochs=10, patience=3, gpu_memory_frac=1.0, verbose='False', random_seed=42 ) def test_check_X_positive(self): X = ['abc', 'defgh', '4wdffg'] ELMo_NER.check_X(X, 'X_train') self.assertTrue(True) def test_check_X_negative01(self): X = {'abc', 'defgh', '4wdffg'} true_err_msg = re.escape('`X_train` is wrong, because it is not list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_X(X, 'X_train') def test_check_X_negative02(self): X = np.random.uniform(-1.0, 1.0, (10, 2)) true_err_msg = re.escape('`X_train` is wrong, because it is not 1-D list!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_X(X, 'X_train') def test_check_X_negative03(self): X = ['abc', 23, '4wdffg'] true_err_msg = re.escape('Item 1 of `X_train` is wrong, because it is not string-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_X(X, 'X_train') def text_check_Xy_positive(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_classes_list = ('LOC', 'ORG', 'PER') self.assertEqual(true_classes_list, ELMo_NER.check_Xy(X, 'X_train', y, 'y_train')) def text_check_Xy_negative01(self): X = { 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' } y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('`X_train` is wrong, because it is not list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative02(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = { '1': { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, '2': { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } } true_err_msg = re.escape('`y_train` is wrong, because it is not a list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative03(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = np.random.uniform(-1.0, 1.0, (10, 2)) true_err_msg = re.escape('`y_train` is wrong, because it is not 1-D list!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative04(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] }, { 'LOC': [(17, 24), (117, 130)] } ] true_err_msg = re.escape('Length of `X_train` does not correspond to length of `y_train`! 2 != 3') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative05(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, 4 ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because it is not a dictionary-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative06(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 1: [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because its key `1` is not a string-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative07(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'O': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `O` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative08(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], '123': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `123` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative09(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'loc': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because its key `loc` incorrectly specifies a named ' 'entity!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative10(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': {1, 2} }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because its value `{0}` is not a list-like ' 'object!'.format(y[0]['PER'])) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative11(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), 63], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because named entity bounds `63` are not specified as ' 'list-like object!') with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative12(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77, 81)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 1 of `y_train` is wrong, because named entity bounds `{0}` are not specified as ' '2-D list!'.format((63, 77, 81))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative13(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (219, 196)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((219, 196))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative14(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(122, 137), (196, 519)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((196, 519))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def text_check_Xy_negative15(self): X = [ 'Встреча с послом Италии в миде Грузии. По инициативе итальянской стороны чрезвычайный и полномочный посол ' 'Италии в Грузии Виторио Сандали встретился с заместителем министра иностранных дел Грузии Александром ' 'Налбандовым.', 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози. Как было объявлено, ' 'президент Франции прибыл в Вашингтон, чтобы обсудить с главой администрации США ряд насущных проблем, ' 'главное место среди которых занимает состояние мировой экономики и безопасность.' ] y = [ { 'ORG': [(26, 37)], 'PER': [(-1, 137), (196, 219)] }, { 'ORG': [(126, 135)], 'PER': [(0, 11), (63, 77)], 'LOC': [(24, 34), (161, 178)] } ] true_err_msg = re.escape('Item 0 of `y_train` is wrong, because named entity bounds `{0}` are ' 'incorrect!'.format((-1, 137))) with self.assertRaisesRegex(ValueError, true_err_msg): ELMo_NER.check_Xy(X, 'X_train', y, 'y_train') def test_calculate_bounds_of_tokens_positive01(self): source_text = 'Совершенно новую технологию перекачки российской водки за рубеж начали использовать ' \ 'контрабандисты.' tokenized_text = ['Совершенно', 'новую', 'технологию', 'перекачки', 'российской', 'водки', 'за', 'рубеж', 'начали', 'использовать', 'контрабандисты', '.'] true_bounds = [(0, 10), (11, 16), (17, 27), (28, 37), (38, 48), (49, 54), (55, 57), (58, 63), (64, 70), (71, 83), (84, 98), (98, 99)] self.assertEqual(true_bounds, ELMo_NER.calculate_bounds_of_tokens(source_text, tokenized_text)) def test_calculate_bounds_of_tokens_positive02(self): source_text = 'Один из последних представителей клады, тираннозавр (Tyrannosaurus rex), живший 66–67 ' \ 'миллионов лет назад, был одним из крупнейших когда-либо живших сухопутных хищников' tokenized_text = ['Один', 'из', 'последних', 'представителей', 'клады', ',', 'тираннозавр', '(', 'Tyrannosaurus', 'rex', ')', ',', 'живший', '66', '–', '67', 'миллионов', 'лет', 'назад', ',', 'был', 'одним', 'из', 'крупнейших', 'когда', '-', 'либо', 'живших', 'сухопутных', 'хищников'] true_bounds = [(0, 4), (5, 7), (8, 17), (18, 32), (33, 38), (38, 39), (40, 51), (52, 53), (53, 66), (67, 70), (70, 71), (71, 72), (73, 79), (80, 82), (82, 83), (83, 85), (86, 95), (96, 99), (100, 105), (105, 106), (107, 110), (111, 116), (117, 119), (120, 130), (131, 136), (136, 137), (137, 141), (142, 148), (149, 159), (160, 168)] self.assertEqual(true_bounds, ELMo_NER.calculate_bounds_of_tokens(source_text, tokenized_text)) def test_detect_token_labels_positive01(self): source_text = 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози.' tokenized_text = ['Барак', 'Обама', 'принимает', 'в', 'Белом', 'доме', 'своего', 'французского', 'коллегу', 'Николя', 'Саркози', '.'] token_bounds = ELMo_NER.calculate_bounds_of_tokens(source_text, tokenized_text) indices_of_named_entities = np.array( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 1} y_true = np.array([2, 1, 0, 0, 4, 3, 0, 0, 0, 2, 1, 0, 0, 0, 0, 0], dtype=np.int32) y_pred = ELMo_NER.detect_token_labels(token_bounds, indices_of_named_entities, label_IDs, 16) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_detect_token_labels_positive02(self): source_text = 'С 1876 г Павлов ассистирует профессору К. Н. Устимовичу в Медико-хирургической академии и ' \ 'параллельно изучает физиологию кровообращения.' tokenized_text = ['С', '1876', 'г', 'Павлов', 'ассистирует', 'профессору', 'К', '.', 'Н', '.', 'Устимовичу', 'в', 'Медико', '-', 'хирургической', 'академии', 'и', 'параллельно', 'изучает', 'физиологию', 'кровообращения', '.'] token_bounds = ELMo_NER.calculate_bounds_of_tokens(source_text, tokenized_text) indices_of_named_entities = np.array( [0, 0, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) label_IDs = {1: 1, 2: 2, 3: 3, 4: 2, 5: 4} y_true = np.array( [0, 2, 1, 4, 0, 6, 4, 3, 3, 3, 3, 0, 8, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int32 ) y_pred = ELMo_NER.detect_token_labels(token_bounds, indices_of_named_entities, label_IDs, 32) self.assertIsInstance(y_pred, np.ndarray) self.assertEqual(y_true.shape, y_pred.shape) self.assertEqual(y_true.tolist(), y_pred.tolist()) def test_calculate_indices_of_named_entities(self): source_text = 'Барак Обама принимает в Белом доме своего французского коллегу Николя Саркози.' classes_list = ('LOCATION', 'ORG', 'PERSON') named_entities = {'PERSON': [(0, 11), (63, 77)], 'LOCATION': [(24, 34)]} true_indices = np.array( [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0], dtype=np.int32 ) true_labels_to_classes = {1: 1, 2: 3, 3: 3} indices, labels_to_classes = ELMo_NER.calculate_indices_of_named_entities(source_text, classes_list, named_entities) self.assertIsInstance(indices, np.ndarray) self.assertIsInstance(labels_to_classes, dict) self.assertEqual(true_indices.shape, indices.shape) self.assertEqual(true_indices.tolist(), indices.tolist()) self.assertEqual(set(true_labels_to_classes.keys()), set(labels_to_classes.keys())) for label_ID in true_labels_to_classes: self.assertEqual(true_labels_to_classes[label_ID], labels_to_classes[label_ID]) def test_fit_positive01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = ELMo_NER(finetune_elmo=False, max_epochs=3, batch_size=4, max_seq_length=64, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, elmo_hub_module_handle=self.ELMO_HUB_MODULE) X_train, y_train = load_dataset(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, ELMo_NER) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'elmo_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_elmo')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.elmo_hub_module_handle, str) self.assertIsInstance(res.finetune_elmo, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'logits_')) self.assertTrue(hasattr(res, 'transition_params_')) self.assertTrue(hasattr(res, 'input_tokens_')) self.assertTrue(hasattr(res, 'sequence_lengths_')) self.assertTrue(hasattr(res, 'additional_features_')) self.assertTrue(hasattr(res, 'y_ph_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) def test_fit_positive02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = ELMo_NER(finetune_elmo=True, max_epochs=3, batch_size=2, max_seq_length=64, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=42, elmo_hub_module_handle=self.ELMO_HUB_MODULE) X_train, y_train = load_dataset(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, ELMo_NER) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'elmo_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_elmo')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.elmo_hub_module_handle, str) self.assertIsInstance(res.finetune_elmo, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertEqual(res.random_seed, 42) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'logits_')) self.assertTrue(hasattr(res, 'transition_params_')) self.assertTrue(hasattr(res, 'input_tokens_')) self.assertTrue(hasattr(res, 'sequence_lengths_')) self.assertTrue(hasattr(res, 'additional_features_')) self.assertTrue(hasattr(res, 'y_ph_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 0) def test_fit_positive03(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = ELMo_NER(finetune_elmo=False, max_epochs=3, batch_size=4, max_seq_length=64, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, elmo_hub_module_handle=self.ELMO_HUB_MODULE) X_train, y_train = load_dataset(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, ELMo_NER) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'elmo_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_elmo')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.elmo_hub_module_handle, str) self.assertIsInstance(res.finetune_elmo, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'logits_')) self.assertTrue(hasattr(res, 'transition_params_')) self.assertTrue(hasattr(res, 'input_tokens_')) self.assertTrue(hasattr(res, 'sequence_lengths_')) self.assertTrue(hasattr(res, 'additional_features_')) self.assertTrue(hasattr(res, 'y_ph_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 0) def test_fit_predict(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = ELMo_NER(finetune_elmo=False, max_epochs=3, batch_size=4, max_seq_length=64, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, elmo_hub_module_handle=self.ELMO_HUB_MODULE) X_train, y_train = load_dataset(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, ELMo_NER) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'elmo_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_elmo')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.elmo_hub_module_handle, str) self.assertIsInstance(res.finetune_elmo, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'logits_')) self.assertTrue(hasattr(res, 'transition_params_')) self.assertTrue(hasattr(res, 'input_tokens_')) self.assertTrue(hasattr(res, 'sequence_lengths_')) self.assertTrue(hasattr(res, 'additional_features_')) self.assertTrue(hasattr(res, 'y_ph_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 0) y_pred = res.predict(X_train) self.assertIsInstance(y_pred, list) self.assertEqual(len(X_train), len(y_pred)) for sample_idx in range(len(y_pred)): self.assertIsInstance(y_pred[sample_idx], dict) f1, precision, recall, _ = calculate_prediction_quality(y_train, y_pred, res.classes_list_) self.assertGreater(f1, 0.0) self.assertGreater(precision, 0.0) self.assertGreater(recall, 0.0) def test_predict_negative(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = ELMo_NER(finetune_elmo=False, max_epochs=3, batch_size=4, random_seed=None, elmo_hub_module_handle=self.ELMO_HUB_MODULE) X_train, y_train = load_dataset(os.path.join(base_dir, 'true_named_entities.json')) with self.assertRaises(NotFittedError): _ = self.ner.predict(X_train) def test_serialize_positive01(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = ELMo_NER(finetune_elmo=False, max_epochs=3, batch_size=4, max_seq_length=64, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, elmo_hub_module_handle=self.ELMO_HUB_MODULE) X_train, y_train = load_dataset(os.path.join(base_dir, 'true_named_entities.json')) res = self.ner.fit(X_train, y_train) self.assertIsInstance(res, ELMo_NER) self.assertTrue(hasattr(res, 'batch_size')) self.assertTrue(hasattr(res, 'lr')) self.assertTrue(hasattr(res, 'l2_reg')) self.assertTrue(hasattr(res, 'elmo_hub_module_handle')) self.assertTrue(hasattr(res, 'finetune_elmo')) self.assertTrue(hasattr(res, 'max_epochs')) self.assertTrue(hasattr(res, 'patience')) self.assertTrue(hasattr(res, 'random_seed')) self.assertTrue(hasattr(res, 'gpu_memory_frac')) self.assertTrue(hasattr(res, 'max_seq_length')) self.assertTrue(hasattr(res, 'validation_fraction')) self.assertTrue(hasattr(res, 'verbose')) self.assertIsInstance(res.batch_size, int) self.assertIsInstance(res.lr, float) self.assertIsInstance(res.l2_reg, float) self.assertIsInstance(res.elmo_hub_module_handle, str) self.assertIsInstance(res.finetune_elmo, bool) self.assertIsInstance(res.max_epochs, int) self.assertIsInstance(res.patience, int) self.assertIsInstance(res.random_seed, int) self.assertIsInstance(res.gpu_memory_frac, float) self.assertIsInstance(res.max_seq_length, int) self.assertIsInstance(res.validation_fraction, float) self.assertIsInstance(res.verbose, bool) self.assertTrue(hasattr(res, 'classes_list_')) self.assertTrue(hasattr(res, 'shapes_list_')) self.assertTrue(hasattr(res, 'logits_')) self.assertTrue(hasattr(res, 'transition_params_')) self.assertTrue(hasattr(res, 'input_tokens_')) self.assertTrue(hasattr(res, 'sequence_lengths_')) self.assertTrue(hasattr(res, 'additional_features_')) self.assertTrue(hasattr(res, 'y_ph_')) self.assertTrue(hasattr(res, 'sess_')) self.assertEqual(res.classes_list_, ('LOCATION', 'ORG', 'PERSON')) self.assertIsInstance(res.shapes_list_, tuple) self.assertGreater(len(res.shapes_list_), 0) y_pred1 = res.predict(X_train) self.assertIsInstance(y_pred1, list) self.assertEqual(len(X_train), len(y_pred1)) for sample_idx in range(len(y_pred1)): self.assertIsInstance(y_pred1[sample_idx], dict) f1, precision, recall, _ = calculate_prediction_quality(y_train, y_pred1, res.classes_list_) self.assertGreater(f1, 0.0) self.assertGreater(precision, 0.0) self.assertGreater(recall, 0.0) self.temp_file_name = tempfile.NamedTemporaryFile(mode='w').name with open(self.temp_file_name, mode='wb') as fp: pickle.dump(res, fp) del res, self.ner gc.collect() with open(self.temp_file_name, mode='rb') as fp: self.ner = pickle.load(fp) y_pred2 = self.ner.predict(X_train) self.assertIsInstance(y_pred2, list) self.assertEqual(len(y_pred2), len(y_pred2)) for sample_idx in range(len(y_pred2)): self.assertIsInstance(y_pred2[sample_idx], dict) self.assertEqual(set(y_pred1[sample_idx]), set(y_pred2[sample_idx])) for ne_type in y_pred1[sample_idx]: self.assertEqual(y_pred1[sample_idx][ne_type], y_pred2[sample_idx][ne_type]) def test_serialize_positive02(self): self.ner = ELMo_NER(random_seed=31, elmo_hub_module_handle=self.ELMO_HUB_MODULE) old_batch_size = self.ner.batch_size old_lr = self.ner.lr old_l2_reg = self.ner.l2_reg old_elmo_hub_module_handle = self.ner.elmo_hub_module_handle old_finetune_elmo = self.ner.finetune_elmo old_max_epochs = self.ner.max_epochs old_patience = self.ner.patience old_random_seed = self.ner.random_seed old_gpu_memory_frac = self.ner.gpu_memory_frac old_max_seq_length = self.ner.max_seq_length old_validation_fraction = self.ner.validation_fraction old_verbose = self.ner.verbose self.temp_file_name = tempfile.NamedTemporaryFile().name with open(self.temp_file_name, mode='wb') as fp: pickle.dump(self.ner, fp) del self.ner gc.collect() with open(self.temp_file_name, mode='rb') as fp: self.ner = pickle.load(fp) self.assertIsInstance(self.ner, ELMo_NER) self.assertTrue(hasattr(self.ner, 'batch_size')) self.assertTrue(hasattr(self.ner, 'lr')) self.assertTrue(hasattr(self.ner, 'l2_reg')) self.assertTrue(hasattr(self.ner, 'elmo_hub_module_handle')) self.assertTrue(hasattr(self.ner, 'finetune_elmo')) self.assertTrue(hasattr(self.ner, 'max_epochs')) self.assertTrue(hasattr(self.ner, 'patience')) self.assertTrue(hasattr(self.ner, 'random_seed')) self.assertTrue(hasattr(self.ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.ner, 'max_seq_length')) self.assertTrue(hasattr(self.ner, 'validation_fraction')) self.assertTrue(hasattr(self.ner, 'verbose')) self.assertEqual(self.ner.batch_size, old_batch_size) self.assertAlmostEqual(self.ner.lr, old_lr) self.assertAlmostEqual(self.ner.l2_reg, old_l2_reg) self.assertEqual(self.ner.elmo_hub_module_handle, old_elmo_hub_module_handle) self.assertEqual(self.ner.finetune_elmo, old_finetune_elmo) self.assertEqual(self.ner.max_epochs, old_max_epochs) self.assertEqual(self.ner.patience, old_patience) self.assertAlmostEqual(self.ner.gpu_memory_frac, old_gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, old_max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, old_validation_fraction) self.assertEqual(self.ner.verbose, old_verbose) self.assertEqual(self.ner.random_seed, old_random_seed) def test_copy_positive01(self): self.ner = ELMo_NER(random_seed=0, elmo_hub_module_handle=self.ELMO_HUB_MODULE) self.another_ner = copy.copy(self.ner) self.assertIsInstance(self.another_ner, ELMo_NER) self.assertIsNot(self.ner, self.another_ner) self.assertTrue(hasattr(self.another_ner, 'batch_size')) self.assertTrue(hasattr(self.another_ner, 'lr')) self.assertTrue(hasattr(self.another_ner, 'l2_reg')) self.assertTrue(hasattr(self.another_ner, 'elmo_hub_module_handle')) self.assertTrue(hasattr(self.another_ner, 'finetune_elmo')) self.assertTrue(hasattr(self.another_ner, 'max_epochs')) self.assertTrue(hasattr(self.another_ner, 'patience')) self.assertTrue(hasattr(self.another_ner, 'random_seed')) self.assertTrue(hasattr(self.another_ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.another_ner, 'max_seq_length')) self.assertTrue(hasattr(self.another_ner, 'validation_fraction')) self.assertTrue(hasattr(self.another_ner, 'verbose')) self.assertEqual(self.ner.batch_size, self.another_ner.batch_size) self.assertAlmostEqual(self.ner.lr, self.another_ner.lr) self.assertAlmostEqual(self.ner.l2_reg, self.another_ner.l2_reg) self.assertEqual(self.ner.elmo_hub_module_handle, self.another_ner.elmo_hub_module_handle) self.assertEqual(self.ner.finetune_elmo, self.another_ner.finetune_elmo) self.assertEqual(self.ner.max_epochs, self.another_ner.max_epochs) self.assertEqual(self.ner.patience, self.another_ner.patience) self.assertEqual(self.ner.random_seed, self.another_ner.random_seed) self.assertAlmostEqual(self.ner.gpu_memory_frac, self.another_ner.gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, self.another_ner.max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, self.another_ner.validation_fraction) self.assertEqual(self.ner.verbose, self.another_ner.verbose) def test_copy_positive02(self): base_dir = os.path.join(os.path.dirname(__file__), 'testdata') self.ner = ELMo_NER(finetune_elmo=False, max_epochs=3, batch_size=4, max_seq_length=64, gpu_memory_frac=0.9, validation_fraction=0.3, random_seed=None, elmo_hub_module_handle=self.ELMO_HUB_MODULE) X_train, y_train = load_dataset(os.path.join(base_dir, 'true_named_entities.json')) self.ner.fit(X_train, y_train) self.another_ner = copy.copy(self.ner) self.assertIsInstance(self.another_ner, ELMo_NER) self.assertIsNot(self.ner, self.another_ner) self.assertTrue(hasattr(self.another_ner, 'batch_size')) self.assertTrue(hasattr(self.another_ner, 'lr')) self.assertTrue(hasattr(self.another_ner, 'l2_reg')) self.assertTrue(hasattr(self.another_ner, 'elmo_hub_module_handle')) self.assertTrue(hasattr(self.another_ner, 'finetune_elmo')) self.assertTrue(hasattr(self.another_ner, 'max_epochs')) self.assertTrue(hasattr(self.another_ner, 'patience')) self.assertTrue(hasattr(self.another_ner, 'random_seed')) self.assertTrue(hasattr(self.another_ner, 'gpu_memory_frac')) self.assertTrue(hasattr(self.another_ner, 'max_seq_length')) self.assertTrue(hasattr(self.another_ner, 'validation_fraction')) self.assertTrue(hasattr(self.another_ner, 'verbose')) self.assertTrue(hasattr(self.another_ner, 'classes_list_')) self.assertTrue(hasattr(self.another_ner, 'shapes_list_')) self.assertTrue(hasattr(self.another_ner, 'logits_')) self.assertTrue(hasattr(self.another_ner, 'transition_params_')) self.assertTrue(hasattr(self.another_ner, 'input_tokens_')) self.assertTrue(hasattr(self.another_ner, 'sequence_lengths_')) self.assertTrue(hasattr(self.another_ner, 'additional_features_')) self.assertTrue(hasattr(self.another_ner, 'y_ph_')) self.assertTrue(hasattr(self.another_ner, 'sess_')) self.assertEqual(self.ner.batch_size, self.another_ner.batch_size) self.assertAlmostEqual(self.ner.lr, self.another_ner.lr) self.assertAlmostEqual(self.ner.l2_reg, self.another_ner.l2_reg) self.assertEqual(self.ner.elmo_hub_module_handle, self.another_ner.elmo_hub_module_handle) self.assertEqual(self.ner.finetune_elmo, self.another_ner.finetune_elmo) self.assertEqual(self.ner.max_epochs, self.another_ner.max_epochs) self.assertEqual(self.ner.patience, self.another_ner.patience) self.assertEqual(self.ner.random_seed, self.another_ner.random_seed) self.assertAlmostEqual(self.ner.gpu_memory_frac, self.another_ner.gpu_memory_frac) self.assertEqual(self.ner.max_seq_length, self.another_ner.max_seq_length) self.assertAlmostEqual(self.ner.validation_fraction, self.another_ner.validation_fraction) self.assertEqual(self.ner.verbose, self.another_ner.verbose) self.assertIs(self.ner.classes_list_, self.another_ner.classes_list_) self.assertIs(self.ner.shapes_list_, self.another_ner.shapes_list_) self.assertIs(self.ner.logits_, self.another_ner.logits_) self.assertIs(self.ner.transition_params_, self.another_ner.transition_params_) self.assertIs(self.ner.input_tokens_, self.another_ner.input_tokens_) self.assertIs(self.ner.sequence_lengths_, self.another_ner.sequence_lengths_) self.assertIs(self.ner.additional_features_, self.another_ner.additional_features_) self.assertIs(self.ner.y_ph_, self.another_ner.y_ph_) self.assertIs(self.ner.sess_, self.another_ner.sess_) def test_calculate_bounds_of_named_entities(self): bounds_of_tokens = [(0, 2), (2, 5), (5, 8), (8, 10), (11, 16), (17, 20), (20, 22), (22, 26), (26, 27), (28, 31), (31, 34), (34, 37), (38, 48), (49, 52), (52, 54), (55, 57), (58, 59), (59, 61), (61, 63), (64, 70), (71, 83), (84, 87), (87, 90), (90, 93), (93, 95), (95, 98), (98, 99)] classes_list = ('LOCATION', 'ORG', 'PERSON') labels_of_tokens = [0, 0, 2, 1, 1, 2, 1, 0, 0, 0, 4, 3, 0, 6, 5, 5, 5, 0, 5, 5, 0, 2, 2, 3, 3, 6, 5] true_entities = { 'LOCATION': [(5, 16), (17, 22), (84, 87), (87, 90)], 'ORG': [(31, 37), (90, 95)], 'PERSON': [(49, 59), (61, 70), (95, 99)] } calc_entities = ELMo_NER.calculate_bounds_of_named_entities(bounds_of_tokens, classes_list, labels_of_tokens) self.assertIsInstance(calc_entities, dict) self.assertEqual(set(true_entities.keys()), set(calc_entities.keys())) for entity_type in true_entities: self.assertEqual(true_entities[entity_type], calc_entities[entity_type]) def test_get_shape_of_string_positive01(self): src = 'уже' dst = 'a' self.assertEqual(dst, ELMo_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive02(self): src = 'К' dst = 'A' self.assertEqual(dst, ELMo_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive03(self): src = 'Однако' dst = 'Aa' self.assertEqual(dst, ELMo_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive04(self): src = '66–67' dst = 'D-D' self.assertEqual(dst, ELMo_NER.get_shape_of_string(src)) def test_get_shape_of_string_positive05(self): src = '…' dst = 'U' self.assertEqual(dst, ELMo_NER.get_shape_of_string(src)) def test_get_shape_of_string_negative(self): src = '' dst = '' self.assertEqual(dst, ELMo_NER.get_shape_of_string(src)) if __name__ == '__main__': unittest.main(verbosity=2)

54.537246

120

0.633126

9,447

72,480

4.622737

0.052821

0.008243

0.07742

0.013098

0.913673

0.892171

0.868357

0.854549

0.84555

0.83694

0

0.045023

0.250442

72,480

1,328

121

54.578313

0.758706

0

0.59292

0

0.004827

0.21061

0.006871

0

0.320998

1

0.06436

false

0

0.012872

0

0.078037

0

null

0

1

0

null

0

6

e84d23fb4b720cca566d2742904cd909b7187d70

34

py

Python

31/00/list.remove.5.py

pylangstudy/201705

c69de524faa67fa2d96267d5a51ed9794208f0e4

[ "CC0-1.0" ]

null

31/00/list.remove.5.py

pylangstudy/201705

c69de524faa67fa2d96267d5a51ed9794208f0e4

[ "CC0-1.0" ]

