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Types & classes31 in github.com/GiantSeaweed/DECREE

⨍ Functions 140◇ Types & classes 31
↓ 18 callersClassCIFAR10Mem
CIFAR10 Dataset.
datasets/backdoor_dataset.py:244
↓ 6 callersClassBadEncoderTestBackdoor
datasets/backdoor_dataset.py:95
↓ 5 callersClassLayerNorm
Subclass torch's LayerNorm to handle fp16.
clip/model.py:153
↓ 4 callersClassReferenceImg
datasets/backdoor_dataset.py:17
↓ 4 callersClassResNet
models/imagenet_model.py:105
↓ 4 callersClassSimCLR
models/simclr_model.py:34
↓ 3 callersClassBadEncoderDataset
datasets/backdoor_dataset.py:45
↓ 2 callersClassBottleneck
clip/model.py:10
↓ 2 callersClassBottleneck
models/clip_model.py:9
↓ 2 callersClassCIFAR10Pair
CIFAR10 Dataset.
datasets/backdoor_dataset.py:232
↓ 2 callersClassCLIP
models/clip_model.py:152
↓ 2 callersClassTransformer
clip/model.py:191
↓ 1 callersClassAttentionPool2d
clip/model.py:56
↓ 1 callersClassAttentionPool2d
models/clip_model.py:55
↓ 1 callersClassBackdoorImageNet
imagenet.py:133
↓ 1 callersClassCLIP
clip/model.py:239
↓ 1 callersClassImageNetResNet
models/imagenet_model.py:215
↓ 1 callersClassImageNetTensorDataset
imagenet.py:183
↓ 1 callersClassModifiedResNet
A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1, wi
clip/model.py:93
↓ 1 callersClassModifiedResNet
A ResNet class that is similar to torchvision's but contains the following changes: - There are now 3 "stem" convolutions as opposed to 1, wi
models/clip_model.py:92
↓ 1 callersClassNeuralNet
evaluation/nn_classifier.py:11
↓ 1 callersClassQuickGELU
clip/model.py:162
↓ 1 callersClassResidualAttentionBlock
clip/model.py:167
↓ 1 callersClassSimCLRBase
models/simclr_model.py:6
↓ 1 callersClassVisualTransformer
clip/model.py:202
ClassBadEncoderImgText
datasets/backdoor_dataset.py:154
ClassBasicBlock
models/imagenet_model.py:20
ClassBottleneck
models/imagenet_model.py:61
ClassCIFAR10CUSTOM
datasets/backdoor_dataset.py:195
ClassCIFAR10MemIndex
CIFAR10 Dataset with sample index
datasets/backdoor_dataset.py:255
ClassSimpleTokenizer
clip/simple_tokenizer.py:63