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Types & classes48 in github.com/LAMDA-CL/CVPR22-Fact

⨍ Functions 209◇ Types & classes 48
↓ 75 callersClassSubPolicy
dataloader/cifar100/autoaugment.py:200
↓ 74 callersClassSubPolicy
dataloader/imagenet1000/autoaugment.py:137
↓ 74 callersClassSubPolicy
dataloader/miniimagenet/autoaugment.py:137
↓ 74 callersClassSubPolicy
dataloader/cub200/autoaugment.py:137
↓ 74 callersClassSubPolicy
dataloader/imagenet100/autoaugment.py:137
↓ 22 callersClassAverager
utils.py:47
↓ 2 callersClassCIFAR100
`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset. This is a subclass of the `CIFAR10` Dataset.
dataloader/cifar100/cifar.py:257
↓ 2 callersClassTimer
utils.py:61
↓ 1 callersClassAutoAugImageNetPolicy
dataloader/imagenet1000/autoaugment.py:12
↓ 1 callersClassAutoAugImageNetPolicy
dataloader/miniimagenet/autoaugment.py:12
↓ 1 callersClassAutoAugImageNetPolicy
dataloader/cub200/autoaugment.py:12
↓ 1 callersClassAutoAugImageNetPolicy
dataloader/imagenet100/autoaugment.py:12
↓ 1 callersClassCIFAR10Policy
Randomly choose one of the best 25 Sub-policies on CIFAR10. Example: >>> policy = CIFAR10Policy() >>> transformed = policy(i
dataloader/cifar100/autoaugment.py:90
↓ 1 callersClassCUB200
dataloader/cub200/cub200.py:11
↓ 1 callersClassCategoriesSampler
dataloader/sampler.py:6
↓ 1 callersClassCutout
dataloader/cifar100/autoaugment.py:7
↓ 1 callersClassImageNet
dataloader/imagenet1000/ImageNet.py:11
↓ 1 callersClassImageNet
dataloader/imagenet100/ImageNet.py:11
↓ 1 callersClassMYNET
models/fact/Network.py:10
↓ 1 callersClassMYNET
models/base/Network.py:10
↓ 1 callersClassMiniImageNet
dataloader/miniimagenet/miniimagenet.py:11
↓ 1 callersClassResNet
models/resnet18_encoder.py:240
↓ 1 callersClassResNet
models/resnet20_cifar.py:43
ClassAutoAugCIFAR10Policy
dataloader/imagenet1000/autoaugment.py:53
ClassAutoAugCIFAR10Policy
dataloader/miniimagenet/autoaugment.py:53
ClassAutoAugCIFAR10Policy
dataloader/cub200/autoaugment.py:53
ClassAutoAugCIFAR10Policy
dataloader/imagenet100/autoaugment.py:53
ClassAutoAugSVHNPolicy
dataloader/imagenet1000/autoaugment.py:95
ClassAutoAugSVHNPolicy
dataloader/miniimagenet/autoaugment.py:95
ClassAutoAugSVHNPolicy
dataloader/cub200/autoaugment.py:95
ClassAutoAugSVHNPolicy
dataloader/imagenet100/autoaugment.py:95
ClassBasePreserverCategoriesSampler
dataloader/sampler.py:41
ClassBasicBlock
models/resnet18_encoder.py:157
ClassBasicBlock
models/resnet20_cifar.py:10
ClassBottleneck
models/resnet18_encoder.py:197
ClassCIFAR10
`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset. Args: root (string): Root directory of dataset where directory
dataloader/cifar100/cifar.py:14
ClassCIFAR_concate
dataloader/cifar100/cifar.py:221
ClassCUB200_concate
dataloader/cub200/cub200.py:153
ClassFSCILTrainer
models/fact/fscil_trainer.py:11
ClassFSCILTrainer
models/base/fscil_trainer.py:11
ClassImageNetPolicy
Randomly choose one of the best 24 Sub-policies on ImageNet. Example: >>> policy = ImageNetPolicy() >>> transformed = policy
dataloader/cifar100/autoaugment.py:35
ClassMiniImageNet_concate
dataloader/imagenet1000/ImageNet.py:139
ClassMiniImageNet_concate
dataloader/miniimagenet/miniimagenet.py:136
ClassMiniImageNet_concate
dataloader/imagenet100/ImageNet.py:139
ClassNewCategoriesSampler
dataloader/sampler.py:74
ClassSVHNPolicy
Randomly choose one of the best 25 Sub-policies on SVHN. Example: >>> policy = SVHNPolicy() >>> transformed = policy(image)
dataloader/cifar100/autoaugment.py:145
ClassTrainer
models/fact/base.py:12
ClassTrainer
models/base/base.py:12