38

2017-05-25T07:08:48.000Z

2017-05-31T01:42:41.000Z

31/00/list.remove.5.py

pylangstudy/201705

c69de524faa67fa2d96267d5a51ed9794208f0e4

[ "CC0-1.0" ]

null

l = [1,2,3,4] del l[100] print(l)

8.5

13

0.529412

10

34

1.8

0.8

0

0.25

0.176471

34

3

14

11.333333

0.392857

0

1

0

false

0

0.333333

1

null

0

1

0

1

0

1

0

null

0

6

e8c9dd477284f0273b3698331381a0b72005ecde

1,423

py

Python

util/data/gen/textinputframework.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/textinputframework.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

util/data/gen/textinputframework.dll.py

56kyle/bloons_auto

419d55b51d1cddc49099593970adf1c67985b389

[ "MIT" ]

null

symbols = [] exports = [{'type': 'function', 'name': 'DllCanUnloadNow', 'address': '0x7ffb110e4e00'}, {'type': 'function', 'name': 'DllGetActivationFactory', 'address': '0x7ffb110e4e30'}, {'type': 'function', 'name': 'DllGetClassObject', 'address': '0x7ffb110e4e70'}, {'type': 'function', 'name': 'InputFocusChanged', 'address': '0x7ffb110b7a50'}, {'type': 'function', 'name': 'NavigateFocusInfoCreate', 'address': '0x7ffb1108b790'}, {'type': 'function', 'name': 'TextInputClientCreate', 'address': '0x7ffb110ae3f0'}, {'type': 'function', 'name': 'TextInputClientCreate2', 'address': '0x7ffb110ae420'}, {'type': 'function', 'name': 'TextInputHostCreate', 'address': '0x7ffb110a1a90'}, {'type': 'function', 'name': 'TextInputHostCreate2', 'address': '0x7ffb110e91c0'}, {'type': 'function', 'name': 'TextInputHostCreateEx', 'address': '0x7ffb110a1ba0'}, {'type': 'function', 'name': 'TextInputHostGetCurrent', 'address': '0x7ffb110e9270'}, {'type': 'function', 'name': 'TextInputHostSiteCreate', 'address': '0x7ffb110846f0'}, {'type': 'function', 'name': 'TextInputServerCreate', 'address': '0x7ffb110b1290'}, {'type': 'function', 'name': 'TsfOneCreate', 'address': '0x7ffb110a0a40'}, {'type': 'function', 'name': 'tsfGetAsyncKeyState', 'address': '0x7ffb110e92f0'}, {'type': 'function', 'name': 'tsfGetKeyState', 'address': '0x7ffb110a2020'}, {'type': 'function', 'name': 'tsfGetKeyboardState', 'address': '0x7ffb110e9380'}]

711.5

1,410

0.683064

104

1,423

9.346154

0.384615

0.209877

0.279835

0

0.107821

0.074491

1,423

2

1,410

711.5

0.63022

0

0.672753

0.124298

0

0.167135

0

1

0

false

0

null

1

0

1

0

1

0

null

0

6

e8d6e06c6af45d7061e8784a4a529ba02df0350a

16,985

py

Python

sdk/python/pulumi_kubernetes/core/v1/ServiceAccount.py

csssuf/pulumi-kubernetes

8d007166d0e8968fcabaeecd0cee13f9c08d97f1

[ "Apache-2.0" ]

null

sdk/python/pulumi_kubernetes/core/v1/ServiceAccount.py

csssuf/pulumi-kubernetes

8d007166d0e8968fcabaeecd0cee13f9c08d97f1

[ "Apache-2.0" ]

null

sdk/python/pulumi_kubernetes/core/v1/ServiceAccount.py

csssuf/pulumi-kubernetes

8d007166d0e8968fcabaeecd0cee13f9c08d97f1

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by pulumigen. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from ... import _utilities from . import outputs from ... import meta as _meta from ._inputs import * __all__ = ['ServiceAccountArgs', 'ServiceAccount'] @pulumi.input_type class ServiceAccountArgs: def __init__(__self__, *, api_version: Optional[pulumi.Input[str]] = None, automount_service_account_token: Optional[pulumi.Input[bool]] = None, image_pull_secrets: Optional[pulumi.Input[Sequence[pulumi.Input['LocalObjectReferenceArgs']]]] = None, kind: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']] = None, secrets: Optional[pulumi.Input[Sequence[pulumi.Input['ObjectReferenceArgs']]]] = None): """ The set of arguments for constructing a ServiceAccount resource. :param pulumi.Input[str] api_version: APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources :param pulumi.Input[bool] automount_service_account_token: AutomountServiceAccountToken indicates whether pods running as this service account should have an API token automatically mounted. Can be overridden at the pod level. :param pulumi.Input[Sequence[pulumi.Input['LocalObjectReferenceArgs']]] image_pull_secrets: ImagePullSecrets is a list of references to secrets in the same namespace to use for pulling any images in pods that reference this ServiceAccount. ImagePullSecrets are distinct from Secrets because Secrets can be mounted in the pod, but ImagePullSecrets are only accessed by the kubelet. More info: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod :param pulumi.Input[str] kind: Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds :param pulumi.Input['_meta.v1.ObjectMetaArgs'] metadata: Standard object's metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata :param pulumi.Input[Sequence[pulumi.Input['ObjectReferenceArgs']]] secrets: Secrets is the list of secrets allowed to be used by pods running using this ServiceAccount. More info: https://kubernetes.io/docs/concepts/configuration/secret """ if api_version is not None: pulumi.set(__self__, "api_version", 'v1') if automount_service_account_token is not None: pulumi.set(__self__, "automount_service_account_token", automount_service_account_token) if image_pull_secrets is not None: pulumi.set(__self__, "image_pull_secrets", image_pull_secrets) if kind is not None: pulumi.set(__self__, "kind", 'ServiceAccount') if metadata is not None: pulumi.set(__self__, "metadata", metadata) if secrets is not None: pulumi.set(__self__, "secrets", secrets) @property @pulumi.getter(name="apiVersion") def api_version(self) -> Optional[pulumi.Input[str]]: """ APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources """ return pulumi.get(self, "api_version") @api_version.setter def api_version(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "api_version", value) @property @pulumi.getter(name="automountServiceAccountToken") def automount_service_account_token(self) -> Optional[pulumi.Input[bool]]: """ AutomountServiceAccountToken indicates whether pods running as this service account should have an API token automatically mounted. Can be overridden at the pod level. """ return pulumi.get(self, "automount_service_account_token") @automount_service_account_token.setter def automount_service_account_token(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "automount_service_account_token", value) @property @pulumi.getter(name="imagePullSecrets") def image_pull_secrets(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['LocalObjectReferenceArgs']]]]: """ ImagePullSecrets is a list of references to secrets in the same namespace to use for pulling any images in pods that reference this ServiceAccount. ImagePullSecrets are distinct from Secrets because Secrets can be mounted in the pod, but ImagePullSecrets are only accessed by the kubelet. More info: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod """ return pulumi.get(self, "image_pull_secrets") @image_pull_secrets.setter def image_pull_secrets(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['LocalObjectReferenceArgs']]]]): pulumi.set(self, "image_pull_secrets", value) @property @pulumi.getter def kind(self) -> Optional[pulumi.Input[str]]: """ Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds """ return pulumi.get(self, "kind") @kind.setter def kind(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "kind", value) @property @pulumi.getter def metadata(self) -> Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]: """ Standard object's metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata """ return pulumi.get(self, "metadata") @metadata.setter def metadata(self, value: Optional[pulumi.Input['_meta.v1.ObjectMetaArgs']]): pulumi.set(self, "metadata", value) @property @pulumi.getter def secrets(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ObjectReferenceArgs']]]]: """ Secrets is the list of secrets allowed to be used by pods running using this ServiceAccount. More info: https://kubernetes.io/docs/concepts/configuration/secret """ return pulumi.get(self, "secrets") @secrets.setter def secrets(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ObjectReferenceArgs']]]]): pulumi.set(self, "secrets", value) class ServiceAccount(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, api_version: Optional[pulumi.Input[str]] = None, automount_service_account_token: Optional[pulumi.Input[bool]] = None, image_pull_secrets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LocalObjectReferenceArgs']]]]] = None, kind: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']]] = None, secrets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ObjectReferenceArgs']]]]] = None, __props__=None): """ ServiceAccount binds together: * a name, understood by users, and perhaps by peripheral systems, for an identity * a principal that can be authenticated and authorized * a set of secrets :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] api_version: APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources :param pulumi.Input[bool] automount_service_account_token: AutomountServiceAccountToken indicates whether pods running as this service account should have an API token automatically mounted. Can be overridden at the pod level. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LocalObjectReferenceArgs']]]] image_pull_secrets: ImagePullSecrets is a list of references to secrets in the same namespace to use for pulling any images in pods that reference this ServiceAccount. ImagePullSecrets are distinct from Secrets because Secrets can be mounted in the pod, but ImagePullSecrets are only accessed by the kubelet. More info: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod :param pulumi.Input[str] kind: Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds :param pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']] metadata: Standard object's metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ObjectReferenceArgs']]]] secrets: Secrets is the list of secrets allowed to be used by pods running using this ServiceAccount. More info: https://kubernetes.io/docs/concepts/configuration/secret """ ... @overload def __init__(__self__, resource_name: str, args: Optional[ServiceAccountArgs] = None, opts: Optional[pulumi.ResourceOptions] = None): """ ServiceAccount binds together: * a name, understood by users, and perhaps by peripheral systems, for an identity * a principal that can be authenticated and authorized * a set of secrets :param str resource_name: The name of the resource. :param ServiceAccountArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(ServiceAccountArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, api_version: Optional[pulumi.Input[str]] = None, automount_service_account_token: Optional[pulumi.Input[bool]] = None, image_pull_secrets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['LocalObjectReferenceArgs']]]]] = None, kind: Optional[pulumi.Input[str]] = None, metadata: Optional[pulumi.Input[pulumi.InputType['_meta.v1.ObjectMetaArgs']]] = None, secrets: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ObjectReferenceArgs']]]]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = ServiceAccountArgs.__new__(ServiceAccountArgs) __props__.__dict__["api_version"] = 'v1' __props__.__dict__["automount_service_account_token"] = automount_service_account_token __props__.__dict__["image_pull_secrets"] = image_pull_secrets __props__.__dict__["kind"] = 'ServiceAccount' __props__.__dict__["metadata"] = metadata __props__.__dict__["secrets"] = secrets super(ServiceAccount, __self__).__init__( 'kubernetes:core/v1:ServiceAccount', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'ServiceAccount': """ Get an existing ServiceAccount resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = ServiceAccountArgs.__new__(ServiceAccountArgs) __props__.__dict__["api_version"] = None __props__.__dict__["automount_service_account_token"] = None __props__.__dict__["image_pull_secrets"] = None __props__.__dict__["kind"] = None __props__.__dict__["metadata"] = None __props__.__dict__["secrets"] = None return ServiceAccount(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="apiVersion") def api_version(self) -> pulumi.Output[Optional[str]]: """ APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources """ return pulumi.get(self, "api_version") @property @pulumi.getter(name="automountServiceAccountToken") def automount_service_account_token(self) -> pulumi.Output[Optional[bool]]: """ AutomountServiceAccountToken indicates whether pods running as this service account should have an API token automatically mounted. Can be overridden at the pod level. """ return pulumi.get(self, "automount_service_account_token") @property @pulumi.getter(name="imagePullSecrets") def image_pull_secrets(self) -> pulumi.Output[Optional[Sequence['outputs.LocalObjectReference']]]: """ ImagePullSecrets is a list of references to secrets in the same namespace to use for pulling any images in pods that reference this ServiceAccount. ImagePullSecrets are distinct from Secrets because Secrets can be mounted in the pod, but ImagePullSecrets are only accessed by the kubelet. More info: https://kubernetes.io/docs/concepts/containers/images/#specifying-imagepullsecrets-on-a-pod """ return pulumi.get(self, "image_pull_secrets") @property @pulumi.getter def kind(self) -> pulumi.Output[Optional[str]]: """ Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds """ return pulumi.get(self, "kind") @property @pulumi.getter def metadata(self) -> pulumi.Output[Optional['_meta.v1.outputs.ObjectMeta']]: """ Standard object's metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata """ return pulumi.get(self, "metadata") @property @pulumi.getter def secrets(self) -> pulumi.Output[Optional[Sequence['outputs.ObjectReference']]]: """ Secrets is the list of secrets allowed to be used by pods running using this ServiceAccount. More info: https://kubernetes.io/docs/concepts/configuration/secret """ return pulumi.get(self, "secrets")

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0.710863

2,033

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5.757501

0.11215

0.055446

0.048697

0.043059

0.831269

0.804955

0.757112

0.719863

0.682358

0.649381

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16,985

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510

63.141264

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0

1

0

1

0

null

0

6

2cdbdc43a6f9fe1e2fbc0e42b941e72a584fc3cc

6,572

py

Python

tests/unit/commands/status_test.py

gamechanger/dusty

dd9778e3a4f0c623209e53e98aa9dc1fe76fc309

[ "MIT" ]

421

2015-06-02T16:29:59.000Z

2021-06-03T18:44:42.000Z

tests/unit/commands/status_test.py

gamechanger/dusty

dd9778e3a4f0c623209e53e98aa9dc1fe76fc309

[ "MIT" ]

404

2015-06-02T20:23:42.000Z

2019-08-21T16:59:41.000Z

tests/unit/commands/status_test.py

gamechanger/dusty

dd9778e3a4f0c623209e53e98aa9dc1fe76fc309

[ "MIT" ]

16

2015-06-16T17:21:02.000Z

2020-03-27T02:27:09.000Z

from mock import patch, Mock, call from ...testcases import DustyTestCase from dusty.commands.status import _has_active_container, get_dusty_status from dusty.schemas.base_schema_class import DustySchema from ..utils import get_app_dusty_schema, get_bundle_dusty_schema, get_lib_dusty_schema class TestStatusCommands(DustyTestCase): @patch('dusty.commands.status.get_dusty_containers') def test_has_active_container_lib_active(self, fake_get_dusty_containers): fake_get_dusty_containers.return_value = ['some_container'] self.assertEquals(False, _has_active_container('lib', 'lib-a')) @patch('dusty.commands.status.get_dusty_containers') def test_has_active_container_lib_inactive(self, fake_get_dusty_containers): fake_get_dusty_containers.return_value = [] self.assertEquals(False, _has_active_container('lib', 'lib-a')) @patch('dusty.commands.status.get_dusty_containers') def test_has_active_container_app_active(self, fake_get_dusty_containers): fake_get_dusty_containers.return_value = ['some_container'] self.assertEquals(True, _has_active_container('app', 'app-a')) @patch('dusty.commands.status.get_dusty_containers') def test_has_active_container_app_inactive(self, fake_get_dusty_containers): fake_get_dusty_containers.return_value = [] self.assertEquals(False, _has_active_container('app', 'app-a')) @patch('dusty.commands.status.get_dusty_containers') def test_has_active_container_service_active(self, fake_get_dusty_containers): fake_get_dusty_containers.return_value = ['some_container'] self.assertEquals(True, _has_active_container('service', 'service-a')) @patch('dusty.commands.status.get_dusty_containers') def test_has_active_container_service_inactive(self, fake_get_dusty_containers): fake_get_dusty_containers.return_value = [] self.assertEquals(False, _has_active_container('service', 'service-a')) @patch('dusty.commands.status.docker_vm_is_running') @patch('dusty.systems.docker.get_docker_client') @patch('dusty.commands.status.PrettyTable') @patch('dusty.commands.status.get_dusty_containers') @patch('dusty.schemas.base_schema_class.get_specs_from_path') @patch('dusty.compiler.spec_assembler._get_referenced_apps') @patch('dusty.compiler.spec_assembler._get_referenced_libs') @patch('dusty.compiler.spec_assembler._get_referenced_services') def test_get_dusty_status_active_1(self, fake_get_services, fake_get_libs, fake_get_apps, fake_get_specs, fake_get_dusty_containers, fake_pretty_table, fake_get_docker_client, fake_vm_is_running): fake_get_services.return_value = set(['ser1', 'ser2', 'ser3']) fake_get_libs.return_value = set(['lib1']) fake_get_apps.return_value = set(['app1', 'app2']) fake_table = Mock() fake_pretty_table.return_value = fake_table fake_get_dusty_containers.return_value = ['some_container'] fake_get_specs.return_value = {'apps': {'app1': get_app_dusty_schema({}, 'app1'), 'app2':get_app_dusty_schema({}, 'app2')}, 'libs': {'lib1': get_lib_dusty_schema({}, 'lib1')}, 'services': {'ser1': DustySchema(None, {}, 'ser1', 'services'), 'ser2': DustySchema(None, {}, 'ser2', 'services'), 'ser3': DustySchema(None, {}, 'ser3', 'services')}, 'bundles': get_lib_dusty_schema({}, 'bundle')} fake_get_docker_client.return_value = None fake_vm_is_running.return_value = True get_dusty_status() call_args_list = fake_table.add_row.call_args_list self.assertTrue(call(['app1', 'app', 'X']) in call_args_list) self.assertTrue(call(['app2', 'app', 'X']) in call_args_list) self.assertTrue(call(['lib1', 'lib', '']) in call_args_list) self.assertTrue(call(['ser1', 'service', 'X']) in call_args_list) self.assertTrue(call(['ser2', 'service', 'X']) in call_args_list) self.assertTrue(call(['ser3', 'service', 'X']) in call_args_list) self.assertTrue(call(['dustyInternalNginx', '', 'X']) in call_args_list) self.assertEquals(len(call_args_list), 7) @patch('dusty.commands.status.docker_vm_is_running') @patch('dusty.systems.docker.get_docker_client') @patch('dusty.commands.status.PrettyTable') @patch('dusty.commands.status.get_dusty_containers') @patch('dusty.schemas.base_schema_class.get_specs_from_path') @patch('dusty.compiler.spec_assembler._get_referenced_apps') @patch('dusty.compiler.spec_assembler._get_referenced_libs') @patch('dusty.compiler.spec_assembler._get_referenced_services') def test_get_dusty_status_active_2(self, fake_get_services, fake_get_libs, fake_get_apps, fake_get_specs, fake_get_dusty_containers, fake_pretty_table, fake_get_docker_client, fake_vm_is_running): fake_get_services.return_value = set(['ser1', 'ser2', 'ser3']) fake_get_libs.return_value = set(['lib1']) fake_get_apps.return_value = set(['app1', 'app2']) fake_table = Mock() fake_pretty_table.return_value = fake_table fake_get_dusty_containers.return_value = [] fake_get_specs.return_value = {'apps': {'app1': get_app_dusty_schema({}, 'app1'), 'app2':get_app_dusty_schema({}, 'app2')}, 'libs': {'lib1': get_lib_dusty_schema({}, 'lib1')}, 'services': {'ser1': DustySchema(None, {}, 'ser1', 'services'), 'ser2': DustySchema(None, {}, 'ser2', 'services'), 'ser3': DustySchema(None, {}, 'ser3', 'services')}, 'bundles': get_lib_dusty_schema({}, 'bundle')} fake_get_docker_client.return_value = None fake_vm_is_running.return_value = True get_dusty_status() call_args_list = fake_table.add_row.call_args_list self.assertTrue(call(['app1', 'app', '']) in call_args_list) self.assertTrue(call(['app2', 'app', '']) in call_args_list) self.assertTrue(call(['lib1', 'lib', '']) in call_args_list) self.assertTrue(call(['ser1', 'service', '']) in call_args_list) self.assertTrue(call(['ser2', 'service', '']) in call_args_list) self.assertTrue(call(['ser3', 'service', '']) in call_args_list) self.assertTrue(call(['dustyInternalNginx', '', '']) in call_args_list) self.assertEquals(len(call_args_list), 7)

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823

6,572

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0.09356

0.060215

0.103226

0.08411

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null

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1

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null

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6

2ced6ac8779a2621bf68d4b0f580e7b3aeab83a4

58

py

Python

prt_mail_messages/__init__.py

sagar-organiztion/custom_repo2

30687143b3b6a7820305075d15fd15fbd61141d1

[ "Apache-2.0" ]

null

prt_mail_messages/__init__.py

sagar-organiztion/custom_repo2

30687143b3b6a7820305075d15fd15fbd61141d1

[ "Apache-2.0" ]

null

prt_mail_messages/__init__.py

sagar-organiztion/custom_repo2

30687143b3b6a7820305075d15fd15fbd61141d1

[ "Apache-2.0" ]

null

from . import models # noqa from . import wizard # noqa

19.333333

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58

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true

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1

0

null

0

1

0

1

0

1

0

6

fa201b797e417adad9763e6671010a321eaecba1

725

py

Python

test/test_cluster_plugin.py

RyanSiu1995/argocd-python-client

2e8f097fe09f247a46ac70692241a93d1acd076a

[ "MIT" ]

1

2021-11-20T13:37:43.000Z

test/test_cluster_plugin.py

RyanSiu1995/argocd-python-client

2e8f097fe09f247a46ac70692241a93d1acd076a

[ "MIT" ]

null

test/test_cluster_plugin.py

RyanSiu1995/argocd-python-client

2e8f097fe09f247a46ac70692241a93d1acd076a

[ "MIT" ]

null

""" Consolidate Services Description of all APIs # noqa: E501 The version of the OpenAPI document: version not set Generated by: https://openapi-generator.tech """ import sys import unittest import argocd_python_client from argocd_python_client.model.cluster_plugin import ClusterPlugin class TestClusterPlugin(unittest.TestCase): """ClusterPlugin unit test stubs""" def setUp(self): pass def tearDown(self): pass def testClusterPlugin(self): """Test ClusterPlugin""" # FIXME: construct object with mandatory attributes with example values # model = ClusterPlugin() # noqa: E501 pass if __name__ == '__main__': unittest.main()

20.138889

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0.07438

0

0.010811

0.234483

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20.714286

0.861261

0.434483

0

0.230769

1

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0.02139

0

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false

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null

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1

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1

0

1

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6

fa575a9ef17332478ba773f79636bab15ead7d4d

69

py

Python

connectedcars/__init__.py

heslegrave/connectedcars-python

29dca7bb33d549dbb4803688032ae3a13b932eba

[ "MIT" ]

4

2019-11-11T00:21:33.000Z

2020-10-27T19:47:35.000Z

connectedcars/__init__.py

heslegrave/connectedcars-python

29dca7bb33d549dbb4803688032ae3a13b932eba

[ "MIT" ]

2

2020-06-29T20:17:55.000Z

2020-10-25T19:17:38.000Z

connectedcars/__init__.py

heslegrave/connectedcars-python

29dca7bb33d549dbb4803688032ae3a13b932eba

[ "MIT" ]

2

2020-07-20T16:08:48.000Z

2020-11-01T14:55:20.000Z

from .client import * from .models import * from .exceptions import *

23

25

0.753623

9

69

5.777778

0.555556

0.384615

0

0.15942

69

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25

23

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0

true

0

1

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null

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1

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1

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1

0

6

d7087e9ee4cdb577e50b32daacbb521e85f4e480

492

py

Python

opendeep/optimization/loss/__init__.py

vitruvianscience/OpenDeep

e96efc449101094354b615cf15afe6d03644fc36

[ "Apache-2.0" ]

252

2015-03-13T21:55:22.000Z

2021-09-06T21:37:38.000Z

opendeep/optimization/loss/__init__.py

afcarl/OpenDeep

e96efc449101094354b615cf15afe6d03644fc36

[ "Apache-2.0" ]

16

2015-03-14T06:47:04.000Z

2016-09-23T19:13:35.000Z

opendeep/optimization/loss/__init__.py

afcarl/OpenDeep

e96efc449101094354b615cf15afe6d03644fc36

[ "Apache-2.0" ]

68

2015-03-14T00:05:53.000Z

2020-06-04T13:36:13.000Z

from __future__ import division, absolute_import, print_function from opendeep.optimization.loss.loss import * from opendeep.optimization.loss.binary_crossentropy import * from opendeep.optimization.loss.categorical_crossentropy import * from opendeep.optimization.loss.isotropic_gaussian_LL import * from opendeep.optimization.loss.mse import * from opendeep.optimization.loss.neg_LL import * from opendeep.optimization.loss.zero_one import * from opendeep.optimization.loss import utils

37.846154

65

0.851626

62

492

6.564516

0.33871

0.235872

0.471744

0.550369

0.653563

0.402948

0

0.083333

492

12

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true

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0

null

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0

null

0

1

0

1

0

1

0

6

ad7ba4397c5c934384de131b1688608495888b8a

153,371

py

Python

lib/ult/ult.py

abreza/HOI-CL

c5be517bb26eac73ef88a39d6ec9e564c3379714

[ "MIT" ]

40

2021-04-09T17:53:08.000Z

2022-03-30T02:38:10.000Z

lib/ult/ult.py

abreza/HOI-CL

c5be517bb26eac73ef88a39d6ec9e564c3379714

[ "MIT" ]

21

2021-04-09T19:05:47.000Z

2022-01-31T23:17:16.000Z

lib/ult/ult.py

abreza/HOI-CL

c5be517bb26eac73ef88a39d6ec9e564c3379714

[ "MIT" ]

