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a/_images/watermark_white.png b/_images/watermark_white.png new file mode 100644 index 00000000..4839ba47 Binary files /dev/null and b/_images/watermark_white.png differ diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt new file mode 100644 index 00000000..03a08142 --- /dev/null +++ b/_sources/index.rst.txt @@ -0,0 +1,24 @@ + +TrojanZoo documentation +======================== +TrojanZoo is a universal pytorch platform for conducting security researches (especially for backdoor attacks/defenses) for image classification in deep learning. + +.. toctree:: + :caption: Tutorials + :titlesonly: + + basic + +.. toctree:: + :caption: TrojanZoo Documentation + :titlesonly: + + trojanzoo + trojanvision + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` diff --git a/_sources/trojanvision/attacks/adv.rst.txt b/_sources/trojanvision/attacks/adv.rst.txt new file mode 100644 index 00000000..401cf220 --- /dev/null +++ b/_sources/trojanvision/attacks/adv.rst.txt @@ -0,0 +1,6 @@ +.. _trojanvision.attacks.adv: + +adv +================ + +.. currentmodule:: trojanvision.attacks diff --git a/_sources/trojanvision/attacks/backdoor/clean_label.rst.txt b/_sources/trojanvision/attacks/backdoor/clean_label.rst.txt new file mode 100644 index 00000000..d340d192 --- /dev/null +++ b/_sources/trojanvision/attacks/backdoor/clean_label.rst.txt @@ -0,0 +1,12 @@ +.. _trojanvision.attacks.backdoor.clean_label: + +clean_label +================ + +.. currentmodule:: trojanvision.attacks + +.. autoclass:: InvisiblePoison + :members: + +.. autoclass:: Refool + :members: diff --git a/_sources/trojanvision/attacks/backdoor/dynamic.rst.txt b/_sources/trojanvision/attacks/backdoor/dynamic.rst.txt new file mode 100644 index 00000000..b140a658 --- /dev/null +++ b/_sources/trojanvision/attacks/backdoor/dynamic.rst.txt @@ -0,0 +1,9 @@ +.. _trojanvision.attacks.backdoor.dynamic: + +dynamic +================ + +.. currentmodule:: trojanvision.attacks + +.. autoclass:: InputAwareDynamic + :members: diff --git a/_sources/trojanvision/attacks/backdoor/index.rst.txt b/_sources/trojanvision/attacks/backdoor/index.rst.txt new file mode 100644 index 00000000..4628b2a6 --- /dev/null +++ b/_sources/trojanvision/attacks/backdoor/index.rst.txt @@ -0,0 +1,13 @@ +.. _trojanvision.attacks.backdoor: + +backdoor +================ + +.. currentmodule:: trojanvision.attacks + +.. toctree:: + :maxdepth: 2 + + normal + clean_label + dynamic diff --git a/_sources/trojanvision/attacks/backdoor/normal.rst.txt b/_sources/trojanvision/attacks/backdoor/normal.rst.txt new file mode 100644 index 00000000..a039412f --- /dev/null +++ b/_sources/trojanvision/attacks/backdoor/normal.rst.txt @@ -0,0 +1,21 @@ +.. _trojanvision.attacks.backdoor.normal: + +normal +================ + +.. currentmodule:: trojanvision.attacks + +.. autoclass:: BadNet + :members: + +.. autoclass:: TrojanNN + :members: + +.. autoclass:: IMC + :members: + +.. autoclass:: LatentBackdoor + :members: + +.. autoclass:: TrojanNet + :members: diff --git a/_sources/trojanvision/attacks/index.rst.txt b/_sources/trojanvision/attacks/index.rst.txt new file mode 100644 index 00000000..0c75b9b7 --- /dev/null +++ b/_sources/trojanvision/attacks/index.rst.txt @@ -0,0 +1,26 @@ +.. _trojanvision.attacks: + +attacks +======== + +.. currentmodule:: trojanvision.attacks + + +.. toctree:: + :maxdepth: 3 + + adv + backdoor/index + + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: BackdoorAttack + :members: + +.. autoclass:: CleanLabelBackdoor + :members: + +.. autoclass:: DynamicBackdoor + :members: diff --git a/_sources/trojanvision/configs.rst.txt b/_sources/trojanvision/configs.rst.txt new file mode 100644 index 00000000..c0f979f6 --- /dev/null +++ b/_sources/trojanvision/configs.rst.txt @@ -0,0 +1,9 @@ +.. _trojanvision.configs: + +configs +======== + +.. currentmodule:: trojanvision.configs + +.. automodule:: trojanvision.configs + :members: diff --git a/_sources/trojanvision/datasets/folder.rst.txt b/_sources/trojanvision/datasets/folder.rst.txt new file mode 100644 index 00000000..c288a657 --- /dev/null +++ b/_sources/trojanvision/datasets/folder.rst.txt @@ -0,0 +1,23 @@ +.. _trojanvision.datasets.folder: + +folder +======= +.. currentmodule:: trojanvision.datasets + +.. autoclass:: CUB200 + :members: + +.. autoclass:: CUB200_2011 + :members: + +.. autoclass:: GTSRB + :members: + +.. autoclass:: ImageNet + :members: + +.. autoclass:: ISIC2018 + :members: + +.. autoclass:: VGGface2 + :members: diff --git a/_sources/trojanvision/datasets/index.rst.txt b/_sources/trojanvision/datasets/index.rst.txt new file mode 100644 index 00000000..edeb5ef5 --- /dev/null +++ b/_sources/trojanvision/datasets/index.rst.txt @@ -0,0 +1,20 @@ +.. _trojanvision.datasets: + +datasets +========= +.. currentmodule:: trojanvision.datasets + +.. toctree:: + :maxdepth: 2 + + normal + folder + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: ImageSet + :members: + +.. autoclass:: ImageFolder + :members: diff --git a/_sources/trojanvision/datasets/normal.rst.txt b/_sources/trojanvision/datasets/normal.rst.txt new file mode 100644 index 00000000..7cd851ae --- /dev/null +++ b/_sources/trojanvision/datasets/normal.rst.txt @@ -0,0 +1,23 @@ +.. _trojanvision.datasets.normal: + +normal +======= +.. currentmodule:: trojanvision.datasets + +.. autoclass:: MNIST + :members: + +.. autoclass:: CIFAR10 + :members: + +.. autoclass:: CIFAR100 + :members: + +.. autoclass:: ImageNet16 + :members: + +.. autoclass:: ImageNet32 + :members: + +.. autoclass:: STL10 + :members: diff --git a/_sources/trojanvision/defenses/adv.rst.txt b/_sources/trojanvision/defenses/adv.rst.txt new file mode 100644 index 00000000..e53fc1cd --- /dev/null +++ b/_sources/trojanvision/defenses/adv.rst.txt @@ -0,0 +1,6 @@ +.. _trojanvision.defenses.adv: + +adv +==== + +.. currentmodule:: trojanvision.defenses diff --git a/_sources/trojanvision/defenses/backdoor/attack_agnostic.rst.txt b/_sources/trojanvision/defenses/backdoor/attack_agnostic.rst.txt new file mode 100644 index 00000000..d20ce92f --- /dev/null +++ b/_sources/trojanvision/defenses/backdoor/attack_agnostic.rst.txt @@ -0,0 +1,21 @@ +.. _trojanvision.defenses.backdoor.attack_agnostic: + +attack_agnostic +================ + +.. currentmodule:: trojanvision.defenses + +.. autoclass:: AdvTrain + :members: + +.. autoclass:: FinePruning + :members: + +.. autoclass:: MagNet + :members: + +.. autoclass:: RandomizedSmooth + :members: + +.. autoclass:: Recompress + :members: diff --git a/_sources/trojanvision/defenses/backdoor/index.rst.txt b/_sources/trojanvision/defenses/backdoor/index.rst.txt new file mode 100644 index 00000000..94114b67 --- /dev/null +++ b/_sources/trojanvision/defenses/backdoor/index.rst.txt @@ -0,0 +1,14 @@ +.. _trojanvision.defenses.backdoor: + +backdoor +========= + +.. currentmodule:: trojanvision.defenses + +.. toctree:: + :maxdepth: 2 + + attack_agnostic + input_filtering + training_filtering + model_inspection diff --git a/_sources/trojanvision/defenses/backdoor/input_filtering.rst.txt b/_sources/trojanvision/defenses/backdoor/input_filtering.rst.txt new file mode 100644 index 00000000..e1e64568 --- /dev/null +++ b/_sources/trojanvision/defenses/backdoor/input_filtering.rst.txt @@ -0,0 +1,12 @@ +.. _trojanvision.defenses.backdoor.input_filtering: + +input_filtering +================ + +.. currentmodule:: trojanvision.defenses + +.. autoclass:: Neo + :members: + +.. autoclass:: Strip + :members: diff --git a/_sources/trojanvision/defenses/backdoor/model_inspection.rst.txt b/_sources/trojanvision/defenses/backdoor/model_inspection.rst.txt new file mode 100644 index 00000000..dd97a5b4 --- /dev/null +++ b/_sources/trojanvision/defenses/backdoor/model_inspection.rst.txt @@ -0,0 +1,21 @@ +.. _trojanvision.defenses.backdoor.model_inspection: + +model_inspection +================= + +.. currentmodule:: trojanvision.defenses + +.. autoclass:: ABS + :members: + +.. autoclass:: DeepInspect + :members: + +.. autoclass:: NeuralCleanse + :members: + +.. autoclass:: NeuronInspect + :members: + +.. autoclass:: Tabor + :members: diff --git a/_sources/trojanvision/defenses/backdoor/training_filtering.rst.txt b/_sources/trojanvision/defenses/backdoor/training_filtering.rst.txt new file mode 100644 index 00000000..b0d8ad30 --- /dev/null +++ b/_sources/trojanvision/defenses/backdoor/training_filtering.rst.txt @@ -0,0 +1,12 @@ +.. _trojanvision.defenses.backdoor.training_filtering: + +training_filtering +=================== + +.. currentmodule:: trojanvision.defenses + +.. autoclass:: ActivationClustering + :members: + +.. .. autoclass:: SpectralSignature +.. :members: diff --git a/_sources/trojanvision/defenses/index.rst.txt b/_sources/trojanvision/defenses/index.rst.txt new file mode 100644 index 00000000..d50679b3 --- /dev/null +++ b/_sources/trojanvision/defenses/index.rst.txt @@ -0,0 +1,28 @@ +.. _trojanvision.defenses: + +defenses +========= + +.. currentmodule:: trojanvision.defenses + +.. toctree:: + :maxdepth: 3 + + adv + backdoor/index + + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: BackdoorDefense + :members: + +.. autoclass:: InputFiltering + :members: + +.. autoclass:: TrainingFiltering + :members: + +.. autoclass:: ModelInspection + :members: diff --git a/_sources/trojanvision/environ.rst.txt b/_sources/trojanvision/environ.rst.txt new file mode 100644 index 00000000..b42aacbc --- /dev/null +++ b/_sources/trojanvision/environ.rst.txt @@ -0,0 +1,8 @@ +.. _trojanvision.environ: + +environ +======== + +.. currentmodule:: trojanvision.environ + +.. automodule:: trojanvision.environ diff --git a/_sources/trojanvision/index.rst.txt b/_sources/trojanvision/index.rst.txt new file mode 100644 index 00000000..02197536 --- /dev/null +++ b/_sources/trojanvision/index.rst.txt @@ -0,0 +1,18 @@ +.. _trojanvision: + +trojanvision +============= + +.. toctree:: + :titlesonly: + + attacks/index + configs + datasets/index + defenses/index + environ + marks + models/index + optim + trainer + utils diff --git a/_sources/trojanvision/marks.rst.txt b/_sources/trojanvision/marks.rst.txt new file mode 100644 index 00000000..acf37c3d --- /dev/null +++ b/_sources/trojanvision/marks.rst.txt @@ -0,0 +1,12 @@ +.. _trojanvision.marks: + +marks +====== + +.. currentmodule:: trojanvision.marks + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: Watermark + :members: diff --git a/_sources/trojanvision/models/index.rst.txt b/_sources/trojanvision/models/index.rst.txt new file mode 100644 index 00000000..e41a59c0 --- /dev/null +++ b/_sources/trojanvision/models/index.rst.txt @@ -0,0 +1,20 @@ +.. _trojanvision.models: + +models +======= + +.. currentmodule:: trojanvision.models + +.. toctree:: + :maxdepth: 2 + + torchvision + nas + others + +.. autofunction:: add_argument +.. autofunction:: create +.. autofunction:: output_available_models + +.. autoclass:: ImageModel + :members: diff --git a/_sources/trojanvision/models/nas.rst.txt b/_sources/trojanvision/models/nas.rst.txt new file mode 100644 index 00000000..21161794 --- /dev/null +++ b/_sources/trojanvision/models/nas.rst.txt @@ -0,0 +1,21 @@ +.. _trojanvision.models.nas: + +nas +============ + +.. currentmodule:: trojanvision.models + +.. autoclass:: DARTS + :members: + +.. autoclass:: ENAS + :members: + +.. autoclass:: NATSbench + :members: + +.. autoclass:: PNASNet + :members: + +.. autoclass:: ProxylessNAS + :members: diff --git a/_sources/trojanvision/models/others.rst.txt b/_sources/trojanvision/models/others.rst.txt new file mode 100644 index 00000000..9b87e7b0 --- /dev/null +++ b/_sources/trojanvision/models/others.rst.txt @@ -0,0 +1,9 @@ +.. _trojanvision.models.others: + +others +============ + +.. currentmodule:: trojanvision.models + +.. autoclass:: MagNet + :members: diff --git a/_sources/trojanvision/models/torchvision.rst.txt b/_sources/trojanvision/models/torchvision.rst.txt new file mode 100644 index 00000000..fb9f91c3 --- /dev/null +++ b/_sources/trojanvision/models/torchvision.rst.txt @@ -0,0 +1,30 @@ +.. _trojanvision.models.torchvision: + +torchvision +============ + +.. currentmodule:: trojanvision.models + +.. autoclass:: AlexNet + :members: + +.. autoclass:: DenseNet + :members: + +.. autoclass:: EfficientNet + :members: + +.. autoclass:: MNASNet + :members: + +.. autoclass:: MobileNet + :members: + +.. autoclass:: ResNet + :members: + +.. autoclass:: ShuffleNetV2 + :members: + +.. autoclass:: VGG + :members: diff --git a/_sources/trojanvision/optim.rst.txt b/_sources/trojanvision/optim.rst.txt new file mode 100644 index 00000000..983096cb --- /dev/null +++ b/_sources/trojanvision/optim.rst.txt @@ -0,0 +1,6 @@ +.. _trojanvision.optim: + +optim +====== + +.. currentmodule:: trojanvision.optim diff --git a/_sources/trojanvision/trainer.rst.txt b/_sources/trojanvision/trainer.rst.txt new file mode 100644 index 00000000..cd945afd --- /dev/null +++ b/_sources/trojanvision/trainer.rst.txt @@ -0,0 +1,8 @@ +.. _trojanvision.trainer: + +trainer +======== + +.. currentmodule:: trojanvision.trainer + +.. automodule:: trojanvision.trainer diff --git a/_sources/trojanvision/utils.rst.txt b/_sources/trojanvision/utils.rst.txt new file mode 100644 index 00000000..14e7fcc4 --- /dev/null +++ b/_sources/trojanvision/utils.rst.txt @@ -0,0 +1,6 @@ +.. _trojanvision.utils: + +utils +====== + +.. currentmodule:: trojanvision.utils diff --git a/_sources/trojanzoo/attacks.rst.txt b/_sources/trojanzoo/attacks.rst.txt new file mode 100644 index 00000000..84689fdd --- /dev/null +++ b/_sources/trojanzoo/attacks.rst.txt @@ -0,0 +1,12 @@ +.. _trojanzoo.attacks: + +attacks +======== + +.. currentmodule:: trojanzoo.attacks + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: Attack + :members: diff --git a/_sources/trojanzoo/configs.rst.txt b/_sources/trojanzoo/configs.rst.txt new file mode 100644 index 00000000..d3facc78 --- /dev/null +++ b/_sources/trojanzoo/configs.rst.txt @@ -0,0 +1,9 @@ +.. _trojanzoo.configs: + +configs +======== + +.. currentmodule:: trojanzoo.configs + +.. automodule:: trojanzoo.configs + :members: diff --git a/_sources/trojanzoo/datasets.rst.txt b/_sources/trojanzoo/datasets.rst.txt new file mode 100644 index 00000000..f14c264a --- /dev/null +++ b/_sources/trojanzoo/datasets.rst.txt @@ -0,0 +1,12 @@ +.. _trojanzoo.datasets: + +datasets +========= + +.. currentmodule:: trojanzoo.datasets + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: Dataset + :members: diff --git a/_sources/trojanzoo/defenses.rst.txt b/_sources/trojanzoo/defenses.rst.txt new file mode 100644 index 00000000..57d3842d --- /dev/null +++ b/_sources/trojanzoo/defenses.rst.txt @@ -0,0 +1,12 @@ +.. _trojanzoo.defenses: + +defenses +========= + +.. currentmodule:: trojanzoo.defenses + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: Defense + :members: diff --git a/_sources/trojanzoo/environ.rst.txt b/_sources/trojanzoo/environ.rst.txt new file mode 100644 index 00000000..3fd975bf --- /dev/null +++ b/_sources/trojanzoo/environ.rst.txt @@ -0,0 +1,12 @@ +.. _trojanzoo.environ: + +environ +======== + +.. currentmodule:: trojanzoo.environ + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: Env + :members: diff --git a/_sources/trojanzoo/index.rst.txt b/_sources/trojanzoo/index.rst.txt new file mode 100644 index 00000000..9f7db84a --- /dev/null +++ b/_sources/trojanzoo/index.rst.txt @@ -0,0 +1,17 @@ +.. _trojanzoo: + +trojanzoo +========== + +.. toctree:: + :titlesonly: + + attacks + configs + datasets + defenses + environ + models + optim + trainer + utils/index diff --git a/_sources/trojanzoo/models.rst.txt b/_sources/trojanzoo/models.rst.txt new file mode 100644 index 00000000..ed8c3224 --- /dev/null +++ b/_sources/trojanzoo/models.rst.txt @@ -0,0 +1,18 @@ +.. _trojanzoo.models: + +models +======= + +.. currentmodule:: trojanzoo.models + +.. autofunction:: add_argument +.. autofunction:: create +.. autofunction:: output_available_models + +.. autoclass:: Model + :members: + :private-members: _train, _validate + +.. autoclass:: _Model + :members: + diff --git a/_sources/trojanzoo/optim.rst.txt b/_sources/trojanzoo/optim.rst.txt new file mode 100644 index 00000000..9209df78 --- /dev/null +++ b/_sources/trojanzoo/optim.rst.txt @@ -0,0 +1,9 @@ +.. _trojanzoo.optim: + +optim +====== + +.. currentmodule:: trojanzoo.optim + +.. autoclass:: Optimizer + :members: diff --git a/_sources/trojanzoo/trainer.rst.txt b/_sources/trojanzoo/trainer.rst.txt new file mode 100644 index 00000000..ecb68f50 --- /dev/null +++ b/_sources/trojanzoo/trainer.rst.txt @@ -0,0 +1,12 @@ +.. _trojanzoo.trainer: + +trainer +======== + +.. currentmodule:: trojanzoo.trainer + +.. autofunction:: add_argument +.. autofunction:: create + +.. autoclass:: Trainer + :members: diff --git a/_sources/trojanzoo/utils/data.rst.txt b/_sources/trojanzoo/utils/data.rst.txt new file mode 100644 index 00000000..42bdb273 --- /dev/null +++ b/_sources/trojanzoo/utils/data.rst.txt @@ -0,0 +1,12 @@ +.. _trojanzoo.utils.data: + +data +===== + +.. currentmodule:: trojanzoo.utils.data + +.. autoclass:: TensorListDataset +.. autofunction:: dataset_to_tensor +.. autofunction:: sample_batch +.. autofunction:: split_dataset +.. autofunction:: get_class_subset diff --git a/_sources/trojanzoo/utils/fim.rst.txt b/_sources/trojanzoo/utils/fim.rst.txt new file mode 100644 index 00000000..60fd7acb --- /dev/null +++ b/_sources/trojanzoo/utils/fim.rst.txt @@ -0,0 +1,12 @@ +.. _trojanzoo.utils.fim: + +fim +==== + +.. currentmodule:: trojanzoo.utils.fim + +.. autoclass:: BaseKFAC +.. autoclass:: KFAC +.. autoclass:: EKFAC +.. autoclass:: BaseState +.. autoclass:: KFACState diff --git a/_sources/trojanzoo/utils/index.rst.txt b/_sources/trojanzoo/utils/index.rst.txt new file mode 100644 index 00000000..0544efa8 --- /dev/null +++ b/_sources/trojanzoo/utils/index.rst.txt @@ -0,0 +1,23 @@ +.. _trojanzoo.utils: + +utils +====== + +.. currentmodule:: trojanzoo.utils + +.. toctree:: + :maxdepth: 2 + + data + fim + logger + model + module + tensor + train + +.. autoclass:: trojanzoo.utils.lock.Lock + :members: + +.. autofunction:: trojanzoo.utils.memory.empty_cache +.. autofunction:: trojanzoo.utils.memory.output_memory diff --git a/_sources/trojanzoo/utils/logger.rst.txt b/_sources/trojanzoo/utils/logger.rst.txt new file mode 100644 index 00000000..08db161d --- /dev/null +++ b/_sources/trojanzoo/utils/logger.rst.txt @@ -0,0 +1,15 @@ +.. _trojanzoo.utils.logger: + +logger +======= + +.. currentmodule:: trojanzoo.utils.logger + +.. autoclass:: SmoothedValue + :members: + +.. autoclass:: MetricLogger + :members: + +.. autoclass:: AverageMeter + :members: \ No newline at end of file diff --git a/_sources/trojanzoo/utils/model.rst.txt b/_sources/trojanzoo/utils/model.rst.txt new file mode 100644 index 00000000..dffc8533 --- /dev/null +++ b/_sources/trojanzoo/utils/model.rst.txt @@ -0,0 +1,16 @@ +.. _trojanzoo.utils.model: + +model +====== + +.. currentmodule:: trojanzoo.utils.model + +.. autofunction:: init_weights +.. autofunction:: get_layer_name +.. autofunction:: get_all_layer +.. autofunction:: get_layer +.. autofunction:: summary +.. autofunction:: activate_params +.. autofunction:: accuracy +.. autofunction:: generate_target +.. autoclass:: ExponentialMovingAverage diff --git a/_sources/trojanzoo/utils/module.rst.txt b/_sources/trojanzoo/utils/module.rst.txt new file mode 100644 index 00000000..77c9e30a --- /dev/null +++ b/_sources/trojanzoo/utils/module.rst.txt @@ -0,0 +1,29 @@ +.. _trojanzoo.utils.module: + +module +======= + +.. currentmodule:: trojanzoo.utils.module + +.. process +.. -------------------------------------- + +.. autoclass:: BasicObject + :members: + +.. autoclass:: Process + :members: + +.. autoclass:: ModelProcess + :members: + +.. param +.. -------------------------------------- + +.. autoclass:: Module + :members: + :undoc-members: + +.. autoclass:: Param + :members: + :undoc-members: diff --git a/_sources/trojanzoo/utils/tensor.rst.txt b/_sources/trojanzoo/utils/tensor.rst.txt new file mode 100644 index 00000000..2eed45f2 --- /dev/null +++ b/_sources/trojanzoo/utils/tensor.rst.txt @@ -0,0 +1,11 @@ +.. _trojanzoo.utils.tensor: + +tensor +======= + +.. currentmodule:: trojanzoo.utils.tensor + +.. autofunction:: tanh_func +.. autofunction:: atan_func +.. autofunction:: repeat_to_batch +.. autofunction:: add_noise diff --git a/_sources/trojanzoo/utils/train.rst.txt b/_sources/trojanzoo/utils/train.rst.txt new file mode 100644 index 00000000..1eebdd6e --- /dev/null +++ b/_sources/trojanzoo/utils/train.rst.txt @@ -0,0 +1,9 @@ +.. _trojanzoo.utils.train: + +train +====== + +.. currentmodule:: trojanzoo.utils.train + +.. autofunction:: train +.. autofunction:: validate diff --git a/_sources/tutorials/basic.rst.txt b/_sources/tutorials/basic.rst.txt new file mode 100644 index 00000000..95b4042b --- /dev/null +++ b/_sources/tutorials/basic.rst.txt @@ -0,0 +1,41 @@ +.. _tutorials.basic: + +basic +====== + +You can view `example files at GitHub `_. + +Following is `backdoor_defense.py `_ +to show the basic workflow. + +.. code-block:: python + + #!/usr/bin/env python3 + + # CUDA_VISIBLE_DEVICES=0 python ./examples/backdoor_defense.py --color --verbose 1 --attack badnet --defense neural_cleanse --pretrained --validate_interval 1 --epochs 50 --lr 1e-2 + + import trojanvision + import argparse + + if __name__ == '__main__': + parser = argparse.ArgumentParser() + trojanvision.environ.add_argument(parser) + trojanvision.datasets.add_argument(parser) + trojanvision.models.add_argument(parser) + trojanvision.trainer.add_argument(parser) + trojanvision.marks.add_argument(parser) + trojanvision.attacks.add_argument(parser) + trojanvision.defenses.add_argument(parser) + kwargs = parser.parse_args().__dict__ + + env = trojanvision.environ.create(**kwargs) + dataset = trojanvision.datasets.create(**kwargs) + model = trojanvision.models.create(dataset=dataset, **kwargs) + trainer = trojanvision.trainer.create(dataset=dataset, model=model, **kwargs) + mark = trojanvision.marks.create(dataset=dataset, **kwargs) + attack = trojanvision.attacks.create(dataset=dataset, model=model, mark=mark, **kwargs) + defense = trojanvision.defenses.create(dataset=dataset, model=model, attack=attack, **kwargs) + + if env['verbose']: + trojanvision.summary(env=env, dataset=dataset, model=model, mark=mark, trainer=trainer, attack=attack, defense=defense) + defense.detect(**trainer) diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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+ var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); 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+ display: flex; + top: .3em; + right: .3em; + width: 1.7em; + height: 1.7em; + opacity: 0; + transition: opacity 0.3s, border .3s, background-color .3s; + user-select: none; + padding: 0; + border: none; + outline: none; + border-radius: 0.4em; + /* The colors that GitHub uses */ + border: #1b1f2426 1px solid; + background-color: #f6f8fa; + color: #57606a; +} + +button.copybtn.success { + border-color: #22863a; + color: #22863a; +} + +button.copybtn svg { + stroke: currentColor; + width: 1.5em; + height: 1.5em; + padding: 0.1em; +} + +div.highlight { + position: relative; +} + +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { + opacity: 1; +} + +.highlight button.copybtn:hover { + background-color: rgb(235, 235, 235); +} + +.highlight button.copybtn:active { + background-color: rgb(187, 187, 187); +} + +/** + * A minimal CSS-only tooltip copied from: + * https://codepen.io/mildrenben/pen/rVBrpK + * + * To use, write HTML like the following: + * + *

