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Functions140 in github.com/GiantSeaweed/DECREE

⨍ Functions 140◇ Types & classes 31
↓ 8 callersFunctionpredict_feature
(net, data_loader, args=None)
evaluation/nn_classifier.py:109
↓ 7 callersFunctionepsilon
()
utils.py:66
↓ 6 callersMethod__init__
(self, embed_dim: int, # vision image_resolution: int,
clip/model.py:240
↓ 5 callersFunctionassert_range
(val, vmin, vmax, ratio=0.7)
utils.py:34
↓ 5 callersMethodsample
(self, rate)
datasets/backdoor_dataset.py:211
↓ 4 callersMethod_make_layer
(self, planes, blocks, stride=1)
clip/model.py:126
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1, dilate=False)
models/imagenet_model.py:158
↓ 4 callersMethod_make_layer
(self, planes, blocks, stride=1)
models/clip_model.py:125
↓ 4 callersFunctionget_encoder_architecture_usage
(args)
models/__init__.py:15
↓ 4 callersFunctionget_processing
(dataset, augment=True, is_tensor=False, need_norm=True, size=None)
imagenet.py:231
↓ 4 callersFunctionnet_test
(net, test_loader, epoch, criterion, args, keyword='Accuracy')
evaluation/nn_classifier.py:81
↓ 3 callersMethod__init__
(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=6
models/imagenet_model.py:107
↓ 3 callersMethod__init__
(self, embed_dim: int, # vision image_resolution: int,
models/clip_model.py:153
↓ 3 callersFunctionconv1x1
1x1 convolution
models/imagenet_model.py:15
↓ 3 callersFunctionconv3x3
3x3 convolution with padding
models/imagenet_model.py:9
↓ 3 callersFunctioncreate_torch_dataloader
(feature_bank, label_bank, batch_size, shuffle=False, num_workers=2, pin_memory=True)
evaluation/nn_classifier.py:49
↓ 2 callersFunctionbytes_to_unicode
Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you
clip/simple_tokenizer.py:17
↓ 2 callersMethodencode_text
(self, text)
clip/model.py:308
↓ 2 callersFunctiongetBackdoorImageNet
(trigger_file, train_transform, test_transform, reference_word, split='val', sample_rate=1.0, poison_
imagenet.py:261
↓ 2 callersFunctionget_pairs
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings).
clip/simple_tokenizer.py:39
↓ 2 callersFunctionknn_predict
(feature, feature_bank, feature_labels, classes, knn_k, knn_t)
evaluation/__init__.py:62
↓ 2 callersFunctionmain
(args)
compute_zscore.py:17
↓ 2 callersFunctionpatch_device
(module)
clip/clip.py:85
↓ 2 callersFunctionpatch_float
(module)
clip/clip.py:105
↓ 2 callersFunctiontest
(net, memory_data_loader, test_data_clean_loader, test_data_backdoor_loader, epoch, args)
evaluation/__init__.py:13
↓ 2 callersFunctiontrain
(backdoored_encoder, clean_encoder, data_loader, train_optimizer, args)
attack_encoder.py:21
↓ 1 callersMethod__init__
(self, feature_dim=128, arch='resnet18')
models/simclr_model.py:35
↓ 1 callersFunction_download
(url: str, root: str = os.path.expanduser("~/.cache/clip"))
clip/clip.py:25
↓ 1 callersMethod_forward_impl
(self, x)
models/imagenet_model.py:182
↓ 1 callersFunctionadjust_learning_rate
(optimizer, epoch, args)
main.py:29
↓ 1 callersMethodattention
(self, x: torch.Tensor)
clip/model.py:181
↓ 1 callersFunctionavailable_models
()
clip/clip.py:57
↓ 1 callersFunctionbasic_clean
(text)
clip/simple_tokenizer.py:51
↓ 1 callersMethodbpe
(self, token)
clip/simple_tokenizer.py:81
↓ 1 callersMethodbuild_attention_mask
(self)
clip/model.py:293
↓ 1 callersFunctionbuild_model
(state_dict: dict)
clip/model.py:364
↓ 1 callersFunctioncompute_self_cos_sim
(mat_a)
utils.py:6
↓ 1 callersFunctionconvert_weights
Convert applicable model parameters to fp16
clip/model.py:340
↓ 1 callersMethoddecode
(self, tokens)
clip/simple_tokenizer.py:130
↓ 1 callersFunctiondefault_bpe
()
clip/simple_tokenizer.py:12
↓ 1 callersMethodencode
(self, text)
clip/simple_tokenizer.py:122
↓ 1 callersMethodencode_image
(self, image)
clip/model.py:305
↓ 1 callersMethodencode_image
(self, image)
models/clip_model.py:176
↓ 1 callersMethodforward
(self, image, text)
clip/model.py:323
↓ 1 callersFunctiongenerate_mask
(mask_size, t_x, t_y, r)
main.py:19
↓ 1 callersFunctiongetTensorImageNet
(transform, split='val')
imagenet.py:208
↓ 1 callersFunctionget_dataset_evaluation
(args)
datasets/__init__.py:33
↓ 1 callersFunctionget_downstream_cifar10
(args)
datasets/cifar10_dataset.py:111
↓ 1 callersFunctionget_downstream_gtsrb
(args)
datasets/gtsrb_dataset.py:66
↓ 1 callersFunctionget_downstream_stl10
(args)
datasets/stl10_dataset.py:92
↓ 1 callersFunctionget_downstream_svhn
(args)