8

2021-05-30T12:37:00.000Z

2022-03-14T03:13:57.000Z

# -------------------------------------------------------- # Tensorflow VCL # Licensed under The MIT License [see LICENSE for details] # Written by Zhi Hou # -------------------------------------------------------- """ Generating training instance """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from functools import partial import numpy as np import json import pickle import random from random import randint import tensorflow as tf import cv2 from .config import cfg # for merge COCO and HICO dataset MAX_COCO_ID = 650000 MAX_HICO_ID = 40000 def bbox_trans(human_box_ori, object_box_ori, ratio, size=64): human_box = human_box_ori.copy() object_box = object_box_ori.copy() InteractionPattern = [min(human_box[0], object_box[0]), min(human_box[1], object_box[1]), max(human_box[2], object_box[2]), max(human_box[3], object_box[3])] height = InteractionPattern[3] - InteractionPattern[1] + 1 width = InteractionPattern[2] - InteractionPattern[0] + 1 if height > width: ratio = 'height' else: ratio = 'width' # shift the top-left corner to (0,0) human_box[0] -= InteractionPattern[0] human_box[2] -= InteractionPattern[0] human_box[1] -= InteractionPattern[1] human_box[3] -= InteractionPattern[1] object_box[0] -= InteractionPattern[0] object_box[2] -= InteractionPattern[0] object_box[1] -= InteractionPattern[1] object_box[3] -= InteractionPattern[1] if ratio == 'height': # height is larger than width human_box[0] = 0 + size * human_box[0] / height human_box[1] = 0 + size * human_box[1] / height human_box[2] = (size * width / height - 1) - size * (width - 1 - human_box[2]) / height human_box[3] = (size - 1) - size * (height - 1 - human_box[3]) / height object_box[0] = 0 + size * object_box[0] / height object_box[1] = 0 + size * object_box[1] / height object_box[2] = (size * width / height - 1) - size * (width - 1 - object_box[2]) / height object_box[3] = (size - 1) - size * (height - 1 - object_box[3]) / height # Need to shift horizontally InteractionPattern = [min(human_box[0], object_box[0]), min(human_box[1], object_box[1]), max(human_box[2], object_box[2]), max(human_box[3], object_box[3])] # assert (InteractionPattern[0] == 0) & (InteractionPattern[1] == 0) & (InteractionPattern[3] == 63) & (InteractionPattern[2] <= 63) if human_box[3] > object_box[3]: human_box[3] = size - 1 else: object_box[3] = size - 1 shift = size / 2 - (InteractionPattern[2] + 1) / 2 human_box += [shift, 0, shift, 0] object_box += [shift, 0, shift, 0] else: # width is larger than height human_box[0] = 0 + size * human_box[0] / width human_box[1] = 0 + size * human_box[1] / width human_box[2] = (size - 1) - size * (width - 1 - human_box[2]) / width human_box[3] = (size * height / width - 1) - size * (height - 1 - human_box[3]) / width object_box[0] = 0 + size * object_box[0] / width object_box[1] = 0 + size * object_box[1] / width object_box[2] = (size - 1) - size * (width - 1 - object_box[2]) / width object_box[3] = (size * height / width - 1) - size * (height - 1 - object_box[3]) / width # Need to shift vertically InteractionPattern = [min(human_box[0], object_box[0]), min(human_box[1], object_box[1]), max(human_box[2], object_box[2]), max(human_box[3], object_box[3])] # assert (InteractionPattern[0] == 0) & (InteractionPattern[1] == 0) & (InteractionPattern[2] == 63) & (InteractionPattern[3] <= 63) if human_box[2] > object_box[2]: human_box[2] = size - 1 else: object_box[2] = size - 1 shift = size / 2 - (InteractionPattern[3] + 1) / 2 human_box = human_box + [0, shift, 0, shift] object_box = object_box + [0, shift, 0, shift] return np.round(human_box), np.round(object_box) def Get_next_sp(human_box, object_box): InteractionPattern = [min(human_box[0], object_box[0]), min(human_box[1], object_box[1]), max(human_box[2], object_box[2]), max(human_box[3], object_box[3])] height = InteractionPattern[3] - InteractionPattern[1] + 1 width = InteractionPattern[2] - InteractionPattern[0] + 1 if height > width: H, O = bbox_trans(human_box, object_box, 'height') else: H, O = bbox_trans(human_box, object_box, 'width') Pattern = np.zeros((64, 64, 2)) Pattern[int(H[1]):int(H[3]) + 1, int(H[0]):int(H[2]) + 1, 0] = 1 Pattern[int(O[1]):int(O[3]) + 1, int(O[0]):int(O[2]) + 1, 1] = 1 return Pattern # # def Get_next_sp_with_pose(human_box, object_box, human_pose, num_joints=17): # InteractionPattern = [min(human_box[0], object_box[0]), min(human_box[1], object_box[1]), # max(human_box[2], object_box[2]), max(human_box[3], object_box[3])] # height = InteractionPattern[3] - InteractionPattern[1] + 1 # width = InteractionPattern[2] - InteractionPattern[0] + 1 # if height > width: # H, O = bbox_trans(human_box, object_box, 'height') # else: # H, O = bbox_trans(human_box, object_box, 'width') # # Pattern = np.zeros((64, 64, 2), dtype='float32') # Pattern[int(H[1]):int(H[3]) + 1, int(H[0]):int(H[2]) + 1, 0] = 1 # Pattern[int(O[1]):int(O[3]) + 1, int(O[0]):int(O[2]) + 1, 1] = 1 # # if human_pose != None and len(human_pose) == 51: # skeleton = get_skeleton(human_box, human_pose, H, num_joints) # else: # skeleton = np.zeros((64, 64, 1), dtype='float32') # skeleton[int(H[1]):int(H[3]) + 1, int(H[0]):int(H[2]) + 1, 0] = 0.05 # # Pattern = np.concatenate((Pattern, skeleton), axis=2) # # return Pattern def get_skeleton(human_box, human_pose, human_pattern, num_joints=17, size=64): width = human_box[2] - human_box[0] + 1 height = human_box[3] - human_box[1] + 1 pattern_width = human_pattern[2] - human_pattern[0] + 1 pattern_height = human_pattern[3] - human_pattern[1] + 1 joints = np.zeros((num_joints + 1, 2), dtype='int32') for i in range(num_joints): joint_x, joint_y, joint_score = human_pose[3 * i: 3 * (i + 1)] x_ratio = (joint_x - human_box[0]) / float(width) y_ratio = (joint_y - human_box[1]) / float(height) joints[i][0] = min(size - 1, int(round(x_ratio * pattern_width + human_pattern[0]))) joints[i][1] = min(size - 1, int(round(y_ratio * pattern_height + human_pattern[1]))) joints[num_joints] = (joints[5] + joints[6]) / 2 return draw_relation(human_pattern, joints) def draw_relation(human_pattern, joints, size=64): joint_relation = [[1, 3], [2, 4], [0, 1], [0, 2], [0, 17], [5, 17], [6, 17], [5, 7], [6, 8], [7, 9], [8, 10], [11, 17], [12, 17], [11, 13], [12, 14], [13, 15], [14, 16]] color = [0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95] skeleton = np.zeros((size, size, 1), dtype="float32") for i in range(len(joint_relation)): cv2.line(skeleton, tuple(joints[joint_relation[i][0]]), tuple(joints[joint_relation[i][1]]), (color[i])) # cv2.rectangle(skeleton, (int(human_pattern[0]), int(human_pattern[1])), (int(human_pattern[2]), int(human_pattern[3])), (255)) # cv2.imshow("Joints", skeleton) # cv2.waitKey(0) # print(skeleton[:,:,0]) return skeleton def bb_IOU(boxA, boxB): ixmin = np.maximum(boxA[0], boxB[0]) iymin = np.maximum(boxA[1], boxB[1]) ixmax = np.minimum(boxA[2], boxB[2]) iymax = np.minimum(boxA[3], boxB[3]) iw = np.maximum(ixmax - ixmin + 1., 0.) ih = np.maximum(iymax - iymin + 1., 0.) inters = iw * ih # union uni = ((boxB[2] - boxB[0] + 1.) * (boxB[3] - boxB[1] + 1.) + (boxA[2] - boxA[0] + 1.) * (boxA[3] - boxA[1] + 1.) - inters) overlaps = inters / uni return overlaps def Augmented_box(bbox, shape, image_id, augment=15): thres_ = 0.7 box = np.array([0, bbox[0], bbox[1], bbox[2], bbox[3]]).reshape(1, 5) box = box.astype(np.float64) if bbox[0] >= bbox[2] or bbox[1] >= bbox[3]: return box count = 0 time_count = 0 while count < augment: time_count += 1 height = bbox[3] - bbox[1] width = bbox[2] - bbox[0] height_cen = (bbox[3] + bbox[1]) / 2 width_cen = (bbox[2] + bbox[0]) / 2 ratio = 1 + randint(-10, 10) * 0.01 height_shift = randint(-np.floor(height), np.floor(height)) * 0.1 width_shift = randint(-np.floor(width), np.floor(width)) * 0.1 H_0 = max(0, width_cen + width_shift - ratio * width / 2) H_2 = min(shape[1] - 1, width_cen + width_shift + ratio * width / 2) H_1 = max(0, height_cen + height_shift - ratio * height / 2) H_3 = min(shape[0] - 1, height_cen + height_shift + ratio * height / 2) if bb_IOU(bbox, np.array([H_0, H_1, H_2, H_3])) > thres_: box_ = np.array([0, H_0, H_1, H_2, H_3]).reshape(1, 5) box = np.concatenate((box, box_), axis=0) count += 1 if time_count > 150: return box return box def Generate_action(action_list, nums=29): action_ = np.zeros(nums) for GT_idx in action_list: action_[GT_idx] = 1 action_ = action_.reshape(1, nums) return action_ def Get_Next_Instance_HO_Neg(trainval_GT, Trainval_Neg, iter, Pos_augment, Neg_select, Data_length): GT = trainval_GT[iter % Data_length] image_id = GT[0] im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill(12) + '.jpg' import os if not os.path.exists(im_file): print("not existing:", im_file) im = cv2.imread(im_file) im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) Pattern, Human_augmented, Human_augmented_solo, Object_augmented, action_HO, action_H, mask_HO, mask_H = Augmented_HO_Neg( GT, Trainval_Neg, im_shape, Pos_augment, Neg_select) blobs = {} blobs['image'] = im_orig blobs['H_boxes_solo'] = Human_augmented_solo blobs['H_boxes'] = Human_augmented blobs['O_boxes'] = Object_augmented blobs['gt_class_HO'] = action_HO blobs['gt_class_H'] = action_H blobs['Mask_HO'] = mask_HO blobs['Mask_H'] = mask_H blobs['sp'] = Pattern blobs['H_num'] = len(action_H) return blobs def Augmented_HO_Neg(GT, Trainval_Neg, shape, Pos_augment, Neg_select): image_id = GT[0] Human = GT[2] Object = GT[3] action_HO_ = Generate_action(GT[1]) action_H_ = Generate_action(GT[4]) mask_HO_ = np.asarray( [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1]).reshape(1, 29) mask_H_ = np.asarray( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]).reshape(1, 29) Human_augmented = Augmented_box(Human, shape, image_id, Pos_augment) Object_augmented = Augmented_box(Object, shape, image_id, Pos_augment) Human_augmented_solo = Human_augmented.copy() Human_augmented = Human_augmented[:min(len(Human_augmented), len(Object_augmented))] Object_augmented = Object_augmented[:min(len(Human_augmented), len(Object_augmented))] num_pos = len(Human_augmented) if image_id in Trainval_Neg: if len(Trainval_Neg[image_id]) < Neg_select: for Neg in Trainval_Neg[image_id]: Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) else: List = random.sample(range(len(Trainval_Neg[image_id])), len(Trainval_Neg[image_id])) for i in range(Neg_select): Neg = Trainval_Neg[image_id][List[i]] Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) num_pos_neg = len(Human_augmented) action_HO = action_HO_ action_H = action_H_ mask_HO = mask_HO_ mask_H = mask_H_ Pattern = np.empty((0, 64, 64, 2), dtype=np.float32) for i in range(num_pos - 1): action_HO = np.concatenate((action_HO, action_HO_), axis=0) action_H = np.concatenate((action_H, action_H_), axis=0) mask_H = np.concatenate((mask_H, mask_H_), axis=0) for i in range(num_pos_neg - 1): mask_HO = np.concatenate((mask_HO, mask_HO_), axis=0) for i in range(num_pos_neg - num_pos): action_HO = np.concatenate((action_HO, np.zeros(29).reshape(1, 29)), axis=0) for i in range(num_pos_neg): Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]).reshape(1, 64, 64, 2) Pattern = np.concatenate((Pattern, Pattern_), axis=0) Pattern = Pattern.reshape(num_pos_neg, 64, 64, 2) Human_augmented = Human_augmented.reshape(num_pos_neg, 5) Human_augmented_solo = Human_augmented_solo.reshape(num_pos, 5) Object_augmented = Object_augmented.reshape(num_pos_neg, 5) action_HO = action_HO.reshape(num_pos_neg, 29) action_H = action_H.reshape(num_pos, 29) mask_HO = mask_HO.reshape(num_pos_neg, 29) mask_H = mask_H.reshape(num_pos, 29) return Pattern, Human_augmented, Human_augmented_solo, Object_augmented, action_HO, action_H, mask_HO, mask_H def Augmented_HO_spNeg(GT, Trainval_Neg, shape, Pos_augment, Neg_select): image_id = GT[0] Human = GT[2] Object = GT[3] set_list = [(0, 38), (1, 31), (1, 32), (2, 43), (2, 44), (2, 77), (4, 1), (4, 19), (4, 28), (4, 46), (4, 47), (4, 48), (4, 49), (4, 51), (4, 52), (4, 54), (4, 55), (4, 56), (5, 2), (5, 3), (5, 4), (5, 6), (5, 7), (5, 8), (5, 9), (5, 18), (5, 21), (6, 68), (7, 33), (8, 64), (9, 47), (9, 48), (9, 49), (9, 50), (9, 51), (9, 52), (9, 53), (9, 54), (9, 55), (9, 56), (10, 2), (10, 4), (10, 14), (10, 18), (10, 21), (10, 25), (10, 27), (10, 29), (10, 57), (10, 58), (10, 60), (10, 61), (10, 62), (10, 64), (11, 31), (11, 32), (11, 37), (11, 38), (12, 14), (12, 57), (12, 58), (12, 60), (12, 61), (13, 40), (13, 41), (13, 42), (13, 46), (14, 1), (14, 25), (14, 26), (14, 27), (14, 29), (14, 30), (14, 31), (14, 32), (14, 33), (14, 34), (14, 35), (14, 37), (14, 38), (14, 39), (14, 40), (14, 41), (14, 42), (14, 47), (14, 50), (14, 68), (14, 74), (14, 75), (14, 78), (15, 30), (15, 33), (16, 43), (16, 44), (16, 45), (18, 1), (18, 2), (18, 3), (18, 4), (18, 5), (18, 6), (18, 7), (18, 8), (18, 11), (18, 14), (18, 15), (18, 16), (18, 17), (18, 18), (18, 19), (18, 20), (18, 21), (18, 24), (18, 25), (18, 26), (18, 27), (18, 28), (18, 29), (18, 30), (18, 31), (18, 32), (18, 33), (18, 34), (18, 35), (18, 36), (18, 37), (18, 38), (18, 39), (18, 40), (18, 41), (18, 42), (18, 43), (18, 44), (18, 45), (18, 46), (18, 47), (18, 48), (18, 49), (18, 51), (18, 53), (18, 54), (18, 55), (18, 56), (18, 57), (18, 61), (18, 62), (18, 63), (18, 64), (18, 65), (18, 66), (18, 67), (18, 68), (18, 73), (18, 74), (18, 75), (18, 77), (19, 35), (19, 39), (20, 33), (21, 31), (21, 32), (23, 1), (23, 11), (23, 19), (23, 20), (23, 24), (23, 28), (23, 34), (23, 49), (23, 53), (23, 56), (23, 61), (23, 63), (23, 64), (23, 67), (23, 68), (23, 73), (24, 74), (25, 1), (25, 2), (25, 4), (25, 8), (25, 9), (25, 14), (25, 15), (25, 16), (25, 17), (25, 18), (25, 19), (25, 21), (25, 25), (25, 26), (25, 27), (25, 28), (25, 29), (25, 30), (25, 31), (25, 32), (25, 33), (25, 34), (25, 35), (25, 36), (25, 37), (25, 38), (25, 39), (25, 40), (25, 41), (25, 42), (25, 43), (25, 44), (25, 45), (25, 46), (25, 47), (25, 48), (25, 49), (25, 50), (25, 51), (25, 52), (25, 53), (25, 54), (25, 55), (25, 56), (25, 57), (25, 64), (25, 65), (25, 66), (25, 67), (25, 68), (25, 73), (25, 74), (25, 77), (25, 78), (25, 79), (25, 80), (26, 32), (26, 37), (28, 30), (28, 33)] action_sp_ = Generate_action(GT[1]) action_HO_ = Generate_action(GT[1]) obj_cls = GT[-1] action_compose = [set_list.index(item) for item in [(ho, obj_cls[0]) for ho in GT[1]]] action_compose_ = Generate_action(action_compose, nums=len(set_list)) action_H_ = Generate_action(GT[4]) mask_sp_ = np.asarray( [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1]).reshape(1, 29) mask_HO_ = np.asarray( [1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1]).reshape(1, 29) mask_H_ = np.asarray( [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]).reshape(1, 29) Human_augmented = Augmented_box(Human, shape, image_id, Pos_augment) Object_augmented = Augmented_box(Object, shape, image_id, Pos_augment) if Human[0] == 0 and Human[1] == 0 and Human[2] == 0: while len(Human_augmented) < Pos_augment + 1: Human_augmented = np.concatenate( [Human_augmented, Human_augmented[-(Pos_augment + 1 - len(Human_augmented)):]], axis=0) Human_augmented = Human_augmented[:min(len(Human_augmented), len(Object_augmented))] Object_augmented = Object_augmented[:min(len(Human_augmented), len(Object_augmented))] num_pos = len(Human_augmented) if image_id in Trainval_Neg: if len(Trainval_Neg[image_id]) < Neg_select: for Neg in Trainval_Neg[image_id]: Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) else: List = random.sample(range(len(Trainval_Neg[image_id])), len(Trainval_Neg[image_id])) for i in range(Neg_select): Neg = Trainval_Neg[image_id][List[i]] Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) num_pos_neg = len(Human_augmented) action_sp = action_sp_ action_HO = action_HO_ action_H = action_H_ action_compose = action_compose_ mask_sp = mask_sp_ mask_HO = mask_HO_ mask_H = mask_H_ Pattern = np.empty((0, 64, 64, 2), dtype=np.float32) for i in range(num_pos - 1): action_sp = np.concatenate((action_sp, action_sp_), axis=0) action_HO = np.concatenate((action_HO, action_HO_), axis=0) action_H = np.concatenate((action_H, action_H_), axis=0) action_compose = np.concatenate((action_compose, action_compose_), axis=0) mask_HO = np.concatenate((mask_HO, mask_HO_), axis=0) mask_H = np.concatenate((mask_H, mask_H_), axis=0) for i in range(num_pos_neg - 1): mask_sp = np.concatenate((mask_sp, mask_sp_), axis=0) for i in range(num_pos_neg - num_pos): action_sp = np.concatenate((action_sp, np.zeros(29).reshape(1, 29)), axis=0) action_compose = np.concatenate((action_compose, np.zeros(len(set_list)).reshape(1, len(set_list))), axis=0) for i in range(num_pos_neg): Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]).reshape(1, 64, 64, 2) Pattern = np.concatenate((Pattern, Pattern_), axis=0) Pattern = Pattern.reshape(num_pos_neg, 64, 64, 2) Human_augmented_sp = Human_augmented.reshape(num_pos_neg, 5) Object_augmented = Object_augmented[:num_pos].reshape(num_pos, 5) action_sp = action_sp.reshape(num_pos_neg, 29) action_HO = action_HO.reshape(num_pos, 29) action_H = action_H.reshape(num_pos, 29) action_compose = action_compose.reshape(num_pos, len(set_list)) mask_sp = mask_sp.reshape(num_pos_neg, 29) mask_HO = mask_HO.reshape(num_pos, 29) mask_H = mask_H.reshape(num_pos, 29) return Pattern, Human_augmented_sp, Human_augmented, Object_augmented, action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, action_compose def Augmented_HO_spNeg2(GT, Trainval_Neg, shape, Pos_augment, Neg_select): image_id = GT[0] Human = GT[2] Object = GT[3] set_list = [(0, 38), (1, 31), (1, 32), (2, 43), (2, 44), (2, 77), (3, 1), (3, 19), (3, 28), (3, 46), (3, 47), (3, 48), (3, 49), (3, 51), (3, 52), (3, 54), (3, 55), (3, 56), (4, 2), (4, 3), (4, 4), (4, 6), (4, 7), (4, 8), (4, 9), (4, 18), (4, 21), (5, 68), (6, 33), (7, 64), (8, 47), (8, 48), (8, 49), (8, 50), (8, 51), (8, 52), (8, 53), (8, 54), (8, 55), (8, 56), (9, 2), (9, 4), (9, 14), (9, 18), (9, 21), (9, 25), (9, 27), (9, 29), (9, 57), (9, 58), (9, 60), (9, 61), (9, 62), (9, 64), (10, 31), (10, 32), (10, 37), (10, 38), (11, 14), (11, 57), (11, 58), (11, 60), (11, 61), (12, 40), (12, 41), (12, 42), (12, 46), (13, 1), (13, 25), (13, 26), (13, 27), (13, 29), (13, 30), (13, 31), (13, 32), (13, 33), (13, 34), (13, 35), (13, 37), (13, 38), (13, 39), (13, 40), (13, 41), (13, 42), (13, 47), (13, 50), (13, 68), (13, 74), (13, 75), (13, 78), (14, 30), (14, 33), (15, 43), (15, 44), (15, 45), (16, 1), (16, 2), (16, 3), (16, 4), (16, 5), (16, 6), (16, 7), (16, 8), (16, 11), (16, 14), (16, 15), (16, 16), (16, 17), (16, 18), (16, 19), (16, 20), (16, 21), (16, 24), (16, 25), (16, 26), (16, 27), (16, 28), (16, 29), (16, 30), (16, 31), (16, 32), (16, 33), (16, 34), (16, 35), (16, 36), (16, 37), (16, 38), (16, 39), (16, 40), (16, 41), (16, 42), (16, 43), (16, 44), (16, 45), (16, 46), (16, 47), (16, 48), (16, 49), (16, 51), (16, 53), (16, 54), (16, 55), (16, 56), (16, 57), (16, 61), (16, 62), (16, 63), (16, 64), (16, 65), (16, 66), (16, 67), (16, 68), (16, 73), (16, 74), (16, 75), (16, 77), (17, 35), (17, 39), (18, 33), (19, 31), (19, 32), (20, 74), (21, 1), (21, 2), (21, 4), (21, 8), (21, 9), (21, 14), (21, 15), (21, 16), (21, 17), (21, 18), (21, 19), (21, 21), (21, 25), (21, 26), (21, 27), (21, 28), (21, 29), (21, 30), (21, 31), (21, 32), (21, 33), (21, 34), (21, 35), (21, 36), (21, 37), (21, 38), (21, 39), (21, 40), (21, 41), (21, 42), (21, 43), (21, 44), (21, 45), (21, 46), (21, 47), (21, 48), (21, 49), (21, 50), (21, 51), (21, 52), (21, 53), (21, 54), (21, 55), (21, 56), (21, 57), (21, 64), (21, 65), (21, 66), (21, 67), (21, 68), (21, 73), (21, 74), (21, 77), (21, 78), (21, 79), (21, 80), (22, 32), (22, 37), (23, 30), (23, 33)] action_sp_ = Generate_action(GT[1], nums=24) action_HO_ = Generate_action(GT[1], nums=24) obj_cls = GT[-1] action_compose = [set_list.index(item) for item in [(ho, obj_cls[0]) for ho in GT[1]]] action_compose_ = Generate_action(action_compose, nums=len(set_list)) action_H_ = Generate_action(GT[4], nums=24) mask_sp_ = np.ones([1, 24], np.int32) mask_HO_ = np.ones([1, 24], np.int32) mask_H_ = np.ones([1, 24], np.int32) Human_augmented = Augmented_box(Human, shape, image_id, Pos_augment) Object_augmented = Augmented_box(Object, shape, image_id, Pos_augment) Human_augmented = Human_augmented[:min(len(Human_augmented), len(Object_augmented))] Object_augmented = Object_augmented[:min(len(Human_augmented), len(Object_augmented))] num_pos = len(Human_augmented) # pose_list = [GT[5]] * num_pos if image_id in Trainval_Neg: if len(Trainval_Neg[image_id]) < Neg_select: for Neg in Trainval_Neg[image_id]: # pose_list.append(Neg[7]) Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) else: List = random.sample(range(len(Trainval_Neg[image_id])), len(Trainval_Neg[image_id])) for i in range(Neg_select): Neg = Trainval_Neg[image_id][List[i]] # pose_list.append(Neg[7]) Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) num_pos_neg = len(Human_augmented) action_sp = action_sp_ action_HO = action_HO_ action_H = action_H_ action_compose = action_compose_ mask_sp = mask_sp_ mask_HO = mask_HO_ mask_H = mask_H_ Pattern = np.empty((0, 64, 64, 2), dtype=np.float32) pose_box = [] # print('pose infor:', GT[5], pose_list) # pose_box = obtain_pose_box(Human_augmented, pose_list, shape) for item in Human_augmented: pose_box.extend([item] * 17) for i in range(num_pos - 1): action_sp = np.concatenate((action_sp, action_sp_), axis=0) action_HO = np.concatenate((action_HO, action_HO_), axis=0) action_H = np.concatenate((action_H, action_H_), axis=0) action_compose = np.concatenate((action_compose, action_compose_), axis=0) mask_HO = np.concatenate((mask_HO, mask_HO_), axis=0) mask_H = np.concatenate((mask_H, mask_H_), axis=0) for i in range(num_pos_neg - 1): mask_sp = np.concatenate((mask_sp, mask_sp_), axis=0) for i in range(num_pos_neg - num_pos): action_sp = np.concatenate((action_sp, np.zeros(24).reshape(1, 24)), axis=0) action_compose = np.concatenate((action_compose, np.zeros(len(set_list)).reshape(1, len(set_list))), axis=0) for i in range(num_pos_neg): Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]).reshape(1, 64, 64, 2) Pattern = np.concatenate((Pattern, Pattern_), axis=0) mask = np.zeros(shape=(1, shape[0] // 16, shape[1] // 16, 1), dtype=np.float32) # obj_box = Object_augmented[i][1:].astype(np.int32) # print(obj_box) # mask[:, obj_box[0]:obj_box[2], obj_box[1]:obj_box[3]] = 1 # from skimage import transform # mask = transform.resize(mask, [1, shape[0] // 16, shape[1] // 16, 1], order=0, preserve_range=True) Pattern = Pattern.reshape(num_pos_neg, 64, 64, 2) Human_augmented_sp = Human_augmented.reshape(num_pos_neg, 5) Object_augmented = Object_augmented[:num_pos].reshape(num_pos, 5) action_sp = action_sp.reshape(num_pos_neg, 24) action_HO = action_HO.reshape(num_pos, 24) action_H = action_H.reshape(num_pos, 24) action_compose = action_compose.reshape(num_pos_neg, len(set_list)) mask_sp = mask_sp.reshape(num_pos_neg, 24) mask_HO = mask_HO.reshape(num_pos, 24) mask_H = mask_H.reshape(num_pos, 24) return Pattern, Human_augmented_sp, Human_augmented, Object_augmented, action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, action_compose def Augmented_HO_spNeg3(GT, Trainval_Neg, shape, Pos_augment, Neg_select): image_id = GT[0] Human = GT[2] Object = GT[3] set_list = [(0, 38), (1, 31), (1, 32), (2, 1), (2, 19), (2, 28), (2, 43), (2, 44), (2, 46), (2, 47), (2, 48), (2, 49), (2, 51), (2, 52), (2, 54), (2, 55), (2, 56), (2, 77), (3, 2), (3, 3), (3, 4), (3, 6), (3, 7), (3, 8), (3, 9), (3, 18), (3, 21), (4, 68), (5, 33), (6, 64), (7, 43), (7, 44), (7, 45), (7, 47), (7, 48), (7, 49), (7, 50), (7, 51), (7, 52), (7, 53), (7, 54), (7, 55), (7, 56), (8, 2), (8, 4), (8, 14), (8, 18), (8, 21), (8, 25), (8, 27), (8, 29), (8, 57), (8, 58), (8, 60), (8, 61), (8, 62), (8, 64), (9, 31), (9, 32), (9, 37), (9, 38), (10, 14), (10, 57), (10, 58), (10, 60), (10, 61), (11, 40), (11, 41), (11, 42), (11, 46), (12, 1), (12, 25), (12, 26), (12, 27), (12, 29), (12, 30), (12, 31), (12, 32), (12, 33), (12, 34), (12, 35), (12, 37), (12, 38), (12, 39), (12, 40), (12, 41), (12, 42), (12, 47), (12, 50), (12, 68), (12, 74), (12, 75), (12, 78), (13, 30), (13, 33), (14, 1), (14, 2), (14, 3), (14, 4), (14, 5), (14, 6), (14, 7), (14, 8), (14, 11), (14, 14), (14, 15), (14, 16), (14, 17), (14, 18), (14, 19), (14, 20), (14, 21), (14, 24), (14, 25), (14, 26), (14, 27), (14, 28), (14, 29), (14, 30), (14, 31), (14, 32), (14, 33), (14, 34), (14, 35), (14, 36), (14, 37), (14, 38), (14, 39), (14, 40), (14, 41), (14, 42), (14, 43), (14, 44), (14, 45), (14, 46), (14, 47), (14, 48), (14, 49), (14, 51), (14, 53), (14, 54), (14, 55), (14, 56), (14, 57), (14, 61), (14, 62), (14, 63), (14, 64), (14, 65), (14, 66), (14, 67), (14, 68), (14, 73), (14, 74), (14, 75), (14, 77), (15, 33), (15, 35), (15, 39), (16, 31), (16, 32), (17, 74), (18, 1), (18, 2), (18, 4), (18, 8), (18, 9), (18, 14), (18, 15), (18, 16), (18, 17), (18, 18), (18, 19), (18, 21), (18, 25), (18, 26), (18, 27), (18, 28), (18, 29), (18, 30), (18, 31), (18, 32), (18, 33), (18, 34), (18, 35), (18, 36), (18, 37), (18, 38), (18, 39), (18, 40), (18, 41), (18, 42), (18, 43), (18, 44), (18, 45), (18, 46), (18, 47), (18, 48), (18, 49), (18, 50), (18, 51), (18, 52), (18, 53), (18, 