Short

+ */ + .o-tooltip--left { + position: relative; + } + + .o-tooltip--left:after { + opacity: 0; + visibility: hidden; + position: absolute; + content: attr(data-tooltip); + padding: .2em; + font-size: .8em; + left: -.2em; + background: grey; + color: white; + white-space: nowrap; + z-index: 2; + border-radius: 2px; + transform: translateX(-102%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); +} + +.o-tooltip--left:hover:after { + display: block; + opacity: 1; + visibility: visible; + transform: translateX(-100%) translateY(0); + transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); + transition-delay: .5s; +} + +/* By default the copy button shouldn't show up when printing a page */ +@media print { + button.copybtn { + display: none; + } +} diff --git a/_static/copybutton.js b/_static/copybutton.js new file mode 100644 index 00000000..2ea7ff3e --- /dev/null +++ b/_static/copybutton.js @@ -0,0 +1,248 @@ +// Localization support +const messages = { + 'en': { + 'copy': 'Copy', + 'copy_to_clipboard': 'Copy to clipboard', + 'copy_success': 'Copied!', + 'copy_failure': 'Failed to copy', + }, + 'es' : { + 'copy': 'Copiar', + 'copy_to_clipboard': 'Copiar al portapapeles', + 'copy_success': '¡Copiado!', + 'copy_failure': 'Error al copiar', + }, + 'de' : { + 'copy': 'Kopieren', + 'copy_to_clipboard': 'In die Zwischenablage kopieren', + 'copy_success': 'Kopiert!', + 'copy_failure': 'Fehler beim Kopieren', + }, + 'fr' : { + 'copy': 'Copier', + 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_success': 'Copié !', + 'copy_failure': 'Échec de la copie', + }, + 'ru': { + 'copy': 'Скопировать', + 'copy_to_clipboard': 'Скопировать в буфер', + 'copy_success': 'Скопировано!', + 'copy_failure': 'Не удалось скопировать', + }, + 'zh-CN': { + 'copy': '复制', + 'copy_to_clipboard': '复制到剪贴板', + 'copy_success': '复制成功!', + 'copy_failure': '复制失败', + }, + 'it' : { + 'copy': 'Copiare', + 'copy_to_clipboard': 'Copiato negli appunti', + 'copy_success': 'Copiato!', + 'copy_failure': 'Errore durante la copia', + } +} + +let locale = 'en' +if( document.documentElement.lang !== undefined + && messages[document.documentElement.lang] !== undefined ) { + locale = document.documentElement.lang +} + +let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; +if (doc_url_root == '#') { + doc_url_root = ''; +} + +/** + * SVG files for our copy buttons + */ +let iconCheck = ` + ${messages[locale]['copy_success']} + + +` + +// If the user specified their own SVG use that, otherwise use the default +let iconCopy = ``; +if (!iconCopy) { + iconCopy = ` + ${messages[locale]['copy_to_clipboard']} + + + +` +} + +/** + * Set up copy/paste for code blocks + */ + +const runWhenDOMLoaded = cb => { + if (document.readyState != 'loading') { + cb() + } else if (document.addEventListener) { + document.addEventListener('DOMContentLoaded', cb) + } else { + document.attachEvent('onreadystatechange', function() { + if (document.readyState == 'complete') cb() + }) + } +} + +const codeCellId = index => `codecell${index}` + +// Clears selected text since ClipboardJS will select the text when copying +const clearSelection = () => { + if (window.getSelection) { + window.getSelection().removeAllRanges() + } else if (document.selection) { + document.selection.empty() + } +} + +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + +const temporarilyChangeTooltip = (el, oldText, newText) => { + el.setAttribute('data-tooltip', newText) + el.classList.add('success') + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) +} + +// Changes the copy button icon for two seconds, then changes it back +const temporarilyChangeIcon = (el) => { + el.innerHTML = iconCheck; + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) +} + +const addCopyButtonToCodeCells = () => { + // If ClipboardJS hasn't loaded, wait a bit and try again. This + // happens because we load ClipboardJS asynchronously. + if (window.ClipboardJS === undefined) { + setTimeout(addCopyButtonToCodeCells, 250) + return + } + + // Add copybuttons to all of our code cells + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + codeCells.forEach((codeCell, index) => { + const id = codeCellId(index) + codeCell.setAttribute('id', id) + + const clipboardButton = id => + `` + codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) + }) + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} + + +var copyTargetText = (trigger) => { + var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') +} + + // Initialize with a callback so we can modify the text before copy + const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) + + // Update UI with error/success messages + clipboard.on('success', event => { + clearSelection() + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) + temporarilyChangeIcon(event.trigger) + }) + + clipboard.on('error', event => { + temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) + }) +} + +runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js new file mode 100644 index 00000000..dbe1aaad --- /dev/null +++ b/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string +} + +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + +// Callback when a copy button is clicked. Will be passed the node that was clicked +// should then grab the text and replace pieces of text that shouldn't be used in output +export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; + var match; + + // Do we check for line continuation characters and "HERE-documents"? + var useLineCont = !!lineContinuationChar + var useHereDoc = !!hereDocDelim + + // create regexp to capture prompt and remaining line + if (isRegexp) { + regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') + } else { + regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') + } + + const outputLines = []; + var promptFound = false; + var gotLineCont = false; + var gotHereDoc = false; + const lineGotPrompt = []; + for (const line of textContent.split('\n')) { + match = line.match(regexp) + if (match || gotLineCont || gotHereDoc) { + promptFound = regexp.test(line) + lineGotPrompt.push(promptFound) + if (removePrompts && promptFound) { + outputLines.push(match[2]) + } else { + outputLines.push(line) + } + gotLineCont = line.endsWith(lineContinuationChar) & useLineCont + if (line.includes(hereDocDelim) & useHereDoc) + gotHereDoc = !gotHereDoc + } else if (!onlyCopyPromptLines) { + outputLines.push(line) + } else if (copyEmptyLines && line.trim() === '') { + outputLines.push(line) + } + } + + // If no lines with the prompt were found then just use original lines + if (lineGotPrompt.some(v => v === true)) { + textContent = outputLines.join('\n'); + } + + // Remove a trailing newline to avoid auto-running when pasting + if (textContent.endsWith("\n")) { + textContent = textContent.slice(0, -1) + } + return textContent +} diff --git a/_static/css/theme.css b/_static/css/theme.css new file mode 100644 index 00000000..9fdabdf8 --- /dev/null +++ b/_static/css/theme.css @@ -0,0 +1,12380 @@ +@charset "UTF-8"; +/*! + * Bootstrap v4.0.0 (https://getbootstrap.com) + * Copyright 2011-2018 The Bootstrap Authors + * Copyright 2011-2018 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */ +:root { + --blue: #007bff; + --indigo: #6610f2; + --purple: #6f42c1; + --pink: #e83e8c; + --red: #dc3545; + --orange: #fd7e14; + --yellow: #ffc107; + --green: #28a745; + --teal: #20c997; + --cyan: #17a2b8; + --white: #fff; + --gray: #6c757d; + --gray-dark: #343a40; + --primary: #007bff; + --secondary: #6c757d; + --success: #28a745; + --info: #17a2b8; + --warning: #ffc107; + --danger: #dc3545; + --light: #f8f9fa; + --dark: #343a40; + --breakpoint-xs: 0; + --breakpoint-sm: 576px; + --breakpoint-md: 768px; + --breakpoint-lg: 992px; + --breakpoint-xl: 1200px; + --font-family-sans-serif: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; + --font-family-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +*, +*::before, +*::after { + -webkit-box-sizing: border-box; + box-sizing: border-box; +} + +html { + font-family: sans-serif; + line-height: 1.15; + -webkit-text-size-adjust: 100%; + -ms-text-size-adjust: 100%; + -ms-overflow-style: scrollbar; + -webkit-tap-highlight-color: rgba(0, 0, 0, 0); +} + +@-ms-viewport { + width: device-width; +} +article, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section { + display: block; +} + +body { + margin: 0; + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; + font-size: 1rem; + font-weight: 400; + line-height: 1.5; + color: #212529; + text-align: left; + background-color: #fff; + + --lightning: #792ee5; + --pytorch: #e44c2c; + --pytorch-red: #ee4c2c; + --readthedoc: #2980b9; + --google-blue: #4285f4; + --google-red: #db4437; + --google-yellow: #f4b400; + --google-green: #0f9d58; +} + +[tabindex="-1"]:focus { + outline: 0 !important; +} + +hr { + -webkit-box-sizing: content-box; + box-sizing: content-box; + height: 0; + overflow: visible; +} + +h1, h2, h3, h4, h5, h6 { + margin-top: 0; + margin-bottom: 0.5rem; +} + +p { + margin-top: 0; + margin-bottom: 1rem; +} + +abbr[title], +abbr[data-original-title] { + text-decoration: underline; + -webkit-text-decoration: underline dotted; + text-decoration: underline dotted; + cursor: help; + border-bottom: 0; +} + +address { + margin-bottom: 1rem; + font-style: normal; + line-height: inherit; +} + +ol, +ul, +dl { + margin-top: 0; + margin-bottom: 1rem; +} + +ol ol, +ul ul, +ol ul, +ul ol { + margin-bottom: 0; +} + +dt { + font-weight: 700; +} + +dd { + margin-bottom: .5rem; + margin-left: 0; +} + +blockquote { + margin: 0 0 1rem; +} + +dfn { + font-style: italic; +} + +b, +strong { + font-weight: bolder; +} + +small { + font-size: 80%; +} + +sub, +sup { + position: relative; + font-size: 75%; + line-height: 0; + vertical-align: baseline; +} + +sub { + bottom: -.25em; +} + +sup { + top: -.5em; +} + +a { + color: #007bff; + text-decoration: none; + background-color: transparent; + -webkit-text-decoration-skip: objects; +} +a:hover { + color: #0056b3; + text-decoration: underline; +} + +a:not([href]):not([tabindex]) { + color: inherit; + text-decoration: none; +} +a:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus { + color: inherit; + text-decoration: none; +} +a:not([href]):not([tabindex]):focus { + outline: 0; +} + +pre, +code, +kbd, +samp { + font-family: monospace, monospace; + font-size: 1em; +} + +pre { + margin-top: 0; + margin-bottom: 1rem; + overflow: auto; + -ms-overflow-style: scrollbar; +} + +figure { + margin: 0 0 1rem; +} + +img { + vertical-align: middle; + border-style: none; +} + +svg:not(:root) { + overflow: hidden; +} + +table { + border-collapse: collapse; +} + +caption { + padding-top: 0.75rem; + padding-bottom: 0.75rem; + color: #6c757d; + text-align: left; + caption-side: bottom; +} + +th { + text-align: inherit; +} + +label { + display: inline-block; + margin-bottom: .5rem; +} + +button { + border-radius: 0; +} + +button:focus { + outline: 1px dotted; + outline: 5px auto -webkit-focus-ring-color; +} + +input, +button, +select, +optgroup, +textarea { + margin: 0; + font-family: inherit; + font-size: inherit; + line-height: inherit; +} + +button, +input { + overflow: visible; +} + +button, +select { + text-transform: none; +} + +button, +html [type="button"], +[type="reset"], +[type="submit"] { + -webkit-appearance: button; +} + +button::-moz-focus-inner, +[type="button"]::-moz-focus-inner, +[type="reset"]::-moz-focus-inner, +[type="submit"]::-moz-focus-inner { + padding: 0; + border-style: none; +} + +input[type="radio"], +input[type="checkbox"] { + -webkit-box-sizing: border-box; + box-sizing: border-box; + padding: 0; +} + +input[type="date"], +input[type="time"], +input[type="datetime-local"], +input[type="month"] { + -webkit-appearance: listbox; +} + +textarea { + overflow: auto; + resize: vertical; +} + +fieldset { + min-width: 0; + padding: 0; + margin: 0; + border: 0; +} + +legend { + display: block; + width: 100%; + max-width: 100%; + padding: 0; + margin-bottom: .5rem; + font-size: 1.5rem; + line-height: inherit; + color: inherit; + white-space: normal; +} + +progress { + vertical-align: baseline; +} + +[type="number"]::-webkit-inner-spin-button, +[type="number"]::-webkit-outer-spin-button { + height: auto; +} + +[type="search"] { + outline-offset: -2px; + -webkit-appearance: none; +} + +[type="search"]::-webkit-search-cancel-button, +[type="search"]::-webkit-search-decoration { + -webkit-appearance: none; +} + +::-webkit-file-upload-button { + font: inherit; + -webkit-appearance: button; +} + +output { + display: inline-block; +} + +summary { + display: list-item; + cursor: pointer; +} + +template { + display: none; +} + +[hidden] { + display: none !important; +} + +h1, h2, h3, h4, h5, h6, +.h1, .h2, .h3, .h4, .h5, .h6 { + margin-bottom: 0.5rem; + font-family: inherit; + font-weight: 500; + line-height: 1.2; + color: inherit; +} + +h1, .h1 { + font-size: 2.5rem; +} + +h2, .h2 { + font-size: 2rem; +} + +h3, .h3 { + font-size: 1.75rem; +} + +h4, .h4 { + font-size: 1.5rem; +} + +h5, .h5 { + font-size: 1.25rem; +} + +h6, .h6 { + font-size: 1rem; +} + +.lead { + font-size: 1.25rem; + font-weight: 300; +} + +.display-1 { + font-size: 6rem; + font-weight: 300; + line-height: 1.2; +} + +.display-2 { + font-size: 5.5rem; + font-weight: 300; + line-height: 1.2; +} + +.display-3 { + font-size: 4.5rem; + font-weight: 300; + line-height: 1.2; +} + +.display-4 { + font-size: 3.5rem; + font-weight: 300; + line-height: 1.2; +} + +hr { + margin-top: 1rem; + margin-bottom: 1rem; + border: 0; + border-top: 1px solid rgba(0, 0, 0, 0.1); +} + +small, +.small { + font-size: 80%; + font-weight: 400; +} + +mark, +.mark { + padding: 0.2em; + background-color: #fcf8e3; +} + +.list-unstyled { + padding-left: 0; + list-style: none; +} + +.list-inline { + padding-left: 0; + list-style: none; +} + +.list-inline-item { + display: inline-block; +} +.list-inline-item:not(:last-child) { + margin-right: 0.5rem; +} + +.initialism { + font-size: 90%; + text-transform: uppercase; +} + +.blockquote { + margin-bottom: 1rem; + font-size: 1.25rem; +} + +.blockquote-footer { + display: block; + font-size: 80%; + color: #6c757d; +} +.blockquote-footer::before { + content: "\2014 \00A0"; +} + +.img-fluid { + max-width: 100%; + height: auto; +} + +.img-thumbnail { + padding: 0.25rem; + background-color: #fff; + border: 1px solid #dee2e6; + border-radius: 0.25rem; + max-width: 100%; + height: auto; +} + +.figure { + display: inline-block; +} + +.figure-img { + margin-bottom: 0.5rem; + line-height: 1; +} + +.figure-caption { + font-size: 90%; + color: #6c757d; +} + +code, +kbd, +pre, +samp { + font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +code { + font-size: 87.5%; + color: #e83e8c; + word-break: break-word; +} +a > code { + color: inherit; +} + +kbd { + padding: 0.2rem 0.4rem; + font-size: 87.5%; + color: #fff; + background-color: #212529; + border-radius: 0.2rem; +} +kbd kbd { + padding: 0; + font-size: 100%; + font-weight: 700; +} + +pre { + display: block; + font-size: 87.5%; + color: #212529; +} +pre code { + font-size: inherit; + color: inherit; + word-break: normal; +} + +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} + +.container { + width: 100%; + padding-right: 15px; + padding-left: 15px; + margin-right: auto; + margin-left: auto; +} +@media (min-width: 576px) { + .container { + max-width: 540px; + } +} +@media (min-width: 768px) { + .container { + max-width: 720px; + } +} +@media (min-width: 992px) { + .container { + max-width: 960px; + } +} +@media (min-width: 1200px) { + .container { + max-width: 1140px; + } +} + +.container-fluid { + width: 100%; + padding-right: 15px; + padding-left: 15px; + margin-right: auto; + margin-left: auto; +} + +.row { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + margin-right: -15px; + margin-left: -15px; +} + +.no-gutters { + margin-right: 0; + margin-left: 0; +} +.no-gutters > .col, +.no-gutters > [class*="col-"] { + padding-right: 0; + padding-left: 0; +} + +.col-1, .col-2, .col-3, .col-4, .col-5, .col-6, .col-7, .col-8, .col-9, .col-10, .col-11, .col-12, .col, +.col-auto, .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12, .col-sm, +.col-sm-auto, .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12, .col-md, +.col-md-auto, .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12, .col-lg, +.col-lg-auto, .col-xl-1, .col-xl-2, .col-xl-3, .col-xl-4, .col-xl-5, .col-xl-6, .col-xl-7, .col-xl-8, .col-xl-9, .col-xl-10, .col-xl-11, .col-xl-12, .col-xl, +.col-xl-auto { + position: relative; + width: 100%; + min-height: 1px; + padding-right: 15px; + padding-left: 15px; +} + +.col { + -ms-flex-preferred-size: 0; + flex-basis: 0; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + max-width: 100%; +} + +.col-auto { + -webkit-box-flex: 0; + -ms-flex: 0 0 auto; + flex: 0 0 auto; + width: auto; + max-width: none; +} + +.col-1 { + -webkit-box-flex: 0; + -ms-flex: 0 0 8.3333333333%; + flex: 0 0 8.3333333333%; + max-width: 8.3333333333%; +} + +.col-2 { + -webkit-box-flex: 0; + -ms-flex: 0 0 16.6666666667%; + flex: 0 0 16.6666666667%; + max-width: 16.6666666667%; +} + +.col-3 { + -webkit-box-flex: 0; + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25%; +} + +.col-4 { + -webkit-box-flex: 0; + -ms-flex: 0 0 33.3333333333%; + flex: 0 0 33.3333333333%; + max-width: 33.3333333333%; +} + +.col-5 { + -webkit-box-flex: 0; + -ms-flex: 0 0 41.6666666667%; + flex: 0 0 41.6666666667%; + max-width: 41.6666666667%; +} + +.col-6 { + -webkit-box-flex: 0; + -ms-flex: 0 0 50%; + flex: 0 0 50%; + max-width: 50%; +} + +.col-7 { + -webkit-box-flex: 0; + -ms-flex: 0 0 58.3333333333%; + flex: 0 0 58.3333333333%; + max-width: 58.3333333333%; +} + +.col-8 { + -webkit-box-flex: 0; + -ms-flex: 0 0 66.6666666667%; + flex: 0 0 66.6666666667%; + max-width: 66.6666666667%; +} + +.col-9 { + -webkit-box-flex: 0; + -ms-flex: 0 0 75%; + flex: 0 0 75%; + max-width: 75%; +} + +.col-10 { + -webkit-box-flex: 0; + -ms-flex: 0 0 83.3333333333%; + flex: 0 0 83.3333333333%; + max-width: 83.3333333333%; +} + +.col-11 { + -webkit-box-flex: 0; + -ms-flex: 0 0 91.6666666667%; + flex: 0 0 91.6666666667%; + max-width: 91.6666666667%; +} + +.col-12 { + -webkit-box-flex: 0; + -ms-flex: 0 0 100%; + flex: 0 0 100%; + max-width: 100%; +} + +.order-first { + -webkit-box-ordinal-group: 0; + -ms-flex-order: -1; + order: -1; +} + +.order-last { + -webkit-box-ordinal-group: 14; + -ms-flex-order: 13; + order: 13; +} + +.order-0 { + -webkit-box-ordinal-group: 1; + -ms-flex-order: 0; + order: 0; +} + +.order-1 { + -webkit-box-ordinal-group: 2; + -ms-flex-order: 1; + order: 1; +} + +.order-2 { + -webkit-box-ordinal-group: 3; + -ms-flex-order: 2; + order: 2; +} + +.order-3 { + -webkit-box-ordinal-group: 4; + -ms-flex-order: 3; + order: 3; +} + +.order-4 { + -webkit-box-ordinal-group: 5; + -ms-flex-order: 4; + order: 4; +} + +.order-5 { + -webkit-box-ordinal-group: 6; + -ms-flex-order: 5; + order: 5; +} + +.order-6 { + -webkit-box-ordinal-group: 7; + -ms-flex-order: 6; + order: 6; +} + +.order-7 { + -webkit-box-ordinal-group: 8; + -ms-flex-order: 7; + order: 7; +} + +.order-8 { + -webkit-box-ordinal-group: 9; + -ms-flex-order: 8; + order: 8; +} + +.order-9 { + -webkit-box-ordinal-group: 10; + -ms-flex-order: 9; + order: 9; +} + +.order-10 { + -webkit-box-ordinal-group: 11; + -ms-flex-order: 10; + order: 10; +} + +.order-11 { + -webkit-box-ordinal-group: 12; + -ms-flex-order: 11; + order: 11; +} + +.order-12 { + -webkit-box-ordinal-group: 13; + -ms-flex-order: 12; + order: 12; +} + +.offset-1 { + margin-left: 8.3333333333%; +} + +.offset-2 { + margin-left: 16.6666666667%; +} + +.offset-3 { + margin-left: 25%; +} + +.offset-4 { + margin-left: 33.3333333333%; +} + +.offset-5 { + margin-left: 41.6666666667%; +} + +.offset-6 { + margin-left: 50%; +} + +.offset-7 { + margin-left: 58.3333333333%; +} + +.offset-8 { + margin-left: 66.6666666667%; +} + +.offset-9 { + margin-left: 75%; +} + +.offset-10 { + margin-left: 83.3333333333%; +} + +.offset-11 { + margin-left: 91.6666666667%; +} + +@media (min-width: 576px) { + .col-sm { + -ms-flex-preferred-size: 0; + flex-basis: 0; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + max-width: 100%; + } + + .col-sm-auto { + -webkit-box-flex: 0; + -ms-flex: 0 0 auto; + flex: 0 0 auto; + width: auto; + max-width: none; + } + + .col-sm-1 { + -webkit-box-flex: 0; + -ms-flex: 0 0 8.3333333333%; + flex: 0 0 8.3333333333%; + max-width: 8.3333333333%; + } + + .col-sm-2 { + -webkit-box-flex: 0; + -ms-flex: 0 0 16.6666666667%; + flex: 0 0 16.6666666667%; + max-width: 16.6666666667%; + } + + .col-sm-3 { + -webkit-box-flex: 0; + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25%; + } + + .col-sm-4 { + -webkit-box-flex: 0; + -ms-flex: 0 0 33.3333333333%; + flex: 0 0 33.3333333333%; + max-width: 33.3333333333%; + } + + .col-sm-5 { + -webkit-box-flex: 0; + -ms-flex: 0 0 41.6666666667%; + flex: 0 0 41.6666666667%; + max-width: 41.6666666667%; + } + + .col-sm-6 { + -webkit-box-flex: 0; + -ms-flex: 0 0 50%; + flex: 0 0 50%; + max-width: 50%; + } + + .col-sm-7 { + -webkit-box-flex: 0; + -ms-flex: 0 0 58.3333333333%; + flex: 0 0 58.3333333333%; + max-width: 58.3333333333%; + } + + .col-sm-8 { + -webkit-box-flex: 0; + -ms-flex: 0 0 66.6666666667%; + flex: 0 0 66.6666666667%; + max-width: 66.6666666667%; + } + + .col-sm-9 { + -webkit-box-flex: 0; + -ms-flex: 0 0 75%; + flex: 0 0 75%; + max-width: 75%; + } + + .col-sm-10 { + -webkit-box-flex: 0; + -ms-flex: 0 0 83.3333333333%; + flex: 0 0 83.3333333333%; + max-width: 83.3333333333%; + } + + .col-sm-11 { + -webkit-box-flex: 0; + -ms-flex: 0 0 91.6666666667%; + flex: 0 0 91.6666666667%; + max-width: 91.6666666667%; + } + + .col-sm-12 { + -webkit-box-flex: 0; + -ms-flex: 0 0 100%; + flex: 0 0 100%; + max-width: 100%; + } + + .order-sm-first { + -webkit-box-ordinal-group: 0; + -ms-flex-order: -1; + order: -1; + } + + .order-sm-last { + -webkit-box-ordinal-group: 14; + -ms-flex-order: 13; + order: 13; + } + + .order-sm-0 { + -webkit-box-ordinal-group: 1; + -ms-flex-order: 0; + order: 0; + } + + .order-sm-1 { + -webkit-box-ordinal-group: 2; + -ms-flex-order: 1; + order: 1; + } + + .order-sm-2 { + -webkit-box-ordinal-group: 3; + -ms-flex-order: 2; + order: 2; + } + + .order-sm-3 { + -webkit-box-ordinal-group: 4; + -ms-flex-order: 3; + order: 3; + } + + .order-sm-4 { + -webkit-box-ordinal-group: 5; + -ms-flex-order: 4; + order: 4; + } + + .order-sm-5 { + -webkit-box-ordinal-group: 6; + -ms-flex-order: 5; + order: 5; + } + + .order-sm-6 { + -webkit-box-ordinal-group: 7; + -ms-flex-order: 6; + order: 6; + } + + .order-sm-7 { + -webkit-box-ordinal-group: 8; + -ms-flex-order: 7; + order: 7; + } + + .order-sm-8 { + -webkit-box-ordinal-group: 9; + -ms-flex-order: 8; + order: 8; + } + + .order-sm-9 { + -webkit-box-ordinal-group: 10; + -ms-flex-order: 9; + order: 9; + } + + .order-sm-10 { + -webkit-box-ordinal-group: 11; + -ms-flex-order: 10; + order: 10; + } + + .order-sm-11 { + -webkit-box-ordinal-group: 12; + -ms-flex-order: 11; + order: 11; + } + + .order-sm-12 { + -webkit-box-ordinal-group: 13; + -ms-flex-order: 12; + order: 12; + } + + .offset-sm-0 { + margin-left: 0; + } + + .offset-sm-1 { + margin-left: 8.3333333333%; + } + + .offset-sm-2 { + margin-left: 16.6666666667%; + } + + .offset-sm-3 { + margin-left: 25%; + } + + .offset-sm-4 { + margin-left: 33.3333333333%; + } + + .offset-sm-5 { + margin-left: 41.6666666667%; + } + + .offset-sm-6 { + margin-left: 50%; + } + + .offset-sm-7 { + margin-left: 58.3333333333%; + } + + .offset-sm-8 { + margin-left: 66.6666666667%; + } + + .offset-sm-9 { + margin-left: 75%; + } + + .offset-sm-10 { + margin-left: 83.3333333333%; + } + + .offset-sm-11 { + margin-left: 91.6666666667%; + } +} +@media (min-width: 768px) { + .col-md { + -ms-flex-preferred-size: 0; + flex-basis: 0; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + max-width: 100%; + } + + .col-md-auto { + -webkit-box-flex: 0; + -ms-flex: 0 0 auto; + flex: 0 0 auto; + width: auto; + max-width: none; + } + + .col-md-1 { + -webkit-box-flex: 0; + -ms-flex: 0 0 8.3333333333%; + flex: 0 0 8.3333333333%; + max-width: 8.3333333333%; + } + + .col-md-2 { + -webkit-box-flex: 0; + -ms-flex: 0 0 16.6666666667%; + flex: 0 0 16.6666666667%; + max-width: 16.6666666667%; + } + + .col-md-3 { + -webkit-box-flex: 0; + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25%; + } + + .col-md-4 { + -webkit-box-flex: 0; + -ms-flex: 0 0 33.3333333333%; + flex: 0 0 33.3333333333%; + max-width: 33.3333333333%; + } + + .col-md-5 { + -webkit-box-flex: 0; + -ms-flex: 0 0 41.6666666667%; + flex: 0 0 41.6666666667%; + max-width: 41.6666666667%; + } + + .col-md-6 { + -webkit-box-flex: 0; + -ms-flex: 0 0 50%; + flex: 0 0 50%; + max-width: 50%; + } + + .col-md-7 { + -webkit-box-flex: 0; + -ms-flex: 0 0 58.3333333333%; + flex: 0 0 58.3333333333%; + max-width: 58.3333333333%; + } + + .col-md-8 { + -webkit-box-flex: 0; + -ms-flex: 0 0 66.6666666667%; + flex: 0 0 66.6666666667%; + max-width: 66.6666666667%; + } + + .col-md-9 { + -webkit-box-flex: 0; + -ms-flex: 0 0 75%; + flex: 0 0 75%; + max-width: 75%; + } + + .col-md-10 { + -webkit-box-flex: 0; + -ms-flex: 0 0 83.3333333333%; + flex: 0 0 83.3333333333%; + max-width: 83.3333333333%; + } + + .col-md-11 { + -webkit-box-flex: 0; + -ms-flex: 0 0 91.6666666667%; + flex: 0 0 91.6666666667%; + max-width: 91.6666666667%; + } + + .col-md-12 { + -webkit-box-flex: 0; + -ms-flex: 0 0 100%; + flex: 0 0 100%; + max-width: 100%; + } + + .order-md-first { + -webkit-box-ordinal-group: 0; + -ms-flex-order: -1; + order: -1; + } + + .order-md-last { + -webkit-box-ordinal-group: 14; + -ms-flex-order: 13; + order: 13; + } + + .order-md-0 { + -webkit-box-ordinal-group: 1; + -ms-flex-order: 0; + order: 0; + } + + .order-md-1 { + -webkit-box-ordinal-group: 2; + -ms-flex-order: 1; + order: 1; + } + + .order-md-2 { + -webkit-box-ordinal-group: 3; + -ms-flex-order: 2; + order: 2; + } + + .order-md-3 { + -webkit-box-ordinal-group: 4; + -ms-flex-order: 3; + order: 3; + } + + .order-md-4 { + -webkit-box-ordinal-group: 5; + -ms-flex-order: 4; + order: 4; + } + + .order-md-5 { + -webkit-box-ordinal-group: 6; + -ms-flex-order: 5; + order: 5; + } + + .order-md-6 { + -webkit-box-ordinal-group: 7; + -ms-flex-order: 6; + order: 6; + } + + .order-md-7 { + -webkit-box-ordinal-group: 8; + -ms-flex-order: 7; + order: 7; + } + + .order-md-8 { + -webkit-box-ordinal-group: 9; + -ms-flex-order: 8; + order: 8; + } + + .order-md-9 { + -webkit-box-ordinal-group: 10; + -ms-flex-order: 9; + order: 9; + } + + .order-md-10 { + -webkit-box-ordinal-group: 11; + -ms-flex-order: 10; + order: 10; + } + + .order-md-11 { + -webkit-box-ordinal-group: 12; + -ms-flex-order: 11; + order: 11; + } + + .order-md-12 { + -webkit-box-ordinal-group: 13; + -ms-flex-order: 12; + order: 12; + } + + .offset-md-0 { + margin-left: 0; + } + + .offset-md-1 { + margin-left: 8.3333333333%; + } + + .offset-md-2 { + margin-left: 16.6666666667%; + } + + .offset-md-3 { + margin-left: 25%; + } + + .offset-md-4 { + margin-left: 33.3333333333%; + } + + .offset-md-5 { + margin-left: 41.6666666667%; + } + + .offset-md-6 { + margin-left: 50%; + } + + .offset-md-7 { + margin-left: 58.3333333333%; + } + + .offset-md-8 { + margin-left: 66.6666666667%; + } + + .offset-md-9 { + margin-left: 75%; + } + + .offset-md-10 { + margin-left: 83.3333333333%; + } + + .offset-md-11 { + margin-left: 91.6666666667%; + } +} +@media (min-width: 992px) { + .col-lg { + -ms-flex-preferred-size: 0; + flex-basis: 0; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + max-width: 100%; + } + + .col-lg-auto { + -webkit-box-flex: 0; + -ms-flex: 0 0 auto; + flex: 0 0 auto; + width: auto; + max-width: none; + } + + .col-lg-1 { + -webkit-box-flex: 0; + -ms-flex: 0 0 8.3333333333%; + flex: 0 0 8.3333333333%; + max-width: 8.3333333333%; + } + + .col-lg-2 { + -webkit-box-flex: 0; + -ms-flex: 0 0 16.6666666667%; + flex: 0 0 16.6666666667%; + max-width: 16.6666666667%; + } + + .col-lg-3 { + -webkit-box-flex: 0; + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25%; + } + + .col-lg-4 { + -webkit-box-flex: 0; + -ms-flex: 0 0 33.3333333333%; + flex: 0 0 33.3333333333%; + max-width: 33.3333333333%; + } + + .col-lg-5 { + -webkit-box-flex: 0; + -ms-flex: 0 0 41.6666666667%; + flex: 0 0 41.6666666667%; + max-width: 41.6666666667%; + } + + .col-lg-6 { + -webkit-box-flex: 0; + -ms-flex: 0 0 50%; + flex: 0 0 50%; + max-width: 50%; + } + + .col-lg-7 { + -webkit-box-flex: 0; + -ms-flex: 0 0 58.3333333333%; + flex: 0 0 58.3333333333%; + max-width: 58.3333333333%; + } + + .col-lg-8 { + -webkit-box-flex: 0; + -ms-flex: 0 0 66.6666666667%; + flex: 0 0 66.6666666667%; + max-width: 66.6666666667%; + } + + .col-lg-9 { + -webkit-box-flex: 0; + -ms-flex: 0 0 75%; + flex: 0 0 75%; + max-width: 75%; + } + + .col-lg-10 { + -webkit-box-flex: 0; + -ms-flex: 0 0 83.3333333333%; + flex: 0 0 83.3333333333%; + max-width: 83.3333333333%; + } + + .col-lg-11 { + -webkit-box-flex: 0; + -ms-flex: 0 0 91.6666666667%; + flex: 0 0 91.6666666667%; + max-width: 91.6666666667%; + } + + .col-lg-12 { + -webkit-box-flex: 0; + -ms-flex: 0 0 100%; + flex: 0 0 100%; + max-width: 100%; + } + + .order-lg-first { + -webkit-box-ordinal-group: 0; + -ms-flex-order: -1; + order: -1; + } + + .order-lg-last { + -webkit-box-ordinal-group: 14; + -ms-flex-order: 13; + order: 13; + } + + .order-lg-0 { + -webkit-box-ordinal-group: 1; + -ms-flex-order: 0; + order: 0; + } + + .order-lg-1 { + -webkit-box-ordinal-group: 2; + -ms-flex-order: 1; + order: 1; + } + + .order-lg-2 { + -webkit-box-ordinal-group: 3; + -ms-flex-order: 2; + order: 2; + } + + .order-lg-3 { + -webkit-box-ordinal-group: 4; + -ms-flex-order: 3; + order: 3; + } + + .order-lg-4 { + -webkit-box-ordinal-group: 5; + -ms-flex-order: 4; + order: 4; + } + + .order-lg-5 { + -webkit-box-ordinal-group: 6; + -ms-flex-order: 5; + order: 5; + } + + .order-lg-6 { + -webkit-box-ordinal-group: 7; + -ms-flex-order: 6; + order: 6; + } + + .order-lg-7 { + -webkit-box-ordinal-group: 8; + -ms-flex-order: 7; + order: 7; + } + + .order-lg-8 { + -webkit-box-ordinal-group: 9; + -ms-flex-order: 8; + order: 8; + } + + .order-lg-9 { + -webkit-box-ordinal-group: 10; + -ms-flex-order: 9; + order: 9; + } + + .order-lg-10 { + -webkit-box-ordinal-group: 11; + -ms-flex-order: 10; + order: 10; + } + + .order-lg-11 { + -webkit-box-ordinal-group: 12; + -ms-flex-order: 11; + order: 11; + } + + .order-lg-12 { + -webkit-box-ordinal-group: 13; + -ms-flex-order: 12; + order: 12; + } + + .offset-lg-0 { + margin-left: 0; + } + + .offset-lg-1 { + margin-left: 8.3333333333%; + } + + .offset-lg-2 { + margin-left: 16.6666666667%; + } + + .offset-lg-3 { + margin-left: 25%; + } + + .offset-lg-4 { + margin-left: 33.3333333333%; + } + + .offset-lg-5 { + margin-left: 41.6666666667%; + } + + .offset-lg-6 { + margin-left: 50%; + } + + .offset-lg-7 { + margin-left: 58.3333333333%; + } + + .offset-lg-8 { + margin-left: 66.6666666667%; + } + + .offset-lg-9 { + margin-left: 75%; + } + + .offset-lg-10 { + margin-left: 83.3333333333%; + } + + .offset-lg-11 { + margin-left: 91.6666666667%; + } +} +@media (min-width: 1200px) { + .col-xl { + -ms-flex-preferred-size: 0; + flex-basis: 0; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + max-width: 100%; + } + + .col-xl-auto { + -webkit-box-flex: 0; + -ms-flex: 0 0 auto; + flex: 0 0 auto; + width: auto; + max-width: none; + } + + .col-xl-1 { + -webkit-box-flex: 0; + -ms-flex: 0 0 8.3333333333%; + flex: 0 0 8.3333333333%; + max-width: 8.3333333333%; + } + + .col-xl-2 { + -webkit-box-flex: 0; + -ms-flex: 0 0 16.6666666667%; + flex: 0 0 16.6666666667%; + max-width: 16.6666666667%; + } + + .col-xl-3 { + -webkit-box-flex: 0; + -ms-flex: 0 0 25%; + flex: 0 0 25%; + max-width: 25%; + } + + .col-xl-4 { + -webkit-box-flex: 0; + -ms-flex: 0 0 33.3333333333%; + flex: 0 0 33.3333333333%; + max-width: 33.3333333333%; + } + + .col-xl-5 { + -webkit-box-flex: 0; + -ms-flex: 0 0 41.6666666667%; + flex: 0 0 41.6666666667%; + max-width: 41.6666666667%; + } + + .col-xl-6 { + -webkit-box-flex: 0; + -ms-flex: 0 0 50%; + flex: 0 0 50%; + max-width: 50%; + } + + .col-xl-7 { + -webkit-box-flex: 0; + -ms-flex: 0 0 58.3333333333%; + flex: 0 0 58.3333333333%; + max-width: 58.3333333333%; + } + + .col-xl-8 { + -webkit-box-flex: 0; + -ms-flex: 0 0 66.6666666667%; + flex: 0 0 66.6666666667%; + max-width: 66.6666666667%; + } + + .col-xl-9 { + -webkit-box-flex: 0; + -ms-flex: 0 0 75%; + flex: 0 0 75%; + max-width: 75%; + } + + .col-xl-10 { + -webkit-box-flex: 0; + -ms-flex: 0 0 83.3333333333%; + flex: 0 0 83.3333333333%; + max-width: 83.3333333333%; + } + + .col-xl-11 { + -webkit-box-flex: 0; + -ms-flex: 0 0 91.6666666667%; + flex: 0 0 91.6666666667%; + max-width: 91.6666666667%; + } + + .col-xl-12 { + -webkit-box-flex: 0; + -ms-flex: 0 0 100%; + flex: 0 0 100%; + max-width: 100%; + } + + .order-xl-first { + -webkit-box-ordinal-group: 0; + -ms-flex-order: -1; + order: -1; + } + + .order-xl-last { + -webkit-box-ordinal-group: 14; + -ms-flex-order: 13; + order: 13; + } + + .order-xl-0 { + -webkit-box-ordinal-group: 1; + -ms-flex-order: 0; + order: 0; + } + + .order-xl-1 { + -webkit-box-ordinal-group: 2; + -ms-flex-order: 1; + order: 1; + } + + .order-xl-2 { + -webkit-box-ordinal-group: 3; + -ms-flex-order: 2; + order: 2; + } + + .order-xl-3 { + -webkit-box-ordinal-group: 4; + -ms-flex-order: 3; + order: 3; + } + + .order-xl-4 { + -webkit-box-ordinal-group: 5; + -ms-flex-order: 4; + order: 4; + } + + .order-xl-5 { + -webkit-box-ordinal-group: 6; + -ms-flex-order: 5; + order: 5; + } + + .order-xl-6 { + -webkit-box-ordinal-group: 7; + -ms-flex-order: 6; + order: 6; + } + + .order-xl-7 { + -webkit-box-ordinal-group: 8; + -ms-flex-order: 7; + order: 7; + } + + .order-xl-8 { + -webkit-box-ordinal-group: 9; + -ms-flex-order: 8; + order: 8; + } + + .order-xl-9 { + -webkit-box-ordinal-group: 10; + -ms-flex-order: 9; + order: 9; + } + + .order-xl-10 { + -webkit-box-ordinal-group: 11; + -ms-flex-order: 10; + order: 10; + } + + .order-xl-11 { + -webkit-box-ordinal-group: 12; + -ms-flex-order: 11; + order: 11; + } + + .order-xl-12 { + -webkit-box-ordinal-group: 13; + -ms-flex-order: 12; + order: 12; + } + + .offset-xl-0 { + margin-left: 0; + } + + .offset-xl-1 { + margin-left: 8.3333333333%; + } + + .offset-xl-2 { + margin-left: 16.6666666667%; + } + + .offset-xl-3 { + margin-left: 25%; + } + + .offset-xl-4 { + margin-left: 33.3333333333%; + } + + .offset-xl-5 { + margin-left: 41.6666666667%; + } + + .offset-xl-6 { + margin-left: 50%; + } + + .offset-xl-7 { + margin-left: 58.3333333333%; + } + + .offset-xl-8 { + margin-left: 66.6666666667%; + } + + .offset-xl-9 { + margin-left: 75%; + } + + .offset-xl-10 { + margin-left: 83.3333333333%; + } + + .offset-xl-11 { + margin-left: 91.6666666667%; + } +} +.table { + width: 100%; + max-width: 100%; + margin-bottom: 1rem; + background-color: transparent; +} +.table th, +.table td { + padding: 0.75rem; + vertical-align: top; + border-top: 1px solid #dee2e6; +} +.table thead th { + vertical-align: bottom; + border-bottom: 2px solid #dee2e6; +} +.table tbody + tbody { + border-top: 2px solid #dee2e6; +} +.table .table { + background-color: #fff; +} + +.table-sm th, +.table-sm td { + padding: 0.3rem; +} + +.table-bordered { + border: 1px solid #dee2e6; +} +.table-bordered th, +.table-bordered td { + border: 1px solid #dee2e6; +} +.table-bordered thead th, +.table-bordered thead td { + border-bottom-width: 2px; +} + +.table-striped tbody tr:nth-of-type(odd) { + background-color: rgba(0, 0, 0, 0.05); +} + +.table-hover tbody tr:hover { + background-color: rgba(0, 0, 0, 0.075); +} + +.table-primary, +.table-primary > th, +.table-primary > td { + background-color: #b8daff; +} + +.table-hover .table-primary:hover { + background-color: #9fcdff; +} +.table-hover .table-primary:hover > td, +.table-hover .table-primary:hover > th { + background-color: #9fcdff; +} + +.table-secondary, +.table-secondary > th, +.table-secondary > td { + background-color: #d6d8db; +} + +.table-hover .table-secondary:hover { + background-color: #c8cbcf; +} +.table-hover .table-secondary:hover > td, +.table-hover .table-secondary:hover > th { + background-color: #c8cbcf; +} + +.table-success, +.table-success > th, +.table-success > td { + background-color: #c3e6cb; +} + +.table-hover .table-success:hover { + background-color: #b1dfbb; +} +.table-hover .table-success:hover > td, +.table-hover .table-success:hover > th { + background-color: #b1dfbb; +} + +.table-info, +.table-info > th, +.table-info > td { + background-color: #bee5eb; +} + +.table-hover .table-info:hover { + background-color: #abdde5; +} +.table-hover .table-info:hover > td, +.table-hover .table-info:hover > th { + background-color: #abdde5; +} + +.table-warning, +.table-warning > th, +.table-warning > td { + background-color: #ffeeba; +} + +.table-hover .table-warning:hover { + background-color: #ffe8a1; +} +.table-hover .table-warning:hover > td, +.table-hover .table-warning:hover > th { + background-color: #ffe8a1; +} + +.table-danger, +.table-danger > th, +.table-danger > td { + background-color: #f5c6cb; +} + +.table-hover .table-danger:hover { + background-color: #f1b0b7; +} +.table-hover .table-danger:hover > td, +.table-hover .table-danger:hover > th { + background-color: #f1b0b7; +} + +.table-light, +.table-light > th, +.table-light > td { + background-color: #fdfdfe; +} + +.table-hover .table-light:hover { + background-color: #ececf6; +} +.table-hover .table-light:hover > td, +.table-hover .table-light:hover > th { + background-color: #ececf6; +} + +.table-dark, +.table-dark > th, +.table-dark > td { + background-color: #c6c8ca; +} + +.table-hover .table-dark:hover { + background-color: #b9bbbe; +} +.table-hover .table-dark:hover > td, +.table-hover .table-dark:hover > th { + background-color: #b9bbbe; +} + +.table-active, +.table-active > th, +.table-active > td { + background-color: rgba(0, 0, 0, 0.075); +} + +.table-hover .table-active:hover { + background-color: rgba(0, 0, 0, 0.075); +} +.table-hover .table-active:hover > td, +.table-hover .table-active:hover > th { + background-color: rgba(0, 0, 0, 0.075); +} + +.table .thead-dark th { + color: #fff; + background-color: #212529; + border-color: #32383e; +} +.table .thead-light th { + color: #495057; + background-color: #e9ecef; + border-color: #dee2e6; +} + +.table-dark { + color: #fff; + background-color: #212529; +} +.table-dark th, +.table-dark td, +.table-dark thead th { + border-color: #32383e; +} +.table-dark.table-bordered { + border: 0; +} +.table-dark.table-striped tbody tr:nth-of-type(odd) { + background-color: rgba(255, 255, 255, 0.05); +} +.table-dark.table-hover tbody tr:hover { + background-color: rgba(255, 255, 255, 0.075); +} + +@media (max-width: 575.98px) { + .table-responsive-sm { + display: block; + width: 100%; + overflow-x: auto; + -webkit-overflow-scrolling: touch; + -ms-overflow-style: -ms-autohiding-scrollbar; + } + .table-responsive-sm > .table-bordered { + border: 0; + } +} +@media (max-width: 767.98px) { + .table-responsive-md { + display: block; + width: 100%; + overflow-x: auto; + -webkit-overflow-scrolling: touch; + -ms-overflow-style: -ms-autohiding-scrollbar; + } + .table-responsive-md > .table-bordered { + border: 0; + } +} +@media (max-width: 991.98px) { + .table-responsive-lg { + display: block; + width: 100%; + overflow-x: auto; + -webkit-overflow-scrolling: touch; + -ms-overflow-style: -ms-autohiding-scrollbar; + } + .table-responsive-lg > .table-bordered { + border: 0; + } +} +@media (max-width: 1199.98px) { + .table-responsive-xl { + display: block; + width: 100%; + overflow-x: auto; + -webkit-overflow-scrolling: touch; + -ms-overflow-style: -ms-autohiding-scrollbar; + } + .table-responsive-xl > .table-bordered { + border: 0; + } +} +.table-responsive { + display: block; + width: 100%; + overflow-x: auto; + -webkit-overflow-scrolling: touch; + -ms-overflow-style: -ms-autohiding-scrollbar; +} +.table-responsive > .table-bordered { + border: 0; +} + +.form-control { + display: block; + width: 100%; + padding: 0.375rem 0.75rem; + font-size: 1rem; + line-height: 1.5; + color: #495057; + background-color: #fff; + background-clip: padding-box; + border: 1px solid #ced4da; + border-radius: 0.25rem; + -webkit-transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; + transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; + transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; + transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; +} +.form-control::-ms-expand { + background-color: transparent; + border: 0; +} +.form-control:focus { + color: #495057; + background-color: #fff; + border-color: #80bdff; + outline: 0; + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); +} +.form-control::-webkit-input-placeholder { + color: #6c757d; + opacity: 1; +} +.form-control::-moz-placeholder { + color: #6c757d; + opacity: 1; +} +.form-control:-ms-input-placeholder { + color: #6c757d; + opacity: 1; +} +.form-control::-ms-input-placeholder { + color: #6c757d; + opacity: 1; +} +.form-control::placeholder { + color: #6c757d; + opacity: 1; +} +.form-control:disabled, .form-control[readonly] { + background-color: #e9ecef; + opacity: 1; +} + +select.form-control:not([size]):not([multiple]) { + height: calc(2.25rem + 2px); +} +select.form-control:focus::-ms-value { + color: #495057; + background-color: #fff; +} + +.form-control-file, +.form-control-range { + display: block; + width: 100%; +} + +.col-form-label { + padding-top: calc(0.375rem + 1px); + padding-bottom: calc(0.375rem + 1px); + margin-bottom: 0; + font-size: inherit; + line-height: 1.5; +} + +.col-form-label-lg { + padding-top: calc(0.5rem + 1px); + padding-bottom: calc(0.5rem + 1px); + font-size: 1.25rem; + line-height: 1.5; +} + +.col-form-label-sm { + padding-top: calc(0.25rem + 1px); + padding-bottom: calc(0.25rem + 1px); + font-size: 0.875rem; + line-height: 1.5; +} + +.form-control-plaintext { + display: block; + width: 100%; + padding-top: 0.375rem; + padding-bottom: 0.375rem; + margin-bottom: 0; + line-height: 1.5; + background-color: transparent; + border: solid transparent; + border-width: 1px 0; +} +.form-control-plaintext.form-control-sm, .input-group-sm > .form-control-plaintext.form-control, +.input-group-sm > .input-group-prepend > .form-control-plaintext.input-group-text, +.input-group-sm > .input-group-append > .form-control-plaintext.input-group-text, +.input-group-sm > .input-group-prepend > .form-control-plaintext.btn, +.input-group-sm > .input-group-append > .form-control-plaintext.btn, .form-control-plaintext.form-control-lg, .input-group-lg > .form-control-plaintext.form-control, +.input-group-lg > .input-group-prepend > .form-control-plaintext.input-group-text, +.input-group-lg > .input-group-append > .form-control-plaintext.input-group-text, +.input-group-lg > .input-group-prepend > .form-control-plaintext.btn, +.input-group-lg > .input-group-append > .form-control-plaintext.btn { + padding-right: 0; + padding-left: 0; +} + +.form-control-sm, .input-group-sm > .form-control, +.input-group-sm > .input-group-prepend > .input-group-text, +.input-group-sm > .input-group-append > .input-group-text, +.input-group-sm > .input-group-prepend > .btn, +.input-group-sm > .input-group-append > .btn { + padding: 0.25rem 0.5rem; + font-size: 0.875rem; + line-height: 1.5; + border-radius: 0.2rem; +} + +select.form-control-sm:not([size]):not([multiple]), .input-group-sm > select.form-control:not([size]):not([multiple]), +.input-group-sm > .input-group-prepend > select.input-group-text:not([size]):not([multiple]), +.input-group-sm > .input-group-append > select.input-group-text:not([size]):not([multiple]), +.input-group-sm > .input-group-prepend > select.btn:not([size]):not([multiple]), +.input-group-sm > .input-group-append > select.btn:not([size]):not([multiple]) { + height: calc(1.8125rem + 2px); +} + +.form-control-lg, .input-group-lg > .form-control, +.input-group-lg > .input-group-prepend > .input-group-text, +.input-group-lg > .input-group-append > .input-group-text, +.input-group-lg > .input-group-prepend > .btn, +.input-group-lg > .input-group-append > .btn { + padding: 0.5rem 1rem; + font-size: 1.25rem; + line-height: 1.5; + border-radius: 0.3rem; +} + +select.form-control-lg:not([size]):not([multiple]), .input-group-lg > select.form-control:not([size]):not([multiple]), +.input-group-lg > .input-group-prepend > select.input-group-text:not([size]):not([multiple]), +.input-group-lg > .input-group-append > select.input-group-text:not([size]):not([multiple]), +.input-group-lg > .input-group-prepend > select.btn:not([size]):not([multiple]), +.input-group-lg > .input-group-append > select.btn:not([size]):not([multiple]) { + height: calc(2.875rem + 2px); +} + +.form-group { + margin-bottom: 1rem; +} + +.form-text { + display: block; + margin-top: 0.25rem; +} + +.form-row { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + margin-right: -5px; + margin-left: -5px; +} +.form-row > .col, +.form-row > [class*="col-"] { + padding-right: 5px; + padding-left: 5px; +} + +.form-check { + position: relative; + display: block; + padding-left: 1.25rem; +} + +.form-check-input { + position: absolute; + margin-top: 0.3rem; + margin-left: -1.25rem; +} +.form-check-input:disabled ~ .form-check-label { + color: #6c757d; +} + +.form-check-label { + margin-bottom: 0; +} + +.form-check-inline { + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + padding-left: 0; + margin-right: 0.75rem; +} +.form-check-inline .form-check-input { + position: static; + margin-top: 0; + margin-right: 0.3125rem; + margin-left: 0; +} + +.valid-feedback { + display: none; + width: 100%; + margin-top: 0.25rem; + font-size: 80%; + color: #28a745; +} + +.valid-tooltip { + position: absolute; + top: 100%; + z-index: 5; + display: none; + max-width: 100%; + padding: .5rem; + margin-top: .1rem; + font-size: .875rem; + line-height: 1; + color: #fff; + background-color: rgba(40, 167, 69, 0.8); + border-radius: .2rem; +} + +.was-validated .form-control:valid, .form-control.is-valid, +.was-validated .custom-select:valid, +.custom-select.is-valid { + border-color: #28a745; +} +.was-validated .form-control:valid:focus, .form-control.is-valid:focus, +.was-validated .custom-select:valid:focus, +.custom-select.is-valid:focus { + border-color: #28a745; + -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); + box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); +} +.was-validated .form-control:valid ~ .valid-feedback, +.was-validated .form-control:valid ~ .valid-tooltip, .form-control.is-valid ~ .valid-feedback, +.form-control.is-valid ~ .valid-tooltip, +.was-validated .custom-select:valid ~ .valid-feedback, +.was-validated .custom-select:valid ~ .valid-tooltip, +.custom-select.is-valid ~ .valid-feedback, +.custom-select.is-valid ~ .valid-tooltip { + display: block; +} + +.was-validated .form-check-input:valid ~ .form-check-label, .form-check-input.is-valid ~ .form-check-label { + color: #28a745; +} +.was-validated .form-check-input:valid ~ .valid-feedback, +.was-validated .form-check-input:valid ~ .valid-tooltip, .form-check-input.is-valid ~ .valid-feedback, +.form-check-input.is-valid ~ .valid-tooltip { + display: block; +} + +.was-validated .custom-control-input:valid ~ .custom-control-label, .custom-control-input.is-valid ~ .custom-control-label { + color: #28a745; +} +.was-validated .custom-control-input:valid ~ .custom-control-label::before, .custom-control-input.is-valid ~ .custom-control-label::before { + background-color: #71dd8a; +} +.was-validated .custom-control-input:valid ~ .valid-feedback, +.was-validated .custom-control-input:valid ~ .valid-tooltip, .custom-control-input.is-valid ~ .valid-feedback, +.custom-control-input.is-valid ~ .valid-tooltip { + display: block; +} +.was-validated .custom-control-input:valid:checked ~ .custom-control-label::before, .custom-control-input.is-valid:checked ~ .custom-control-label::before { + background-color: #34ce57; +} +.was-validated .custom-control-input:valid:focus ~ .custom-control-label::before, .custom-control-input.is-valid:focus ~ .custom-control-label::before { + -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(40, 167, 69, 0.25); + box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(40, 167, 69, 0.25); +} + +.was-validated .custom-file-input:valid ~ .custom-file-label, .custom-file-input.is-valid ~ .custom-file-label { + border-color: #28a745; +} +.was-validated .custom-file-input:valid ~ .custom-file-label::before, .custom-file-input.is-valid ~ .custom-file-label::before { + border-color: inherit; +} +.was-validated .custom-file-input:valid ~ .valid-feedback, +.was-validated .custom-file-input:valid ~ .valid-tooltip, .custom-file-input.is-valid ~ .valid-feedback, +.custom-file-input.is-valid ~ .valid-tooltip { + display: block; +} +.was-validated .custom-file-input:valid:focus ~ .custom-file-label, .custom-file-input.is-valid:focus ~ .custom-file-label { + -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); + box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25); +} + +.invalid-feedback { + display: none; + width: 100%; + margin-top: 0.25rem; + font-size: 80%; + color: #dc3545; +} + +.invalid-tooltip { + position: absolute; + top: 100%; + z-index: 5; + display: none; + max-width: 100%; + padding: .5rem; + margin-top: .1rem; + font-size: .875rem; + line-height: 1; + color: #fff; + background-color: rgba(220, 53, 69, 0.8); + border-radius: .2rem; +} + +.was-validated .form-control:invalid, .form-control.is-invalid, +.was-validated .custom-select:invalid, +.custom-select.is-invalid { + border-color: #dc3545; +} +.was-validated .form-control:invalid:focus, .form-control.is-invalid:focus, +.was-validated .custom-select:invalid:focus, +.custom-select.is-invalid:focus { + border-color: #dc3545; + -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); + box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); +} +.was-validated .form-control:invalid ~ .invalid-feedback, +.was-validated .form-control:invalid ~ .invalid-tooltip, .form-control.is-invalid ~ .invalid-feedback, +.form-control.is-invalid ~ .invalid-tooltip, +.was-validated .custom-select:invalid ~ .invalid-feedback, +.was-validated .custom-select:invalid ~ .invalid-tooltip, +.custom-select.is-invalid ~ .invalid-feedback, +.custom-select.is-invalid ~ .invalid-tooltip { + display: block; +} + +.was-validated .form-check-input:invalid ~ .form-check-label, .form-check-input.is-invalid ~ .form-check-label { + color: #dc3545; +} +.was-validated .form-check-input:invalid ~ .invalid-feedback, +.was-validated .form-check-input:invalid ~ .invalid-tooltip, .form-check-input.is-invalid ~ .invalid-feedback, +.form-check-input.is-invalid ~ .invalid-tooltip { + display: block; +} + +.was-validated .custom-control-input:invalid ~ .custom-control-label, .custom-control-input.is-invalid ~ .custom-control-label { + color: #dc3545; +} +.was-validated .custom-control-input:invalid ~ .custom-control-label::before, .custom-control-input.is-invalid ~ .custom-control-label::before { + background-color: #efa2a9; +} +.was-validated .custom-control-input:invalid ~ .invalid-feedback, +.was-validated .custom-control-input:invalid ~ .invalid-tooltip, .custom-control-input.is-invalid ~ .invalid-feedback, +.custom-control-input.is-invalid ~ .invalid-tooltip { + display: block; +} +.was-validated .custom-control-input:invalid:checked ~ .custom-control-label::before, .custom-control-input.is-invalid:checked ~ .custom-control-label::before { + background-color: #e4606d; +} +.was-validated .custom-control-input:invalid:focus ~ .custom-control-label::before, .custom-control-input.is-invalid:focus ~ .custom-control-label::before { + -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(220, 53, 69, 0.25); + box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(220, 53, 69, 0.25); +} + +.was-validated .custom-file-input:invalid ~ .custom-file-label, .custom-file-input.is-invalid ~ .custom-file-label { + border-color: #dc3545; +} +.was-validated .custom-file-input:invalid ~ .custom-file-label::before, .custom-file-input.is-invalid ~ .custom-file-label::before { + border-color: inherit; +} +.was-validated .custom-file-input:invalid ~ .invalid-feedback, +.was-validated .custom-file-input:invalid ~ .invalid-tooltip, .custom-file-input.is-invalid ~ .invalid-feedback, +.custom-file-input.is-invalid ~ .invalid-tooltip { + display: block; +} +.was-validated .custom-file-input:invalid:focus ~ .custom-file-label, .custom-file-input.is-invalid:focus ~ .custom-file-label { + -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); + box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25); +} + +.form-inline { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row wrap; + flex-flow: row wrap; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; +} +.form-inline .form-check { + width: 100%; +} +@media (min-width: 576px) { + .form-inline label { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + margin-bottom: 0; + } + .form-inline .form-group { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-flex: 0; + -ms-flex: 0 0 auto; + flex: 0 0 auto; + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row wrap; + flex-flow: row wrap; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + margin-bottom: 0; + } + .form-inline .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .form-inline .form-control-plaintext { + display: inline-block; + } + .form-inline .input-group { + width: auto; + } + .form-inline .form-check { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + width: auto; + padding-left: 0; + } + .form-inline .form-check-input { + position: relative; + margin-top: 0; + margin-right: 0.25rem; + margin-left: 0; + } + .form-inline .custom-control { + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + } + .form-inline .custom-control-label { + margin-bottom: 0; + } +} + +.btn { + display: inline-block; + font-weight: 400; + text-align: center; + white-space: nowrap; + vertical-align: middle; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + border: 1px solid transparent; + padding: 0.375rem 0.75rem; + font-size: 1rem; + line-height: 1.5; + border-radius: 0.25rem; + -webkit-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; + transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; + transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out; + transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out; +} +.btn:hover, .btn:focus { + text-decoration: none; +} +.btn:focus, .btn.focus { + outline: 0; + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); +} +.btn.disabled, .btn:disabled { + opacity: 0.65; +} +.btn:not(:disabled):not(.disabled) { + cursor: pointer; +} +.btn:not(:disabled):not(.disabled):active, .btn:not(:disabled):not(.disabled).active { + background-image: none; +} + +a.btn.disabled, +fieldset:disabled a.btn { + pointer-events: none; +} + +.btn-primary { + color: #fff; + background-color: #007bff; + border-color: #007bff; +} +.btn-primary:hover { + color: #fff; + background-color: #0069d9; + border-color: #0062cc; +} +.btn-primary:focus, .btn-primary.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); +} +.btn-primary.disabled, .btn-primary:disabled { + color: #fff; + background-color: #007bff; + border-color: #007bff; +} +.btn-primary:not(:disabled):not(.disabled):active, .btn-primary:not(:disabled):not(.disabled).active, .show > .btn-primary.dropdown-toggle { + color: #fff; + background-color: #0062cc; + border-color: #005cbf; +} +.btn-primary:not(:disabled):not(.disabled):active:focus, .btn-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-primary.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); +} + +.btn-secondary { + color: #fff; + background-color: #6c757d; + border-color: #6c757d; +} +.btn-secondary:hover { + color: #fff; + background-color: #5a6268; + border-color: #545b62; +} +.btn-secondary:focus, .btn-secondary.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); + box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); +} +.btn-secondary.disabled, .btn-secondary:disabled { + color: #fff; + background-color: #6c757d; + border-color: #6c757d; +} +.btn-secondary:not(:disabled):not(.disabled):active, .btn-secondary:not(:disabled):not(.disabled).active, .show > .btn-secondary.dropdown-toggle { + color: #fff; + background-color: #545b62; + border-color: #4e555b; +} +.btn-secondary:not(:disabled):not(.disabled):active:focus, .btn-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-secondary.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); + box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); +} + +.btn-success { + color: #fff; + background-color: #28a745; + border-color: #28a745; +} +.btn-success:hover { + color: #fff; + background-color: #218838; + border-color: #1e7e34; +} +.btn-success:focus, .btn-success.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); +} +.btn-success.disabled, .btn-success:disabled { + color: #fff; + background-color: #28a745; + border-color: #28a745; +} +.btn-success:not(:disabled):not(.disabled):active, .btn-success:not(:disabled):not(.disabled).active, .show > .btn-success.dropdown-toggle { + color: #fff; + background-color: #1e7e34; + border-color: #1c7430; +} +.btn-success:not(:disabled):not(.disabled):active:focus, .btn-success:not(:disabled):not(.disabled).active:focus, .show > .btn-success.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); +} + +.btn-info { + color: #fff; + background-color: #17a2b8; + border-color: #17a2b8; +} +.btn-info:hover { + color: #fff; + background-color: #138496; + border-color: #117a8b; +} +.btn-info:focus, .btn-info.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); + box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); +} +.btn-info.disabled, .btn-info:disabled { + color: #fff; + background-color: #17a2b8; + border-color: #17a2b8; +} +.btn-info:not(:disabled):not(.disabled):active, .btn-info:not(:disabled):not(.disabled).active, .show > .btn-info.dropdown-toggle { + color: #fff; + background-color: #117a8b; + border-color: #10707f; +} +.btn-info:not(:disabled):not(.disabled):active:focus, .btn-info:not(:disabled):not(.disabled).active:focus, .show > .btn-info.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); + box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); +} + +.btn-warning { + color: #212529; + background-color: #ffc107; + border-color: #ffc107; +} +.btn-warning:hover { + color: #212529; + background-color: #e0a800; + border-color: #d39e00; +} +.btn-warning:focus, .btn-warning.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); + box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); +} +.btn-warning.disabled, .btn-warning:disabled { + color: #212529; + background-color: #ffc107; + border-color: #ffc107; +} +.btn-warning:not(:disabled):not(.disabled):active, .btn-warning:not(:disabled):not(.disabled).active, .show > .btn-warning.dropdown-toggle { + color: #212529; + background-color: #d39e00; + border-color: #c69500; +} +.btn-warning:not(:disabled):not(.disabled):active:focus, .btn-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-warning.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); + box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); +} + +.btn-danger { + color: #fff; + background-color: #dc3545; + border-color: #dc3545; +} +.btn-danger:hover { + color: #fff; + background-color: #c82333; + border-color: #bd2130; +} +.btn-danger:focus, .btn-danger.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); +} +.btn-danger.disabled, .btn-danger:disabled { + color: #fff; + background-color: #dc3545; + border-color: #dc3545; +} +.btn-danger:not(:disabled):not(.disabled):active, .btn-danger:not(:disabled):not(.disabled).active, .show > .btn-danger.dropdown-toggle { + color: #fff; + background-color: #bd2130; + border-color: #b21f2d; +} +.btn-danger:not(:disabled):not(.disabled):active:focus, .btn-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-danger.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); +} + +.btn-light { + color: #212529; + background-color: #f8f9fa; + border-color: #f8f9fa; +} +.btn-light:hover { + color: #212529; + background-color: #e2e6ea; + border-color: #dae0e5; +} +.btn-light:focus, .btn-light.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); + box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); +} +.btn-light.disabled, .btn-light:disabled { + color: #212529; + background-color: #f8f9fa; + border-color: #f8f9fa; +} +.btn-light:not(:disabled):not(.disabled):active, .btn-light:not(:disabled):not(.disabled).active, .show > .btn-light.dropdown-toggle { + color: #212529; + background-color: #dae0e5; + border-color: #d3d9df; +} +.btn-light:not(:disabled):not(.disabled):active:focus, .btn-light:not(:disabled):not(.disabled).active:focus, .show > .btn-light.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); + box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); +} + +.btn-dark { + color: #fff; + background-color: #343a40; + border-color: #343a40; +} +.btn-dark:hover { + color: #fff; + background-color: #23272b; + border-color: #1d2124; +} +.btn-dark:focus, .btn-dark.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); + box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); +} +.btn-dark.disabled, .btn-dark:disabled { + color: #fff; + background-color: #343a40; + border-color: #343a40; +} +.btn-dark:not(:disabled):not(.disabled):active, .btn-dark:not(:disabled):not(.disabled).active, .show > .btn-dark.dropdown-toggle { + color: #fff; + background-color: #1d2124; + border-color: #171a1d; +} +.btn-dark:not(:disabled):not(.disabled):active:focus, .btn-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-dark.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); + box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); +} + +.btn-outline-primary { + color: #007bff; + background-color: transparent; + background-image: none; + border-color: #007bff; +} +.btn-outline-primary:hover { + color: #fff; + background-color: #007bff; + border-color: #007bff; +} +.btn-outline-primary:focus, .btn-outline-primary.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); +} +.btn-outline-primary.disabled, .btn-outline-primary:disabled { + color: #007bff; + background-color: transparent; +} +.btn-outline-primary:not(:disabled):not(.disabled):active, .btn-outline-primary:not(:disabled):not(.disabled).active, .show > .btn-outline-primary.dropdown-toggle { + color: #fff; + background-color: #007bff; + border-color: #007bff; +} +.btn-outline-primary:not(:disabled):not(.disabled):active:focus, .btn-outline-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-primary.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5); +} + +.btn-outline-secondary { + color: #6c757d; + background-color: transparent; + background-image: none; + border-color: #6c757d; +} +.btn-outline-secondary:hover { + color: #fff; + background-color: #6c757d; + border-color: #6c757d; +} +.btn-outline-secondary:focus, .btn-outline-secondary.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); + box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); +} +.btn-outline-secondary.disabled, .btn-outline-secondary:disabled { + color: #6c757d; + background-color: transparent; +} +.btn-outline-secondary:not(:disabled):not(.disabled):active, .btn-outline-secondary:not(:disabled):not(.disabled).active, .show > .btn-outline-secondary.dropdown-toggle { + color: #fff; + background-color: #6c757d; + border-color: #6c757d; +} +.btn-outline-secondary:not(:disabled):not(.disabled):active:focus, .btn-outline-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-secondary.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); + box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5); +} + +.btn-outline-success { + color: #28a745; + background-color: transparent; + background-image: none; + border-color: #28a745; +} +.btn-outline-success:hover { + color: #fff; + background-color: #28a745; + border-color: #28a745; +} +.btn-outline-success:focus, .btn-outline-success.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); +} +.btn-outline-success.disabled, .btn-outline-success:disabled { + color: #28a745; + background-color: transparent; +} +.btn-outline-success:not(:disabled):not(.disabled):active, .btn-outline-success:not(:disabled):not(.disabled).active, .show > .btn-outline-success.dropdown-toggle { + color: #fff; + background-color: #28a745; + border-color: #28a745; +} +.btn-outline-success:not(:disabled):not(.disabled):active:focus, .btn-outline-success:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-success.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5); +} + +.btn-outline-info { + color: #17a2b8; + background-color: transparent; + background-image: none; + border-color: #17a2b8; +} +.btn-outline-info:hover { + color: #fff; + background-color: #17a2b8; + border-color: #17a2b8; +} +.btn-outline-info:focus, .btn-outline-info.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); + box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); +} +.btn-outline-info.disabled, .btn-outline-info:disabled { + color: #17a2b8; + background-color: transparent; +} +.btn-outline-info:not(:disabled):not(.disabled):active, .btn-outline-info:not(:disabled):not(.disabled).active, .show > .btn-outline-info.dropdown-toggle { + color: #fff; + background-color: #17a2b8; + border-color: #17a2b8; +} +.btn-outline-info:not(:disabled):not(.disabled):active:focus, .btn-outline-info:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-info.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); + box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5); +} + +.btn-outline-warning { + color: #ffc107; + background-color: transparent; + background-image: none; + border-color: #ffc107; +} +.btn-outline-warning:hover { + color: #212529; + background-color: #ffc107; + border-color: #ffc107; +} +.btn-outline-warning:focus, .btn-outline-warning.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); + box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); +} +.btn-outline-warning.disabled, .btn-outline-warning:disabled { + color: #ffc107; + background-color: transparent; +} +.btn-outline-warning:not(:disabled):not(.disabled):active, .btn-outline-warning:not(:disabled):not(.disabled).active, .show > .btn-outline-warning.dropdown-toggle { + color: #212529; + background-color: #ffc107; + border-color: #ffc107; +} +.btn-outline-warning:not(:disabled):not(.disabled):active:focus, .btn-outline-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-warning.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); + box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5); +} + +.btn-outline-danger { + color: #dc3545; + background-color: transparent; + background-image: none; + border-color: #dc3545; +} +.btn-outline-danger:hover { + color: #fff; + background-color: #dc3545; + border-color: #dc3545; +} +.btn-outline-danger:focus, .btn-outline-danger.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); +} +.btn-outline-danger.disabled, .btn-outline-danger:disabled { + color: #dc3545; + background-color: transparent; +} +.btn-outline-danger:not(:disabled):not(.disabled):active, .btn-outline-danger:not(:disabled):not(.disabled).active, .show > .btn-outline-danger.dropdown-toggle { + color: #fff; + background-color: #dc3545; + border-color: #dc3545; +} +.btn-outline-danger:not(:disabled):not(.disabled):active:focus, .btn-outline-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-danger.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); + box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5); +} + +.btn-outline-light { + color: #f8f9fa; + background-color: transparent; + background-image: none; + border-color: #f8f9fa; +} +.btn-outline-light:hover { + color: #212529; + background-color: #f8f9fa; + border-color: #f8f9fa; +} +.btn-outline-light:focus, .btn-outline-light.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); + box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); +} +.btn-outline-light.disabled, .btn-outline-light:disabled { + color: #f8f9fa; + background-color: transparent; +} +.btn-outline-light:not(:disabled):not(.disabled):active, .btn-outline-light:not(:disabled):not(.disabled).active, .show > .btn-outline-light.dropdown-toggle { + color: #212529; + background-color: #f8f9fa; + border-color: #f8f9fa; +} +.btn-outline-light:not(:disabled):not(.disabled):active:focus, .btn-outline-light:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-light.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); + box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5); +} + +.btn-outline-dark { + color: #343a40; + background-color: transparent; + background-image: none; + border-color: #343a40; +} +.btn-outline-dark:hover { + color: #fff; + background-color: #343a40; + border-color: #343a40; +} +.btn-outline-dark:focus, .btn-outline-dark.focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); + box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); +} +.btn-outline-dark.disabled, .btn-outline-dark:disabled { + color: #343a40; + background-color: transparent; +} +.btn-outline-dark:not(:disabled):not(.disabled):active, .btn-outline-dark:not(:disabled):not(.disabled).active, .show > .btn-outline-dark.dropdown-toggle { + color: #fff; + background-color: #343a40; + border-color: #343a40; +} +.btn-outline-dark:not(:disabled):not(.disabled):active:focus, .btn-outline-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-dark.dropdown-toggle:focus { + -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); + box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5); +} + +.btn-link { + font-weight: 400; + color: #007bff; + background-color: transparent; +} +.btn-link:hover { + color: #0056b3; + text-decoration: underline; + background-color: transparent; + border-color: transparent; +} +.btn-link:focus, .btn-link.focus { + text-decoration: underline; + border-color: transparent; + -webkit-box-shadow: none; + box-shadow: none; +} +.btn-link:disabled, .btn-link.disabled { + color: #6c757d; +} + +.btn-lg, .btn-group-lg > .btn { + padding: 0.5rem 1rem; + font-size: 1.25rem; + line-height: 1.5; + border-radius: 0.3rem; +} + +.btn-sm, .btn-group-sm > .btn { + padding: 0.25rem 0.5rem; + font-size: 0.875rem; + line-height: 1.5; + border-radius: 0.2rem; +} + +.btn-block { + display: block; + width: 100%; +} +.btn-block + .btn-block { + margin-top: 0.5rem; +} + +input[type="submit"].btn-block, +input[type="reset"].btn-block, +input[type="button"].btn-block { + width: 100%; +} + +.fade { + opacity: 0; + -webkit-transition: opacity 0.15s linear; + transition: opacity 0.15s linear; +} +.fade.show { + opacity: 1; +} + +.collapse { + display: none; +} +.collapse.show { + display: block; +} + +tr.collapse.show { + display: table-row; +} + +tbody.collapse.show { + display: table-row-group; +} + +.collapsing { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition: height 0.35s ease; + transition: height 0.35s ease; +} + +.dropup, +.dropdown { + position: relative; +} + +.dropdown-toggle::after { + display: inline-block; + width: 0; + height: 0; + margin-left: 0.255em; + vertical-align: 0.255em; + content: ""; + border-top: 0.3em solid; + border-right: 0.3em solid transparent; + border-bottom: 0; + border-left: 0.3em solid transparent; +} +.dropdown-toggle:empty::after { + margin-left: 0; +} + +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 10rem; + padding: 0.5rem 0; + margin: 0.125rem 0 0; + font-size: 1rem; + color: #212529; + text-align: left; + list-style: none; + background-color: #fff; + background-clip: padding-box; + border: 1px solid rgba(0, 0, 0, 0.15); + border-radius: 0.25rem; +} + +.dropup .dropdown-menu { + margin-top: 0; + margin-bottom: 0.125rem; +} +.dropup .dropdown-toggle::after { + display: inline-block; + width: 0; + height: 0; + margin-left: 0.255em; + vertical-align: 0.255em; + content: ""; + border-top: 0; + border-right: 0.3em solid transparent; + border-bottom: 0.3em solid; + border-left: 0.3em solid transparent; +} +.dropup .dropdown-toggle:empty::after { + margin-left: 0; +} + +.dropright .dropdown-menu { + margin-top: 0; + margin-left: 0.125rem; +} +.dropright .dropdown-toggle::after { + display: inline-block; + width: 0; + height: 0; + margin-left: 0.255em; + vertical-align: 0.255em; + content: ""; + border-top: 0.3em solid transparent; + border-bottom: 0.3em solid transparent; + border-left: 0.3em solid; +} +.dropright .dropdown-toggle:empty::after { + margin-left: 0; +} +.dropright .dropdown-toggle::after { + vertical-align: 0; +} + +.dropleft .dropdown-menu { + margin-top: 0; + margin-right: 0.125rem; +} +.dropleft .dropdown-toggle::after { + display: inline-block; + width: 0; + height: 0; + margin-left: 0.255em; + vertical-align: 0.255em; + content: ""; +} +.dropleft .dropdown-toggle::after { + display: none; +} +.dropleft .dropdown-toggle::before { + display: inline-block; + width: 0; + height: 0; + margin-right: 0.255em; + vertical-align: 0.255em; + content: ""; + border-top: 0.3em solid transparent; + border-right: 0.3em solid; + border-bottom: 0.3em solid transparent; +} +.dropleft .dropdown-toggle:empty::after { + margin-left: 0; +} +.dropleft .dropdown-toggle::before { + vertical-align: 0; +} + +.dropdown-divider { + height: 0; + margin: 0.5rem 0; + overflow: hidden; + border-top: 1px solid #e9ecef; +} + +.dropdown-item { + display: block; + width: 100%; + padding: 0.25rem 1.5rem; + clear: both; + font-weight: 400; + color: #212529; + text-align: inherit; + white-space: nowrap; + background-color: transparent; + border: 0; +} +.dropdown-item:hover, .dropdown-item:focus { + color: #16181b; + text-decoration: none; + background-color: #f8f9fa; +} +.dropdown-item.active, .dropdown-item:active { + color: #fff; + text-decoration: none; + background-color: #007bff; +} +.dropdown-item.disabled, .dropdown-item:disabled { + color: #6c757d; + background-color: transparent; +} + +.dropdown-menu.show { + display: block; +} + +.dropdown-header { + display: block; + padding: 0.5rem 1.5rem; + margin-bottom: 0; + font-size: 0.875rem; + color: #6c757d; + white-space: nowrap; +} + +.btn-group, +.btn-group-vertical { + position: relative; + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + vertical-align: middle; +} +.btn-group > .btn, +.btn-group-vertical > .btn { + position: relative; + -webkit-box-flex: 0; + -ms-flex: 0 1 auto; + flex: 0 1 auto; +} +.btn-group > .btn:hover, +.btn-group-vertical > .btn:hover { + z-index: 1; +} +.btn-group > .btn:focus, .btn-group > .btn:active, .btn-group > .btn.active, +.btn-group-vertical > .btn:focus, +.btn-group-vertical > .btn:active, +.btn-group-vertical > .btn.active { + z-index: 1; +} +.btn-group .btn + .btn, +.btn-group .btn + .btn-group, +.btn-group .btn-group + .btn, +.btn-group .btn-group + .btn-group, +.btn-group-vertical .btn + .btn, +.btn-group-vertical .btn + .btn-group, +.btn-group-vertical .btn-group + .btn, +.btn-group-vertical .btn-group + .btn-group { + margin-left: -1px; +} + +.btn-toolbar { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + -webkit-box-pack: start; + -ms-flex-pack: start; + justify-content: flex-start; +} +.btn-toolbar .input-group { + width: auto; +} + +.btn-group > .btn:first-child { + margin-left: 0; +} +.btn-group > .btn:not(:last-child):not(.dropdown-toggle), +.btn-group > .btn-group:not(:last-child) > .btn { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.btn-group > .btn:not(:first-child), +.btn-group > .btn-group:not(:first-child) > .btn { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} + +.dropdown-toggle-split { + padding-right: 0.5625rem; + padding-left: 0.5625rem; +} +.dropdown-toggle-split::after { + margin-left: 0; +} + +.btn-sm + .dropdown-toggle-split, .btn-group-sm > .btn + .dropdown-toggle-split { + padding-right: 0.375rem; + padding-left: 0.375rem; +} + +.btn-lg + .dropdown-toggle-split, .btn-group-lg > .btn + .dropdown-toggle-split { + padding-right: 0.75rem; + padding-left: 0.75rem; +} + +.btn-group-vertical { + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + -webkit-box-align: start; + -ms-flex-align: start; + align-items: flex-start; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; +} +.btn-group-vertical .btn, +.btn-group-vertical .btn-group { + width: 100%; +} +.btn-group-vertical > .btn + .btn, +.btn-group-vertical > .btn + .btn-group, +.btn-group-vertical > .btn-group + .btn, +.btn-group-vertical > .btn-group + .btn-group { + margin-top: -1px; + margin-left: 0; +} +.btn-group-vertical > .btn:not(:last-child):not(.dropdown-toggle), +.btn-group-vertical > .btn-group:not(:last-child) > .btn { + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn:not(:first-child), +.btn-group-vertical > .btn-group:not(:first-child) > .btn { + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.btn-group-toggle > .btn, +.btn-group-toggle > .btn-group > .btn { + margin-bottom: 0; +} +.btn-group-toggle > .btn input[type="radio"], +.btn-group-toggle > .btn input[type="checkbox"], +.btn-group-toggle > .btn-group > .btn input[type="radio"], +.btn-group-toggle > .btn-group > .btn input[type="checkbox"] { + position: absolute; + clip: rect(0, 0, 0, 0); + pointer-events: none; +} + +.input-group { + position: relative; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + -webkit-box-align: stretch; + -ms-flex-align: stretch; + align-items: stretch; + width: 100%; +} +.input-group > .form-control, +.input-group > .custom-select, +.input-group > .custom-file { + position: relative; + -webkit-box-flex: 1; + -ms-flex: 1 1 auto; + flex: 1 1 auto; + width: 1%; + margin-bottom: 0; +} +.input-group > .form-control:focus, +.input-group > .custom-select:focus, +.input-group > .custom-file:focus { + z-index: 3; +} +.input-group > .form-control + .form-control, +.input-group > .form-control + .custom-select, +.input-group > .form-control + .custom-file, +.input-group > .custom-select + .form-control, +.input-group > .custom-select + .custom-select, +.input-group > .custom-select + .custom-file, +.input-group > .custom-file + .form-control, +.input-group > .custom-file + .custom-select, +.input-group > .custom-file + .custom-file { + margin-left: -1px; +} +.input-group > .form-control:not(:last-child), +.input-group > .custom-select:not(:last-child) { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.input-group > .form-control:not(:first-child), +.input-group > .custom-select:not(:first-child) { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} +.input-group > .custom-file { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; +} +.input-group > .custom-file:not(:last-child) .custom-file-label, .input-group > .custom-file:not(:last-child) .custom-file-label::before { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} +.input-group > .custom-file:not(:first-child) .custom-file-label, .input-group > .custom-file:not(:first-child) .custom-file-label::before { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} + +.input-group-prepend, +.input-group-append { + display: -webkit-box; + display: -ms-flexbox; + display: flex; +} +.input-group-prepend .btn, +.input-group-append .btn { + position: relative; + z-index: 2; +} +.input-group-prepend .btn + .btn, +.input-group-prepend .btn + .input-group-text, +.input-group-prepend .input-group-text + .input-group-text, +.input-group-prepend .input-group-text + .btn, +.input-group-append .btn + .btn, +.input-group-append .btn + .input-group-text, +.input-group-append .input-group-text + .input-group-text, +.input-group-append .input-group-text + .btn { + margin-left: -1px; +} + +.input-group-prepend { + margin-right: -1px; +} + +.input-group-append { + margin-left: -1px; +} + +.input-group-text { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + padding: 0.375rem 0.75rem; + margin-bottom: 0; + font-size: 1rem; + font-weight: 400; + line-height: 1.5; + color: #495057; + text-align: center; + white-space: nowrap; + background-color: #e9ecef; + border: 1px solid #ced4da; + border-radius: 0.25rem; +} +.input-group-text input[type="radio"], +.input-group-text input[type="checkbox"] { + margin-top: 0; +} + +.input-group > .input-group-prepend > .btn, +.input-group > .input-group-prepend > .input-group-text, +.input-group > .input-group-append:not(:last-child) > .btn, +.input-group > .input-group-append:not(:last-child) > .input-group-text, +.input-group > .input-group-append:last-child > .btn:not(:last-child):not(.dropdown-toggle), +.input-group > .input-group-append:last-child > .input-group-text:not(:last-child) { + border-top-right-radius: 0; + border-bottom-right-radius: 0; +} + +.input-group > .input-group-append > .btn, +.input-group > .input-group-append > .input-group-text, +.input-group > .input-group-prepend:not(:first-child) > .btn, +.input-group > .input-group-prepend:not(:first-child) > .input-group-text, +.input-group > .input-group-prepend:first-child > .btn:not(:first-child), +.input-group > .input-group-prepend:first-child > .input-group-text:not(:first-child) { + border-top-left-radius: 0; + border-bottom-left-radius: 0; +} + +.custom-control { + position: relative; + display: block; + min-height: 1.5rem; + padding-left: 1.5rem; +} + +.custom-control-inline { + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + margin-right: 1rem; +} + +.custom-control-input { + position: absolute; + z-index: -1; + opacity: 0; +} +.custom-control-input:checked ~ .custom-control-label::before { + color: #fff; + background-color: #007bff; +} +.custom-control-input:focus ~ .custom-control-label::before { + -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25); + box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25); +} +.custom-control-input:active ~ .custom-control-label::before { + color: #fff; + background-color: #b3d7ff; +} +.custom-control-input:disabled ~ .custom-control-label { + color: #6c757d; +} +.custom-control-input:disabled ~ .custom-control-label::before { + background-color: #e9ecef; +} + +.custom-control-label { + margin-bottom: 0; +} +.custom-control-label::before { + position: absolute; + top: 0.25rem; + left: 0; + display: block; + width: 1rem; + height: 1rem; + pointer-events: none; + content: ""; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + background-color: #dee2e6; +} +.custom-control-label::after { + position: absolute; + top: 0.25rem; + left: 0; + display: block; + width: 1rem; + height: 1rem; + content: ""; + background-repeat: no-repeat; + background-position: center center; + background-size: 50% 50%; +} + +.custom-checkbox .custom-control-label::before { + border-radius: 0.25rem; +} +.custom-checkbox .custom-control-input:checked ~ .custom-control-label::before { + background-color: #007bff; +} +.custom-checkbox .custom-control-input:checked ~ .custom-control-label::after { + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3E%3Cpath fill='%23fff' d='M6.564.75l-3.59 3.612-1.538-1.55L0 4.26 2.974 7.25 8 2.193z'/%3E%3C/svg%3E"); +} +.custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::before { + background-color: #007bff; +} +.custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::after { + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 4'%3E%3Cpath stroke='%23fff' d='M0 2h4'/%3E%3C/svg%3E"); +} +.custom-checkbox .custom-control-input:disabled:checked ~ .custom-control-label::before { + background-color: rgba(0, 123, 255, 0.5); +} +.custom-checkbox .custom-control-input:disabled:indeterminate ~ .custom-control-label::before { + background-color: rgba(0, 123, 255, 0.5); +} + +.custom-radio .custom-control-label::before { + border-radius: 50%; +} +.custom-radio .custom-control-input:checked ~ .custom-control-label::before { + background-color: #007bff; +} +.custom-radio .custom-control-input:checked ~ .custom-control-label::after { + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3E%3Ccircle r='3' fill='%23fff'/%3E%3C/svg%3E"); +} +.custom-radio .custom-control-input:disabled:checked ~ .custom-control-label::before { + background-color: rgba(0, 123, 255, 0.5); +} + +.custom-select { + display: inline-block; + width: 100%; + height: calc(2.25rem + 2px); + padding: 0.375rem 1.75rem 0.375rem 0.75rem; + line-height: 1.5; + color: #495057; + vertical-align: middle; + background: #fff url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 5'%3E%3Cpath fill='%23343a40' d='M2 0L0 2h4zm0 5L0 3h4z'/%3E%3C/svg%3E") no-repeat right 0.75rem center; + background-size: 8px 10px; + border: 1px solid #ced4da; + border-radius: 0.25rem; + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; +} +.custom-select:focus { + border-color: #80bdff; + outline: 0; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075), 0 0 5px rgba(128, 189, 255, 0.5); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075), 0 0 5px rgba(128, 189, 255, 0.5); +} +.custom-select:focus::-ms-value { + color: #495057; + background-color: #fff; +} +.custom-select[multiple], .custom-select[size]:not([size="1"]) { + height: auto; + padding-right: 0.75rem; + background-image: none; +} +.custom-select:disabled { + color: #6c757d; + background-color: #e9ecef; +} +.custom-select::-ms-expand { + opacity: 0; +} + +.custom-select-sm { + height: calc(1.8125rem + 2px); + padding-top: 0.375rem; + padding-bottom: 0.375rem; + font-size: 75%; +} + +.custom-select-lg { + height: calc(2.875rem + 2px); + padding-top: 0.375rem; + padding-bottom: 0.375rem; + font-size: 125%; +} + +.custom-file { + position: relative; + display: inline-block; + width: 100%; + height: calc(2.25rem + 2px); + margin-bottom: 0; +} + +.custom-file-input { + position: relative; + z-index: 2; + width: 100%; + height: calc(2.25rem + 2px); + margin: 0; + opacity: 0; +} +.custom-file-input:focus ~ .custom-file-control { + border-color: #80bdff; + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); +} +.custom-file-input:focus ~ .custom-file-control::before { + border-color: #80bdff; +} +.custom-file-input:lang(en) ~ .custom-file-label::after { + content: "Browse"; +} + +.custom-file-label { + position: absolute; + top: 0; + right: 0; + left: 0; + z-index: 1; + height: calc(2.25rem + 2px); + padding: 0.375rem 0.75rem; + line-height: 1.5; + color: #495057; + background-color: #fff; + border: 1px solid #ced4da; + border-radius: 0.25rem; +} +.custom-file-label::after { + position: absolute; + top: 0; + right: 0; + bottom: 0; + z-index: 3; + display: block; + height: calc(calc(2.25rem + 2px) - 1px * 2); + padding: 0.375rem 0.75rem; + line-height: 1.5; + color: #495057; + content: "Browse"; + background-color: #e9ecef; + border-left: 1px solid #ced4da; + border-radius: 0 0.25rem 0.25rem 0; +} + +.nav { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + padding-left: 0; + margin-bottom: 0; + list-style: none; +} + +.nav-link { + display: block; + padding: 0.5rem 1rem; +} +.nav-link:hover, .nav-link:focus { + text-decoration: none; +} +.nav-link.disabled { + color: #6c757d; +} + +.nav-tabs { + border-bottom: 1px solid #dee2e6; +} +.nav-tabs .nav-item { + margin-bottom: -1px; +} +.nav-tabs .nav-link { + border: 1px solid transparent; + border-top-left-radius: 0.25rem; + border-top-right-radius: 0.25rem; +} +.nav-tabs .nav-link:hover, .nav-tabs .nav-link:focus { + border-color: #e9ecef #e9ecef #dee2e6; +} +.nav-tabs .nav-link.disabled { + color: #6c757d; + background-color: transparent; + border-color: transparent; +} +.nav-tabs .nav-link.active, +.nav-tabs .nav-item.show .nav-link { + color: #495057; + background-color: #fff; + border-color: #dee2e6 #dee2e6 #fff; +} +.nav-tabs .dropdown-menu { + margin-top: -1px; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.nav-pills .nav-link { + border-radius: 0.25rem; +} +.nav-pills .nav-link.active, +.nav-pills .show > .nav-link { + color: #fff; + background-color: #007bff; +} + +.nav-fill .nav-item { + -webkit-box-flex: 1; + -ms-flex: 1 1 auto; + flex: 1 1 auto; + text-align: center; +} + +.nav-justified .nav-item { + -ms-flex-preferred-size: 0; + flex-basis: 0; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + text-align: center; +} + +.tab-content > .tab-pane { + display: none; +} +.tab-content > .active { + display: block; +} + +.navbar { + position: relative; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: justify; + -ms-flex-pack: justify; + justify-content: space-between; + padding: 0.5rem 1rem; +} +.navbar > .container, +.navbar > .container-fluid { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: justify; + -ms-flex-pack: justify; + justify-content: space-between; +} + +.navbar-brand { + display: inline-block; + padding-top: 0.3125rem; + padding-bottom: 0.3125rem; + margin-right: 1rem; + font-size: 1.25rem; + line-height: inherit; + white-space: nowrap; +} +.navbar-brand:hover, .navbar-brand:focus { + text-decoration: none; +} + +.navbar-nav { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + padding-left: 0; + margin-bottom: 0; + list-style: none; +} +.navbar-nav .nav-link { + padding-right: 0; + padding-left: 0; +} +.navbar-nav .dropdown-menu { + position: static; + float: none; +} + +.navbar-text { + display: inline-block; + padding-top: 0.5rem; + padding-bottom: 0.5rem; +} + +.navbar-collapse { + -ms-flex-preferred-size: 100%; + flex-basis: 100%; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; +} + +.navbar-toggler { + padding: 0.25rem 0.75rem; + font-size: 1.25rem; + line-height: 1; + background-color: transparent; + border: 1px solid transparent; + border-radius: 0.25rem; +} +.navbar-toggler:hover, .navbar-toggler:focus { + text-decoration: none; +} +.navbar-toggler:not(:disabled):not(.disabled) { + cursor: pointer; +} + +.navbar-toggler-icon { + display: inline-block; + width: 1.5em; + height: 1.5em; + vertical-align: middle; + content: ""; + background: no-repeat center center; + background-size: 100% 100%; +} + +@media (max-width: 575.98px) { + .navbar-expand-sm > .container, + .navbar-expand-sm > .container-fluid { + padding-right: 0; + padding-left: 0; + } +} +@media (min-width: 576px) { + .navbar-expand-sm { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row nowrap; + flex-flow: row nowrap; + -webkit-box-pack: start; + -ms-flex-pack: start; + justify-content: flex-start; + } + .navbar-expand-sm .navbar-nav { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-direction: row; + flex-direction: row; + } + .navbar-expand-sm .navbar-nav .dropdown-menu { + position: absolute; + } + .navbar-expand-sm .navbar-nav .dropdown-menu-right { + right: 0; + left: auto; + } + .navbar-expand-sm .navbar-nav .nav-link { + padding-right: 0.5rem; + padding-left: 0.5rem; + } + .navbar-expand-sm > .container, + .navbar-expand-sm > .container-fluid { + -ms-flex-wrap: nowrap; + flex-wrap: nowrap; + } + .navbar-expand-sm .navbar-collapse { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + -ms-flex-preferred-size: auto; + flex-basis: auto; + } + .navbar-expand-sm .navbar-toggler { + display: none; + } + .navbar-expand-sm .dropup .dropdown-menu { + top: auto; + bottom: 100%; + } +} +@media (max-width: 767.98px) { + .navbar-expand-md > .container, + .navbar-expand-md > .container-fluid { + padding-right: 0; + padding-left: 0; + } +} +@media (min-width: 768px) { + .navbar-expand-md { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row nowrap; + flex-flow: row nowrap; + -webkit-box-pack: start; + -ms-flex-pack: start; + justify-content: flex-start; + } + .navbar-expand-md .navbar-nav { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-direction: row; + flex-direction: row; + } + .navbar-expand-md .navbar-nav .dropdown-menu { + position: absolute; + } + .navbar-expand-md .navbar-nav .dropdown-menu-right { + right: 0; + left: auto; + } + .navbar-expand-md .navbar-nav .nav-link { + padding-right: 0.5rem; + padding-left: 0.5rem; + } + .navbar-expand-md > .container, + .navbar-expand-md > .container-fluid { + -ms-flex-wrap: nowrap; + flex-wrap: nowrap; + } + .navbar-expand-md .navbar-collapse { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + -ms-flex-preferred-size: auto; + flex-basis: auto; + } + .navbar-expand-md .navbar-toggler { + display: none; + } + .navbar-expand-md .dropup .dropdown-menu { + top: auto; + bottom: 100%; + } +} +@media (max-width: 991.98px) { + .navbar-expand-lg > .container, + .navbar-expand-lg > .container-fluid { + padding-right: 0; + padding-left: 0; + } +} +@media (min-width: 992px) { + .navbar-expand-lg { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row nowrap; + flex-flow: row nowrap; + -webkit-box-pack: start; + -ms-flex-pack: start; + justify-content: flex-start; + } + .navbar-expand-lg .navbar-nav { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-direction: row; + flex-direction: row; + } + .navbar-expand-lg .navbar-nav .dropdown-menu { + position: absolute; + } + .navbar-expand-lg .navbar-nav .dropdown-menu-right { + right: 0; + left: auto; + } + .navbar-expand-lg .navbar-nav .nav-link { + padding-right: 0.5rem; + padding-left: 0.5rem; + } + .navbar-expand-lg > .container, + .navbar-expand-lg > .container-fluid { + -ms-flex-wrap: nowrap; + flex-wrap: nowrap; + } + .navbar-expand-lg .navbar-collapse { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + -ms-flex-preferred-size: auto; + flex-basis: auto; + } + .navbar-expand-lg .navbar-toggler { + display: none; + } + .navbar-expand-lg .dropup .dropdown-menu { + top: auto; + bottom: 100%; + } +} +@media (max-width: 1199.98px) { + .navbar-expand-xl > .container, + .navbar-expand-xl > .container-fluid { + padding-right: 0; + padding-left: 0; + } +} +@media (min-width: 1200px) { + .navbar-expand-xl { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row nowrap; + flex-flow: row nowrap; + -webkit-box-pack: start; + -ms-flex-pack: start; + justify-content: flex-start; + } + .navbar-expand-xl .navbar-nav { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-direction: row; + flex-direction: row; + } + .navbar-expand-xl .navbar-nav .dropdown-menu { + position: absolute; + } + .navbar-expand-xl .navbar-nav .dropdown-menu-right { + right: 0; + left: auto; + } + .navbar-expand-xl .navbar-nav .nav-link { + padding-right: 0.5rem; + padding-left: 0.5rem; + } + .navbar-expand-xl > .container, + .navbar-expand-xl > .container-fluid { + -ms-flex-wrap: nowrap; + flex-wrap: nowrap; + } + .navbar-expand-xl .navbar-collapse { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + -ms-flex-preferred-size: auto; + flex-basis: auto; + } + .navbar-expand-xl .navbar-toggler { + display: none; + } + .navbar-expand-xl .dropup .dropdown-menu { + top: auto; + bottom: 100%; + } +} +.navbar-expand { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row nowrap; + flex-flow: row nowrap; + -webkit-box-pack: start; + -ms-flex-pack: start; + justify-content: flex-start; +} +.navbar-expand > .container, +.navbar-expand > .container-fluid { + padding-right: 0; + padding-left: 0; +} +.navbar-expand .navbar-nav { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-direction: row; + flex-direction: row; +} +.navbar-expand .navbar-nav .dropdown-menu { + position: absolute; +} +.navbar-expand .navbar-nav .dropdown-menu-right { + right: 0; + left: auto; +} +.navbar-expand .navbar-nav .nav-link { + padding-right: 0.5rem; + padding-left: 0.5rem; +} +.navbar-expand > .container, +.navbar-expand > .container-fluid { + -ms-flex-wrap: nowrap; + flex-wrap: nowrap; +} +.navbar-expand .navbar-collapse { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + -ms-flex-preferred-size: auto; + flex-basis: auto; +} +.navbar-expand .navbar-toggler { + display: none; +} +.navbar-expand .dropup .dropdown-menu { + top: auto; + bottom: 100%; +} + +.navbar-light .navbar-brand { + color: rgba(0, 0, 0, 0.9); +} +.navbar-light .navbar-brand:hover, .navbar-light .navbar-brand:focus { + color: rgba(0, 0, 0, 0.9); +} +.navbar-light .navbar-nav .nav-link { + color: rgba(0, 0, 0, 0.5); +} +.navbar-light .navbar-nav .nav-link:hover, .navbar-light .navbar-nav .nav-link:focus { + color: rgba(0, 0, 0, 0.7); +} +.navbar-light .navbar-nav .nav-link.disabled { + color: rgba(0, 0, 0, 0.3); +} +.navbar-light .navbar-nav .show > .nav-link, +.navbar-light .navbar-nav .active > .nav-link, +.navbar-light .navbar-nav .nav-link.show, +.navbar-light .navbar-nav .nav-link.active { + color: rgba(0, 0, 0, 0.9); +} +.navbar-light .navbar-toggler { + color: rgba(0, 0, 0, 0.5); + border-color: rgba(0, 0, 0, 0.1); +} +.navbar-light .navbar-toggler-icon { + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgba(0, 0, 0, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E"); +} +.navbar-light .navbar-text { + color: rgba(0, 0, 0, 0.5); +} +.navbar-light .navbar-text a { + color: rgba(0, 0, 0, 0.9); +} +.navbar-light .navbar-text a:hover, .navbar-light .navbar-text a:focus { + color: rgba(0, 0, 0, 0.9); +} + +.navbar-dark .navbar-brand { + color: #fff; +} +.navbar-dark .navbar-brand:hover, .navbar-dark .navbar-brand:focus { + color: #fff; +} +.navbar-dark .navbar-nav .nav-link { + color: rgba(255, 255, 255, 0.5); +} +.navbar-dark .navbar-nav .nav-link:hover, .navbar-dark .navbar-nav .nav-link:focus { + color: rgba(255, 255, 255, 0.75); +} +.navbar-dark .navbar-nav .nav-link.disabled { + color: rgba(255, 255, 255, 0.25); +} +.navbar-dark .navbar-nav .show > .nav-link, +.navbar-dark .navbar-nav .active > .nav-link, +.navbar-dark .navbar-nav .nav-link.show, +.navbar-dark .navbar-nav .nav-link.active { + color: #fff; +} +.navbar-dark .navbar-toggler { + color: rgba(255, 255, 255, 0.5); + border-color: rgba(255, 255, 255, 0.1); +} +.navbar-dark .navbar-toggler-icon { + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgba(255, 255, 255, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E"); +} +.navbar-dark .navbar-text { + color: rgba(255, 255, 255, 0.5); +} +.navbar-dark .navbar-text a { + color: #fff; +} +.navbar-dark .navbar-text a:hover, .navbar-dark .navbar-text a:focus { + color: #fff; +} + +.card { + position: relative; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + min-width: 0; + word-wrap: break-word; + background-color: #fff; + background-clip: border-box; + border: 1px solid rgba(0, 0, 0, 0.125); + border-radius: 0.25rem; +} +.card > hr { + margin-right: 0; + margin-left: 0; +} +.card > .list-group:first-child .list-group-item:first-child { + border-top-left-radius: 0.25rem; + border-top-right-radius: 0.25rem; +} +.card > .list-group:last-child .list-group-item:last-child { + border-bottom-right-radius: 0.25rem; + border-bottom-left-radius: 0.25rem; +} + +.card-body { + -webkit-box-flex: 1; + -ms-flex: 1 1 auto; + flex: 1 1 auto; + padding: 1.25rem; +} + +.card-title { + margin-bottom: 0.75rem; +} + +.card-subtitle { + margin-top: -0.375rem; + margin-bottom: 0; +} + +.card-text:last-child { + margin-bottom: 0; +} + +.card-link:hover { + text-decoration: none; +} +.card-link + .card-link { + margin-left: 1.25rem; +} + +.card-header { + padding: 0.75rem 1.25rem; + margin-bottom: 0; + background-color: rgba(0, 0, 0, 0.03); + border-bottom: 1px solid rgba(0, 0, 0, 0.125); +} +.card-header:first-child { + border-radius: calc(0.25rem - 1px) calc(0.25rem - 1px) 0 0; +} +.card-header + .list-group .list-group-item:first-child { + border-top: 0; +} + +.card-footer { + padding: 0.75rem 1.25rem; + background-color: rgba(0, 0, 0, 0.03); + border-top: 1px solid rgba(0, 0, 0, 0.125); +} +.card-footer:last-child { + border-radius: 0 0 calc(0.25rem - 1px) calc(0.25rem - 1px); +} + +.card-header-tabs { + margin-right: -0.625rem; + margin-bottom: -0.75rem; + margin-left: -0.625rem; + border-bottom: 0; +} + +.card-header-pills { + margin-right: -0.625rem; + margin-left: -0.625rem; +} + +.card-img-overlay { + position: absolute; + top: 0; + right: 0; + bottom: 0; + left: 0; + padding: 1.25rem; +} + +.card-img { + width: 100%; + border-radius: calc(0.25rem - 1px); +} + +.card-img-top { + width: 100%; + border-top-left-radius: calc(0.25rem - 1px); + border-top-right-radius: calc(0.25rem - 1px); +} + +.card-img-bottom { + width: 100%; + border-bottom-right-radius: calc(0.25rem - 1px); + border-bottom-left-radius: calc(0.25rem - 1px); +} + +.card-deck { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; +} +.card-deck .card { + margin-bottom: 15px; +} +@media (min-width: 576px) { + .card-deck { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row wrap; + flex-flow: row wrap; + margin-right: -15px; + margin-left: -15px; + } + .card-deck .card { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-flex: 1; + -ms-flex: 1 0 0%; + flex: 1 0 0%; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + margin-right: 15px; + margin-bottom: 0; + margin-left: 15px; + } +} + +.card-group { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; +} +.card-group > .card { + margin-bottom: 15px; +} +@media (min-width: 576px) { + .card-group { + -webkit-box-orient: horizontal; + -webkit-box-direction: normal; + -ms-flex-flow: row wrap; + flex-flow: row wrap; + } + .card-group > .card { + -webkit-box-flex: 1; + -ms-flex: 1 0 0%; + flex: 1 0 0%; + margin-bottom: 0; + } + .card-group > .card + .card { + margin-left: 0; + border-left: 0; + } + .card-group > .card:first-child { + border-top-right-radius: 0; + border-bottom-right-radius: 0; + } + .card-group > .card:first-child .card-img-top, + .card-group > .card:first-child .card-header { + border-top-right-radius: 0; + } + .card-group > .card:first-child .card-img-bottom, + .card-group > .card:first-child .card-footer { + border-bottom-right-radius: 0; + } + .card-group > .card:last-child { + border-top-left-radius: 0; + border-bottom-left-radius: 0; + } + .card-group > .card:last-child .card-img-top, + .card-group > .card:last-child .card-header { + border-top-left-radius: 0; + } + .card-group > .card:last-child .card-img-bottom, + .card-group > .card:last-child .card-footer { + border-bottom-left-radius: 0; + } + .card-group > .card:only-child { + border-radius: 0.25rem; + } + .card-group > .card:only-child .card-img-top, + .card-group > .card:only-child .card-header { + border-top-left-radius: 0.25rem; + border-top-right-radius: 0.25rem; + } + .card-group > .card:only-child .card-img-bottom, + .card-group > .card:only-child .card-footer { + border-bottom-right-radius: 0.25rem; + border-bottom-left-radius: 0.25rem; + } + .card-group > .card:not(:first-child):not(:last-child):not(:only-child) { + border-radius: 0; + } + .