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Functions1,054 in github.com/JDAI-CV/FaceX-Zoo

⨍ Functions 1,054◇ Types & classes 307

↓ 1 callersFunctiontrain

Total training procedure.

training_mode/swin_training/train.py:110

↓ 1 callersFunctiontrain

Total training procedure.

training_mode/distributed_training/train.py:102

↓ 1 callersFunctiontrain

Total training procedure.

training_mode/siamese-triplet_training/train.py:91

↓ 1 callersFunctiontrain

Total training procedure.

training_mode/conventional_training/train.py:102

↓ 1 callersFunctiontrain

Total training procedure.

training_mode/conventional_training/train_amp.py:100

↓ 1 callersFunctiontrain

addition_module/DMUE/train.py:36

↓ 1 callersFunctiontrain

addition_module/DMUE/train_ddp.py:49

↓ 1 callersFunctiontrain

addition_module/DMUE/pretrain/train_mv_softmax.py:84

↓ 1 callersFunctiontrain

Total training procedure.

addition_module/face_lightning/KDF/training_mode/kd_training/train.py:116

↓ 1 callersFunctiontrain

Total training procedure.

addition_module/face_lightning/KDF/training_mode/kd_training/train_ft.py:130

↓ 1 callersFunctiontrain

Total training procedure.

addition_module/face_lightning/KDF/training_mode/cls_training/train.py:104

↓ 1 callersFunctiontrain

Total training procedure.

addition_module/HSST/train.py:98

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by semi-siamese training.

training_mode/semi-siamese_training/train.py:54

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

training_mode/swin_training/train.py:63

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

training_mode/distributed_training/train.py:59

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

training_mode/siamese-triplet_training/train.py:60

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

training_mode/conventional_training/train.py:56

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

training_mode/conventional_training/train_amp.py:57

↓ 1 callersFunctiontrain_one_epoch

(train_loader, val_loader, model, criterions, optimizer, epoch, cfg)

addition_module/DMUE/train.py:77

↓ 1 callersFunctiontrain_one_epoch

(train_loader, val_loader, model, criterions, optimizer, epoch, cfg)

addition_module/DMUE/train_ddp.py:119

↓ 1 callersFunctiontrain_one_epoch

(data_loader, model, optimizer, criteria, cur_epoch, loss_meter, args)

addition_module/DMUE/pretrain/train_mv_softmax.py:51

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

addition_module/face_lightning/KDF/training_mode/kd_training/train.py:59

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

addition_module/face_lightning/KDF/training_mode/kd_training/train_ft.py:82

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by traditional training.

addition_module/face_lightning/KDF/training_mode/cls_training/train.py:58

↓ 1 callersFunctiontrain_one_epoch

Tain one epoch by semi-siamese training.

addition_module/HSST/train.py:54

↓ 1 callersFunctiontrain_para

(train_loader, teacher_model, paraphraser, optimizer_para, criterionPara, total_epoch, conf)

addition_module/face_lightning/KDF/training_mode/kd_training/train_ft.py:61

↓ 1 callersFunctiontrain_test

addition_module/DSDG/DUM/train.py:77

↓ 1 callersMethodupdate_queue

(self, vis_g, nir_g, cur_ids, batch_size)

addition_module/HSST/HSST_Prototype.py:57

↓ 1 callersMethodverification

(self, template_norm_feats, unique_templates)

test_protocol/ijbc/ijbc_evaluator.py:68

↓ 1 callersMethodverification

(self, template_norm_feats, unique_templates)

addition_module/face_lightning/KDF/test_protocol/ijbc/ijbc_evaluator.py:68

↓ 1 callersMethodweight_call

backbone/TF_NAS.py:128

↓ 1 callersFunctionwindow_reverse

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

backbone/Swin_Transformer.py:53

FunctionHardestNegativeTripletSelector

(margin, cpu=False)

training_mode/siamese-triplet_training/pair_selector.py:182

FunctionLightCNN_29Layers

(drop_ratio, out_h, out_w, feat_dim)

backbone/LightCNN.py:188

FunctionLightCNN_29v2

(num_classes=10000, is_train=True)

addition_module/DSDG/networks/__init__.py:29

FunctionMMD_Loss

(fc_nir, fc_vis)

addition_module/DSDG/misc/util.py:79

FunctionRandomNegativeTripletSelector

(margin, cpu=False)

training_mode/siamese-triplet_training/pair_selector.py:187

FunctionResNet18_u

addition_module/DSDG/DUM/models/ResNet_u.py:111

FunctionSemihardNegativeTripletSelector

(margin, cpu=False)

training_mode/siamese-triplet_training/pair_selector.py:192

addition_module/DMUE/datasets/preprocessing.py:24

(self, outputs_x, targets_x, softlabel_x, epoch=None)

addition_module/DMUE/losses/softLoss.py:7

(self, outputs_x, targets_x, softlabel_x, epoch=None)

addition_module/DMUE/losses/softLoss.py:17

(self, G_matrixs, G_main_matrixs)