54), (18, 55), (18, 56), (18, 57), (18, 64), (18, 65), (18, 66), (18, 67), (18, 68), (18, 73), (18, 74), (18, 77), (18, 78), (18, 79), (18, 80), (19, 32), (19, 37), (20, 30), (20, 33)] action_sp_ = Generate_action(GT[1], nums=21) action_HO_ = Generate_action(GT[1], nums=21) obj_cls = GT[-1] action_compose = [set_list.index(item) for item in [(ho, obj_cls[0]) for ho in GT[1]]] action_compose_ = Generate_action(action_compose, nums=len(set_list)) action_H_ = Generate_action(GT[4], nums=21) mask_sp_ = np.ones([1, 21], np.int32) mask_HO_ = np.ones([1, 21], np.int32) mask_H_ = np.ones([1, 21], np.int32) Human_augmented = Augmented_box(Human, shape, image_id, Pos_augment) Object_augmented = Augmented_box(Object, shape, image_id, Pos_augment) Human_augmented = Human_augmented[:min(len(Human_augmented), len(Object_augmented))] Object_augmented = Object_augmented[:min(len(Human_augmented), len(Object_augmented))] num_pos = len(Human_augmented) # pose_list = [GT[5]] * num_pos if image_id in Trainval_Neg: if len(Trainval_Neg[image_id]) < Neg_select: for Neg in Trainval_Neg[image_id]: # pose_list.append(Neg[7]) Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) else: List = random.sample(range(len(Trainval_Neg[image_id])), len(Trainval_Neg[image_id])) for i in range(Neg_select): Neg = Trainval_Neg[image_id][List[i]] # pose_list.append(Neg[7]) Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) num_pos_neg = len(Human_augmented) action_sp = action_sp_ action_HO = action_HO_ action_H = action_H_ action_compose = action_compose_ mask_sp = mask_sp_ mask_HO = mask_HO_ mask_H = mask_H_ Pattern = np.empty((0, 64, 64, 2), dtype=np.float32) pose_box = [] # print('pose infor:', GT[5], pose_list) # pose_box = obtain_pose_box(Human_augmented, pose_list, shape) for item in Human_augmented: pose_box.extend([item] * 17) for i in range(num_pos - 1): action_sp = np.concatenate((action_sp, action_sp_), axis=0) action_HO = np.concatenate((action_HO, action_HO_), axis=0) action_H = np.concatenate((action_H, action_H_), axis=0) action_compose = np.concatenate((action_compose, action_compose_), axis=0) mask_HO = np.concatenate((mask_HO, mask_HO_), axis=0) mask_H = np.concatenate((mask_H, mask_H_), axis=0) for i in range(num_pos_neg - 1): mask_sp = np.concatenate((mask_sp, mask_sp_), axis=0) for i in range(num_pos_neg - num_pos): action_sp = np.concatenate((action_sp, np.zeros(21).reshape(1, 21)), axis=0) action_compose = np.concatenate((action_compose, np.zeros(len(set_list)).reshape(1, len(set_list))), axis=0) for i in range(num_pos_neg): Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]).reshape([1, 64, 64, 2]) # Pattern_ = np.concatenate([Pattern_, np.zeros([1, 64, 64, 1])], axis=-1) Pattern = np.concatenate((Pattern, Pattern_), axis=0) mask = np.zeros(shape=(1, shape[0] // 16, shape[1] // 16, 1), dtype=np.float32) Pattern = Pattern.reshape(num_pos_neg, 64, 64, 2) Human_augmented_sp = Human_augmented.reshape(num_pos_neg, 5) Object_augmented = Object_augmented[:num_pos].reshape(num_pos, 5) action_sp = action_sp.reshape(num_pos_neg, 21) action_HO = action_HO.reshape(num_pos, 21) action_H = action_H.reshape(num_pos, 21) action_compose = action_compose.reshape(num_pos_neg, len(set_list)) mask_sp = mask_sp.reshape(num_pos_neg, 21) mask_HO = mask_HO.reshape(num_pos, 21) mask_H = mask_H.reshape(num_pos, 21) return Pattern, Human_augmented_sp, Human_augmented, Object_augmented, action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, action_compose def Generate_action_HICO(action_list): action_ = np.zeros(600) for GT_idx in action_list: action_[GT_idx] = 1 action_ = action_.reshape(1, 600) return action_ def Get_Next_Instance_HO_Neg_HICO(trainval_GT, Trainval_Neg, iter, Pos_augment, Neg_select, Data_length): GT = trainval_GT[iter % Data_length] image_id = GT[0] im_file = cfg.DATA_DIR + '/' + 'hico_20160224_det/images/train2015/HICO_train2015_' + (str(image_id)).zfill( 8) + '.jpg' im = cv2.imread(im_file) im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) Pattern, Human_augmented, Object_augmented, action_HO, num_pos = Augmented_HO_Neg_HICO(GT, Trainval_Neg, im_shape, Pos_augment, Neg_select) blobs = {} blobs['image'] = im_orig blobs['H_boxes'] = Human_augmented blobs['O_boxes'] = Object_augmented blobs['gt_class_HO'] = action_HO blobs['sp'] = Pattern blobs['H_num'] = num_pos return blobs def Augmented_neg_box(bbox, shape, image_id, augment=15, bbox_list=[]): thres_ = 0.25 # box = np.array([0, bbox[0], bbox[1], bbox[2], bbox[3]]).reshape(1, 5) # box = box.astype(np.float64) box = np.empty([1, 5], np.float64) count = 0 time_count = 0 while count < augment: time_count += 1 height = bbox[3] - bbox[1] width = bbox[2] - bbox[0] height_cen = (bbox[3] + bbox[1]) / 2 width_cen = (bbox[2] + bbox[0]) / 2 ratio = 1 + randint(-10, 10) * 0.01 height_shift = randint(-np.floor(height), np.floor(height)) height_shift = np.sign(height_shift) * 0.5 * height + height_shift width_shift = randint(-np.floor(width), np.floor(width)) * 0.1 width_shift = np.sign(width_shift) * 0.5 * width + width_shift H_0 = max(0, width_cen + width_shift - ratio * width / 2) H_2 = min(shape[1] - 1, width_cen + width_shift + ratio * width / 2) H_1 = max(0, height_cen + height_shift - ratio * height / 2) H_3 = min(shape[0] - 1, height_cen + height_shift + ratio * height / 2) valid_neg_box = True for bbox1 in bbox_list: if bb_IOU(bbox1, np.array([H_0, H_1, H_2, H_3])) > thres_: valid_neg_box = False break if valid_neg_box: box_ = np.array([0, H_0, H_1, H_2, H_3]).reshape(1, 5) box = np.concatenate((box, box_), axis=0) count += 1 if time_count > 150: return box return box def obtain_data2_large(Pos_augment=15, Neg_select=60, augment_type=0, model_name='', pattern_type=False, zero_shot_type=0, isalign=False, bnum=2, neg_type_ratio=0): # bnum = 2 if pattern_type == 1: Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_with_pose.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO_with_pose.pkl', "rb"), encoding='latin1') else: Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO.pkl', "rb"), encoding='latin1') g_func = generator2 def generator3(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type): buffer = [[] for i in range(7)] import time st = time.time() count_time = 0 avg_time = 0 # np.random.seed(0) for im_orig, image_id, num_pos, Human_augmented, Object_augmented, \ action_HO, Pattern in g_func(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, pattern_type, zero_shot_type, isalign, 0, neg_type_ratio): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(Human_augmented) buffer[4].append(Object_augmented) buffer[5].append(action_HO) buffer[6].append(Pattern) buffer[3][-1][:, 0] = len(buffer[3]) - 1 buffer[4][-1][:, 0] = len(buffer[3]) - 1 if len(buffer[0]) >= bnum: # if len(buffer[3][0]) < len(buffer[3][1]): # # make sure the second batch is less. # for i in range(len(buffer)): # tmp = buffer[i][0] # buffer[i][0] = buffer[i][1] # buffer[i][1] = tmp # print("inner:", buffer[0][0].shape, buffer[0][1].shape, buffer[1], buffer[2], buffer[3].shape, buffer[4].shape, buffer[5].shape, buffer[6].shape) # print("inner:", buffer[1], buffer[2][0], buffer[2][1], buffer[3][0].shape, buffer[3][1].shape, buffer[5][0].shape, buffer[5][1].shape) # yield buffer[0][0], buffer[0][1], buffer[1], buffer[2], buffer[3], buffer[4], buffer[5], buffer[6] # print("inner hint:", buffer[1], 'num_pos:', buffer[2], 'len of h boxes:',len(buffer[3][0]), len(buffer[3][1]), # len(buffer[4][0]), len(buffer[4][1]), len(buffer[5][0]), len(buffer[5][1]), len(buffer[6][0]), len(buffer[6][1])) pos_semi_list = [] if model_name.__contains__('x5new'): for b in range(bnum): pos_semi_list.append(int(buffer[2][b] + (len(buffer[3][b]) - buffer[2][b]) // 8)) else: for b in range(bnum): pos_semi_list.append(buffer[2][b]) for ii in range(3, 7): pos_h_boxes = np.concatenate([buffer[ii][pi][:pos2] for pi, pos2 in enumerate(pos_semi_list)], axis=0) neg_h_boxes = np.concatenate([buffer[ii][pi][pos2:] for pi, pos2 in enumerate(pos_semi_list)], axis=0) buffer[ii] = np.concatenate([pos_h_boxes, neg_h_boxes], axis=0) width = max([buffer[0][b].shape[1] for b in range(bnum)]) height = max([buffer[0][b].shape[2] for b in range(bnum)]) im_list = [] for b in range(bnum): im_list.append(np.pad(buffer[0][b], [(0, 0), (0, max(0, width - buffer[0][b].shape[1])), (0, max(0, height - buffer[0][b].shape[2])), (0, 0)], mode='constant')) width = max([buffer[7][b].shape[1] for b in range(bnum)]) height = max([buffer[7][b].shape[2] for b in range(bnum)]) yield np.concatenate(im_list, axis=0), buffer[1], sum(pos_semi_list), \ buffer[3], buffer[4], buffer[5], buffer[6], pos_semi_list[0] buffer = [[] for i in range(8)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() if pattern_type == 1: pattern_channel = 3 else: pattern_channel = 2 dataset = tf.data.Dataset.from_generator( partial(generator3, Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type), output_types=( tf.float32, tf.int32, tf.int64, tf.float32, tf.float32, tf.float32, tf.float32, tf.int32), output_shapes=( tf.TensorShape([bnum, None, None, 3]), tf.TensorShape([bnum, ]), tf.TensorShape([]), tf.TensorShape([None, 5]), tf.TensorShape([None, 5]), tf.TensorShape([None, 600]), tf.TensorShape([None, 64, 64, pattern_channel]), tf.TensorShape([]) # tf.TensorShape([2, None, None, None, 1]) ) ) # dataset = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32), # output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]))) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx = iterator.get_next() return image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx def Augmented_HO_Neg_HICO(GT, Trainval_Neg, shape, Pos_augment, Neg_select, pattern_type=False, isalign=False, box_list=[], real_neg_ratio=0): """ :param GT: :param Trainval_Neg: :param shape: :param Pos_augment: :param Neg_select: :param pattern_type: :param isalign: :param box_list: :param real_neg_ratio: This is for no action HOI (all zeros) :return: """ image_id = GT[0] Human = GT[2] Object = GT[3] action_HO_ = Generate_action_HICO(GT[1]) action_HO = action_HO_ Human_augmented = Augmented_box(Human, shape, image_id, Pos_augment) Object_augmented = Augmented_box(Object, shape, image_id, Pos_augment) max_augmented_nums = max(len(Human_augmented), len(Object_augmented)) if isalign: while len(Human_augmented) < max_augmented_nums: Human_augmented = np.concatenate( [Human_augmented, Human_augmented[-(max_augmented_nums - len(Human_augmented)):]], axis=0) if isalign: while len(Object_augmented) < max_augmented_nums: Object_augmented = np.concatenate( [Object_augmented, Object_augmented[-(max_augmented_nums - len(Object_augmented)):]], axis=0) # print("shape:", Human_augmented.shape, Object_augmented.shape) Human_augmented = Human_augmented[:min(len(Human_augmented), len(Object_augmented))] Object_augmented = Object_augmented[:min(len(Human_augmented), len(Object_augmented))] action_HO = np.tile(action_HO, [len(Human_augmented), 1]) if len(box_list) > 0 and real_neg_ratio > 0: aug_neg_objs = Augmented_neg_box(Object, shape, image_id, int(Pos_augment * real_neg_ratio), bbox_list=box_list) if len(aug_neg_objs) > 0: aug_neg_humans = np.tile([Human_augmented[0]], [len(aug_neg_objs), 1]) aug_neg_actions = np.zeros([len(aug_neg_objs), 600], ) # print(aug_neg_objs.shape, Object_augmented.shape, Human_augmented.shape, aug_neg_humans.shape) Human_augmented = np.concatenate([Human_augmented, aug_neg_humans]) Object_augmented = np.concatenate([Object_augmented, aug_neg_objs]) action_HO = np.concatenate([action_HO, aug_neg_actions]) num_pos = len(Human_augmented) pose_list = [] if image_id in Trainval_Neg: if len(Trainval_Neg[image_id]) < Neg_select: for Neg in Trainval_Neg[image_id]: Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) action_HO = np.concatenate((action_HO, Generate_action_HICO([Neg[1]])), axis=0) else: List = random.sample(range(len(Trainval_Neg[image_id])), len(Trainval_Neg[image_id])) for i in range(Neg_select): Neg = Trainval_Neg[image_id][List[i]] Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) action_HO = np.concatenate((action_HO, Generate_action_HICO([Neg[1]])), axis=0) num_pos_neg = len(Human_augmented) pattern_channel = 2 Pattern = np.empty((0, 64, 64, pattern_channel), dtype=np.float32) for i in range(num_pos_neg): # Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]).reshape(1, 64, 64, 2) # there are poses for the negative sample Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]) Pattern_ = Pattern_.reshape(1, 64, 64, pattern_channel) Pattern = np.concatenate((Pattern, Pattern_), axis=0) Pattern = Pattern.reshape(num_pos_neg, 64, 64, pattern_channel) Human_augmented = Human_augmented.reshape(num_pos_neg, 5) Object_augmented = Object_augmented.reshape(num_pos_neg, 5) action_HO = action_HO.reshape(num_pos_neg, 600) # print("shape1:", Human_augmented.shape, Object_augmented.shape, num_pos, Neg_select) return Pattern, Human_augmented, Object_augmented, action_HO, num_pos def obtain_data2(Pos_augment=15, Neg_select=60, augment_type=0, model_name='', pattern_type=False, zero_shot_type=0, isalign=False, neg_type_ratio=0): b_num = 2 Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO.pkl', "rb"), encoding='latin1') g_func = generator2 def generator3(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type): buffer = [[] for i in range(7)] import time st = time.time() count_time = 0 avg_time = 0 for im_orig, image_id, num_pos, Human_augmented, \ Object_augmented, action_HO, Pattern in g_func(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, pattern_type, zero_shot_type, isalign, 0): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(Human_augmented) buffer[4].append(Object_augmented) buffer[5].append(action_HO) buffer[6].append(Pattern) # buffer[8].append(pose_list) # print(im_orig.shape, image_id, num_pos, if len(buffer[0]) >= b_num: # print("inner:", buffer[0][0].shape, buffer[0][1].shape, buffer[1], buffer[2], buffer[3].shape, buffer[4].shape, buffer[5].shape, buffer[6].shape) # print("inner:", buffer[1], buffer[2][0], buffer[2][1], buffer[3][0].shape, buffer[3][1].shape, buffer[5][0].shape, buffer[5][1].shape) # yield buffer[0][0], buffer[0][1], buffer[1], buffer[2], buffer[3], buffer[4], buffer[5], buffer[6] if len(buffer[3][0]) < len(buffer[3][1]): # make sure the second batch is less. for i in range(len(buffer)): tmp = buffer[i][0] buffer[i][0] = buffer[i][1] buffer[i][1] = tmp buffer[3][1][:, 0] = 1 buffer[4][1][:, 0] = 1 # print("inner hint:", buffer[1], 'num_pos:', buffer[2], 'len of h boxes:',len(buffer[3][0]), len(buffer[3][1]), # len(buffer[4][0]), len(buffer[4][1]), len(buffer[5][0]), len(buffer[5][1]), len(buffer[6][0]), len(buffer[6][1])) if model_name.__contains__('x5new'): pos1 = int(buffer[2][0] + (len(buffer[3][0]) - buffer[2][0]) // 8) pos2 = int(buffer[2][1] + (len(buffer[3][1]) - buffer[2][1]) // 8) else: pos1 = buffer[2][0] pos2 = buffer[2][1] for ii in list(range(3, 7)): pos_h_boxes = np.concatenate([buffer[ii][0][:pos1], buffer[ii][1][:pos2]], axis=0) neg_h_boxes = np.concatenate([buffer[ii][0][pos1:], buffer[ii][1][pos2:]], axis=0) buffer[ii] = np.concatenate([pos_h_boxes, neg_h_boxes], axis=0) # buffer[ii] = np.concatenate([buffer[ii][0], buffer[ii][1]], axis=0) buffer = buffer[:-1] + buffer[-1:] im_shape1 = buffer[0][0].shape im_shape2 = buffer[0][1].shape width = max(im_shape1[1], im_shape2[1]) height = max(im_shape1[2], im_shape2[2]) im1 = np.pad(buffer[0][0], [(0, 0), (0, max(0, width - im_shape1[1])), (0, max(0, height - im_shape1[2])), (0, 0)], mode='constant') im2 = np.pad(buffer[0][1], [(0, 0), (0, max(0, width - im_shape2[1])), (0, max(0, height - im_shape2[2])), (0, 0)], mode='constant') split_idx = pos1 yield np.concatenate([im1, im2], axis=0), buffer[1], pos1 + pos2, buffer[3], buffer[4], buffer[5], \ buffer[6], split_idx buffer = [[] for i in range(7 )] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() if pattern_type == 1: pattern_channel = 3 else: pattern_channel = 2 dataset = tf.data.Dataset.from_generator( partial(generator3, Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type), output_types=( tf.float32, tf.int32, tf.int64, tf.float32, tf.float32, tf.float32, tf.float32, tf.int32), output_shapes=( tf.TensorShape([2, None, None, 3]), tf.TensorShape([2, ]), tf.TensorShape([]), tf.TensorShape([None, 5]), tf.TensorShape([None, 5]), tf.TensorShape([None, 600]), tf.TensorShape([None, 64, 64, pattern_channel]), tf.TensorShape([]) # tf.TensorShape([2, None, None, None, 1]) ) ) # dataset = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32), # output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]))) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx = iterator.get_next() return image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx def get_new_Trainval_GT(Trainval_GT, is_zero_shot, unseen_idx): unseen_idx = set(unseen_idx) if is_zero_shot > 0: new_Trainval_GT = [] for item in Trainval_GT: if len(set(list(item[1])).intersection(unseen_idx)) == 0: new_Trainval_GT.append(item) Trainval_GT = new_Trainval_GT return Trainval_GT def extract_semi_data(semi_type, model_name): print(semi_type, '===========') semi_pkl_path = cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl' if semi_type == 'default': semi_pkl_path = cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl' elif semi_type == 'coco': semi_pkl_path = cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_semi.pkl' elif semi_type == 'coco2': semi_pkl_path = cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_semi_coco2.pkl' elif semi_type == 'coco1': # train2017 semi_pkl_path = cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_semi1.pkl' elif semi_type == 'rehico': semi_pkl_path = cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl' elif semi_type == 'vcoco': semi_pkl_path = cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_vcoco_semi.pkl' if semi_type == 'both': Trainval_semi = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_semi.pkl', "rb"), encoding='latin1') Trainval_semi1 = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb"), encoding='latin1') # Trainval_semi = Trainval_semi[:5000] for item in Trainval_semi: item[0] += MAX_HICO_ID Trainval_semi.extend(Trainval_semi1) elif semi_type == 'both1': Trainval_semi = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_vcoco_semi.pkl', "rb"), encoding='latin1') Trainval_semi1 = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb"), encoding='latin1') for item in Trainval_semi: item[0] += MAX_HICO_ID Trainval_semi.extend(Trainval_semi1) pass elif semi_type == 'bothzs': Trainval_semi = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_semi.pkl', "rb"), encoding='latin1') Trainval_semi1 = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb"), encoding='latin1') # ids1 = [item[0] for item in Trainval_semi] # ids2 = [item[0] for item in Trainval_semi1] # ids = set(ids1).intersection(set(ids2)) # Trainval_semi = [item for item in Trainval_semi if item[0] not in ids] zero_shot_type = get_zero_shot_type(model_name) unseen_idx = get_unseen_index(zero_shot_type) print(unseen_idx) new_semi = [] print(len(Trainval_semi)) # 604907 for item in Trainval_semi: item[0] += MAX_HICO_ID # print(item) if len(item[1]) > 0 and len(list(set(item[1]).intersection(set(unseen_idx)))) > 0: new_semi.append(item) print(len(new_semi), 'bothzs semi') # 524239 bothzs semi zs3 517008 bothzs semi zs4 print(type(Trainval_semi)) Trainval_semi = new_semi Trainval_semi.extend(Trainval_semi1) elif semi_type == 'cocozs': Trainval_semi = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_semi1.pkl', "rb"), encoding='latin1') # ids1 = [item[0] for item in Trainval_semi] # ids2 = [item[0] for item in Trainval_semi1] # ids = set(ids1).intersection(set(ids2)) # Trainval_semi = [item for item in Trainval_semi if item[0] not in ids] zero_shot_type = get_zero_shot_type(model_name) unseen_idx = get_unseen_index(zero_shot_type) # Trainval_semi1 = [item for item in Trainval_semi1 if len(list(set(item[1]).intersection(set(unseen_idx)))) == 0] # remove unseen objects. print(unseen_idx) new_semi = [] for item in Trainval_semi: item[0] += MAX_HICO_ID # print(item) if len(item[1]) > 0 and len(list(set(item[1]).intersection(set(unseen_idx)))) > 0: new_semi.append(item) print(type(Trainval_semi)) Trainval_semi = new_semi elif semi_type == 'coco3': Trainval_semi = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_semi1.pkl', "rb"), encoding='latin1') Trainval_semi1 = pickle.load( open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_obj365_coco_semi_obj365_coco.pkl', "rb"), encoding='latin1') for item in Trainval_semi: item[0] += MAX_HICO_ID for item in Trainval_semi1: item[0] += MAX_COCO_ID Trainval_semi.extend(Trainval_semi1) else: with open(semi_pkl_path, "rb") as f: Trainval_semi = pickle.load(f, encoding='latin1') if semi_type == 'coco' or semi_type == 'coco2' or semi_type == 'coco1' or semi_type == 'vcoco': for item in Trainval_semi: item[0] += MAX_HICO_ID if semi_type == 'rehico' and model_name.__contains__('_zs11'): zero_shot_type = get_zero_shot_type(model_name) unseen_idx = get_unseen_index(zero_shot_type) Trainval_semi = get_new_Trainval_GT(Trainval_semi, zero_shot_type, unseen_idx) # Trainval_semi = [item for item in Trainval_semi if # len(list(set(item[1]).intersection(set(unseen_idx)))) == 0] # remove unseen objects. pass return Trainval_semi def obtain_data2_large(Pos_augment=15, Neg_select=60, augment_type=0, model_name='', pattern_type=False, zero_shot_type=0, isalign=False, bnum=2, neg_type_ratio=0): # bnum = 2 if pattern_type == 1: Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_with_pose.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO_with_pose.pkl', "rb"), encoding='latin1') else: Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO.pkl', "rb"), encoding='latin1') g_func = generator2 def generator3(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type): buffer = [[] for i in range(8)] import time st = time.time() count_time = 0 avg_time = 0 # np.random.seed(0) for im_orig, image_id, num_pos, Human_augmented, Object_augmented, \ action_HO, Pattern in g_func(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, pattern_type, zero_shot_type, isalign, 0): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(Human_augmented) buffer[4].append(Object_augmented) buffer[5].append(action_HO) buffer[6].append(Pattern) buffer[3][-1][:, 0] = len(buffer[3]) - 1 buffer[4][-1][:, 0] = len(buffer[3]) - 1 if len(buffer[0]) >= bnum: # if len(buffer[3][0]) < len(buffer[3][1]): # # make sure the second batch is less. # for i in range(len(buffer)): # tmp = buffer[i][0] # buffer[i][0] = buffer[i][1] # buffer[i][1] = tmp # print("inner:", buffer[0][0].shape, buffer[0][1].shape, buffer[1], buffer[2], buffer[3].shape, buffer[4].shape, buffer[5].shape, buffer[6].shape) # print("inner:", buffer[1], buffer[2][0], buffer[2][1], buffer[3][0].shape, buffer[3][1].shape, buffer[5][0].shape, buffer[5][1].shape) # yield buffer[0][0], buffer[0][1], buffer[1], buffer[2], buffer[3], buffer[4], buffer[5], buffer[6] # print("inner hint:", buffer[1], 'num_pos:', buffer[2], 'len of h boxes:',len(buffer[3][0]), len(buffer[3][1]), # len(buffer[4][0]), len(buffer[4][1]), len(buffer[5][0]), len(buffer[5][1]), len(buffer[6][0]), len(buffer[6][1])) pos_semi_list = [] if model_name.__contains__('x5new'): for b in range(bnum): pos_semi_list.append(int(buffer[2][b] + (len(buffer[3][b]) - buffer[2][b]) // 8)) else: for b in range(bnum): pos_semi_list.append(buffer[2][b]) for ii in range(3, 7): pos_h_boxes = np.concatenate([buffer[ii][pi][:pos2] for pi, pos2 in enumerate(pos_semi_list)], axis=0) neg_h_boxes = np.concatenate([buffer[ii][pi][pos2:] for pi, pos2 in enumerate(pos_semi_list)], axis=0) buffer[ii] = np.concatenate([pos_h_boxes, neg_h_boxes], axis=0) width = max([buffer[0][b].shape[1] for b in range(bnum)]) height = max([buffer[0][b].shape[2] for b in range(bnum)]) im_list = [] for b in range(bnum): im_list.append(np.pad(buffer[0][b], [(0, 0), (0, max(0, width - buffer[0][b].shape[1])), (0, max(0, height - buffer[0][b].shape[2])), (0, 0)], mode='constant')) yield np.concatenate(im_list, axis=0), buffer[1], sum(pos_semi_list), \ buffer[3], buffer[4], buffer[5], buffer[6], pos_semi_list[0] buffer = [[] for i in range(8)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() if pattern_type == 1: pattern_channel = 3 else: pattern_channel = 2 dataset = tf.data.Dataset.from_generator( partial(generator3, Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type), output_types=( tf.float32, tf.int32, tf.int64, tf.float32, tf.float32, tf.float32, tf.float32, tf.int32), output_shapes=( tf.TensorShape([bnum, None, None, 3]), tf.TensorShape([bnum, ]), tf.TensorShape([]), tf.TensorShape([None, 5]), tf.TensorShape([None, 5]), tf.TensorShape([None, 600]), tf.TensorShape([None, 64, 64, pattern_channel]), tf.TensorShape([]) ) ) # dataset = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32), # output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]))) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx = iterator.get_next() return image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx def obtain_batch_data_semi1(Pos_augment=15, Neg_select=60, augment_type=0, model_name='', pattern_type=0, zero_shot_type=0, isalign=False, epoch=0, semi_type='default', bnum=2, neg_type_ratio=0): assert len(model_name) > 1, model_name with open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb") as f: Trainval_GT = pickle.load(f, encoding='latin1') Trainval_semi = extract_semi_data(semi_type, model_name) with open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO.pkl', "rb") as f: Trainval_N = pickle.load(f, encoding='latin1') g_func = generator2 def generator3(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type): buffer = [[] for i in range(7)] import time st = time.time() count_time = 0 avg_time = 0 # np.random.seed(0) semi_g = generator2(Trainval_semi, {}, Pos_augment, Neg_select, augment_type, False, zero_shot_type, isalign, epoch, ) for im_orig, image_id, num_pos, Human_augmented, Object_augmented, \ action_HO, Pattern in g_func(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, pattern_type, zero_shot_type, False, epoch, ): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(Human_augmented) buffer[4].append(Object_augmented) buffer[5].append(action_HO) buffer[6].append(Pattern) for b in range(bnum): im_orig, image_id, num_pos, Human_augmented, Object_augmented, action_HO, Pattern, = next(semi_g) buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(Human_augmented) buffer[4].append(Object_augmented) buffer[5].append(action_HO) buffer[6].append(Pattern) buffer[3][b + 1][:, 0] = b + 1 buffer[4][b + 1][:, 0] = b + 1 assert num_pos == len(Human_augmented) # print(buffer[3]) # print(len(buffer[0])) # print("inner hint:", buffer[1], 'num_pos:', buffer[2], 'len of h boxes:',len(buffer[3][0]), len(buffer[3][1]), # len(buffer[4][0]), len(buffer[4][1]), len(buffer[5][0]), len(buffer[5][1]), len(buffer[6][0]), len(buffer[6][1])) pos_semi_list = [] if model_name.