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-img-top, + .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-img-bottom, + .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-header, + .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-footer { + border-radius: 0; + } +} + +.card-columns .card { + margin-bottom: 0.75rem; +} +@media (min-width: 576px) { + .card-columns { + -webkit-column-count: 3; + -moz-column-count: 3; + column-count: 3; + -webkit-column-gap: 1.25rem; + -moz-column-gap: 1.25rem; + column-gap: 1.25rem; + } + .card-columns .card { + display: inline-block; + width: 100%; + } +} + +.breadcrumb { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + padding: 0.75rem 1rem; + margin-bottom: 1rem; + list-style: none; + background-color: #e9ecef; + border-radius: 0.25rem; +} + +.breadcrumb-item + .breadcrumb-item::before { + display: inline-block; + padding-right: 0.5rem; + padding-left: 0.5rem; + color: #6c757d; + content: "/"; +} +.breadcrumb-item + .breadcrumb-item:hover::before { + text-decoration: underline; +} +.breadcrumb-item + .breadcrumb-item:hover::before { + text-decoration: none; +} +.breadcrumb-item.active { + color: #6c757d; +} + +.pagination { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + padding-left: 0; + list-style: none; + border-radius: 0.25rem; +} + +.page-link { + position: relative; + display: block; + padding: 0.5rem 0.75rem; + margin-left: -1px; + line-height: 1.25; + color: #007bff; + background-color: #fff; + border: 1px solid #dee2e6; +} +.page-link:hover { + color: #0056b3; + text-decoration: none; + background-color: #e9ecef; + border-color: #dee2e6; +} +.page-link:focus { + z-index: 2; + outline: 0; + -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); + box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25); +} +.page-link:not(:disabled):not(.disabled) { + cursor: pointer; +} + +.page-item:first-child .page-link { + margin-left: 0; + border-top-left-radius: 0.25rem; + border-bottom-left-radius: 0.25rem; +} +.page-item:last-child .page-link { + border-top-right-radius: 0.25rem; + border-bottom-right-radius: 0.25rem; +} +.page-item.active .page-link { + z-index: 1; + color: #fff; + background-color: #007bff; + border-color: #007bff; +} +.page-item.disabled .page-link { + color: #6c757d; + pointer-events: none; + cursor: auto; + background-color: #fff; + border-color: #dee2e6; +} + +.pagination-lg .page-link { + padding: 0.75rem 1.5rem; + font-size: 1.25rem; + line-height: 1.5; +} +.pagination-lg .page-item:first-child .page-link { + border-top-left-radius: 0.3rem; + border-bottom-left-radius: 0.3rem; +} +.pagination-lg .page-item:last-child .page-link { + border-top-right-radius: 0.3rem; + border-bottom-right-radius: 0.3rem; +} + +.pagination-sm .page-link { + padding: 0.25rem 0.5rem; + font-size: 0.875rem; + line-height: 1.5; +} +.pagination-sm .page-item:first-child .page-link { + border-top-left-radius: 0.2rem; + border-bottom-left-radius: 0.2rem; +} +.pagination-sm .page-item:last-child .page-link { + border-top-right-radius: 0.2rem; + border-bottom-right-radius: 0.2rem; +} + +.badge { + display: inline-block; + padding: 0.25em 0.4em; + font-size: 75%; + font-weight: 700; + line-height: 1; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + border-radius: 0.25rem; +} +.badge:empty { + display: none; +} + +.btn .badge { + position: relative; + top: -1px; +} + +.badge-pill { + padding-right: 0.6em; + padding-left: 0.6em; + border-radius: 10rem; +} + +.badge-primary { + color: #fff; + background-color: #007bff; +} +.badge-primary[href]:hover, .badge-primary[href]:focus { + color: #fff; + text-decoration: none; + background-color: #0062cc; +} + +.badge-secondary { + color: #fff; + background-color: #6c757d; +} +.badge-secondary[href]:hover, .badge-secondary[href]:focus { + color: #fff; + text-decoration: none; + background-color: #545b62; +} + +.badge-success { + color: #fff; + background-color: #28a745; +} +.badge-success[href]:hover, .badge-success[href]:focus { + color: #fff; + text-decoration: none; + background-color: #1e7e34; +} + +.badge-info { + color: #fff; + background-color: #17a2b8; +} +.badge-info[href]:hover, .badge-info[href]:focus { + color: #fff; + text-decoration: none; + background-color: #117a8b; +} + +.badge-warning { + color: #212529; + background-color: #ffc107; +} +.badge-warning[href]:hover, .badge-warning[href]:focus { + color: #212529; + text-decoration: none; + background-color: #d39e00; +} + +.badge-danger { + color: #fff; + background-color: #dc3545; +} +.badge-danger[href]:hover, .badge-danger[href]:focus { + color: #fff; + text-decoration: none; + background-color: #bd2130; +} + +.badge-light { + color: #212529; + background-color: #f8f9fa; +} +.badge-light[href]:hover, .badge-light[href]:focus { + color: #212529; + text-decoration: none; + background-color: #dae0e5; +} + +.badge-dark { + color: #fff; + background-color: #343a40; +} +.badge-dark[href]:hover, .badge-dark[href]:focus { + color: #fff; + text-decoration: none; + background-color: #1d2124; +} + +.jumbotron { + padding: 2rem 1rem; + margin-bottom: 2rem; + background-color: #e9ecef; + border-radius: 0.3rem; +} +@media (min-width: 576px) { + .jumbotron { + padding: 4rem 2rem; + } +} + +.jumbotron-fluid { + padding-right: 0; + padding-left: 0; + border-radius: 0; +} + +.alert { + position: relative; + padding: 0.75rem 1.25rem; + margin-bottom: 1rem; + border: 1px solid transparent; + border-radius: 0.25rem; +} + +.alert-heading { + color: inherit; +} + +.alert-link { + font-weight: 700; +} + +.alert-dismissible { + padding-right: 4rem; +} +.alert-dismissible .close { + position: absolute; + top: 0; + right: 0; + padding: 0.75rem 1.25rem; + color: inherit; +} + +.alert-primary { + color: #004085; + background-color: #cce5ff; + border-color: #b8daff; +} +.alert-primary hr { + border-top-color: #9fcdff; +} +.alert-primary .alert-link { + color: #002752; +} + +.alert-secondary { + color: #383d41; + background-color: #e2e3e5; + border-color: #d6d8db; +} +.alert-secondary hr { + border-top-color: #c8cbcf; +} +.alert-secondary .alert-link { + color: #202326; +} + +.alert-success { + color: #155724; + background-color: #d4edda; + border-color: #c3e6cb; +} +.alert-success hr { + border-top-color: #b1dfbb; +} +.alert-success .alert-link { + color: #0b2e13; +} + +.alert-info { + color: #0c5460; + background-color: #d1ecf1; + border-color: #bee5eb; +} +.alert-info hr { + border-top-color: #abdde5; +} +.alert-info .alert-link { + color: #062c33; +} + +.alert-warning { + color: #856404; + background-color: #fff3cd; + border-color: #ffeeba; +} +.alert-warning hr { + border-top-color: #ffe8a1; +} +.alert-warning .alert-link { + color: #533f03; +} + +.alert-danger { + color: #721c24; + background-color: #f8d7da; + border-color: #f5c6cb; +} +.alert-danger hr { + border-top-color: #f1b0b7; +} +.alert-danger .alert-link { + color: #491217; +} + +.alert-light { + color: #818182; + background-color: #fefefe; + border-color: #fdfdfe; +} +.alert-light hr { + border-top-color: #ececf6; +} +.alert-light .alert-link { + color: #686868; +} + +.alert-dark { + color: #1b1e21; + background-color: #d6d8d9; + border-color: #c6c8ca; +} +.alert-dark hr { + border-top-color: #b9bbbe; +} +.alert-dark .alert-link { + color: #040505; +} + +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 1rem 0; + } + to { + background-position: 0 0; + } +} + +@keyframes progress-bar-stripes { + from { + background-position: 1rem 0; + } + to { + background-position: 0 0; + } +} +.progress { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + height: 1rem; + overflow: hidden; + font-size: 0.75rem; + background-color: #e9ecef; + border-radius: 0.25rem; +} + +.progress-bar { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + color: #fff; + text-align: center; + background-color: #007bff; + -webkit-transition: width 0.6s ease; + transition: width 0.6s ease; +} + +.progress-bar-striped { + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-size: 1rem 1rem; +} + +.progress-bar-animated { + -webkit-animation: progress-bar-stripes 1s linear infinite; + animation: progress-bar-stripes 1s linear infinite; +} + +.media { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: start; + -ms-flex-align: start; + align-items: flex-start; +} + +.media-body { + -webkit-box-flex: 1; + -ms-flex: 1; + flex: 1; +} + +.list-group { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + padding-left: 0; + margin-bottom: 0; +} + +.list-group-item-action { + width: 100%; + color: #495057; + text-align: inherit; +} +.list-group-item-action:hover, .list-group-item-action:focus { + color: #495057; + text-decoration: none; + background-color: #f8f9fa; +} +.list-group-item-action:active { + color: #212529; + background-color: #e9ecef; +} + +.list-group-item { + position: relative; + display: block; + padding: 0.75rem 1.25rem; + margin-bottom: -1px; + background-color: #fff; + border: 1px solid rgba(0, 0, 0, 0.125); +} +.list-group-item:first-child { + border-top-left-radius: 0.25rem; + border-top-right-radius: 0.25rem; +} +.list-group-item:last-child { + margin-bottom: 0; + border-bottom-right-radius: 0.25rem; + border-bottom-left-radius: 0.25rem; +} +.list-group-item:hover, .list-group-item:focus { + z-index: 1; + text-decoration: none; +} +.list-group-item.disabled, .list-group-item:disabled { + color: #6c757d; + background-color: #fff; +} +.list-group-item.active { + z-index: 2; + color: #fff; + background-color: #007bff; + border-color: #007bff; +} + +.list-group-flush .list-group-item { + border-right: 0; + border-left: 0; + border-radius: 0; +} +.list-group-flush:first-child .list-group-item:first-child { + border-top: 0; +} +.list-group-flush:last-child .list-group-item:last-child { + border-bottom: 0; +} + +.list-group-item-primary { + color: #004085; + background-color: #b8daff; +} +.list-group-item-primary.list-group-item-action:hover, .list-group-item-primary.list-group-item-action:focus { + color: #004085; + background-color: #9fcdff; +} +.list-group-item-primary.list-group-item-action.active { + color: #fff; + background-color: #004085; + border-color: #004085; +} + +.list-group-item-secondary { + color: #383d41; + background-color: #d6d8db; +} +.list-group-item-secondary.list-group-item-action:hover, .list-group-item-secondary.list-group-item-action:focus { + color: #383d41; + background-color: #c8cbcf; +} +.list-group-item-secondary.list-group-item-action.active { + color: #fff; + background-color: #383d41; + border-color: #383d41; +} + +.list-group-item-success { + color: #155724; + background-color: #c3e6cb; +} +.list-group-item-success.list-group-item-action:hover, .list-group-item-success.list-group-item-action:focus { + color: #155724; + background-color: #b1dfbb; +} +.list-group-item-success.list-group-item-action.active { + color: #fff; + background-color: #155724; + border-color: #155724; +} + +.list-group-item-info { + color: #0c5460; + background-color: #bee5eb; +} +.list-group-item-info.list-group-item-action:hover, .list-group-item-info.list-group-item-action:focus { + color: #0c5460; + background-color: #abdde5; +} +.list-group-item-info.list-group-item-action.active { + color: #fff; + background-color: #0c5460; + border-color: #0c5460; +} + +.list-group-item-warning { + color: #856404; + background-color: #ffeeba; +} +.list-group-item-warning.list-group-item-action:hover, .list-group-item-warning.list-group-item-action:focus { + color: #856404; + background-color: #ffe8a1; +} +.list-group-item-warning.list-group-item-action.active { + color: #fff; + background-color: #856404; + border-color: #856404; +} + +.list-group-item-danger { + color: #721c24; + background-color: #f5c6cb; +} +.list-group-item-danger.list-group-item-action:hover, .list-group-item-danger.list-group-item-action:focus { + color: #721c24; + background-color: #f1b0b7; +} +.list-group-item-danger.list-group-item-action.active { + color: #fff; + background-color: #721c24; + border-color: #721c24; +} + +.list-group-item-light { + color: #818182; + background-color: #fdfdfe; +} +.list-group-item-light.list-group-item-action:hover, .list-group-item-light.list-group-item-action:focus { + color: #818182; + background-color: #ececf6; +} +.list-group-item-light.list-group-item-action.active { + color: #fff; + background-color: #818182; + border-color: #818182; +} + +.list-group-item-dark { + color: #1b1e21; + background-color: #c6c8ca; +} +.list-group-item-dark.list-group-item-action:hover, .list-group-item-dark.list-group-item-action:focus { + color: #1b1e21; + background-color: #b9bbbe; +} +.list-group-item-dark.list-group-item-action.active { + color: #fff; + background-color: #1b1e21; + border-color: #1b1e21; +} + +.close { + float: right; + font-size: 1.5rem; + font-weight: 700; + line-height: 1; + color: #000; + text-shadow: 0 1px 0 #fff; + opacity: .5; +} +.close:hover, .close:focus { + color: #000; + text-decoration: none; + opacity: .75; +} +.close:not(:disabled):not(.disabled) { + cursor: pointer; +} + +button.close { + padding: 0; + background-color: transparent; + border: 0; + -webkit-appearance: none; +} + +.modal-open { + overflow: hidden; +} + +.modal { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1050; + display: none; + overflow: hidden; + outline: 0; +} +.modal-open .modal { + overflow-x: hidden; + overflow-y: auto; +} + +.modal-dialog { + position: relative; + width: auto; + margin: 0.5rem; + pointer-events: none; +} +.modal.fade .modal-dialog { + -webkit-transition: -webkit-transform 0.3s ease-out; + transition: -webkit-transform 0.3s ease-out; + transition: transform 0.3s ease-out; + transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out; + -webkit-transform: translate(0, -25%); + transform: translate(0, -25%); +} +.modal.show .modal-dialog { + -webkit-transform: translate(0, 0); + transform: translate(0, 0); +} + +.modal-dialog-centered { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + min-height: calc(100% - (0.5rem * 2)); +} + +.modal-content { + position: relative; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + width: 100%; + pointer-events: auto; + background-color: #fff; + background-clip: padding-box; + border: 1px solid rgba(0, 0, 0, 0.2); + border-radius: 0.3rem; + outline: 0; +} + +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000; +} +.modal-backdrop.fade { + opacity: 0; +} +.modal-backdrop.show { + opacity: 0.5; +} + +.modal-header { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: start; + -ms-flex-align: start; + align-items: flex-start; + -webkit-box-pack: justify; + -ms-flex-pack: justify; + justify-content: space-between; + padding: 1rem; + border-bottom: 1px solid #e9ecef; + border-top-left-radius: 0.3rem; + border-top-right-radius: 0.3rem; +} +.modal-header .close { + padding: 1rem; + margin: -1rem -1rem -1rem auto; +} + +.modal-title { + margin-bottom: 0; + line-height: 1.5; +} + +.modal-body { + position: relative; + -webkit-box-flex: 1; + -ms-flex: 1 1 auto; + flex: 1 1 auto; + padding: 1rem; +} + +.modal-footer { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: end; + -ms-flex-pack: end; + justify-content: flex-end; + padding: 1rem; + border-top: 1px solid #e9ecef; +} +.modal-footer > :not(:first-child) { + margin-left: .25rem; +} +.modal-footer > :not(:last-child) { + margin-right: .25rem; +} + +.modal-scrollbar-measure { + position: absolute; + top: -9999px; + width: 50px; + height: 50px; + overflow: scroll; +} + +@media (min-width: 576px) { + .modal-dialog { + max-width: 500px; + margin: 1.75rem auto; + } + + .modal-dialog-centered { + min-height: calc(100% - (1.75rem * 2)); + } + + .modal-sm { + max-width: 300px; + } +} +@media (min-width: 992px) { + .modal-lg { + max-width: 800px; + } +} +.tooltip { + position: absolute; + z-index: 1070; + display: block; + margin: 0; + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; + font-style: normal; + font-weight: 400; + line-height: 1.5; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + letter-spacing: normal; + word-break: normal; + word-spacing: normal; + white-space: normal; + line-break: auto; + font-size: 0.875rem; + word-wrap: break-word; + opacity: 0; +} +.tooltip.show { + opacity: 0.9; +} +.tooltip .arrow { + position: absolute; + display: block; + width: 0.8rem; + height: 0.4rem; +} +.tooltip .arrow::before { + position: absolute; + content: ""; + border-color: transparent; + border-style: solid; +} + +.bs-tooltip-top, .bs-tooltip-auto[x-placement^="top"] { + padding: 0.4rem 0; +} +.bs-tooltip-top .arrow, .bs-tooltip-auto[x-placement^="top"] .arrow { + bottom: 0; +} +.bs-tooltip-top .arrow::before, .bs-tooltip-auto[x-placement^="top"] .arrow::before { + top: 0; + border-width: 0.4rem 0.4rem 0; + border-top-color: #000; +} + +.bs-tooltip-right, .bs-tooltip-auto[x-placement^="right"] { + padding: 0 0.4rem; +} +.bs-tooltip-right .arrow, .bs-tooltip-auto[x-placement^="right"] .arrow { + left: 0; + width: 0.4rem; + height: 0.8rem; +} +.bs-tooltip-right .arrow::before, .bs-tooltip-auto[x-placement^="right"] .arrow::before { + right: 0; + border-width: 0.4rem 0.4rem 0.4rem 0; + border-right-color: #000; +} + +.bs-tooltip-bottom, .bs-tooltip-auto[x-placement^="bottom"] { + padding: 0.4rem 0; +} +.bs-tooltip-bottom .arrow, .bs-tooltip-auto[x-placement^="bottom"] .arrow { + top: 0; +} +.bs-tooltip-bottom .arrow::before, .bs-tooltip-auto[x-placement^="bottom"] .arrow::before { + bottom: 0; + border-width: 0 0.4rem 0.4rem; + border-bottom-color: #000; +} + +.bs-tooltip-left, .bs-tooltip-auto[x-placement^="left"] { + padding: 0 0.4rem; +} +.bs-tooltip-left .arrow, .bs-tooltip-auto[x-placement^="left"] .arrow { + right: 0; + width: 0.4rem; + height: 0.8rem; +} +.bs-tooltip-left .arrow::before, .bs-tooltip-auto[x-placement^="left"] .arrow::before { + left: 0; + border-width: 0.4rem 0 0.4rem 0.4rem; + border-left-color: #000; +} + +.tooltip-inner { + max-width: 200px; + padding: 0.25rem 0.5rem; + color: #fff; + text-align: center; + background-color: #000; + border-radius: 0.25rem; +} + +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1060; + display: block; + max-width: 276px; + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol"; + font-style: normal; + font-weight: 400; + line-height: 1.5; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + letter-spacing: normal; + word-break: normal; + word-spacing: normal; + white-space: normal; + line-break: auto; + font-size: 0.875rem; + word-wrap: break-word; + background-color: #fff; + background-clip: padding-box; + border: 1px solid rgba(0, 0, 0, 0.2); + border-radius: 0.3rem; +} +.popover .arrow { + position: absolute; + display: block; + width: 1rem; + height: 0.5rem; + margin: 0 0.3rem; +} +.popover .arrow::before, .popover .arrow::after { + position: absolute; + display: block; + content: ""; + border-color: transparent; + border-style: solid; +} + +.bs-popover-top, .bs-popover-auto[x-placement^="top"] { + margin-bottom: 0.5rem; +} +.bs-popover-top .arrow, .bs-popover-auto[x-placement^="top"] .arrow { + bottom: calc((0.5rem + 1px) * -1); +} +.bs-popover-top .arrow::before, .bs-popover-auto[x-placement^="top"] .arrow::before, +.bs-popover-top .arrow::after, +.bs-popover-auto[x-placement^="top"] .arrow::after { + border-width: 0.5rem 0.5rem 0; +} +.bs-popover-top .arrow::before, .bs-popover-auto[x-placement^="top"] .arrow::before { + bottom: 0; + border-top-color: rgba(0, 0, 0, 0.25); +} +.bs-popover-top .arrow::after, .bs-popover-auto[x-placement^="top"] .arrow::after { + bottom: 1px; + border-top-color: #fff; +} + +.bs-popover-right, .bs-popover-auto[x-placement^="right"] { + margin-left: 0.5rem; +} +.bs-popover-right .arrow, .bs-popover-auto[x-placement^="right"] .arrow { + left: calc((0.5rem + 1px) * -1); + width: 0.5rem; + height: 1rem; + margin: 0.3rem 0; +} +.bs-popover-right .arrow::before, .bs-popover-auto[x-placement^="right"] .arrow::before, +.bs-popover-right .arrow::after, +.bs-popover-auto[x-placement^="right"] .arrow::after { + border-width: 0.5rem 0.5rem 0.5rem 0; +} +.bs-popover-right .arrow::before, .bs-popover-auto[x-placement^="right"] .arrow::before { + left: 0; + border-right-color: rgba(0, 0, 0, 0.25); +} +.bs-popover-right .arrow::after, .bs-popover-auto[x-placement^="right"] .arrow::after { + left: 1px; + border-right-color: #fff; +} + +.bs-popover-bottom, .bs-popover-auto[x-placement^="bottom"] { + margin-top: 0.5rem; +} +.bs-popover-bottom .arrow, .bs-popover-auto[x-placement^="bottom"] .arrow { + top: calc((0.5rem + 1px) * -1); +} +.bs-popover-bottom .arrow::before, .bs-popover-auto[x-placement^="bottom"] .arrow::before, +.bs-popover-bottom .arrow::after, +.bs-popover-auto[x-placement^="bottom"] .arrow::after { + border-width: 0 0.5rem 0.5rem 0.5rem; +} +.bs-popover-bottom .arrow::before, .bs-popover-auto[x-placement^="bottom"] .arrow::before { + top: 0; + border-bottom-color: rgba(0, 0, 0, 0.25); +} +.bs-popover-bottom .arrow::after, .bs-popover-auto[x-placement^="bottom"] .arrow::after { + top: 1px; + border-bottom-color: #fff; +} +.bs-popover-bottom .popover-header::before, .bs-popover-auto[x-placement^="bottom"] .popover-header::before { + position: absolute; + top: 0; + left: 50%; + display: block; + width: 1rem; + margin-left: -0.5rem; + content: ""; + border-bottom: 1px solid #f7f7f7; +} + +.bs-popover-left, .bs-popover-auto[x-placement^="left"] { + margin-right: 0.5rem; +} +.bs-popover-left .arrow, .bs-popover-auto[x-placement^="left"] .arrow { + right: calc((0.5rem + 1px) * -1); + width: 0.5rem; + height: 1rem; + margin: 0.3rem 0; +} +.bs-popover-left .arrow::before, .bs-popover-auto[x-placement^="left"] .arrow::before, +.bs-popover-left .arrow::after, +.bs-popover-auto[x-placement^="left"] .arrow::after { + border-width: 0.5rem 0 0.5rem 0.5rem; +} +.bs-popover-left .arrow::before, .bs-popover-auto[x-placement^="left"] .arrow::before { + right: 0; + border-left-color: rgba(0, 0, 0, 0.25); +} +.bs-popover-left .arrow::after, .bs-popover-auto[x-placement^="left"] .arrow::after { + right: 1px; + border-left-color: #fff; +} + +.popover-header { + padding: 0.5rem 0.75rem; + margin-bottom: 0; + font-size: 1rem; + color: inherit; + background-color: #f7f7f7; + border-bottom: 1px solid #ebebeb; + border-top-left-radius: calc(0.3rem - 1px); + border-top-right-radius: calc(0.3rem - 1px); +} +.popover-header:empty { + display: none; +} + +.popover-body { + padding: 0.5rem 0.75rem; + color: #212529; +} + +.carousel { + position: relative; +} + +.carousel-inner { + position: relative; + width: 100%; + overflow: hidden; +} + +.carousel-item { + position: relative; + display: none; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + width: 100%; + -webkit-transition: -webkit-transform 0.6s ease; + transition: -webkit-transform 0.6s ease; + transition: transform 0.6s ease; + transition: transform 0.6s ease, -webkit-transform 0.6s ease; + -webkit-backface-visibility: hidden; + backface-visibility: hidden; + -webkit-perspective: 1000px; + perspective: 1000px; +} + +.carousel-item.active, +.carousel-item-next, +.carousel-item-prev { + display: block; +} + +.carousel-item-next, +.carousel-item-prev { + position: absolute; + top: 0; +} + +.carousel-item-next.carousel-item-left, +.carousel-item-prev.carousel-item-right { + -webkit-transform: translateX(0); + transform: translateX(0); +} +@supports (transform-style: preserve-3d) { + .carousel-item-next.carousel-item-left, + .carousel-item-prev.carousel-item-right { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +.carousel-item-next, +.active.carousel-item-right { + -webkit-transform: translateX(100%); + transform: translateX(100%); +} +@supports (transform-style: preserve-3d) { + .carousel-item-next, + .active.carousel-item-right { + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + } +} + +.carousel-item-prev, +.active.carousel-item-left { + -webkit-transform: translateX(-100%); + transform: translateX(-100%); +} +@supports (transform-style: preserve-3d) { + .carousel-item-prev, + .active.carousel-item-left { + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + } +} + +.carousel-control-prev, +.carousel-control-next { + position: absolute; + top: 0; + bottom: 0; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + width: 15%; + color: #fff; + text-align: center; + opacity: 0.5; +} +.carousel-control-prev:hover, .carousel-control-prev:focus, +.carousel-control-next:hover, +.carousel-control-next:focus { + color: #fff; + text-decoration: none; + outline: 0; + opacity: .9; +} + +.carousel-control-prev { + left: 0; +} + +.carousel-control-next { + right: 0; +} + +.carousel-control-prev-icon, +.carousel-control-next-icon { + display: inline-block; + width: 20px; + height: 20px; + background: transparent no-repeat center center; + background-size: 100% 100%; +} + +.carousel-control-prev-icon { + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3E%3Cpath d='M5.25 0l-4 4 4 4 1.5-1.5-2.5-2.5 2.5-2.5-1.5-1.5z'/%3E%3C/svg%3E"); +} + +.carousel-control-next-icon { + background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3E%3Cpath d='M2.75 0l-1.5 1.5 2.5 2.5-2.5 2.5 1.5 1.5 4-4-4-4z'/%3E%3C/svg%3E"); +} + +.carousel-indicators { + position: absolute; + right: 0; + bottom: 10px; + left: 0; + z-index: 15; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + padding-left: 0; + margin-right: 15%; + margin-left: 15%; + list-style: none; +} +.carousel-indicators li { + position: relative; + -webkit-box-flex: 0; + -ms-flex: 0 1 auto; + flex: 0 1 auto; + width: 30px; + height: 3px; + margin-right: 3px; + margin-left: 3px; + text-indent: -999px; + background-color: rgba(255, 255, 255, 0.5); +} +.carousel-indicators li::before { + position: absolute; + top: -10px; + left: 0; + display: inline-block; + width: 100%; + height: 10px; + content: ""; +} +.carousel-indicators li::after { + position: absolute; + bottom: -10px; + left: 0; + display: inline-block; + width: 100%; + height: 10px; + content: ""; +} +.carousel-indicators .active { + background-color: #fff; +} + +.carousel-caption { + position: absolute; + right: 15%; + bottom: 20px; + left: 15%; + z-index: 10; + padding-top: 20px; + padding-bottom: 20px; + color: #fff; + text-align: center; +} + +.align-baseline { + vertical-align: baseline !important; +} + +.align-top { + vertical-align: top !important; +} + +.align-middle { + vertical-align: middle !important; +} + +.align-bottom { + vertical-align: bottom !important; +} + +.align-text-bottom { + vertical-align: text-bottom !important; +} + +.align-text-top { + vertical-align: text-top !important; +} + +.bg-primary { + background-color: #007bff !important; +} + +a.bg-primary:hover, a.bg-primary:focus, +button.bg-primary:hover, +button.bg-primary:focus { + background-color: #0062cc !important; +} + +.bg-secondary { + background-color: #6c757d !important; +} + +a.bg-secondary:hover, a.bg-secondary:focus, +button.bg-secondary:hover, +button.bg-secondary:focus { + background-color: #545b62 !important; +} + +.bg-success { + background-color: #28a745 !important; +} + +a.bg-success:hover, a.bg-success:focus, +button.bg-success:hover, +button.bg-success:focus { + background-color: #1e7e34 !important; +} + +.bg-info { + background-color: #17a2b8 !important; +} + +a.bg-info:hover, a.bg-info:focus, +button.bg-info:hover, +button.bg-info:focus { + background-color: #117a8b !important; +} + +.bg-warning { + background-color: #ffc107 !important; +} + +a.bg-warning:hover, a.bg-warning:focus, +button.bg-warning:hover, +button.bg-warning:focus { + background-color: #d39e00 !important; +} + +.bg-danger { + background-color: #dc3545 !important; +} + +a.bg-danger:hover, a.bg-danger:focus, +button.bg-danger:hover, +button.bg-danger:focus { + background-color: #bd2130 !important; +} + +.bg-light { + background-color: #f8f9fa !important; +} + +a.bg-light:hover, a.bg-light:focus, +button.bg-light:hover, +button.bg-light:focus { + background-color: #dae0e5 !important; +} + +.bg-dark { + background-color: #343a40 !important; +} + +a.bg-dark:hover, a.bg-dark:focus, +button.bg-dark:hover, +button.bg-dark:focus { + background-color: #1d2124 !important; +} + +.bg-white { + background-color: #fff !important; +} + +.bg-transparent { + background-color: transparent !important; +} + +.border { + border: 1px solid #dee2e6 !important; +} + +.border-top { + border-top: 1px solid #dee2e6 !important; +} + +.border-right { + border-right: 1px solid #dee2e6 !important; +} + +.border-bottom { + border-bottom: 1px solid #dee2e6 !important; +} + +.border-left { + border-left: 1px solid #dee2e6 !important; +} + +.border-0 { + border: 0 !important; +} + +.border-top-0 { + border-top: 0 !important; +} + +.border-right-0 { + border-right: 0 !important; +} + +.border-bottom-0 { + border-bottom: 0 !important; +} + +.border-left-0 { + border-left: 0 !important; +} + +.border-primary { + border-color: #007bff !important; +} + +.border-secondary { + border-color: #6c757d !important; +} + +.border-success { + border-color: #28a745 !important; +} + +.border-info { + border-color: #17a2b8 !important; +} + +.border-warning { + border-color: #ffc107 !important; +} + +.border-danger { + border-color: #dc3545 !important; +} + +.border-light { + border-color: #f8f9fa !important; +} + +.border-dark { + border-color: #343a40 !important; +} + +.border-white { + border-color: #fff !important; +} + +.rounded { + border-radius: 0.25rem !important; +} + +.rounded-top { + border-top-left-radius: 0.25rem !important; + border-top-right-radius: 0.25rem !important; +} + +.rounded-right { + border-top-right-radius: 0.25rem !important; + border-bottom-right-radius: 0.25rem !important; +} + +.rounded-bottom { + border-bottom-right-radius: 0.25rem !important; + border-bottom-left-radius: 0.25rem !important; +} + +.rounded-left { + border-top-left-radius: 0.25rem !important; + border-bottom-left-radius: 0.25rem !important; +} + +.rounded-circle { + border-radius: 50% !important; +} + +.rounded-0 { + border-radius: 0 !important; +} + +.clearfix::after { + display: block; + clear: both; + content: ""; +} + +.d-none { + display: none !important; +} + +.d-inline { + display: inline !important; +} + +.d-inline-block { + display: inline-block !important; +} + +.d-block { + display: block !important; +} + +.d-table { + display: table !important; +} + +.d-table-row { + display: table-row !important; +} + +.d-table-cell { + display: table-cell !important; +} + +.d-flex { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; +} + +.d-inline-flex { + display: -webkit-inline-box !important; + display: -ms-inline-flexbox !important; + display: inline-flex !important; +} + +@media (min-width: 576px) { + .d-sm-none { + display: none !important; + } + + .d-sm-inline { + display: inline !important; + } + + .d-sm-inline-block { + display: inline-block !important; + } + + .d-sm-block { + display: block !important; + } + + .d-sm-table { + display: table !important; + } + + .d-sm-table-row { + display: table-row !important; + } + + .d-sm-table-cell { + display: table-cell !important; + } + + .d-sm-flex { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + } + + .d-sm-inline-flex { + display: -webkit-inline-box !important; + display: -ms-inline-flexbox !important; + display: inline-flex !important; + } +} +@media (min-width: 768px) { + .d-md-none { + display: none !important; + } + + .d-md-inline { + display: inline !important; + } + + .d-md-inline-block { + display: inline-block !important; + } + + .d-md-block { + display: block !important; + } + + .d-md-table { + display: table !important; + } + + .d-md-table-row { + display: table-row !important; + } + + .d-md-table-cell { + display: table-cell !important; + } + + .d-md-flex { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + } + + .d-md-inline-flex { + display: -webkit-inline-box !important; + display: -ms-inline-flexbox !important; + display: inline-flex !important; + } +} +@media (min-width: 992px) { + .d-lg-none { + display: none !important; + } + + .d-lg-inline { + display: inline !important; + } + + .d-lg-inline-block { + display: inline-block !important; + } + + .d-lg-block { + display: block !important; + } + + .d-lg-table { + display: table !important; + } + + .d-lg-table-row { + display: table-row !important; + } + + .d-lg-table-cell { + display: table-cell !important; + } + + .d-lg-flex { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + } + + .d-lg-inline-flex { + display: -webkit-inline-box !important; + display: -ms-inline-flexbox !important; + display: inline-flex !important; + } +} +@media (min-width: 1200px) { + .d-xl-none { + display: none !important; + } + + .d-xl-inline { + display: inline !important; + } + + .d-xl-inline-block { + display: inline-block !important; + } + + .d-xl-block { + display: block !important; + } + + .d-xl-table { + display: table !important; + } + + .d-xl-table-row { + display: table-row !important; + } + + .d-xl-table-cell { + display: table-cell !important; + } + + .d-xl-flex { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + } + + .d-xl-inline-flex { + display: -webkit-inline-box !important; + display: -ms-inline-flexbox !important; + display: inline-flex !important; + } +} +@media print { + .d-print-none { + display: none !important; + } + + .d-print-inline { + display: inline !important; + } + + .d-print-inline-block { + display: inline-block !important; + } + + .d-print-block { + display: block !important; + } + + .d-print-table { + display: table !important; + } + + .d-print-table-row { + display: table-row !important; + } + + .d-print-table-cell { + display: table-cell !important; + } + + .d-print-flex { + display: -webkit-box !important; + display: -ms-flexbox !important; + display: flex !important; + } + + .d-print-inline-flex { + display: -webkit-inline-box !important; + display: -ms-inline-flexbox !important; + display: inline-flex !important; + } +} +.embed-responsive { + position: relative; + display: block; + width: 100%; + padding: 0; + overflow: hidden; +} +.embed-responsive::before { + display: block; + content: ""; +} +.embed-responsive .embed-responsive-item, +.embed-responsive iframe, +.embed-responsive embed, +.embed-responsive object, +.embed-responsive video { + position: absolute; + top: 0; + bottom: 0; + left: 0; + width: 100%; + height: 100%; + border: 0; +} + +.embed-responsive-21by9::before { + padding-top: 42.8571428571%; +} + +.embed-responsive-16by9::before { + padding-top: 56.25%; +} + +.embed-responsive-4by3::before { + padding-top: 75%; +} + +.embed-responsive-1by1::before { + padding-top: 100%; +} + +.flex-row { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: row !important; + flex-direction: row !important; +} + +.flex-column { + -webkit-box-orient: vertical !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: column !important; + flex-direction: column !important; +} + +.flex-row-reverse { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: row-reverse !important; + flex-direction: row-reverse !important; +} + +.flex-column-reverse { + -webkit-box-orient: vertical !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: column-reverse !important; + flex-direction: column-reverse !important; +} + +.flex-wrap { + -ms-flex-wrap: wrap !important; + flex-wrap: wrap !important; +} + +.flex-nowrap { + -ms-flex-wrap: nowrap !important; + flex-wrap: nowrap !important; +} + +.flex-wrap-reverse { + -ms-flex-wrap: wrap-reverse !important; + flex-wrap: wrap-reverse !important; +} + +.justify-content-start { + -webkit-box-pack: start !important; + -ms-flex-pack: start !important; + justify-content: flex-start !important; +} + +.justify-content-end { + -webkit-box-pack: end !important; + -ms-flex-pack: end !important; + justify-content: flex-end !important; +} + +.justify-content-center { + -webkit-box-pack: center !important; + -ms-flex-pack: center !important; + justify-content: center !important; +} + +.justify-content-between { + -webkit-box-pack: justify !important; + -ms-flex-pack: justify !important; + justify-content: space-between !important; +} + +.justify-content-around { + -ms-flex-pack: distribute !important; + justify-content: space-around !important; +} + +.align-items-start { + -webkit-box-align: start !important; + -ms-flex-align: start !important; + align-items: flex-start !important; +} + +.align-items-end { + -webkit-box-align: end !important; + -ms-flex-align: end !important; + align-items: flex-end !important; +} + +.align-items-center { + -webkit-box-align: center !important; + -ms-flex-align: center !important; + align-items: center !important; +} + +.align-items-baseline { + -webkit-box-align: baseline !important; + -ms-flex-align: baseline !important; + align-items: baseline !important; +} + +.align-items-stretch { + -webkit-box-align: stretch !important; + -ms-flex-align: stretch !important; + align-items: stretch !important; +} + +.align-content-start { + -ms-flex-line-pack: start !important; + align-content: flex-start !important; +} + +.align-content-end { + -ms-flex-line-pack: end !important; + align-content: flex-end !important; +} + +.align-content-center { + -ms-flex-line-pack: center !important; + align-content: center !important; +} + +.align-content-between { + -ms-flex-line-pack: justify !important; + align-content: space-between !important; +} + +.align-content-around { + -ms-flex-line-pack: distribute !important; + align-content: space-around !important; +} + +.align-content-stretch { + -ms-flex-line-pack: stretch !important; + align-content: stretch !important; +} + +.align-self-auto { + -ms-flex-item-align: auto !important; + align-self: auto !important; +} + +.align-self-start { + -ms-flex-item-align: start !important; + align-self: flex-start !important; +} + +.align-self-end { + -ms-flex-item-align: end !important; + align-self: flex-end !important; +} + +.align-self-center { + -ms-flex-item-align: center !important; + align-self: center !important; +} + +.align-self-baseline { + -ms-flex-item-align: baseline !important; + align-self: baseline !important; +} + +.align-self-stretch { + -ms-flex-item-align: stretch !important; + align-self: stretch !important; +} + +@media (min-width: 576px) { + .flex-sm-row { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: row !important; + flex-direction: row !important; + } + + .flex-sm-column { + -webkit-box-orient: vertical !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: column !important; + flex-direction: column !important; + } + + .flex-sm-row-reverse { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: row-reverse !important; + flex-direction: row-reverse !important; + } + + .flex-sm-column-reverse { + -webkit-box-orient: vertical !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: column-reverse !important; + flex-direction: column-reverse !important; + } + + .flex-sm-wrap { + -ms-flex-wrap: wrap !important; + flex-wrap: wrap !important; + } + + .flex-sm-nowrap { + -ms-flex-wrap: nowrap !important; + flex-wrap: nowrap !important; + } + + .flex-sm-wrap-reverse { + -ms-flex-wrap: wrap-reverse !important; + flex-wrap: wrap-reverse !important; + } + + .justify-content-sm-start { + -webkit-box-pack: start !important; + -ms-flex-pack: start !important; + justify-content: flex-start !important; + } + + .justify-content-sm-end { + -webkit-box-pack: end !important; + -ms-flex-pack: end !important; + justify-content: flex-end !important; + } + + .justify-content-sm-center { + -webkit-box-pack: center !important; + -ms-flex-pack: center !important; + justify-content: center !important; + } + + .justify-content-sm-between { + -webkit-box-pack: justify !important; + -ms-flex-pack: justify !important; + justify-content: space-between !important; + } + + .justify-content-sm-around { + -ms-flex-pack: distribute !important; + justify-content: space-around !important; + } + + .align-items-sm-start { + -webkit-box-align: start !important; + -ms-flex-align: start !important; + align-items: flex-start !important; + } + + .align-items-sm-end { + -webkit-box-align: end !important; + -ms-flex-align: end !important; + align-items: flex-end !important; + } + + .align-items-sm-center { + -webkit-box-align: center !important; + -ms-flex-align: center !important; + align-items: center !important; + } + + .align-items-sm-baseline { + -webkit-box-align: baseline !important; + -ms-flex-align: baseline !important; + align-items: baseline !important; + } + + .align-items-sm-stretch { + -webkit-box-align: stretch !important; + -ms-flex-align: stretch !important; + align-items: stretch !important; + } + + .align-content-sm-start { + -ms-flex-line-pack: start !important; + align-content: flex-start !important; + } + + .align-content-sm-end { + -ms-flex-line-pack: end !important; + align-content: flex-end !important; + } + + .align-content-sm-center { + -ms-flex-line-pack: center !important; + align-content: center !important; + } + + .align-content-sm-between { + -ms-flex-line-pack: justify !important; + align-content: space-between !important; + } + + .align-content-sm-around { + -ms-flex-line-pack: distribute !important; + align-content: space-around !important; + } + + .align-content-sm-stretch { + -ms-flex-line-pack: stretch !important; + align-content: stretch !important; + } + + .align-self-sm-auto { + -ms-flex-item-align: auto !important; + align-self: auto !important; + } + + .align-self-sm-start { + -ms-flex-item-align: start !important; + align-self: flex-start !important; + } + + .align-self-sm-end { + -ms-flex-item-align: end !important; + align-self: flex-end !important; + } + + .align-self-sm-center { + -ms-flex-item-align: center !important; + align-self: center !important; + } + + .align-self-sm-baseline { + -ms-flex-item-align: baseline !important; + align-self: baseline !important; + } + + .align-self-sm-stretch { + -ms-flex-item-align: stretch !important; + align-self: stretch !important; + } +} +@media (min-width: 768px) { + .flex-md-row { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: row !important; + flex-direction: row !important; + } + + .flex-md-column { + -webkit-box-orient: vertical !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: column !important; + flex-direction: column !important; + } + + .flex-md-row-reverse { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: row-reverse !important; + flex-direction: row-reverse !important; + } + + .flex-md-column-reverse { + -webkit-box-orient: vertical !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: column-reverse !important; + flex-direction: column-reverse !important; + } + + .flex-md-wrap { + -ms-flex-wrap: wrap !important; + flex-wrap: wrap !important; + } + + .flex-md-nowrap { + -ms-flex-wrap: nowrap !important; + flex-wrap: nowrap !important; + } + + .flex-md-wrap-reverse { + -ms-flex-wrap: wrap-reverse !important; + flex-wrap: wrap-reverse !important; + } + + .justify-content-md-start { + -webkit-box-pack: start !important; + -ms-flex-pack: start !important; + justify-content: flex-start !important; + } + + .justify-content-md-end { + -webkit-box-pack: end !important; + -ms-flex-pack: end !important; + justify-content: flex-end !important; + } + + .justify-content-md-center { + -webkit-box-pack: center !important; + -ms-flex-pack: center !important; + justify-content: center !important; + } + + .justify-content-md-between { + -webkit-box-pack: justify !important; + -ms-flex-pack: justify !important; + justify-content: space-between !important; + } + + .justify-content-md-around { + -ms-flex-pack: distribute !important; + justify-content: space-around !important; + } + + .align-items-md-start { + -webkit-box-align: start !important; + -ms-flex-align: start !important; + align-items: flex-start !important; + } + + .align-items-md-end { + -webkit-box-align: end !important; + -ms-flex-align: end !important; + align-items: flex-end !important; + } + + .align-items-md-center { + -webkit-box-align: center !important; + -ms-flex-align: center !important; + align-items: center !important; + } + + .align-items-md-baseline { + -webkit-box-align: baseline !important; + -ms-flex-align: baseline !important; + align-items: baseline !important; + } + + .align-items-md-stretch { + -webkit-box-align: stretch !important; + -ms-flex-align: stretch !important; + align-items: stretch !important; + } + + .align-content-md-start { + -ms-flex-line-pack: start !important; + align-content: flex-start !important; + } + + .align-content-md-end { + -ms-flex-line-pack: end !important; + align-content: flex-end !important; + } + + .align-content-md-center { + -ms-flex-line-pack: center !important; + align-content: center !important; + } + + .align-content-md-between { + -ms-flex-line-pack: justify !important; + align-content: space-between !important; + } + + .align-content-md-around { + -ms-flex-line-pack: distribute !important; + align-content: space-around !important; + } + + .align-content-md-stretch { + -ms-flex-line-pack: stretch !important; + align-content: stretch !important; + } + + .align-self-md-auto { + -ms-flex-item-align: auto !important; + align-self: auto !important; + } + + .align-self-md-start { + -ms-flex-item-align: start !important; + align-self: flex-start !important; + } + + .align-self-md-end { + -ms-flex-item-align: end !important; + align-self: flex-end !important; + } + + .align-self-md-center { + -ms-flex-item-align: center !important; + align-self: center !important; + } + + .align-self-md-baseline { + -ms-flex-item-align: baseline !important; + align-self: baseline !important; + } + + .align-self-md-stretch { + -ms-flex-item-align: stretch !important; + align-self: stretch !important; + } +} +@media (min-width: 992px) { + .flex-lg-row { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: row !important; + flex-direction: row !important; + } + + .flex-lg-column { + -webkit-box-orient: vertical !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: column !important; + flex-direction: column !important; + } + + .flex-lg-row-reverse { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: row-reverse !important; + flex-direction: row-reverse !important; + } + + .flex-lg-column-reverse { + -webkit-box-orient: vertical !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: column-reverse !important; + flex-direction: column-reverse !important; + } + + .flex-lg-wrap { + -ms-flex-wrap: wrap !important; + flex-wrap: wrap !important; + } + + .flex-lg-nowrap { + -ms-flex-wrap: nowrap !important; + flex-wrap: nowrap !important; + } + + .flex-lg-wrap-reverse { + -ms-flex-wrap: wrap-reverse !important; + flex-wrap: wrap-reverse !important; + } + + .justify-content-lg-start { + -webkit-box-pack: start !important; + -ms-flex-pack: start !important; + justify-content: flex-start !important; + } + + .justify-content-lg-end { + -webkit-box-pack: end !important; + -ms-flex-pack: end !important; + justify-content: flex-end !important; + } + + .justify-content-lg-center { + -webkit-box-pack: center !important; + -ms-flex-pack: center !important; + justify-content: center !important; + } + + .justify-content-lg-between { + -webkit-box-pack: justify !important; + -ms-flex-pack: justify !important; + justify-content: space-between !important; + } + + .justify-content-lg-around { + -ms-flex-pack: distribute !important; + justify-content: space-around !important; + } + + .align-items-lg-start { + -webkit-box-align: start !important; + -ms-flex-align: start !important; + align-items: flex-start !important; + } + + .align-items-lg-end { + -webkit-box-align: end !important; + -ms-flex-align: end !important; + align-items: flex-end !important; + } + + .align-items-lg-center { + -webkit-box-align: center !important; + -ms-flex-align: center !important; + align-items: center !important; + } + + .align-items-lg-baseline { + -webkit-box-align: baseline !important; + -ms-flex-align: baseline !important; + align-items: baseline !important; + } + + .align-items-lg-stretch { + -webkit-box-align: stretch !important; + -ms-flex-align: stretch !important; + align-items: stretch !important; + } + + .align-content-lg-start { + -ms-flex-line-pack: start !important; + align-content: flex-start !important; + } + + .align-content-lg-end { + -ms-flex-line-pack: end !important; + align-content: flex-end !important; + } + + .align-content-lg-center { + -ms-flex-line-pack: center !important; + align-content: center !important; + } + + .align-content-lg-between { + -ms-flex-line-pack: justify !important; + align-content: space-between !important; + } + + .align-content-lg-around { + -ms-flex-line-pack: distribute !important; + align-content: space-around !important; + } + + .align-content-lg-stretch { + -ms-flex-line-pack: stretch !important; + align-content: stretch !important; + } + + .align-self-lg-auto { + -ms-flex-item-align: auto !important; + align-self: auto !important; + } + + .align-self-lg-start { + -ms-flex-item-align: start !important; + align-self: flex-start !important; + } + + .align-self-lg-end { + -ms-flex-item-align: end !important; + align-self: flex-end !important; + } + + .align-self-lg-center { + -ms-flex-item-align: center !important; + align-self: center !important; + } + + .align-self-lg-baseline { + -ms-flex-item-align: baseline !important; + align-self: baseline !important; + } + + .align-self-lg-stretch { + -ms-flex-item-align: stretch !important; + align-self: stretch !important; + } +} +@media (min-width: 1200px) { + .flex-xl-row { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: row !important; + flex-direction: row !important; + } + + .flex-xl-column { + -webkit-box-orient: vertical !important; + -webkit-box-direction: normal !important; + -ms-flex-direction: column !important; + flex-direction: column !important; + } + + .flex-xl-row-reverse { + -webkit-box-orient: horizontal !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: row-reverse !important; + flex-direction: row-reverse !important; + } + + .flex-xl-column-reverse { + -webkit-box-orient: vertical !important; + -webkit-box-direction: reverse !important; + -ms-flex-direction: column-reverse !important; + flex-direction: column-reverse !important; + } + + .flex-xl-wrap { + -ms-flex-wrap: wrap !important; + flex-wrap: wrap !important; + } + + .flex-xl-nowrap { + -ms-flex-wrap: nowrap !important; + flex-wrap: nowrap !important; + } + + .flex-xl-wrap-reverse { + -ms-flex-wrap: wrap-reverse !important; + flex-wrap: wrap-reverse !important; + } + + .justify-content-xl-start { + -webkit-box-pack: start !important; + -ms-flex-pack: start !important; + justify-content: flex-start !important; + } + + .justify-content-xl-end { + -webkit-box-pack: end !important; + -ms-flex-pack: end !important; + justify-content: flex-end !important; + } + + .justify-content-xl-center { + -webkit-box-pack: center !important; + -ms-flex-pack: center !important; + justify-content: center !important; + } + + .justify-content-xl-between { + -webkit-box-pack: justify !important; + -ms-flex-pack: justify !important; + justify-content: space-between !important; + } + + .justify-content-xl-around { + -ms-flex-pack: distribute !important; + justify-content: space-around !important; + } + + .align-items-xl-start { + -webkit-box-align: start !important; + -ms-flex-align: start !important; + align-items: flex-start !important; + } + + .align-items-xl-end { + -webkit-box-align: end !important; + -ms-flex-align: end !important; + align-items: flex-end !important; + } + + .align-items-xl-center { + -webkit-box-align: center !important; + -ms-flex-align: center !important; + align-items: center !important; + } + + .align-items-xl-baseline { + -webkit-box-align: baseline !important; + -ms-flex-align: baseline !important; + align-items: baseline !important; + } + + .align-items-xl-stretch { + -webkit-box-align: stretch !important; + -ms-flex-align: stretch !important; + align-items: stretch !important; + } + + .align-content-xl-start { + -ms-flex-line-pack: start !important; + align-content: flex-start !important; + } + + .align-content-xl-end { + -ms-flex-line-pack: end !important; + align-content: flex-end !important; + } + + .align-content-xl-center { + -ms-flex-line-pack: center !important; + align-content: center !important; + } + + .align-content-xl-between { + -ms-flex-line-pack: justify !important; + align-content: space-between !important; + } + + .align-content-xl-around { + -ms-flex-line-pack: distribute !important; + align-content: space-around !important; + } + + .align-content-xl-stretch { + -ms-flex-line-pack: stretch !important; + align-content: stretch !important; + } + + .align-self-xl-auto { + -ms-flex-item-align: auto !important; + align-self: auto !important; + } + + .align-self-xl-start { + -ms-flex-item-align: start !important; + align-self: flex-start !important; + } + + .align-self-xl-end { + -ms-flex-item-align: end !important; + align-self: flex-end !important; + } + + .align-self-xl-center { + -ms-flex-item-align: center !important; + align-self: center !important; + } + + .align-self-xl-baseline { + -ms-flex-item-align: baseline !important; + align-self: baseline !important; + } + + .align-self-xl-stretch { + -ms-flex-item-align: stretch !important; + align-self: stretch !important; + } +} +.float-left { + float: left !important; +} + +.float-right { + float: right !important; +} + +.float-none { + float: none !important; +} + +@media (min-width: 576px) { + .float-sm-left { + float: left !important; + } + + .float-sm-right { + float: right !important; + } + + .float-sm-none { + float: none !important; + } +} +@media (min-width: 768px) { + .float-md-left { + float: left !important; + } + + .float-md-right { + float: right !important; + } + + .float-md-none { + float: none !important; + } +} +@media (min-width: 992px) { + .float-lg-left { + float: left !important; + } + + .float-lg-right { + float: right !important; + } + + .float-lg-none { + float: none !important; + } +} +@media (min-width: 1200px) { + .float-xl-left { + float: left !important; + } + + .float-xl-right { + float: right !important; + } + + .float-xl-none { + float: none !important; + } +} +.position-static { + position: static !important; +} + +.position-relative { + position: relative !important; +} + +.position-absolute { + position: absolute !important; +} + +.position-fixed { + position: fixed !important; +} + +.position-sticky { + position: sticky !important; +} + +.fixed-top { + position: fixed; + top: 0; + right: 0; + left: 0; + z-index: 1030; +} + +.fixed-bottom { + position: fixed; + right: 0; + bottom: 0; + left: 0; + z-index: 1030; +} + +@supports (position: sticky) { + .sticky-top { + position: sticky; + top: 0; + z-index: 1020; + } +} + +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + -webkit-clip-path: inset(50%); + clip-path: inset(50%); + border: 0; +} + +.sr-only-focusable:active, .sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + overflow: visible; + clip: auto; + white-space: normal; + -webkit-clip-path: none; + clip-path: none; +} + +.w-25 { + width: 25% !important; +} + +.w-50 { + width: 50% !important; +} + +.w-75 { + width: 75% !important; +} + +.w-100 { + width: 100% !important; +} + +.h-25 { + height: 25% !important; +} + +.h-50 { + height: 50% !important; +} + +.h-75 { + height: 75% !important; +} + +.h-100 { + height: 100% !important; +} + +.mw-100 { + max-width: 100% !important; +} + +.mh-100 { + max-height: 100% !important; +} + +.m-0 { + margin: 0 !important; +} + +.mt-0, +.my-0 { + margin-top: 0 !important; +} + +.mr-0, +.mx-0 { + margin-right: 0 !important; +} + +.mb-0, +.my-0 { + margin-bottom: 0 !important; +} + +.ml-0, +.mx-0 { + margin-left: 0 !important; +} + +.m-1 { + margin: 0.25rem !important; +} + +.mt-1, +.my-1 { + margin-top: 0.25rem !important; +} + +.mr-1, +.mx-1 { + margin-right: 0.25rem !important; +} + +.mb-1, +.my-1 { + margin-bottom: 0.25rem !important; +} + +.ml-1, +.mx-1 { + margin-left: 0.25rem !important; +} + +.m-2 { + margin: 0.5rem !important; +} + +.mt-2, +.my-2 { + margin-top: 0.5rem !important; +} + +.mr-2, +.mx-2 { + margin-right: 0.5rem !important; +} + +.mb-2, +.my-2 { + margin-bottom: 0.5rem !important; +} + +.ml-2, +.mx-2 { + margin-left: 0.5rem !important; +} + +.m-3 { + margin: 1rem !important; +} + +.mt-3, +.my-3 { + margin-top: 1rem !important; +} + +.mr-3, +.mx-3 { + margin-right: 1rem !important; +} + +.mb-3, +.my-3 { + margin-bottom: 1rem !important; +} + +.ml-3, +.mx-3 { + margin-left: 1rem !important; +} + +.m-4 { + margin: 1.5rem !important; +} + +.mt-4, +.my-4 { + margin-top: 1.5rem !important; +} + +.mr-4, +.mx-4 { + margin-right: 1.5rem !important; +} + +.mb-4, +.my-4 { + margin-bottom: 1.5rem !important; +} + +.ml-4, +.mx-4 { + margin-left: 1.5rem !important; +} + +.m-5 { + margin: 3rem !important; +} + +.mt-5, +.my-5 { + margin-top: 3rem !important; +} + +.mr-5, +.mx-5 { + margin-right: 3rem !important; +} + +.mb-5, +.my-5 { + margin-bottom: 3rem !important; +} + +.ml-5, +.mx-5 { + margin-left: 3rem !important; +} + +.p-0 { + padding: 0 !important; +} + +.pt-0, +.py-0 { + padding-top: 0 !important; +} + +.pr-0, +.px-0 { + padding-right: 0 !important; +} + +.pb-0, +.py-0 { + padding-bottom: 0 !important; +} + +.pl-0, +.px-0 { + padding-left: 0 !important; +} + +.p-1 { + padding: 0.25rem !important; +} + +.pt-1, +.py-1 { + padding-top: 0.25rem !important; +} + +.pr-1, +.px-1 { + padding-right: 0.25rem !important; +} + +.pb-1, +.py-1 { + padding-bottom: 0.25rem !important; +} + +.pl-1, +.px-1 { + padding-left: 0.25rem !important; +} + +.p-2 { + padding: 0.5rem !important; +} + +.pt-2, +.py-2 { + padding-top: 0.5rem !important; +} + +.pr-2, +.px-2 { + padding-right: 0.5rem !important; +} + +.pb-2, +.py-2 { + padding-bottom: 0.5rem !important; +} + +.pl-2, +.px-2 { + padding-left: 0.5rem !important; +} + +.p-3 { + padding: 1rem !important; +} + +.pt-3, +.py-3 { + padding-top: 1rem !important; +} + +.pr-3, +.px-3 { + padding-right: 1rem !important; +} + +.pb-3, +.py-3 { + padding-bottom: 1rem !important; +} + +.pl-3, +.px-3 { + padding-left: 1rem !important; +} + +.p-4 { + padding: 1.5rem !important; +} + +.pt-4, +.py-4 { + padding-top: 1.5rem !important; +} + +.pr-4, +.px-4 { + padding-right: 1.5rem !important; +} + +.pb-4, +.py-4 { + padding-bottom: 1.5rem !important; +} + +.pl-4, +.px-4 { + padding-left: 1.5rem !important; +} + +.p-5 { + padding: 3rem !important; +} + +.pt-5, +.py-5 { + padding-top: 3rem !important; +} + +.pr-5, +.px-5 { + padding-right: 3rem !important; +} + +.pb-5, +.py-5 { + padding-bottom: 3rem !important; +} + +.pl-5, +.px-5 { + padding-left: 3rem !important; +} + +.m-auto { + margin: auto !important; +} + +.mt-auto, +.my-auto { + margin-top: auto !important; +} + +.mr-auto, +.mx-auto { + margin-right: auto !important; +} + +.mb-auto, +.my-auto { + margin-bottom: auto !important; +} + +.ml-auto, +.mx-auto { + margin-left: auto !important; +} + +@media (min-width: 576px) { + .m-sm-0 { + margin: 0 !important; + } + + .mt-sm-0, + .my-sm-0 { + margin-top: 0 !important; + } + + .mr-sm-0, + .mx-sm-0 { + margin-right: 0 !important; + } + + .mb-sm-0, + .my-sm-0 { + margin-bottom: 0 !important; + } + + .ml-sm-0, + .mx-sm-0 { + margin-left: 0 !important; + } + + .m-sm-1 { + margin: 0.25rem !important; + } + + .mt-sm-1, + .my-sm-1 { + margin-top: 0.25rem !important; + } + + .mr-sm-1, + .mx-sm-1 { + margin-right: 0.25rem !important; + } + + .mb-sm-1, + .my-sm-1 { + margin-bottom: 0.25rem !important; + } + + .ml-sm-1, + .mx-sm-1 { + margin-left: 0.25rem !important; + } + + .m-sm-2 { + margin: 0.5rem !important; + } + + .mt-sm-2, + .my-sm-2 { + margin-top: 0.5rem !important; + } + + .mr-sm-2, + .mx-sm-2 { + margin-right: 0.5rem !important; + } + + .mb-sm-2, + .my-sm-2 { + margin-bottom: 0.5rem !important; + } + + .ml-sm-2, + .mx-sm-2 { + margin-left: 0.5rem !important; + } + + .m-sm-3 { + margin: 1rem !important; + } + + .mt-sm-3, + .my-sm-3 { + margin-top: 1rem !important; + } + + .mr-sm-3, + .mx-sm-3 { + margin-right: 1rem !important; + } + + .mb-sm-3, + .my-sm-3 { + margin-bottom: 1rem !important; + } + + .ml-sm-3, + .mx-sm-3 { + margin-left: 1rem !important; + } + + .m-sm-4 { + margin: 1.5rem !important; + } + + .mt-sm-4, + .my-sm-4 { + margin-top: 1.5rem !important; + } + + .mr-sm-4, + .mx-sm-4 { + margin-right: 1.5rem !important; + } + + .mb-sm-4, + .my-sm-4 { + margin-bottom: 1.5rem !important; + } + + .ml-sm-4, + .mx-sm-4 { + margin-left: 1.5rem !important; + } + + .m-sm-5 { + margin: 3rem !important; + } + + .mt-sm-5, + .my-sm-5 { + margin-top: 3rem !important; + } + + .mr-sm-5, + .mx-sm-5 { + margin-right: 3rem !important; + } + + .mb-sm-5, + .my-sm-5 { + margin-bottom: 3rem !important; + } + + .ml-sm-5, + .mx-sm-5 { + margin-left: 3rem !important; + } + + .p-sm-0 { + padding: 0 !important; + } + + .pt-sm-0, + .py-sm-0 { + padding-top: 0 !important; + } + + .pr-sm-0, + .px-sm-0 { + padding-right: 0 !important; + } + + .pb-sm-0, + .py-sm-0 { + padding-bottom: 0 !important; + } + + .pl-sm-0, + .px-sm-0 { + padding-left: 0 !important; + } + + .p-sm-1 { + padding: 0.25rem !important; + } + + .pt-sm-1, + .py-sm-1 { + padding-top: 0.25rem !important; + } + + .pr-sm-1, + .px-sm-1 { + padding-right: 0.25rem !important; + } + + .pb-sm-1, + .py-sm-1 { + padding-bottom: 0.25rem !important; + } + + .pl-sm-1, + .px-sm-1 { + padding-left: 0.25rem !important; + } + + .p-sm-2 { + padding: 0.5rem !important; + } + + .pt-sm-2, + .py-sm-2 { + padding-top: 0.5rem !important; + } + + .pr-sm-2, + .px-sm-2 { + padding-right: 0.5rem !important; + } + + .pb-sm-2, + .py-sm-2 { + padding-bottom: 0.5rem !important; + } + + .pl-sm-2, + .px-sm-2 { + padding-left: 0.5rem !important; + } + + .p-sm-3 { + padding: 1rem !important; + } + + .pt-sm-3, + .py-sm-3 { + padding-top: 1rem !important; + } + + .pr-sm-3, + .px-sm-3 { + padding-right: 1rem !important; + } + + .pb-sm-3, + .py-sm-3 { + padding-bottom: 1rem !important; + } + + .pl-sm-3, + .px-sm-3 { + padding-left: 1rem !important; + } + + .p-sm-4 { + padding: 1.5rem !important; + } + + .pt-sm-4, + .py-sm-4 { + padding-top: 1.5rem !important; + } + + .pr-sm-4, + .px-sm-4 { + padding-right: 1.5rem !important; + } + + .pb-sm-4, + .py-sm-4 { + padding-bottom: 1.5rem !important; + } + + .pl-sm-4, + .px-sm-4 { + padding-left: 1.5rem !important; + } + + .p-sm-5 { + padding: 3rem !important; + } + + .pt-sm-5, + .py-sm-5 { + padding-top: 3rem !important; + } + + .pr-sm-5, + .px-sm-5 { + padding-right: 3rem !important; + } + + .pb-sm-5, + .py-sm-5 { + padding-bottom: 3rem !important; + } + + .pl-sm-5, + .px-sm-5 { + padding-left: 3rem !important; + } + + .m-sm-auto { + margin: auto !important; + } + + .mt-sm-auto, + .my-sm-auto { + margin-top: auto !important; + } + + .mr-sm-auto, + .mx-sm-auto { + margin-right: auto !important; + } + + .mb-sm-auto, + .my-sm-auto { + margin-bottom: auto !important; + } + + .ml-sm-auto, + .mx-sm-auto { + margin-left: auto !important; + } +} +@media (min-width: 768px) { + .m-md-0 { + margin: 0 !important; + } + + .mt-md-0, + .my-md-0 { + margin-top: 0 !important; + } + + .mr-md-0, + .mx-md-0 { + margin-right: 0 !important; + } + + .mb-md-0, + .my-md-0 { + margin-bottom: 0 !important; + } + + .ml-md-0, + .mx-md-0 { + margin-left: 0 !important; + } + + .m-md-1 { + margin: 0.25rem !important; + } + + .mt-md-1, + .my-md-1 { + margin-top: 0.25rem !important; + } + + .mr-md-1, + .mx-md-1 { + margin-right: 0.25rem !important; + } + + .mb-md-1, + .my-md-1 { + margin-bottom: 0.25rem !important; + } + + .ml-md-1, + .mx-md-1 { + margin-left: 0.25rem !important; + } + + .m-md-2 { + margin: 0.5rem !important; + } + + .mt-md-2, + .my-md-2 { + margin-top: 0.5rem !important; + } + + .mr-md-2, + .mx-md-2 { + margin-right: 0.5rem !important; + } + + .mb-md-2, + .my-md-2 { + margin-bottom: 0.5rem !important; + } + + .ml-md-2, + .mx-md-2 { + margin-left: 0.5rem !important; + } + + .m-md-3 { + margin: 1rem !important; + } + + .mt-md-3, + .my-md-3 { + margin-top: 1rem !important; + } + + .mr-md-3, + .mx-md-3 { + margin-right: 1rem !important; + } + + .mb-md-3, + .my-md-3 { + margin-bottom: 1rem !important; + } + + .ml-md-3, + .mx-md-3 { + margin-left: 1rem !important; + } + + .m-md-4 { + margin: 1.5rem !important; + } + + .mt-md-4, + .my-md-4 { + margin-top: 1.5rem !important; + } + + .mr-md-4, + .mx-md-4 { + margin-right: 1.5rem !important; + } + + .mb-md-4, + .my-md-4 { + margin-bottom: 1.5rem !important; + } + + .ml-md-4, + .mx-md-4 { + margin-left: 1.5rem !important; + } + + .m-md-5 { + margin: 3rem !important; + } + + .mt-md-5, + .my-md-5 { + margin-top: 3rem !important; + } + + .mr-md-5, + .mx-md-5 { + margin-right: 3rem !important; + } + + .mb-md-5, + .my-md-5 { + margin-bottom: 3rem !important; + } + + .ml-md-5, + .mx-md-5 { + margin-left: 3rem !important; + } + + .p-md-0 { + padding: 0 !important; + } + + .pt-md-0, + .py-md-0 { + padding-top: 0 !important; + } + + .pr-md-0, + .px-md-0 { + padding-right: 0 !important; + } + + .pb-md-0, + .py-md-0 { + padding-bottom: 0 !important; + } + + .pl-md-0, + .px-md-0 { + padding-left: 0 !important; + } + + .p-md-1 { + padding: 0.25rem !important; + } + + .pt-md-1, + .py-md-1 { + padding-top: 0.25rem !important; + } + + .pr-md-1, + .px-md-1 { + padding-right: 0.25rem !important; + } + + .pb-md-1, + .py-md-1 { + padding-bottom: 0.25rem !important; + } + + .pl-md-1, + .px-md-1 { + padding-left: 0.25rem !important; + } + + .p-md-2 { + padding: 0.5rem !important; + } + + .pt-md-2, + .py-md-2 { + padding-top: 0.5rem !important; + } + + .pr-md-2, + .px-md-2 { + padding-right: 0.5rem !important; + } + + .pb-md-2, + .py-md-2 { + padding-bottom: 0.5rem !important; + } + + .pl-md-2, + .px-md-2 { + padding-left: 0.5rem !important; + } + + .p-md-3 { + padding: 1rem !important; + } + + .pt-md-3, + .py-md-3 { + padding-top: 1rem !important; + } + + .pr-md-3, + .px-md-3 { + padding-right: 1rem !important; + } + + .pb-md-3, + .py-md-3 { + padding-bottom: 1rem !important; + } + + .pl-md-3, + .px-md-3 { + padding-left: 1rem !important; + } + + .p-md-4 { + padding: 1.5rem !important; + } + + .pt-md-4, + .py-md-4 { + padding-top: 1.5rem !important; + } + + .pr-md-4, + .px-md-4 { + padding-right: 1.5rem !important; + } + + .pb-md-4, + .py-md-4 { + padding-bottom: 1.5rem !important; + } + + .pl-md-4, + .px-md-4 { + padding-left: 1.5rem !important; + } + + .p-md-5 { + padding: 3rem !important; + } + + .pt-md-5, + .py-md-5 { + padding-top: 3rem !important; + } + + .pr-md-5, + .px-md-5 { + padding-right: 3rem !important; + } + + .pb-md-5, + .py-md-5 { + padding-bottom: 3rem !important; + } + + .pl-md-5, + .px-md-5 { + padding-left: 3rem !important; + } + + .m-md-auto { + margin: auto !important; + } + + .mt-md-auto, + .my-md-auto { + margin-top: auto !important; + } + + .mr-md-auto, + .mx-md-auto { + margin-right: auto !important; + } + + .mb-md-auto, + .my-md-auto { + margin-bottom: auto !important; + } + + .ml-md-auto, + .mx-md-auto { + margin-left: auto !important; + } +} +@media (min-width: 992px) { + .m-lg-0 { + margin: 0 !important; + } + + .mt-lg-0, + .my-lg-0 { + margin-top: 0 !important; + } + + .mr-lg-0, + .mx-lg-0 { + margin-right: 0 !important; + } + + .mb-lg-0, + .my-lg-0 { + margin-bottom: 0 !important; + } + + .ml-lg-0, + .mx-lg-0 { + margin-left: 0 !important; + } + + .m-lg-1 { + margin: 0.25rem !important; + } + + .mt-lg-1, + .my-lg-1 { + margin-top: 0.25rem !important; + } + + .mr-lg-1, + .mx-lg-1 { + margin-right: 0.25rem !important; + } + + .mb-lg-1, + .my-lg-1 { + margin-bottom: 0.25rem !important; + } + + .ml-lg-1, + .mx-lg-1 { + margin-left: 0.25rem !important; + } + + .m-lg-2 { + margin: 0.5rem !important; + } + + .mt-lg-2, + .my-lg-2 { + margin-top: 0.5rem !important; + } + + .mr-lg-2, + .mx-lg-2 { + margin-right: 0.5rem !important; + } + + .mb-lg-2, + .my-lg-2 { + margin-bottom: 0.5rem !important; + } + + .ml-lg-2, + .mx-lg-2 { + margin-left: 0.5rem !important; + } + + .m-lg-3 { + margin: 1rem !important; + } + + .mt-lg-3, + .my-lg-3 { + margin-top: 1rem !important; + } + + .mr-lg-3, + .mx-lg-3 { + margin-right: 1rem !important; + } + + .mb-lg-3, + .my-lg-3 { + margin-bottom: 1rem !important; + } + + .ml-lg-3, + .mx-lg-3 { + margin-left: 1rem !important; + } + + .m-lg-4 { + margin: 1.5rem !important; + } + + .mt-lg-4, + .my-lg-4 { + margin-top: 1.5rem !important; + } + + .mr-lg-4, + .mx-lg-4 { + margin-right: 1.5rem !important; + } + + .mb-lg-4, + .my-lg-4 { + margin-bottom: 1.5rem !important; + } + + .ml-lg-4, + .mx-lg-4 { + margin-left: 1.5rem !important; + } + + .m-lg-5 { + margin: 3rem !important; + } + + .mt-lg-5, + .my-lg-5 { + margin-top: 3rem !important; + } + + .mr-lg-5, + .mx-lg-5 { + margin-right: 3rem !important; + } + + .mb-lg-5, + .my-lg-5 { + margin-bottom: 3rem !important; + } + + .ml-lg-5, + .mx-lg-5 { + margin-left: 3rem !important; + } + + .p-lg-0 { + padding: 0 !important; + } + + .pt-lg-0, + .py-lg-0 { + padding-top: 0 !important; + } + + .pr-lg-0, + .px-lg-0 { + padding-right: 0 !important; + } + + .pb-lg-0, + .py-lg-0 { + padding-bottom: 0 !important; + } + + .pl-lg-0, + .px-lg-0 { + padding-left: 0 !important; + } + + .p-lg-1 { + padding: 0.25rem !important; + } + + .pt-lg-1, + .py-lg-1 { + padding-top: 0.25rem !important; + } + + .pr-lg-1, + .px-lg-1 { + padding-right: 0.25rem !important; + } + + .pb-lg-1, + .py-lg-1 { + padding-bottom: 0.25rem !important; + } + + .pl-lg-1, + .px-lg-1 { + padding-left: 0.25rem !important; + } + + .p-lg-2 { + padding: 0.5rem !important; + } + + .pt-lg-2, + .py-lg-2 { + padding-top: 0.5rem !important; + } + + .pr-lg-2, + .px-lg-2 { + padding-right: 0.5rem !important; + } + + .pb-lg-2, + .py-lg-2 { + padding-bottom: 0.5rem !important; + } + + .pl-lg-2, + .px-lg-2 { + padding-left: 0.5rem !important; + } + + .p-lg-3 { + padding: 1rem !important; + } + + .pt-lg-3, + .py-lg-3 { + padding-top: 1rem !important; + } + + .pr-lg-3, + .px-lg-3 { + padding-right: 1rem !important; + } + + .pb-lg-3, + .py-lg-3 { + padding-bottom: 1rem !important; + } + + .pl-lg-3, + .px-lg-3 { + padding-left: 1rem !important; + } + + .p-lg-4 { + padding: 1.5rem !important; + } + + .pt-lg-4, + .py-lg-4 { + padding-top: 1.5rem !important; + } + + .pr-lg-4, + .px-lg-4 { + padding-right: 1.5rem !important; + } + + .pb-lg-4, + .py-lg-4 { + padding-bottom: 1.5rem !important; + } + + .pl-lg-4, + .px-lg-4 { + padding-left: 1.5rem !important; + } + + .p-lg-5 { + padding: 3rem !important; + } + + .pt-lg-5, + .py-lg-5 { + padding-top: 3rem !important; + } + + .pr-lg-5, + .px-lg-5 { + padding-right: 3rem !important; + } + + .pb-lg-5, + .py-lg-5 { + padding-bottom: 3rem !important; + } + + .pl-lg-5, + .px-lg-5 { + padding-left: 3rem !important; + } + + .m-lg-auto { + margin: auto !important; + } + + .mt-lg-auto, + .my-lg-auto { + margin-top: auto !important; + } + + .mr-lg-auto, + .mx-lg-auto { + margin-right: auto !important; + } + + .mb-lg-auto, + .my-lg-auto { + margin-bottom: auto !important; + } + + .ml-lg-auto, + .mx-lg-auto { + margin-left: auto !important; + } +} +@media (min-width: 1200px) { + .m-xl-0 { + margin: 0 !important; + } + + .mt-xl-0, + .my-xl-0 { + margin-top: 0 !important; + } + + .mr-xl-0, + .mx-xl-0 { + margin-right: 0 !important; + } + + .mb-xl-0, + .my-xl-0 { + margin-bottom: 0 !important; + } + + .ml-xl-0, + .mx-xl-0 { + margin-left: 0 !important; + } + + .m-xl-1 { + margin: 0.25rem !important; + } + + .mt-xl-1, + .my-xl-1 { + margin-top: 0.25rem !important; + } + + .mr-xl-1, + .mx-xl-1 { + margin-right: 0.25rem !important; + } + + .mb-xl-1, + .my-xl-1 { + margin-bottom: 0.25rem !important; + } + + .ml-xl-1, + .mx-xl-1 { + margin-left: 0.25rem !important; + } + + .m-xl-2 { + margin: 0.5rem !important; + } + + .mt-xl-2, + .my-xl-2 { + margin-top: 0.5rem !important; + } + + .mr-xl-2, + .mx-xl-2 { + margin-right: 0.5rem !important; + } + + .mb-xl-2, + .my-xl-2 { + margin-bottom: 0.5rem !important; + } + + .ml-xl-2, + .mx-xl-2 { + margin-left: 0.5rem !important; + } + + .m-xl-3 { + margin: 1rem !important; + } + + .mt-xl-3, + .my-xl-3 { + margin-top: 1rem !important; + } + + .mr-xl-3, + .mx-xl-3 { + margin-right: 1rem !important; + } + + .mb-xl-3, + .my-xl-3 { + margin-bottom: 1rem !important; + } + + .ml-xl-3, + .mx-xl-3 { + margin-left: 1rem !important; + } + + .m-xl-4 { + margin: 1.5rem !important; + } + + .mt-xl-4, + .my-xl-4 { + margin-top: 1.5rem !important; + } + + .mr-xl-4, + .mx-xl-4 { + margin-right: 1.5rem !important; + } + + .mb-xl-4, + .my-xl-4 { + margin-bottom: 1.5rem !important; + } + + .ml-xl-4, + .mx-xl-4 { + margin-left: 1.5rem !important; + } + + .m-xl-5 { + margin: 3rem !important; + } + + .mt-xl-5, + .my-xl-5 { + margin-top: 3rem !important; + } + + .mr-xl-5, + .mx-xl-5 { + margin-right: 3rem !important; + } + + .mb-xl-5, + .my-xl-5 { + margin-bottom: 3rem !important; + } + + .ml-xl-5, + .mx-xl-5 { + margin-left: 3rem !important; + } + + .p-xl-0 { + padding: 0 !important; + } + + .pt-xl-0, + .py-xl-0 { + padding-top: 0 !important; + } + + .pr-xl-0, + .px-xl-0 { + padding-right: 0 !important; + } + + .pb-xl-0, + .py-xl-0 { + padding-bottom: 0 !important; + } + + .pl-xl-0, + .px-xl-0 { + padding-left: 0 !important; + } + + .p-xl-1 { + padding: 0.25rem !important; + } + + .pt-xl-1, + .py-xl-1 { + padding-top: 0.25rem !important; + } + + .pr-xl-1, + .px-xl-1 { + padding-right: 0.25rem !important; + } + + .pb-xl-1, + .py-xl-1 { + padding-bottom: 0.25rem !important; + } + + .pl-xl-1, + .px-xl-1 { + padding-left: 0.25rem !important; + } + + .p-xl-2 { + padding: 0.5rem !important; + } + + .pt-xl-2, + .py-xl-2 { + padding-top: 0.5rem !important; + } + + .pr-xl-2, + .px-xl-2 { + padding-right: 0.5rem !important; + } + + .pb-xl-2, + .py-xl-2 { + padding-bottom: 0.5rem !important; + } + + .pl-xl-2, + .px-xl-2 { + padding-left: 0.5rem !important; + } + + .p-xl-3 { + padding: 1rem !important; + } + + .pt-xl-3, + .py-xl-3 { + padding-top: 1rem !important; + } + + .pr-xl-3, + .px-xl-3 { + padding-right: 1rem !important; + } + + .pb-xl-3, + .py-xl-3 { + padding-bottom: 1rem !important; + } + + .pl-xl-3, + .px-xl-3 { + padding-left: 1rem !important; + } + + .p-xl-4 { + padding: 1.5rem !important; + } + + .pt-xl-4, + .py-xl-4 { + padding-top: 1.5rem !important; + } + + .pr-xl-4, + .px-xl-4 { + padding-right: 1.5rem !important; + } + + .pb-xl-4, + .py-xl-4 { + padding-bottom: 1.5rem !important; + } + + .pl-xl-4, + .px-xl-4 { + padding-left: 1.5rem !important; + } + + .p-xl-5 { + padding: 3rem !important; + } + + .pt-xl-5, + .py-xl-5 { + padding-top: 3rem !important; + } + + .pr-xl-5, + .px-xl-5 { + padding-right: 3rem !important; + } + + .pb-xl-5, + .py-xl-5 { + padding-bottom: 3rem !important; + } + + .pl-xl-5, + .px-xl-5 { + padding-left: 3rem !important; + } + + .m-xl-auto { + margin: auto !important; + } + + .mt-xl-auto, + .my-xl-auto { + margin-top: auto !important; + } + + .mr-xl-auto, + .mx-xl-auto { + margin-right: auto !important; + } + + .mb-xl-auto, + .my-xl-auto { + margin-bottom: auto !important; + } + + .ml-xl-auto, + .mx-xl-auto { + margin-left: auto !important; + } +} +.text-justify { + text-align: justify !important; +} + +.text-nowrap { + white-space: nowrap !important; +} + +.text-truncate { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.text-left { + text-align: left !important; +} + +.text-right { + text-align: right !important; +} + +.text-center { + text-align: center !important; +} + +@media (min-width: 576px) { + .text-sm-left { + text-align: left !important; + } + + .text-sm-right { + text-align: right !important; + } + + .text-sm-center { + text-align: center !important; + } +} +@media (min-width: 768px) { + .text-md-left { + text-align: left !important; + } + + .text-md-right { + text-align: right !important; + } + + .text-md-center { + text-align: center !important; + } +} +@media (min-width: 992px) { + .text-lg-left { + text-align: left !important; + } + + .text-lg-right { + text-align: right !important; + } + + .text-lg-center { + text-align: center !important; + } +} +@media (min-width: 1200px) { + .text-xl-left { + text-align: left !important; + } + + .text-xl-right { + text-align: right !important; + } + + .text-xl-center { + text-align: center !important; + } +} +.text-lowercase { + text-transform: lowercase !important; +} + +.text-uppercase { + text-transform: uppercase !important; +} + +.text-capitalize { + text-transform: capitalize !important; +} + +.font-weight-light { + font-weight: 300 !important; +} + +.font-weight-normal { + font-weight: 400 !important; +} + +.font-weight-bold { + font-weight: 700 !important; +} + +.font-italic { + font-style: italic !important; +} + +.text-white { + color: #fff !important; +} + +.text-primary { + color: #007bff !important; +} + +a.text-primary:hover, a.text-primary:focus { + color: #0062cc !important; +} + +.text-secondary { + color: #6c757d !important; +} + +a.text-secondary:hover, a.text-secondary:focus { + color: #545b62 !important; +} + +.text-success { + color: #28a745 !important; +} + +a.text-success:hover, a.text-success:focus { + color: #1e7e34 !important; +} + +.text-info { + color: #17a2b8 !important; +} + +a.text-info:hover, a.text-info:focus { + color: #117a8b !important; +} + +.text-warning { + color: #ffc107 !important; +} + +a.text-warning:hover, a.text-warning:focus { + color: #d39e00 !important; +} + +.text-danger { + color: #dc3545 !important; +} + +a.text-danger:hover, a.text-danger:focus { + color: #bd2130 !important; +} + +.text-light { + color: #f8f9fa !important; +} + +a.text-light:hover, a.text-light:focus { + color: #dae0e5 !important; +} + +.text-dark { + color: #343a40 !important; +} + +a.text-dark:hover, a.text-dark:focus { + color: #1d2124 !important; +} + +.text-muted { + color: #6c757d !important; +} + +.text-hide { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} + +.visible { + visibility: visible !important; +} + +.invisible { + visibility: hidden !important; +} + +@media print { + *, + *::before, + *::after { + text-shadow: none !important; + -webkit-box-shadow: none !important; + box-shadow: none !important; + } + + a:not(.btn) { + text-decoration: underline; + } + + abbr[title]::after { + content: " (" attr(title) ")"; + } + + pre { + white-space: pre-wrap !important; + } + + pre, + blockquote { + border: 1px solid #999; + page-break-inside: avoid; + } + + thead { + display: table-header-group; + } + + tr, + img { + page-break-inside: avoid; + } + + p, + h2, + h3 { + orphans: 3; + widows: 3; + } + + h2, + h3 { + page-break-after: avoid; + } + + @page { + size: a3; + } + body { + min-width: 992px !important; + } + + .container { + min-width: 992px !important; + } + + .navbar { + display: none; + } + + .badge { + border: 1px solid #000; + } + + .table { + border-collapse: collapse !important; + } + .table td, + .table th { + background-color: #fff !important; + } + + .table-bordered th, + .table-bordered td { + border: 1px solid #ddd !important; + } +} +/*Github syntax highlighting theme via Rouge*/ +.highlight table td { + padding: 5px; +} + +.highlight table pre { + margin: 0; +} + +.highlight .cm { + color: #999988; + font-style: italic; +} + +.highlight .cp { + color: #999999; + font-weight: bold; +} + +.highlight .c1 { + color: #999988; + font-style: italic; +} + +.highlight .cs { + color: #999999; + font-weight: bold; + font-style: italic; +} + +.highlight .c, .highlight .cd { + color: #999988; + font-style: italic; +} + +.highlight .err { + color: #a61717; + background-color: #e3d2d2; +} + +.highlight .gd { + color: #000000; + background-color: #ffdddd; +} + +.highlight .ge { + color: #000000; + font-style: italic; +} + +.highlight .gr { + color: #aa0000; +} + +.highlight .gh { + color: #999999; +} + +.highlight .gi { + color: #000000; + background-color: #ddffdd; +} + +.highlight .go { + color: #888888; +} + +.highlight .gp { + color: #555555; +} + +.highlight .gs { + font-weight: bold; +} + +.highlight .gu { + color: #aaaaaa; +} + +.highlight .gt { + color: #aa0000; +} + +.highlight .kc { + color: #000000; + font-weight: bold; +} + +.highlight .kd { + color: #000000; + font-weight: bold; +} + +.highlight .kn { + color: #000000; + font-weight: bold; +} + +.highlight .kp { + color: #000000; + font-weight: bold; +} + +.highlight .kr { + color: #000000; + font-weight: bold; +} + +.highlight .kt { + color: #445588; + font-weight: bold; +} + +.highlight .k, .highlight .kv { + color: #000000; + font-weight: bold; +} + +.highlight .mf { + color: #009999; +} + +.highlight .mh { + color: #009999; +} + +.highlight .il { + color: #009999; +} + +.highlight .mi { + color: #009999; +} + +.highlight .mo { + color: #009999; +} + +.highlight .m, .highlight .mb, .highlight .mx { + color: #009999; +} + +.highlight .sb { + color: #d14; +} + +.highlight .sc { + color: #d14; +} + +.highlight .sd { + color: #d14; +} + +.highlight .s2 { + color: #d14; +} + +.highlight .se { + color: #d14; +} + +.highlight .sh { + color: #d14; +} + +.highlight .si { + color: #d14; +} + +.highlight .sx { + color: #d14; +} + +.highlight .sr { + color: #009926; +} + +.highlight .s1 { + color: #d14; +} + +.highlight .ss { + color: #990073; +} + +.highlight .s { + color: #d14; +} + +.highlight .na { + color: #008080; +} + +.highlight .bp { + color: #525252; +} + +.highlight .nb { + color: #0086B3; +} + +.highlight .nc { + color: #445588; + font-weight: bold; +} + +.highlight .no { + color: #008080; +} + +.highlight .nd { + color: #3c5d5d; + font-weight: bold; +} + +.highlight .ni { + color: #800080; +} + +.highlight .ne { + color: #990000; + font-weight: bold; +} + +.highlight .nf { + color: #990000; + font-weight: bold; +} + +.highlight .nl { + color: #990000; + font-weight: bold; +} + +.highlight .nn { + color: #555555; +} + +.highlight .nt { + color: #000080; +} + +.highlight .vc { + color: #008080; +} + +.highlight .vg { + color: #008080; +} + +.highlight .vi { + color: #008080; +} + +.highlight .nv { + color: #008080; +} + +.highlight .ow { + color: #000000; + font-weight: bold; +} + +.highlight .o { + color: #000000; + font-weight: bold; +} + +.highlight .n { + color: #000000; + font-weight: bold; +} + +.highlight .p { + color: #000000; + font-weight: bold; +} + +.highlight .w { + color: #bbbbbb; +} + +.highlight { + background-color: #f8f8f8; +} + +@font-face { + font-family: FreightSans; + font-weight: 700; + font-style: normal; + src: url("../fonts/FreightSans/freight-sans-bold.