datasets/svhn_dataset.py:22
↓ 1 callersFunctionget_norm
(dataset)
imagenet.py:217
↓ 1 callersFunctionget_pretraining_cifar10
(data_dir)
datasets/cifar10_dataset.py:49
↓ 1 callersFunctionget_pretraining_stl10
(data_dir)
datasets/stl10_dataset.py:42
↓ 1 callersFunctionget_resize
(size)
imagenet.py:225
↓ 1 callersFunctionget_shadow_cifar10
(args)
datasets/cifar10_dataset.py:58
↓ 1 callersFunctionget_shadow_cifar10_224
(args)
datasets/cifar10_dataset.py:90
↓ 1 callersFunctionget_shadow_dataset
(args)
datasets/__init__.py:22
↓ 1 callersFunctionget_shadow_stl10
(args)
datasets/stl10_dataset.py:50
↓ 1 callersFunctionmain
(args)
main.py:71
↓ 1 callersFunctionnet_train
(net, train_loader, optimizer, epoch, criterion, args)
evaluation/nn_classifier.py:64
↓ 1 callersMethodrand_sample
(self, ratio)
imagenet.py:202
↓ 1 callersFunctionrun
(gpu, model_flag, mask_init, enc_path, arch, result_file, encoder_usage_info, lr=0.5, batch_size=128, id=
run_decree.py:5
↓ 1 callersFunctionrun
(gpu, encoder_path, res_file, batch_size=64 , id='_vrfy' )
validate/script_compute_zscore.py:5
↓ 1 callersFunctionrun_finetune
(gpu, lr, epoch, batch_size, encoder_usage_info, shadow_dataset, downstream_dataset, trigger, referen
scripts/run_attack_encoder.py:6
↓ 1 callersFunctiontrain_text
(backdoored_encoder, clean_img_encoder, clean_clip, data_loader, train_optimizer, args)
attack_encoder.py:110
↓ 1 callersFunctionwhitespace_clean
(text)
clip/simple_tokenizer.py:57
Method__getitem__
(self, index)
imagenet.py:160
Method__getitem__
(self, index)
imagenet.py:192
Method__getitem__
(self, index)
datasets/backdoor_dataset.py:33
Method__getitem__
(self, index)
datasets/backdoor_dataset.py:66
Method__getitem__
(self,index)
datasets/backdoor_dataset.py:117
Method__getitem__
(self, index)
datasets/backdoor_dataset.py:179
Method__getitem__
(self, index)
datasets/backdoor_dataset.py:235
Method__getitem__
(self, index)
datasets/backdoor_dataset.py:247
Method__getitem__
(self, index)
datasets/backdoor_dataset.py:258
Method__init__
(self, dataset, trigger_file, reference_word, train_transform, test_transform,
imagenet.py:134
Method__init__
(self, dataset, transform)
imagenet.py:184
Method__init__
(self, bpe_path: str = default_bpe())
clip/simple_tokenizer.py:64
Method__init__
(self, inplanes, planes, stride=1)
clip/model.py:13
Method__init__
(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None)
clip/model.py:57
Method__init__
(self, layers, output_dim, heads, input_resolution=224, width=64)
clip/model.py:101
Method__init__
(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None)
clip/model.py:168
Method__init__
(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None)
clip/model.py:192
Method__init__
(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int)
clip/model.py:203
Method__init__
Args: numpy_file (string): Path to the numpy file. transform (callable, optional): Optional transform to be applied
datasets/backdoor_dataset.py:19
Method__init__
(self, numpy_file, trigger_file, reference_file, indices, class_type, transform=None,
datasets/backdoor_dataset.py:47
Method__init__
Args: numpy_file (string): Path to the numpy file. transform (callable, optional): Optional transform to be applied
datasets/backdoor_dataset.py:96
Method__init__
Args: numpy_file (string): Path to the numpy file. transform (callable, optional): Optional transform to be applied
datasets/backdoor_dataset.py:156
Method__init__
Args: numpy_file (string): Path to the numpy file. transform (callable, optional): Optional transform to be applied
datasets/backdoor_dataset.py:197
Method__init__
(self, arch='resnet18')
models/simclr_model.py:8
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
models/imagenet_model.py:24
Method__init__
(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm
models/imagenet_model.py:65
Method__init__
(self, # embed_dim: int, # # vision # image_resolution: int
models/imagenet_model.py:216
Method__init__
(self, inplanes, planes, stride=1)
models/clip_model.py:12
Method__init__
(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None)
models/clip_model.py:56
Method__init__
(self, layers, output_dim, heads, input_resolution=224, width=64)
models/clip_model.py:100
Method__init__
(self, input_size, hidden_size_list, num_classes)
evaluation/nn_classifier.py:12
Method__len__
(self)
imagenet.py:180
Method__len__
(self)
imagenet.py:199
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