__contains__('x5new'): pos1 = int(buffer[2][0] + (len(buffer[3][0]) - buffer[2][0]) // 8) assert len(buffer[3][1]) == buffer[2][1], (len(buffer[3][1]), buffer[2][1],) # print(pos1, (len(buffer[3][b+1]) - buffer[2][b+1]) // 8) for b in range(bnum): pos_semi_list.append(int(buffer[2][b + 1] + (len(buffer[3][b + 1]) - buffer[2][b + 1]) // 8)) else: pos1 = buffer[2][0] for b in range(bnum): pos_semi_list.append(buffer[2][b + 1]) # print('before', buffer[3]) for ii in range(3, 7): pos_h_boxes = np.concatenate( [buffer[ii][0][:pos1]] + [buffer[ii][pi + 1][:pos2] for pi, pos2 in enumerate(pos_semi_list)], axis=0) neg_h_boxes = np.concatenate( [buffer[ii][0][pos1:]] + [buffer[ii][pi + 1][pos2:] for pi, pos2 in enumerate(pos_semi_list)], axis=0) buffer[ii] = np.concatenate([pos_h_boxes, neg_h_boxes], axis=0) # buffer[ii] = np.concatenate([buffer[ii][0], buffer[ii][1]], axis=0) # print('after', buffer[3]) width = max([buffer[0][b].shape[1] for b in range(bnum + 1)]) height = max([buffer[0][b].shape[2] for b in range(bnum + 1)]) im_list = [] for b in range(bnum + 1): im_list.append(np.pad(buffer[0][b], [(0, 0), (0, max(0, width - buffer[0][b].shape[1])), (0, max(0, height - buffer[0][b].shape[2])), (0, 0)], mode='constant')) width = max([buffer[7][b].shape[1] for b in range(bnum + 1)]) height = max([buffer[7][b].shape[2] for b in range(bnum + 1)]) split_idx = pos1 yield np.concatenate(im_list, axis=0), buffer[1], pos1 + sum(pos_semi_list), \ buffer[3], buffer[4], buffer[5], buffer[6], split_idx buffer = [[] for i in range(7)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() pattern_channel = 2 dataset = tf.data.Dataset.from_generator( partial(generator3, Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type), output_types=( tf.float32, tf.int32, tf.int64, tf.float32, tf.float32, tf.float32, tf.float32, tf.int32), output_shapes=( tf.TensorShape([bnum + 1, None, None, 3]), tf.TensorShape([bnum + 1, ]), tf.TensorShape([]), tf.TensorShape([None, 5]), tf.TensorShape([None, 5]), tf.TensorShape([None, 600]), tf.TensorShape([None, 64, 64, pattern_channel]), tf.TensorShape([]) # tf.TensorShape([2, None, None, None, 1]) ) ) # dataset = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32), # output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]))) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx = iterator.get_next() return image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx def Augmented_HO_Neg_HICO2(GT, Trainval_Neg, shape, Pos_augment, Neg_select, pose_type=0, isalign=False): image_id = GT[0] Human = GT[2] Object = GT[3] action_HO_ = Generate_action_HICO(GT[1]) action_HO = action_HO_ Human_augmented = Augmented_box(Human, shape, image_id, Pos_augment) Object_augmented = Augmented_box(Object, shape, image_id, Pos_augment) if isalign: while len(Human_augmented) < Pos_augment + 1: Human_augmented = np.concatenate( [Human_augmented, Human_augmented[-(Pos_augment + 1 - len(Human_augmented)):]], axis=0) if isalign: while len(Object_augmented) < Pos_augment + 1: Object_augmented = np.concatenate( [Object_augmented, Object_augmented[-(Pos_augment + 1 - len(Human_augmented)):]], axis=0) # print("shape:", Human_augmented.shape, Object_augmented.shape) Human_augmented = Human_augmented[:min(len(Human_augmented), len(Object_augmented))] Object_augmented = Object_augmented[:min(len(Human_augmented), len(Object_augmented))] if isalign: assert len(Human_augmented) == Pos_augment + 1, (len(Human_augmented), Pos_augment) num_pos = len(Human_augmented) if pose_type > 0: pose_list = [GT[5]] * num_pos for i in range(num_pos - 1): action_HO = np.concatenate((action_HO, action_HO_), axis=0) if image_id in Trainval_Neg: if len(Trainval_Neg[image_id]) < Neg_select: for Neg in Trainval_Neg[image_id]: if pose_type > 0: pose_list.append(Neg[7]) Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) action_HO = np.concatenate((action_HO, Generate_action_HICO([Neg[1]])), axis=0) else: List = random.sample(range(len(Trainval_Neg[image_id])), len(Trainval_Neg[image_id])) for i in range(Neg_select): Neg = Trainval_Neg[image_id][List[i]] if pose_type > 0: pose_list.append(Neg[7]) Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) action_HO = np.concatenate((action_HO, Generate_action_HICO([Neg[1]])), axis=0) num_pos_neg = len(Human_augmented) if pose_type > 0: pattern_channel = 3 else: pattern_channel = 2 Pattern = np.empty((0, 64, 64, pattern_channel), dtype=np.float32) for i in range(num_pos_neg): # Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]).reshape(1, 64, 64, 2) # there are poses for the negative sample Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]) Pattern_ = Pattern_.reshape(1, 64, 64, pattern_channel) Pattern = np.concatenate((Pattern, Pattern_), axis=0) Pattern = Pattern.reshape(num_pos_neg, 64, 64, pattern_channel) Human_augmented = Human_augmented.reshape(num_pos_neg, 5) Object_augmented = Object_augmented.reshape(num_pos_neg, 5) action_HO = action_HO.reshape(num_pos_neg, 600) # print("shape1:", Human_augmented.shape, Object_augmented.shape, num_pos, Neg_select) return Pattern, Human_augmented, Object_augmented, action_HO, num_pos def coco_generator1(Pos_augment=15, Neg_select=30, augment_type=0, with_pose=False, is_zero_shot=0): Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_VCOCO.pkl', "rb"), encoding='latin1') Neg_select1, Pos_augment1, inters_per_img = get_aug_params(Neg_select, Pos_augment, augment_type) index_list = list(range(0, len(Trainval_GT))) print("generator1", inters_per_img, Pos_augment1, 'Neg_select:', Neg_select1, augment_type) import math img_id_index_map = {} for i, gt in enumerate(Trainval_GT): img_id = gt[0] if img_id in img_id_index_map: img_id_index_map[img_id].append(i) else: img_id_index_map[img_id] = [i] img_id_list = list(img_id_index_map.keys()) for k, v in img_id_index_map.items(): for i in range(math.ceil(len(v) * 1.0 / inters_per_img) - 1): img_id_list.append(k) import copy while True: running_map = copy.deepcopy(img_id_index_map) # print('Step: ', i) np.random.shuffle(index_list) for k in running_map.keys(): np.random.shuffle(running_map[k]) for img_id_tmp in img_id_list: gt_ids = running_map[img_id_tmp][:inters_per_img] running_map[img_id_tmp] = running_map[img_id_tmp][inters_per_img:] image_id = img_id_tmp im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill( 12) + '.jpg' import os if not os.path.exists(im_file): print('not exist', im_file) import cv2 im = cv2.imread(im_file) im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im.shape blobs = {} blobs['H_boxes'] = np.empty([0, 5], dtype=np.float32) blobs['Hsp_boxes'] = np.empty([0, 5], dtype=np.float32) blobs['O_boxes'] = np.empty([0, 5], dtype=np.float32) blobs['gt_class_sp'] = np.empty([0, 29], dtype=np.float32) blobs['gt_class_HO'] = np.empty([0, 29], dtype=np.float32) blobs['gt_class_H'] = np.empty([0, 29], dtype=np.float32) blobs['gt_class_C'] = np.empty([0, 238], dtype=np.float32) blobs['Mask_sp'] = np.empty([0, 29], dtype=np.float32) blobs['Mask_HO'] = np.empty([0, 29], dtype=np.float32) blobs['Mask_H'] = np.empty([0, 29], dtype=np.float32) blobs['sp'] = np.empty([0, 64, 64, 2], dtype=np.float32) for i in gt_ids: GT = Trainval_GT[i] assert GT[0] == image_id # im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) cur_neg_select = Neg_select1 cur_pos_augment = Pos_augment1 if augment_type > 1: if i == gt_ids[-1]: cur_neg_select = Neg_select1 * len(gt_ids) else: cur_neg_select = 0 else: cur_neg_select = Neg_select1 Pattern, Human_augmented_sp, Human_augmented, Object_augmented, \ action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, action_compose = Augmented_HO_spNeg(GT, Trainval_N, im_shape, Pos_augment=cur_pos_augment, Neg_select=cur_neg_select) # blobs['image'] = im_orig blobs['H_boxes'] = np.concatenate((blobs['H_boxes'], Human_augmented), axis=0) blobs['Hsp_boxes'] = np.concatenate((blobs['Hsp_boxes'], Human_augmented_sp), axis=0) blobs['O_boxes'] = np.concatenate((blobs['O_boxes'], Object_augmented), axis=0) blobs['gt_class_sp'] = np.concatenate((blobs['gt_class_sp'], action_sp), axis=0) blobs['gt_class_HO'] = np.concatenate((blobs['gt_class_HO'], action_HO), axis=0) blobs['gt_class_H'] = np.concatenate((blobs['gt_class_H'], action_H), axis=0) blobs['gt_class_C'] = np.concatenate((blobs['gt_class_C'], action_compose), axis=0) blobs['Mask_sp'] = np.concatenate((blobs['Mask_sp'], mask_sp), axis=0) blobs['Mask_HO'] = np.concatenate((blobs['Mask_HO'], mask_HO), axis=0) blobs['Mask_H'] = np.concatenate((blobs['Mask_H'], mask_H), axis=0) blobs['sp'] = np.concatenate((blobs['sp'], Pattern), axis=0) yield (im_orig, image_id, len(blobs['gt_class_H']), blobs) def coco_generator(Pos_augment=15, Neg_select=30, augment_type=0, with_pose=False, is_zero_shot=0): Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_with_pose_obj.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_VCOCO_with_pose_obj.pkl', "rb"), encoding='latin1') i = 0 index_list = list(range(0, len(Trainval_GT))) set_list = [(0, 38), (1, 31), (1, 32), (2, 43), (2, 44), (2, 77), (4, 1), (4, 19), (4, 28), (4, 46), (4, 47), (4, 48), (4, 49), (4, 51), (4, 52), (4, 54), (4, 55), (4, 56), (5, 2), (5, 3), (5, 4), (5, 6), (5, 7), (5, 8), (5, 9), (5, 18), (5, 21), (6, 68), (7, 33), (8, 64), (9, 47), (9, 48), (9, 49), (9, 50), (9, 51), (9, 52), (9, 53), (9, 54), (9, 55), (9, 56), (10, 2), (10, 4), (10, 14), (10, 18), (10, 21), (10, 25), (10, 27), (10, 29), (10, 57), (10, 58), (10, 60), (10, 61), (10, 62), (10, 64), (11, 31), (11, 32), (11, 37), (11, 38), (12, 14), (12, 57), (12, 58), (12, 60), (12, 61), (13, 40), (13, 41), (13, 42), (13, 46), (14, 1), (14, 25), (14, 26), (14, 27), (14, 29), (14, 30), (14, 31), (14, 32), (14, 33), (14, 34), (14, 35), (14, 37), (14, 38), (14, 39), (14, 40), (14, 41), (14, 42), (14, 47), (14, 50), (14, 68), (14, 74), (14, 75), (14, 78), (15, 30), (15, 33), (16, 43), (16, 44), (16, 45), (18, 1), (18, 2), (18, 3), (18, 4), (18, 5), (18, 6), (18, 7), (18, 8), (18, 11), (18, 14), (18, 15), (18, 16), (18, 17), (18, 18), (18, 19), (18, 20), (18, 21), (18, 24), (18, 25), (18, 26), (18, 27), (18, 28), (18, 29), (18, 30), (18, 31), (18, 32), (18, 33), (18, 34), (18, 35), (18, 36), (18, 37), (18, 38), (18, 39), (18, 40), (18, 41), (18, 42), (18, 43), (18, 44), (18, 45), (18, 46), (18, 47), (18, 48), (18, 49), (18, 51), (18, 53), (18, 54), (18, 55), (18, 56), (18, 57), (18, 61), (18, 62), (18, 63), (18, 64), (18, 65), (18, 66), (18, 67), (18, 68), (18, 73), (18, 74), (18, 75), (18, 77), (19, 35), (19, 39), (20, 33), (21, 31), (21, 32), (23, 1), (23, 11), (23, 19), (23, 20), (23, 24), (23, 28), (23, 34), (23, 49), (23, 53), (23, 56), (23, 61), (23, 63), (23, 64), (23, 67), (23, 68), (23, 73), (24, 74), (25, 1), (25, 2), (25, 4), (25, 8), (25, 9), (25, 14), (25, 15), (25, 16), (25, 17), (25, 18), (25, 19), (25, 21), (25, 25), (25, 26), (25, 27), (25, 28), (25, 29), (25, 30), (25, 31), (25, 32), (25, 33), (25, 34), (25, 35), (25, 36), (25, 37), (25, 38), (25, 39), (25, 40), (25, 41), (25, 42), (25, 43), (25, 44), (25, 45), (25, 46), (25, 47), (25, 48), (25, 49), (25, 50), (25, 51), (25, 52), (25, 53), (25, 54), (25, 55), (25, 56), (25, 57), (25, 64), (25, 65), (25, 66), (25, 67), (25, 68), (25, 73), (25, 74), (25, 77), (25, 78), (25, 79), (25, 80), (26, 32), (26, 37), (28, 30), (28, 33)] while True: # print('Step: ', i) np.random.shuffle(index_list) for i in index_list: GT = Trainval_GT[i] image_id = GT[0] im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill( 12) + '.jpg' im = cv2.imread(im_file) im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) Pattern, Human_augmented_sp, Human_augmented, Object_augmented, \ action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, gt_compose = Augmented_HO_spNeg(GT, Trainval_N, im_shape, Pos_augment, Neg_select) blobs = {} # blobs['image'] = im_orig blobs['H_boxes'] = Human_augmented blobs['Hsp_boxes'] = Human_augmented_sp blobs['O_boxes'] = Object_augmented blobs['gt_class_sp'] = action_sp blobs['gt_class_HO'] = action_HO blobs['gt_class_H'] = action_H blobs['gt_class_C'] = gt_compose blobs['Mask_sp'] = mask_sp blobs['Mask_HO'] = mask_HO blobs['Mask_H'] = mask_H blobs['sp'] = Pattern yield (im_orig, image_id, len(action_H), blobs) def obtain_coco_data(Pos_augment=15, Neg_select=30, augment_type=0): if augment_type == 0: g = coco_generator else: g = coco_generator1 # generator() dataset = tf.data.Dataset.from_generator(partial(g, Pos_augment, Neg_select, augment_type), output_types=(tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }), output_shapes=( tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, 29]), 'gt_class_HO': tf.TensorShape([None, 29]), 'gt_class_H': tf.TensorShape([None, 29]), 'gt_class_C': tf.TensorShape([None, 238]), 'Mask_sp': tf.TensorShape([None, 29]), 'Mask_HO': tf.TensorShape([None, 29]), 'Mask_H': tf.TensorShape([None, 29]), 'sp': tf.TensorShape([None, 64, 64, 3]), })) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, blobs = iterator.get_next() return image, image_id, num_pos, blobs # image, num_pos = iterator.get_next() # return image, num_pos def obtain_coco_data1(Pos_augment=15, Neg_select=30, augment_type=0, with_pose=False, is_zero_shot=0): if augment_type == 0: g_func = coco_generator else: g_func = coco_generator1 def generator3(Pos_augment, Neg_select, augment_type, with_pose, is_zero_shot): buffer = [[] for i in range(4)] import time st = time.time() count_time = 0 avg_time = 0 for im_orig, image_id, num_pos, blobs in g_func(Pos_augment, Neg_select, augment_type, with_pose, is_zero_shot): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(blobs) if len(buffer[0]) > 1: if buffer[2][0] < buffer[2][1]: # make sure the first batch is less. for i in range(len(buffer)): tmp = buffer[i][0] buffer[i][0] = buffer[i][1] buffer[i][1] = tmp yield buffer[0][0], buffer[1][0], buffer[2][0], buffer[3][0], buffer[0][1], buffer[1][1], buffer[2][1], \ buffer[3][1], buffer = [[] for i in range(4)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() # generator() dataset = tf.data.Dataset.from_generator( partial(generator3, Pos_augment, Neg_select, augment_type, with_pose, is_zero_shot), output_types=(tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }, tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }), output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, 29]), 'gt_class_HO': tf.TensorShape([None, 29]), 'gt_class_H': tf.TensorShape([None, 29]), 'gt_class_C': tf.TensorShape([None, 238]), 'Mask_sp': tf.TensorShape([None, 29]), 'Mask_HO': tf.TensorShape([None, 29]), 'Mask_H': tf.TensorShape([None, 29]), 'sp': tf.TensorShape([None, 64, 64, 3]), }, tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, 29]), 'gt_class_HO': tf.TensorShape([None, 29]), 'gt_class_H': tf.TensorShape([None, 29]), 'gt_class_C': tf.TensorShape([None, 238]), 'Mask_sp': tf.TensorShape([None, 29]), 'Mask_HO': tf.TensorShape([None, 29]), 'Mask_H': tf.TensorShape([None, 29]), 'sp': tf.TensorShape([None, 64, 64, 3]), })) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, blobs, image1, image_id1, num_pos1, blobs1 = iterator.get_next() return [image, image1], [image_id, image_id1], [num_pos, num_pos1], [blobs, blobs1] def obtain_coco_data_hoicoco_24(Pos_augment = 15, Neg_select=30, augment_type = 0, pattern_type=False, is_zero_shot=0, type=0): if type == 0: verb_num = 24 g_func = coco_generator2 elif type == 1: verb_num = 21 g_func = coco_generator3 def generator3(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer = [[] for i in range(4)] import time st = time.time() count_time = 0 avg_time = 0 for im_orig, image_id, num_pos, blobs in g_func(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(blobs) # print(im_orig.shape, image_id, num_pos, if len(buffer[0]) > 1: if buffer[2][0] < buffer[2][1]: # make sure the first batch is less. for i in range(len(buffer)): tmp = buffer[i][0] buffer[i][0] = buffer[i][1] buffer[i][1] = tmp yield buffer[0][0], buffer[1][0], buffer[2][0], buffer[3][0],buffer[0][1], buffer[1][1], buffer[2][1],buffer[3][1], buffer = [[] for i in range(4)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() dataset = tf.data.Dataset.from_generator(partial(generator3, Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot), output_types=(tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'pose_box':tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, },tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'pose_box': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }), output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'pose_box': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), },tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'pose_box': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), })) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, blobs, image1, image_id1, num_pos1, blobs1 = iterator.get_next() return [image, image1], [image_id, image_id1], [num_pos, num_pos1], [blobs, blobs1] def get_new_Trainval_N(Trainval_N, is_zero_shot, unseen_idx): if is_zero_shot > 0: new_Trainval_N = {} for k in Trainval_N.keys(): new_Trainval_N[k] = [] for item in Trainval_N[k]: # the original code include a bug (k is wrongly set to 4) if item[1] not in unseen_idx: new_Trainval_N[k].append(item) Trainval_N = new_Trainval_N return Trainval_N def get_zero_shot_type(model_name): zero_shot_type = 0 if model_name.__contains__('_zs_'): # for open long-tailed hoi detection zero_shot_type = 7 elif model_name.__contains__('zsnrare'): zero_shot_type = 4 elif model_name.__contains__('_zsrare_'): zero_shot_type = 3 elif model_name.__contains__('_zsuo_'): # for unseen object zero_shot_type = 11 elif model_name.__contains__('_zs3_'): # for VCL model zero_shot_type = 3 elif model_name.__contains__('_zs4_'): zero_shot_type = 4 return zero_shot_type def get_epoch_iters(model_name): epoch_iters = 43273 if model_name.__contains__('zsnrare'): epoch_iters = 20000 elif model_name.__contains__('zs_'): epoch_iters = 20000 elif model_name.__contains__('_zs4_'): epoch_iters = 20000 elif model_name.__contains__('zsrare'): epoch_iters = 40000 else: epoch_iters = 43273 return epoch_iters def get_augment_type(model_name): augment_type = 0 if model_name.__contains__('_aug5'): augment_type = 4 elif model_name.__contains__('_aug6'): augment_type = 5 else: # raise Exception('params wrong', args.model) pass return augment_type def get_unseen_index(zero_shot_type): unseen_idx = None if zero_shot_type == 3: # rare first unseen_idx = [509, 279, 280, 402, 504, 286, 499, 498, 289, 485, 303, 311, 325, 439, 351, 358, 66, 427, 379, 418, 70, 416, 389, 90, 395, 76, 397, 84, 135, 262, 401, 592, 560, 586, 548, 593, 526, 181, 257, 539, 535, 260, 596, 345, 189, 205, 206, 429, 179, 350, 405, 522, 449, 261, 255, 546, 547, 44, 22, 334, 599, 239, 315, 317, 229, 158, 195, 238, 364, 222, 281, 149, 399, 83, 127, 254, 398, 403, 555, 552, 520, 531, 440, 436, 482, 274, 8, 188, 216, 597, 77, 407, 556, 469, 474, 107, 390, 410, 27, 381, 463, 99, 184, 100, 292, 517, 80, 333, 62, 354, 104, 55, 50, 198, 168, 391, 192, 595, 136, 581] elif zero_shot_type == 4: # non rare first unseen_idx = [38, 41, 20, 18, 245, 11, 19, 154, 459, 42, 155, 139, 60, 461, 577, 153, 582, 89, 141, 576, 75, 212, 472, 61, 457, 146, 208, 94, 471, 131, 248, 544, 515, 566, 370, 481, 226, 250, 470, 323, 169, 480, 479, 230, 385, 73, 159, 190, 377, 176, 249, 371, 284, 48, 583, 53, 162, 140, 185, 106, 294, 56, 320, 152, 374, 338, 29, 594, 346, 456, 589, 45, 23, 67, 478, 223, 493, 228, 240, 215, 91, 115, 337, 559, 7, 218, 518, 297, 191, 266, 304, 6, 572, 529, 312, 9, 308, 417, 197, 193, 163, 455, 25, 54, 575, 446, 387, 483, 534, 340, 508, 110, 329, 246, 173, 506, 383, 93, 516, 64] elif zero_shot_type == 11: unseen_idx = [111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 224, 225, 226, 227, 228, 229, 230, 231, 290, 291, 292, 293, 294, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 336, 337, 338, 339, 340, 341, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 533, 534, 535, 536, 537, 558, 559, 560, 561, 595, 596, 597, 598, 599] # miss [ 5, 6, 28, 56, 88] verbs 006 break 007 brush_with 029 flip 057 move 089 slide elif zero_shot_type == 7: # 24 rare merge of zs3 & zs4 unseen_idx = [509, 279, 280, 402, 504, 286, 499, 498, 289, 485, 303, 311, 325, 439, 351, 358, 66, 427, 379, 418, 70, 416, 389, 90, 38, 41, 20, 18, 245, 11, 19, 154, 459, 42, 155, 139, 60, 461, 577, 153, 582, 89, 141, 576, 75, 212, 472, 61, 457, 146, 208, 94, 471, 131, 248, 544, 515, 566, 370, 481, 226, 250, 470, 323, 169, 480, 479, 230, 385, 73, 159, 190, 377, 176, 249, 371, 284, 48, 583, 53, 162, 140, 185, 106, 294, 56, 320, 152, 374, 338, 29, 594, 346, 456, 589, 45, 23, 67, 478, 223, 493, 228, 240, 215, 91, 115, 337, 559, 7, 218, 518, 297, 191, 266, 304, 6, 572, 529, 312, 9] # 22529, 14830, 22493, 17411, 21912, return unseen_idx def generator2(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, pattern_type, zero_shot_type, isalign, epoch=0): """ :param Trainval_GT: :param Trainval_N: :param Pos_augment: :param Neg_select: :param augment_type: :param pattern_type: :return: """ # import skimage # assert skimage.