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-bold.woff") format("woff"); +} +@font-face { + font-family: FreightSans; + font-weight: 700; + font-style: italic; + src: url("../fonts/FreightSans/freight-sans-bold-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-bold-italic.woff") format("woff"); +} +@font-face { + font-family: FreightSans; + font-weight: 500; + font-style: normal; + src: url("../fonts/FreightSans/freight-sans-medium.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-medium.woff") format("woff"); +} +@font-face { + font-family: FreightSans; + font-weight: 500; + font-style: italic; + src: url("../fonts/FreightSans/freight-sans-medium-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-medium-italic.woff") format("woff"); +} +@font-face { + font-family: FreightSans; + font-weight: 100; + font-style: normal; + src: url("../fonts/FreightSans/freight-sans-light.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-light.woff") format("woff"); +} +@font-face { + font-family: FreightSans; + font-weight: 100; + font-style: italic; + src: url("../fonts/FreightSans/freight-sans-light-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-light-italic.woff") format("woff"); +} +@font-face { + font-family: FreightSans; + font-weight: 400; + font-style: italic; + src: url("../fonts/FreightSans/freight-sans-book-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-book-italic.woff") format("woff"); +} +@font-face { + font-family: FreightSans; + font-weight: 400; + font-style: normal; + src: url("../fonts/FreightSans/freight-sans-book.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-book.woff") format("woff"); +} +@font-face { + font-family: IBMPlexMono; + font-weight: 600; + font-style: normal; + unicode-range: u+0020-007f; + src: local("IBMPlexMono-SemiBold"), url("../fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-SemiBold.woff") format("woff"); +} +@font-face { + font-family: IBMPlexMono; + font-weight: 500; + font-style: normal; + unicode-range: u+0020-007f; + src: local("IBMPlexMono-Medium"), url("../fonts/IBMPlexMono/IBMPlexMono-Medium.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-Medium.woff") format("woff"); +} +@font-face { + font-family: IBMPlexMono; + font-weight: 400; + font-style: normal; + unicode-range: u+0020-007f; + src: local("IBMPlexMono-Regular"), url("../fonts/IBMPlexMono/IBMPlexMono-Regular.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-Regular.woff") format("woff"); +} +@font-face { + font-family: IBMPlexMono; + font-weight: 300; + font-style: normal; + unicode-range: u+0020-007f; + src: local("IBMPlexMono-Light"), url("../fonts/IBMPlexMono/IBMPlexMono-Light.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-Light.woff") format("woff"); +} +@font-face { + font-family: FontAwesome; + font-weight: normal; + font-style: normal; + src: url("../fonts/FontAwesome/fontawesome-webfont.eot?v=4.7.0"); + src: url("../fonts/FontAwesome/fontawesome-webfont.eot?#iefix&v=4.7.0") format("embedded-opentype"), url("../fonts/FontAwesome/fontawesome-webfont.woff2?v=4.7.0") format("woff2"), url("../fonts/FontAwesome/fontawesome-webfont.woff?v=4.7.0") format("woff"), url("../fonts/FontAwesome/fontawesome-webfont.ttf?v=4.7.0") format("truetype"), url("../fonts/FontAwesome/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular") format("svg") +} +html { + position: relative; + min-height: 100%; + font-size: 12px; +} +@media screen and (min-width: 768px) { + html { + font-size: 16px; + } +} + +* { + -webkit-box-sizing: border-box; + box-sizing: border-box; +} + +body { + font-family: FreightSans, Helvetica Neue, Helvetica, Arial, sans-serif; +} + +a:link, +a:visited, +a:hover { + text-decoration: none; + color: var(--pytorch); +} + +a.with-right-arrow, .btn.with-right-arrow { + padding-right: 1.375rem; + position: relative; + background-image: url("../images/chevron-right-orange.svg"); + background-size: 6px 13px; + background-position: center right 5px; + background-repeat: no-repeat; +} +@media screen and (min-width: 768px) { + a.with-right-arrow, .btn.with-right-arrow { + background-size: 8px 14px; + background-position: center right 12px; + padding-right: 2rem; + } +} + +::-webkit-input-placeholder { + color: var(--pytorch); +} + +::-moz-placeholder { + color: var(--pytorch); +} + +:-ms-input-placeholder { + color: var(--pytorch); +} + +:-moz-placeholder { + color: var(--pytorch); +} + +.email-subscribe-form input.email { + color: var(--pytorch); + border: none; + border-bottom: 1px solid #939393; + width: 100%; + background-color: transparent; + outline: none; + font-size: 1.125rem; + letter-spacing: 0.25px; + line-height: 2.25rem; +} +.email-subscribe-form input[type="submit"] { + position: absolute; + right: 0; + top: 10px; + height: 15px; + width: 15px; + background-image: url("../images/arrow-right-with-tail.svg"); + background-color: transparent; + background-repeat: no-repeat; + background-size: 15px 15px; + background-position: center center; + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; + border: 0; +} + +.email-subscribe-form-fields-wrapper { + position: relative; +} + +.anchorjs-link { + color: #6c6c6d !important; +} +@media screen and (min-width: 768px) { + .anchorjs-link:hover { + color: inherit; + text-decoration: none !important; + } +} + +.sphinx-template-article #table-of-contents { + display: none; +} + +code, kbd, pre, samp { + font-family: IBMPlexMono,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace; +} + +pre { + padding: 1.125rem; +} +pre code { + font-size: 0.875rem; +} +pre.highlight { + background-color: #f3f4f7; + line-height: 1.3125rem; +} + +code.highlighter-rouge { + color: #6c6c6d; + background-color: #f3f4f7; + padding: 2px 6px; +} + +a:link code.highlighter-rouge, +a:visited code.highlighter-rouge, +a:hover code.highlighter-rouge { + color: #4974D1; +} + +p code, +h1 code, +h2 code, +h3 code, +h4 code, +h5 code, +h6 code { + font-size: 78.5%; +} + +pre { + white-space: pre-wrap; + white-space: -moz-pre-wrap; + white-space: -pre-wrap; + white-space: -o-pre-wrap; + word-wrap: break-word; +} + +.header-holder { + height: 68px; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + left: 0; + margin-left: auto; + margin-right: auto; + position: fixed; + right: 0; + top: 0; + width: 100%; + z-index: 9999; + background-color: #ffffff; + border-bottom: 1px solid #e2e2e2; +} +@media screen and (min-width: 1100px) { + .header-holder { + height: 90px; + } +} + +.header-container { + position: relative; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; +} +.header-container:before, .header-container:after { + content: ""; + display: table; +} +.header-container:after { + clear: both; +} +.header-container { + *zoom: 1; +} +@media screen and (min-width: 1100px) { + .header-container { + display: block; + } +} + +.header-logo { + height: 23px; + width: 93px; + background-repeat: no-repeat; + background-size: 93px 23px; + display: block; + float: left; + z-index: 10; +} +@media screen and (min-width: 1100px) { + .header-logo { + background-size: 108px 27px; + position: absolute; + height: 27px; + width: 108px; + top: 4px; + float: none; + } +} + +.main-menu-open-button { + background-image: url("../images/icon-menu-dots.svg"); + background-position: center center; + background-size: 25px 7px; + background-repeat: no-repeat; + width: 25px; + height: 17px; + position: absolute; + right: 0; + top: 4px; +} +@media screen and (min-width: 1100px) { + .main-menu-open-button { + display: none; + } +} + +.header-holder .main-menu { + display: none; +} +@media screen and (min-width: 1100px) { + .header-holder .main-menu { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: end; + -ms-flex-pack: end; + justify-content: flex-end; + } +} +.header-holder .main-menu ul { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + margin: 0; +} +.header-holder .main-menu ul li { + display: inline-block; + margin-right: 40px; + position: relative; +} +.header-holder .main-menu ul li.active:after { + content: "•"; + bottom: -24px; + color: var(--pytorch); + font-size: 1.375rem; + left: 0; + position: absolute; + right: 0; + text-align: center; +} +.header-holder .main-menu ul li.active a { + color: var(--pytorch); +} +.header-holder .main-menu ul li.docs-active:after { + content: "•"; + bottom: -24px; + color: var(--pytorch); + font-size: 1.375rem; + left: -24px; + position: absolute; + right: 0; + text-align: center; +} +.header-holder .main-menu ul li:last-of-type { + margin-right: 0; +} +.header-holder .main-menu ul li a { + color: #ffffff; + font-size: 1.3rem; + letter-spacing: 0; + line-height: 2.125rem; + text-align: center; + text-decoration: none; +} +@media screen and (min-width: 1100px) { + .header-holder .main-menu ul li a:hover { + color: var(--pytorch); + } +} + +.mobile-main-menu { + display: none; +} +.mobile-main-menu.open { + background-color: #262626; + display: block; + height: 100%; + left: 0; + margin-left: auto; + margin-right: auto; + min-height: 100%; + position: fixed; + right: 0; + top: 0; + width: 100%; + z-index: 99999; +} + +.mobile-main-menu .container-fluid { + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + height: 68px; + position: relative; +} +.mobile-main-menu .container-fluid:before, .mobile-main-menu .container-fluid:after { + content: ""; + display: table; +} +.mobile-main-menu .container-fluid:after { + clear: both; +} +.mobile-main-menu .container-fluid { + *zoom: 1; +} + +.mobile-main-menu.open ul { + list-style-type: none; + padding: 0; +} +.mobile-main-menu.open ul li a, .mobile-main-menu.open .resources-mobile-menu-title { + font-size: 2rem; + color: #ffffff; + letter-spacing: 0; + line-height: 4rem; + text-decoration: none; +} +.mobile-main-menu.open ul li.active a { + color: var(--pytorch); +} + +.main-menu-close-button { + background-image: url("../images/icon-close.svg"); + background-position: center center; + background-repeat: no-repeat; + background-size: 24px 24px; + height: 24px; + position: absolute; + right: 0; + width: 24px; + top: -4px; +} + +.mobile-main-menu-header-container { + position: relative; +} + +.mobile-main-menu-links-container { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + padding-left: 2.8125rem; + height: 90vh; + margin-top: -25px; + padding-top: 50%; + overflow-y: scroll; +} +.mobile-main-menu-links-container .main-menu { + height: 100vh; +} + +.mobile-main-menu-links-container ul.resources-mobile-menu-items li { + padding-left: 30px; +} +.mobile-main-menu-links-container ul.resources-mobile-menu-items li { + padding-left: 30px; +} +.mobile-main-menu-links-container ul.resources-mobile-menu-items li.active { + padding-left: 15px; +} +.mobile-main-menu-links-container ul.resources-mobile-menu-items li.active a { + color: var(--google-green); +} +.mobile-main-menu-links-container ul.resources-mobile-menu-items li.active:before { + content: "•"; + color: var(--google-green); + font-size: 1.375rem; + text-align: left; + display: inline-block; + width: 13px; +} + +.site-footer { + padding: 2.5rem 0; + width: 100%; + background: #000000; + background-size: 100%; + margin-left: 0; + margin-right: 0; + position: relative; + z-index: 201; +} +@media screen and (min-width: 768px) { + .site-footer { + padding: 5rem 0; + } +} +.site-footer p { + color: #ffffff; +} +.site-footer ul { + list-style-type: none; + padding-left: 0; + margin-bottom: 0; +} +.site-footer ul li { + font-size: 1.125rem; + line-height: 2rem; + color: #A0A0A1; + padding-bottom: 0.375rem; +} +.site-footer ul li.list-title { + padding-bottom: 0.75rem; + color: #ffffff; +} +.site-footer a:link, +.site-footer a:visited { + color: inherit; +} +@media screen and (min-width: 768px) { + .site-footer a:hover { + color: var(--pytorch); + } +} + +.docs-resources { + background-color: #262626; + color: #ffffff; + padding-top: 2.5rem; + padding-bottom: 2.5rem; + position: relative; + z-index: 201; +} +@media screen and (min-width: 768px) { + .docs-resources { + padding-top: 5rem; + padding-bottom: 5rem; + } +} +.docs-resources p { + color: #929292; + font-size: 1.125rem; +} +.docs-resources h2 { + font-size: 1.5rem; + letter-spacing: -0.25px; + text-transform: none; + margin-bottom: 0.25rem; +} +@media screen and (min-width: 768px) { + .docs-resources h2 { + margin-bottom: 1.25rem; + } +} +.docs-resources .col-md-4 { + margin-bottom: 2rem; + text-align: center; +} +@media screen and (min-width: 768px) { + .docs-resources .col-md-4 { + margin-bottom: 0; + } +} +.docs-resources .with-right-arrow { + margin-left: 12px; +} +.docs-resources .with-right-arrow:hover { + background-image: url("../images/chevron-right-white.svg"); +} +.docs-resources p { + font-size: 1rem; + line-height: 1.5rem; + letter-spacing: 0.22px; + color: #939393; + margin-bottom: 0; +} +@media screen and (min-width: 768px) { + .docs-resources p { + margin-bottom: 1.25rem; + } +} +.docs-resources a { + font-size: 1.125rem; + color: var(--pytorch); +} +.docs-resources a:hover { + color: #ffffff; +} + +.footer-container { + position: relative; +} + +@media screen and (min-width: 768px) { + .footer-logo-wrapper { + position: absolute; + top: 0; + left: 30px; + } +} + +.footer-logo { + background-position: center; + background-repeat: no-repeat; + background-size: 20px 24px; + display: block; + height: 24px; + margin-bottom: 2.8125rem; + width: 20px; +} +@media screen and (min-width: 768px) { + .footer-logo { + background-size: 29px 36px; + height: 36px; + margin-bottom: 0; + margin-bottom: 0; + width: 29px; + } +} + +.footer-links-wrapper { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; +} +@media screen and (min-width: 768px) { + .footer-links-wrapper { + -ms-flex-wrap: initial; + flex-wrap: initial; + -webkit-box-pack: end; + -ms-flex-pack: end; + justify-content: flex-end; + } +} + +.footer-links-col { + margin-bottom: 3.75rem; + width: 50%; +} +@media screen and (min-width: 768px) { + .footer-links-col { + margin-bottom: 0; + width: 14%; + margin-right: 23px; + } + .footer-links-col.follow-us-col { + width: 18%; + margin-right: 0; + } +} +@media (min-width: 768px) and (max-width: 1239px) { + .footer-links-col { + width: 18%; + margin-right: 30px; + } +} + +.footer-social-icons { + margin: 8.5625rem 0 2.5rem 0; +} +.footer-social-icons a { + height: 32px; + width: 32px; + display: inline-block; + background-color: #CCCDD1; + border-radius: 50%; + margin-right: 5px; +} +.footer-social-icons a.facebook { + background-image: url("../images/logo-facebook-dark.svg"); + background-position: center center; + background-size: 9px 18px; + background-repeat: no-repeat; +} +.footer-social-icons a.twitter { + background-image: url("../images/logo-twitter-dark.svg"); + background-position: center center; + background-size: 17px 17px; + background-repeat: no-repeat; +} +.footer-social-icons a.youtube { + background-image: url("../images/logo-youtube-dark.svg"); + background-position: center center; + background-repeat: no-repeat; +} + +.site-footer .mc-field-group { + margin-top: -2px; +} + +article.sphinx-template-article { + max-width: 920px; + margin: 0 auto; +} +article.sphinx-template-article h2, +article.sphinx-template-article h3, +article.sphinx-template-article h4, +article.sphinx-template-article h5, +article.sphinx-template-article h6 { + margin: 1.375rem 0; + color: #262626; +} +article.sphinx-template-article h2 { + font-size: 1.625rem; + letter-spacing: 1.33px; + line-height: 2rem; + text-transform: none; +} +article.sphinx-template-article h3 { + font-size: 1.5rem; + letter-spacing: -0.25px; + line-height: 1.875rem; + text-transform: none; +} +article.sphinx-template-article h4, +article.sphinx-template-article h5, +article.sphinx-template-article h6 { + font-size: 1.125rem; + letter-spacing: -0.19px; + line-height: 1.875rem; +} +article.sphinx-template-article p { + margin-bottom: 1.125rem; +} +article.sphinx-template-article p, +article.sphinx-template-article ul li, +article.sphinx-template-article ol li, +article.sphinx-template-article dl dt, +article.sphinx-template-article dl dd, +article.sphinx-template-article blockquote { + font-size: 1rem; + line-height: 1.375rem; + color: #262626; + letter-spacing: 0.01px; + font-weight: 500; +} +article.sphinx-template-article table { + margin-bottom: 2.5rem; + width: 100%; +} +article.sphinx-template-article table thead { + border-bottom: 1px solid #cacaca; +} +article.sphinx-template-article table th { + padding: 0.625rem; + color: #262626; +} +article.sphinx-template-article table td { + padding: 0.3125rem; +} +article.sphinx-template-article table tr th:first-of-type, +article.sphinx-template-article table tr td:first-of-type { + padding-left: 0; +} +article.sphinx-template-article table.docutils.field-list th.field-name { + padding: 0.3125rem; + padding-left: 0; +} +article.sphinx-template-article table.docutils.field-list td.field-body { + padding: 0.3125rem; +} +article.sphinx-template-article table.docutils.field-list td.field-body p:last-of-type { + margin-bottom: 0; +} +article.sphinx-template-article ul, +article.sphinx-template-article ol { + margin: 1.5rem 0 3.125rem 0; +} +@media screen and (min-width: 768px) { + article.sphinx-template-article ul, + article.sphinx-template-article ol { + padding-left: 6.25rem; + } +} +article.sphinx-template-article ul li, +article.sphinx-template-article ol li { + margin-bottom: 0.625rem; +} +article.sphinx-template-article dl { + margin-bottom: 1.5rem; +} +article.sphinx-template-article dl dt { + margin-bottom: 0.75rem; +} +article.sphinx-template-article pre { + margin-bottom: 2.5rem; +} +article.sphinx-template-article hr { + margin-top: 4.6875rem; + margin-bottom: 4.6875rem; +} +article.sphinx-template-article blockquote { + margin: 0 auto; + margin-bottom: 2.5rem; + width: 65%; +} + +html { + height: 100%; +} +@media screen and (min-width: 768px) { + html { + font-size: 16px; + } +} + +body { + background: #ffffff; + height: 100%; + margin: 0; +} +body.no-scroll { + height: 100%; + overflow: hidden; +} + +p { + margin-top: 0; + margin-bottom: 1.125rem; +} +p a:link, +p a:visited, +p a:hover { + color: var(--pytorch); + text-decoration: none; +} +@media screen and (min-width: 768px) { + p a:hover { + text-decoration: underline; + } +} +p a:link, +p a:visited, +p a:hover { + color: var(--pytorch-red); +} + +.sphinx-template-breadcrumbs li a { + color: var(--pytorch-red); +} + +ul.sphinx-template-breadcrumbs { + padding-left: 0; + list-style-type: none; +} +ul.sphinx-template-breadcrumbs li { + display: inline-block; + font-size: 0.875rem; +} +ul.sphinx-template-breadcrumbs a { + color: var(--pytorch-red); + text-decoration: none; +} + +.table-of-contents-link-wrapper { + display: block; + margin-top: 0; + padding: 1.25rem 1.875rem; + background-color: #f3f4f7; + position: relative; + color: #262626; + font-size: 1.25rem; +} +.table-of-contents-link-wrapper.is-open .toggle-table-of-contents { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); +} +@media screen and (min-width: 1100px) { + .table-of-contents-link-wrapper { + display: none; + } +} + +.toggle-table-of-contents { + background-image: url("../images/chevron-down-grey.svg"); + background-position: center center; + background-repeat: no-repeat; + background-size: 18px 18px; + height: 100%; + position: absolute; + right: 21px; + width: 30px; + top: 0; +} + +.docs-header .main-menu ul li a { + color: #262626; +} +.docs-header .main-menu-open-button { + background-image: url("../images/icon-menu-dots-dark.svg"); +} + +.rst-content footer .rating-hr.hr-top { + margin-bottom: -0.0625rem; +} +.rst-content footer .rating-hr.hr-bottom { + margin-top: -0.0625rem; +} +.rst-content footer .rating-container { + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + font-size: 1.125rem; +} +.rst-content footer .rating-container .rating-prompt, .rst-content footer .rating-container .was-helpful-thank-you { + padding: 0.625rem 1.25rem 0.625rem 1.25rem; +} +.rst-content footer .rating-container .was-helpful-thank-you { + display: none; +} +.rst-content footer .rating-container .rating-prompt.yes-link, .rst-content footer .rating-container .rating-prompt.no-link { + color: var(--pytorch); + cursor: pointer; +} +.rst-content footer .rating-container .rating-prompt.yes-link:hover, .rst-content footer .rating-container .rating-prompt.no-link:hover { + background-color: var(--pytorch); + color: #ffffff; +} +.rst-content footer .rating-container .stars-outer { + display: inline-block; + position: relative; + font-family: FontAwesome; + padding: 0.625rem 1.25rem 0.625rem 1.25rem; +} +.rst-content footer .rating-container .stars-outer i { + cursor: pointer; +} +.rst-content footer .rating-container .stars-outer .star-fill { + color: var(--pytorch-red); +} +.rst-content footer div[role="contentinfo"] { + padding-top: 2.5rem; +} +.rst-content footer div[role="contentinfo"] p { + margin-bottom: 0; +} +.rst-content .highlighted { + background: var(--yellow); + box-shadow: 0 0 0 2 var(--yellow); + display: inline; + font-weight: bold; + margin-bottom: 0; +} +.rst-content .style-external-links a.reference.external:after { + font-family: FontAwesome; + content: "\f08e"; + color: var(--readthedoc); + vertical-align: super; + font-size: 60%; + margin: 0 0.2em; +} +.rst-content :not(h1, h2, h3, h4, h5, h6, +.toctree-l1, .toctree-l2, .toctree-l3, .toctree-l4) > a.reference.external code { + color: var(--google-blue); +} +.rst-content :not(h1, h2, h3, h4, h5, h6, +.toctree-l1, .toctree-l2, .toctree-l3, .toctree-l4) > a.reference.internal code { + color: var(--google-green); +} + + +h1 { + font-size: 2rem; + letter-spacing: 1.78px; + line-height: 2.5rem; + text-transform: uppercase; + margin: 1.375rem 0; +} + +code.xref.py.py-attr.docutils.literal.notranslate { + color: #6c6c6d; + background-color: #f3f4f7; + padding: 2px 0px; +} + +pre { + padding: 1.375rem; +} + +.highlight .c1 { + color: #6c6c6d; +} + +/* .headerlink { + color: #0A507A !important; + font-family: FontAwesome; + float: right; + visibility: hidden; + position: absolute; +} +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} */ + + +a:link.has-code code, +a:hover.has-code code, +a:visited.has-code code { + color: #4974D1; + text-decoration: inherit; +} + +.sphinx-template-left-menu code, .sphinx-template-right-menu code, .sphinx-template-breadcrumbs code, +.toctree-l1 code, .toctree-l1 a:link.has-code code, .toctree-l1 a:hover.has-code code, .toctree-l1 a:visited.has-code code, +h1 code, h1 a:link.has-code code, h1 a:hover.has-code code, h1 a:visited.has-code code { + font-family: inherit; + font-size: inherit; + color: inherit; +} + +article.sphinx-template-article ul, +article.sphinx-template-article ol { + padding-left: 1.875rem; + margin: 0; +} +article.sphinx-template-article ul li, +article.sphinx-template-article ol li { + margin: 0; + line-height: 1.75rem; +} +article.sphinx-template-article ul p, +article.sphinx-template-article ol p { + line-height: 1.75rem; + margin-bottom: 0; +} +article.sphinx-template-article ul ul, +article.sphinx-template-article ul ol, +article.sphinx-template-article ol ul, +article.sphinx-template-article ol ol { + margin: 0; +} +article.sphinx-template-article h1, +article.sphinx-template-article h2, +article.sphinx-template-article h3, +article.sphinx-template-article h4, +article.sphinx-template-article h5, +article.sphinx-template-article h6 { + font-weight: normal; +} +article.sphinx-template-article h1 a, +article.sphinx-template-article h2 a, +article.sphinx-template-article h3 a, +article.sphinx-template-article h4 a, +article.sphinx-template-article h5 a, +article.sphinx-template-article h6 a { + color: #262626; +} +article.sphinx-template-article p.caption { + margin-top: 1.25rem; +} + +article.sphinx-template-article h1:first-of-type { + margin-top: 0; +} + +article.sphinx-template-article .sphx-glr-thumbcontainer { + margin: 0; + border: 1px solid #d6d7d8; + border-radius: 0; + width: 45%; + text-align: center; + margin-bottom: 5%; +} +@media screen and (max-width: 1100px) { + article.sphinx-template-article .sphx-glr-thumbcontainer:nth-child(odd) { + margin-left: 0; + margin-right: 2.5%; + } + article.sphinx-template-article .sphx-glr-thumbcontainer:nth-child(even) { + margin-right: 0; + margin-left: 2.5%; + } + article.sphinx-template-article .sphx-glr-thumbcontainer .figure { + width: 40%; + } +} +@media screen and (min-width: 1101px) { + article.sphinx-template-article .sphx-glr-thumbcontainer { + margin-right: 3%; + margin-bottom: 3%; + width: 30%; + } +} +article.sphinx-template-article .sphx-glr-thumbcontainer .caption-text a { + font-size: 1rem; + color: #262626; + letter-spacing: 0; + line-height: 1.5rem; + text-decoration: none; +} +article.sphinx-template-article .sphx-glr-thumbcontainer:hover { + -webkit-box-shadow: none; + box-shadow: none; + border-bottom-color: #ffffff; +} +article.sphinx-template-article .sphx-glr-thumbcontainer:hover .figure:before { + bottom: 100%; +} +article.sphinx-template-article .sphx-glr-thumbcontainer .figure { + width: 80%; +} +article.sphinx-template-article .sphx-glr-thumbcontainer .figure:before { + content: ""; + display: block; + position: absolute; + top: 0; + bottom: 35%; + left: 0; + right: 0; + background: #8A94B3; + opacity: 0.10; +} +article.sphinx-template-article .sphx-glr-thumbcontainer .figure a.reference.internal { + text-align: left; +} +@media screen and (min-width: 768px) { + article.sphinx-template-article .sphx-glr-thumbcontainer:after { + content: ""; + display: block; + width: 0; + height: 1px; + position: absolute; + bottom: 0; + left: 0; + background-color: var(--pytorch); + -webkit-transition: width .250s ease-in-out; + transition: width .250s ease-in-out; + } + article.sphinx-template-article .sphx-glr-thumbcontainer:hover:after { + width: 100%; + } +} +@media screen and (min-width: 768px) { + article.sphinx-template-article .sphx-glr-thumbcontainer:after { + background-color: var(--pytorch-red); + } +} + +article.sphinx-template-article section :not(dt) > code { + color: #262626; + border-top: 2px; + border-bottom: 2px; + padding: 0px 3px; + -webkit-box-decoration-break: clone; + box-decoration-break: clone; +} +article.sphinx-template-article section :not(dt) > code .pre { + outline: 0px; + padding: 0px; +} +article.sphinx-template-article .py.function dt, article.sphinx-template-article .attribute dt, article.sphinx-template-article .class .attribute dt, article.sphinx-template-article .class dt { + position: relative; + background: #f3f4f7; + padding: 0.5rem; + border-left: 3px solid var(--pytorch-red); + word-wrap: break-word; + padding-right: 100px; +} +article.sphinx-template-article .py.function dd dt, article.sphinx-template-article .attribute dd dt { + border-left: 3px solid var(--lightning); +} +article.sphinx-template-article .class .attribute dd dt, article.sphinx-template-article .class dd dt { + border-top: 3px solid var(--lightning); +} +article.sphinx-template-article .py.function dt em.property, article.sphinx-template-article .attribute dt em.property, article.sphinx-template-article .class dt em.property { + font-family: inherit; +} +article.sphinx-template-article .py.function dt em, article.sphinx-template-article .attribute dt em, article.sphinx-template-article .class .attribute dt em, article.sphinx-template-article .class dt em, article.sphinx-template-article .py.function dt .sig-paren, article.sphinx-template-article .attribute dt .sig-paren, article.sphinx-template-article .class dt .sig-paren { + font-family: IBMPlexMono,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace; + font-size: 87.5%; +} +article.sphinx-template-article .py.function dt a, article.sphinx-template-article .attribute dt a, article.sphinx-template-article .class .attribute dt a, article.sphinx-template-article .class dt a { + right: 30px; + padding-right: 0; + top: 50%; + -webkit-transform: perspective(1px) translateY(-50%); + transform: perspective(1px) translateY(-50%); +} +article.sphinx-template-article .py.function dt:hover .viewcode-link, article.sphinx-template-article .attribute dt:hover .viewcode-link, article.sphinx-template-article .class dt:hover .viewcode-link { + color: var(--pytorch-red); +} +article.sphinx-template-article .py.function .anchorjs-link, article.sphinx-template-article .attribute .anchorjs-link, article.sphinx-template-article .class .anchorjs-link { + display: inline; + position: absolute; + right: 8px; + font-size: 1.5625rem !important; + padding-left: 0; +} +article.sphinx-template-article .py.function dt > code, article.sphinx-template-article .attribute dt > code, article.sphinx-template-article .class .attribute dt > code, article.sphinx-template-article .class dt > code { + color: #262626; + border-top: solid 2px #f3f4f7; + background-color: #f3f4f7; + border-bottom: solid 2px #f3f4f7; + -webkit-box-decoration-break: clone; + box-decoration-break: clone; +} +article.sphinx-template-article .py.function .viewcode-link, article.sphinx-template-article .attribute .viewcode-link, article.sphinx-template-article .class .viewcode-link { + padding-left: 0.6rem; + position: absolute; + font-size: 0.875rem; + color: #979797; + letter-spacing: 0; + line-height: 1.5rem; + text-transform: uppercase; +} +article.sphinx-template-article .py.function dd, article.sphinx-template-article .attribute dd, article.sphinx-template-article .class .attribute dd, article.sphinx-template-article .class dd { + padding-left: 3.75rem; +} +article.sphinx-template-article .py.function table tbody tr th.field-name, article.sphinx-template-article .attribute table tbody tr th.field-name, article.sphinx-template-article .class table tbody tr th.field-name { + white-space: nowrap; + color: #262626; + width: 20%; +} +@media screen and (min-width: 768px) { + article.sphinx-template-article .py.function table tbody tr th.field-name, article.sphinx-template-article .attribute table tbody tr th.field-name, article.sphinx-template-article .class table tbody tr th.field-name { + width: 15%; + } +} +article.sphinx-template-article .py.function table tbody tr td.field-body, article.sphinx-template-article .attribute table tbody tr td.field-body, article.sphinx-template-article .class table tbody tr td.field-body { + padding: 0.625rem; + width: 80%; + color: #262626; +} +@media screen and (min-width: 768px) { + article.sphinx-template-article .py.function table tbody tr td.field-body, article.sphinx-template-article .attribute table tbody tr td.field-body, article.sphinx-template-article .class table tbody tr td.field-body { + width: 85%; + } +} +@media screen and (min-width: 1600px) { + article.sphinx-template-article .py.function table tbody tr td.field-body, article.sphinx-template-article .attribute table tbody tr td.field-body, article.sphinx-template-article .class table tbody tr td.field-body { + padding-left: 1.25rem; + } +} +article.sphinx-template-article .py.function table tbody tr td.field-body p, article.sphinx-template-article .attribute table tbody tr td.field-body p, article.sphinx-template-article .class table tbody tr td.field-body p { + padding-left: 0px; +} +article.sphinx-template-article .py.function table tbody tr td.field-body p:last-of-type, article.sphinx-template-article .attribute table tbody tr td.field-body p:last-of-type, article.sphinx-template-article .class table tbody tr td.field-body p:last-of-type { + margin-bottom: 0; +} +article.sphinx-template-article .py.function table tbody tr td.field-body ol, article.sphinx-template-article .attribute table tbody tr td.field-body ol, article.sphinx-template-article .class table tbody tr td.field-body ol, article.sphinx-template-article .py.function table tbody tr td.field-body ul, article.sphinx-template-article .attribute table tbody tr td.field-body ul, article.sphinx-template-article .class table tbody tr td.field-body ul { + padding-left: 1rem; + padding-bottom: 0; +} +article.sphinx-template-article .py.function table.docutils.field-list, article.sphinx-template-article .attribute table.docutils.field-list, article.sphinx-template-article .class table.docutils.field-list { + margin-bottom: 0.75rem; +} +article.sphinx-template-article .attribute .has-code { + float: none; +} +article.sphinx-template-article .class dt { + border-left: none; + border-top: 3px solid var(--pytorch-red); + padding-left: 4em; +} +article.sphinx-template-article .class dt em.property { + position: absolute; + left: 0.5rem; +} +article.sphinx-template-article .class dd .docutils dt { + padding-left: 0.5rem; +} +article.sphinx-template-article .class em.property { + text-transform: uppercase; + font-style: normal; + color: var(--pytorch-red); + font-size: 1rem; + letter-spacing: 0; + padding-right: 0.75rem; +} +article.sphinx-template-article .class dl dt em.property { + position: static; + left: 0; + padding-right: 0; +} +article.sphinx-template-article .class .method dt, +article.sphinx-template-article .class .staticmethod dt { + border-left: 3px solid var(--lightning); + border-top: none; + padding-left: 0.5rem; +} +article.sphinx-template-article .class .method dd dt, +article.sphinx-template-article .class .staticmethod dd dt { + border-left: 3px solid var(--google-green); +} +article.sphinx-template-article .class .attribute dt { + border-top: none; +} +article.sphinx-template-article .class .attribute dt em.property { + position: relative; + left: 0; +} +article.sphinx-template-article table { + table-layout: fixed; +} + +div.deprecated p { + display: inline; +} +div.deprecated, +div.versionchanged { + margin-top: 0.5rem; + padding: 0.5rem; + margin-bottom: 0.5rem; + border: none; +} +div.versionadded { + margin: 1rem 0; +} +div.deprecated p:last-child, +div.versionchanged p:last-child, +div.versionadded p:last-child { + margin-bottom: 0 +} +div.deprecated { + color: #b94a48; + background-color: #fdede9; +} +div.versionchanged { + background-color: #fffbe8; +} + +article.sphinx-template-article .note, +article.sphinx-template-article .warning, +article.sphinx-template-article .tip, +article.sphinx-template-article .seealso, +article.sphinx-template-article .hint, +article.sphinx-template-article .important, +article.sphinx-template-article .caution, +article.sphinx-template-article .danger, +article.sphinx-template-article .attention, +article.sphinx-template-article .error { + background: #f3f4f7; + margin-top: 1.875rem; + margin-bottom: 1.125rem; +} +article.sphinx-template-article .note .admonition-title, +article.sphinx-template-article .warning .admonition-title, +article.sphinx-template-article .tip .admonition-title, +article.sphinx-template-article .seealso .admonition-title, +article.sphinx-template-article .hint .admonition-title, +article.sphinx-template-article .important .admonition-title, +article.sphinx-template-article .caution .admonition-title, +article.sphinx-template-article .danger .admonition-title, +article.sphinx-template-article .attention .admonition-title, +article.sphinx-template-article .error .admonition-title { + color: #ffffff; + letter-spacing: 1px; + text-transform: uppercase; + margin-bottom: 1.125rem; + padding: 3px 0 3px 1.375rem; + position: relative; + font-size: 0.875rem; +} +article.sphinx-template-article .note .admonition-title:before, +article.sphinx-template-article .warning .admonition-title:before, +article.sphinx-template-article .tip .admonition-title:before, +article.sphinx-template-article .seealso .admonition-title:before, +article.sphinx-template-article .hint .admonition-title:before, +article.sphinx-template-article .important .admonition-title:before, +article.sphinx-template-article .caution .admonition-title:before, +article.sphinx-template-article .danger .admonition-title:before, +article.sphinx-template-article .attention .admonition-title:before, +article.sphinx-template-article .error .admonition-title:before { + content: "\2022"; + position: absolute; + left: 9px; + color: #ffffff; + top: 2px; +} +article.sphinx-template-article .note p:nth-child(n + 2), +article.sphinx-template-article .warning p:nth-child(n + 2), +article.sphinx-template-article .tip p:nth-child(n + 2), +article.sphinx-template-article .seealso p:nth-child(n + 2), +article.sphinx-template-article .hint p:nth-child(n + 2), +article.sphinx-template-article .important p:nth-child(n + 2), +article.sphinx-template-article .caution p:nth-child(n + 2), +article.sphinx-template-article .danger p:nth-child(n + 2), +article.sphinx-template-article .attention p:nth-child(n + 2), +article.sphinx-template-article .error p:nth-child(n + 2) { + padding: 0 1.375rem; +} +article.sphinx-template-article .note table, +article.sphinx-template-article .warning table, +article.sphinx-template-article .tip table, +article.sphinx-template-article .seealso table, +article.sphinx-template-article .hint table, +article.sphinx-template-article .important table, +article.sphinx-template-article .caution table, +article.sphinx-template-article .danger table, +article.sphinx-template-article .attention table, +article.sphinx-template-article .error table { + margin: 0 2rem; + width: auto; +} +article.sphinx-template-article .note :not(dt) > code, +article.sphinx-template-article .warning :not(dt) > code, +article.sphinx-template-article .tip :not(dt) > code, +article.sphinx-template-article .seealso :not(dt) > code, +article.sphinx-template-article .hint :not(dt) > code, +article.sphinx-template-article .important :not(dt) > code, +article.sphinx-template-article .caution :not(dt) > code, +article.sphinx-template-article .danger :not(dt) > code, +article.sphinx-template-article .attention :not(dt) > code, +article.sphinx-template-article .error :not(dt) > code { + border-top: 2px; + border-bottom: 2px; + padding: 0px 3px; + -webkit-box-decoration-break: clone; + box-decoration-break: clone; +} +article.sphinx-template-article .note :not(dt) > code .pre, +article.sphinx-template-article .warning :not(dt) > code .pre, +article.sphinx-template-article .tip :not(dt) > code .pre, +article.sphinx-template-article .seealso :not(dt) > code .pre, +article.sphinx-template-article .hint :not(dt) > code .pre, +article.sphinx-template-article .important :not(dt) > code .pre, +article.sphinx-template-article .caution :not(dt) > code .pre, +article.sphinx-template-article .danger :not(dt) > code .pre, +article.sphinx-template-article .attention :not(dt) > code .pre, +article.sphinx-template-article .error :not(dt) > code .pre { + outline: 0px; + padding: 0px; +} +article.sphinx-template-article .note pre, +article.sphinx-template-article .warning pre, +article.sphinx-template-article .tip pre, +article.sphinx-template-article .seealso pre, +article.sphinx-template-article .hint pre, +article.sphinx-template-article .important pre, +article.sphinx-template-article .caution pre, +article.sphinx-template-article .danger pre, +article.sphinx-template-article .attention pre, +article.sphinx-template-article .error pre { + margin-bottom: 0; +} +article.sphinx-template-article .note .highlight, +article.sphinx-template-article .warning .highlight, +article.sphinx-template-article .tip .highlight, +article.sphinx-template-article .seealso .highlight, +article.sphinx-template-article .hint .highlight, +article.sphinx-template-article .important .highlight, +article.sphinx-template-article .caution .highlight, +article.sphinx-template-article .danger .highlight, +article.sphinx-template-article .attention .highlight, +article.sphinx-template-article .error .highlight { + margin: 0 2rem 1.125rem 2rem; +} +article.sphinx-template-article .note ul, +article.sphinx-template-article .note ol, +article.sphinx-template-article .warning ul, +article.sphinx-template-article .warning ol, +article.sphinx-template-article .tip ul, +article.sphinx-template-article .tip ol, +article.sphinx-template-article .seealso ul, +article.sphinx-template-article .seealso ol, +article.sphinx-template-article .hint ul, +article.sphinx-template-article .hint ol, +article.sphinx-template-article .important ul, +article.sphinx-template-article .important ol, +article.sphinx-template-article .caution ul, +article.sphinx-template-article .caution ol, +article.sphinx-template-article .danger ul, +article.sphinx-template-article .danger ol, +article.sphinx-template-article .attention ul, +article.sphinx-template-article .attention ol, +article.sphinx-template-article .error ul, +article.sphinx-template-article .error ol { + padding-left: 3.25rem; +} +article.sphinx-template-article .note ul li, +article.sphinx-template-article .note ol li, +article.sphinx-template-article .warning ul li, +article.sphinx-template-article .warning ol li, +article.sphinx-template-article .tip ul li, +article.sphinx-template-article .tip ol li, +article.sphinx-template-article .seealso ul li, +article.sphinx-template-article .seealso ol li, +article.sphinx-template-article .hint ul li, +article.sphinx-template-article .hint ol li, +article.sphinx-template-article .important ul li, +article.sphinx-template-article .important ol li, +article.sphinx-template-article .caution ul li, +article.sphinx-template-article .caution ol li, +article.sphinx-template-article .danger ul li, +article.sphinx-template-article .danger ol li, +article.sphinx-template-article .attention ul li, +article.sphinx-template-article .attention ol li, +article.sphinx-template-article .error ul li, +article.sphinx-template-article .error ol li { + color: #262626; +} +article.sphinx-template-article .note p, +article.sphinx-template-article .warning p, +article.sphinx-template-article .tip p, +article.sphinx-template-article .seealso p, +article.sphinx-template-article .hint p, +article.sphinx-template-article .important p, +article.sphinx-template-article .caution p, +article.sphinx-template-article .danger p, +article.sphinx-template-article .attention p, +article.sphinx-template-article .error p { + margin-top: 1.125rem; +} +article.sphinx-template-article .note .admonition-title { + background: #54c7ec; +} +article.sphinx-template-article .warning .admonition-title { + background: #e94f3b; +} +article.sphinx-template-article .tip .admonition-title { + background: #6bcebb; +} +article.sphinx-template-article .seealso .admonition-title { + background: #6bcebb; +} +article.sphinx-template-article .hint .admonition-title { + background: #a2cdde; +} +article.sphinx-template-article .important .admonition-title { + background: #5890ff; +} +article.sphinx-template-article .caution .admonition-title { + background: #f7923a; +} +article.sphinx-template-article .danger .admonition-title { + background: #db2c49; +} +article.sphinx-template-article .attention .admonition-title { + background: #f5a623; +} +article.sphinx-template-article .error .admonition-title { + background: #cc2f90; +} +article.sphinx-template-article .sphx-glr-download-link-note.admonition.note, +article.sphinx-template-article .reference.download.internal, article.sphinx-template-article .sphx-glr-signature { + display: none; +} +article.sphinx-template-article .admonition > p:last-of-type { + margin-bottom: 0; + padding-bottom: 1.125rem !important; +} + +.sphinx-template-article div.sphx-glr-download a { + background-color: #f3f4f7; + background-image: url("../images/arrow-down-orange.svg"); + background-repeat: no-repeat; + background-position: left 10px center; + background-size: 15px 15px; + border-radius: 0; + border: none; + display: block; + text-align: left; + padding: 0.9375rem 3.125rem; + position: relative; + margin: 1.25rem auto; +} +@media screen and (min-width: 768px) { + .sphinx-template-article div.sphx-glr-download a:after { + content: ""; + display: block; + width: 0; + height: 1px; + position: absolute; + bottom: 0; + left: 0; + background-color: var(--pytorch); + -webkit-transition: width .250s ease-in-out; + transition: width .250s ease-in-out; + } + .sphinx-template-article div.sphx-glr-download a:hover:after { + width: 100%; + } +} +@media screen and (min-width: 768px) { + .sphinx-template-article div.sphx-glr-download a:after { + background-color: var(--pytorch-red); + } +} +@media screen and (min-width: 768px) { + .sphinx-template-article div.sphx-glr-download a { + background-position: left 20px center; + } +} +.sphinx-template-article div.sphx-glr-download a:hover { + -webkit-box-shadow: none; + box-shadow: none; + text-decoration: none; + background-image: url("../images/arrow-down-orange.svg"); + background-color: #f3f4f7; +} +.sphinx-template-article div.sphx-glr-download a span.pre { + background-color: transparent; + font-size: 1.125rem; + padding: 0; + color: #262626; +} +.sphinx-template-article div.sphx-glr-download a code, .sphinx-template-article div.sphx-glr-download a kbd, .sphinx-template-article div.sphx-glr-download a pre, .sphinx-template-article div.sphx-glr-download a samp, .sphinx-template-article div.sphx-glr-download a span.pre { + font-family: FreightSans, Helvetica Neue, Helvetica, Arial, sans-serif; +} + +.sphinx-template-article p.sphx-glr-script-out { + margin-bottom: 1.125rem; +} + +.sphinx-template-article div.sphx-glr-script-out { + margin-bottom: 2.5rem; +} +.sphinx-template-article div.sphx-glr-script-out .highlight { + margin-left: 0; + margin-top: 0; +} +.sphinx-template-article div.sphx-glr-script-out .highlight pre { + background-color: #fdede9; + padding: 1.5625rem; + color: #837b79; +} +.sphinx-template-article div.sphx-glr-script-out + p { + margin-top: unset; +} + +article.sphinx-template-article .sphinx-template-table-responsive table { + border: none; + border-color: #ffffff !important; + table-layout: fixed; +} +article.sphinx-template-article .sphinx-template-table-responsive table thead tr { + border-bottom: 2px solid #6c6c6d; +} +article.sphinx-template-article .sphinx-template-table-responsive table thead th { + line-height: 1.75rem; + padding-left: 0.9375rem; + padding-right: 0.9375rem; +} +article.sphinx-template-article .sphinx-template-table-responsive table tbody .row-odd { + background-color: #f3f4f7; +} +article.sphinx-template-article .sphinx-template-table-responsive table tbody td { + color: #6c6c6d; + white-space: normal; + padding: 0.9375rem; + font-size: 1rem; + line-height: 1.375rem; +} +article.sphinx-template-article .sphinx-template-table-responsive table tbody td .pre { + background: #ffffff; + color: var(--pytorch-red); + font-size: 87.5%; +} +article.sphinx-template-article .sphinx-template-table-responsive table tbody td code { + font-size: 87.5%; +} + +a[rel~="prev"], a[rel~="next"] { + padding: 0.375rem 0 0 0; +} + +img.next-page, +img.previous-page { + width: 8px; + height: 10px; + position: relative; + top: -1px; +} + +img.previous-page { + -webkit-transform: scaleX(-1); + transform: scaleX(-1); +} + +.rst-footer-buttons { + margin-top: 1.875rem; + margin-bottom: 1.875rem; +} +.rst-footer-buttons .btn:focus, +.rst-footer-buttons .btn.focus { + -webkit-box-shadow: none; + box-shadow: none; +} + +article.sphinx-template-article blockquote { + margin-left: 3.75rem; + color: #6c6c6d; +} + +article.sphinx-template-article .caption { + color: #6c6c6d; + letter-spacing: 0.25px; + line-height: 2.125rem; +} + +article.sphinx-template-article .math { + color: #262626; + width: auto; + text-align: center; + } + article.sphinx-template-article .math img { + width: auto; + } + +.sphinx-template-breadcrumbs-wrapper { + width: 100%; +} +@media screen and (min-width: 1101px) { + .sphinx-template-breadcrumbs-wrapper { + float: left; + margin-left: 3%; + width: 75%; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-breadcrumbs-wrapper { + width: 850px; + margin-left: 1.875rem; + } +} +.sphinx-template-breadcrumbs-wrapper .sphinx-template-breadcrumbs-aside { + float: right; +} +.sphinx-template-breadcrumbs-wrapper .sphinx-template-breadcrumbs-aside .fa.fa-github { + margin-top: 5px; + display: block; +} + +.sphinx-template-article .container { + padding-left: 0; + padding-right: 0; + max-width: none; +} + +a:link, +a:visited, +a:hover { + color: var(--pytorch-red); +} + +::-webkit-input-placeholder { + color: var(--pytorch-red); +} + +::-moz-placeholder { + color: var(--pytorch-red); +} + +:-ms-input-placeholder { + color: var(--pytorch-red); +} + +:-moz-placeholder { + color: var(--pytorch-red); +} + +@media screen and (min-width: 768px) { + .site-footer a:hover { + color: var(--pytorch-red); + } +} + +.docs-resources a { + color: var(--pytorch-red); +} + +.header-holder { + position: relative; + z-index: 201; +} + +.header-holder .main-menu ul li.active:after { + color: var(--pytorch-red); +} +.header-holder .main-menu ul li.active a { + color: var(--pytorch-red); +} +@media screen and (min-width: 1100px) { + .header-holder .main-menu ul li a:hover { + color: var(--pytorch-red); + } +} + +.version { + padding-bottom: 1rem; +} + +.sphinx-template-call-to-action-links { + padding-top: 0; + display: -webkit-box; + display: -ms-flexbox; + display: flex; +} +@media screen and (min-width: 768px) { + .sphinx-template-call-to-action-links { + padding-top: 2.5rem; + } +} +@media (min-width: 768px) and (max-width: 1239px) { + .sphinx-template-call-to-action-links { + padding-top: 0; + } +} +@media (min-width: 1100px) and (max-width: 1239px) { + .sphinx-template-call-to-action-links { + padding-top: 2.5rem; + } +} +.sphinx-template-call-to-action-links .call-to-action-img, .sphinx-template-call-to-action-links .call-to-action-notebook-img { + height: 1.375rem; + width: 1.375rem; + margin-right: 10px; +} +.sphinx-template-call-to-action-links .call-to-action-notebook-img { + height: 1rem; +} +.sphinx-template-call-to-action-links a { + padding-right: 1.25rem; + color: #000000; + cursor: pointer; +} +.sphinx-template-call-to-action-links a:hover { + color: var(--pytorch); +} +.sphinx-template-call-to-action-links a .call-to-action-desktop-view { + display: none; +} +@media screen and (min-width: 768px) { + .sphinx-template-call-to-action-links a .call-to-action-desktop-view { + display: block; + } +} +.sphinx-template-call-to-action-links a .call-to-action-mobile-view { + display: block; +} +@media screen and (min-width: 768px) { + .sphinx-template-call-to-action-links a .call-to-action-mobile-view { + display: none; + } +} +.sphinx-template-call-to-action-links a #google-colab-link, .sphinx-template-call-to-action-links a #download-notebook-link, +.sphinx-template-call-to-action-links a #github-view-link { + padding-bottom: 0.625rem; + border-bottom: 1px solid #f3f4f7; + padding-right: 2.5rem; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; +} +.sphinx-template-call-to-action-links a #google-colab-link:hover, .sphinx-template-call-to-action-links a #download-notebook-link:hover, +.sphinx-template-call-to-action-links a #github-view-link:hover { + border-bottom-color: var(--pytorch); + color: var(--pytorch); +} + +.sphinx-template-container { + margin: 0 auto; + padding: 0 1.875rem; + width: auto; + position: relative; +} +@media screen and (min-width: 1100px) { + .sphinx-template-container { + padding: 0; + } +} +@media screen and (min-width: 1101px) { + .sphinx-template-container { + margin-left: 25%; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-container { + margin-left: 350px; + } +} +.sphinx-template-container:before, .sphinx-template-container:after { + content: ""; + display: table; +} +.sphinx-template-container:after { + clear: both; +} +.sphinx-template-container { + *zoom: 1; +} + +.sphinx-template-content-wrap { + background-color: #ffffff; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + position: relative; + padding-top: 0; +} +.sphinx-template-content-wrap:before, .sphinx-template-content-wrap:after { + content: ""; + display: table; +} +.sphinx-template-content-wrap:after { + clear: both; +} +.sphinx-template-content-wrap { + *zoom: 1; +} +@media screen and (min-width: 1101px) { + .sphinx-template-content-wrap { + padding-top: 45px; + float: left; + width: 100%; + display: block; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-content-wrap { + width: 100%; + } +} + +.sphinx-template-content { + background: #ffffff; + width: 100%; + max-width: 700px; + position: relative; +} + +.sphinx-template-content-left { + min-height: 100vh; + margin-top: 2.5rem; + width: 100%; +} +@media screen and (min-width: 1101px) { + .sphinx-template-content-left { + margin-top: 0; + margin-left: 3%; + width: 75%; + float: left; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-content-left { + width: 850px; + margin-left: 30px; + } +} +.sphinx-template-content-left .main-content { + padding-top: 0.9375rem; +} +.sphinx-template-content-left .main-content ul.simple { + padding-bottom: 1.25rem; +} +.sphinx-template-content-left .main-content .note:nth-child(1), .sphinx-template-content-left .main-content .warning:nth-child(1) { + margin-top: 0; +} + +.sphinx-template-content-right { + display: none; + position: relative; + overflow-x: hidden; + overflow-y: hidden; +} +@media screen and (min-width: 1101px) { + .sphinx-template-content-right { + display: block; + margin-left: 0; + width: 19%; + float: left; + height: 100%; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-content-right { + width: 280px; + } +} + +@media screen and (min-width: 1101px) { + .sphinx-template-side-scroll { + position: relative; + overflow-x: hidden; + overflow-y: scroll; + height: 100%; + } +} + +.sphinx-template-menu-vertical { + padding: 1.25rem 1.875rem 2.5rem 1.875rem; +} +@media screen and (min-width: 1101px) { + .sphinx-template-menu-vertical { + display: block; + padding-top: 0; + padding-right: 13.5%; + padding-bottom: 5.625rem; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-menu-vertical { + padding-left: 0; + padding-right: 1.5625rem; + } +} + +.sphinx-template-left-menu { + display: none; + background-color: #f3f4f7; + color: #262626; + overflow: scroll; +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu { + display: block; + overflow-x: hidden; + overflow-y: hidden; + padding-bottom: 110px; + padding: 0 1.875rem 0 0; + width: 25%; + z-index: 200; + float: left; + } + .sphinx-template-left-menu.make-fixed { + position: fixed; + top: 0; + bottom: 0; + left: 0; + float: none; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-left-menu { + padding: 0 0 0 1.875rem; + width: 350px; + } +} + +.expand-menu, .hide-menu { + color: #6c6c6d; + padding-left: 10px; + cursor: pointer; +} + +.collapse { + display: none; +} + +.left-nav-top-caption { + padding-top: 1rem; +} + +.sphinx-template-left-menu p.caption { + color: #262626; + display: block; + font-size: 1.2rem; + line-height: 1.375rem; + margin-bottom: 1rem; + text-transform: none; + white-space: normal; +} + +.sphinx-template-left-menu-search { + margin-bottom: 2.5rem; +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu-search { + margin: 1.25rem 0.625rem 1.875rem 0; + } +} + +.sphinx-template-left-menu-search ::-webkit-input-placeholder { + color: #262626; +} +.sphinx-template-left-menu-search ::-moz-placeholder { + color: #262626; +} +.sphinx-template-left-menu-search :-ms-input-placeholder { + color: #262626; +} +.sphinx-template-left-menu-search ::-ms-input-placeholder { + color: #262626; +} +.sphinx-template-left-menu-search ::placeholder { + color: #262626; +} + +.sphinx-template-left-menu-search :focus::-webkit-input-placeholder { + color: transparent; +} +.sphinx-template-left-menu-search :focus::-moz-placeholder { + color: transparent; +} +.sphinx-template-left-menu-search :focus:-ms-input-placeholder { + color: transparent; +} +.sphinx-template-left-menu-search :focus::-ms-input-placeholder { + color: transparent; +} +.sphinx-template-left-menu-search :focus::placeholder { + color: transparent; +} + +.sphinx-template-left-menu-search input[type=text] { + border-radius: 0; + padding: 0.5rem 0.75rem; + border-color: #ffffff; + color: #262626; + border-style: solid; + font-size: 1rem; + width: 100%; + background-color: #f3f4f7; + background-image: url("../images/search-icon.svg"); + background-repeat: no-repeat; + background-size: 18px 18px; + background-position: 12px 10px; + padding-left: 40px; + background-color: #ffffff; +} +.sphinx-template-left-menu-search input[type=text]:focus { + outline: 0; +} + +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu .sphinx-template-side-scroll { + width: 120%; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-left-menu .sphinx-template-side-scroll { + width: 340px; + } +} + +.sphinx-template-right-menu { + min-height: 100px; + overflow-x: hidden; + overflow-y: hidden; + left: 0; + z-index: 200; + padding-top: 0; + position: relative; +} +@media screen and (min-width: 1101px) { + .sphinx-template-right-menu { + width: 100%; + } + .sphinx-template-right-menu.scrolling-fixed { + position: fixed; + top: 45px; + left: 83.5%; + width: 14%; + } + .sphinx-template-right-menu.scrolling-absolute { + position: absolute; + left: 0; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-right-menu { + left: 0; + width: 380px; + } + .sphinx-template-right-menu.scrolling-fixed { + position: fixed; + top: 45px; + left: 1230px; + } + .sphinx-template-right-menu.scrolling-absolute { + position: absolute; + left: 0; + } +} + +.sphinx-template-left-menu ul, +.sphinx-template-right-menu ul { + list-style-type: none; + padding-left: 0; + margin-bottom: 2.5rem; +} +.sphinx-template-left-menu > ul, +.sphinx-template-right-menu > ul { + margin-bottom: 2.5rem; +} +.sphinx-template-left-menu a:link, +.sphinx-template-left-menu a:visited, +.sphinx-template-left-menu a:hover, +.sphinx-template-right-menu a:link, +.sphinx-template-right-menu a:visited, +.sphinx-template-right-menu a:hover { + color: #6c6c6d; + font-size: 0.875rem; + line-height: 1rem; + padding: 0; + text-decoration: none; +} +.sphinx-template-left-menu a:link.reference.internal, +.sphinx-template-left-menu a:visited.reference.internal, +.sphinx-template-left-menu a:hover.reference.internal, +.sphinx-template-right-menu a:link.reference.internal, +.sphinx-template-right-menu a:visited.reference.internal, +.sphinx-template-right-menu a:hover.reference.internal { + margin-bottom: 0.3125rem; + margin-left: 1.5rem; + position: relative; +} +.sphinx-template-left-menu li code, +.sphinx-template-right-menu li code { + border: none; + background: inherit; + color: inherit; + padding-left: 0; + padding-right: 0; +} +.search li code { + font-family: inherit; + font-size: inherit; +} +.sphinx-template-left-menu li span.toctree-expand, +.sphinx-template-right-menu li span.toctree-expand { + display: block; + float: left; + margin-left: -1.2em; + font-size: 0.8em; + line-height: 1.6em; +} +.sphinx-template-left-menu li.on a, .sphinx-template-left-menu li.current > a, +.sphinx-template-right-menu li.on a, +.sphinx-template-right-menu li.current > a { + position: relative; + border: none; +} +.sphinx-template-left-menu li.on a span.toctree-expand, .sphinx-template-left-menu li.current > a span.toctree-expand, +.sphinx-template-right-menu li.on a span.toctree-expand, +.sphinx-template-right-menu li.current > a span.toctree-expand { + display: block; + font-size: 0.8em; + line-height: 1.6em; +} + +/* l1 text */ +.sphinx-template-left-menu li.toctree-l1 > a, +.sphinx-template-right-menu li.toctree-l1 > a { + color: #262626; + font-size: 1rem; +} + +/* l1 current text */ +.sphinx-template-left-menu li.toctree-l1.current > a, +.sphinx-template-right-menu li.toctree-l1.current > a { + color: var(--pytorch-red); +} + + +/* l2 text */ +.sphinx-template-left-menu li.toctree-l2 > a, +.sphinx-template-right-menu li.toctree-l2 > a { + left: 15px; + font-size: 1rem; +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu li.toctree-l2 > a, + .sphinx-template-right-menu li.toctree-l2 > a { + left: 20px; + } +} +/* l2 current text */ +.sphinx-template-left-menu li.toctree-l2.current > a, +.sphinx-template-right-menu li.toctree-l2.current > a { + color: var(--lightning); +} + +/* l1 current point */ +.sphinx-template-left-menu li.toctree-l1.current > a:before, +.sphinx-template-right-menu li.toctree-l1.current > a:before { + content: "\2022"; + display: inline-block; + position: absolute; + left: -15px; + top: -10%; + font-size: 1.375rem; + color: var(--pytorch-red); /* point color */ +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu li.toctree-l1.current > a:before, + .sphinx-template-right-menu li.toctree-l1.current > a:before { + left: -20px; + } +} + +/* l2 current point */ +.sphinx-template-left-menu li.toctree-l2.current > a:before, +.sphinx-template-right-menu li.toctree-l2.current > a:before { + content: "\2022"; + display: inline-block; + position: absolute; + left: -15px; + top: -10%; + font-size: 1.375rem; + color: var(--lightning); +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu li.toctree-l2.current > a:before, + .sphinx-template-right-menu li.toctree-l2.current > a:before { + left: -20px; + } +} + + +.sphinx-template-left-menu li.toctree-l1.current li.toctree-l2 > ul, .sphinx-template-left-menu li.toctree-l2.current li.toctree-l3 > ul, +.sphinx-template-right-menu li.toctree-l1.current li.toctree-l2 > ul, +.sphinx-template-right-menu li.toctree-l2.current li.toctree-l3 > ul { + display: none; +} +.sphinx-template-left-menu li.toctree-l1.current li.toctree-l2.current > ul, .sphinx-template-left-menu li.toctree-l2.current li.toctree-l3.current > ul, +.sphinx-template-right-menu li.toctree-l1.current li.toctree-l2.current > ul, +.sphinx-template-right-menu li.toctree-l2.current li.toctree-l3.current > ul { + display: block; +} +.sphinx-template-left-menu li.toctree-l2.current li.toctree-l3 > a, +.sphinx-template-right-menu li.toctree-l2.current li.toctree-l3 > a { + display: block; +} + +/* l3 text */ +.sphinx-template-left-menu li.toctree-l3 > a, +.sphinx-template-right-menu li.toctree-l3 > a { + left: 30px; + font-size: 0.9em; +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu li.toctree-l3 > a, + .sphinx-template-right-menu li.toctree-l3 > a { + left: 40px; + } +} +/* l3 current text */ +.sphinx-template-left-menu li.toctree-l3.current > a, +.sphinx-template-right-menu li.toctree-l3.current > a { + color: var(--google-green); +} +/* l3 current point */ +.sphinx-template-left-menu li.toctree-l3.current > a:before, +.sphinx-template-right-menu li.toctree-l3.current > a:before { + content: "\2022"; + display: inline-block; + position: absolute; + left: -15px; + top: -10%; + font-size: 1.2rem; + color: var(--google-green); +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu li.toctree-l3.current > a:before, + .sphinx-template-right-menu li.toctree-l3.current > a:before { + left: -20px; + } +} + +.sphinx-template-left-menu li.toctree-l3.current li.toctree-l4 > a, +.sphinx-template-right-menu li.toctree-l3.current li.toctree-l4 > a { + display: block; +} + +/* l4 text */ +.sphinx-template-left-menu li.toctree-l4 > a, +.sphinx-template-right-menu li.toctree-l4 > a { + left: 45px; + font-size: 0.9em; +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu li.toctree-l4 > a, + .sphinx-template-right-menu li.toctree-l4 > a { + left: 60px; + } +} +/* l4 current text */ +.sphinx-template-left-menu li.toctree-l4.current > a, +.sphinx-template-right-menu li.toctree-l4.current > a { + color: #f4b400; +} +/* l4 current point */ +.sphinx-template-left-menu li.toctree-l4.current > a:before, +.sphinx-template-right-menu li.toctree-l4.current > a:before { + content: "\2022"; + display: inline-block; + position: absolute; + left: -15px; + top: -10%; + font-size: 1.2rem; + color: #f4b400; +} +@media screen and (min-width: 1101px) { + .sphinx-template-left-menu li.toctree-l4.current > a:before, + .sphinx-template-right-menu li.toctree-l4.current > a:before { + left: -20px; + } +} + +.sphinx-template-left-menu li[aria-expanded="true"] > ul, +.sphinx-template-right-menu li[aria-expanded="true"] > ul { + display: block; +} +.sphinx-template-left-menu li ul, +.sphinx-template-right-menu li ul { + margin-bottom: 0; + display: none; +} +.sphinx-template-left-menu li ul li a, +.sphinx-template-right-menu li ul li a { + margin-bottom: 0; +} +.sphinx-template-left-menu a, +.sphinx-template-right-menu a { + display: inline-block; + position: relative; +} +.sphinx-template-left-menu a:hover, +.sphinx-template-right-menu a:hover { + cursor: pointer; +} +.sphinx-template-left-menu a:active, +.sphinx-template-right-menu a:active { + cursor: pointer; +} + +.sphinx-template-left-menu ul { + padding-left: 0; +} + +/* .sphinx-template-right-menu a:link, +.sphinx-template-right-menu a:visited, +.sphinx-template-right-menu a:hover { + color: #6c6c6d; +} +.sphinx-template-right-menu a:link span.pre, +.sphinx-template-right-menu a:visited span.pre, +.sphinx-template-right-menu a:hover span.pre { + color: #6c6c6d; +} */ +.sphinx-template-right-menu a.reference.internal button { + display: block; + padding: 0; + border: 0; + margin: 0; + color: inherit; +} +.sphinx-template-right-menu a.reference.internal[aria-expanded="true"] button:before { + content: "-"; + font-family: monospace; + position: absolute; + left: -12px; +} +.sphinx-template-right-menu a.reference.internal[aria-expanded="false"] button:before { + content: "+"; + font-family: monospace; + position: absolute; + left: -12px; +} +.sphinx-template-right-menu li.active > a { + color: var(--pytorch-red); +} +.sphinx-template-right-menu li.active > a span.pre, .sphinx-template-right-menu li.active > a:before { + color: var(--pytorch-red); +} +.sphinx-template-right-menu li.active > a:after { + content: "\2022"; + color: var(--pytorch); + display: inline-block; + font-size: 1.375rem; + left: -17px; + position: absolute; + top: 1px; +} +.sphinx-template-right-menu .sphinx-template-side-scroll > ul > li > ul > li { + margin-bottom: 0; +} +.sphinx-template-right-menu ul ul { + padding-left: 0; +} +.sphinx-template-right-menu ul ul li { + padding-left: 0px; +} +.sphinx-template-right-menu ul ul li a.reference.internal { + padding-left: 0; +} +.sphinx-template-right-menu ul ul li ul { + display: none; + padding-left: 10px; +} +.sphinx-template-right-menu ul ul li li a.reference.internal { + padding-left: 0; +} +.sphinx-template-right-menu li ul { + display: block; +} + +.sphinx-template-right-menu .sphinx-template-side-scroll { + padding-top: 20px; +} +@media screen and (min-width: 1101px) { + .sphinx-template-right-menu .sphinx-template-side-scroll { + width: 120%; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-right-menu .sphinx-template-side-scroll { + width: 400px; + } +} +.sphinx-template-right-menu .sphinx-template-side-scroll > ul { + padding-left: 10%; + padding-right: 10%; + margin-bottom: 0; +} +@media screen and (min-width: 1600px) { + .sphinx-template-right-menu .sphinx-template-side-scroll > ul { + padding-left: 25px; + } +} +.sphinx-template-right-menu .sphinx-template-side-scroll > ul > li > a.reference.internal { + color: #262626; + font-weight: 500; +} +.sphinx-template-right-menu .sphinx-template-side-scroll ul li { + position: relative; +} + +#sphinx-template-right-menu ul li ul li .side-scroll-highlight { + color: var(--pytorch-red); +} +#sphinx-template-right-menu ul li ul li ul li .side-scroll-highlight { + color: var(--lightning); +} +#sphinx-template-right-menu ul li ul li ul li ul li .side-scroll-highlight { + color: var(--google-green); +} +#sphinx-template-right-menu ul li ul li ul li ul li ul li .side-scroll-highlight { + color: var(--readthedoc); +} + +.header-container { + max-width: none; + margin-top: 4px; +} +@media screen and (min-width: 1101px) { + .header-container { + margin-top: 0; + } +} +@media screen and (min-width: 1600px) { + .header-container { + margin-top: 0; + } +} + +.container-fluid.header-holder { + padding-right: 0; + padding-left: 0; +} + +.header-holder .container { + max-width: none; + padding-right: 1.875rem; + padding-left: 1.875rem; +} +@media screen and (min-width: 1101px) { + .header-holder .container { + padding-right: 1.875rem; + padding-left: 1.875rem; + } +} + +.header-holder .main-menu { + -webkit-box-pack: unset; + -ms-flex-pack: unset; + justify-content: unset; + position: relative; +} +@media screen and (min-width: 1101px) { + .header-holder .main-menu ul { + padding-left: 0; + margin-left: 26%; + } +} +@media screen and (min-width: 1600px) { + .header-holder .main-menu ul { + padding-left: 38px; + margin-left: 310px; + } +} + +.sphinx-template-page-level-bar { + display: none; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + background-color: #ffffff; + border-bottom: 1px solid #e2e2e2; + width: 100%; + z-index: 201; +} +@media screen and (min-width: 1101px) { + .sphinx-template-page-level-bar { + left: 0; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + height: 45px; + padding-left: 0; + width: 100%; + position: absolute; + z-index: 1; + } + .sphinx-template-page-level-bar.left-menu-is-fixed { + position: fixed; + top: 0; + left: 25%; + padding-left: 0; + right: 0; + width: 75%; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-page-level-bar { + left: 0; + right: 0; + width: auto; + z-index: 1; + } + .sphinx-template-page-level-bar.left-menu-is-fixed { + left: 350px; + right: 0; + width: auto; + } +} +.sphinx-template-page-level-bar ul, .sphinx-template-page-level-bar li { + margin: 0; +} + +.sphinx-template-shortcuts-wrapper { + display: none; +} +@media screen and (min-width: 1101px) { + .sphinx-template-shortcuts-wrapper { + font-size: 0.875rem; + float: left; + margin-left: 2%; + } +} +@media screen and (min-width: 1600px) { + .sphinx-template-shortcuts-wrapper { + margin-left: 1.875rem; + } +} + +.cookie-banner-wrapper { + display: none; +} +.cookie-banner-wrapper .container { + padding-left: 1.875rem; + padding-right: 1.875rem; + max-width: 1240px; +} +.cookie-banner-wrapper.is-visible { + display: block; + position: fixed; + bottom: 0; + background-color: #f3f4f7; + min-height: 100px; + width: 100%; + z-index: 401; + border-top: 3px solid #ededee; +} +.cookie-banner-wrapper .gdpr-notice { + color: #6c6c6d; + margin-top: 1.5625rem; + text-align: left; + max-width: 1440px; +} +@media screen and (min-width: 768px) { + .cookie-banner-wrapper .gdpr-notice { + width: 77%; + } +} +@media (min-width: 768px) and (max-width: 1239px) { + .cookie-banner-wrapper .gdpr-notice { + width: inherit; + } +} +.cookie-banner-wrapper .gdpr-notice .cookie-policy-link { + color: #343434; +} +.cookie-banner-wrapper .close-button { + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; + background: transparent; + border: 1px solid #f3f4f7; + height: 1.3125rem; + position: absolute; + bottom: 42px; + right: 0; + top: 0; + cursor: pointer; + outline: none; +} +@media screen and (min-width: 768px) { + .cookie-banner-wrapper .close-button { + right: 20%; + top: inherit; + } +} +@media (min-width: 768px) and (max-width: 1239px) { + .cookie-banner-wrapper .close-button { + right: 0; + top: 0; + } +} + +.main-menu ul li .resources-dropdown a { + cursor: pointer; +} +.main-menu ul li .dropdown-menu { + border-radius: 0; + padding: 0; +} +.main-menu ul li .dropdown-menu .dropdown-item { + color: #6c6c6d; + border-bottom: 1px solid #e2e2e2; +} +.main-menu ul li .dropdown-menu .dropdown-item:last-of-type { + border-bottom-color: transparent; +} +.main-menu ul li .dropdown-menu .dropdown-item:hover { + background-color: var(--pytorch); +} +.main-menu ul li .dropdown-menu .dropdown-item p { + font-size: 1rem; + color: #979797; +} +.main-menu ul li .dropdown-menu a.dropdown-item:hover { + color: #ffffff; +} +.main-menu ul li .dropdown-menu a.dropdown-item:hover p { + color: #ffffff; +} + +.resources-dropdown-menu { + left: -75px; + width: 226px; + display: none; + position: absolute; + z-index: 1000; + display: none; + float: left; + min-width: 10rem; + padding: 0.5rem 0; + font-size: 1rem; + color: #212529; + text-align: left; + list-style: none; + background-color: #ffffff; + background-clip: padding-box; + border: 1px solid rgba(0, 0, 0, 0.15); + border-radius: 0.25rem; +} + +.resources-dropdown:hover .resources-dropdown-menu { + display: block; +} + +.main-menu ul li .resources-dropdown-menu { + border-radius: 0; + padding: 0; +} +.main-menu ul li.active:hover .resources-dropdown-menu { + display: block; +} + +.main-menu ul li .resources-dropdown-menu .dropdown-item { + color: #6c6c6d; + border-bottom: 1px solid #e2e2e2; +} + +.resources-dropdown .with-down-orange-arrow { + padding-right: 2rem; + position: relative; + background: url("../images/chevron-down-orange.svg"); + background-size: 14px 18px; + background-position: top 7px right 10px; + background-repeat: no-repeat; +} + +.with-down-arrow { + padding-right: 2rem; + position: relative; + background-image: url("../images/chevron-down-black.svg"); + background-size: 14px 18px; + background-position: top 7px right 10px; + background-repeat: no-repeat; +} +.with-down-arrow:hover { + background-image: url("../images/chevron-down-orange.svg"); + background-repeat: no-repeat; +} + +.header-holder .main-menu ul li .resources-dropdown .doc-dropdown-option { + padding-top: 1rem; +} + +.header-holder .main-menu ul li a.nav-dropdown-item { + display: block; + font-size: 1rem; + line-height: 1.3125rem; + width: 100%; + padding: 0.25rem 1.5rem; + clear: both; + font-weight: 400; + color: #979797; + text-align: center; + background-color: transparent; + border-bottom: 1px solid #e2e2e2; +} +.header-holder .main-menu ul li a.nav-dropdown-item.active:before { + content: "\F0A3"; + font-family: FontAwesome; + color: var(--lightning); + padding: 0.5rem 0rem; + font-size: 1.3rem; + left: 3px; + position: absolute; + text-align: left; +} +.header-holder .main-menu ul li a.nav-dropdown-item.active:hover:before { + color: var(--google-green); +} +.header-holder .main-menu ul li a.nav-dropdown-item:last-of-type { + border-bottom-color: transparent; +} +.header-holder .main-menu ul li a.nav-dropdown-item:hover { + background-color: var(--pytorch); + color: white; +} +.header-holder .main-menu ul li a.nav-dropdown-item .dropdown-title { + font-size: 1.125rem; + color: #6c6c6d; + letter-spacing: 0; + line-height: 34px; +} + +.header-holder .main-menu ul li a.nav-dropdown-item:hover .dropdown-title { + background-color: var(--pytorch); + color: white; +} + +.fa:before { + font-family: FontAwesome; + display: inline-block; + font-style: normal; + font-weight: 400; + font-size: inherit; + line-height: 1; + padding-right: 2px; +} +.fa-black:before { + color: 'black'; +} +.fa-blue:before { + color: var(--readthedoc); +} +.fa-large:before { + font-size: large; +} +.fa.fa-home:before { + content: "\F015"; +} +.fa.fa-github:before { + content: "\F09B"; +} +.fa.fa-arrow-circle-left:before { + content: "\F0A8"; +} +.fa.fa-arrow-circle-right:before { + content: "\F0A9"; +} + +.toctree-wrapper .toctree-l2 a { + color: var(--lightning); +} +.toctree-wrapper .toctree-l2 .toctree-l3 a { + color: var(--google-green); +} +.toctree-wrapper .toctree-l2 .toctree-l3 .toctree-l4 a { + color: var(--readthedoc); +} + +/*# sourceMappingURL=theme.css.map */ diff --git a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 00000000..d06a71d7 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/_static/documentation_options.js b/_static/documentation_options.js new file mode 100644 index 00000000..138ae34f --- /dev/null +++ b/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '2.0.3', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/_static/favicon.ico b/_static/favicon.ico new file mode 100644 index 00000000..c679534a Binary files /dev/null and b/_static/favicon.ico differ diff --git a/_static/file.png b/_static/file.png new file mode 100644 index 00000000..a858a410 Binary files /dev/null and b/_static/file.png differ diff --git a/_static/fonts/FontAwesome/fontawesome-webfont.eot b/_static/fonts/FontAwesome/fontawesome-webfont.eot new file mode 100644 index 00000000..e9f60ca9 Binary files /dev/null and b/_static/fonts/FontAwesome/fontawesome-webfont.eot differ diff --git a/_static/fonts/FontAwesome/fontawesome-webfont.svg b/_static/fonts/FontAwesome/fontawesome-webfont.svg new file mode 100644 index 00000000..855c845e --- /dev/null +++ b/_static/fonts/FontAwesome/fontawesome-webfont.svg @@ -0,0 +1,2671 @@ + + + + +Created by FontForge 20120731 at Mon Oct 24 17:37:40 2016 + By ,,, +Copyright Dave Gandy 2016. 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file mode 100644 index 00000000..250f5665 --- /dev/null +++ b/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, is available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/_static/logo/trojanzoo-logo-dark.svg b/_static/logo/trojanzoo-logo-dark.svg new file mode 100644 index 00000000..ad6e3730 --- /dev/null +++ b/_static/logo/trojanzoo-logo-dark.svg @@ -0,0 +1,83 @@ + + + + + + + + + + + + + + + + + + + + + + + diff --git a/_static/logo/trojanzoo-logo-icon.svg b/_static/logo/trojanzoo-logo-icon.svg new file mode 100644 index 00000000..e0537f0a --- /dev/null +++ b/_static/logo/trojanzoo-logo-icon.svg @@ -0,0 +1,54 @@ + + + + + + + + + + + + diff --git a/_static/logo/trojanzoo-logo.svg b/_static/logo/trojanzoo-logo.svg 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Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + `Search finished, found ${resultCount} page(s) matching the search query.` + ); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() }); + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent !== undefined) return docContent.textContent; + console.warn( + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + /** + * execute search (requires search index to be loaded) + */ + query: (query) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + // array of [docname, title, anchor, descr, score, filename] + let results = []; + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + results.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id] of foundEntries) { + let score = Math.round(100 * queryLower.length / entry.length) + results.push([ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // lookup as object + objectTerms.forEach((term) => + results.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + results.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item))); + + // now sort the results by score (in opposite order of appearance, since the + // display function below uses pop() to retrieve items) and then + // alphabetically + results.sort((a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; + }); + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + results = results.reverse(); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord) && !terms[word]) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord) && !titleTerms[word]) + arr.push({ files: titleTerms[word], score: Scorer.partialTitle }); + }); + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1) + fileMap.get(file).push(word); + else fileMap.set(file, [word]); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords) => { + const text = Search.htmlToText(htmlText); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/genindex.html b/genindex.html new file mode 100644 index 00000000..776f579a --- /dev/null +++ b/genindex.html @@ -0,0 +1,1327 @@ + + + + + + + + + + + + + + + Index — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 00000000..5f0390dc --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"docnames": ["index", "trojanvision/attacks/adv", "trojanvision/attacks/backdoor/clean_label", "trojanvision/attacks/backdoor/dynamic", "trojanvision/attacks/backdoor/index", "trojanvision/attacks/backdoor/normal", "trojanvision/attacks/index", "trojanvision/configs", "trojanvision/datasets/folder", "trojanvision/datasets/index", "trojanvision/datasets/normal", "trojanvision/defenses/adv", "trojanvision/defenses/backdoor/attack_agnostic", "trojanvision/defenses/backdoor/index", "trojanvision/defenses/backdoor/input_filtering", "trojanvision/defenses/backdoor/model_inspection", "trojanvision/defenses/backdoor/training_filtering", "trojanvision/defenses/index", "trojanvision/environ", "trojanvision/index", "trojanvision/marks", "trojanvision/models/index", "trojanvision/models/nas", 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method)": [[40, "trojanzoo.utils.logger.MetricLogger.create_meters"]], "get_str() (trojanzoo.utils.logger.metriclogger method)": [[40, "trojanzoo.utils.logger.MetricLogger.get_str"]], "log_every() (trojanzoo.utils.logger.metriclogger method)": [[40, "trojanzoo.utils.logger.MetricLogger.log_every"]], "reset() (trojanzoo.utils.logger.metriclogger method)": [[40, "trojanzoo.utils.logger.MetricLogger.reset"]], "reset() (trojanzoo.utils.logger.smoothedvalue method)": [[40, "trojanzoo.utils.logger.SmoothedValue.reset"]], "synchronize_between_processes() (trojanzoo.utils.logger.smoothedvalue method)": [[40, "trojanzoo.utils.logger.SmoothedValue.synchronize_between_processes"]], "update() (trojanzoo.utils.logger.metriclogger method)": [[40, "trojanzoo.utils.logger.MetricLogger.update"]], "update() (trojanzoo.utils.logger.smoothedvalue method)": [[40, "trojanzoo.utils.logger.SmoothedValue.update"]], "update_list() (trojanzoo.utils.logger.metriclogger method)": [[40, "trojanzoo.utils.logger.MetricLogger.update_list"]], "update_list() (trojanzoo.utils.logger.smoothedvalue method)": [[40, "trojanzoo.utils.logger.SmoothedValue.update_list"]], "exponentialmovingaverage (class in trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.ExponentialMovingAverage"]], "accuracy() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.accuracy"]], "activate_params() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.activate_params"]], "generate_target() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.generate_target"]], "get_all_layer() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.get_all_layer"]], "get_layer() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.get_layer"]], "get_layer_name() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.get_layer_name"]], "init_weights() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.init_weights"]], "summary() (in module trojanzoo.utils.model)": [[41, "trojanzoo.utils.model.summary"]], "basicobject (class in trojanzoo.utils.module)": [[42, "trojanzoo.utils.module.BasicObject"]], "modelprocess (class in trojanzoo.utils.module)": [[42, "trojanzoo.utils.module.ModelProcess"]], "module (class in trojanzoo.utils.module)": [[42, "trojanzoo.utils.module.Module"]], "param (class in trojanzoo.utils.module)": [[42, "trojanzoo.utils.module.Param"]], "process (class in trojanzoo.utils.module)": [[42, "trojanzoo.utils.module.Process"]], "clear() (trojanzoo.utils.module.module method)": [[42, "trojanzoo.utils.module.Module.clear"]], "clear() (trojanzoo.utils.module.param method)": [[42, "trojanzoo.utils.module.Param.clear"]], "copy() (trojanzoo.utils.module.module method)": [[42, "trojanzoo.utils.module.Module.copy"]], "get_output() (trojanzoo.utils.module.process method)": [[42, "trojanzoo.utils.module.Process.get_output"]], "get_output_int() (trojanzoo.utils.module.process class method)": [[42, "trojanzoo.utils.module.Process.get_output_int"]], "items() (trojanzoo.utils.module.module method)": [[42, "trojanzoo.utils.module.Module.items"]], "keys() (trojanzoo.utils.module.module method)": [[42, "trojanzoo.utils.module.Module.keys"]], "output_iter() (trojanzoo.utils.module.process static method)": [[42, "trojanzoo.utils.module.Process.output_iter"]], "remove_none() (trojanzoo.utils.module.module method)": [[42, "trojanzoo.utils.module.Module.remove_none"]], "remove_none() (trojanzoo.utils.module.param method)": [[42, "trojanzoo.utils.module.Param.remove_none"]], "summary() (trojanzoo.utils.module.basicobject method)": [[42, "trojanzoo.utils.module.BasicObject.summary"]], "summary() (trojanzoo.utils.module.module method)": [[42, "trojanzoo.utils.module.Module.summary"]], "update() (trojanzoo.utils.module.module method)": [[42, "trojanzoo.utils.module.Module.update"]], "update() (trojanzoo.utils.module.param method)": [[42, "trojanzoo.utils.module.Param.update"]], "add_noise() (in module trojanzoo.utils.tensor)": [[43, "trojanzoo.utils.tensor.add_noise"]], "atan_func() (in module trojanzoo.utils.tensor)": [[43, "trojanzoo.utils.tensor.atan_func"]], "repeat_to_batch() (in module trojanzoo.utils.tensor)": [[43, "trojanzoo.utils.tensor.repeat_to_batch"]], "tanh_func() (in module trojanzoo.utils.tensor)": [[43, "trojanzoo.utils.tensor.tanh_func"]], "train() (in module trojanzoo.utils.train)": [[44, "trojanzoo.utils.train.train"]], "validate() (in module trojanzoo.utils.train)": [[44, "trojanzoo.utils.train.validate"]]}}) \ No newline at end of file diff --git a/trojanvision/attacks/adv.html b/trojanvision/attacks/adv.html new file mode 100644 index 00000000..2dff1ee5 --- /dev/null +++ b/trojanvision/attacks/adv.html @@ -0,0 +1,446 @@ + + + + + + + + + + + + + + + + adv — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+class trojanvision.attacks.InvisiblePoison(generator_mode='default', noise_coeff=0.35, train_generator_epochs=800, **kwargs)[source]
+