addition_module/DMUE/losses/SP_KD_Loss.py:7

addition_module/DMUE/preprocess/face_alignmenet/dataloader.py:29

addition_module/DMUE/preprocess/face_alignmenet/dataloader.py:194

addition_module/DMUE/preprocess/face_alignmenet/dataloader.py:208

addition_module/DMUE/preprocess/face_alignmenet/core/dataloader.py:28

addition_module/DMUE/preprocess/face_alignmenet/core/dataloader.py:193

addition_module/DMUE/preprocess/face_alignmenet/core/dataloader.py:207

addition_module/DSDG/DUM/Load_OULUNPUcrop_train.py:46

addition_module/DSDG/DUM/Load_OULUNPUcrop_train.py:76

addition_module/DSDG/DUM/Load_OULUNPUcrop_train.py:102

addition_module/DSDG/DUM/Load_OULUNPUcrop_train.py:112

addition_module/DSDG/DUM/Load_OULUNPUcrop_train.py:137

addition_module/DSDG/DUM/Load_OULUNPUcrop_valtest.py:23

addition_module/DSDG/DUM/Load_OULUNPUcrop_valtest.py:37

Method__getitem__

data_processor/test_dataset.py:39

Method__getitem__

data_processor/train_dataset.py:63

Method__getitem__

data_processor/train_dataset.py:103

Method__getitem__

addition_module/DMUE/datasets/bases.py:36

Method__getitem__

(self, index, transform=None)

addition_module/DMUE/pretrain/utils/dataset.py:42

Method__getitem__

addition_module/DMUE/preprocess/face_alignmenet/dataloader.py:254

Method__getitem__

addition_module/DMUE/preprocess/face_alignmenet/core/dataloader.py:253

Method__getitem__

addition_module/DSDG/data/generation_dataset.py:32

Method__getitem__

addition_module/DSDG/data/recognition_dataset.py:39

Method__getitem__

addition_module/DSDG/data/recognition_dataset.py:148

Method__getitem__

addition_module/DSDG/DUM/Load_OULUNPUcrop_train.py:169

Method__getitem__

addition_module/DSDG/DUM/Load_OULUNPUcrop_valtest.py:70

Method__getitem__

addition_module/HSST/train_dataset.py:63

Method__getitem__

addition_module/HSST/train_dataset.py:103

Method__getitem__

addition_module/HSST/train_dataset.py:155

(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, pad

backbone/RepVGG.py:28

(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True)

backbone/EfficientNets.py:230

(self, in_channels, out_channels, kernel_size, stride=1, image_size=None, **kwargs)

backbone/EfficientNets.py:253

(self, kernel_size, stride, padding=0, dilation=1, return_indices=False, ceil_mode=False)

backbone/EfficientNets.py:298

(self, kernel_size, stride, image_size=None, **kwargs)

backbone/EfficientNets.py:321

(self, out_h, out_w, feat_dim, blocks_args=None, global_params=None)

backbone/EfficientNets.py:774

(self, in_channels, out_channels, size1=(56, 56), size2=(28, 28), size3=(14, 14))

backbone/AttentionNets.py:55

(self, in_channels, out_channels, size1=(28, 28), size2=(14, 14))

backbone/AttentionNets.py:118

(self, in_channels, out_channels, size1=(14, 14))

backbone/AttentionNets.py:167

(self, stage1_modules, stage2_modules, stage3_modules, feat_dim, out_h, out_w)

backbone/AttentionNets.py:202

(self, inplanes, planes, stride=1, downsample=None)

backbone/HRNet.py:70

(self, num_branches, blocks, num_blocks, num_inchannels, num_channels, fuse_method, multi_sca

backbone/HRNet.py:109

(self, cfg, **kwargs)

backbone/HRNet.py:258

(self, channels, reduction)

backbone/ResNets.py:21

(self, in_channel, depth, stride)

backbone/ResNets.py:41

(self, in_channel, depth, stride)

backbone/ResNets.py:59

(self, in_channels, out_channels, kernel_size, stride, padding)

backbone/LightCNN.py:31

(self, in_channels, out_channels)

backbone/LightCNN.py:42

(self, num_classes=79077)

backbone/LightCNN.py:55

(self, block, layers, num_classes=79077)

backbone/LightCNN.py:80

(self, block, layers, drop_ratio, out_h, out_w, feat_dim)

backbone/LightCNN.py:131

(self, inplace=False)

backbone/TF_NAS.py:52

( self, in_channels, out_channels, use_bn=True, affine = True, act_func='relu6', ops_orde

backbone/TF_NAS.py:65

( self, in_channels, out_channels, kernel_size=3, stride=1, groups=1, has_shuffle=False,

backbone/TF_NAS.py:148

( self, in_features, out_features, bias=True, use_bn=False, affine=False, act_func=None,

backbone/TF_NAS.py:194

( self, in_channels, mid_channels, se_channels, out_channels, kernel_size=3, stride=1,

backbone/TF_NAS.py:272

(self, out_h, out_w, feat_dim, drop_ratio=0.0)

backbone/TF_NAS.py:405

(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.)

backbone/Swin_Transformer.py:84

(self, dim, input_resolution, num_heads, window_size=7, shift_size=0, mlp_ratio=4., qkv_bias=

backbone/Swin_Transformer.py:187

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