__version__ == '0.14.2', "The version of skimage might affect the speed largely. I use 0.14.2" Neg_select1, Pos_augment1, inters_per_img = get_aug_params(Neg_select, Pos_augment, augment_type) unseen_idx = get_unseen_index(zero_shot_type) Trainval_N = get_new_Trainval_N(Trainval_N, zero_shot_type, unseen_idx) print("generator2", inters_per_img, Pos_augment1, 'Neg_select:', Neg_select1, augment_type, 'zero shot:', zero_shot_type) import math img_id_index_map = {} for i, gt in enumerate(Trainval_GT): img_id = gt[0] if img_id in img_id_index_map: img_id_index_map[img_id].append(i) else: img_id_index_map[img_id] = [i] img_id_list = list(img_id_index_map.keys()) for k, v in img_id_index_map.items(): for i in range(math.ceil(len(v) * 1.0 / inters_per_img) - 1): img_id_list.append(k) import copy import time st = time.time() count_time = 0 avg_time = 0 while True: running_map = copy.deepcopy(img_id_index_map) # print('Step: ', i) np.random.shuffle(img_id_list) for k in running_map.keys(): np.random.shuffle(running_map[k]) for img_id_tmp in img_id_list: gt_ids = running_map[img_id_tmp][:inters_per_img] running_map[img_id_tmp] = running_map[img_id_tmp][inters_per_img:] Pattern_list = [] Human_augmented_list = [] Object_augmented_list = [] action_HO_list = [] num_pos_list = 0 mask_all_list = [] image_id = img_id_tmp if image_id in [528, 791, 1453, 2783, 3489, 3946, 3946, 11747, 11978, 12677, 16946, 17833, 19218, 19218, 22347, 27293, 27584, 28514, 33683, 35399]: # This is a list contain multiple objects within the same object box. It seems like wrong annotations. # We remove those images. This do not affect the performance in our experiment. continue im_file = cfg.DATA_DIR + '/' + 'hico_20160224_det/images/train2015/HICO_train2015_' + ( str(image_id)).zfill( 8) + '.jpg' # id, gt, h, o # print(gt_ids, gt_ids[0], Trainval_GT[gt_ids[0]]) import cv2 import os if not os.path.exists(im_file): print('not exist', im_file) continue im = cv2.imread(im_file) if im is None: print('node', im_file) continue im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im.shape import os # print('generate batch read image:', time.time() - st, "average;", avg_time) for i in gt_ids: GT = Trainval_GT[i] # rare data if zero_shot_type > 0: has_rare = False for label in GT[1]: if label in unseen_idx: has_rare = True if has_rare: continue assert GT[0] == image_id # im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) cur_pos_augment = Pos_augment1 if augment_type > 1: if i == gt_ids[-1]: # This must be -1 cur_neg_select = Neg_select1 * len(gt_ids) else: cur_neg_select = 0 else: cur_neg_select = Neg_select1 # st1 = time.time() Pattern, Human_augmented, Object_augmented, action_HO, num_pos = Augmented_HO_Neg_HICO( GT, Trainval_N, im_shape, Pos_augment=cur_pos_augment, Neg_select=cur_neg_select, pattern_type=pattern_type, isalign=isalign) # maintain same number of augmentation, # print('generate batch read image:', i, time.time() - st1, cur_neg_select, len(Trainval_N[image_id]) if image_id in Trainval_N else 0) Pattern_list.append(Pattern) Human_augmented_list.append(Human_augmented) Object_augmented_list.append(Object_augmented) action_HO_list.append(action_HO) num_pos_list += num_pos # print('item:', Pattern.shape, num_pos) if len(Pattern_list) <= 0: continue Pattern = np.concatenate(Pattern_list, axis=0) Human_augmented = np.concatenate(Human_augmented_list, axis=0) Object_augmented = np.concatenate(Object_augmented_list, axis=0) action_HO = np.concatenate(action_HO_list, axis=0) num_pos = num_pos_list im_orig = np.expand_dims(im_orig, axis=0) yield (im_orig, image_id, num_pos, Human_augmented, Object_augmented, action_HO, Pattern) if augment_type < 0: break def get_aug_params(Neg_select, Pos_augment, augment_type): Pos_augment1 = Pos_augment Neg_select1 = Neg_select inters_per_img = 2 if augment_type == 0: inters_per_img = 1 Pos_augment1 = 15 Neg_select1 = 60 elif augment_type == 4: inters_per_img = 5 Pos_augment1 = 6 Neg_select1 = 24 elif augment_type == 5: inters_per_img = 7 Pos_augment1 = 10 Neg_select1 = 40 return Neg_select1, Pos_augment1, inters_per_img def get_vcoco_aug_params(Neg_select, Pos_augment, augment_type): Pos_augment1 = Pos_augment Neg_select1 = Neg_select inters_per_img = 2 if augment_type == 0: inters_per_img = 1 Pos_augment1 = 15 Neg_select1 = 30 elif augment_type == 1: inters_per_img = 2 Pos_augment1 = 15 Neg_select1 = 30 elif augment_type == 2: inters_per_img = 3 Pos_augment1 = 15 Neg_select1 = 30 elif augment_type == -1: inters_per_img = 1 Pos_augment1 = 0 Neg_select1 = 0 return Neg_select1, Pos_augment1, inters_per_img def obtain_data(Pos_augment=15, Neg_select=60, augment_type=0, pattern_type=0, zero_shot_type=0, isalign=False, epoch=0, coco=False, neg_type=0): with open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO.pkl', "rb") as f: Trainval_N = pickle.load(f, encoding='latin1') if not coco: with open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb") as f: Trainval_GT = pickle.load(f, encoding='latin1') elif coco == 2: # 115904 with open(cfg.DATA_DIR + '/' + 'new_list_pickle_2.pkl', "rb") as f: Trainval_GT = pickle.load(f, encoding='latin1') elif coco == 3: # 115904 with open(cfg.DATA_DIR + '/' + 'new_list_pickle_3.pkl', "rb") as f: Trainval_GT = pickle.load(f, encoding='latin1') with open(cfg.DATA_DIR + '/' + 'new_neg_dict.pkl', "rb") as f: Trainval_N1 = pickle.load(f, encoding='latin1') for k in Trainval_N: if k in Trainval_N1: Trainval_N[k].extend(Trainval_N1[k]) else: print('Trainval_GT_HICO_COCO') Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO_COCO.pkl', "rb"), encoding='latin1') dataset = tf.data.Dataset.from_generator(partial(generator2, Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, pattern_type, zero_shot_type, isalign, epoch, ), output_types=( tf.float32, tf.int32, tf.int64, tf.float32, tf.float32, tf.float32, tf.float32), output_shapes=( tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), tf.TensorShape([None, 5]), tf.TensorShape([None, 5]), tf.TensorShape([None, 600]), tf.TensorShape([None, 64, 64, 2]))) # (im_orig, image_id, num_pos, Human_augmented, Object_augmented, action_HO, Pattern) # dataset = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32), # output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]))) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp = iterator.get_next() return image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp def obtain_test_data(Pos_augment=15, Neg_select=60, augment_type=0, with_pose=False, large_neg_for_ho=False, isalign=False): Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Test_GT_HICO.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Test_GT_HICO.pkl', "rb"), encoding='latin1') g = generator2 dataset = tf.data.Dataset.from_generator( partial(g, Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, with_pose, 0, isalign), output_types=( tf.float32, tf.int32, tf.int64, tf.float32, tf.float32, tf.float32, tf.float32, tf.float32), output_shapes=( tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), tf.TensorShape([None, 5]), tf.TensorShape([None, 5]), tf.TensorShape([None, 600]), tf.TensorShape([None, 64, 64, 2]), )) # (im_orig, image_id, num_pos, Human_augmented, Object_augmented, action_HO, Pattern) # dataset = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32), # output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]))) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp = iterator.get_next() return image, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp def obtain_coco_data_hoicoco(Pos_augment=15, Neg_select=30, augment_type=0, pattern_type=False, is_zero_shot=0, type=0): if type == 1: verb_num = 21 g_func = coco_generator3 def generator3(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer = [[] for i in range(4)] import time st = time.time() count_time = 0 avg_time = 0 for im_orig, image_id, num_pos, blobs in g_func(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(blobs) # print(im_orig.shape, image_id, num_pos, if len(buffer[0]) > 1: if buffer[2][0] < buffer[2][1]: # make sure the first batch is less. for i in range(len(buffer)): tmp = buffer[i][0] buffer[i][0] = buffer[i][1] buffer[i][1] = tmp yield buffer[0][0], buffer[1][0], buffer[2][0], buffer[3][0], buffer[0][1], buffer[1][1], buffer[2][1], \ buffer[3][1], buffer = [[] for i in range(4)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() # generator() dataset = tf.data.Dataset.from_generator( partial(generator3, Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot), output_types=(tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }, tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }), output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), }, tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), })) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, blobs, image1, image_id1, num_pos1, blobs1 = iterator.get_next() return [image, image1], [image_id, image_id1], [num_pos, num_pos1], [blobs, blobs1] def coco_generator2(Pos_augment = 15, Neg_select=30, augment_type = 0, pattern_type=False, is_zero_shot=0): Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_obj_24.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_VCOCO_obj_24.pkl', "rb"), encoding='latin1') i = 0 index_list = list(range(0, len(Trainval_GT))) while True: # print('Step: ', i) np.random.shuffle(index_list) for i in index_list: GT = Trainval_GT[i] image_id = GT[0] im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill( 12) + '.jpg' im = cv2.imread(im_file) im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) Pattern, Human_augmented_sp, Human_augmented, Object_augmented, \ action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, gt_compose = Augmented_HO_spNeg2(GT, Trainval_N, im_shape, Pos_augment, Neg_select) blobs = {} # blobs['image'] = im_orig blobs['H_boxes'] = Human_augmented blobs['Hsp_boxes'] = Human_augmented_sp blobs['O_boxes'] = Object_augmented blobs['gt_class_sp'] = action_sp blobs['gt_class_HO'] = action_HO blobs['gt_class_H'] = action_H blobs['gt_class_C'] = gt_compose blobs['Mask_sp'] = mask_sp blobs['Mask_HO'] = mask_HO blobs['Mask_H'] = mask_H blobs['sp'] = Pattern # blobs['H_num'] = len(action_H) # print(image_id, len(action_H)) yield (im_orig, image_id, len(action_H), blobs) # print(i, image_id, len(Trainval_GT)) # i += 1 # i = i % len(Trainval_GT) def coco_generator3(Pos_augment = 15, Neg_select=30, augment_type = 0, pattern_type=False, is_zero_shot=0): Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_obj_21.pkl', "rb"), encoding='latin1') Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_VCOCO_obj_21.pkl', "rb"), encoding='latin1') i = 0 index_list = list(range(0, len(Trainval_GT))) print(len(index_list)) while True: # print('Step: ', i) np.random.shuffle(index_list) for i in index_list: GT = Trainval_GT[i] image_id = GT[0] im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill( 12) + '.jpg' im = cv2.imread(im_file) im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) Pattern, Human_augmented_sp, Human_augmented, Object_augmented, \ action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, gt_compose = Augmented_HO_spNeg3(GT, Trainval_N, im_shape, Pos_augment, Neg_select) blobs = {} # blobs['image'] = im_orig blobs['H_boxes'] = Human_augmented blobs['Hsp_boxes'] = Human_augmented_sp blobs['O_boxes'] = Object_augmented blobs['gt_class_sp'] = action_sp blobs['gt_class_HO'] = action_HO blobs['gt_class_H'] = action_H blobs['gt_class_C'] = gt_compose blobs['Mask_sp'] = mask_sp blobs['Mask_HO'] = mask_HO blobs['Mask_H'] = mask_H blobs['sp'] = Pattern yield (im_orig, image_id, len(action_H), blobs) if augment_type < 0: break def coco_generator_atl(Pos_augment = 15, Neg_select=0, augment_type = 0, pattern_type=False, is_zero_shot=0, type =0, vcoco_type = 21): """ Here, the name semi means atl. For objects, we do not have verb labels. Thus, we can only provide object id. """ print(type) if type == 0: # coco 2014 570834 length Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_obj_semi.pkl', "rb"), encoding='latin1') elif type == 2: # hico 68389 length Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_hico_obj_semi_21.pkl', "rb"), encoding='latin1') elif type == 3: # both Trainval_GT_hico = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_hico_obj_semi_21.pkl', "rb"), encoding='latin1') Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_obj_semi_21.pkl', "rb"), encoding='latin1') for item in Trainval_GT: item[0] += MAX_HICO_ID Trainval_GT.extend(Trainval_GT_hico) elif type == 4: # --- 42631 Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_vcoco_obj_semi_21.pkl', "rb"), encoding='latin1') elif type == 5: # vcoco Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_vcoco1_obj_semi_21.pkl', "rb"), encoding='latin1') else: # coco 2014 train 570834 Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO_obj_semi_21.pkl', "rb"), encoding='latin1') i = 0 index_list = list(range(0, len(Trainval_GT))) if vcoco_type == 24: g_func = Augmented_HO_spNeg2 else: g_func = Augmented_HO_spNeg3 while True: # print('Step: ', i) np.random.shuffle(index_list) for i in index_list: GT = Trainval_GT[i] image_id = GT[0] if type == 2: im_file = cfg.DATA_DIR + '/' + 'hico_20160224_det/images/train2015/HICO_train2015_' + ( str(image_id)).zfill( 8) + '.jpg' elif type == 3: if image_id < MAX_HICO_ID: # obj365 tmp_id = image_id im_file = cfg.DATA_DIR + '/' + 'hico_20160224_det/images/train2015/HICO_train2015_' + ( str(image_id)).zfill( 8) + '.jpg' pass else: tmp_id = image_id - MAX_HICO_ID im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(tmp_id)).zfill( 12) + '.jpg' import os if not os.path.exists(im_file): im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/val2014/COCO_val2014_' + ( str(tmp_id)).zfill(12) + '.jpg' if not os.path.exists(im_file): print(im_file) import os if not os.path.exists(im_file): print(im_file) elif type == 6: im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill( 12) + '.jpg' import os if not os.path.exists(im_file): im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/val2014/COCO_val2014_' + ( str(image_id)).zfill(12) + '.jpg' if not os.path.exists(im_file): print(im_file) elif type == 7: if image_id >= MAX_COCO_ID: # obj365 tmp_id = image_id - MAX_COCO_ID im_file = cfg.LOCAL_DATA + '/dataset/Objects365/Images/train/train/obj365_train_' + (str(tmp_id)).zfill( 12) + '.jpg' pass else: tmp_id = image_id im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(tmp_id)).zfill( 12) + '.jpg' import os if not os.path.exists(im_file): im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/val2014/COCO_val2014_' + ( str(tmp_id)).zfill(12) + '.jpg' if not os.path.exists(im_file): print(im_file) import os if not os.path.exists(im_file): print(im_file) else: im_file = cfg.DATA_DIR + '/' + 'v-coco/coco/images/train2014/COCO_train2014_' + (str(image_id)).zfill( 12) + '.jpg' im = cv2.imread(im_file) im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_orig = im_orig.reshape(1, im_shape[0], im_shape[1], 3) Pattern, Human_augmented_sp, Human_augmented, Object_augmented, \ action_sp, action_HO, action_H, mask_sp, mask_HO, mask_H, gt_compose = g_func(GT, {}, im_shape, Pos_augment, Neg_select) blobs = {} # blobs['image'] = im_orig blobs['H_boxes'] = Human_augmented blobs['Hsp_boxes'] = Human_augmented_sp blobs['O_boxes'] = Object_augmented blobs['gt_class_sp'] = action_sp blobs['gt_class_HO'] = action_HO blobs['gt_class_H'] = action_H blobs['gt_class_C'] = gt_compose blobs['Mask_sp'] = mask_sp blobs['Mask_HO'] = mask_HO blobs['Mask_H'] = mask_H blobs['sp'] = Pattern # blobs['H_num'] = len(action_H) # print(image_id, len(action_H)) yield (im_orig, image_id, len(action_H), blobs) # print(i, image_id, len(Trainval_GT)) # i += 1 # i = i % len(Trainval_GT) def obtain_coco_data2(Pos_augment = 15, Neg_select=30, augment_type = 0, type =0 ): # Trainval_GT = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_GT_VCOCO.pkl', "rb"), encoding='latin1') # Trainval_N = pickle.load(open(cfg.DATA_DIR + '/' + 'Trainval_Neg_VCOCO.pkl', "rb"), encoding='latin1') if type == 0: compose_classes = 222 verb_num = 24 g_func = coco_generator2 elif type == 1: compose_classes = 222 verb_num = 21 g_func = coco_generator3 elif type == 2: compose_classes = 238 verb_num = 29 g_func = coco_generator1 # generator() dataset = tf.data.Dataset.from_generator(partial(g_func, Pos_augment, Neg_select, augment_type), output_types=(tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }), output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, compose_classes]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), })) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, blobs = iterator.get_next() return image, image_id, num_pos, blobs # image, num_pos = iterator.get_next() # return image, num_pos def obtain_coco_data_atl(Pos_augment=15, Neg_select=30, augment_type=0, pattern_type=False, is_zero_shot=0, type=0, vcoco_type=21): if vcoco_type == 21: verb_num = 21 g_func = coco_generator3 elif vcoco_type == 24: verb_num = 24 g_func = coco_generator2 else: # default verb_num = 21 g_func = coco_generator3 def generator3(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer = [[] for i in range(4)] import time st = time.time() count_time = 0 avg_time = 0 semi_func = coco_generator_atl(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot, type, vcoco_type = vcoco_type) # semi is atl. a weak-supervised manner. for im_orig, image_id, num_pos, blobs in g_func(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(blobs) im_orig, image_id, num_pos, blobs = next(semi_func) buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(blobs) # print(im_orig.shape, image_id, num_pos, yield buffer[0][0], buffer[1][0], buffer[2][0], buffer[3][0], buffer[0][1], buffer[1][1], buffer[2][1], \ buffer[3][1], buffer = [[] for i in range(4)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() # generator() dataset = tf.data.Dataset.from_generator( partial(generator3, Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot), output_types=(tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }, tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }), output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), }, tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), })) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, blobs, image1, image_id1, num_pos1, blobs1 = iterator.get_next() return [image, image1], [image_id, image_id1], [num_pos, num_pos1], [blobs, blobs1] def obtain_coco_data_hoicoco_24_atl(Pos_augment=15, Neg_select=30, augment_type=0, pattern_type=False, is_zero_shot=0, type=0): # default verb_num = 24 g_func = coco_generator2 def generator3(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer = [[] for i in range(4)] import time st = time.time() count_time = 0 avg_time = 0 semi_func = coco_generator_atl(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot, type) # semi is atl. a weak-supervised manner. for im_orig, image_id, num_pos, blobs in g_func(Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(blobs) im_orig, image_id, num_pos, blobs = next(semi_func) buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(blobs) # print(im_orig.shape, image_id, num_pos, yield buffer[0][0], buffer[1][0], buffer[2][0], buffer[3][0], buffer[0][1], buffer[1][1], buffer[2][1], \ buffer[3][1], buffer = [[] for i in range(4)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() # generator() dataset = tf.data.Dataset.from_generator( partial(generator3, Pos_augment, Neg_select, augment_type, pattern_type, is_zero_shot), output_types=(tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }, tf.float32, tf.int32, tf.int32, { 'H_boxes': tf.float32, 'Hsp_boxes': tf.float32, 'O_boxes': tf.float32, 'gt_class_sp': tf.float32, 'gt_class_HO': tf.float32, 'gt_class_H': tf.float32, 'gt_class_C': tf.float32, 'Mask_sp': tf.float32, 'Mask_HO': tf.float32, 'Mask_H': tf.float32, 'sp': tf.float32, }), output_shapes=(tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), }, tf.TensorShape([1, None, None, 3]), tf.TensorShape([]), tf.TensorShape([]), { 'H_boxes': tf.TensorShape([None, 5]), 'Hsp_boxes': tf.TensorShape([None, 5]), 'O_boxes': tf.TensorShape([None, 5]), 'gt_class_sp': tf.TensorShape([None, verb_num]), 'gt_class_HO': tf.TensorShape([None, verb_num]), 'gt_class_H': tf.TensorShape([None, verb_num]), 'gt_class_C': tf.TensorShape([None, 222]), 'Mask_sp': tf.TensorShape([None, verb_num]), 'Mask_HO': tf.TensorShape([None, verb_num]), 'Mask_H': tf.TensorShape([None, verb_num]), 'sp': tf.TensorShape([None, 64, 64, 2]), })) dataset = dataset.prefetch(100) # dataset = dataset.shuffle(1000) # dataset = dataset.repeat(100) # dataset = dataset.repeat(1000).shuffle(1000) # dataset._dataset.batch(3) iterator = dataset.make_one_shot_iterator() image, image_id, num_pos, blobs, image1, image_id1, num_pos1, blobs1 = iterator.get_next() return [image, image1], [image_id, image_id1], [num_pos, num_pos1], [blobs, blobs1] def get_epoch_iters(model_name): epoch_iters = 43273 if model_name.__contains__('zsnrare'): epoch_iters = 20000 elif model_name.__contains__('zs_'): epoch_iters = 20000 elif model_name.__contains__('zsrare'): epoch_iters = 40000 else: epoch_iters = 43273 return epoch_iters def obtain_data_vcl_hico(Pos_augment=15, Neg_select=60, augment_type=0, with_pose=False, zero_shot_type=0, isalign=False, epoch=0): # we do not use pose, thus we remove it. with open(cfg.DATA_DIR + '/' + 'Trainval_GT_HICO.pkl', "rb") as f: Trainval_GT = pickle.load(f, encoding='latin1') with open(cfg.DATA_DIR + '/' + 'Trainval_Neg_HICO.pkl', "rb") as f: Trainval_N = pickle.load(f, encoding='latin1') g_func = generator2 def generator3(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type): buffer = [[] for i in range(7)] import time st = time.time() count_time = 0 avg_time = 0 for im_orig, image_id, num_pos, Human_augmented, Object_augmented, action_HO, Pattern in g_func(Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type, with_pose, zero_shot_type, isalign, epoch): buffer[0].append(im_orig) buffer[1].append(image_id) buffer[2].append(num_pos) buffer[3].append(Human_augmented) buffer[4].append(Object_augmented) buffer[5].append(action_HO) buffer[6].append(Pattern) if len(buffer[0]) > 1: # print("inner:", buffer[0][0].shape, buffer[0][1].shape, buffer[1], buffer[2], buffer[3].shape, buffer[4].shape, buffer[5].shape, buffer[6].shape) # print("inner:", buffer[1], buffer[2][0], buffer[2][1], buffer[3][0].shape, buffer[3][1].shape, buffer[5][0].shape, buffer[5][1].shape) # yield buffer[0][0], buffer[0][1], buffer[1], buffer[2], buffer[3], buffer[4], buffer[5], buffer[6] if len(buffer[3][0]) < len(buffer[3][1]): # make sure the second batch is less. for i in range(len(buffer)): tmp = buffer[i][0] buffer[i][0] = buffer[i][1] buffer[i][1] = tmp split_idx = len(buffer[5][0]) buffer = buffer[:3] + [np.concatenate(item, axis=0) for item in buffer[3:]] + buffer[-1:] yield buffer[0][0], buffer[0][1], buffer[1], buffer[2], buffer[3], buffer[4], buffer[5], buffer[ 6], split_idx buffer = [[] for i in range(7)] # avg_time = ((time.time() - st) + avg_time * count_time) / (count_time + 1) # count_time += 1 # print('generate batch:', time.time() - st, "average;", avg_time) # st = time.time() if with_pose: pattern_channel = 3 else: pattern_channel = 2 dataset = tf.data.Dataset.from_generator( partial(generator3, Trainval_GT, Trainval_N, Pos_augment, Neg_select, augment_type), output_types=( tf.float32, tf.float32, tf.int32, tf.int64, tf.float32, tf.float32, tf.float32, tf.float32, tf.int32), output_shapes=( tf.TensorShape([1, None, None, 3]), tf.TensorShape([1, None, None, 3]), tf.TensorShape([2, ]), tf.TensorShape([2, ]), tf.TensorShape([None, 5]), tf.TensorShape([None, 5]), tf.TensorShape([None, 600]), tf.TensorShape([None, 64, 64, pattern_channel]), tf.TensorShape([]) ) ) dataset = dataset.prefetch(100) iterator = dataset.make_one_shot_iterator() image, image2, image_id, num_pos, Human_augmented, Object_augmented, action_HO, sp, split_idx = iterator.get_next() return [image, image2], image_id, num_pos, [Human_augmented[:split_idx], Human_augmented[split_idx:]], \ [Object_augmented[:split_idx], Object_augmented[split_idx:]], \ [action_HO[:split_idx], action_HO[split_idx:]], \ [sp[:split_idx], sp[split_idx:]] def Augmented_HO_Neg_HICO_inner(GT, negs, shape, Pos_augment, Neg_select, with_pose): image_id = GT[0] Human = GT[2] Object = GT[3] pose_list = [] if Pos_augment < 0: action_HO = np.empty([0, 600]) Human_augmented = np.empty([0, 5]) Object_augmented = np.empty([0, 5]) num_pos = 0 else: action_HO_ = Generate_action_HICO(GT[1]) action_HO = action_HO_ Human_augmented = Augmented_box(Human, shape, image_id, Pos_augment) Object_augmented = Augmented_box(Object, shape, image_id, Pos_augment) Human_augmented = Human_augmented[:min(len(Human_augmented), len(Object_augmented))] Object_augmented = Object_augmented[:min(len(Human_augmented), len(Object_augmented))] num_pos = len(Human_augmented) for i in range(num_pos - 1): action_HO = np.concatenate((action_HO, action_HO_), axis=0) if with_pose: pose_list = [GT[5]] * num_pos num_pos_neg = len(Human_augmented) if with_pose: pattern_channel = 3 else: pattern_channel = 2 Pattern = get_pattern(Human_augmented, Object_augmented, num_pos_neg, pose_list, shape, with_pose) if negs is not None and Neg_select > 0: if len(negs) < Neg_select: Neg_select = len(negs) List = range(Neg_select) else: List = random.sample(range(len(negs)), Neg_select) _Human_augmented, _Object_augmented, _action_HO, _Pattern = get_neg_items(List, negs, shape, with_pose) Human_augmented = np.concatenate([Human_augmented, _Human_augmented], axis=0) Object_augmented = np.concatenate([Object_augmented, _Object_augmented], axis=0) action_HO = np.concatenate([action_HO, _action_HO], axis=0) Pattern = np.concatenate([Pattern, _Pattern], axis=0) num_pos_neg = len(Human_augmented) Pattern = Pattern.reshape(num_pos_neg, 64, 64, pattern_channel) Human_augmented = Human_augmented.reshape(num_pos_neg, 5) Object_augmented = Object_augmented.reshape(num_pos_neg, 5) action_HO = action_HO.reshape(num_pos_neg, 600) return Pattern, Human_augmented, Object_augmented, action_HO, num_pos def get_pattern(Human_augmented, Object_augmented, num_pos_neg, pose_list, shape, with_pose): pattern_channel = 2 Pattern = np.empty((0, 64, 64, pattern_channel), dtype=np.float32) for i in range(num_pos_neg): # Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]).reshape(1, 64, 64, 2) # there are poses for the negative sample Pattern_ = Get_next_sp(Human_augmented[i][1:], Object_augmented[i][1:]) Pattern_ = Pattern_.reshape(1, 64, 64, pattern_channel) Pattern = np.concatenate((Pattern, Pattern_), axis=0) return Pattern def get_neg_items(neg_select_list, negs, shape, with_pose): action_HO = np.empty([0, 600]) Human_augmented = np.empty([0, 5]) Object_augmented = np.empty([0, 5]) pose_list = [] for i in range(len(neg_select_list)): Neg = negs[neg_select_list[i]] if with_pose: pose_list.append(Neg[7]) Human_augmented = np.concatenate( (Human_augmented, np.array([0, Neg[2][0], Neg[2][1], Neg[2][2], Neg[2][3]]).reshape(1, 5)), axis=0) Object_augmented = np.concatenate( (Object_augmented, np.array([0, Neg[3][0], Neg[3][1], Neg[3][2], Neg[3][3]]).reshape(1, 5)), axis=0) action_HO = np.concatenate((action_HO, Generate_action_HICO([Neg[1]])), axis=0) num_pos_neg = len(Human_augmented) Pattern = get_pattern(Human_augmented, Object_augmented, num_pos_neg, pose_list, shape, with_pose) return Human_augmented, Object_augmented, action_HO, Pattern