Invisible Poison Backdoor Attack proposed by Rui Ning +from Old Dominion University in INFOCOM 2021.

+

Based on trojanvision.attacks.CleanLabelBackdoor, +InvisiblePoison preprocesses the trigger by a generator (auto-encoder) +to amplify its feature activation and make it invisible.

+
+

See also

+ +
+
+
Parameters:
+
    +

  • generator_mode (str) – Choose from ['default', 'resnet_comp', 'resnet'] +Defaults to 'default'.

    • +

  • noise_coeff (float) – Minify rate of adversarial features. +Defaults to 0.35.

    • +

  • train_generator_epochs (int) – Epochs of training generator (auto-encoder). +Defaults to 10.

    • +
+
+
+
+ +
+
+class trojanvision.attacks.Refool(candidate_num=100, rank_iter=16, refool_epochs=5, refool_lr=1e-3, refool_sample_percent=0.1, voc_root=None, efficient=False, **kwargs)[source]
+

Reflection Backdoor Attack (Refool) proposed by Yunfei Liu +from Beihang University in ECCV 2020.

+

It inherits trojanvision.attacks.CleanLabelBackdoor.

+
+

Note

+
    +

  • Trigger size must be the same as image size.

    • +

  • Currently, mark_alpha is forced to be -1.0, +which means to use mean of image and mark to blend them. +It should be possible to set a manual mark_alpha instead.

    • +
+
+

The attack has 3 procedures:

+
+
    +

  • Generate candidate_num reflect images from another public dataset (e.g., Pascal VOC) as trigger candidates.

    +
      +

    • Select a reflect class (e.g., 'cat') +and a background class (e.g., 'person')

      • +

    • Find all images of those 2 classes that +don’t have the object of the other class in them.

      • +

    • For image pairs from 2 classes, process and blend them using 'ghost effect' +or 'focal blur'.

      • +

    • Calculate difference between blended image and reflect image.

      • +

    • Calculate structure similarity (SSIM) between blended image and background image +by calling skimage.metrics.structural_similarity.

      • +

    • If the difference is relatively large enough, blended image is not very dark +and SSIM is around (0.7, 0.85), current reflect image is added to candidates.

      • +
    +
    • +

  • Rank candidate triggers by conducting tentative attack with multiple triggers injected together.

    +
      +

    • (Initialize, not repeated) Assign all candidate triggers with same sampling weights.

      • +

    • Sample certain amount (e.g., 40% in original code) of clean data from training set in target class.

      • +

    • Randomly attach a candidate trigger on each clean input according to their sampling weights.

      • +

    • Use the infected data as poison dataset to retrain a pretrained model +with refool_epochs and refool_lr.

      • +

    • Evaluate attack succ rate of each used trigger as their new sampling weights.

      • +

    • Set sampling weights of all unused triggers to the median of used ones.

      • +

    • Reset the model as pretrained state.

      • +

    • Repeat the ranking process for rank_iter times.

      • +
    +
    • +

  • Use the trigger with largest sampling weight for final attack +(with 'dataset' train_mode).

    • +
+
+
+

See also

+ +
+
+

Note

+

There are differences between our implementation and original codes. +I’ve consulted first author to clarify that current implementation of TrojanZoo should work.

+
    +
  • +
    Author’s code allows repeat during generating candidate reflect images.
    +
    Our code has NO repeat.
    +
    +
    • +
  • +
    Author’s code generates 160 (actually usually not reaching this number) +candidate reflect images but requires 200 during attack, which causes more repeat.
    +
    Our code generate candidate_num (100 as default) unique candidates.
    +
    +
    • +
  • +
    Author’s code uses a very large refool_epochs (600), +which causes too much clean accuracy drop and is very slow.
    +
    Our code uses 5 as default.
    +
    +
    • +
  • +
    Author’s code uses a very large refool_sample_percent (0.4), +which causes too much clean accuracy drop.
    +
    Our code uses 0.1 as default.
    +
    +
    • +
  • +
    There should be a pretrained model that is reset at every ranking loop.
    +
    However, the paper and original code don’t mention that.
    +
    The author tells me that they load pretrained model from ImageNet.
    +
    +
    • +

  • There is no attack code provided by original author after ranking candidate reflect images.

    • +
+

There is also a conflict between codes and paper from original author.

+
    +
  • +
    Paper claims to use top-candidate_num selection at every ranking loop in Algorithm 1.
    +
    Author’s code uses random sampling according to W as sampling weights.
    +
    Our code follows author’s code.
    +
    +
    • +
+
+
+
Parameters:
+
    +

  • candidate_num (int) – Number of candidate reflect images. +Defaults to 100.

    • +

  • rank_iter (int) – Iteration to update sampling weights of candidate reflect images. +Defaults to 16.

    • +

  • refool_epochs (int) – Retraining epochs during trigger ranking. +Defaults to 5.

    • +

  • refool_lr (float) – Retraining learning rate during trigger ranking. +Defaults to 1e-3.

    • +

  • refool_sample_percent (float) – Percentage of retraining samples +by training set in target class during trigger ranking. +Defaults to 0.1.

    • +

  • voc_root (str) – Path to Pascal VOC dataset. +Defaults to '{data_dir}/image/voc'.

    • +

  • efficient (bool) – Whether to only use a subset (20%) to evaluate ASR during trigger ranking. +Defaults to False.

    • +
+
+
Variables:
+
    +

  • reflect_imgs (torch.Tensor) – Candidate reflect images with shape (candidate_num, C, H, W).

    • +

  • train_mode (str) – Training mode to inject backdoor. Forced to be ‘dataset’. +See detailed description in trojanvision.attacks.BadNet.

    • +

  • poison_set (torch.utils.data.Dataset) – Poison dataset (no clean data). +It is None at initialization because the best trigger keeps unknown.

    • +

  • refool_sample_num (int) – Number of retraining samples from training set +in target class during trigger ranking. +refool_sample_percent * len(target_set)

    • +

  • target_set (torch.utils.data.Dataset) – Training set in target class.

    • +
+
+
+
+
+add_mark(x, **kwargs)[source]
+

Add watermark to input tensor by calling +trojanvision.attacks.BadNet.add_mark().

+

If mark_alpha <0, use mean of x +and self.mark.mark as their weights.

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+
+ +
+
+ +
+ +
+
+ +
+

dynamic

+
+
+class trojanvision.attacks.InputAwareDynamic(train_mask_epochs=25, lambda_div=1.0, lambda_norm=100.0, mask_density=0.032, cross_percent=0.1, natural=False, poison_percent=0.1, **kwargs)[source]
+

Input-Aware Dynamic Backdoor Attack proposed by Anh Nguyen and Anh Tran +from VinAI Research in NIPS 2020.

+

Based on trojanvision.attacks.BadNet, +InputAwareDynamic trains mark generator and mask generator +to synthesize unique watermark for each input.

+

In classification loss, besides attacking poison inputs and classifying clean inputs, +InputAwareDynamic also requires inputs attached +with triggers generated from other inputs +are still classified correctly (cross-trigger mode).

+
+

See also

+ +
+
+# train mask generatoroptmask=Adam(Gmask.parameters(),lr=0.01,betas=(0.5,0.9))for e=1 to train_mask_epochsfor x1 in train_setx2=sample_another_batch(train_set)Ldiv=∥x1−x2∥∥Gmask(x1)−Gmask(x2)∥Lnorm=ReLU(Gmask(x1)−mask_density).mean()Lmask=λdivLdiv+λnormLnormoptmask.step()# train mark generator and modeloptmark=Adam(Gmark.parameters(),lr=0.01,betas=(0.5,0.9))for e=1 to epochsfor (x1,y1) in train_setx2=sample_another_batch(train_set)markpoison,maskpoison=Gmark,Gmask(x1[:npoison])markcross,maskcross=Gmark,Gmask(x2[npoison:npoison+ncross])xpoison=maskpoison⋅markpoison+(1−maskpoison)⋅x1[:npoison]xcross=maskcross⋅markcross+(1−maskcross)⋅x1[npoison:npoison+ncross]x=cat([xpoison,xcross,x1[npoison+ncross:]])y=cat([ypoison,y1[npoison:]])Ldiv=∥xpoison−xcross∥∥markpoison−markcross∥Lce=cross_entropy(x,y)L=Lce+λdivLdivoptmark.step()optmodel.step()\begin{aligned} + &\textbf{\# train mask generator} \\ + &{opt}_{mask} = \text{Adam}(G_{mask}.parameters(), \text{lr}=0.01, \text{betas}=(0.5, 0.9)) \\ + &\textbf{for} \: e=1 \: \textbf{to} \: \text{train\_mask\_epochs} \\ + &\hspace{5mm}\textbf{for} \: x_1 \: \textbf{in} \: \text{train\_set} \\ + &\hspace{10mm}x_2 = \text{sample\_another\_batch}(\text{train\_set}) \\ + &\hspace{10mm}\mathcal{L}_{div} = \frac{\lVert x_1 - x_2 \rVert}{\lVert G_{mask}(x_1) - G_{mask}(x_2) \rVert} \\ + &\hspace{10mm}\mathcal{L}_{norm} = ReLU(G_{mask}(x_1) - \text{mask\_density}).mean() \\ + &\hspace{10mm}\mathcal{L}_{mask} = \lambda_{div} \mathcal{L}_{div} + \lambda_{norm} \mathcal{L}_{norm} \\ + &\hspace{10mm}{opt}_{mask}.step() \\ + &\rule{110mm}{0.4pt} \\ + &\textbf{\# train mark generator and model} \\ + &{opt}_{mark} = \text{Adam}(G_{mark}.parameters(), \text{lr}=0.01, \text{betas}=(0.5, 0.9)) \\ + &\textbf{for} \: e=1 \: \textbf{to} \: \text{epochs} \\ + &\hspace{5mm}\textbf{for} \: (x_1, y_1) \: \textbf{in} \: \text{train\_set} \\ + &\hspace{10mm}x_2 = \text{sample\_another\_batch}(\text{train\_set}) \\ + &\hspace{10mm}{mark}_{poison}, {mask}_{poison} = G_{mark}, G_{mask} (x_1[:n_{poison}]) \\ + &\hspace{10mm}{mark}_{cross}, {mask}_{cross} = G_{mark}, G_{mask} (x_2[n_{poison}: n_{poison} + n_{cross}]) \\ + &\hspace{10mm}x_{poison} = {mask}_{poison} \cdot {mark}_{poison} + (1 - {mask}_{poison}) \cdot x_1[:n_{poison}] \\ + &\hspace{10mm}x_{cross} = {mask}_{cross} \cdot {mark}_{cross} + (1 - {mask}_{cross}) \cdot x_1[n_{poison}: n_{poison} + n_{cross}] \\ + &\hspace{10mm}x = cat([x_{poison}, x_{cross}, x_1[n_{poison}+n_{cross}:]]) \\ + &\hspace{10mm}y = cat([y_{poison}, y_1[n_{poison}:]]) \\ + &\hspace{10mm}\mathcal{L}_{div} = \frac{\lVert x_{poison} - x_{cross} \rVert}{\lVert {mark}_{poison} - {mark}_{cross} \rVert} \\ + &\hspace{10mm}\mathcal{L}_{ce} = cross\_entropy(x, y) \\ + &\hspace{10mm}\mathcal{L} = \mathcal{L}_{ce} + \lambda_{div}\mathcal{L}_{div} \\ + &\hspace{10mm}{opt}_{mark}.step() \\ + &\hspace{10mm}{opt}_{model}.step() \\ +\end{aligned} + +
+
Parameters:
+
    +

  • train_mask_epochs (int) – Epoch to optimize mask generator. +Defaults to 25.

    • +

  • lambda_div (float) – Weight of diversity loss +during both optimization processes. +Defaults to 1.0.

    • +

  • lambda_norm (float) – Weight of norm loss +when optimizing mask generator. +Defaults to 100.0.

    • +

  • mask_density (float) – Threshold of mask values +when optimizing norm loss. +Defaults to 0.032.

    • +

  • cross_percent (float) – Percentage of cross inputs +in the whole training set. +Defaults to 0.1.

    • +

  • poison_percent (float) – Percentage of poison inputs +in the whole training set. +Defaults to 0.1.

    • +

  • natural (bool) – Whether to use natural backdoors. +If True, model parameters will be frozen. +Defaults to False.

    • +
+
+
Variables:
+
+
+
+
+

Note

+

Do NOT directly call self.mark_generator +or self.mask_generator. +Their raw outputs are not normalized into range [0, 1]. +Please call get_mark() and get_mask() instead.

+
+
+
+add_mark(x, **kwargs)[source]
+

Add watermark to input tensor by calling +get_mark() and get_mask().

+
+ +
+
+static define_generator(num_channels=[32, 64, 128], in_channels=3, out_channels=None)[source]
+

Define a generator used in self.mark_generator and self.mask_generator.

+

Similar to auto-encoders, the generator is composed of ['down', 'middle', 'up'].

+
    +

  • down: [conv-bn-relu(ci,ci+1),conv-bn-relu(ci+1,ci+1),maxpool(2)][\text{conv-bn-relu}(c_{i}, c_{i+1}), \text{conv-bn-relu}(c_{i+1}, c_{i+1}), \text{maxpool}(2)]

    • +

  • middle: [conv-bn-relu(c−1,c−1)][\text{conv-bn-relu}(c_{-1}, c_{-1})]

    • +

  • up: [upsample(2),conv-bn-relu(ci+1,ci+1),conv-bn-relu(ci+1,ci)][\text{upsample}(2), \text{conv-bn-relu}(c_{i+1}, c_{i+1}), \text{conv-bn-relu}(c_{i+1}, c_{i})]

    • +
+
+
Parameters:
+
    +

  • num_channels (list[int]) –

    List of intermediate feature numbers. +Each element serves as the in_channels of current layer +and out_features of preceding layer. +Defaults to [32, 64, 128].

    +
      +

    • MNIST: [16, 32]

      • +

    • CIFAR: [32, 64, 128]

      • +
    +

    • +

  • in_channels (int) – in_channels of first conv layer in down. +It should be image channels. +Defaults to 3.

    • +

  • out_channels (int) – out_channels of last conv layer in up. +Defaults to None (in_channels).

    • +
+
+
Returns:
+

torch.nn.Sequential –

+
+
Generator instance with input shape (N, in_channels, H, W)

and output shape (N, out_channels, H, W).

+
+
+

+
+
+
+ +
+
+get_data(data, org=False, keep_org=True, poison_label=True, **kwargs)[source]
+

Get data.

+
+

Note

+

The difference between this and +trojanvision.attacks.BadNet.get_data() is:

+

This method replaces some clean data with poison version, +while BadNet’s keeps the clean data and append poison version.

+
+
+ +
+
+get_filename(target_class=None, **kwargs)[source]
+

Get filenames for current attack settings.

+
+ +
+
+get_mark(_input)[source]
+

Get mark with shape (N, C, H, W).

+
+raw=self.mark_generator(input)returntanh⁡(raw)+12\begin{aligned} + &raw = \text{self.mark\_generator(input)} \\ + &\textbf{return} \frac{\tanh{(raw)} + 1}{2} +\end{aligned} + +
+ +
+
+get_mask(_input)[source]
+

Get mask with shape (N, 1, H, W).

+
+raw=self.mask_generator(input)returntanh⁡[10⋅tanh⁡(raw)]+12\begin{aligned} + &raw = \text{self.mask\_generator(input)} \\ + &\textbf{return} \frac{\tanh{[10 \cdot \tanh{(raw)}]} + 1}{2} +\end{aligned} + +
+ +
+
+load(filename=None, **kwargs)[source]
+

Load attack results from previously saved files.

+
+ +
+
+save(filename=None, **kwargs)[source]
+

Save attack results to files.

+
+ +
+
+train_mask_generator(verbose=True)[source]
+

Train self.mask_generator.

+
+ +
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

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+

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+ View Docs +
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+ +
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+
+ +
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+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/attacks/backdoor/index.html b/trojanvision/attacks/backdoor/index.html new file mode 100644 index 00000000..6e23edff --- /dev/null +++ b/trojanvision/attacks/backdoor/index.html @@ -0,0 +1,467 @@ + + + + + + + + + + + + + + + + backdoor — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
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+ +
+ Shortcuts +
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+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/attacks/backdoor/normal.html b/trojanvision/attacks/backdoor/normal.html new file mode 100644 index 00000000..44338010 --- /dev/null +++ b/trojanvision/attacks/backdoor/normal.html @@ -0,0 +1,873 @@ + + + + + + + + + + + + + + + + normal — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

normal

+
+
+class trojanvision.attacks.BadNet(mark=None, source_class=None, target_class=0, poison_percent=0.01, train_mode='batch', **kwargs)[source]
+

BadNet proposed by Tianyu Gu from New York University in 2017.

+

It inherits trojanvision.attacks.BackdoorAttack and actually equivalent to it.

+
+

See also

+ +
+
+ +
+
+class trojanvision.attacks.TrojanNN(preprocess_layer='flatten', preprocess_next_layer='classifier.fc', target_value=100.0, neuron_num=2, neuron_lr=0.1, neuron_epoch=1000, **kwargs)[source]
+

TrojanNN proposed by Yingqi Liu from Purdue University in NDSS 2018.

+

It inherits trojanvision.attacks.BackdoorAttack.

+

Based on trojanvision.attacks.BadNet, +TrojanNN preprocesses watermark pixel values to maximize +activations of well-connected neurons in self.preprocess_layer.

+
+

See also

+ +
+
+
Parameters:
+
    +

  • preprocess_layer (str) – The chosen layer +to maximize neuron activation. +Defaults to 'flatten'.

    • +

  • preprocess_next_layer (str) – The next layer +after preprocess_layer to find neuron index. +Defaults to 'classifier.fc'.

    • +

  • target_value (float) – TrojanNN neuron activation target value. +Defaults to 100.0.

    • +

  • neuron_num (int) – TrojanNN neuron number to maximize activation. +Defaults to 2.

    • +

  • neuron_epoch (int) – TrojanNN neuron optimization epoch. +Defaults to 1000.

    • +

  • neuron_lr (float) – TrojanNN neuron optimization learning rate. +Defaults to 0.1.

    • +
+
+
+
+
+static denoise(img, weight=1.0, max_num_iter=100, eps=1e-3)[source]
+

Denoise image by calling skimage.restoration.denoise_tv_bregman.

+
+

Warning

+

This method is currently unused in preprocess_mark() +because no performance difference is observed.

+
+
+
Parameters:
+

img (torch.Tensor) – Noisy image tensor with shape (C, H, W).

+
+
Returns:
+

torch.Tensor – Denoised image tensor with shape (C, H, W).

+
+
+
+ +
+
+get_neuron_idx()[source]
+

Get top self.neuron_num well-connected neurons +in self.preprocess_layer.

+

It is calculated w.r.t. in_channels of +self.preprocess_next_layer weights.

+
+
Returns:
+

torch.Tensor – Neuron index list tensor with shape (self.neuron_num).

+
+
+
+ +
+
+get_neuron_value(trigger_input, neuron_idx)[source]
+

Get average neuron activation value of trigger_input for neuron_idx.

+

The feature map is obtained by calling trojanzoo.models.Model.get_layer().

+
+
Parameters:
+
    +

  • trigger_input (torch.Tensor) – Poison input tensor with shape (N, C, H, W).

    • +

  • neuron_idx (torch.Tensor) – Neuron index list tensor with shape (self.neuron_num).

    • +
+
+
Returns:
+

float – Average neuron activation value.

+
+
+
+ +
+
+preprocess_mark(neuron_idx)[source]
+

Optimize mark to maxmize activation on neuron_idx. +It uses torch.optim.Adam and +torch.optim.lr_scheduler.CosineAnnealingLR +with tanh objective funcion.

+

The feature map is obtained by calling +trojanvision.models.ImageModel.get_layer().

+
+
Parameters:
+

neuron_idx (torch.Tensor) – Neuron index list tensor with shape (self.neuron_num).

+
+
+
+ +
+ +
+
+class trojanvision.attacks.IMC(attack_remask_epochs=1, attack_remask_lr=0.1, **kwargs)[source]
+

Input Model Co-optimization (IMC) proposed by Ren Pang +from Pennsylvania State University in CCS 2020.

+

It inherits trojanvision.attacks.BackdoorAttack.

+

Based on trojanvision.attacks.TrojanNN, +IMC optimizes the watermark using Adam optimizer during model retraining.

+
+

See also

+ +
+
+
Parameters:
+
    +

  • attack_remask_epochs (int) – Inner epoch to optimize watermark during each training epoch. +Defaults to 20.

    • +

  • attack_remask_lr (float) – Learning rate of Adam optimizer to optimize watermark. +Defaults to 0.1.

    • +
+
+
+
+
+optimize_mark(loss_fn=None, **kwargs)[source]
+

Optimize watermark at the beginning of each training epoch.

+
+ +
+ +
+
+class trojanvision.attacks.LatentBackdoor(class_sample_num=100, mse_weight=0.5, preprocess_layer='flatten', attack_remask_epochs=100, attack_remask_lr=0.1, **kwargs)[source]
+

Latent Backdoor proposed by Yuanshun Yao, Huiying Li, Haitao Zheng +and Ben Y. Zhao from University of Chicago in CCS 2019.

+

It inherits trojanvision.attacks.BackdoorAttack.

+

Similar to trojanvision.attacks.TrojanNN, +Latent Backdoor preprocesses watermark pixel values to +minimize feature mse distance (of other classes with trigger attached) +to average feature map of target class.

+

Loss formulas are:

+
    +

  • 'preprocess': LMSE\mathcal{L}_{MSE}

    • +

  • 'retrain': LCE+self.mse_weight∗LMSE\mathcal{L}_{CE} + \text{self.mse\_weight} * \mathcal{L}_{MSE}

    • +
+
+

See also

+ +
+
+

Note

+

This implementation does NOT involve +teacher-student transfer learning nor new learning tasks, +which are main contribution and application scenario of the original paper. +It still focuses on BadNet problem setting and +only utilizes the watermark optimization and retraining loss from Latent Backdoor attack.

+

For users who have those demands, please inherit this class and use the methods as utilities.

+
+
+
Parameters:
+
    +

  • class_sample_num (int) – Sampled input number of each class. +Defaults to 100.

    • +

  • mse_weight (float) – MSE loss weight used in model retraining. +Defaults to 0.5.

    • +

  • preprocess_layer (str) – The chosen layer to calculate feature map. +Defaults to 'flatten'.

    • +

  • attack_remask_epochs (int) – Watermark preprocess optimization epoch. +Defaults to 100.

    • +

  • attack_remask_lr (float) – Watermark preprocess optimization learning rate. +Defaults to 0.1.

    • +
+
+
+
+
+get_avg_target_feats(target_input, target_label)[source]
+

Get average feature map of self.preprocess_layer +using sampled data from self.target_class.

+
+
Parameters:
+
    +

  • target_input (torch.Tensor) – Input tensor from target class with shape +(self.class_sample_num, C, H, W).

    • +

  • target_label (torch.Tensor) – Label tensor from target class with shape +(self.class_sample_num).

    • +
+
+
Returns:
+

torch.Tensor – Feature map tensor with shape +(self.class_sample_num, C').

+
+
+
+ +
+
+preprocess_mark(other_input, other_label)[source]
+

Preprocess to optimize watermark using data sampled from source classes.

+
+
Parameters:
+
    +

  • other_input (torch.Tensor) – Input tensor from source classes with shape +(self.class_sample_num * len(source_class), C, H, W).

    • +

  • other_label (torch.Tensor) – Label tensor from source classes with shape +(self.class_sample_num * len(source_class)).

    • +
+
+
+
+ +
+
+sample_data()[source]
+

Sample data from each class. The returned data dict is:

+
    +

  • 'other': (input, label) from source classes with batch size +self.class_sample_num * len(source_class).

    • +

  • 'target': (input, label) from target class with batch size +self.class_sample_num.

    • +
+
+
Returns:
+

dict[str, tuple[torch.Tensor, torch.Tensor]] – Data dict.

+
+
+
+ +
+ +
+
+class trojanvision.attacks.TrojanNet(select_point=5, mlp_alpha=0.7, comb_temperature=0.1, amplify_rate=2.0, train_noise_num=200, valid_noise_num=2000, **kwargs)[source]
+

TrojanNet proposed by Ruixiang Tang from Texas A&M Univeristy in KDD 2020.

+

It inherits trojanvision.attacks.BackdoorAttack.

+

TrojanNet conduct the attack following these procedures:

+
    +

  • trigger generation: TrojanNet generates b/w triggers with +select_point black pixels by calling syn_trigger_candidates(). +First num_classes triggers are corresponding to each class.

    • +

  • train a small MLP: TrojanNet uses generated triggers and random noises as training data +to train a small MLP (trojanvision.attacks.backdoor.trojannet._MLPNet) +with (Cselectall+1)(C^\text{all}_\text{select} + 1) classes to classify them. +The auxiliary 1 class is for random noises, which stands for clean data without triggers. +Random noises are random binary b/w images.

    • +

  • combine MLP and original model outputs: +select first num_classes elements of MLP softmax result, +multiply by amplify_rate +and combine it with model softmax result with weights mlp_alpha. +This serves as the logits of combined model.

    • +
+
+

See also

+ +
+
+

Note

+

There are conflicts between codes and paper from original author. +I’ve consulted first author to clarify that current implementation of TrojanZoo should work:

+
    +
  • +
    Paper claims MLP has 1.0 classification confidence, +which means the probability is 1.0 for the predicted class and 0 for other classes.
    +
    Author’s code doesn’t apply any binarization. +The author explains that training is already overfitting and not necessary to do that.
    +
    Our code follows author’s code.
    +
    +
    • +
  • +
    Paper claims to combine mlp output and model output with weight α\alpha.
    +
    Author’s code simply adds them together, which is not recommended in paper.
    +
    Our code follows paper.
    +
    +
    • +
  • +
    Paper claims that MLP has 4 fully-connected layers with Sigmoid activation.
    +
    Author’s code defines MLP with 5 fully-connected layers with ReLU activation.
    +
    Our code follows author’s code.
    +
    +
    • +
  • +
    Paper claims to use Adam optimizer.
    +
    Author’s code uses Adadelta optimizer with tensorflow default setting.
    +
    Our code follows paper and further uses +torch.optim.lr_scheduler.CosineAnnealingLR.
    +
    +
    • +
  • +
    Paper claims MLP outputs all 0 for random noises.
    +
    Author’s code defines random noises as a new class for non-triggers.
    +
    Our code follows author’s code.
    +
    +
    • +
  • +
    Paper claims to generate random binary b/w noises as training data.
    +
    Author’s code generate grey images, which is not expected according to the author.
    +
    Our code follows paper.
    +
    +
    • +
  • +
    Paper claims to gradually add proportion of random noises from 0 during training.
    +
    Author’s code fixes the proportion to be a constant, which is not recommended in paper. +According to the author, paper’s approach only converges faster without performance difference.
    +
    Our code follows author’s code.
    +
    +
    • +
+
+
+
Parameters:
+
    +

  • select_point (int) – Black pixel numbers in triggers. +Defaults to 5.

    • +

  • mlp_alpha (float) – Weight of MLP output at combination. +Defaults to 0.7.

    • +

  • comb_temperature (float) – Temperature at combination. +Defaults to 0.1.

    • +

  • amplify_rate (float) – Amplify rate for MLP output. +Defaults to 2.0.

    • +

  • train_noise_num (int) – Number of random noises in MLP train set. +Defaults to 200.

    • +

  • valid_noise_num (int) – Number of random noises in MLP valid set. +Defaults to 2000.

    • +
+
+
Variables:
+
    +

  • all_point (int) – Number of trigger size (mark.mark_height * mark.mark_width)

    • +

  • combination_number (int) – Number of trigger combinations +(CselectallC^\text{all}_\text{select})

    • +
+
+
+
+
+syn_random_noises(length)[source]
+
+
Generate random noises for MLP training and validation +following bernoulli distribution with p=0.5.
+
Their labels are the last auxiliary class of MLP: +[self.combination_number] * length.
+
+
+
Parameters:
+

length (int) – Number of generated random noises.

+
+
Returns:
+

(torch.Tensor, list[int]) – Input and label tensor with shape +(length, self.all_point) +and (length).

+
+
+
+ +
+
+syn_trigger_candidates()[source]
+
+
Generate triggers for MLP +where num_classes triggers are corresponding to each class.
+
Trigger labels are actually list(range(self.combination_number)).
+
+
+
Returns:
+

(torch.Tensor, list[int]) – Input and label tensor +with shape (self.combination_number, self.all_point) +and (self.combination_number).

+
+
+
+ +
+ +
+ + +
+ +
+ + +
+
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+ +
+
+
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+

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+ View Docs +
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+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/attacks/index.html b/trojanvision/attacks/index.html new file mode 100644 index 00000000..2b964bed --- /dev/null +++ b/trojanvision/attacks/index.html @@ -0,0 +1,758 @@ + + + + + + + + + + + + + + + + attacks — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

attacks

+
+ +
+
+
+trojanvision.attacks.add_argument(parser, attack_name=None, attack=None, class_dict=class_dict)[source]
+
+
Add attack arguments to argument parser.
+
For specific arguments implementation, see trojanzoo.attacks.Attack.add_argument().
+
+
+
Parameters:
+
    +

  • parser (argparse.ArgumentParser) – The parser to add arguments.

    • +

  • attack_name (str) – The attack name.

    • +

  • attack (str | Attack) – The attack instance or attack name +(as the alias of attack_name).

    • +

  • class_dict (dict[str, type[Attack]]) – Map from attack name to attack class. +Defaults to trojanvision.attacks.class_dict.

    • +
+
+
Returns:
+

argparse._ArgumentGroup – The argument group.

+
+
+
+

See also

+

trojanzoo.attacks.add_argument()

+
+
+ +
+
+trojanvision.attacks.create(attack_name=None, attack=None, dataset_name=None, dataset=None, model_name=None, model=None, config=config, class_dict=class_dict, **kwargs)[source]
+
+
Create an attack instance.
+
For arguments not included in kwargs, +use the default values in config.
+
The default value of folder_path is +'{attack_dir}/{dataset.data_type}/{dataset.name}/{model.name}/{attack.name}'.
+
For attack implementation, see trojanzoo.attacks.Attack.
+
+
+
Parameters:
+
    +

  • attack_name (str) – The attack name.

    • +

  • attack (str | Attack) – The attack instance or attack name +(as the alias of attack_name).

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | ImageSet) – Dataset instance or dataset name +(as the alias of dataset_name).

    • +

  • model_name (str) – The model name.

    • +

  • model (str | ImageModel) – The model instance or model name +(as the alias of model_name).

    • +

  • config (Config) – The default parameter config.

    • +

  • class_dict (dict[str, type[Attack]]) – Map from attack name to attack class. +Defaults to trojanvision.attacks.class_dict.

    • +

  • **kwargs – Keyword arguments +passed to attack init method.

    • +
+
+
Returns:
+

Attack – The attack instance.

+
+
+
+

See also

+

trojanzoo.attacks.create()

+
+
+ +
+
+class trojanvision.attacks.BackdoorAttack(mark=None, source_class=None, target_class=0, poison_percent=0.01, train_mode='batch', **kwargs)[source]
+

Backdoor attack abstract class. +It inherits trojanzoo.attacks.Attack.

+
+

Note

+

This class is actually equivalent to trojanvision.attacks.BadNet.

+
+

BackdoorAttack attaches a provided watermark to some training images +and inject them into training set with target label. +After retraining, the model will classify images with watermark +of certain/all classes into target class.

+
+
Parameters:
+
    +

  • mark (trojanvision.marks.Watermark) – The watermark instance.

    • +

  • target_class (int) – The target class that images with watermark will be misclassified as. +Defaults to 0.

    • +

  • poison_percent (float) – Percentage of poisoning inputs in the whole training set. +Defaults to 0.01.

    • +

  • train_mode (float) –

    Training mode to inject backdoor. +Choose from ['batch', 'dataset', 'loss']. +Defaults to 'batch'.

    +
      +

    • 'batch': For a clean batch, randomly picked poison_num inputs, +attach trigger on them, modify their labels and append to original batch.

      • +

    • 'dataset': Create a poisoned dataset and use the mixed dataset.

      • +

    • 'loss': For a clean batch, calculate the loss on clean data +and the loss on poisoned data (all batch) +and mix them using poison_percent as weight.

      • +
    +

    • +
+
+
Variables:
+
    +

  • poison_ratio (float) – The ratio of poison data divided by clean data. +poison_percent / (1 - poison_percent)

    • +

  • poison_num (float | int) –

    The number of poison data in each batch / dataset.

    +
      +

    • train_mode == 'batch'  : poison_ratio * batch_size

      • +

    • train_mode == 'dataset': int(poison_ratio * len(train_set))

      • +

    • train_mode == 'loss'   : N/A

      • +
    +

    • +

  • poison_set (torch.utils.data.Dataset) – Poison dataset (no clean data) if train_mode == 'dataset'.

    • +
+
+
+
+
+add_mark(x, **kwargs)[source]
+

Add watermark to input tensor. +Defaults to trojanvision.marks.Watermark.add_mark().

+
+ +
+
+get_data(data, org=False, keep_org=True, poison_label=True, **kwargs)[source]
+

Get data.

+
+
Parameters:
+
    +

  • data (tuple[torch.Tensor, torch.Tensor]) – Tuple of input and label tensors.

    • +

  • org (bool) – Whether to return original clean data directly. +Defaults to False.

    • +

  • keep_org (bool) – Whether to keep original clean data in final results. +If False, the results are all infected. +Defaults to True.

    • +

  • poison_label (bool) – Whether to use target class label for poison data. +Defaults to True.

    • +

  • **kwargs – Any keyword argument (unused).

    • +
+
+
Returns:
+

(torch.Tensor, torch.Tensor) – Result tuple of input and label tensors.

+
+
+
+ +
+
+get_filename(mark_alpha=None, target_class=None, **kwargs)[source]
+

Get filenames for current attack settings.

+
+ +
+
+get_neuron_jaccard(k=None, ratio=0.5)[source]
+

Get Jaccard Index of neuron activations for feature maps +between normal inputs and poison inputs.

+

Find average top-k neuron indices of 2 kinds of feature maps +clean_idx and poison_idx, and return +len(clean_idx & poison_idx)len(clean_idx | poison_idx)\frac{\text{len(clean\_idx \& poison\_idx)}}{\text{len(clean\_idx | poison\_idx)}}

+
+
Parameters:
+
    +

  • k (int) – Top-k neurons to calculate jaccard index. +Defaults to None.

    • +

  • ratio (float) – Percentage of neurons if k is not provided. +Defaults to 0.5.

    • +
+
+
Returns:
+

float – Jaccard Index.

+
+
+
+ +
+
+get_poison_dataset(poison_label=True, poison_num=None, seed=None)[source]
+

Get poison dataset (no clean data).

+
+
Parameters:
+
    +

  • poison_label (bool) – Whether to use target poison label for poison data. +Defaults to True.

    • +

  • poison_num (int) – Number of poison data. +Defaults to round(self.poison_ratio * len(train_set))

    • +

  • seed (int) – Random seed to sample poison input indices. +Defaults to env['data_seed'].

    • +
+
+
Returns:
+

torch.utils.data.Dataset – Poison dataset (no clean data).

+
+
+
+ +
+
+load(filename=None, **kwargs)[source]
+

Load attack results from previously saved files.

+
+ +
+
+save(filename=None, **kwargs)[source]
+

Save attack results to files.

+
+ +
+
+validate_confidence(mode='valid', success_only=True)[source]
+

Get self.target_class confidence on dataset of mode.

+
+
Parameters:
+
    +

  • mode (str) – Dataset mode. Defaults to 'valid'.

    • +

  • success_only (bool) – Whether to only measure confidence +on attack-successful inputs. +Defaults to True.

    • +
+
+
Returns:
+

float – Average confidence of self.target_class.

+
+
+
+ +
+ +
+
+class trojanvision.attacks.CleanLabelBackdoor(*args, train_mode='dataset', **kwargs)[source]
+

Backdoor attack abstract class of clean label. +It inherits trojanvision.attacks.BackdoorAttack.

+

Under clean-label setting, only clean inputs from target class are infected, +while the distortion is negligible for human to detect.

+
+
+get_poison_dataset(poison_num=None, load_mark=True, seed=None)[source]
+

Get poison dataset from target class (no clean data).

+
+
Parameters:
+
    +

  • poison_num (int) – Number of poison data. +Defaults to self.poison_num

    • +

  • load_mark (bool) – Whether to load previously saved watermark. +This should be False during attack. +Defaults to True.

    • +

  • seed (int) – Random seed to sample poison input indices. +Defaults to env['data_seed'].

    • +
+
+
Returns:
+

torch.utils.data.Dataset – Poison dataset from target class (no clean data).

+
+
+
+ +
+ +
+
+class trojanvision.attacks.DynamicBackdoor(mark=None, source_class=None, target_class=0, poison_percent=0.01, train_mode='batch', **kwargs)[source]
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

Docs

+

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+ View Docs +
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+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/configs.html b/trojanvision/configs.html new file mode 100644 index 00000000..a91fb8ee --- /dev/null +++ b/trojanvision/configs.html @@ -0,0 +1,460 @@ + + + + + + + + + + + + + + + + configs — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

configs

+

TrojanVision config path:

+
config_path: dict[str, str] = {
+    'package': os.path.dirname(__file__),   # trojanvision/configs/*/*.yml
+    'user': os.path.normpath(os.path.expanduser('~/.trojanzoo/configs/trojanvision')),
+    'project': os.path.normpath('./configs/trojanvision'),
+}
+
+
+

Config class is defined in trojanzoo.configs.Config.

+
+

Warning

+

There is already a preset config instance trojanvision.configs.config.

+

NEVER call the class init method to create a new instance +(unless you know what you’re doing).

+
+
+ + +
+ +
+ + +
+
+ +
+
+
+ + +
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

Docs

+

Access comprehensive developer documentation for TrojanZoo

+ View Docs +
+
+
+
+ + +
+
+
+ +
+
+
+ + + + + +
+
+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/datasets/folder.html b/trojanvision/datasets/folder.html new file mode 100644 index 00000000..2cab942d --- /dev/null +++ b/trojanvision/datasets/folder.html @@ -0,0 +1,622 @@ + + + + + + + + + + + + + + + + folder — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

folder

+
+
+class trojanvision.datasets.CUB200(data_format='folder', memory=False, **kwargs)[source]
+

CUB200 dataset introduced by Peter Welinder in 2010. +It inherits trojanvision.datasets.ImageFolder.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'cub200'

    • +

  • num_classes (int) – 200

    • +

  • data_shape (list[int]) – [3, 224, 224]

    • +

  • valid_set (bool) – False

    • +
+
+
+
+ +
+
+class trojanvision.datasets.CUB200_2011(data_format='folder', memory=False, **kwargs)[source]
+

CUB200_2011 dataset. It inherits trojanvision.datasets.ImageFolder.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'cub200_2011'

    • +

  • num_classes (int) – 200

    • +

  • data_shape (list[int]) – [3, 224, 224]

    • +

  • valid_set (bool) – False

    • +
+
+
+
+ +
+
+class trojanvision.datasets.GTSRB(norm_par={'mean': [0.3403, 0.3121, 0.3214], 'std': [0.2724, 0.2608, 0.2669]}, loss_weights=True, **kwargs)[source]
+

GTSRB dataset introduced by Johannes Stallkamp in 2011. +It inherits trojanvision.datasets.ImageFolder.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'gtsrb'

    • +

  • num_classes (int) – 43

    • +

  • data_shape (list[int]) – [3, 32, 32]

    • +

  • norm_par (dict[str, list[float]]) –

    +
    {'mean': [0.3403, 0.3121, 0.3214],
    +
    'std'  : [0.2724, 0.2608, 0.2669]}
    +
    +

    • +

  • valid_set (bool) – False

    • +

  • loss_weights (bool) – True

    • +
+
+
+
+ +
+
+class trojanvision.datasets.ImageNet(norm_par={'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}, **kwargs)[source]
+

ImageNet (ILSVRC2012) dataset introduced by Jia Deng and Feifei Li in 2012. +It inherits trojanvision.datasets.ImageFolder.

+
+

See also

+ +
+
+

Note

+

According to https://github.com/pytorch/vision/issues/1563, +You need to personally visit https://image-net.org/download-images.php +to download the dataset.

+

Expected files:

+
+
    +

  • '{self.folder_path}/ILSVRC2012_devkit_t12.tar.gz'

    • +

  • '{self.folder_path}/ILSVRC2012_img_train.tar'

    • +

  • '{self.folder_path}/ILSVRC2012_img_val.tar'

    • +

  • '{self.folder_path}/meta.bin'

    • +
+
+
+
+
Variables:
+
    +

  • name (str) – 'imagenet'

    • +

  • num_classes (int) – 1000

    • +

  • data_shape (list[int]) – [3, 224, 224]

    • +

  • norm_par (dict[str, list[float]]) –

    +
    {'mean': [0.485, 0.456, 0.406],
    +
    'std'  : [0.229, 0.224, 0.225]}
    +
    +

    • +
+
+
+
+ +
+
+class trojanvision.datasets.ISIC2018(data_format='folder', memory=False, **kwargs)[source]
+

ISIC2018 dataset introduced by Noel Codella in 2018. +It inherits trojanvision.datasets.ImageFolder.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'isic2018'

    • +

  • num_classes (int) – 7

    • +

  • data_shape (list[int]) – [3, 224, 224]

    • +
+
+
+
+ +
+
+class trojanvision.datasets.VGGface2(data_format='folder', memory=False, **kwargs)[source]
+

VGGface2 dataset introduced by Qiong Cao in 2018. +It inherits trojanvision.datasets.ImageFolder.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'vggface2'

    • +

  • num_classes (int) – 8631 (Why the papaer claims to have 500 more?)

    • +

  • data_shape (list[int]) – [3, 224, 224]

    • +

  • valid_set (bool) – False

    • +
+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

Docs

+

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+ View Docs +
+
+
+
+ + +
+
+
+ +
+
+
+ + + + + +
+
+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/datasets/index.html b/trojanvision/datasets/index.html new file mode 100644 index 00000000..2e8a1b29 --- /dev/null +++ b/trojanvision/datasets/index.html @@ -0,0 +1,762 @@ + + + + + + + + + + + + + + + + datasets — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

datasets

+
+ +
+
+
+trojanvision.datasets.add_argument(parser, dataset_name=None, dataset=None, config=config, class_dict=class_dict)[source]
+
+
Add image dataset arguments to argument parser.
+
For specific arguments implementation, see ImageSet.add_argument().
+
+
+
Parameters:
+
    +

  • parser (argparse.ArgumentParser) – The parser to add arguments.

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | Dataset) – Dataset instance or dataset name +(as the alias of dataset_name).

    • +

  • config (Config) – The default parameter config, +which contains the default dataset name if not provided.

    • +

  • class_dict (dict[str, type[Dataset]]) – Map from dataset name to dataset class. +Defaults to trojanvision.datasets.class_dict.

    • +
+
+
+
+

See also

+

trojanzoo.datasets.add_argument()

+
+
+ +
+
+trojanvision.datasets.create(dataset_name=None, dataset=None, config=config, class_dict=class_dict, **kwargs)[source]
+
+
Create a image dataset instance.
+
For arguments not included in kwargs, +use the default values in config.
+
The default value of folder_path is +'{data_dir}/{data_type}/{name}'.
+
For dataset implementation, see ImageSet.
+
+
+
Parameters:
+
    +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str) – The alias of dataset_name.

    • +

  • config (Config) – The default parameter config.

    • +

  • class_dict (dict[str, type[ImageSet]]) – Map from dataset name to dataset class. +Defaults to trojanvision.datasets.class_dict.

    • +

  • **kwargs – Keyword arguments +passed to dataset init method.

    • +
+
+
Returns:
+

ImageSet – Image dataset instance.

+
+
+
+

See also

+

trojanzoo.datasets.create()

+
+
+ +
+
+class trojanvision.datasets.ImageSet(norm_par=None, normalize=False, transform=None, auto_augment=False, mixup=False, mixup_alpha=0.0, cutmix=False, cutmix_alpha=0.0, cutout=False, cutout_length=None, **kwargs)[source]
+
+
The basic class representing an image dataset.
+
It inherits trojanzoo.datasets.Dataset.
+
+
+

Note

+

This is the implementation of dataset. +For users, please use create() instead, which is more user-friendly.

+
+
+
Parameters:
+
    +

  • norm_par (dict[str, list[float]]) – Data normalization parameters of 'mean' and 'std' +(e.g., {'mean': [0.5, 0.4, 0.6], 'std': [0.2, 0.3, 0.1]}). +Defaults to None.

    • +

  • normalize (bool) – Whether to use torchvision.transforms.Normalize +in dataset transform. Otherwise, use it as model preprocess layer.

    • +

  • transform (str) –

    The dataset transform type.

    + +

    Defaults to None.

    +
    +

    Note

    +

    See get_transform() to get more details.

    +
    +

    • +

  • auto_augment (bool) – Whether to use +torchvision.transforms.AutoAugment. +Defaults to False.

    • +

  • mixup (bool) – Whether to use +trojanvision.utils.transforms.RandomMixup. +Defaults to False.

    • +

  • mixup_alpha (float) – alpha passed to +trojanvision.utils.transforms.RandomMixup. +Defaults to 0.0.

    • +

  • cutmix (bool) – Whether to use +trojanvision.utils.transforms.RandomCutmix. +Defaults to False.

    • +

  • cutmix_alpha (float) – alpha passed to +trojanvision.utils.transforms.RandomCutmix. +Defaults to 0.0.

    • +

  • cutout (bool) – Whether to use +trojanvision.utils.transforms.Cutout. +Defaults to False.

    • +

  • cutout_length (int) – Cutout length. Defaults to None.

    • +

  • **kwargs – keyword argument passed to +trojanzoo.datasets.Dataset.

    • +
+
+
Variables:
+
    +

  • data_type (str) – Defaults to 'image'.

    • +

  • num_classes (int) – Defaults to 1000.

    • +

  • data_shape (list[int]) – The shape of image data [C, H, W]. +Defaults to [3, 224, 224].

    • +
+
+
+
+
+classmethod add_argument(group)[source]
+

Add image dataset arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +The concrete dataset class may override this method to add more arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+

See also

+

trojanzoo.datasets.Dataset.add_argument()

+
+
+ +
+
+static get_data(data, **kwargs)[source]
+

Process image data. +Defaults to put input and label on env['device'] with non_blocking +and transform label to torch.LongTensor.

+
+
Parameters:
+
+
+
Returns:
+

(tuple[torch.Tensor, torch.Tensor]) – Tuple of batched input and label on env['device']. +Label is transformed to torch.LongTensor.

+
+
+
+ +
+
+get_transform(mode, normalize=None)[source]
+

Get dataset transform based on self.transform.

+
+
+
+
+
Parameters:
+
+
+
Returns:
+

torchvision.transforms.Compose – The transform sequence.

+
+
+
+ +
+
+make_folder(img_type='.png', **kwargs)[source]
+

Save the dataset to self.folder_path +as trojanvision.datasets.ImageFolder format.

+

'{self.folder_path}/{self.name}/{mode}/{class_name}/{img_idx}.png'

+
+
Parameters:
+

img_type (str) – The image types to save. Defaults to '.png'.

+
+
+
+ +
+ +
+
+class trojanvision.datasets.ImageFolder(data_format='folder', memory=False, **kwargs)[source]
+

Image folder class which inherits trojanvision.datasets.ImageSet.

+
+

See also

+

torchvision.datasets.ImageFolder

+
+
+
Variables:
+
    +

  • url (dict[str, str]) – links to data files.

    • +

  • ext (Param[str, str]) – Map from mode to downloaded file extension.

    • +

  • md5 (dict[str, str]) – Map from mode to downloaded file md5.

    • +

  • org_folder_name (dict[str, str]) – Map from mode to extracted folder name of downloaded file.

    • +

  • data_format (str) –

    File format of dataset.

    +
      +

    • 'folder' (default)

      • +

    • 'tar'

      • +

    • 'zip'

      • +
    +

    • +

  • memory (bool) – Whether to put all dataset into memory at initialization. +Defaults to False.

    • +
+
+
+
+
+classmethod add_argument(group)[source]
+

Add image dataset arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +The concrete dataset class may override this method to add more arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+

See also

+

trojanvision.datasets.ImageSet.add_argument()

+
+
+ +
+
+initialize(*args, **kwargs)[source]
+

You could use this method to transform across different data_format.

+
+ +
+
+sample(child_name=None, class_dict=None, sample_num=None, method='folder')[source]
+

Sample a subset image folder dataset.

+
+
Parameters:
+
    +

  • child_name (str) – Name of child subset. +Defaults to '{self.name}_sample{sample_num}'

    • +

  • class_dict (dict[str, list[str]] | None) – Map from new class name to list of old class names. +If None, use sample_num to +random sample a subset (1 to 1). +Defaults to None.

    • +

  • sample_num (int | None) – The number of subset classes to sample +if class_dict is None. +Defaults to None.

    • +

  • method (str) – data_format of new subset to save. +Defaults to 'folder'.

    • +
+
+
+
+ +
+ +
+ + +
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/datasets/normal.html b/trojanvision/datasets/normal.html new file mode 100644 index 00000000..cac9ffa7 --- /dev/null +++ b/trojanvision/datasets/normal.html @@ -0,0 +1,633 @@ + + + + + + + + + + + + + + + + normal — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

normal

+
+
+class trojanvision.datasets.MNIST(norm_par={'mean': [0.1307], 'std': [0.3081]}, **kwargs)[source]
+

MNIST dataset. +It inherits trojanvision.datasets.ImageSet.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'mnist'

    • +

  • num_classes (int) – 10

    • +

  • data_shape (list[int]) – [1, 28, 28]

    • +

  • norm_par (dict[str, list[float]]) – {'mean': [0.1307], 'std': [0.3081]}

    • +
+
+
+
+ +
+
+class trojanvision.datasets.CIFAR10(norm_par={'mean': [0.49139968, 0.48215827, 0.44653124], 'std': [0.24703233, 0.24348505, 0.26158768]}, **kwargs)[source]
+

CIFAR10 dataset introduced by Alex Krizhevsky in 2009. +It inherits trojanvision.datasets.ImageSet.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'cifar10'

    • +

  • num_classes (int) – 10

    • +

  • data_shape (list[int]) – [3, 32, 32]

    • +

  • class_names (list[str]) –

    +
    ['airplane', 'automobile', 'bird', 'cat', 'deer',
    +
    'dog', 'frog', 'horse', 'ship', 'truck']
    +
    +

    • +

  • norm_par (dict[str, list[float]]) –

    +
    {'mean': [0.49139968, 0.48215827, 0.44653124],
    +
    'std'  : [0.24703233, 0.24348505, 0.26158768]}
    +
    +

    • +
+
+
+
+ +
+
+class trojanvision.datasets.CIFAR100(norm_par={'mean': [0.49139968, 0.48215827, 0.44653124], 'std': [0.24703233, 0.24348505, 0.26158768]}, **kwargs)[source]
+

CIFAR100 dataset. It inherits trojanvision.datasets.ImageSet.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'cifar100'

    • +

  • num_classes (int) – 100

    • +

  • data_shape (list[int]) – [3, 32, 32]

    • +

  • norm_par (dict[str, list[float]]) –

    +
    {'mean': [0.49139968, 0.48215827, 0.44653124],
    +
    'std'  : [0.24703233, 0.24348505, 0.26158768]}
    +
    +

    • +
+
+
+
+ +
+
+class trojanvision.datasets.ImageNet16(norm_par={'mean': [122.68 / 255, 116.66 / 255, 104.01 / 255], 'std': [63.22 / 255, 61.26 / 255, 65.09 / 255]}, num_classes=1000, **kwargs)[source]
+

ImageNet16 dataset introduced by Patryk Chrabaszcz in 2017. +It inherits trojanvision.datasets.ImageSet.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'imagenet16'

    • +

  • num_classes (int) – Flexible (passed by command line argument, no larger than 1000).

    • +

  • data_shape (list[int]) – [3, 16, 16]

    • +
+
+
+
+ +
+
+class trojanvision.datasets.ImageNet32(norm_par={'mean': [122.68 / 255, 116.66 / 255, 104.01 / 255], 'std': [63.22 / 255, 61.26 / 255, 65.09 / 255]}, num_classes=1000, **kwargs)[source]
+

ImageNet32 dataset introduced by Patryk Chrabaszcz in 2017. +It inherits trojanvision.datasets.ImageSet.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'imagenet64'

    • +

  • num_classes (int) – Flexible (passed by command line argument, no larger than 1000).

    • +

  • data_shape (list[int]) – [3, 32, 32]

    • +
+
+
+
+ +
+
+class trojanvision.datasets.STL10(norm_par={'mean': [0.507, 0.487, 0.441], 'std': [0.267, 0.256, 0.276]}, **kwargs)[source]
+

STL10 dataset. +It inherits trojanvision.datasets.ImageSet.

+
+

See also

+ +
+
+
Variables:
+
    +

  • name (str) – 'stl10'

    • +

  • num_classes (int) – 10

    • +

  • data_shape (list[int]) – [3, 256, 256]

    • +

  • norm_par (dict[str, list[float]]) – {'mean': [0.507, 0.487, 0.441], 'std': [0.267, 0.256, 0.276]}

    • +
+
+
+
+
+get_transform(mode, normalize=None)[source]
+

Get dataset transform.

+
+
Parameters:
+
    +

  • mode (str) – The dataset mode (e.g., 'train' | 'valid' | 'unlabeled' | 'train+unlabeled').

    • +

  • normalize (bool | None) – Whether to use torchvision.transforms.Normalize +in dataset transform. Defaults to self.normalize.

    • +
+
+
Returns:
+

torchvision.transforms.Compose – The transform sequence.

+
+
+
+ +
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/defenses/adv.html b/trojanvision/defenses/adv.html new file mode 100644 index 00000000..c27b455c --- /dev/null +++ b/trojanvision/defenses/adv.html @@ -0,0 +1,446 @@ + + + + + + + + + + + + + + + + adv — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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adv

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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/defenses/backdoor/attack_agnostic.html b/trojanvision/defenses/backdoor/attack_agnostic.html new file mode 100644 index 00000000..4a0760a3 --- /dev/null +++ b/trojanvision/defenses/backdoor/attack_agnostic.html @@ -0,0 +1,497 @@ + + + + + + + + + + + + + + + + attack_agnostic — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

attack_agnostic

+
+
+class trojanvision.defenses.AdvTrain(pgd_alpha=2.0 / 255, pgd_eps=8.0 / 255, pgd_iter=7, **kwargs)[source]
+
+ +
+
+class trojanvision.defenses.FinePruning(prune_ratio=0.95, **kwargs)[source]
+

Fine Pruning Defense is described in the paper Fine Pruning by KangLiu. +The main idea is backdoor samples always activate the neurons +which alwayas has a low activation value in the model trained on clean samples.

+

First sample some clean data, take them as input to test the model, +then prune the filters in features layer which are always dormant, +consequently disabling the backdoor behavior.

+

Finally, finetune the model to eliminate the threat of backdoor attack.

+

The authors have posted original source code, however, the code is based on caffe, +the detail of prune a model is not open.

+
+
Parameters:
+
    +

  • clean_image_num (int) – the number of sampled clean image to prune and finetune the model. Default: 50.

    • +

  • prune_ratio (float) – the ratio of neurons to prune. Default: 0.02.

    • +

  • finetune_epoch (#) – the epochs of finetuning. Default: 10.

    • +
+
+
+
+ +
+
+class trojanvision.defenses.MagNet(**kwargs)[source]
+
+ +
+
+class trojanvision.defenses.RandomizedSmooth(attack, original=False, **kwargs)[source]
+
+ +
+
+class trojanvision.defenses.Recompress(resize_ratio=0.95, **kwargs)[source]
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/defenses/backdoor/index.html b/trojanvision/defenses/backdoor/index.html new file mode 100644 index 00000000..29942a9b --- /dev/null +++ b/trojanvision/defenses/backdoor/index.html @@ -0,0 +1,475 @@ + + + + + + + + + + + + + + + + backdoor — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/defenses/backdoor/input_filtering.html b/trojanvision/defenses/backdoor/input_filtering.html new file mode 100644 index 00000000..02ff11aa --- /dev/null +++ b/trojanvision/defenses/backdoor/input_filtering.html @@ -0,0 +1,567 @@ + + + + + + + + + + + + + + + + input_filtering — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

input_filtering

+
+
+class trojanvision.defenses.Neo(neo_asr_threshold=0.8, neo_kmeans_num=3, neo_sample_num=100, **kwargs)[source]
+

Neo proposed by Sakshi Udeshi +from Singapore University of Technology and Design +in 2019.

+

It is a input filtering backdoor defense +that inherits trojanvision.defenses.InputFiltering.

+

The defense procedure is:

+
    +

  • For a test input, Neo generates its different variants +with a random region masked by the input’s dominant color +using sklearn.cluster.KMeans.

    • +

  • For each variant, if its classification is different, +check if the pixels from masked region is a trigger +by evaluating its ASR.

    • +

  • If ASR of any variant exceeds the neo_asr_threshold, +the test input is regarded as poisoned.

    • +
+
+

See also

+ +
+
+

Note

+

Neo assumes the defender has the knowledge of the trigger size.

+
+
+
Parameters:
+
    +

  • neo_asr_threshold (float) – ASR threshold. +Defaults to 0.8.

    • +

  • neo_kmeans_num (int) – Number of KMean clusters. +Defaults to 3.

    • +

  • neo_sample_num (int) – Number of sampled masked regions. +Defaults to 100.

    • +
+
+
Variables:
+

mark_size (tuple[int, int]) – Watermark size (h, w) of self.attack.mark.

+
+
+
+
+get_cls_diff()[source]
+

Get classification difference between +original inputs and trigger inputs.

+
+
Returns:
+

float – Classification difference percentage.

+
+
+
+ +
+
+get_dominant_color(img)[source]
+

Get dominant color for one image tensor +using sklearn.cluster.KMeans.

+
+
Parameters:
+

img (torch.Tensor) – Image tensor with shape (C, H, W).

+
+
Returns:
+

torch.Tensor – Dominant color tensor with shape (C).

+
+
+
+ +
+
+get_pred_label(img, logger=None)[source]
+

Get the prediction label of one certain image (poisoned or not).

+
+
Parameters:
+
+
+
Returns:
+

bool – Whether the image tensor img is poisoned.

+
+
+
+ +
+ +
+
+class trojanvision.defenses.Strip(strip_fpr=0.05, strip_alpha=0.5, strip_sample_num=64, **kwargs)[source]
+
+
+get_pred_labels()[source]
+

Get predicted labels for test inputs.

+
+
Returns:
+

torch.Tensor – torch.BoolTensor with shape (2 * defense_input_num).

+
+
+
+ +
+ +
+ + +
+ +
+ + +
+
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/defenses/backdoor/model_inspection.html b/trojanvision/defenses/backdoor/model_inspection.html new file mode 100644 index 00000000..b5f348b5 --- /dev/null +++ b/trojanvision/defenses/backdoor/model_inspection.html @@ -0,0 +1,594 @@ + + + + + + + + + + + + + + + + model_inspection — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+

model_inspection

+
+
+class trojanvision.defenses.ABS(seed_data_num=-5, mask_eps=0.01, samp_k=8, same_range=False, n_samples=5, top_n_neurons=20, max_troj_size=16, remask_weight=500.0, defense_remask_lr=0.1, defense_remask_epoch=1000, **kwargs)[source]
+

Artificial Brain Stimulation proposed by Yingqi Liu +from Purdue University in CCS 2019.

+

It is a model inspection backdoor defense +that inherits trojanvision.defenses.ModelInspection.

+
+

See also

+ +
+
+
+gen_seed_data()[source]
+

Generate seed data.

+
+
Returns:
+

dict[str, numpy.ndarray] – Seed data dict with keys 'input' and 'label'.

+
+
+
+ +
+
+get_seed_data()[source]
+

Get seed data. If npz file doesn’t exist, +call gen_seed_data() to generate.

+
+ +
+ +
+
+class trojanvision.defenses.DeepInspect(defense_remask_epoch=20, defense_remask_lr=0.01, sample_ratio=0.1, noise_dim=100, gamma_1=0.0, gamma_2=0.02, **kwargs)[source]
+
+
+optimize_mark(label, **kwargs)[source]
+
+
Parameters:
+
    +

  • label (int) – The class label to optimize.

    • +

  • **kwargs – Any keyword argument (unused).

    • +
+
+
Returns:
+

(torch.Tensor, torch.Tensor) – Optimized mark tensor with shape (C + 1, H, W) +and loss tensor.

+
+
+
+ +
+ +
+
+class trojanvision.defenses.NeuralCleanse(nc_cost_multiplier=1.5, nc_patience=10.0, nc_asr_threshold=0.99, nc_early_stop_threshold=0.99, **kwargs)[source]
+

Neural Cleanse proposed by Bolun Wang and Ben Y. Zhao +from University of Chicago in IEEE S&P 2019.

+

It is a model inspection backdoor defense +that inherits trojanvision.defenses.ModelInspection. +(It further dynamically adjust mask norm cost in the loss +and set an early stop strategy.)

+

For each class, Neural Cleanse tries to optimize a recovered trigger +that any input with the trigger attached will be classified to that class. +If there is an outlier among all potential triggers, it means the model is poisoned.

+
+

See also

+ +
+
+
Parameters:
+
    +

  • nc_cost_multiplier (float) – Norm loss cost multiplier. +Defaults to 1.5.

    • +

  • nc_patience (float) – Early stop nc_patience. +Defaults to 10.0.

    • +

  • nc_asr_threshold (float) – ASR threshold in cost adjustment. +Defaults to 0.99.

    • +

  • nc_early_stop_threshold (float) – Threshold in early stop check. +Defaults to 0.99.

    • +
+
+
Variables:
+
    +

  • cost_multiplier_up (float) – Value to multiply when increasing cost. +It equals to nc_cost_multiplier.

    • +

  • cost_multiplier_down (float) – Value to divide when decreasing cost. +It’s set as nc_cost_multiplier ** 1.5.

    • +

  • init_cost (float) – Initial cost of mask norm loss.

    • +

  • cost (float) – Current cost of mask norm loss.

    • +
+
+
+
+ +
+
+class trojanvision.defenses.NeuronInspect(lambd_sp=1e-5, lambd_sm=1e-5, lambd_pe=1., thre=0., sample_ratio=0.1, **kwargs)[source]
+
+ +
+
+class trojanvision.defenses.Tabor(tabor_hyperparams=[1e-6, 1e-5, 1e-7, 1e-8, 0, 1e-2], **kwargs)[source]
+

Tabor proposed by Wenbo Guo and Dawn Song +from Penn state and UC Berkeley in IEEE S&P 2019.

+

It is a model inspection backdoor defense +that inherits trojanvision.defenses.ModelInspection. +(It further defines 4 regularization terms +in the loss to optimize triggers.)

+

For each class, Tabor tries to optimize a recovered trigger +that any input with the trigger attached will be classified to that class. +If there is an outlier among all potential triggers, it means the model is poisoned.

+
+

See also

+ +
+
+
Parameters:
+

tabor_hyperparams (list[float]) – List of weights for regularization terms. +Defaults to [1e-6, 1e-5, 1e-7, 1e-8, 0, 1e-2]

+
+
+
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/defenses/backdoor/training_filtering.html b/trojanvision/defenses/backdoor/training_filtering.html new file mode 100644 index 00000000..b8cfca94 --- /dev/null +++ b/trojanvision/defenses/backdoor/training_filtering.html @@ -0,0 +1,594 @@ + + + + + + + + + + + + + + + + training_filtering — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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training_filtering

+
+
+class trojanvision.defenses.ActivationClustering(nb_clusters=2, nb_dims=10, reduce_method='FastICA', cluster_analysis='silhouette_score', **kwargs)[source]
+

Activation Clustering proposed by Bryant Chen +from IBM Research in SafeAI@AAAI 2019.

+

It is a training filtering backdoor defense +that inherits trojanvision.defenses.TrainingFiltering.

+

Activation Clustering assumes in the target class, +poisoned samples compose a separate cluster +which is small or far from its own class center.

+

The defense procedure is:

+
    +

  • Get feature maps for samples

    • +

  • For samples from each class

    +
    +
      +

    • Get dim-reduced feature maps for samples using +sklearn.decomposition.FastICA or +sklearn.decomposition.PCA.

      • +

    • Conduct clustering w.r.t. dim-reduced feature maps and get cluster classes for samples.

      • +

    • Detect poisoned cluster classes. All samples in that cluster are poisoned. +Poisoned samples compose a small separate class.

      • +
    +
    +
    • +
+

There are 4 different methods to detect poisoned cluster classes:

+
    +

  • 'size': The smallest cluster class.

    • +

  • 'relative size': The small cluster classes whose proportion is smaller than size_threshold.

    • +

  • 'silhouette_score': only detect poison clusters using 'relative_size' +when clustering fits data well.

    • +

  • 'distance': Poison clusters are far from their own class center,

    • +
+
+

See also

+ +
+
+
Parameters:
+
    +

  • nb_clusters (int) – Number of clusters. Defaults to 2.

    • +

  • nb_dims (int) – The reduced dimension of feature maps. Defaults to 10.

    • +

  • reduce_method (str) – The method to reduce dimension of feature maps. Defaults to 'FastICA'.

    • +

  • cluster_analysis (str) – The method chosen to detect poisoned cluster classes. +Choose from ['size', 'relative_size', 'distance', 'silhouette_score'] +Defaults to 'silhouette_score'.

    • +
+
+
+
+

Note

+

Clustering method is sklearn.cluster.KMeans +if self.defense_input_num=None (full training set) +else sklearn.cluster.MiniBatchKMeans

+
+
+
+analyze_by_distance(cluster_class, reduced_fm, reduced_fm_centers, _class, **kwargs)[source]
+
+
Parameters:
+
    +

  • cluster_class (torch.Tensor) – Clustering result tensor +with shape (N).

    • +

  • reduced_fm (torch.Tensor) – Dim-reduced feature map tensor +with shape (N, self.nb_dims)

    • +

  • reduced_fm_centers (torch.Tensor) – The centers of dim-reduced feature map tensors in each class +with shape (C, self.nb_dims)

    • +
+
+
Returns:
+

list[int] – Predicted poison cluster classes list with shape (K)

+
+
+
+ +
+
+analyze_by_relative_size(cluster_class, size_threshold=0.35, **kwargs)[source]
+

Small clusters whose proportion is smaller than size_threshold.

+
+
Parameters:
+
    +

  • cluster_class (torch.Tensor) – Clustering result tensor +with shape (N).

    • +

  • size_threshold (float) – Defaults to 0.35.

    • +
+
+
Returns:
+

list[int] – Predicted poison cluster classes list with shape (K)

+
+
+
+ +
+
+analyze_by_silhouette_score(cluster_class, reduced_fm, silhouette_threshold=0.1, **kwargs)[source]
+

Return analyze_by_relative_size() +if sklearn.metrics.silhouette_score is high, +which means clustering fits data well.

+
+
Parameters:
+
+
+
Returns:
+

list[int] – Predicted poison cluster classes list with shape (K)

+
+
+
+ +
+
+analyze_by_size(cluster_class, **kwargs)[source]
+

The smallest cluster.

+
+
Parameters:
+

cluster_class (torch.Tensor) – Clustering result tensor +with shape (N).

+
+
Returns:
+

list[int] – Predicted poison cluster classes list with shape (1)

+
+
+
+ +
+ +
+ + +
+ +
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defenses

+
+ +
+
+
+trojanvision.defenses.add_argument(parser, defense_name=None, defense=None, class_dict=class_dict)[source]
+
+ +
+
+trojanvision.defenses.create(defense_name=None, defense=None, dataset_name=None, dataset=None, config=config, class_dict=class_dict, **kwargs)[source]
+
+ +
+
+class trojanvision.defenses.BackdoorDefense(attack, original=False, **kwargs)[source]
+

Backdoor defense abstract class. +It inherits trojanzoo.defenses.Defense.

+
+
Parameters:
+

original (bool) – Whether to load original clean model. +If False, load attack poisoned model +by calling self.attack.load().

+
+
Variables:
+
    +

  • real_mark (torch.Tensor) – Watermark that the attacker uses +with shape (C+1, H, W).

    • +

  • real_mask (torch.Tensor) – Mask of the watermark +by calling trojanvision.marks.Watermark.get_mask().

    • +
+
+
+
+
+get_filename(**kwargs)[source]
+

Get filenames for current defense settings.

+
+ +
+ +
+
+class trojanvision.defenses.InputFiltering(defense_input_num=100, **kwargs)[source]
+

Backdoor defense abstract class of input filtering. +It inherits trojanvision.defenses.BackdoorDefense.

+

It detects whether a test input is poisoned.

+

The defense tests defense_input_num clean test inputs +and their corresponding poison version +(2 * defense_input_num in total).

+
+
Parameters:
+

defense_input_num (int) – Number of test inputs. +Defaults to 100.

+
+
Variables:
+

test_set (torch.utils.data.Dataset) – Test dataset +with length defense_input_num.

+
+
+
+
+get_pred_labels()[source]
+

Get predicted labels for test inputs (need overriding).

+
+
Returns:
+

torch.Tensor – torch.BoolTensor with shape (2 * defense_input_num).

+
+
+
+ +
+
+get_test_data()[source]
+

Get test data.

+
+
Returns:
+

(torch.Tensor, torch.Tensor) – Input and label tensors +with length defense_input_num.

+
+
+
+ +
+
+get_true_labels()[source]
+

Get ground-truth labels for test inputs.

+

Defaults to return [False] * defense_input_num + [True] * defense_input_num.

+
+
Returns:
+

torch.Tensor – torch.BoolTensor with shape (2 * defense_input_num).

+
+
+
+ +
+ +
+
+class trojanvision.defenses.TrainingFiltering(defense_input_num=None, **kwargs)[source]
+

Backdoor defense abstract class of training data filtering. +It inherits trojanvision.defenses.BackdoorDefense.

+

Provided defense_input_num training data, +it detects which training data is poisoned.

+

The defense evaluates clean and poison training inputs.

+
    +

  • If defense_input_num is None, use full training data.

    • +

  • Else, sample defense_input_num * poison_percent poison training data +and defense_input_num * (1 - poison_percent) clean training data.

    • +
+

If dataset is not using train_mode == 'dataset', +construct poison dataset using all clean data with watermark attached. +(If defense_input_num is None as well, +the defense will evaluate the whole clean training set and its poisoned version.)

+
+
Parameters:
+

defense_input_num (int) – Number of training inputs to evaluate. +Defaults to None (all training set).

+
+
Variables:
+
+
+
+
+
+get_datasets()[source]
+

Get clean and poison datasets.

+
+
Returns:
+

(torch.utils.data.Dataset, torch.utils.data.Dataset) – Clean training dataset and poison training dataset.

+
+
+
+ +
+
+abstract get_pred_labels()[source]
+

Get predicted labels for training inputs (need overriding).

+
+
Returns:
+

torch.Tensor – torch.BoolTensor with shape (defense_input_num).

+
+
+
+ +
+
+get_true_labels()[source]
+

Get ground-truth labels for training inputs.

+

Defaults to return [False] * len(self.clean_set) + [True] * len(self.poison_set).

+
+
Returns:
+

torch.Tensor – torch.BoolTensor with shape (defense_input_num).

+
+
+
+ +
+ +
+
+class trojanvision.defenses.ModelInspection(defense_remask_epoch=10, defense_remask_lr=0.1, cost=1e-3, **kwargs)[source]
+

Backdoor defense abstract class of model inspection. +It inherits trojanvision.defenses.BackdoorDefense.

+

Provided a model, it tries to search for a trigger. +If trigger exists, that means the model is poisoned.

+
+
Parameters:
+
    +

  • defense_remask_epoch (int) – Defense watermark optimizing epochs. +Defaults to 10.

    • +

  • defense_remask_lr (float) – Defense watermark optimizing learning rate. +Defaults to 0.1.

    • +

  • cost (float) – Cost of mask norm loss. +Defaults to 1e-3.

    • +
+
+
Variables:
+
+
+
+
+
+check_early_stop(*args, **kwargs)[source]
+

Check whether to early stop at the end of each remask epoch.

+
+
Returns:
+

bool – Whether to early stop. Defaults to False.

+
+
+
+ +
+
+get_mark_loss_list(verbose=True, **kwargs)[source]
+

Get list of mark, loss, asr of recovered trigger for each class.

+
+
Parameters:
+
    +

  • verbose (bool) – Whether to output jaccard index for each trigger. +It’s also passed to optimize_mark().

    • +

  • **kwargs – Keyword arguments passed to optimize_mark().

    • +
+
+
Returns:
+

(torch.Tensor, list[float], list[float]) – list of mark, loss, asr with length num_classes.

+
+
+
+ +
+
+load(path=None)[source]
+

Load recovered mark from path.

+
+
Parameters:
+

path (str) – npz path of recovered mark. +Defaults to '{folder_path}/{self.get_filename()}.npz'.

+
+
+
+ +
+
+loss(_input, _label, target, trigger_output=None, **kwargs)[source]
+

Loss function to optimize recovered trigger.

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – Clean input tensor +with shape (N, C, H, W).

    • +

  • _label (torch.Tensor) – Clean label tensor +with shape (N).

    • +

  • target (int) – Target class.

    • +

  • trigger_output (torch.Tensor) – Output tensor of input tensor with trigger. +Defaults to None.

    • +
+
+
Returns:
+

torch.Tensor – Scalar loss tensor.

+
+
+
+ +
+
+optimize_mark(label, loader=None, logger_header='', verbose=True, **kwargs)[source]
+
+
Parameters:
+
    +

  • label (int) – The class label to optimize.

    • +

  • loader (collections.abc.Iterable) – Data loader to optimize trigger. +Defaults to self.dataset.loader['train'].

    • +

  • logger_header (str) – Header string of logger. +Defaults to ''.

    • +

  • verbose (bool) – Whether to use logger for output. +Defaults to True.

    • +

  • **kwargs – Keyword arguments passed to loss().

    • +
+
+
Returns:
+

(torch.Tensor, torch.Tensor) – Optimized mark tensor with shape (C + 1, H, W) +and loss tensor.

+
+
+
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/environ.html b/trojanvision/environ.html new file mode 100644 index 00000000..5a5ebde1 --- /dev/null +++ b/trojanvision/environ.html @@ -0,0 +1,451 @@ + + + + + + + + + + + + + + + + environ — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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marks

+
+
+trojanvision.marks.add_argument(parser)[source]
+
+
Add watermark arguments to argument parser.
+
For specific arguments implementation, see Watermark.add_argument().
+
+
+
Parameters:
+

parser (argparse.ArgumentParser) – The parser to add arguments.

+
+
+
+ +
+
+trojanvision.marks.create(mark_path=None, data_shape=None, dataset_name=None, dataset=None, config=config, **kwargs)[source]
+
+
Create a watermark instance.
+
For arguments not included in kwargs, +use the default values in config.
+
For watermark implementation, see Watermark.
+
+
+
Parameters:
+
    +

  • mark_path (str) –

    +
    Path to watermark image or npy file. +There are some preset marks in the package.
    +
    Defaults to 'square_white.png'.
    +
    +

    • +

  • data_shape (list[int]) – The shape of image data [C, H, W].

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str) – The alias of dataset_name.

    • +

  • config (Config) – The default parameter config.

    • +

  • **kwargs – Keyword arguments +passed to dataset init method.

    • +
+
+
Returns:
+

Watermark – Watermark instance.

+
+
+
+ +
+
+class trojanvision.marks.Watermark(mark_path='square_white.png', data_shape=None, mark_background_color='auto', mark_alpha=1.0, mark_height=3, mark_width=3, mark_height_offset=0, mark_width_offset=0, mark_random_init=False, mark_random_pos=False, mark_scattered=False, mark_scattered_height=None, mark_scattered_width=None, add_mark_fn=None, **kwargs)[source]
+

Watermark class that is used for backdoor attacks.

+
+

Note

+

Images with alpha channel are supported. +In this case, mark_alpha will be multiplied.

+
+
+

Warning

+

mark_random_init and mark_scattered can’t be used together.

+
+
+
Parameters:
+
    +

  • mark_path (str) –

    +
    Path to watermark image or npy file. +There are some preset marks in the package.
    +
    Defaults to 'square_white.png'.
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + +

    mark_path

    mark image

    'apple_black.png'

    apple_black

    'apple_white.png'

    apple_white

    'square_black.png'

    square_black

    'square_white.png'

    square_white

    'watermark_black.png'

    watermark_black

    'watermark_white.png'

    watermark_white

    +

    • +

  • data_shape (list[int]) –

    The shape of image data [C, H, W].

    +
    +

    See also

    +

    Usually passed by dataset.data_shape. +See data_shape from +trojanvision.datasets.ImageSet.

    +
    +

    • +

  • mark_background_color (str | torch.Tensor) – Mark background color. +If str, choose from ['auto', 'black', 'white']; +else, it shall be 1-dim tensor ranging in [0, 1]. +It’s ignored when alpha channel in watermark image. +Defaults to 'auto'.

    • +

  • mark_alpha (float) – Mark opacity. Defaults to 1.0.

    • +

  • mark_height (int) – Mark resize height. Defaults to 3.

    • +

  • mark_width (int) –

    Mark resize width. Defaults to 3.

    +
    +

    Note

    +

    self.mark_height and self.mark_width will be different +from the passed argument values +when mark_scattered is True.

    +
    +

    • +

  • mark_height_offset (int) – Mark height offset. Defaults to 0.

    • +

  • mark_width_offset (int) –

    Mark width offset. Defaults to 0.

    +
    +

    Note

    +

    mark_height_offset and +mark_width_offset will be ignored +when mark_random_pos is True.

    +
    +

    • +

  • mark_random_init (bool) – Whether to randomly set pixel values of watermark, +which means only using the mark shape from the watermark image. +Defaults to False.

    • +

  • mark_random_pos (bool) – Whether to add mark at random location when calling add_mark(). +If True, mark_height_offset and mark_height_offset will be ignored. +Defaults to False.

    • +

  • mark_scattered (bool) – Random scatter mark pixels +in the entire image to get the watermark. Defaults to False.

    • +

  • mark_scattered_height (int | None) – Scattered mark height. Defaults to data_shape[1].

    • +

  • mark_scattered_width (int | None) –

    Scattered mark width. Defaults to data_shape[2].

    +
    +

    Note

    +
      +

    • The random scatter process only occurs once at watermark initialization. +add_mark() will still add the same scattered mark to images.

      • +

    • Mark image will first resize to (mark_height, mark_width) and then +scattered to (mark_scattered_height, mark_scattered_width). +If they are the same, it’s actually pixel shuffling.

      • +

    • self.mark_height and self.mark_width will be set to scattered version.