49.442618

163

0.532989

20,492

153,371

3.761419

0.04026

0.040478

0.03573

0.004567

0.870406

0.848882

0.829993

0.81074

0.79028

0.774738

0

0.089126

0.318404

153,371

3,101

164

49.458562

0.648211

0.102066

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false

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0.009291

0

null

0

1

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null

0

6

0f0e142729a14317a69e8d385afb46bde4284923

61

py

Python

ContactHands/contact_hands_two_stream/evaluation/__init__.py

seoyoon130/Graduation_Project

9082cb93fb4f73c3a1577f63e906e6eb7f147dc4

[ "Apache-2.0" ]

26

2020-10-20T01:58:26.000Z

2022-02-24T11:48:10.000Z

ContactHands/contact_hands_two_stream/evaluation/__init__.py

seoyoon130/Graduation_Project

9082cb93fb4f73c3a1577f63e906e6eb7f147dc4

[ "Apache-2.0" ]

5

2020-10-21T05:39:08.000Z

2021-09-17T13:57:29.000Z

contact_hands_two_stream/evaluation/__init__.py

cvlab-stonybrook/ContactHands

6aba9a5f098b50529e589b7835264df9264844e9

[ "MIT" ]

1

2022-02-24T11:48:14.000Z

from .evaluator_ourdata import PascalVOCContactHandsEvaluator

61

0.934426

5

61

11.2

1

0

0.04918

61

1

61

0.965517

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true

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null

0

1

0

1

0

1

0

6

0f0f1ff11d8e7394f9595e6cacdd63bb00caad24

368

py

Python

unsup_spatial_pred/__init__.py

alaflaquiere/unsupervised-spatial-predictor

3c8aa02dc20782d31d1df791dd5e92dce275aec2

[ "MIT" ]

null

unsup_spatial_pred/__init__.py

alaflaquiere/unsupervised-spatial-predictor

3c8aa02dc20782d31d1df791dd5e92dce275aec2

[ "MIT" ]

null

unsup_spatial_pred/__init__.py

alaflaquiere/unsupervised-spatial-predictor

3c8aa02dc20782d31d1df791dd5e92dce275aec2

[ "MIT" ]

null

from unsup_spatial_pred.utils.data_utils import get_dataloader, load_regular_grid from unsup_spatial_pred.network.siamese_network import SiameseSMPredictor from unsup_spatial_pred.analyze.evaluator import Evaluator from unsup_spatial_pred.analyze.live_visualizer import save_embedding, start_display_server from unsup_spatial_pred.train.training import run_experiment

61.333333

91

0.907609

52

368

6.038462

0.538462

0.143312

0.254777

0.318471

0.171975

0

0.059783

368

5

92

73.6

0.907514

0

1

0

true

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

1

0

6

0f36515b28109285aae7c23e030b46c1cbf8a95e

151

py

Python

src/rendering/__init__.py

CorentinBrtx/image-stitching

7ee7eda3bd8717dd9996eacb9d58eb4ed1e6ad80

[ "MIT" ]

9

2022-01-25T14:59:57.000Z

2022-03-24T13:25:23.000Z

src/rendering/__init__.py

CorentinBrtx/image-stitching

7ee7eda3bd8717dd9996eacb9d58eb4ed1e6ad80

[ "MIT" ]

null

src/rendering/__init__.py

CorentinBrtx/image-stitching

7ee7eda3bd8717dd9996eacb9d58eb4ed1e6ad80

[ "MIT" ]

null

from .gain_compensation import set_gain_compensations from .multiband_blending import multi_band_blending from .simple_blending import simple_blending

37.75

53

0.900662

20

151

6.4

0.55

0.21875

0

0.07947

151

3

54

50.333333

0.920863

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

0f4d4816c48653d27898204dea4f719c0e7b0d70

18,887

py

Python

steprocker.py

RavnikM/Py-TMCM-1110-Steprocker

7e6e833514b0da38c0ef4c2420246c0b940e2f47

[ "MIT" ]

null

steprocker.py

RavnikM/Py-TMCM-1110-Steprocker

7e6e833514b0da38c0ef4c2420246c0b940e2f47

[ "MIT" ]

null

steprocker.py

RavnikM/Py-TMCM-1110-Steprocker

7e6e833514b0da38c0ef4c2420246c0b940e2f47

[ "MIT" ]

null

import serial import struct from threading import Lock class Steprocker(): def __init__(self,port): self.lock = Lock() self.serial = serial.Serial(port=port, baudrate=9600, bytesize=serial.EIGHTBITS, parity=serial.PARITY_NONE, stopbits=serial.STOPBITS_ONE, timeout=None) # behavior of read(x) -> wait forever / until requested number of bytes are received def __del__(self): if self.serial.isOpen(): self.serial.close() def rs232_checksum(self,the_bytes): value = b'%02X' % (sum(the_bytes) & 0xFF) return struct.pack(">B", int(value,16)) def get_maximum_positioning_speed(self): # Fiksni del # x01 - Target address (1 = board number) # x06 - Instruction number (6 = GAP(Get axis parameter)) # x04 - Type (4 = Maximum positioning speed) # x00 - Motor / Bank # x00 - Value byte3 (not used) # x00 - Value byte2 (not used) # x00 - Value byte1 (not used) # x00 - Value byte0 (not used) try: self.lock.acquire() packet = b'\x01\x06\x04\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) return struct.unpack('>i', reply[4:8])[0] finally: self.lock.release() def set_maximum_positioning_speed(self, value): # Fiksni del # x01 - Target address (1 = board number) # x05 - Instruction number (5 = SAP(Set axis parameter)) # x04 - Type (4 = Maximum positioning speed) # x00 - Motor / Bank # Variabilni del # Value byte3 # Value byte2 # Value byte1 # Value byte0 try: self.lock.acquire() fixed_part = b'\x01\x05\x04\x00' uvalue_bytes = struct.pack(">i", value) packet = fixed_part + uvalue_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def get_maximum_acceleration(self): # Fiksni del # x01 - Target address (1 = board number) # x06 - Instruction number (6 = GAP(Get axis parameter)) # x05 - Type (5 = Maximum acceleration) # x00 - Motor / Bank # x00 - Value byte3 (not used) # x00 - Value byte2 (not used) # x00 - Value byte1 (not used) # x00 - Value byte0 (not used) try: self.lock.acquire() packet = b'\x01\x06\x05\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) return struct.unpack('>i', reply[4:8])[0] finally: self.lock.release() def set_maximum_acceleration(self, value): # Fiksni del # x01 - Target address (1 = board number) # x05 - Instruction number (5 = SAP(Set axis parameter)) # x05 - Type (5 = Maximum acceleration) # x00 - Motor / Bank # Variabilni del # Value byte3 # Value byte2 # Value byte1 # Value byte0 try: self.lock.acquire() fixed_part = b'\x01\x05\x05\x00' uvalue_bytes = struct.pack(">i", value) packet = fixed_part + uvalue_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def get_actual_position(self): # Fiksni del # x01 - Target address (1 = board number) # x06 - Instruction number (6 = GAP(Get axis parameter)) # x01 - Type (1 = Actual position) # x00 - Motor / Bank # x00 - Value byte3 (not used) # x00 - Value byte2 (not used) # x00 - Value byte1 (not used) # x00 - Value byte0 (not used) try: self.lock.acquire() packet = b'\x01\x06\x01\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) return struct.unpack('>i', reply[4:8])[0] finally: self.lock.release() def set_actual_position(self, value): # Fiksni del # x01 - Target address (1 = board number) # x05 - Instruction number (5 = SAP(Set axis parameter)) # x01 - Type (1 = Actual position) # x00 - Motor / Bank # Variabilni del # Value byte3 # Value byte2 # Value byte1 # Value byte0 try: self.lock.acquire() fixed_part = b'\x01\x05\x01\x00' uvalue_bytes = struct.pack(">i", value) packet = fixed_part + uvalue_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def get_target_position(self): # Fiksni del # x01 - Target address (1 = board number) # x06 - Instruction number (6 = GAP(Get axis parameter)) # x00 - Type (1 = Target position) # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 (not used) # x00 - Value byte2 (not used) # x00 - Value byte1 (not used) # x00 - Value byte0 (not used) try: self.lock.acquire() packet = b'\x01\x06\x00\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) return struct.unpack('>i', reply[4:8])[0] finally: self.lock.release() def set_target_position(self, value): # Fiksni del # x01 - Target address (1 = board number) # x05 - Instruction number (5 = SAP(Set axis parameter)) # x00 - Type (0 = Target position) # x00 - Motor / Bank # Variabilni del # Value byte3 # Value byte2 # Value byte1 # Value byte0 try: self.lock.acquire() fixed_part = b'\x01\x05\x00\x00' uvalue_bytes = struct.pack(">i", value) packet = fixed_part + uvalue_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def get_run_current(self): # Fiksni del # x01 - Target address 1 = board number # x06 - Instruction number 5 = GAP(Get axis parameter) # x06 - Type 6 = run currnet # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 (not used) # x00 - Value byte2 (not used) # x00 - Value byte1 (not used) # x00 - Value byte0 (not used) try: self.lock.acquire() packet = b'\x01\x06\x06\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) return reply[7] finally: self.lock.release() def set_run_current(self, value): # Fiksni del # x01 - Target address (1 = board number) # x05 - Instruction number (5 = SAP(Set axis parameter)) # x06 - Type (6 = run currnet) # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 - not used # x00 - Value byte2 - not used # x00 - Value byte1 - not used # Value byte0 - parameter value 0..255 try: self.lock.acquire() fixed_part = b'\x01\x05\x06\x00\x00\x00\x00' uvalue_bytes = struct.pack("B", value) packet = fixed_part + uvalue_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def get_standby_current(self): # Fiksni del # x01 - Target address (1 = board number) # x06 - Instruction number (6 = GAP(Get axis parameter)) # x07 - Type (7 = standby currnet) # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 - not used # x00 - Value byte2 - not used # x00 - Value byte1 - not used # x00 - Value byte0 - not used try: self.lock.acquire() packet = b'\x01\x06\x07\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) return reply[7] finally: self.lock.release() def set_standby_current(self, value): # Fiksni del # x01 - Target address (1 = board number) # x05 - Instruction number (5 = SAP(Set axis parameter)) # x07 - Type (7 = standby currnet) # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 - not used # x00 - Value byte2 - not used # x00 - Value byte1 - not used # Value - Value byte0 - parameter value 0..255 try: self.lock.acquire() fixed_part = b'\x01\x05\x07\x00\x00\x00\x00' uvalue_bytes = struct.pack("B", value) packet = fixed_part + uvalue_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def get_micro_steps(self, return_str=False): # microsteps0..8 # Microstep resolutions per full step: # 0 fullstep # 1 halfstep # 2 4 microsteps # 3 8 microsteps # 4 16 microsteps # 5 32 microsteps # 6 64 microsteps # 7 128 microsteps # 8 256 microsteps # Fiksni del # x01 - Target address (1 = board number) # x06 - Instruction number (6 = GAP(Get axis parameter)) # x8C - Type (140 = get microsteps) # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 - not used # x00 - Value byte2 - not used # x00 - Value byte1 - not used # x00 - Value byte0 - not used try: self.lock.acquire() packet = b'\x01\x06\x8C\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[7] == 0: if not return_str: return 1 else: return 'full-steps' elif reply[7] == 1: if not return_str: return 2 else: return 'half-steps' elif reply[7] == 2: if not return_str: return 4 else: return '4-microsteps' elif reply[7] == 3: if not return_str: return 8 else: return '8-microsteps' elif reply[7] == 4: if not return_str: return 16 else: return '16-microsteps' elif reply[7] == 5: if not return_str: return 32 else: return '32-microsteps' elif reply[7] == 6: if not return_str: return 64 else: return '64-microsteps' elif reply[7] == 7: if not return_str: return 128 else: return '128-microsteps' elif reply[7] == 8: if not return_str: return 256 else: return '256-microsteps' finally: self.lock.release() def set_micro_steps(self, value): # microsteps 0..8 # Microstep resolutions per full step: # 0 - fullstep # 1 - halfstep # 2 - 4 microsteps # 3 - 8 microsteps # 4 - 16 microsteps # 5 - 32 microsteps # 6 - 64 microsteps # 7 - 128 microsteps # 8 - 256 microsteps if value == 1: true_value = 0 elif value == 2: true_value = 1 elif value == 4: true_value = 2 elif value == 8: true_value = 3 elif value == 16: true_value = 4 elif value == 32: true_value = 5 elif value == 64: true_value = 6 elif value == 128: true_value = 7 elif value == 256: true_value = 8 # Fiksni del # x01 - Target address 1 = board number # x05 - Instruction number (5 = SAP(Set axis parameter)) # x8C - Type 140 = set microsteps # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 - not used # x00 - Value byte2 - not used # x00 - Value byte1 - not used # Value - Value byte0 - parameter value 0..8 try: self.lock.acquire() fixed_part = b'\x01\x05\x8C\x00\x00\x00\x00' uvalue_bytes = struct.pack("B", true_value) packet = fixed_part + uvalue_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def get_target_position_reached(self): # Fiksni del # x01 - Target address (1 = board number) # x06 - Instruction number (6 = GAP(Get axis parameter)) # x08 - Type (8 = Target position reached flag) # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 - not used # x00 - Value byte2 - not used # x00 - Value byte1 - not used # x00 - Value byte0 - not used try: self.lock.acquire() packet = b'\x01\x06\x08\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[7] == 1: return True elif reply[7] == 0: return False finally: self.lock.release() def motor_stop(self): # Fiksni del # x01 - Target address (1 = board number) # x03 - Instruction number (3 = MST(Motor Stop)) # x00 - Type (0 - not used) # x00 - Motor / Bank # Variabilni del # x00 - Value byte3 - not used # x00 - Value byte2 - not used # x00 - Value byte1 - not used # x00 - Value byte0 - not used try: self.lock.acquire() packet = b'\x01\x03\x00\x00\x00\x00\x00\x00' packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def movetopos_abs(self,microsteps, calculate_to_fullsteps=False): # Fiksni del # x01 - Target address (1 = board number) # x04 - Instruction number (4 = MVP(Move to Position)) # x00 - Type (0 = ABSolute) # x00 - Motor / Bank # Variabilni del # Value byte3 # Value byte2 # Value byte1 # Value byte0 try: if calculate_to_fullsteps: microsteps = microsteps * self.get_micro_steps() self.lock.acquire() fixed_part = b'\x01\x04\x00\x00' usteps_bytes = struct.pack(">i", microsteps) packet = fixed_part + usteps_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release() def movetopos_rel(self,microsteps, calculate_to_fullsteps=False): # Fiksni del # x01 - Target address (1 = board number) # x04 - Instruction number (4 = MVP(Move to Position)) # x01 - Type (1 = Relative) # x00 - Motor / Bank # Variabilni del # Value byte3 # Value byte2 # Value byte1 # Value byte0 try: if calculate_to_fullsteps: microsteps = microsteps * self.get_micro_steps() self.lock.acquire() fixed_part = b'\x01\x04\x01\x00' usteps_bytes = struct.pack(">i", microsteps) packet = fixed_part + usteps_bytes packet = packet + self.rs232_checksum(packet) self.serial.write(packet) reply = self.serial.read(9) if reply[2] == 100: return True else: return False finally: self.lock.release()

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Python

testapp/wagtail_wordpress_importer/test/__init__.py

nickmoreton/wagtail_wordpress_importer

fbe6b60ae624edac3f42a62ce30af4a0c548b4ed

[ "MIT" ]

null

testapp/wagtail_wordpress_importer/test/__init__.py

nickmoreton/wagtail_wordpress_importer

fbe6b60ae624edac3f42a62ce30af4a0c548b4ed

[ "MIT" ]

null

testapp/wagtail_wordpress_importer/test/__init__.py

nickmoreton/wagtail_wordpress_importer

fbe6b60ae624edac3f42a62ce30af4a0c548b4ed

[ "MIT" ]

null

from .api_fetcher_tests import * from .base_importer_command_tests import * from .import_delete_commands_tests import * from .utils_tests import *

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py

Python

autox/autoxserver.py

fanghy06/AutoX

0bad349ef1b047152e2608760fd5d197128be723

[ "Apache-2.0" ]

499

2021-07-27T02:57:58.000Z

2022-03-28T12:08:27.000Z

autox/autoxserver.py

fanghy06/AutoX

0bad349ef1b047152e2608760fd5d197128be723

[ "Apache-2.0" ]

9

2021-08-03T15:14:56.000Z

2022-03-11T07:06:06.000Z

autox/autoxserver.py

fanghy06/AutoX

0bad349ef1b047152e2608760fd5d197128be723

[ "Apache-2.0" ]