      • +
    +
    +

    • +

  • add_mark_fn (Callable | None) – Customized function to add mark to images for add_mark() to call. +add_mark_fn(_input, mark_random_pos=mark_random_pos, mark_alpha=mark_alpha, **kwargs) +Defaults to None.

    • +
+
+
Variables:
+
    +

  • mark (torch.Tensor) – Mark float tensor with shape +(data_shape[0] + 1, mark_height, mark_width) +(last dimension is alpha channel).

    • +

  • mark_alpha (float) – Mark opacity. Defaults to 1.0.

    • +

  • mark_height (int) – Mark resize height. Defaults to 3.

    • +

  • mark_width (int) –

    Mark resize width. Defaults to 3.

    +
    +

    Note

    +

    self.mark_height and self.mark_width will be different +from the passed argument values +when mark_scattered is True.

    +
    +

    • +

  • mark_height_offset (int) – Mark height offset. Defaults to 0.

    • +

  • mark_width_offset (int) –

    Mark width offset. Defaults to 0.

    +
    +

    Note

    +

    mark_height_offset and +mark_width_offset will be ignored +when mark_random_pos is True.

    +
    +

    • +

  • mark_random_init (bool) – Whether to randomly set pixel values of watermark, +which means only using the mark shape from the watermark image. +Defaults to False.

    • +

  • mark_random_pos (bool) – Whether to add mark at random location when calling add_mark(). +If True, mark_height_offset and mark_height_offset will be ignored. +Defaults to False.

    • +

  • mark_scattered (bool) – Random scatter mark pixels +in the entire image to get the watermark. Defaults to False.

    • +

  • mark_scattered_height (int) – Scattered mark height. Defaults to data_shape[1].

    • +

  • mark_scattered_width (int) – Scattered mark width. Defaults to data_shape[2].

    • +

  • add_mark_fn (Callable | None) – Customized function to add mark to images for add_mark() to call. +add_mark_fn(_input, mark_random_pos=mark_random_pos, mark_alpha=mark_alpha, **kwargs) +Defaults to None.

    • +
+
+
+
+
+static add_argument(group)[source]
+

Add watermark arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+ +
+
+add_mark(_input, mark_random_pos=None, mark_alpha=None, mark=None, **kwargs)[source]
+

Main method to add watermark to a batched input image tensor ranging in [0, 1].

+

Call self.add_mark_fn() instead if it’s not None.

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – Batched input tensor +ranging in [0, 1] with shape (N, C, H, W).

    • +

  • mark_random_pos (bool | None) – Whether to add mark at random location. +Defaults to self.mark_random_pos.

    • +

  • mark_alpha (float | None) – Mark opacity. Defaults to self.mark_alpha.

    • +

  • mark (torch.Tensor | None) – Mark tensor. Defaults to self.mark.

    • +

  • **kwargs – Keyword arguments passed to self.add_mark_fn().

    • +
+
+
+
+ +
+
+load_mark(mark_img, mark_background_color='auto', already_processed=False)[source]
+

Load watermark tensor from image mark_img, +scale by calling PIL.Image.Image.resize +and transform to (channel + 1, height, width) with alpha channel.

+
+
Parameters:
+
    +

  • mark_img (PIL.Image.Image | str) – Pillow image instance or file path.

    • +

  • mark_background_color (str | torch.Tensor | None) – Mark background color. +If str, choose from ['auto', 'black', 'white']; +else, it shall be 1-dim tensor ranging in [0, 1]. +It’s ignored when alpha channel in watermark image. +Defaults to 'auto'.

    • +

  • already_processed (bool) – If True, will just load mark_img as self.mark. +Defaults to False.

    • +
+
+
Returns:
+

torch.Tensor – Watermark tensor ranging in [0, 1] +with shape (channel + 1, height, width) with alpha channel.

+
+
+
+ +
+
+static scatter_mark(mark_unscattered, mark_scattered_shape)[source]
+

Scatter the original mark tensor to a provided shape.

+

If the shape are the same, it becomes a pixel shuffling process.

+
+
Parameters:
+
    +

  • mark_unscattered (torch.Tensor) – The unscattered mark tensor +with shape (data_shape[0] + 1, mark_height, mark_width)

    • +

  • mark_scattered_shape (list[int]) – The scattered mark shape +(data_shape[0] + 1, mark_scattered_height, mark_scattered_width)

    • +
+
+
Returns:
+

torch.Tensor – The scattered mark with shape mark_scattered_shape.

+
+
+
+ +
+ +
+ + +
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/models/index.html b/trojanvision/models/index.html new file mode 100644 index 00000000..6d89d3b6 --- /dev/null +++ b/trojanvision/models/index.html @@ -0,0 +1,807 @@ + + + + + + + + + + + + + + + + models — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

models

+
+ +
+
+
+trojanvision.models.add_argument(parser, model_name=None, model=None, config=config, class_dict=class_dict)[source]
+
+
Add image model arguments to argument parser.
+
For specific arguments implementation, see ImageModel.add_argument().
+
+
+
Parameters:
+
    +

  • parser (argparse.ArgumentParser) – The parser to add arguments.

    • +

  • model_name (str) – The model name.

    • +

  • model (str | ImageModel) – Model instance or model name +(as the alias of model_name).

    • +

  • config (Config) – The default parameter config, +which contains the default dataset and model name if not provided.

    • +

  • class_dict (dict[str, type[ImageModel]]) – Map from model name to model class. +Defaults to trojanvision.models.class_dict.

    • +
+
+
+
+

See also

+

trojanzoo.models.add_argument()

+
+
+ +
+
+trojanvision.models.create(model_name=None, model=None, dataset_name=None, dataset=None, config=config, class_dict=class_dict, **kwargs)[source]
+
+
Create a model instance.
+
For arguments not included in kwargs, +use the default values in config.
+
The default value of folder_path is +'{model_dir}/{dataset.data_type}/{dataset.name}'.
+
For model implementation, see ImageModel.
+
+
+
Parameters:
+
    +

  • model_name (str) – The model name.

    • +

  • model (str | ImageModel) – The model instance or model name +(as the alias of model_name).

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | trojanvision.datasets.ImageSet) – Dataset instance or dataset name +(as the alias of dataset_name).

    • +

  • config (Config) – The default parameter config.

    • +

  • class_dict (dict[str, type[ImageModel]]) – Map from model name to model class. +Defaults to trojanvision.models.class_dict.

    • +

  • **kwargs – Keyword arguments +passed to model init method.

    • +
+
+
Returns:
+

ImageModel – The image model instance.

+
+
+
+

See also

+

trojanzoo.models.create()

+
+
+ +
+
+trojanvision.models.output_available_models(class_dict=class_dict, indent=0)[source]
+

Output all available model names.

+
+
Parameters:
+
    +

  • class_dict (dict[str, type[ImageModel]]) – Map from model name to model class. +Defaults to trojanvision.models.class_dict.

    • +

  • indent (int) – The space indent for the entire string. +Defaults to 0.

    • +
+
+
+
+

See also

+

trojanzoo.models.output_available_models()

+
+
+ +
+
+class trojanvision.models.ImageModel(name='imagemodel', layer='', model=_ImageModel, dataset=None, data_shape=None, adv_train=None, adv_train_random_init=False, adv_train_eval_random_init=None, adv_train_iter=7, adv_train_alpha=2 / 255, adv_train_eps=8 / 255, adv_train_eval_iter=None, adv_train_eval_alpha=None, adv_train_eval_eps=None, adv_train_trades_beta=6.0, norm_layer='bn', sgm=False, sgm_gamma=1.0, norm_par=None, suffix=None, modify_first_layer_channel=True, **kwargs)[source]
+
+
A basic image model wrapper class, which should be the most common interface for users.
+
It inherits trojanzoo.models.Model and further extend +adversarial training and Skip Gradient Method (SGM).
+
+
+

See also

+ +
+
+
Parameters:
+

layer (int | str) – Default layer when it’s not provided in name. +Defaults to ''.

+
+
Variables:
+
    +

  • pgd (trojanvision.attacks.PGD) – PGD attacker using eval settings without early stop. +It’s only constructed when adv_train is not None.

    • +

  • adv_train (str | None) –

    Adversarial training strategy. +Choose from [None, 'pgd', 'free', 'trades']. +Defaults to None.

    +
    +

    Note

    +

    If adv_train is not None and suffix is None, +set suffix = f'_at-{adv_train}'.

    +
    +

    • +

  • adv_train_random_init (bool) – Whether to random initialize adversarial noise +using normal distribution with adv_train_eps. +Otherwise, attack starts from the benign inputs. +Defaults to False.

    • +

  • adv_train_iter (int) – Adversarial training PGD iteration. +Defaults to 7.

    • +

  • adv_train_alpha (float) – Adversarial training PGD alpha. +Defaults to 2255\frac{2}{255}.

    • +

  • adv_train_eps (float) – Adversarial training PGD eps. +Defaults to 8255\frac{8}{255}.

    • +

  • adv_train_eval_iter (int) – Adversarial training PGD iteration at evaluation. +Defaults to adv_train_iter.

    • +

  • adv_train_eval_alpha (float) – Adversarial training PGD alpha at evaluation. +Defaults to adv_train_alpha.

    • +

  • adv_train_eval_eps (float) – Adversarial training PGD eps at evaluation. +Defaults to adv_train_eps.

    • +

  • adv_train_trades_beta (float) – regularization factor +(1λ\frac{1}{\lambda} in TRADES) +Defaults to 6.0.

    • +

  • norm_layer (str) – The normalization layer type. +Choose from ['bn', 'gn']. +Defaults to ['bn'].

    • +

  • sgm (bool) – Whether to use Skip Gradient Method. Defaults to False.

    • +

  • sgm_gamma (float) – The gradient factor γ\gamma used in SGM. +Defaults to 1.0.

    • +
+
+
+
+
+classmethod add_argument(group)[source]
+

Add image model arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +The concrete model class may override this method to add more arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+

See also

+

trojanzoo.models.Model.add_argument()

+
+
+ +
+
+get_data(data, adv_train=False, **kwargs)[source]
+
+
Parameters:
+
+
+
+
+ +
+
+get_heatmap(_input, _label, method='grad_cam', cmap=jet, mode='bicubic')[source]
+

Use colormap cmap to get heatmap tensor of _input +w.r.t. _label with method.

+
+
Parameters:
+
+
+
Returns:
+

torch.Tensor – The heatmap tensor with shape ([N], C, H, W).

+
+
+
+

Note

+

Most matplotlib.colors.Colormap will return +a 4-channel heatmap with alpha channel.

+
+
+

See also

+

https://keras.io/examples/vision/grad_cam/

+
+
+
Example:
+
import trojanvision
+from trojanvision.utils import superimpose
+import torchvision
+import torchvision.transforms as transforms
+import PIL.Image as Image
+import os
+import wget
+
+env = trojanvision.environ.create(device='cpu')
+model = trojanvision.models.create(
+    'resnet152', data_shape=[3, 224, 224], official=True,
+    norm_par={'mean': [0.485, 0.456, 0.406],
+              'std': [0.229, 0.224, 0.225]})
+transform = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.PILToTensor(),
+    transforms.ConvertImageDtype(torch.float)])
+url = 'https://i.imgur.com/Bvro0YD.png'
+if not os.path.isfile('african_elephant.png'):
+    wget.download(url, 'african_elephant.png')
+
+img = Image.open('african_elephant.png').convert(mode='RGB')
+
+_input = transform(img).unsqueeze(0).to(env['device'])
+_prob = model.get_prob(_input).squeeze()
+label = _prob.argmax().item()
+conf = _prob[label].item()
+print(f'{label=:}  {conf=:.2%}')
+
+grad_cam = model.get_heatmap(_input, label)[:, :3]
+saliency_map = model.get_heatmap(_input, label,
+                                 method='saliency_map')[:, :3]
+grad_cam_impose = (grad_cam * 0.4 + _input)
+saliency_map_impose = (saliency_map * 0.4 + _input)
+grad_cam_impose = grad_cam_impose.div(grad_cam_impose.max())
+saliency_map_impose = saliency_map_impose.div(saliency_map_impose.max())
+
+torchvision.utils.save_image(_input, './center_cropped.png')
+torchvision.utils.save_image(grad_cam, './grad_cam.png')
+torchvision.utils.save_image(saliency_map, './saliency_map.png')
+torchvision.utils.save_image(grad_cam_impose, './grad_cam_impose.png')
+torchvision.utils.save_image(saliency_map_impose, './saliency_map_impose.png')
+
+
+

label=386  conf=77.74%

+ +++++ + + + + + + + + + + + + + + +

original

original

center_cropped

grad_cam

grad_cam

grad_cam_impose

saliency_map

saliency_map

saliency_map_impose

+
+
+
+ +
+
+classmethod get_name(name, layer='')[source]
+

A useful function to combine name and layer.

+
+

Note

+
    +

  • If there is already layer claimed in name, +layer will be ignored.

    • +

  • You may override this method for concrete model class +on demand.

    • +
+
+
+
Parameters:
+
    +

  • name (str) – Model name string.

    • +

  • layer (int | str) – Model layer. Defaults to ''.

    • +
+
+
Example:
+
>>> from trojanvision.models import ImageModel
+>>>
+>>> ImageModel.get_name('vgg_comp', layer=13)
+'vgg13_comp'
+>>> ImageModel.get_name('vgg16_comp', layer=13)
+'vgg16_comp'
+
+
+
+
+
+ +
+ +
+ + +
+ +
+ + +
+
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+
+

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+ View Docs +
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+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/models/nas.html b/trojanvision/models/nas.html new file mode 100644 index 00000000..153ac7a4 --- /dev/null +++ b/trojanvision/models/nas.html @@ -0,0 +1,662 @@ + + + + + + + + + + + + + + + + nas — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
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+ Shortcuts +
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+ +
+ +
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+ +
+

nas

+
+
+class trojanvision.models.DARTS(name='darts', model_arch='darts', layers=20, init_channels=36, dropout_p=0.2, auxiliary=False, auxiliary_weight=0.4, genotype=None, model=_DARTS, supernet=False, arch_search=False, use_full_train_set=False, arch_lr=3e-4, arch_weight_decay=1e-3, arch_unrolled=False, primitives=PRIMITIVES, **kwargs)[source]
+

DARTS-like models used in Neural Architecture Search.

+
+
Available model names:
+
{'darts'}
+
+
+
+
+
+

See also

+ +
+
+
Parameters:
+
    +

  • supernet (bool) – Whether to use supernet (mixed operations). +Defaults to False.

    • +

  • model_arch (str) –

    Genotype name in trojanvision.utils.model_archs.genotypes to use. +Defaults to 'darts'.

    +
      +

    • 'amoebanet', 'amoebanet_adapt'

      • +

    • 'darts_v1', 'darts_v2'('darts')

      • +

    • 'drnas_cifar10'('drnas'), 'drnas_imagenet'

      • +

    • 'enas', 'enas_adapt'

      • +

    • 'nasnet', 'nasnet_adapt'

      • +

    • 'pc_darts_cifar'('pc_darts'), 'pc_darts_image'

      • +

    • 'pdarts'

      • +

    • 'robust_darts'

      • +

    • 'sgas'

      • +

    • 'snas_mild', 'snas_adapt'

      • +

    • 'random'

      • +

    • 'diy_deep', 'diy_noskip', 'diy_deep_noskip'

      • +
    +

    • +

  • layers (int) – Total number of layers. Defaults to 20.

    • +

  • init_channels (int) – out_channel of stem conv layer. +Defaults to 36.

    • +

  • dropout_p (float) – Dropout probability. +Defaults to 0.2.

    • +

  • auxiliary (bool) – Whether to use auxiliary classifier. +Defaults to False.

    • +

  • auxiliary_weight (float) – Loss weight of auxiliary classifier. +Defaults to 0.4.

    • +

  • arch_search (bool) – Whether to search supernet architecture weight parameters. +Defaults to False.

    • +

  • use_full_train_set (bool) – Whether to use full training data during architecture search. +Defaults to False.

    • +

  • arch_lr (float) – Learning rate for architecture optimizer. +Defaults to 3e-4

    • +

  • arch_weight_decay (float) – Weight decay for architecture optimizer. +Defaults to 1e-3.

    • +

  • arch_unrolled (bool) – Whether to use one-step unrolled validation loss (darts-v2). +Defaults to False.

    • +
+
+
Variables:
+

genotype (Genotype) – Genotype of cell architecture.

+
+
+
+

Note

+

The implementation of DARTS model is in trojanvision.utils.model_archs.darts

+
+
+ +
+
+class trojanvision.models.ENAS(name='enas', model=_ENAS, folder_path=None, **kwargs)[source]
+

This is yet another ENAS implementation based on Microsoft Neural Network Intelligence (NNI) library. +You need to first generate 'enas_macro.pt' using NNI library and put it under folder_path.

+
+

Warning

+

It is highly recommended to use trojanvision.models.DARTS with model_arch='enas' instead.

+
+
+
Available model names:
+
{'enas'}
+
+
+
+
+
+

See also

+ +
+
+ +
+
+class trojanvision.models.NATSbench(name='nats_bench', model=_NATSbench, model_index=0, model_seed=777, hp=200, dataset=None, dataset_name=None, nats_path=None, search_space='tss', **kwargs)[source]
+

NATS-Bench proposed by Xuanyi Dong from University of Technology Sydney.

+
+
Available model names:
+
{'nats_bench'}
+
+
+
+
+
+

Note

+

There are prerequisites to use the benchmark:

+
    +

  • pip install nats_bench.

    • +

  • git clone https://github.com/D-X-Y/AutoDL-Projects.git and pip install .

    • +

  • Extract NATS-tss-v1_0-3ffb9-full to nats_path.

    • +
+
+
+

See also

+ +
+
+
Parameters:
+
    +

  • model_index (int) – model_index passed to +api.get_net_config(). +Ranging from 0 -- 15624. +Defaults to 0.

    • +

  • model_seed (int) – model_seed passed to +api.get_net_param(). +Choose from [777, 888, 999]. +Defaults to 777.

    • +

  • hp (int) – Training epochs. +hp passed to api.get_net_param(). +Choose from [12, 200]. +Defaults to 200.

    • +

  • nats_path (str) – NATS benchmark file path. +It should be set as format like +'**/NATS-tss-v1_0-3ffb9-full'

    • +

  • search_space (str) – Search space of topology or size. +Choose from ['tss', 'sss'].

    • +

  • dataset_name (str) – Dataset name. +Choose from ['cifar10', 'cifar10-valid', 'cifar100', 'imagenet16-120'].

    • +
+
+
+
+ +
+
+class trojanvision.models.PNASNet(name='pnasnet', layer='_b', model=_PNASNet, **kwargs)[source]
+

PNASNet proposed by Chenxi Liu from Johns Hopkins University in ECCV 2018.

+
+

Note

+

The implementation is imported from a third-party github repo. The correctness can’t be guaranteed. +It might be better to reimplement according to tensorflow codes: +https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/pnasnet.py

+
+
+
Available model names:
+
{'pnasnet', 'pnasnet_a', 'pnasnet_b'}
+
+
+
+
+
+

See also

+ +
+
+ +
+
+class trojanvision.models.ProxylessNAS(name='proxylessnas', target_platform='proxyless_cifar', model=_ProxylessNAS, **kwargs)[source]
+

ProxylessNAS proposed by Han Cai from MIT in ICLR 2019.

+
+
Available model names:
+
{'proxylessnas'}
+
+
+
+
+
+

See also

+ +
+
+
Parameters:
+

target_platform (str) – Target platform to load using torch.hub.load. +Choose from ['proxyless_cpu', 'proxyless_gpu', 'proxyless_mobile', 'proxyless_mobile_14', 'proxyless_cifar'] +Defaults to 'proxyless_cifar'.

+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/models/others.html b/trojanvision/models/others.html new file mode 100644 index 00000000..16afb11a --- /dev/null +++ b/trojanvision/models/others.html @@ -0,0 +1,488 @@ + + + + + + + + + + + + + + + + others — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
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+ Shortcuts +
+
+ +
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+ +
+ +
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+ +
+

others

+
+
+class trojanvision.models.MagNet(name='magnet', dataset=None, model=_MagNet, structure=None, activation=None, v_noise=0.1, **kwargs)[source]
+

MagNet proposed by Dongyu Meng from Shanghai Tech University in CCS 2017. +It is an autoencoder for input images to defend against adversarial attacks.

+
+
Available model names:
+
{'magnet'}
+
+
+
+
+
+

See also

+ +
+
+
Parameters:
+
    +

  • structure (list[int | str]) –

    The MagNet model structure. +Defaults to

    +
      +

    • 1-channel images: [3, 'average', 3] (e.g, MNIST)

      • +

    • 3-channel images: [32]

      • +
    +

    • +

  • activation (str) – The activation layer in MagNet model. +Choose from ['sigmoid', 'relu']. +Defaults to 'sigmoid' for 1-channel images (e.g, MNIST) +and 'relu' for 3-channel images.

    • +

  • v_noise (float) – The std of random Gaussian noise added to training data. +Defaults to 0.1.

    • +
+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+
+
+ + +
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

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+

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+ View Docs +
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+ + +
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+ +
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+ + + + + +
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+ + +
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+ +
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+ +
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+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/models/torchvision.html b/trojanvision/models/torchvision.html new file mode 100644 index 00000000..4593f60e --- /dev/null +++ b/trojanvision/models/torchvision.html @@ -0,0 +1,760 @@ + + + + + + + + + + + + + + + + torchvision — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

torchvision

+
+
+class trojanvision.models.AlexNet(name='alexnet', model=_AlexNet, **kwargs)[source]
+

AlexNet proposed by Alex Krizhevsky from Google in 2014.

+
+
Available model names:
+
{'alexnet'}
+
+
+
+
+
+

See also

+ +
+
self.features = nn.Sequential(
+    nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+    nn.ReLU(inplace=True),
+    nn.MaxPool2d(kernel_size=3, stride=2),
+    nn.Conv2d(64, 192, kernel_size=5, padding=2),
+    nn.ReLU(inplace=True),
+    nn.MaxPool2d(kernel_size=3, stride=2),
+    nn.Conv2d(192, 384, kernel_size=3, padding=1),
+    nn.ReLU(inplace=True),
+    nn.Conv2d(384, 256, kernel_size=3, padding=1),
+    nn.ReLU(inplace=True),
+    nn.Conv2d(256, 256, kernel_size=3, padding=1),
+    nn.ReLU(inplace=True),
+    nn.MaxPool2d(kernel_size=3, stride=2),
+)
+
+self.pool = nn.AdaptiveAvgPool2d((6, 6))
+self.flatten = nn.Flatten(1)
+
+self.classifier = nn.Sequential(
+    nn.Dropout(),
+    nn.Linear(256 * 6 * 6, 4096),
+    nn.ReLU(inplace=True),
+    nn.Dropout(),
+    nn.Linear(4096, 4096),
+    nn.ReLU(inplace=True),
+    nn.Linear(4096, num_classes),
+)
+
+
+
+ +
+
+class trojanvision.models.DenseNet(name='densenet', layer=121, model=_DenseNet, **kwargs)[source]
+

DenseNet proposed by Gao Huang from Cornell University in CVPR 2017.

+
+
Available model names:
+
{'densenet', 'densenet_comp',
+ 'densenet121', 'densenet169', 'densenet201', 'densenet161',
+ 'densenet121_comp', 'densenet169_comp', 'densenet201_comp', 'densenet161_comp'}
+
+
+
+
+
+

See also

+ +
+
+

Note

+

_comp reduces the first convolutional layer +from kernel_size=7, stride=2, padding=3

+

to kernel_size=3, stride=1, padding=1, +and removes following norm0, relu0, pool0 +(pool0 is torch.nn.MaxPool2d) +before block layers.

+
+
+ +
+
+class trojanvision.models.EfficientNet(name='efficientnet', layer='_b0', model=_EfficientNet, **kwargs)[source]
+

EfficientNet proposed by Mingxing Tan from Google in ICML 2019.

+
+
Available model names:
+
{'efficientnet', 'efficientnet_comp',
+ 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2',
+ 'efficientnet_b3', 'efficientnet_b4', 'efficientnet_b5',
+ 'efficientnet_b6', 'efficientnet_b7',
+ 'efficientnet_b0_comp', 'efficientnet_b1_comp', 'efficientnet_b2_comp',
+ 'efficientnet_b3_comp', 'efficientnet_b4_comp', 'efficientnet_b5_comp',
+ 'efficientnet_b6_comp', 'efficientnet_b7_comp'}
+
+
+
+
+
+

See also

+ +
+
+

Note

+

_comp reduces the first convolutional layer +from kernel_size=7, stride=2, padding=3

+

to kernel_size=3, stride=1, padding=1.

+
+
+ +
+
+class trojanvision.models.MNASNet(name='mnasnet', mnas_alpha=1.0, model=_MNASNet, **kwargs)[source]
+

MNASNet proposed by Mingxing Tan from Google in CVPR 2019.

+
+
Available model names:
+
{'mnasnet', 'mnasnet0_5', 'mnasnet0_75', 'mnasnet1_0', 'mnasnet1_3'}
+
+
+
+
+
+

See also

+ +
+
+ +
+
+class trojanvision.models.MobileNet(name='mobilenet_v2', model=_MobileNet, **kwargs)[source]
+

MobileNets proposed by Andrew Howard and Liang-Chieh Chen from Google in CVPR 2018.

+
+
Available model names:
+
['mobilenet_v2', 'mobilenet_v3_large', 'mobilenet_v3_small',
+ 'mobilenet_v2_comp', 'mobilenet_v3_large_comp', 'mobilenet_v3_small_comp']
+
+
+
+
+
+

See also

+ +
+
+

Note

+

_comp uses a small inverted_residual_setting +and set first conv layer stride=1.

+
+
+ +
+
+class trojanvision.models.ResNet(name='resnet', layer=18, model=_ResNet, **kwargs)[source]
+

ResNet model series including ResNet, ResNext and WideResNet.

+
+
Available model names:
+
{'resnet', 'resnet_comp', 'resnet_s',
+ 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
+ 'resnet18_comp', 'resnet34_comp', 'resnet50_comp', 'resnet101_comp', 'resnet152_comp',
+ 'resnext50_32x4d', 'resnext101_32x8d',
+ 'resnext50_32x4d_comp', 'resnext101_32x8d_comp',
+ 'wide_resnet50_2', 'wide_resnet101_2',
+ 'wide_resnet50_2_comp', 'wide_resnet101_2_comp',
+
+ 'resnet18_s', 'resnet34_s', 'resnet50_s', 'resnet101_s', 'resnet152_s',
+ 'resnet18_ap_comp'}
+
+
+
+
+
+

See also

+ +
+
+

Note

+ +
+
+ +
+
+class trojanvision.models.ShuffleNetV2(name='shufflenetv2', layer='_x0_5', model=_ShuffleNetV2, **kwargs)[source]
+

ShuffleNet v2 proposed by Ningning Ma from Megvii in ECCV 2018.

+
+
Available model names:
+
{'shufflenetv2', 'shufflenetv2_comp',
+ 'shufflenetv2_x0_5', 'shufflenetv2_x1_0',
+ 'shufflenetv2_x1_5', 'shufflenetv2_x2_0',
+ 'shufflenetv2_x0_5_comp', 'shufflenetv2_x1_0_comp',
+ 'shufflenetv2_x1_5_comp', 'shufflenetv2_x2_0_comp'}
+
+
+
+
+
+

See also

+ +
+
+

Note

+

_comp reduces the first convolutional layer +from kernel_size=7, stride=2, padding=3

+

to kernel_size=3, stride=1, padding=1, +and removes the maxpool layer before block layers.

+
+
+ +
+
+class trojanvision.models.VGG(name='vgg', layer=13, model=_VGG, **kwargs)[source]
+

VGG model proposed by Karen Simonyan from University of Oxford in ICLR 2015.

+
+
Available model names:
+
{'vgg', 'vgg_bn', 'vgg_comp', 'vgg_bn_comp', 'vgg_s', 'vgg_bn_s',
+ 'vgg11', 'vgg13', 'vgg16', 'vgg19',
+ 'vgg11_bn', 'vgg13_bn', 'vgg16_bn', 'vgg19_bn',
+ 'vgg11_comp', 'vgg13_comp', 'vgg16_comp', 'vgg19_comp',
+ 'vgg11_bn_comp', 'vgg13_bn_comp', 'vgg16_bn_comp', 'vgg19_bn_comp'
+ 'vgg11_s', 'vgg13_s', 'vgg16_s', 'vgg19_s',
+ 'vgg11_bn_s', 'vgg13_bn_s', 'vgg16_bn_s', 'vgg19_bn_s'}
+
+
+
+
+
+

See also

+ +
+
+

Note

+
    +

  • _comp sets torch.nn.AdaptiveAvgPool2d from (7, 7) to (1, 1), +update the intermediate feature dimension from 4096 to 512 in self.classifier.

    • +

  • _s further makes self.classifier only one single linear layer based on _comp.

    • +
+
+
+ +
+ + +
+ +
+ + +
+
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/optim.html b/trojanvision/optim.html new file mode 100644 index 00000000..62a0d319 --- /dev/null +++ b/trojanvision/optim.html @@ -0,0 +1,445 @@ + + + + + + + + + + + + + + + + optim — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/trainer.html b/trojanvision/trainer.html new file mode 100644 index 00000000..794ca8ee --- /dev/null +++ b/trojanvision/trainer.html @@ -0,0 +1,451 @@ + + + + + + + + + + + + + + + + trainer — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanvision/utils.html b/trojanvision/utils.html new file mode 100644 index 00000000..094b7075 --- /dev/null +++ b/trojanvision/utils.html @@ -0,0 +1,442 @@ + + + + + + + + + + + + + + + + utils — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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utils

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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/attacks.html b/trojanzoo/attacks.html new file mode 100644 index 00000000..2b9c518f --- /dev/null +++ b/trojanzoo/attacks.html @@ -0,0 +1,578 @@ + + + + + + + + + + + + + + + + attacks — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
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+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

attacks

+
+
+trojanzoo.attacks.add_argument(parser, attack_name=None, attack=None, class_dict={})[source]
+
+
Add attack arguments to argument parser.
+
For specific arguments implementation, see Attack.add_argument().
+
+
+
Parameters:
+
    +

  • parser (argparse.ArgumentParser) – The parser to add arguments.

    • +

  • attack_name (str) – The attack name.

    • +

  • attack (str | Attack) – The attack instance or attack name +(as the alias of attack_name).

    • +

  • class_dict (dict[str, type[Attack]]) – Map from attack name to attack class. +Defaults to {}.

    • +
+
+
Returns:
+

argparse._ArgumentGroup – The argument group.

+
+
+
+ +
+
+trojanzoo.attacks.create(attack_name=None, attack=None, dataset_name=None, dataset=None, model_name=None, model=None, config=config, class_dict={}, **kwargs)[source]
+
+
Create an attack instance.
+
For arguments not included in kwargs, +use the default values in config.
+
The default value of folder_path is +'{attack_dir}/{dataset.data_type}/{dataset.name}/{model.name}/{attack.name}'.
+
For attack implementation, see Attack.
+
+
+
Parameters:
+
    +

  • attack_name (str) – The attack name.

    • +

  • attack (str | Attack) – The attack instance or attack name +(as the alias of attack_name).

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | Dataset) – Dataset instance or dataset name +(as the alias of dataset_name).

    • +

  • model_name (str) – The model name.

    • +

  • model (str | Model) – The model instance or model name +(as the alias of model_name).

    • +

  • config (Config) – The default parameter config.

    • +

  • class_dict (dict[str, type[Attack]]) – Map from attack name to attack class. +Defaults to {}.

    • +

  • **kwargs – The keyword arguments +passed to attack init method.

    • +
+
+
Returns:
+

Attack – The attack instance.

+
+
+
+ +
+
+class trojanzoo.attacks.Attack(dataset=None, model=None, folder_path=None, **kwargs)[source]
+
+
An abstract class representing an attack.
+
It inherits trojanzoo.utils.module.ModelProcess.
+
+
+

Note

+

This is the implementation of attack. +For users, please use create() instead, which is more user-friendly.

+
+
+
+classmethod add_argument(group)[source]
+

Add attack arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +The concrete attack class may override this method to add more arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+ +
+
+abstract attack(**kwargs)[source]
+

Main attack method (need overriding).

+
+ +
+
+generate_target(_input, idx=1, same=False, **kwargs)[source]
+

Generate target labels of a batched input based on +the classification confidence ranking index.

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The input tensor.

    • +

  • idx (int) – The classification confidence +rank of target class. +Defaults to 1.

    • +

  • same (bool) – Generate the same label +for all samples using mod. +Defaults to False.

    • +
+
+
Returns:
+

torch.Tensor – The generated target label with shape (N).

+
+
+
+

See also

+

This method calls +trojanzoo.models.Model.generate_target().

+

The implementation is in +trojanzoo.utils.model.generate_target().

+
+
+ +
+ +
+ + +
+ +
+ + +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/configs.html b/trojanzoo/configs.html new file mode 100644 index 00000000..075dc858 --- /dev/null +++ b/trojanzoo/configs.html @@ -0,0 +1,583 @@ + + + + + + + + + + + + + + + + configs — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+ +
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+ +
+

configs

+

TrojanZoo config path:

+
config_path: dict[str, str] = {
+    'package': os.path.dirname(__file__),   # trojanzoo/configs/*/*.yml
+    'user': os.path.normpath(os.path.expanduser(
+        '~/.trojanzoo/configs/trojanzoo')),
+    'project': os.path.normpath('./configs/trojanzoo'),
+    }
+
+
+
+
+class trojanzoo.configs.Config(cmd_config_path=None, _base=None, **kwargs)[source]
+

Configuration class.

+
+

Warning

+

There is already a preset config instance trojanzoo.configs.config.

+

NEVER call the class init method to create a new instance +(unless you know what you’re doing).

+
+
+

Note

+

ConfigType is Module[str, Module[str, Any]]

+

value = config[config_file][key][dataset_name] +where dataset_name is optional

+

(config[config_file][key] is trojanzoo.utils.module.Param +and has default values).

+
+
+
Parameters:
+
    +

  • _base (Config) – The base config instance. +config_dict of current config instance +will inherit _base.config_dict +and update based on self.config_path. +It’s usually the config in father library +(e.g., trojanvision config inherits trojanzoo config). +Defaults to None.

    • +

  • **kwargs (dict[str, str]) – Map of config paths.

    • +
+
+
Variables:
+
    +

  • cmd_config_path (str) – Path to cmd_config. Defaults to None.

    • +

  • cmd_config (ConfigType) – Config loaded from path cmd_config_path.

    • +

  • config_path (dict[str, str]) – Map from config name +(e.g., 'package', 'user', 'project') +to path string.

    • +

  • config_dict (dict[str, ConfigType]) – Map from config name +(e.g., 'package', 'user', 'project') +to its config.

    • +

  • full_config (ConfigType) – Full config with parameters for all datasets +by calling merge() to merge different configs +in self.config_dict. +value = full_config[config_file][key][dataset_name].

    • +
+
+
+
+
+get_config(dataset_name, config=None, **kwargs)[source]
+

Get config for specific dataset.

+
+
Parameters:
+
    +

  • dataset_name (str) – Dataset name.

    • +

  • config – (ConfigType): The config for all datasets. +value = full_config[config_file][key][dataset_name]. +Defaults to self.full_config.

    • +
+
+
Returns:
+

Param[str, Module[str, Any]] – Config for dataset_name. +value = full_config[config_file][key].

+
+
+
+ +
+
+static load_config(path)[source]
+

Load yaml or json configs from path.

+
+
Parameters:
+

path (str) – Path to config file.

+
+
Returns:
+

ConfigType – Config loaded from path.

+
+
+
+ +
+
+merge(keys=['package', 'user', 'project'])[source]
+

Merge different configs of keys in self.config_dict.

+
+
Parameters:
+

keys (list[str]) – Keys of self.config_dict to merge.

+
+
Returns:
+

ConfigType – Merged config.

+
+
+
+ +
+
+summary(keys=['final'], config=None, indent=0)[source]
+

Summary the config information.

+
+
Parameters:
+
    +

  • keys (list[str] | str) –

    keys of configs to summary.

    +
      +

    • 'final': self.full_config

      • +

    • 'cmd': self.cmd_config

      • +

    • key in self.config_dict.keys()

      • +
    +

    Defaults to ['final'].

    +

    • +

  • indent (int) – The space indent of entire string. +Defaults to 0.

    • +
+
+
+
+ +
+ +
+ + +
+ +
+ + +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/datasets.html b/trojanzoo/datasets.html new file mode 100644 index 00000000..e5accd84 --- /dev/null +++ b/trojanzoo/datasets.html @@ -0,0 +1,863 @@ + + + + + + + + + + + + + + + + datasets — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Shortcuts +
+
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+ +
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+

datasets

+
+
+trojanzoo.datasets.add_argument(parser, dataset_name=None, dataset=None, config=config, class_dict={})[source]
+
+
Add dataset arguments to argument parser.
+
For specific arguments implementation, see Dataset.add_argument().
+
+
+
Parameters:
+
    +

  • parser (argparse.ArgumentParser) – The parser to add arguments.

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | Dataset) – Dataset instance or dataset name +(as the alias of dataset_name).

    • +

  • config (Config) – The default parameter config, +which contains the default dataset name if not provided.

    • +

  • class_dict (dict[str, type[Dataset]]) – Map from dataset name to dataset class. +Defaults to {}.

    • +
+
+
+
+ +
+
+trojanzoo.datasets.create(dataset_name=None, dataset=None, config=config, class_dict={}, **kwargs)[source]
+
+
Create a dataset instance.
+
For arguments not included in kwargs, +use the default values in config.
+
The default value of folder_path is +'{data_dir}/{data_type}/{name}'.
+
For dataset implementation, see Dataset.
+
+
+
Parameters:
+
    +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str) – The alias of dataset_name.

    • +

  • config (Config) – The default parameter config.

    • +

  • class_dict (dict[str, type[Dataset]]) – Map from dataset name to dataset class. +Defaults to {}.

    • +

  • **kwargs – Keyword arguments +passed to dataset init method.

    • +
+
+
Returns:
+

Dataset – Dataset instance.

+
+
+
+ +
+
+class trojanzoo.datasets.Dataset(batch_size=100, valid_batch_size=100, folder_path=None, download=False, split_ratio=0.8, num_workers=4, loss_weights=False, **kwargs)[source]
+
+
An abstract class representing a dataset.
+
It inherits trojanzoo.utils.module.BasicObject.
+
+
+

Note

+

This is the implementation of dataset. +For users, please use create() instead, which is more user-friendly.

+
+
+
Parameters:
+
    +

  • batch_size (int) – Batch size of training set +(negative number means batch size for each gpu). +Defaults to 100.

    • +

  • valid_batch_size (int) – Batch size of validation set. +Defaults to 100.

    • +

  • folder_path (str) –

    Folder path to store dataset. +Defaults to None.

    +
    +

    Note

    +

    folder_path is usually +'{data_dir}/{data_type}/{name}', +which is claimed as the default value of create().

    +
    +

    • +

  • download (bool) – Download dataset if not exist. Defaults to False.

    • +

  • split_ratio (float) –

    +
    Split training set for training and validation +if valid_set is False.
    +
    The ratio stands for +# training subset# total training set\frac{\text{\# training\ subset}}{\text{\# total\ training\ set}}.
    +
    Defaults to 0.8.
    +
    +

    • +

  • num_workers (int) – Used in get_dataloader(). +Defaults to 4.

    • +

  • loss_weights (bool | np.ndarray | torch.Tensor) –

    +
    The loss weights w.r.t. each class.
    +
    if numpy.ndarray or torch.Tensor, +directly set as loss_weights (cpu tensor).
    +
    if True, set loss_weights as get_loss_weights();
    +
    if False, set loss_weights as None.
    +
    +

    • +

  • **kwargs – Any keyword argument (unused).

    • +
+
+
Variables:
+
    +

  • name (str) – Dataset Name. (need overriding)

    • +

  • loader (dict[str, DataLoader]) –

    +
    Preset dataloader for users at dataset initialization.
    +
    It contains 'train' and 'valid' loaders.
    +
    +

    • +

  • batch_size (int) – Batch size of training set (always positive). +Defaults to 100.

    • +

  • valid_batch_size (int) – Batch size of validation set. +Defaults to 100.

    • +

  • num_classes (int) – Number of classes. (need overriding)

    • +

  • folder_path (str) – Folder path to store dataset. +Defaults to None.

    • +

  • data_type (str) – Data type (e.g., 'image'). (need overriding)

    • +

  • label_names (list[int]) – Number of classes. (optional)

    • +

  • valid_set (bool) – Whether having a native validation set. +Defaults to True.

    • +

  • split_ratio (float) –

    +
    Split training set for training and validation +if valid_set is False.
    +
    The ratio stands for +# training subset# total training set\frac{\text{\# training\ subset}}{\text{\# total\ training\ set}}.
    +
    Defaults to 0.8.
    +
    +

    • +

  • loss_weights (torch.Tensor | None) – The loss weights w.r.t. each class.

    • +

  • num_workers (int) – Used in get_dataloader(). +Defaults to 4.

    • +

  • collate_fn (Callable | None) – Used in get_dataloader(). +Defaults to None.

    • +
+
+
+
+
+classmethod add_argument(group)[source]
+

Add dataset arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +The concrete dataset class may override this method to add more arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+ +
+
+check_files(**kwargs)[source]
+

Check if the dataset files are prepared.

+
+
Parameters:
+

**kwargs – Keyword arguments passed to get_org_dataset().

+
+
Returns:
+

bool – Whether the dataset files are prepared.

+
+
+
+ +
+
+static get_class_subset(dataset, class_list)[source]
+

Get a subset from dataset with certain classes.

+
+
Parameters:
+
+
+
Returns:
+

torch.utils.data.Subset – The subset with labels in class_list.

+
+
Example:
+
>>> from trojanzoo.utils.data import TensorListDataset
+>>> from trojanzoo.utils.data import get_class_subset
+>>> import torch
+>>>
+>>> data = torch.ones(11, 3, 32, 32)
+>>> targets = list(range(11))
+>>> dataset = TensorListDataset(data, targets)
+>>> subset = get_class_subset(dataset, class_list=[2, 3])
+>>> len(subset)
+2
+
+
+
+
+
+

See also

+

The implementation is in +trojanzoo.utils.data.get_class_subset().

+
+
+ +
+
+get_data(data, **kwargs)[source]
+

Process data. Defaults to directly return data.

+
+
Parameters:
+
    +

  • data (Any) – Unprocessed data.

    • +

  • **kwargs – Keyword arguments to process data.

    • +
+
+
Returns:
+

Any – Processed data.

+
+
+
+ +
+
+get_dataloader(mode=None, dataset=None, batch_size=None, shuffle=None, num_workers=None, pin_memory=True, drop_last=False, collate_fn=None, **kwargs)[source]
+

Get dataloader. Call get_dataset() if dataset is not provided.

+
+
Parameters:
+
    +

  • mode (str) – Dataset mode (e.g., 'train' or 'valid').

    • +

  • dataset (torch.utils.data.Dataset) – The pytorch dataset.

    • +

  • batch_size (int) – Defaults to self.batch_size for 'train' mode +and self.valid_batch_size for 'valid' mode.

    • +

  • shuffle (bool) – Whether to shuffle. +Defaults to True for 'train' mode +and False for 'valid' mode.

    • +

  • num_workers (int) – Number of workers for dataloader. +Defaults to self.num_workers.

    • +

  • pin_memory (bool) – Whether to use pin memory. +Defaults to True if there is any GPU available.

    • +

  • drop_last (bool) – Whether drop the last batch if not full size. +Defaults to False.

    • +

  • collate_fn (Callable) – Passed to torch.utils.data.DataLoader.

    • +

  • **kwargs – Keyword arguments passed to get_dataset() +if dataset is not provided.

    • +
+
+
Returns:
+

torch.utils.data.DataLoader – The pytorch dataloader.

+
+
+
+ +
+
+get_dataset(mode=None, seed=None, class_list=None, **kwargs)[source]
+

Get dataset. Call split_dataset() to split the training set +if valid_set is False.

+
+
Parameters:
+
    +

  • mode (str) – Dataset mode (e.g., 'train' or 'valid').

    • +

  • seed (int) – The random seed to split dataset +using numpy.random.shuffle. +Defaults to env['data_seed'].

    • +

  • class_list (int | list[int]) – The class list to pick. Defaults to None.

    • +

  • **kwargs – Keyword arguments passed to get_org_dataset().

    • +
+
+
Returns:
+

torch.utils.data.Dataset – The original dataset.

+
+
+
+ +
+
+get_loss_weights(file_path=None, verbose=True)[source]
+

Calculate loss_weights as reciprocal of data size of each class +(to mitigate data imbalance).

+
+
Parameters:
+
    +

  • file_path (str) –

    +
    The file path of saved weights file.
    +
    If exist, just load the file and return;
    +
    else, calculate the weights, save and return.
    +
    Defaults to {folder_path}/loss_weights.npy
    +
    +

    • +

  • verbose (bool) – Whether to print verbose information. +Defaults to True.

    • +
+
+
Returns:
+

torch.Tensor – The tensor of loss weights w.r.t. each class.

+
+
+
+ +
+
+get_org_dataset(mode, **kwargs)[source]
+

Get original dataset that is not splitted.

+
+

Note

+

This is a wrapper and the specific implementation +is in _get_org_dataset(), which needs overriding.

+
+
+
Parameters:
+
    +

  • mode (str) – Dataset mode (e.g., 'train' or 'valid').

    • +

  • transform (Callable) – The transform applied on dataset. +Defaults to get_transform().

    • +

  • **kwargs – Keyword arguments passed to _get_org_dataset().

    • +
+
+
Returns:
+

torch.utils.data.Dataset – The original dataset.

+
+
+
+

See also

+

get_dataset()

+
+
+ +
+
+abstract get_transform(mode)[source]
+

Get dataset transform for mode.

+
+
Parameters:
+

mode (str) – Dataset mode (e.g., 'train' or 'valid').

+
+
Returns:
+

~collections.abc.Callable – A callable transform.

+
+
+
+ +
+
+initialize(*args, **kwargs)[source]
+

Initialize the dataset (download and extract) if it’s not prepared yet +(need overriding).

+
+ +
+
+static split_dataset(dataset, length=None, percent=None, shuffle=True, seed=None)[source]
+

Split a dataset into two subsets.

+
+
Parameters:
+
    +

  • dataset (torch.utils.data.Dataset) – The dataset to split.

    • +

  • length (int) – The length of the first subset. +This argument cannot be used together with percent. +If None, use percent to calculate length instead. +Defaults to None.

    • +

  • percent (float) – The split ratio for the first subset. +This argument cannot be used together with length. +length = percent * len(dataset). +Defaults to None.

    • +

  • shuffle (bool) – Whether to shuffle the dataset. +Defaults to True.

    • +

  • seed (bool) – The random seed to split dataset +using numpy.random.shuffle. +Defaults to env['data_seed'].

    • +
+
+
Returns:
+

(torch.utils.data.Subset, torch.utils.data.Subset) – The two splitted subsets.

+
+
Example:
+
>>> from trojanzoo.utils.data import TensorListDataset
+>>> from trojanzoo.datasets import Dataset
+>>> import torch
+>>>
+>>> data = torch.ones(11, 3, 32, 32)
+>>> targets = list(range(11))
+>>> dataset = TensorListDataset(data, targets)
+>>> set1, set2 = Dataset.split_dataset(dataset, length=3)
+>>> len(set1), len(set2)
+(3, 8)
+>>> set3, set4 = split_dataset(dataset, percent=0.5)
+>>> len(set3), len(set4)
+(5, 6)
+
+
+
+
+
+

See also

+

The implementation is in +trojanzoo.utils.data.split_dataset(). +The difference is that this method will set seed +as env['data_seed'] when it is None.

+
+
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/defenses.html b/trojanzoo/defenses.html new file mode 100644 index 00000000..53ac4025 --- /dev/null +++ b/trojanzoo/defenses.html @@ -0,0 +1,552 @@ + + + + + + + + + + + + + + + + defenses — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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defenses

+
+
+trojanzoo.defenses.add_argument(parser, defense_name=None, defense=None, class_dict={})[source]
+
+
Add defense arguments to argument parser.
+
For specific arguments implementation, see Defense.add_argument().
+
+
+
Parameters:
+
    +

  • parser (argparse.ArgumentParser) – The parser to add arguments.

    • +

  • defense_name (str) – The defense name.

    • +

  • defense (str | Defense) – The defense instance or defense name +(as the alias of defense_name).

    • +

  • class_dict (dict[str, type[Defense]]) – Map from defense name to defense class. +Defaults to {}.

    • +
+
+
Returns:
+

argparse._ArgumentGroup – The argument group.

+
+
+
+ +
+
+trojanzoo.defenses.create(defense_name=None, defense=None, folder_path=None, dataset_name=None, dataset=None, model_name=None, model=None, config=config, class_dict={}, **kwargs)[source]
+
+
Create a defense instance.
+
For arguments not included in kwargs, +use the default values in config.
+
The default value of folder_path is +'{defense_dir}/{dataset.data_type}/{dataset.name}/{model.name}/{defense.name}'.
+
For defense implementation, see Defense.
+
+
+
Parameters:
+
    +

  • defense_name (str) – The defense name.

    • +

  • defense (str | Defense) – The defense instance or defense name +(as the alias of defense_name).

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | trojanzoo.datasets.Dataset) – Dataset instance or dataset name +(as the alias of dataset_name).

    • +

  • model_name (str) – The model name.

    • +

  • model (str | Model) – The model instance or model name +(as the alias of model_name).

    • +

  • config (Config) – The default parameter config.

    • +

  • class_dict (dict[str, type[Defense]]) – Map from defense name to defense class. +Defaults to {}.

    • +

  • **kwargs – The keyword arguments +passed to defense init method.

    • +
+
+
Returns:
+

Defense – The defense instance.

+
+
+
+ +
+
+class trojanzoo.defenses.Defense(attack=None, **kwargs)[source]
+
+
An abstract class representing a defense.
+
It inherits trojanzoo.utils.module.ModelProcess.
+
+
+

Note

+

This is the implementation of defense. +For users, please use create() instead, which is more user-friendly.

+
+
+
Variables:
+

attack (trojanzoo.attacks.Attack | None) – The attack instance.

+
+
+
+
+classmethod add_argument(group)[source]
+

Add defense arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +The concrete defense class may override this method to add more arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+ +
+
+abstract detect(*args, **kwargs)[source]
+

Main detect method (need overriding).

+
+ +
+ +
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+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/environ.html b/trojanzoo/environ.html new file mode 100644 index 00000000..139bc430 --- /dev/null +++ b/trojanzoo/environ.html @@ -0,0 +1,554 @@ + + + + + + + + + + + + + + + + environ — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
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+

environ

+
+
+trojanzoo.environ.add_argument(parser)[source]
+
+
Add environ arguments to argument parser.
+
For specific arguments implementation, see Env.add_argument().
+
+
+
Parameters:
+

parser (argparse.ArgumentParser) – The parser to add arguments.

+
+
Returns:
+

argparse._ArgumentGroup – The argument group.

+
+
+
+ +
+
+trojanzoo.environ.create(cmd_config_path=None, dataset_name=None, dataset=None, seed=None, data_seed=None, cudnn_benchmark=None, config=config, cache_threshold=None, verbose=None, color=None, device=None, tqdm=None, **kwargs)[source]
+
+
Load env values from config and command line.
+
+
+
Parameters:
+
    +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | trojanzoo.datasets.Dataset) – Dataset instance +(required for model_ema) +or dataset name +(as the alias of dataset_name).

    • +

  • model (trojanzoo.models.Model) – Model instance.

    • +

  • config (Config) – The default parameter config.

    • +

  • **kwargs – The keyword arguments in keys of +['optim_args', 'train_args', 'writer_args'].

    • +
+
+
Returns:
+

Env – The env instance.

+
+
+
+ +
+
+class trojanzoo.environ.Env(*args, device='auto', **kwargs)[source]
+

The dict-like environment class that inherits trojanzoo.utils.module.Param. +It should be singleton in most cases.

+
+

Warning

+

There is already an environ instance trojanzoo.environ.env. +call create() to set its value.

+

NEVER call the class init method to create a new instance +(unless you know what you’re doing).

+
+
+
Parameters:
+

device (str | device) –

Defaults to 'auto'.

+
    +

  • 'auto' (use gpu if available)

    • +

  • 'cpu'

    • +

  • 'gpu' | 'cuda'

    • +
+

+
+
Variables:
+
    +

  • color (bool) – Whether to show colorful outputs in console +using ASNI escape characters. +Defaults to False.

    • +

  • num_gpus (int) – Number of available GPUs.

    • +

  • tqdm (bool) – Whether to use tqdm.tqdm to show progress bar. +Defaults to False.

    • +

  • verbose (int) – The output level. Defaults to 0.

    • +

  • cudnn_benchmark (bool) – Whether to use torch.backends.cudnn.benchmark +to accelerate without deterministic. +Defaults to False.

    • +

  • cache_threshold (float) – the threshold (MB) to call torch.cuda.empty_cache. +Defaults to None (never).

    • +

  • seed (int) – The random seed for numpy, torch and cuda.

    • +

  • data_seed (int) – Seed to process data +(e.g., trojanzoo.datasets.Dataset.split_dataset())

    • +

  • device (device) – The default device to store tensors.

    • +

  • world_size (int) – Number of distributed machines. Defaults to 1.

    • +
+
+
+
+
+classmethod add_argument(group)[source]
+

Add environ arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
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models

+
+
+trojanzoo.models.add_argument(parser, model_name=None, model=None, config=config, class_dict={})[source]
+
+
Add model arguments to argument parser.
+
For specific arguments implementation, see Model.add_argument().
+
+
+
Parameters:
+
    +

  • parser (argparse.ArgumentParser) – The parser to add arguments.

    • +

  • model_name (str) – The model name.

    • +

  • model (str | Model) – The model instance or model name +(as the alias of model_name).

    • +

  • config (Config) – The default parameter config, +which contains the default dataset and model name if not provided.

    • +

  • class_dict (dict[str, type[Model]]) – Map from model name to model class. +Defaults to {}.

    • +
+
+
Returns:
+

argparse._ArgumentGroup – The argument group.

+
+
+
+ +
+
+trojanzoo.models.create(model_name=None, model=None, dataset_name=None, dataset=None, config=config, class_dict={}, **kwargs)[source]
+
+
Create a model instance.
+
For arguments not included in kwargs, +use the default values in config.
+
The default value of folder_path is +'{model_dir}/{dataset.data_type}/{dataset.name}'.
+
For model implementation, see Model.
+
+
+
Parameters:
+
    +

  • model_name (str) – The model name.

    • +

  • model (str | Model) – The model instance or model name +(as the alias of model_name).

    • +

  • dataset_name (str) – The dataset name.

    • +

  • dataset (str | trojanzoo.datasets.Dataset) – Dataset instance or dataset name +(as the alias of dataset_name).

    • +

  • config (Config) – The default parameter config.

    • +

  • class_dict (dict[str, type[Model]]) – Map from model name to model class. +Defaults to {}.

    • +

  • **kwargs – The keyword arguments +passed to model init method.

    • +
+
+
Returns:
+

Model – The model instance.

+
+
+
+ +
+
+trojanzoo.models.output_available_models(class_dict={}, indent=0)[source]
+

Output all available model names.

+
+
Parameters:
+
    +

  • class_dict (dict[str, type[Model]]) – Map from model name to model class. +Defaults to {}.

    • +

  • indent (int) – The space indent for the entire string. +Defaults to 0.

    • +
+
+
+
+ +
+
+class trojanzoo.models.Model(name='model', suffix=None, model=_Model, dataset=None, num_classes=None, folder_path=None, official=False, pretrained=False, randomized_smooth=False, rs_sigma=0.01, rs_n=100, **kwargs)[source]
+
+
A general model wrapper class, which should be the most common interface for users.
+
It inherits trojanzoo.utils.module.BasicObject.
+
+
+
Parameters:
+
    +

  • name (str) – Name of model.

    • +

  • suffix (str) –

    +
    Suffix of local model weights file (e.g., '_adv_train'). +Defaults to empty string ''.
    +
    The location of local pretrained weights is +'{folder_path}/{self.name}{self.suffix}.pth'
    +
    +

    • +

  • model (type[_Model] | _Model) – Type of model or a specific model instance.

    • +

  • dataset (trojanzoo.datasets.Dataset | None) – Corresponding dataset (optional). +Defaults to None.

    • +

  • num_classes (int | None) – Number of classes. +If it’s None, fetch the value from dataset. +Defaults to None.

    • +

  • folder_path (str) –

    Folder path to save model weights. +Defaults to None.

    +
    +

    Note

    +

    folder_path is usually +'{model_dir}/{dataset.data_type}/{dataset.name}', +which is claimed as the default value of create().

    +
    +

    • +

  • official (bool) – Whether to use official pretrained weights. +Defaults to False.

    • +

  • pretrained (bool) – Whether to use local pretrained weights +from '{folder_path}/{self.name}{self.suffix}.pth' +Defaults to False.

    • +

  • randomized_smooth (bool) – Whether to use randomized smoothing. +Defaults to False.

    • +

  • rs_sigma (float) – Randomized smoothing sampling std σ\sigma. +Defaults to 0.01.

    • +

  • rs_n (int) – Randomized smoothing sampling number. Defaults to 100.

    • +
+
+
Variables:
+
    +

  • available_models (set[str]) – The list of available model names.

    • +

  • weights (WeightsEnum) – The pretrained weights to use.

    • +

  • name (str) – Name of model.

    • +

  • suffix (str) –

    +
    Suffix of local model weights file (e.g., '_adv_train'). +Defaults to empty string ''.
    +
    The location of local pretrained weights is +'{folder_path}/{self.name}{self.suffix}.pth'
    +
    +

    • +

  • _model (_Model) – torch.nn.Module model instance.

    • +

  • model (torch.nn.DataParallel | _Model) – Parallel version of _model if there is more than 1 GPU available. +Generated by get_parallel_model().

    • +

  • dataset (trojanzoo.datasets.Dataset | None) – Corresponding dataset (optional). +Defaults to None.

    • +

  • num_classes (int | None) – Number of classes. +If it’s None, fetch the value from dataset. +Defaults to None.

    • +

  • folder_path (str) – Folder path to save model weights. +Defaults to None.

    • +

  • randomized_smooth (bool) – Whether to use randomized smoothing. +Defaults to False.

    • +

  • rs_sigma (float) – Randomized smoothing sampling std σ\sigma.

    • +

  • rs_n (int) – Randomized smoothing sampling number. Defaults to 100.

    • +

  • criterion (Callable) – The criterion used to calculate loss().

    • +

  • criterion_noreduction (Callable) – The criterion used to calculate loss() +when reduction='none'.

    • +

  • softmax (torch.nn.Module) – torch.nn.Softmax (dim=1). +Used in get_prob().

    • +
+
+
+
+
+_train(epochs, optimizer, module=None, num_classes=None, lr_scheduler=None, lr_warmup_epochs=0, model_ema=None, model_ema_steps=32, grad_clip=None, pre_conditioner=None, print_prefix='Train', start_epoch=0, resume=0, validate_interval=10, save=False, amp=False, loader_train=None, loader_valid=None, epoch_fn=None, get_data_fn=None, loss_fn=None, after_loss_fn=None, validate_fn=None, save_fn=None, file_path=None, folder_path=None, suffix=None, writer=None, main_tag='train', tag='', metric_fn=None, verbose=True, indent=0, **kwargs)[source]
+

Train the model

+
+ +
+
+_validate(module=None, num_classes=None, loader=None, print_prefix='Validate', indent=0, verbose=True, get_data_fn=None, loss_fn=None, writer=None, main_tag='valid', tag='', _epoch=None, metric_fn=None, **kwargs)[source]
+

Evaluate the model.

+
+
Returns:
+

(float, float) – Accuracy and loss.

+
+
+
+ +
+
+accuracy(_output, _label, topk=(1, 5), **kwargs)[source]
+

Computes the accuracy over the k top predictions +for the specified values of k.

+
+
Parameters:
+
+
+
Returns:
+

dict[str, float] – Top-k accuracies.

+
+
+
+

Note

+

The implementation is in trojanzoo.utils.model.accuracy().

+
+
+ +
+
+activate_params(params=[])[source]
+

Set requires_grad=True for selected params of module. +All other params are frozen.

+
+
Parameters:
+

params (Iterator[torch.nn.parameter.Parameter]) – The parameters to requires_grad. +Defaults to [].

+
+
+
+ +
+
+static add_argument(group)[source]
+

Add model arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +The concrete model class may override this method to add more arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+ +
+
+apply(fn)[source]
+

Applies fn recursively to every submodule (as returned by .children()) +as well as self. Typical use includes initializing the parameters of a model

+
+

See also

+

torch.nn.Module.apply.

+
+
+ +
+
+children()[source]
+

Returns an iterator over immediate children modules.

+
+

See also

+

torch.nn.Module.children.

+
+
+ +
+
+cpu()[source]
+

Moves all model parameters and buffers to the CPU.

+
+

See also

+

torch.nn.Module.cpu.

+
+
+ +
+
+cuda(device=None)[source]
+

Moves all model parameters and buffers to the GPU.

+
+

See also

+

torch.nn.Module.cuda.

+
+
+ +
+
+define_criterion(**kwargs)[source]
+

Define criterion to calculate loss. +Defaults to use torch.nn.CrossEntropyLoss.

+
+
Parameters:
+
+
+
+
+ +
+
+define_optimizer(parameters='full', OptimType='SGD', lr=0.1, momentum=0.0, weight_decay=0.0, lr_scheduler=False, lr_scheduler_type='CosineAnnealingLR', lr_step_size=30, lr_gamma=0.1, epochs=None, lr_min=0.0, lr_warmup_epochs=0, lr_warmup_method='constant', lr_warmup_decay=0.01, **kwargs)[source]
+

Define optimizer and lr_scheduler.

+
+
Parameters:
+
    +

  • parameters (str | Iterable[torch.nn.parameter.Parameter]) –

    The parameters to optimize while other model parameters are frozen. +If str, set parameters as:

    +
    +
      +

    • 'features': self._model.features

      • +

    • 'classifier' | 'partial': self._model.classifier

      • +

    • 'full': self._model

      • +
    +
    +

    Defaults to 'full'.

    +

    • +

  • OptimType (str | type[Optimizer]) – The optimizer type. +If str, load from module torch.optim. +Defaults to 'SGD'.

    • +

  • lr (float) – The learning rate of optimizer. Defaults to 0.1.

    • +

  • momentum (float) – The momentum of optimizer. Defaults to 0.0.

    • +

  • weight_decay (float) – The momentum of optimizer. Defaults to 0.0.

    • +

  • lr_scheduler (bool) – Whether to enable lr_scheduler. Defaults to False.

    • +

  • lr_scheduler_type (str) –

    The type of lr_scheduler. +Defaults to 'CosineAnnealingLR'.

    +

    Available lr_scheduler types (use string rather than type):

    +
    +
    +

    • +

  • lr_step_size (int) – step_size for torch.optim.lr_scheduler.StepLR. +Defaults to 30.

    • +

  • lr_gamma (float) – gamma for torch.optim.lr_scheduler.StepLR +or torch.optim.lr_scheduler.ExponentialLR. +Defaults to 0.1.

    • +

  • epochs (int) – Total training epochs. +epochs - lr_warmup_epochs is passed as T_max +to any:torch.optim.lr_scheduler.CosineAnnealingLR. +Defaults to None.

    • +

  • lr_min (float) – The minimum of learning rate. +It’s passed as eta_min +to any:torch.optim.lr_scheduler.CosineAnnealingLR. +Defaults to 0.0.

    • +

  • lr_warmup_epochs (int) – Learning rate warmup epochs. +Passed as total_iters to lr_scheduler. +Defaults to 0.

    • +

  • lr_warmup_method (str) – Learning rate warmup methods. +Choose from ['constant', 'linear']. +Defaults to 'constant'.

    • +

  • lr_warmup_decay (float) – Learning rate warmup decay factor. +Passed as factor (start_factor) to lr_scheduler. +Defaults to 0.01.

    • +

  • **kwargs – Keyword arguments passed to optimizer init method.

    • +
+
+
Returns:
+

(torch.optim.Optimizer, torch.optim.lr_scheduler._LRScheduler) – The tuple of optimizer and lr_scheduler.

+
+
+
+ +
+
+eval()[source]
+

Sets the module in evaluation mode.

+
+

See also

+

torch.nn.Module.eval.

+
+
+ +
+
+generate_target(_input, idx=1, same=False)[source]
+
+
Generate target labels of a batched input based on

the classification confidence ranking index.

+
+
+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The input tensor.

    • +

  • idx (int) – The classification confidence +rank of target class. +Defaults to 1.

    • +

  • same (bool) – Generate the same label +for all samples using mod. +Defaults to False.

    • +
+
+
Returns:
+

torch.Tensor – The generated target label with shape (N).

+
+
+
+

See also

+

The implementation is in +trojanzoo.utils.model.generate_target().

+
+
+ +
+
+get_all_layer(_input, layer_input='input', depth=-1, prefix='', use_filter=True, non_leaf=False, seq_only=True, verbose=0)[source]
+

Get all intermediate layer outputs of +_input from any intermediate layer.

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The batched input tensor +from layer_input.

    • +

  • layer_input (str) – The intermediate layer name of _input. +Defaults to 'input'.

    • +

  • depth (int) – The traverse depth. +Defaults to -1 (∞\infty).

    • +

  • prefix (str) – The prefix string to all elements. +Defaults to empty string ''.

    • +

  • use_filter (bool) –

    Whether to filter out certain layer types.

    + +

    • +

  • non_leaf (bool) – Whether to include non-leaf nodes. +Defaults to False.

    • +

  • seq_only (bool) – Whether to only traverse children +of torch.nn.Sequential. +If False, will traverse children of all torch.nn.Module. +Defaults to False.

    • +

  • verbose (bool) –

    The output level to show information +including layer name, output shape and module information. +Setting it larger than 0 will enable the output. +Different integer values stands for different module information. +Defaults to 0.

    +
      +

    • 0: No output

      • +

    • 1: Show layer class name.

      • +

    • 2: Show layer string (first line).

      • +

    • 3: Show layer string (full).

      • +
    +

    • +
+
+
Returns:
+

dict[str, torch.Tensor] – The dict of all layer outputs.

+
+
+
+

See also

+

The implementation is in +trojanzoo.utils.model.get_all_layer().

+
+
+ +
+
+get_class(_input, **kwargs)[source]
+

Get the class classification result of _input +(using torch.argmax).

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The batched input tensor +passed to _Model.get_logits().

    • +

  • **kwargs – Keyword arguments passed to get_logits().

    • +
+
+
Returns:
+

torch.Tensor – The classes tensor with shape (N).

+
+
+
+ +
+
+get_data(data, **kwargs)[source]
+

Process data. Defaults to be self.dataset.get_data. +If self.dataset is None, return data directly.

+
+
Parameters:
+
    +

  • data (Any) – Unprocessed data.

    • +

  • **kwargs – Keyword arguments passed to +self.dataset.get_data().

    • +
+
+
Returns:
+

Any – Processed data.

+
+
+
+ +
+
+get_final_fm(_input, **kwargs)[source]
+

Get the final feature map of _input, +which is the output of self.flatten +and input of self.classifier. +Call _Model.get_final_fm().

+
+
Parameters:
+
+
+
Returns:
+

torch.Tensor – The feature tensor with shape (N, dim).

+
+
+
+ +
+
+get_fm(_input, **kwargs)[source]
+

Get the feature map of _input, +which is the output of self.features +and input of self.pool. +Call _Model.get_fm().

+
+
Parameters:
+
+
+
Returns:
+

torch.Tensor – The feature tensor with shape (N, C', H', W').

+
+
+
+ +
+
+get_layer(_input, layer_output='classifier', layer_input='input', seq_only=True)[source]
+

Get one certain intermediate layer output +of _input from any intermediate layer.

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The batched input tensor +from layer_input.

    • +

  • layer_output (str) – The intermediate output layer name. +Defaults to 'classifier'.

    • +

  • layer_input (str) – The intermediate layer name of _input. +Defaults to 'input'.

    • +

  • seq_only (bool) – Whether to only traverse children +of torch.nn.Sequential. +If False, will traverse children of all torch.nn.Module. +Defaults to True.

    • +
+
+
Returns:
+

torch.Tensor – The output of layer layer_output.

+
+
+
+

See also

+

The implementation is in +trojanzoo.utils.model.get_layer().

+
+
+ +
+
+get_layer_name(depth=-1, prefix='', use_filter=True, non_leaf=False, seq_only=False)[source]
+

Get layer names of model instance.

+
+
Parameters:
+
+
+
Returns:
+

list[str] – The list of all layer names.

+
+
+
+

See also

+

The implementation is in +trojanzoo.utils.model.get_layer_name().

+
+
+ +
+
+get_logits(_input, parallel=False, randomized_smooth=None, rs_sigma=None, rs_n=None, **kwargs)[source]
+

Get logits of _input.

+
+

Note

+

Users should use model as Callable function +rather than call this method directly, +because __call__ supports torch.cuda.amp.

+
+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The batched input tensor.

    • +

  • parallel (bool) – Whether to use parallel model +self.model rather than self._model. +Defautls to False.

    • +

  • randomized_smooth (bool | None) – Whether to use randomized smoothing. +If it’s None, use self.randmized_smooth instead. +Defaults to None.

    • +

  • rs_sigma (float | None) – Randomized smoothing sampling std σ\sigma. +If it’s None, use self.rs_sigma instead. +Defaults to None.

    • +

  • rs_n (int) – Randomized smoothing sampling number. +If it’s None, use self.rs_n instead. +Defaults to None.

    • +

  • **kwargs – Keyword arguments passed to forward().

    • +
+
+
Returns:
+

torch.Tensor – The logit tensor with shape (N, C).

+
+
+
+ +
+
+get_official_weights(weights=None, progress=True, map_location='cpu', **kwargs)[source]
+

Get official model weights from weights.

+
+
Parameters:
+
+
+
Returns:
+

OrderedDict[str, torch.Tensor] – The model weights OrderedDict.

+
+
+
+ +
+
+static get_parallel_model(_model)[source]
+

Get the parallel model if there are more than 1 GPU avaiable.

+
+

Warning

+

torch.nn.DataParallel would be deprecated according to +https://github.com/pytorch/pytorch/issues/65936. +We need to consider using +torch.nn.parallel.DistributedDataParallel instead.

+
+
+
Parameters:
+

_model (_Model) – The non-parallel model.

+
+
Returns:
+

_Model | nn.DataParallel – The parallel model if there are more than 1 GPU avaiable.

+
+
+
+ +
+
+get_prob(_input, **kwargs)[source]
+

Get the probability classification vector of _input.

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The batched input tensor +passed to _Model.get_logits().

    • +

  • **kwargs – Keyword arguments passed to get_logits().

    • +
+
+
Returns:
+

torch.Tensor – The probability tensor with shape (N, C).

+
+
+
+ +
+
+get_target_prob(_input, target, **kwargs)[source]
+

Get the probability w.r.t. target class of _input +(using torch.gather).

+
+
Parameters:
+
+
+
Returns:
+

torch.Tensor – The probability tensor with shape (N).

+
+
+
+ +
+
+load(file_path=None, folder_path=None, suffix=None, inplace=True, map_location='cpu', component='full', strict=True, verbose=False, indent=0, **kwargs)[source]
+

Load pretrained model weights.

+
+
Parameters:
+
    +

  • file_path (str | None) – The file path to load pretrained weights. +If 'official', call get_official_weights(). +Defaults to '{folder_path}/{self.name}{suffix}.pth'.

    • +

  • folder_path (str | None) – The folder path containing model checkpoint. +It is used when file_path is not provided. +Defaults to self.folder_path.

    • +

  • suffix (str | None) – The suffix string to model weights file. +Defaults to self.suffix.

    • +

  • inplace (bool) – Whether to change model parameters. +If False, will only return the dict but not change model parameters. +Defaults to True.

    • +

  • map_location (str | device | dict) –

    Passed to torch.load. +Defaults to 'cpu'.

    +
    +

    Note

    +

    The device of model parameters will still be 'cuda' +if there is any cuda available. +This argument only affects intermediate operation.

    +
    +

    • +

  • component (str) – Specify which part of the weights to load. +Choose from ['full', 'features', 'classifier']. +Defaults to 'full'.

    • +

  • strict (bool) – Passed to torch.nn.Module.load_state_dict. +Defaults to True.

    • +

  • verbose (bool) – Whether to output auxiliary information. +Defaults to False.

    • +

  • indent (int) – The indent of output auxialiary information.

    • +

  • **kwargs – Keyword arguments passed to torch.load.

    • +
+
+
Returns:
+

OrderedDict[str, torch.Tensor] – The model weights OrderedDict.

+
+
+
+ +
+
+load_state_dict(state_dict, strict=True)[source]
+

Copies parameters and buffers from state_dict +into this module and its descendants.

+
+

See also

+

torch.nn.Module.load_state_dict.

+
+
+ +
+
+loss(_input=None, _label=None, _output=None, reduction='mean', **kwargs)[source]
+

Calculate the loss using self.criterion +(self.criterion_noreduction).

+
+
Parameters:
+
    +

  • _input (torch.Tensor | None) – The batched input tensor. +If _output is provided, this argument will be ignored. +Defaults to None.

    • +

  • _label (torch.Tensor) – The label of the batch with shape (N).

    • +

  • _output (torch.Tensor | None) – The logits of _input. +If None, use _input to calculate logits. +Defaults to None.

    • +

  • reduction (str) – Specifies the reduction to apply to the output. +Choose from ['none', 'mean']. +Defaults to 'mean'.

    • +

  • **kwargs – Keyword arguments passed to get_logits() +if _output is not provided.

    • +
+
+
Returns:
+

torch.Tensor – A scalar loss tensor (with shape (N) if reduction='none').

+
+
+
+ +
+
+modules()[source]
+

Returns an iterator over all modules in the network.

+
+

See also

+

torch.nn.Module.modules.

+
+
+ +
+
+named_children()[source]
+

Returns an iterator over immediate children modules, yielding both +the name of the module as well as the module itself.

+
+

See also

+

torch.nn.Module.named_children.

+
+
+ +
+
+named_modules(memo=None, prefix='')[source]
+

Returns an iterator over all modules in the network, yielding +both the name of the module as well as the module itself.

+
+

See also

+

torch.nn.Module.named_modules.

+
+
+ +
+
+named_parameters(prefix='', recurse=True)[source]
+

Returns an iterator over module parameters, yielding both the +name of the parameter as well as the parameter itself.

+
+

See also

+

torch.nn.Module.named_parameters.

+
+
+ +
+
+parameters(recurse=True)[source]
+

Returns an iterator over module parameters.

+
+

See also

+

torch.nn.Module.parameters.

+
+
+ +
+
+remove_misclassify(data, **kwargs)[source]
+

Remove misclassified samples in a data batch.

+
+
Parameters:
+
+
+
Returns:
+

(torch.Tensor, torch.Tensor) – The processed input and label with shape (N - k, *) and (N - k).

+
+
+
+ +
+
+requires_grad_(requires_grad=True)[source]
+

Change if autograd should record operations on parameters in this +module.

+
+

See also

+

torch.nn.Module.requires_grad_.

+
+
+ +
+
+save(file_path=None, folder_path=None, suffix=None, component='', _epoch=None, verbose=False, indent=0, **kwargs)[source]
+

Save pretrained model weights.

+
+
Parameters:
+
    +

  • file_path (str | None) – The file path to save pretrained weights. +Defaults to '{folder_path}/{self.name}{suffix}.pth'.

    • +

  • folder_path (str | None) – The folder path containing model checkpoint. +It is used when file_path is not provided. +Defaults to self.folder_path.

    • +

  • suffix (str | None) – The suffix string to model weights file. +Defaults to self.suffix.

    • +

  • component (str) – Specify which part of the weights to save. +Choose from ['full', 'features', 'classifier']. +Defaults to 'full'.

    • +

  • verbose (bool) – Whether to output auxiliary information. +Defaults to False.

    • +

  • indent (int) – The indent of output auxialiary information.

    • +

  • **kwargs – Keyword arguments passed to torch.save.

    • +
+
+
+
+ +
+
+state_dict(destination=None, prefix='', keep_vars=False)[source]
+

Returns a dictionary containing a whole state of the module.

+
+

See also

+

torch.nn.Module.state_dict.

+
+
+ +
+
+summary(depth=None, verbose=True, indent=0, **kwargs)[source]
+

Prints a string summary of the model instance by calling +trojanzoo.utils.module.BasicObject.summary() +and trojanzoo.utils.model.summary().

+
+
Parameters:
+
+
+
+
+ +
+
+train(mode=True)[source]
+

Sets the module in training mode.

+
+

See also

+

torch.nn.Module.train.

+
+
+ +
+
+zero_grad(set_to_none=False)[source]
+

Sets gradients of all model parameters to zero.

+
+

See also

+

torch.nn.Module.zero_grad.

+
+
+ +
+ +
+
+class trojanzoo.models._Model(num_classes=None, **kwargs)[source]
+

A specific model class which inherits torch.nn.Module.

+
+
Parameters:
+
+
+
Variables:
+
+
+
+
+
+static define_classifier(num_features=[], num_classes=1000, activation=nn.ReLU, activation_inplace=True, dropout=0.0, **kwargs)[source]
+
+
Define classifier as +(Linear -> Activation -> Dropout ) * (len(num_features) - 1) -> Linear.
+
If there is only 1 linear layer, its name will be 'fc'.
+
Else, all layer names will be indexed starting from 0 +(e.g., 'fc1', 'relu1', 'dropout0').
+
+
+
Parameters:
+
    +

  • num_features (list[int]) – List of feature numbers. +Each element serves as the in_features of current layer +and out_features of preceding layer. +Defaults to [].

    • +

  • num_classes (int) – The number of classes. +This serves as the out_features of last layer. +Defaults to None.

    • +

  • activation (type[torch.nn.Module]) – The type of activation layer. +Defaults to torch.nn.ReLU.

    • +

  • activation_inplace (bool) – Whether to use inplace activation. +Defaults to 'True'

    • +

  • dropout (float) – The drop out probability. +Will NOT add dropout layers if it’s 0. +Defaults to 0.0.

    • +

  • **kwargs – Any keyword argument (unused).

    • +
+
+
Returns:
+

torch.nn.Sequential – The sequential classifier.

+
+
Examples:
+
>>> from trojanzoo.models import _Model
+>>>
+>>> _Model.define_classifier(num_features=[5,4,4], num_classes=10)
+Sequential(
+    (fc1): Linear(in_features=5, out_features=4, bias=True)
+    (relu1): ReLU(inplace=True)
+    (dropout1): Dropout(p=0.5, inplace=False)
+    (fc2): Linear(in_features=4, out_features=4, bias=True)
+    (relu2): ReLU(inplace=True)
+    (dropout2): Dropout(p=0.5, inplace=False)
+    (fc3): Linear(in_features=4, out_features=10, bias=True)
+)
+
+
+
+
+
+ +
+
+static define_features(**kwargs)[source]
+

Define feature extractor.

+
+
Returns:
+

torch.nn.Identity – Identity module.

+
+
+
+ +
+
+classmethod define_preprocess(**kwargs)[source]
+

Define preprocess before feature extractor.

+
+
Returns:
+

torch.nn.Identity – Identity module.

+
+
+
+ +
+
+forward(x, **kwargs)[source]
+

x -> self.get_final_fm -> self.classifier -> return

+
+ +
+
+get_final_fm(x, **kwargs)[source]
+

x -> self.get_fm -> self.pool -> self.flatten -> return

+
+ +
+
+get_fm(x, **kwargs)[source]
+

x -> self.preprocess -> self.features -> return

+
+ +
+ +
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+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/optim.html b/trojanzoo/optim.html new file mode 100644 index 00000000..c2d3faa7 --- /dev/null +++ b/trojanzoo/optim.html @@ -0,0 +1,592 @@ + + + + + + + + + + + + + + + + optim — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

optim

+
+
+class trojanzoo.optim.Optimizer(iteration=20, stop_threshold=None, loss_fn=None, **kwargs)[source]
+

An abstract input optimizer class that inherits +trojanzoo.utils.module.Process.

+
+
Parameters:
+
    +

  • iteration (int) – Optimization iteration. +Defaults to 20.

    • +

  • stop_threshold (float | None) – Threshold used in early stop check. +Defaults to None (no early stop).

    • +

  • loss_fn (Callable) – Loss function (it’s usually reduction='none').

    • +

  • **kwargs – Keyword Arguments passed to +trojanzoo.utils.module.Process.

    • +
+
+
+
+
+early_stop_check(*args, current_idx=None, adv_input=None, loss_values=None, loss_fn=None, stop_threshold=None, loss_kwargs={}, **kwargs)[source]
+

Early stop check using stop_threshold.

+
+
Parameters:
+
    +

  • current_idx (torch.Tensor) – The indices of adv_input need to check +(Other indices have early stopped).

    • +

  • adv_input (torch.Tensor) – The entire batched adversairl input tensor +with shape (N, *).

    • +

  • loss_values (torch.Tensor) – Batched loss tensor with shape (N). +If None, use loss_fn +and adv_input to calculate. +Defaults to None.

    • +

  • loss_fn (collections.abc.Callable | None) – Loss function (it’s usually reduction='none'). +Defaults to self.loss_fn.

    • +

  • stop_threshold (float | None) – Threshold used in early stop check. +None means using self.stop_threshold. +Defaults to self.stop_threshold.

    • +

  • loss_kwargs (dict[str, torch.Tensor]) – Keyword arguments passed to loss_fn, +which will also be selected according to current_idx.

    • +

  • *args – Any positional argument (unused).

    • +

  • **kwargs – Any keyword argument (unused).

    • +
+
+
Returns:
+

torch.Tensor – Batched torch.BoolTensor with shape (N).

+
+
+
+ +
+
+optimize(_input, *args, iteration=None, loss_fn=None, stop_threshold=None, output=None, **kwargs)[source]
+

Main optimize method.

+
+
Parameters:
+
    +

  • _input (torch.Tensor) – The batched input tensor to optimize.

    • +

  • iteration (int | None) – Optimization iteration. +Defaults to self.iteration.

    • +

  • loss_fn (Callable) – Loss function (it’s usually reduction='none'). +Defaults to self.loss_fn.

    • +

  • stop_threshold (float | None) – Threshold used in early stop check. +None means using self.stop_threshold. +Defaults to self.stop_threshold.

    • +

  • output (int | Iterable[str]) – Output level integer or output items. +If int, call get_output_int(). +Defaults to self.output.

    • +
+
+
Returns:
+

(torch.Tensor, torch.Tensor) – batched adversarial input tensor and batched optimization iterations +(-1 if not reaching self.threshold).

+
+
+
+ +
+
+output_info(*args, mode='start', _iter=0, iteration=0, output=None, indent=None, **kwargs)[source]
+

Output information.

+
+
Parameters:
+
    +

  • mode (str) – The output mode +(e.g., 'start', 'end', 'middle', 'memory'). +Should be legal strings in get_output_int(). +Defaults to 'start'.

    • +

  • _iter (int) – Current iteration. Defaults to 0.

    • +

  • iteration (int) – Total iteration. Defaults to 0.

    • +

  • output (Iterable[str]) – Output items. +Defaults to self.output.

    • +

  • indent (int) – The space indent for the entire string. +Defaults to self.indent.

    • +

  • *args – Any positional argument (unused).

    • +

  • **kwargs – Any keyword argument (unused).

    • +
+
+
+
+ +
+
+preprocess_input(*args, adv_input=None, org_input=None, **kwargs)[source]
+

Optimize input tensor for 1 iteration.

+
+
Parameters:
+
    +

  • adv_input (torch.Tensor) – The entire batched adversairl input tensor +with shape (N, *).

    • +

  • org_input (torch.Tensor) – The entire batched original input tensor +with shape (N, *).

    • +
+
+
+
+ +
+
+abstract update_input(current_idx, adv_input, org_input, *args, **kwargs)[source]
+

Optimize input tensor for 1 iteration.

+
+
Parameters:
+
    +

  • current_idx (torch.Tensor) – The indices of adv_input need to optimize +(Other indices have early stopped).

    • +

  • adv_input (torch.Tensor) – The entire batched adversairl input tensor +with shape (N, *).

    • +

  • org_input (torch.Tensor) – The entire batched original input tensor +with shape (N, *).

    • +
+
+
+
+ +
+ +
+ + +
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/trainer.html b/trojanzoo/trainer.html new file mode 100644 index 00000000..12d5dba5 --- /dev/null +++ b/trojanzoo/trainer.html @@ -0,0 +1,580 @@ + + + + + + + + + + + + + + + + trainer — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+
+ +
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+ +
+ +
+
+ +
+

trainer

+
+
+trojanzoo.trainer.add_argument(parser, ClassType=Trainer)[source]
+
+
Add trainer arguments to argument parser.
+
For specific arguments implementation, see Trainer.add_argument().
+
+
+
Parameters:
+
+
+
Returns:
+

argparse._ArgumentGroup – The argument group.

+
+
+
+ +
+
+trojanzoo.trainer.create(dataset_name=None, dataset=None, model=None, model_ema=False, pre_conditioner=None, tensorboard=None, ClassType=Trainer, config=config, **kwargs)[source]
+
+
Create a trainer instance.
+
For arguments not included in kwargs, +use the default values in config.
+
For trainer implementation, see Trainer.
+
+
+
Parameters:
+
+
+
Returns:
+

Trainer – The trainer instance.

+
+
+
+ +
+
+class trojanzoo.trainer.Trainer(optim_args={}, train_args={}, writer_args={}, optimizer=None, lr_scheduler=None, model_ema=None, pre_conditioner=None, writer=None, **kwargs)[source]
+

A dict-like class to contain training arguments +which supports attribute-like view as well.

+

It inherits trojanzoo.utils.module.BasicObject.

+
+

Note

+

The most common usage is train(**trainer). +See keys() for details.

+
+
+
Variables:
+
+
+
+
+
+classmethod add_argument(group)[source]
+

Add trainer arguments to argument parser group. +View source to see specific arguments.

+
+

Note

+

This is the implementation of adding arguments. +For users, please use add_argument() instead, which is more user-friendly.

+
+
+ +
+
+keys()[source]
+

Keys include:

+
+
    +
  • +
    All attributes exclude
    +
    ['optim_args', 'train_args', 'writer_args']
    +
    +
    • +

  • train_args

    • +
+
+
+
Returns:
+

list[str] – The list of keys.

+
+
+
+ +
+ +
+ + +
+ +
+ + +
+
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/data.html b/trojanzoo/utils/data.html new file mode 100644 index 00000000..882b3ae0 --- /dev/null +++ b/trojanzoo/utils/data.html @@ -0,0 +1,643 @@ + + + + + + + + + + + + + + + + data — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

data

+
+
+class trojanzoo.utils.data.TensorListDataset(data=None, targets=None, **kwargs)[source]
+

The dataset class that has a torch.Tensor as inputs +and list[int] as labels. +It inherits torch.utils.data.Dataset.

+
+
Parameters:
+
+
+
Example:
+
>>> import torch
+>>> from trojanzoo.utils.data import TensorListDataset
+>>>
+>>> data = torch.ones(10, 3, 32, 32)
+>>> targets = list(range(10))
+>>> dataset = TensorListDataset(data, targets)
+>>> x, y = dataset[3]
+>>> x.shape
+torch.Size([3, 32, 32])
+>>> y
+3
+
+
+
+
+
+ +
+
+trojanzoo.utils.data.dataset_to_tensor(dataset)[source]
+

Transform a torch.utils.data.Dataset to (data, targets) tensor tuple +by traversing all elements.

+
+
Parameters:
+

dataset (torch.utils.data.Dataset) – The dataset.

+
+
Returns:
+

(torch.Tensor, torch.Tensor) – The tuple of (data, targets).

+
+
Example:
+
>>> from torchvision.datasets import MNIST
+>>> import torchvision.transforms as transforms
+>>> from trojanzoo.utils.data import dataset_to_tensor
+>>>
+>>> transform = transforms.Compose([
+    transforms.PILToTensor(),
+    transforms.ConvertImageDtype(torch.float)])
+>>> dataset = MNIST('./', train=False, download=True,
+                    transform=transform)
+>>> data, targets = dataset_to_tensor(dataset)
+>>> data.shape
+torch.Size([10000, 1, 28, 28])
+>>> targets.shape
+torch.Size([10000])
+>>> targets.dtype
+torch.int64
+
+
+
+
+
+ +
+
+trojanzoo.utils.data.sample_batch(dataset, batch_size=None, idx=[])[source]
+

Sample a batch from dataset by calling

+
dataset_to_tensor()(torch.utils.data.Subset(dataset, idx))
+
+
Parameters:
+
    +

  • dataset (torch.utils.data.Dataset) – The dataset to sample.

    • +

  • batch_size (int) – The batch size to sample +when idx is None. +Defaults to None.

    • +

  • idx (Sequence[int]) – The index list of each sample in dataset. +If empty, randomly sample a batch with given batch_size. +Defaults to [].

    • +
+
+
Returns:
+

(torch.Tensor, torch.Tensor) – The tuple of sampled batch (data, targets).

+
+
Example:
+
>>> import torch
+>>> from trojanzoo.utils.data import TensorListDataset, sample_batch
+>>>
+>>> data = torch.ones(10, 3, 32, 32)
+>>> targets = list(range(10))
+>>> dataset = TensorListDataset(data, targets)
+>>> x, y = sample_batch(dataset, idx=[1, 2])
+>>> x.shape
+torch.Size([2, 3, 32, 32])
+>>> y
+tensor([1, 2])
+>>> x, y = sample_batch(dataset, batch_size=4)
+>>> y
+tensor([6, 3, 2, 5])
+
+
+
+
+
+ +
+
+trojanzoo.utils.data.split_dataset(dataset, length=None, percent=None, shuffle=True, seed=None)[source]
+

Split a dataset into two subsets.

+
+
Parameters:
+
    +

  • dataset (torch.utils.data.Dataset) – The dataset to split.

    • +

  • length (int) – The length of the first subset. +This argument cannot be used together with percent. +If None, use percent to calculate length instead. +Defaults to None.

    • +

  • percent (float) – The split ratio for the first subset. +This argument cannot be used together with length. +length = percent * len(dataset). +Defaults to None.

    • +

  • shuffle (bool) – Whether to shuffle the dataset. +Defaults to True.

    • +

  • seed (bool) – The random seed to split dataset +using numpy.random.shuffle. +Defaults to None.

    • +
+
+
Returns:
+

(torch.utils.data.Subset, torch.utils.data.Subset) – The two splitted subsets.

+
+
Example:
+
>>> import torch
+>>> from trojanzoo.utils.data import TensorListDataset, split_dataset
+>>>
+>>> data = torch.ones(11, 3, 32, 32)
+>>> targets = list(range(11))
+>>> dataset = TensorListDataset(data, targets)
+>>> set1, set2 = split_dataset(dataset, length=3)
+>>> len(set1), len(set2)
+(3, 8)
+>>> set3, set4 = split_dataset(dataset, percent=0.5)
+>>> len(set3), len(set4)
+(5, 6)
+
+
+
+
+
+

Note

+

This is the implementation of trojanzoo.datasets.Dataset.split_dataset(). +The difference is that this method will NOT set seed +as env['data_seed'] when it is None.

+
+
+ +
+
+trojanzoo.utils.data.get_class_subset(dataset, class_list)[source]
+

Get a subset from dataset with certain classes.

+
+
Parameters:
+
+
+
Returns:
+

torch.utils.data.Subset – The subset with labels in class_list.

+
+
Example:
+
>>> import torch
+>>> from trojanzoo.utils.data import get_class_subset, TensorListDataset
+>>>
+>>> data = torch.ones(11, 3, 32, 32)
+>>> targets = list(range(11))
+>>> dataset = TensorListDataset(data, targets)
+>>> subset = get_class_subset(dataset, class_list=[2, 3])
+>>> len(subset)
+2
+
+
+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
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+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/fim.html b/trojanzoo/utils/fim.html new file mode 100644 index 00000000..d2950177 --- /dev/null +++ b/trojanzoo/utils/fim.html @@ -0,0 +1,557 @@ + + + + + + + + + + + + + + + + fim — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Shortcuts +
+
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+ +
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+ +
+

fim

+
+
+class trojanzoo.utils.fim.BaseKFAC(net, eps=0.1, sua=False, update_freq=1, alpha=1.0, constraint_norm=False, state_type=BaseState)[source]
+

Base K-FAC Preconditionner for Linear and Conv2d layers.

+

Compute the K-FAC of the second moment of the gradients. +It works for Linear and Conv2d layers and silently skip other layers.

+
+
Parameters:
+
    +

  • net (torch.nn.Module) – Network to precondition.

    • +

  • eps (float) – Tikhonov regularization parameter for the inverses.

    • +

  • sua (bool) – Applies SUA approximation.

    • +

  • update_freq (int) – Perform inverses every update_freq updates.

    • +

  • alpha (float) – Running average parameter (if == 1, no r. ave.).

    • +

  • constraint_norm (bool) – Scale the gradients by the squared +fisher norm.

    • +
+
+
+
+ +
+
+class trojanzoo.utils.fim.KFAC(*args, pi=False, **kwargs)[source]
+

K-FAC Preconditionner for torch.nn.Linear +and torch.nn.Conv2d layers.

+

Compute the K-FAC of the second moment of the gradients. +It works for Linear and Conv2d layers and silently skip other layers.

+
+

See also

+

https://github.com/Thrandis/EKFAC-pytorch/blob/master/kfac.py

+

https://github.com/n-gao/pytorch-kfac

+
+
+
Parameters:
+
    +

  • net (torch.nn.Module) – Network to precondition.

    • +

  • pi (bool) – Computes pi correction for Tikhonov regularization.

    • +

  • eps (float) – Tikhonov regularization parameter for the inverses.

    • +

  • sua (bool) – Applies SUA approximation.

    • +

  • update_freq (int) – Perform inverses every update_freq updates.

    • +

  • alpha (float) – Running average parameter (if == 1, no r. ave.).

    • +

  • constraint_norm (bool) – Scale the gradients by the squared +fisher norm.

    • +
+
+
+
+ +
+
+class trojanzoo.utils.fim.EKFAC(net, *args, ra=False, **kwargs)[source]
+

EKFAC Preconditionner for torch.nn.Linear +and torch.nn.Conv2d layers.

+

Computes the EKFAC of the second moment of the gradients. +It works for Linear and Conv2d layers and silently skip other layers.

+
+
Parameters:
+
    +

  • net (torch.nn.Module) – Network to precondition.

    • +

  • eps (float) – Tikhonov regularization parameter for the inverses.

    • +

  • sua (bool) – Applies SUA approximation.

    • +

  • ra (bool) – Computes stats using a running average of +averaged gradients instead of using a intra minibatch estimate.

    • +

  • update_freq (int) – Perform inverses every update_freq updates.

    • +

  • alpha (float) – Running average parameter.

    • +

  • constraint_norm (bool) – Scale the gradients by the squared +fisher norm.

    • +
+
+
+
+ +
+
+class trojanzoo.utils.fim.BaseState[source]
+

A basic storage class.

+
+
Variables:
+
+
+
+
+ +
+
+class trojanzoo.utils.fim.KFACState[source]
+

A storage class for KFAC.

+
+
Variables:
+
+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+
+ +
+
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+ +
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+ +
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+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/index.html b/trojanzoo/utils/index.html new file mode 100644 index 00000000..89c2bf9f --- /dev/null +++ b/trojanzoo/utils/index.html @@ -0,0 +1,596 @@ + + + + + + + + + + + + + + + + utils — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + +
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+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

utils

+
+ +
+
+
+class trojanzoo.utils.lock.Lock[source]
+

A boolean lock class used for contextmanager. +It’s used in KFAC +to avoid auxiliary computation operations.

+
+
Example:
+
>>> from trojanzoo.utils.lock import Lock
+>>>
+>>> track = Lock()
+>>> print(bool(track))
+False
+>>> with track():
+>>>     print(bool(track))
+True
+>>> print(bool(track))
+False
+>>> track.enable()
+>>> print(bool(track))
+True
+>>> track.disable()
+>>> print(bool(track))
+False
+
+
+
+
+
+
+disable()[source]
+

Set lock boolean value as False. +It’s used together with enable() +when contextmanager is not suitable for the case.

+
+ +
+
+enable()[source]
+

Set lock boolean value as True. +It’s used together with disable() +when contextmanager is not suitable for the case.

+
+ +
+ +
+
+trojanzoo.utils.memory.empty_cache(threshold=None)[source]
+

Call torch.cuda.empty_cache to empty GPU cache when +torch.cuda.memory_cached() +> thresholdMB.

+
+
Parameters:
+

threshold (float) – The cached memory threshold (MB). +Defaults to env['cache_threshold'].

+
+
+
+ +
+
+trojanzoo.utils.memory.output_memory(device=None, full=False, indent=0, **kwargs)[source]
+

Output memory usage information.

+
+
Parameters:
+
+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
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+

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+
+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/logger.html b/trojanzoo/utils/logger.html new file mode 100644 index 00000000..97dc2dd0 --- /dev/null +++ b/trojanzoo/utils/logger.html @@ -0,0 +1,721 @@ + + + + + + + + + + + + + + + + logger — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

logger

+
+
+class trojanzoo.utils.logger.SmoothedValue(name='', window_size=None, fmt='{global_avg:.3f}')[source]
+

Track a series of values and provide access to smoothed values over a +window or the global series average.

+
+

See also

+

https://github.com/pytorch/vision/blob/main/references/classification/utils.py

+
+
+
Parameters:
+
    +

  • name (str) – Name string.

    • +

  • window_size (int) – The maxlen of deque.

    • +

  • fmt (str) – The format pattern of str(self).

    • +
+
+
Variables:
+
    +

  • name (str) – Name string.

    • +

  • fmt (str) – The string pattern.

    • +

  • deque (deque) – The unique data series.

    • +

  • count (int) – The amount of data.

    • +

  • total (float) – The sum of all data.

    • +

  • median (float) – The median of deque.

    • +

  • avg (float) – The avg of deque.

    • +

  • global_avg (float) – totalcount\frac{\text{total}}{\text{count}}

    • +

  • max (float) – The max of deque.

    • +

  • min (float) – The min of deque.

    • +

  • last_value (float) – The last value of deque.

    • +
+
+
+
+
+reset()[source]
+

Reset deque, count and total to be empty.

+
+
Returns:
+

SmoothedValue – return self for stream usage.

+
+
+
+ +
+
+synchronize_between_processes()[source]
+
+

Warning

+

Does NOT synchronize the deque!

+
+
+ +
+
+update(value, n=1)[source]
+

Update n pieces of data with same value.

+
self.deque.append(value)
+self.total += value * n
+self.count += n
+
+
+
+
Parameters:
+
    +

  • value (float) – the value to update.

    • +

  • n (int) – the number of data with same value.

    • +
+
+
Returns:
+

SmoothedValue – return self for stream usage.

+
+
+
+ +
+
+update_list(value_list)[source]
+

Update value_list.

+
for value in value_list:
+    self.deque.append(value)
+    self.total += value
+self.count += len(value_list)
+
+
+
+
Parameters:
+

value_list (list[float]) – the value list to update.

+
+
Returns:
+

SmoothedValue – return self for stream usage.

+
+
+
+ +
+ +
+
+class trojanzoo.utils.logger.MetricLogger(delimiter='', meter_length=20, tqdm=None, indent=0, **kwargs)[source]
+
+

See also

+

https://github.com/pytorch/vision/blob/main/references/classification/utils.py

+
+
+
Parameters:
+
    +

  • delimiter (str) – The delimiter to join different meter strings. +Defaults to ''.

    • +

  • meter_length (int) – The minimum length for each meter. +Defaults to 20.

    • +

  • tqdm (bool) – Whether to use tqdm to show iteration information. +Defaults to env['tqdm'].

    • +

  • indent (int) – The space indent for the entire string. +Defaults to 0.

    • +
+
+
Variables:
+
+
+
+
+
+create_meters(**kwargs)[source]
+

Create meters with specific fmt in self.meters.

+

self.meters[meter_name] = SmoothedValue(fmt=fmt)

+
+
Parameters:
+

**kwargs – (meter_name: fmt)

+
+
Returns:
+

MetricLogger – return self for stream usage.

+
+
+
+ +
+
+get_str(cut_too_long=True, strip=True, **kwargs)[source]
+

Generate formatted string based on keyword arguments.

+

key: value with max length to be self.meter_length. +The key string is green when env['color'] == True.

+
+
Parameters:
+
    +

  • cut_too_long (bool) – Whether to cut too long values to first 5 characters. +Defaults to True.

    • +

  • strip (bool) – Whether to strip trailing whitespaces. +Defaults to True.

    • +

  • **kwargs – Keyword arguments to generate string.

    • +
+
+
+
+ +
+
+log_every(iterable, header='', tqdm=None, tqdm_header='Iter', indent=None, verbose=None)[source]
+

Wrap an collections.abc.Iterable with formatted outputs.

+
    +

  • Middle Output: +{tqdm_header}: [ current / total ] str(self) {memory} {iter_time} {data_time} {time}<{remaining}

    • +

  • Final Output +{header} str(self) {memory} {iter_time} {data_time} {total_time}

    • +
+
+
Parameters:
+
    +

  • iterable (Iterable) – The raw iterator.

    • +

  • header (str) – The header string for final output. +Defaults to ''.

    • +

  • tqdm (bool) – Whether to use tqdm to show iteration information. +Defaults to self.tqdm.

    • +

  • tqdm_header (str) – The header string for middle output. +Defaults to 'Iter'.

    • +

  • indent (int) – The space indent for the entire string. +if None, use self.indent. +Defaults to None.

    • +

  • verbose (int) – The verbose level of output information. +Defaults to env[verbose]

    • +
+
+
Example:
+
+

See also

+

trojanzoo.utils.train.train()

+
+
+
+
+ +
+
+reset()[source]
+

Reset meter in self.meters by calling SmoothedValue.reset().

+
+
Returns:
+

MetricLogger – return self for stream usage.

+
+
+
+ +
+
+update(n=1, **kwargs)[source]
+

Update values to self.meters by calling SmoothedValue.update().

+

self.meters[meter_name].update(float(value), n=n)

+
+
Parameters:
+
    +

  • n (int) – the number of data with same value.

    • +

  • **kwargs – {meter_name: value}.

    • +
+
+
Returns:
+

MetricLogger – return self for stream usage.

+
+
+
+ +
+
+update_list(**kwargs)[source]
+

Update values to self.meters by calling SmoothedValue.update_list().

+

self.meters[meter_name].update_list(value_list)

+
+
Parameters:
+

**kwargs – {meter_name: value_list}.

+
+
Returns:
+

MetricLogger – return self for stream usage.

+
+
+
+ +
+ +
+
+class trojanzoo.utils.logger.AverageMeter(name, fmt=':f')[source]
+

Computes and stores the average and current value.

+
+

See also

+

https://github.com/pytorch/examples/blob/master/imagenet/main.py

+
+
+

Note

+

It is recommended to use SmoothedValue instead.

+
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

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+

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+ View Docs +
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+ +
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+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/model.html b/trojanzoo/utils/model.html new file mode 100644 index 00000000..4199f18d --- /dev/null +++ b/trojanzoo/utils/model.html @@ -0,0 +1,863 @@ + + + + + + + + + + + + + + + + model — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ Shortcuts +
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+

model

+
+
+trojanzoo.utils.model.init_weights(m, filter_list=[])[source]
+

Traverse module m to intialize weights of all submodules +except for those in filter_list.

+

Module parameters are reset by calling module.reset_parameters().

+
+

Note

+

An alternative implementation is to call m.apply(init_weights) +so that this method could be non-recursive and avoid traverse.

+
+
+
Parameters:
+
    +

  • m (torch.nn.Module) – Module to initialize.

    • +

  • filter_list (tuple[type]) – List of submodule types as exceptions. +Defaults to [] (empty).

    • +
+
+
Example:
+
from trojanzoo.utils.model import init_weights
+import torch.nn as nn
+
+net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
+init_weights(filter_list=[nn.Linear])   # no change
+init_weights(net)                       # init nn.Linear layers
+
+
+
+
+
+ +
+
+trojanzoo.utils.model.get_layer_name(module, depth=-1, prefix='', use_filter=True, non_leaf=False, seq_only=False, init=True)[source]
+

Get layer names of a torch.nn.Module.

+
+
Parameters:
+
+
+
Returns:
+

list[str] – The list of all layer names.

+
+
Example:
+
>>> import torchvision
+>>> from trojanzoo.utils.model import get_layer_name
+>>>
+>>> model = torchvision.models.resnet18()
+>>> get_layer_name(model, depth=0)
+[]
+>>> get_layer_name(model, depth=1)
+['conv1', 'maxpool', 'layer1', 'layer2',
+'layer3', 'layer4', 'avgpool', 'fc']
+>>> get_layer_name(model, depth=2, prefix='model')
+['model.conv1', 'model.maxpool', 'model.layer1.0', 'model.layer1.1',
+'model.layer2.0', 'model.layer2.1', 'model.layer3.0', 'model.layer3.1',
+'model.layer4.0', 'model.layer4.1', 'model.avgpool', 'model.fc']
+>>> get_layer_name(model, seq_only=True)
+['conv1', 'maxpool', 'layer1.0', 'layer1.1', 'layer2.0', 'layer2.1',
+'layer3.0', 'layer3.1', 'layer4.0', 'layer4.1', 'avgpool', 'fc']
+>>> get_layer_name(model, seq_only=True, non_leaf=True)
+['conv1', 'maxpool',
+'layer1.0', 'layer1.1', 'layer1',
+'layer2.0', 'layer2.1', 'layer2',
+'layer3.0', 'layer3.1', 'layer3',
+'layer4.0', 'layer4.1', 'layer4',
+'avgpool', 'fc']
+>>> get_layer_name(model)
+['conv1', 'maxpool',
+'layer1.0.conv1', 'layer1.0.conv2', 'layer1.1.conv1', 'layer1.1.conv2',
+'layer2.0.conv1', 'layer2.0.conv2', 'layer2.0.downsample.0', 'layer2.1.conv1', 'layer2.1.conv2',
+'layer3.0.conv1', 'layer3.0.conv2', 'layer3.0.downsample.0', 'layer3.1.conv1', 'layer3.1.conv2',
+'layer4.0.conv1', 'layer4.0.conv2', 'layer4.0.downsample.0', 'layer4.1.conv1', 'layer4.1.conv2',
+'avgpool', 'fc']
+
+
+
+
+
+ +
+
+trojanzoo.utils.model.get_all_layer(module, x, layer_input='input', depth=-1, prefix='', use_filter=True, non_leaf=False, seq_only=True, verbose=0)[source]
+

Get all intermediate layer outputs of +_input from any intermediate layer +in a torch.nn.Module.

+
+
Parameters:
+
    +

  • module (torch.nn.Module) – the module to process.

    • +

  • x (torch.Tensor) – The batched input tensor +from layer_input.

    • +

  • layer_input (str) – The intermediate layer name of x. +Defaults to 'input'.

    • +

  • depth (int) – The traverse depth. +Defaults to -1 (∞\infty).

    • +

  • prefix (str) – The prefix string to all elements. +Defaults to empty string ''.

    • +

  • use_filter (bool) –

    Whether to filter out certain layer types.

    + +

    • +

  • non_leaf (bool) – Whether to include non-leaf nodes. +Defaults to False.

    • +

  • seq_only (bool) – Whether to only traverse children +of torch.nn.Sequential. +If False, will traverse children of all torch.nn.Module. +Defaults to False.

    • +

  • verbose (int) –

    The output level to show information +including layer name, output shape and module information. +Setting it larger than 0 will enable the output. +Different integer values stands for different module information. +Defaults to 0.

    +
      +

    • 0: No output

      • +

    • 1: Show layer class name.

      • +

    • 2: Show layer string (first line).

      • +

    • 3: Show layer string (full).

      • +
    +

    • +
+
+
Returns:
+

dict[str, torch.Tensor] – The dict of all layer outputs.

+
+
Example:
+
>>> import torch
+>>> import torchvision
+>>> from trojanzoo.utils.model import get_all_layer
+>>>
+>>> model = torchvision.models.densenet121()
+>>> x = torch.randn(5, 3, 224, 224)
+>>> y = get_all_layer(model.features, x, verbose=True)
+layer name                                        output shape        module information
+conv0                                             [5, 64, 112, 112]   Conv2d
+pool0                                             [5, 64, 56, 56]     MaxPool2d
+denseblock1                                       [5, 256, 56, 56]    _DenseBlock
+transition1.conv                                  [5, 128, 56, 56]    Conv2d
+transition1.pool                                  [5, 128, 28, 28]    AvgPool2d
+denseblock2                                       [5, 512, 28, 28]    _DenseBlock
+transition2.conv                                  [5, 256, 28, 28]    Conv2d
+transition2.pool                                  [5, 256, 14, 14]    AvgPool2d
+denseblock3                                       [5, 1024, 14, 14]   _DenseBlock
+transition3.conv                                  [5, 512, 14, 14]    Conv2d
+transition3.pool                                  [5, 512, 7, 7]      AvgPool2d
+denseblock4                                       [5, 1024, 7, 7]     _DenseBlock
+>>> y.keys()
+dict_keys(['conv0', 'pool0',
+'denseblock1', 'transition1.conv', 'transition1.pool',
+'denseblock2', 'transition2.conv', 'transition2.pool',
+'denseblock3', 'transition3.conv', 'transition3.pool',
+'denseblock4'])
+
+
+
+
+
+

Note

+

This method regards module as a torch.nn.Sequential. +Many modules embed flatten operation in their forward method +(e.g., view(n, -1) or flatten(1)), +making get_all_layer raise error. +We suggest to use torch.nn.Flatten instead to keep the module sequential.

+
+
+ +
+
+trojanzoo.utils.model.get_layer(module, x, layer_output='output', layer_input='input', layer_name_list=None, seq_only=True)[source]
+

Get one certain intermediate layer output +of _input from any intermediate layer +in a torch.nn.Module.

+
+
Parameters:
+
    +

  • module (torch.nn.Module) – the module to process.

    • +

  • x (torch.Tensor) – The batched input tensor +from layer_input.

    • +

  • layer_output (str) – The intermediate output layer name. +Defaults to 'classifier'.

    • +

  • layer_input (str) – The intermediate layer name that outputs x. +Defaults to 'input'.

    • +

  • seq_only (bool) – Whether to only traverse children +of torch.nn.Sequential. +If False, will traverse children of all torch.nn.Module. +Defaults to True.

    • +
+
+
Returns:
+

torch.Tensor – The output of layer layer_output.

+
+
Example:
+
>>> import torch
+>>> import torchvision
+>>> from trojanzoo.utils.model import get_all_layer, get_layer
+>>>
+>>> model = torchvision.models.densenet121()
+>>> x = torch.randn(5, 3, 224, 224)
+>>> y = get_all_layer(model.features, x, verbose=True)
+layer name                                        output shape        module information
+conv0                                             [5, 64, 112, 112]   Conv2d
+pool0                                             [5, 64, 56, 56]     MaxPool2d
+denseblock1                                       [5, 256, 56, 56]    _DenseBlock
+transition1.conv                                  [5, 128, 56, 56]    Conv2d
+transition1.pool                                  [5, 128, 28, 28]    AvgPool2d
+denseblock2                                       [5, 512, 28, 28]    _DenseBlock
+transition2.conv                                  [5, 256, 28, 28]    Conv2d
+transition2.pool                                  [5, 256, 14, 14]    AvgPool2d
+denseblock3                                       [5, 1024, 14, 14]   _DenseBlock
+transition3.conv                                  [5, 512, 14, 14]    Conv2d
+transition3.pool                                  [5, 512, 7, 7]      AvgPool2d
+denseblock4                                       [5, 1024, 7, 7]     _DenseBlock
+>>> x = torch.randn(6, 256, 56, 56)
+>>> get_layer(model.features, x, layer_input='denseblock1', layer_output='transition3.conv').shape
+torch.Size([6, 512, 14, 14])
+
+
+
+
+
+ +
+
+trojanzoo.utils.model.summary(module, depth=0, verbose=True, indent=0, tree_length=None, indent_atom=12)[source]
+
+
Prints a string summary of the module.
+
This method is similar to tensorflow.keras.Model.summary().
+
+
+
Parameters:
+
    +

  • module (torch.nn.Module) – The module to process.

    • +

  • depth (int) – The traverse depth. Defaults to 0.

    • +

  • verbose (bool) – Whether to output auxiliary information. +Defaults to True.

    • +

  • indent (int) – The space indent for the entire string. +Defaults to 0.

    • +

  • tree_length (int) – The tree length. +If None, use indent_atom * (depth + 1). +Defaults to None.

    • +

  • indent_atom (int) – The indent incremental for each sub-structure. +Defaults to 12.

    • +
+
+
Example:
+
>>> import torchvision
+>>> from trojanzoo.utils.model import summary
+>>>
+>>> model=torchvision.models.resnet18()
+>>> summary(model)
+>>> summary(model, depth=1)
+conv1                   Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+bn1                     BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+relu                    ReLU(inplace=True)
+maxpool                 MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+layer1                  Sequential
+layer2                  Sequential
+layer3                  Sequential
+layer4                  Sequential
+avgpool                 AdaptiveAvgPool2d(output_size=(1, 1))
+fc                      Linear(in_features=512, out_features=1000, bias=True)
+>>> summary(model, depth=1, verbose=False)
+conv1
+bn1
+relu
+maxpool
+layer1
+layer2
+layer3
+layer4
+avgpool
+fc
+>>> summary(model, depth=2)
+conv1                               Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+bn1                                 BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+relu                                ReLU(inplace=True)
+maxpool                             MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
+layer1                              Sequential
+            0                       BasicBlock
+            1                       BasicBlock
+layer2                              Sequential
+            0                       BasicBlock
+            1                       BasicBlock
+layer3                              Sequential
+            0                       BasicBlock
+            1                       BasicBlock
+layer4                              Sequential
+            0                       BasicBlock
+            1                       BasicBlock
+avgpool                             AdaptiveAvgPool2d(output_size=(1, 1))
+fc                                  Linear(in_features=512, out_features=1000, bias=True)
+
+
+
+
+
+

Note

+

You could use get_all_layer() with verbose=True to see the output tensor shape for each layer.

+
+
+ +
+
+trojanzoo.utils.model.activate_params(module, params=[])[source]
+

Set requires_grad=True for selected params of module. +All other params are frozen.

+
+
Parameters:
+
+
+
+
+ +
+
+trojanzoo.utils.model.accuracy(_output, _label, topk=(1, 5), **kwargs)[source]
+

Computes the accuracy over the k top predictions +for the specified values of k.

+
+
Parameters:
+
    +

  • _output (torch.Tensor) – The batched logit tensor with shape (N, C).

    • +

  • _label (torch.Tensor) – The batched label tensor with shape (N).

    • +

  • topk (Iterable[int]) – Which top-k accuracies to show. +Defaults to (1, 5).

    • +

  • **kwargs – Any keyword argument (unused).

    • +
+
+
Returns:
+

dict[str, float] – Top-k accuracies.

+
+
+
+ +
+
+trojanzoo.utils.model.generate_target(module, _input, idx=1, same=False)[source]
+
+
Generate target labels of a batched input based on

the classification confidence ranking index.

+
+
+
+
Parameters:
+
    +

  • module (torch.nn.Module) – The module to process.

    • +

  • _input (torch.Tensor) – The input tensor.

    • +

  • idx (int) – The classification confidence +rank of target class. +Defaults to 1.

    • +

  • same (bool) – Generate the same label +for all samples using mod. +Defaults to False.

    • +
+
+
Returns:
+

torch.Tensor – The generated target label with shape (N).

+
+
+
+ +
+
+class trojanzoo.utils.model.ExponentialMovingAverage(model, decay)[source]
+

Maintains moving averages of model parameters using an exponential decay. +ema_avg = decay * avg_model_param + (1 - decay) * model_param

+
+

See also

+

https://github.com/pytorch/vision/blob/main/references/classification/utils.py

+

torch.optim.swa_utils.AveragedModel +is used to compute the EMA.

+
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

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+

Access comprehensive developer documentation for TrojanZoo

+ View Docs +
+
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+ +
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+ + + + + +
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+ + +
+
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+ +
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+ +
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+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/module.html b/trojanzoo/utils/module.html new file mode 100644 index 00000000..fee00a76 --- /dev/null +++ b/trojanzoo/utils/module.html @@ -0,0 +1,753 @@ + + + + + + + + + + + + + + + + module — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
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+
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+ +
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+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

module

+
+
+class trojanzoo.utils.module.BasicObject(indent=0, **kwargs)[source]
+

A basic class with a pretty summary() method.

+
+
Variables:
+
    +

  • name (str) – The name of the instance or class.

    • +

  • param_list (dict[str, list[str]]) – Map from category strings to variable name list.

    • +

  • indent (int) – The indent when calling summary(). Defaults to 0.

    • +
+
+
+
+
+summary(indent=None)[source]
+

Summary the variables of the instance +according to param_list.

+
+
Parameters:
+

indent (int) – The space indent for the entire string. +Defaults to self.indent.

+
+
+
+

See also

+

trojanzoo.models.Model.summary().

+
+
+ +
+ +
+
+class trojanzoo.utils.module.Process(output=0, **kwargs)[source]
+

It inherits BasicObject and further specify output levels.

+
+
Parameters:
+

output (int, Iterable[str]) – The level of output or the set of output items.

+
+
Variables:
+

output (set[str]) – The set of output items +generated by get_output().

+
+
+
+

See also

+

trojanzoo.optim.Optimizer +and ModelProcess inherit this class.

+
+
+
+get_output(org_output=None)[source]
+

Get output items based on output level.

+
+
Parameters:
+

org_output (int, Iterable[str]) – Output level integer or output items. +If int, call get_output_int(). +Defaults to self.output.

+
+
Returns:
+

set[str] – The set of output items.

+
+
+
+ +
+
+classmethod get_output_int(org_output=0)[source]
+

Get output items based on output level integer.

+
+
    +

  • 0  - 4 : {'verbose'}

    • +

  • 5  - 9 : {'verbose', 'end'}

    • +

  • 10 - 19: {'verbose', 'end', 'start'}

    • +

  • 20 - 29: {'verbose', 'end', 'start', 'middle'}

    • +

  • 30 - * : {'verbose', 'end', 'start', 'middle', 'memory'}

    • +
+
+
+
Parameters:
+

org_output (int) – Output level integer. +Defaults to 0.

+
+
Returns:
+

set[str] – The set of output items.

+
+
+
+ +
+
+static output_iter(name, _iter, iteration=None)[source]
+

Output an iteration string: +{name} Iter: [ {_iter + 1} / {iteration} ] +or {name} Iter: [ {_iter + 1} ] +if iteration is None.

+
+
Parameters:
+
    +

  • name (str) – The header string.

    • +

  • _iter (int) – The current iteration.

    • +

  • iteration (int) – The total iteration. +Defaults to None.

    • +
+
+
+
+

See also

+

The implementation is in +trojanzoo.utils.output.output_iter().

+
+
+ +
+ +
+
+class trojanzoo.utils.module.ModelProcess(dataset=None, model=None, folder_path=None, **kwargs)[source]
+

It inherits Process +and further specify model related items.

+
+
Variables:
+
+
+
+
+

See also

+

trojanzoo.attacks.Attack +and trojanzoo.defenses.Defense +inherit this class.

+
+
+ +
+
+class trojanzoo.utils.module.Module(*args, **kwargs)[source]
+

A dict-like class which supports attribute-like view as well.

+
+
Parameters:
+
    +

  • *args – Positional dict-like arguments. +All keys will be merged together.

    • +

  • **kwargs – Keyword arguments that compose a dict. +All keys will be merged together.

    • +
+
+
Variables:
+

_marker (str) – The marker of the class, +which is shown in str(self). +Defaults to 'M'.

+
+
+
+
+clear()[source]
+

Remove all keys.

+
+
Returns:
+

Module – return self for stream usage.

+
+
+
+ +
+
+copy()[source]
+

Deepcopy of self.

+
+
Returns:
+

Module – return the deepcopy of self.

+
+
+
+ +
+
+items()[source]
+
+ +
+
+keys()[source]
+
+ +
+
+remove_none()[source]
+

Remove the parameters whose values are None.

+
+
Returns:
+

Module – return self for stream usage.

+
+
+
+ +
+
+summary(indent=0)[source]
+

Output information of self.

+
+
Parameters:
+

indent (int) – The space indent for the entire string. +Defaults to 0.

+
+
+
+ +
+
+update(*args, **kwargs)[source]
+

update values.

+
+
Parameters:
+
    +

  • *args – Positional dict-like arguments. +All keys will be merged together.

    • +

  • **kwargs – Keyword arguments that compose a dict. +All keys will be merged together.

    • +
+
+
Returns:
+

Module – return self for stream usage.

+
+
+
+ +
+ +
+
+class trojanzoo.utils.module.Param(*args, **kwargs)[source]
+

A dict-like class to store parameters config that +inherits Module and further extends default values. +You can view and set keys by attributes as well.

+
+
Parameters:
+
    +

  • *args – Positional dict-like arguments. +All keys will be merged together. +If there is only 1 argument and no keyword argument, +regard it as the default value.

    • +

  • **kwargs – Keyword arguments that compose a dict. +All keys will be merged together.

    • +
+
+
Variables:
+
    +

  • _marker (str) – The marker of the class, +which is shown in str(self). +Defaults to 'P'.

    • +

  • default (Any) – The default value of unknown keys.

    • +
+
+
+
+
+clear()[source]
+
+ +
+
+remove_none()[source]
+
+ +
+
+update(*args, **kwargs)[source]
+
+ +
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

Docs

+

Access comprehensive developer documentation for TrojanZoo

+ View Docs +
+
+
+
+ + +
+
+
+ +
+
+
+ + + + + +
+
+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/tensor.html b/trojanzoo/utils/tensor.html new file mode 100644 index 00000000..de934154 --- /dev/null +++ b/trojanzoo/utils/tensor.html @@ -0,0 +1,539 @@ + + + + + + + + + + + + + + + + tensor — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

tensor

+
+
+trojanzoo.utils.tensor.tanh_func(x)[source]
+

tanh object function.

+
return x.tanh().add(1).mul(0.5)
+
+
+
+
Parameters:
+

x (torch.Tensor) – The tensor ranging from +[−∞,+∞][-\infty, +\infty].

+
+
Returns:
+

torch.Tensor – The tensor ranging in [0, 1]

+
+
+
+ +
+
+trojanzoo.utils.tensor.atan_func(x)[source]
+

arctan object function.

+
return x.atan().div(math.pi).add(0.5)
+
+
+
+
Parameters:
+

x (torch.Tensor) – The tensor ranging from +[−∞],+∞][-\infty], +\infty].

+
+
Returns:
+

torch.Tensor – The tensor ranging in [0, 1]

+
+
+
+ +
+
+trojanzoo.utils.tensor.repeat_to_batch(x, batch_size=1)[source]
+

Repeat a single input tensor to a batch.

+
+
Parameters:
+
    +

  • x (torch.Tensor) – The single input tensor to process +with shape (*).

    • +

  • batch_size (int) – Batch size. Defaults to 1.

    • +
+
+
Returns:
+

torch.Tensor – The batched input tensor with shape (batch_size, *)

+
+
+
+ +
+
+trojanzoo.utils.tensor.add_noise(x, noise=None, mean=0.0, std=1.0, universal=False, clip_min=0.0, clip_max=1.0)[source]
+

Add noise to a batched input tensor.

+
+
Parameters:
+
    +

  • x (torch.Tensor) – The input tensor to process +with shape (N, *).

    • +

  • noise (torch.Tensor | None) – The pre-defined noise. +If None, generate Gaussian noise +using torch.normal. +Defaults to be None.

    • +

  • mean (float) – The mean of generated Gaussian noise. +Defaults to 0.0.

    • +

  • std (float) – The std of generated Gaussian noise. +Defaults to 1.0.

    • +

  • universal (bool) – Whether the noise is universal +for all samples in the batch. +Defaults to False.

    • +

  • clip_min (float | torch.Tensor) – The min value of available input region. +Defaults to 0.0.

    • +

  • clip_max (float | torch.Tensor) – The max value of available input region. +Defaults to 1.0.

    • +
+
+
Returns:
+

torch.Tensor – The noisy batched input tensor +with shape (N, *) ((*) when universal=True).

+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

Docs

+

Access comprehensive developer documentation for TrojanZoo

+ View Docs +
+
+
+
+ + +
+
+
+ +
+
+
+ + + + + +
+
+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/trojanzoo/utils/train.html b/trojanzoo/utils/train.html new file mode 100644 index 00000000..87719b90 --- /dev/null +++ b/trojanzoo/utils/train.html @@ -0,0 +1,467 @@ + + + + + + + + + + + + + + + + train — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

train

+
+
+trojanzoo.utils.train.train(module, num_classes, epochs, optimizer, lr_scheduler=None, lr_warmup_epochs=0, model_ema=None, model_ema_steps=32, grad_clip=None, pre_conditioner=None, print_prefix='Train', start_epoch=0, resume=0, validate_interval=10, save=False, amp=False, loader_train=None, loader_valid=None, epoch_fn=None, get_data_fn=None, forward_fn=None, loss_fn=None, after_loss_fn=None, validate_fn=None, save_fn=None, file_path=None, folder_path=None, suffix=None, writer=None, main_tag='train', tag='', metric_fn=None, verbose=True, output_freq='iter', indent=0, change_train_eval=True, lr_scheduler_freq='epoch', backward_and_step=True, metric_kwargs={}, logger_train=None, logger_valid=None, **kwargs)[source]
+

Train the model

+
+ +
+
+trojanzoo.utils.train.validate(module, num_classes, loader, print_prefix='Validate', indent=0, verbose=True, get_data_fn=None, forward_fn=None, loss_fn=None, writer=None, main_tag='valid', tag='', _epoch=None, metric_fn=None, metric_kwargs={}, logger=None, **kwargs)[source]
+

Evaluate the model.

+
+
Returns:
+

(float, float) – Accuracy and loss.

+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+
+
+ + +
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

Docs

+

Access comprehensive developer documentation for TrojanZoo

+ View Docs +
+
+
+
+ + +
+
+
+ +
+
+
+ + + + + +
+
+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file diff --git a/tutorials/basic.html b/tutorials/basic.html new file mode 100644 index 00000000..8977ce01 --- /dev/null +++ b/tutorials/basic.html @@ -0,0 +1,478 @@ + + + + + + + + + + + + + + + + basic — TrojanZoo 2.0.3 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ Table of Contents + +
+ + + +
+
+
+
+ +
+
+ +
+ Shortcuts +
+
+ +
+
+ +
+ +
+
+ +
+

basic

+

You can view example files at GitHub.

+

Following is backdoor_defense.py +to show the basic workflow.

+
#!/usr/bin/env python3
+
+# CUDA_VISIBLE_DEVICES=0 python ./examples/backdoor_defense.py --color --verbose 1 --attack badnet --defense neural_cleanse --pretrained --validate_interval 1 --epochs 50 --lr 1e-2
+
+import trojanvision
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    trojanvision.environ.add_argument(parser)
+    trojanvision.datasets.add_argument(parser)
+    trojanvision.models.add_argument(parser)
+    trojanvision.trainer.add_argument(parser)
+    trojanvision.marks.add_argument(parser)
+    trojanvision.attacks.add_argument(parser)
+    trojanvision.defenses.add_argument(parser)
+    kwargs = parser.parse_args().__dict__
+
+    env = trojanvision.environ.create(**kwargs)
+    dataset = trojanvision.datasets.create(**kwargs)
+    model = trojanvision.models.create(dataset=dataset, **kwargs)
+    trainer = trojanvision.trainer.create(dataset=dataset, model=model, **kwargs)
+    mark = trojanvision.marks.create(dataset=dataset, **kwargs)
+    attack = trojanvision.attacks.create(dataset=dataset, model=model, mark=mark, **kwargs)
+    defense = trojanvision.defenses.create(dataset=dataset, model=model, attack=attack, **kwargs)
+
+    if env['verbose']:
+        trojanvision.summary(env=env, dataset=dataset, model=model, mark=mark, trainer=trainer, attack=attack, defense=defense)
+    defense.detect(**trainer)
+
+
+
+ + +
+ +
+ + +
+
+ +
+
+
+ + +
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+
+

Docs

+

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+ View Docs +
+
+
+
+ + +
+
+
+ +
+
+
+ + + + + +
+
+
+
+ + +
+
+
+ +
+
+ +
+
+
+ + + + + + + + + + \ No newline at end of file