87

2021-07-27T01:13:02.000Z

2022-03-29T02:14:09.000Z

from autox.autox_server.ensemble import ensemble from autox.autox_server.feature_engineer import fe_count, fe_onehot, fe_shift, fe_time_diff from autox.autox_server.feature_engineer import fe_kv, fe_stat_for_same_prefix, fe_frequency from autox.autox_server.feature_engineer import fe_time_count, fe_window_count, fe_time_rolling_count from autox.autox_server.feature_engineer import fe_window2, fe_txt from autox.autox_server.join_table import join_table from autox.autox_server.model import lgb_with_fe, lgb_for_feature_selection from autox.autox_server.model import model_util from autox.autox_server.pre_process import process_1, process_2, process_3 from autox.autox_server.read_data import read_data from autox.autox_server.util import log, load_obj from autox.autox_server.util import merge_table, save_obj class AutoXServer(): def __init__(self, is_train, server_name, data_info_path=None, train_set_path=None): if is_train: assert(data_info_path is not None and train_set_path is not None) else: assert (data_info_path is None and train_set_path is None) self.is_train = is_train self.data_info_path = data_info_path self.train_set_path = train_set_path self.server_name = server_name def fit(self): data_name = self.server_name log("data name: {}".format(data_name)) lgb_para_dict_1 = model_util.lgb_para_dict_1 lgb_para_dict_2 = model_util.lgb_para_dict_2 params_1 = model_util.params_1 params_2 = model_util.params_2 self.G_hist = {} self.G_hist['val_auc'] = {} self.G_hist['predict'] = {} self.G_hist['delete_column'] = {} phase = 'train' log("*** phase: {}".format(phase)) is_train = True if phase == 'train' else False self.G_df_dict, self.G_data_info, remain_time = read_data.read_data(data_info_path=self.data_info_path, train_set_path=self.train_set_path, is_train=is_train, debug=False) remain_time = process_1.preprocess(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = join_table.join_simple_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = process_2.preprocess_2(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = join_table.join_indirect_1_to_M_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train=is_train, remain_time=remain_time) remain_time = join_table.preprocess_after_join_indirect_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train=is_train, remain_time=remain_time) remain_time = join_table.join_1_to_M_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train=is_train, remain_time=remain_time) remain_time = process_3.preprocess_3(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = fe_kv.fe_kv(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_stat_for_same_prefix.fe_stat_for_same_prefix(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_frequency.fe_frequency(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_count.fe_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_shift.fe_shift(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_time_diff.fe_time_diff(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_time_count.fe_time_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_window_count.fe_window_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_time_rolling_count.fe_time_rolling_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_window2.fe_window2(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_onehot.fe_onehot(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = fe_txt.fe_txt(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = merge_table(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) exp_name = 'feature_selection' remain_time = lgb_for_feature_selection.lgb_for_feature_selection(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, params_1, lgb_para_dict_1, data_name, exp_name) exp_name_1 = 'fe_lgb' remain_time = lgb_with_fe.lgb_with_fe(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, params_1, lgb_para_dict_1, data_name, exp_name_1) exp_name_2 = 'fe_lgb_2' remain_time = lgb_with_fe.lgb_with_fe(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, params_2, lgb_para_dict_2, data_name, exp_name_2) _ = ensemble.ensemble(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, top_k=2) def predict(self, df=None, test_set_path=None): assert ((df is None and test_set_path is not None) or (df is not None and test_set_path is None)) data_name = self.server_name lgb_para_dict_1 = model_util.lgb_para_dict_1 lgb_para_dict_2 = model_util.lgb_para_dict_2 params_1 = model_util.params_1 params_2 = model_util.params_2 phase = 'test' log("*** phase: {}".format(phase)) remain_time = 1e10 is_train = True if phase == 'train' else False self.G_df_dict, self.G_data_info, remain_time = read_data.read_data(data_info=self.G_data_info, test_set_path=test_set_path, df_dict=self.G_df_dict, is_train=is_train, debug=False, remain_time=remain_time) remain_time = process_1.preprocess(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = join_table.join_simple_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = process_2.preprocess_2(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = join_table.join_indirect_1_to_M_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train=is_train, remain_time=remain_time) remain_time = join_table.preprocess_after_join_indirect_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train=is_train, remain_time=remain_time) remain_time = join_table.join_1_to_M_tables(self.G_df_dict, self.G_data_info, self.G_hist, is_train=is_train, remain_time=remain_time) remain_time = process_3.preprocess_3(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = fe_kv.fe_kv(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_stat_for_same_prefix.fe_stat_for_same_prefix(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_frequency.fe_frequency(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_count.fe_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_shift.fe_shift(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_time_diff.fe_time_diff(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_time_count.fe_time_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_window_count.fe_window_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_time_rolling_count.fe_time_rolling_count(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_window2.fe_window2(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, False) remain_time = fe_onehot.fe_onehot(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = fe_txt.fe_txt(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) remain_time = merge_table(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time) exp_name = 'feature_selection' remain_time = lgb_for_feature_selection.lgb_for_feature_selection(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, params_1, lgb_para_dict_1, data_name, exp_name) exp_name_1 = 'fe_lgb' remain_time = lgb_with_fe.lgb_with_fe(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, params_1, lgb_para_dict_1, data_name, exp_name_1) exp_name_2 = 'fe_lgb_2' remain_time = lgb_with_fe.lgb_with_fe(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, params_2, lgb_para_dict_2, data_name, exp_name_2) _ = ensemble.ensemble(self.G_df_dict, self.G_data_info, self.G_hist, is_train, remain_time, top_k=2) sub = self.G_hist['predict']['ensemble'] sub.index = range(len(sub)) return sub def save_server(self, path): data_name = self.server_name save_obj(self.G_df_dict, path + f'/{data_name}_G_df_dict.pkl') save_obj(self.G_data_info, path + f'/{data_name}_G_data_info.pkl') save_obj(self.G_hist, path + f'/{data_name}_G_hist.pkl') def load_server(self, path): data_name = self.server_name self.G_df_dict = load_obj(path + f'/{data_name}_G_df_dict.pkl') self.G_data_info = load_obj(path + f'/{data_name}_G_data_info.pkl') self.G_hist = load_obj(path + f'/{data_name}_G_hist.pkl')

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0

0.161017

0

null

0

1

0

null

0

6

7e2d4fd2ac241c2c37224631a6c00435bed89865

993

py

Python

tests/dfa2re/test_dfa2re.py

xinqin23/ShapeIt

4c643f71bca3b1acc388c688b0e8ffb59109be03

[ "MIT" ]

null

tests/dfa2re/test_dfa2re.py

xinqin23/ShapeIt

4c643f71bca3b1acc388c688b0e8ffb59109be03

[ "MIT" ]

null

tests/dfa2re/test_dfa2re.py

xinqin23/ShapeIt

4c643f71bca3b1acc388c688b0e8ffb59109be03

[ "MIT" ]

null

import pytest import networkx as nx from shapeit.shape_it import ShapeIt def test_dfa2re_0(): aut = nx.MultiDiGraph() aut.add_node(0, initial=True, accepting=False) aut.add_node(1, initial=False, accepting=True) aut.add_edge(0, 1, label='0') aut.add_edge(1, 0, label='0') aut.add_edge(1, 0, label='1') shapeit = ShapeIt() re = shapeit.dfa2re(aut, 0) assert re == '((0)).(((0 + 1)).(0))*' def test_dfa2re_1(): aut = nx.MultiDiGraph() aut.add_node(0, initial=True, accepting=False) aut.add_node(1, initial=False, accepting=True) aut.add_edge(0, 1, label='0') aut.add_edge(1, 0, label='0') shapeit = ShapeIt() re = shapeit.dfa2re(aut, 0) assert re == '((0)).((0).(0))*' def test_dfa2re_2(): aut = nx.MultiDiGraph() aut.add_node(0, initial=True, accepting=True) aut.add_node(1, initial=False, accepting=False) aut.add_edge(0, 1, label='0') aut.add_edge(1, 0, label='0') shapeit = ShapeIt() re = shapeit.dfa2re(aut, 0) assert re == '(eps + ((0)).((0).(0))*.((0)))'

24.825

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993

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993

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null

0

1

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null

0

6

0e521a825cb19d628f10c01686a3109c63584d9c

6,384

py

Python

tests/test_menu_items.py

codingedward/book-a-meal-api

36756abc225bf7e8306330f2c3e223dc32af7869

[ "MIT" ]

null

tests/test_menu_items.py

codingedward/book-a-meal-api

36756abc225bf7e8306330f2c3e223dc32af7869

[ "MIT" ]

null

tests/test_menu_items.py

codingedward/book-a-meal-api

36756abc225bf7e8306330f2c3e223dc32af7869

[ "MIT" ]

2

2018-10-01T17:45:19.000Z

2020-12-07T13:48:25.000Z

import json from app import create_app, db from app.models import User, UserType from .base import BaseTest class MenuItemTests(BaseTest): def setUp(self): self.app = create_app(config_name='testing') self.client = self.app.test_client() with self.app.app_context(): db.create_all() self.setUpAuth() def data(self): return json.dumps({ 'quantity': 30, 'meal_id': self.create_meal()['meal']['id'], 'menu_id': self.create_menu()['menu']['id'], }) def test_can_create_menu_item(self): res = self.client.post( 'api/v1/menu-items', data=self.data(), headers=self.admin_headers) self.assertEqual(res.status_code, 201) self.assertIn(b'Successfully saved menu item', res.data) def test_cannot_create_menu_item_without_quantity(self): res = self.client.post( 'api/v1/menu-items', data=self.data_without(['quantity']), headers=self.admin_headers) self.assertEqual(res.status_code, 400) self.assertIn(b'quantity field is required', res.data) def test_cannot_create_menu_item_without_menu_id(self): res = self.client.post( 'api/v1/menu-items', data=self.data_without(['menu_id']), headers=self.admin_headers) self.assertEqual(res.status_code, 400) self.assertIn(b'id field is required', res.data) def test_cannot_create_menu_item_without_meal_id(self): res = self.client.post( 'api/v1/menu-items', data=self.data_without(['meal_id']), headers=self.admin_headers) self.assertEqual(res.status_code, 400) self.assertIn(b'id field is required', res.data) def test_cannot_create_menu_item_with_nonexistant_meal_id(self): res = self.client.post( 'api/v1/menu-items', data=self.data_with({ 'meal_id': 300 }), headers=self.admin_headers) self.assertEqual(res.status_code, 400) self.assertIn(b'The selected meal id is invalid', res.data) def test_cannot_create_menu_item_with_nonexistant_menu_id(self): res = self.client.post( 'api/v1/menu-items', data=self.data_with({ 'menu_id': 300 }), headers=self.admin_headers) self.assertEqual(res.status_code, 400) self.assertIn(b'The selected menu id is invalid', res.data) def test_can_update_menu_item(self): menu_item = self.create_menu_item(self.data()) meal_id = self.create_meal(name='beef')['meal']['id'] res = self.client.put( 'api/v1/menu-items/{}'.format(menu_item['menu_item']['id']), data=json.dumps({ 'meal_id': meal_id }), headers=self.admin_headers) self.assertEqual(res.status_code, 200) self.assertIn(b'Menu item successfully updated', res.data) def test_cannot_update_menu_item_without_being_unique(self): # create a menu item menu_item = self.create_menu_item(self.data())['menu_item'] meal_id = menu_item['meal']['id'] menu_id = menu_item['menu']['id'] # create another menu item new_meal_id = self.create_meal(name='beef')['meal']['id'] new_menu_item = self.create_menu_item( json.dumps({ 'quantity': 30, 'menu_id': menu_id, 'meal_id': new_meal_id, })) # try to update the first one with the second's values res = self.client.put( 'api/v1/menu-items/{}'.format(new_menu_item['menu_item']['id']), data=json.dumps({ 'meal_id': meal_id, 'menu_id': 2, }), headers=self.admin_headers) self.assertEqual(res.status_code, 400) self.assertIn(b'is invalid', res.data) def test_can_get_menu_item(self): menu_item = self.create_menu_item(self.data()) res = self.client.get( 'api/v1/menu-items/{}'.format(menu_item['menu_item']['id']), headers=self.user_headers) self.assertEqual(res.status_code, 200) self.assertIn(b'successfully retrieved', res.data) def test_can_get_many_menu_items_history(self): self.create_menu_item(self.data()) res = self.client.get( 'api/v1/menu-items?history=1', headers=self.user_headers) self.assertEqual(res.status_code, 200) self.assertIn(b'Successfully retrieved', res.data) def test_can_get_many_menu_items(self): self.create_menu_item(self.data()) res = self.client.get('api/v1/menu-items', headers=self.user_headers) self.assertEqual(res.status_code, 200) self.assertIn(b'Successfully retrieved', res.data) def test_can_delete_menu_item(self): menu_item = self.create_menu_item(self.data()) res = self.client.delete( 'api/v1/menu-items/{}'.format(menu_item['menu_item']['id']), headers=self.admin_headers) self.assertEqual(res.status_code, 200) self.assertIn(b'Menu item successfully deleted', res.data) def create_menu_item(self, data): res = self.client.post( 'api/v1/menu-items', data=data, headers=self.admin_headers) self.assertEqual(res.status_code, 201) self.assertIn(b'Successfully saved menu item', res.data) return self.to_dict(res) def create_meal(self, name='ugali'): res = self.client.post( 'api/v1/meals', data=json.dumps({ 'name': name, 'cost': 30, }), headers=self.admin_headers) self.assertEqual(res.status_code, 201) self.assertIn(b'Successfully saved meal', res.data) return self.to_dict(res) def create_menu(self, name='Lunch'): res = self.client.post( 'api/v1/menus', data=json.dumps({ 'name': name, }), headers=self.admin_headers) self.assertEqual(res.status_code, 201) self.assertIn(b'Successfully saved menu', res.data) return self.to_dict(res) def tearDown(self): with self.app.app_context(): db.drop_all()

37.333333

78

0.60213

824

6,384

4.450243

0.115291

0.087265

0.052359

0.102263

0.818653

0.787565

0.755113

0.737388

0.714208

0.645487

0

0.015924

0.272086

6,384

170

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37.552941

0.773187

0.015038

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0.530612

0

0.136856

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false

0

0.027211

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0.183673

0

null

0

1

0

null

0

6

0e6736307dc21e4437a017717108fb9cd2e908df

41

py

Python

bowling/sort/selection/__init__.py

necromuralist/Bowling-For-Data

8fb2bff206bf419812f96a5ad243e1d82959a00a

[ "MIT" ]

null

bowling/sort/selection/__init__.py

necromuralist/Bowling-For-Data

8fb2bff206bf419812f96a5ad243e1d82959a00a

[ "MIT" ]

null

bowling/sort/selection/__init__.py

necromuralist/Bowling-For-Data

8fb2bff206bf419812f96a5ad243e1d82959a00a

[ "MIT" ]

null

from .selection import selection_counter

20.5

40

0.878049

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41

7

0.8

0

0.097561

41

1

41

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0

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0

1

0

null

0

1

0

1

0

1

0

6

adfbe28608e10d8964372bec46988193eac2c970

250

py

Python

Talent and Weapon Level-Up Material Drops/src/algorithm/__init__.py

RaylaKurosaki1503/Genshin_Impact-Projects

3828663914f2f74da9747cd9ffd983ef313ae0c5

[ "MIT" ]

null

Talent and Weapon Level-Up Material Drops/src/algorithm/__init__.py

RaylaKurosaki1503/Genshin_Impact-Projects

3828663914f2f74da9747cd9ffd983ef313ae0c5

[ "MIT" ]

null

Talent and Weapon Level-Up Material Drops/src/algorithm/__init__.py

RaylaKurosaki1503/Genshin_Impact-Projects

3828663914f2f74da9747cd9ffd983ef313ae0c5

[ "MIT" ]

null

""" Author: Rayla Kurosaki File: __init__.py Description: The initialization file for the package algorithm. """ from algorithm.phase1_get_data import * from algorithm.phase2_print_to_file import * from algorithm.phase3_print_to_workbook import *

20.833333

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0.812

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250

5.617647

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0.204188

0.198953

0

0.013636

0.12

250

11

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0.854545

0.424

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true

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null

0

1

0

1

0

1

0

6

70c1ef8a5b5fef704533a23848def370b4801ae2

5,305

py

Python

tests/test_single_category_update.py

PatrickCmd/Yummy-Recipe-RestAPI

8911678be501d233e39f1b5c5a46aa3e82e5c844

[ "MIT" ]

null

tests/test_single_category_update.py

PatrickCmd/Yummy-Recipe-RestAPI

8911678be501d233e39f1b5c5a46aa3e82e5c844

[ "MIT" ]

41

2017-11-07T00:39:02.000Z

2019-10-21T15:09:58.000Z

tests/test_single_category_update.py

PatrickCmd/Yummy-Recipe-RestAPI

8911678be501d233e39f1b5c5a46aa3e82e5c844

[ "MIT" ]

3

2017-11-18T16:03:34.000Z

2017-12-20T19:49:59.000Z

# tests/test_single_category_update.py import unittest import json import uuid import time from api import db from api.models import User, RecipeCategory from tests.register_login import RegisterLogin class TestUpdateSingleCategoriesBlueprint(RegisterLogin): def test_update_single_recipe_category(self): """ Test for update single recipe category """ with self.client: response = self.register_user( "Patrick", "Walukagga", "pwalukagga@gmail.com", "telnetcmd123" ) # registered user login rep_login = self.login_user("pwalukagga@gmail.com", "telnetcmd123") # valid token headers=dict( Authorization='Bearer ' + json.loads( rep_login.data.decode() )['auth_token'] ) response = self.create_category("Breakfast", "How to make breakfast", headers) category_data = json.dumps({"name": "Lunchfast", "description": "How to make lunchfast"}) response = self.client.put('/recipe_category/1', headers=headers, data=category_data) self.assertEqual(response.status_code, 200) self.assertIn('Recipe Category updated', str(response.data)) self.assertNotIn('How to make breakfast', str(response.data)) # update recipe category not in database response = self.client.put('/recipe_category/3', headers=headers, data=category_data) self.assertEqual(response.status_code, 404) self.assertIn('No category found', str(response.data)) self.assertNotIn('How to make lunchfast', str(response.data)) def test_update_single_recipe_category_id_not_number(self): """ Test for update single recipe category id not number """ with self.client: response = self.register_user( "Patrick", "Walukagga", "pwalukagga@gmail.com", "telnetcmd123" ) # registered user login rep_login = self.login_user("pwalukagga@gmail.com", "telnetcmd123") # valid token headers=dict( Authorization='Bearer ' + json.loads( rep_login.data.decode() )['auth_token'] ) response = self.create_category("Breakfast", "How to make breakfast", headers) category_data = json.dumps({"name": "Lunchfast", "description": "How to make lunchfast"}) response = self.client.put('/recipe_category/a', headers=headers, data=category_data) self.assertEqual(response.status_code, 400) self.assertIn('Category ID must be an integer', str(response.data)) self.assertIn('fail', str(response.data)) def test_update_single_recipe_category_with_one_field(self): """ Test for update single recipe category with one field """ with self.client: response = self.register_user( "Patrick", "Walukagga", "pwalukagga@gmail.com", "telnetcmd123" ) # registered user login rep_login = self.login_user("pwalukagga@gmail.com", "telnetcmd123") # valid token headers=dict( Authorization='Bearer ' + json.loads( rep_login.data.decode() )['auth_token'] ) response = self.create_category("Breakfast", "How to make breakfast", headers) category_data = json.dumps({"name": "Lunchfast"}) response = self.client.put('/recipe_category/1', headers=headers, data=category_data) self.assertEqual(response.status_code, 200) self.assertIn('Recipe Category updated', str(response.data)) category_data = json.dumps({ "description": "How to make lunchfast"}) response = self.client.put('/recipe_category/1', headers=headers, data=category_data) self.assertEqual(response.status_code, 200) self.assertIn('Recipe Category updated', str(response.data)) if __name__ == '__main__': unittest.main()

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0.766787

0.710092

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0.010101

0

null

0

1

0

null

0

6

70c2f075a6dbedbe2aebe6fb9502f022768e876b

119

py

Python

detection/data/util.py

stanford-policylab/surveilling-surveillance

bbb9a147927a6342eecfe07ffa756b3acdb63f35

[ "MIT" ]

8

2021-05-21T03:38:52.000Z

2021-11-21T08:32:41.000Z

detection/data/util.py

stanford-policylab/surveilling-surveillance

bbb9a147927a6342eecfe07ffa756b3acdb63f35

[ "MIT" ]

null

detection/data/util.py

stanford-policylab/surveilling-surveillance

bbb9a147927a6342eecfe07ffa756b3acdb63f35

[ "MIT" ]

1

2021-06-13T21:49:14.000Z

from pathlib import Path, PosixPath def _is_path(file_path): return isinstance(file_path, (str, PosixPath))

17

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0.731092

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119

5.1875

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null

0

1

0

1

0

6

70c62e8e855d3b712588b926c684c8ff454e3bf3

60

py

Python

package-eg-test.py

thinmarwin/python-exercises

2d8ccdf9b0fcf73802b161ca31dd0428e92bbc66

[ "MIT" ]

null

package-eg-test.py

thinmarwin/python-exercises

2d8ccdf9b0fcf73802b161ca31dd0428e92bbc66

[ "MIT" ]

null

package-eg-test.py

thinmarwin/python-exercises

2d8ccdf9b0fcf73802b161ca31dd0428e92bbc66

[ "MIT" ]

null

import package_example.ex41 package-example.ex41.convert()

15

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60

6.125

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true

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0

null

0

1

0

1

0

6

70d0d010999cf9fc72930b2583309f9127a9f629

99

py

Python

custom_django_auth_backend/logging.py

Chiorufarewerin/custom-django-auth-backend

89aa2330933472f8cfb13ad3c488d4347b6db128

[ "MIT" ]

null

custom_django_auth_backend/logging.py

Chiorufarewerin/custom-django-auth-backend

89aa2330933472f8cfb13ad3c488d4347b6db128

[ "MIT" ]

null

custom_django_auth_backend/logging.py

Chiorufarewerin/custom-django-auth-backend

89aa2330933472f8cfb13ad3c488d4347b6db128

[ "MIT" ]

null

from .settings import LOGGER_NAME from .utils import get_logger logger = get_logger(LOGGER_NAME)

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null

0

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6

cb5e57b476ba9567514018000954b065d84a62f5

34

py

Python

digital_signature/__init__.py

syamamura0x00/RedRIbbon

9a035d3e64b70eee7519cccad9d9735e509d5a2f

[ "Apache-2.0" ]

null

digital_signature/__init__.py

syamamura0x00/RedRIbbon

9a035d3e64b70eee7519cccad9d9735e509d5a2f

[ "Apache-2.0" ]

null

digital_signature/__init__.py

syamamura0x00/RedRIbbon

9a035d3e64b70eee7519cccad9d9735e509d5a2f

[ "Apache-2.0" ]

null

from .digital_signature import *

11.333333

32

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34

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0.896552

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1

0

null

0

1

0

1

0

1

0

6

cb8a427f9a37f405511471601f02b428dc29f6f9

96

py

Python

suricate/api/main/__init__.py

marco-buttu/suricate

89cb406ffde7a67db2ac8594d3e9c371924c57bb

[ "BSD-3-Clause" ]

1

2020-03-26T15:27:42.000Z

suricate/api/main/__init__.py

marco-buttu/suricate

89cb406ffde7a67db2ac8594d3e9c371924c57bb

[ "BSD-3-Clause" ]

75

2019-08-19T14:21:08.000Z

2020-03-26T11:24:12.000Z

suricate/api/main/__init__.py

marco-buttu/suricate

89cb406ffde7a67db2ac8594d3e9c371924c57bb

[ "BSD-3-Clause" ]

null

from flask import Blueprint main = Blueprint('main', __name__) from suricate.api import views

16

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96

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0.692308

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0.145833

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null

0

1

0

1

0

6

cba41a7cf1c04aed8dfd66206d9c9b49393f1262

160

py

Python

lambdas/Frontend-Lambda/create_token.py

david-fisher/320-F19-Track-I

cddff29ad9f79a794928eb29d44bc9f53f46f3fd

[ "BSD-3-Clause" ]

8

2019-09-04T14:18:30.000Z

2020-02-04T18:06:50.000Z

lambdas/Frontend-Lambda/create_token.py

david-fisher/320-F19-Track-I

cddff29ad9f79a794928eb29d44bc9f53f46f3fd

[ "BSD-3-Clause" ]

103

2019-09-19T18:15:25.000Z

2020-05-05T01:39:40.000Z

lambdas/Frontend-Lambda/create_token.py

david-fisher/320-F19-Track-I

cddff29ad9f79a794928eb29d44bc9f53f46f3fd

[ "BSD-3-Clause" ]

2

2020-01-17T18:46:46.000Z

2020-05-04T15:53:34.000Z

import random import string def rand_token(N=64): return ''.join(random.choices(string.ascii_uppercase + string.digits + string.ascii_lowercase, k=N))

26.666667

104

0.74375

23

160

5.043478

0.695652

0.189655

0

0.014493

0.1375

160

6

105

26.666667

0.826087

0

1

0.25

false

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null

0

1

0

null

0

1

0

1

0

6

cbb305385a5c951c924dac8441e15d42b33ad71b

46

py

Python

Modulo_5/semana_1/crear_arreglos/arange.py

AutodidactaMx/cocid_python

11628f465ff362807a692c79ede26bf30dd8e26a

[ "MIT" ]

null

Modulo_5/semana_1/crear_arreglos/arange.py

AutodidactaMx/cocid_python

11628f465ff362807a692c79ede26bf30dd8e26a

[ "MIT" ]

null

Modulo_5/semana_1/crear_arreglos/arange.py

AutodidactaMx/cocid_python

11628f465ff362807a692c79ede26bf30dd8e26a

[ "MIT" ]

1

2022-03-04T00:57:18.000Z

import numpy as np print(np.arange(1,50,3))

9.2

24

0.695652

10

46

3.2

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0.152174

46

4

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cbbc3bb7486135cb35b5a26282d6f9a051675662

134

py

Python

python/code_challenges/insertion-sort/tests/test_insertion_sort.py

YAHIAQOUS/data-structures-and-algorithms

73ed1d7f6bb0f60af3cafbac468122c4a035f348

[ "MIT" ]

null

python/code_challenges/insertion-sort/tests/test_insertion_sort.py

YAHIAQOUS/data-structures-and-algorithms

73ed1d7f6bb0f60af3cafbac468122c4a035f348

[ "MIT" ]

8

2021-08-14T14:46:14.000Z

2021-09-13T20:30:29.000Z

python/code_challenges/insertion-sort/tests/test_insertion_sort.py

YAHIAQOUS/data-structures-and-algorithms

73ed1d7f6bb0f60af3cafbac468122c4a035f348

[ "MIT" ]

1

2021-12-05T13:25:31.000Z

from insertion_sort import insertion_sort def test_insertion_sort(): assert insertion_sort([8,4,23,42,16,15]) == [4,8,15,16,23,42]

26.8

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0.746269

25

134

3.8

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0.165289

0.097015

134

4

64

33.5

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0.333333

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0.333333

true

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0.333333

0

0.666667

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null

1

0

1

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null

0

1

0

1

0

6

cbd0908e3f5b3b0101f7b0384d46765a6096579a

48

py

Python

multiply.py

AdvaitShiralkar/calculatorpython

4c9de99c217f2210f574f9c329af887ce728b3dd

[ "MIT" ]

null

multiply.py

AdvaitShiralkar/calculatorpython

4c9de99c217f2210f574f9c329af887ce728b3dd

[ "MIT" ]

null

multiply.py

AdvaitShiralkar/calculatorpython

4c9de99c217f2210f574f9c329af887ce728b3dd

[ "MIT" ]

null

def multiply_2(number): return number * 2

16

24

0.666667

7

48

4.428571

0.714286

0

0.055556

0.25

48

2

25

24

0.805556

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1

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null

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null

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6