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Functions423 in github.com/ZhaoJ9014/face.evoLVe

⨍ Functions 423◇ Types & classes 103

backup/metrics.py:190

applications/align/matlab_cp2tform.py:7

applications/align/align_trans.py:19

paddle/align/align_trans.py:19

Function_encode_block_string

Encode a block to a string. Args: block (namedtuple): A BlockArgs type argument. Returns: block_string: A St

backbone/EfficientNets.py:395

Method_initialize_weights

head/metrics.py:48

(self, img, text, left, top, color=(0, 0, 0), size=20)

paddle/Paddle-Lite-Inference-demo/main.py:109

Functioncalculate_val

Copy from [insightface](https://github.com/deepinsight/insightface) :param thresholds: :param embeddings1: :param embeddings2: :p

util/verification.py:107

Functionde_preprocess

backup/utils.py:121

Functionde_preprocess

util/extract_feature_v1.py:17

Functionde_preprocess

util/utils.py:121

Decode a list of string notations to specify blocks inside the network. Args: string_list (list[str]): A list of strings, each st

backbone/EfficientNets.py:419

paddle/PaddleInference-demo/utils.py:281

Encode a list of BlockArgs to a list of strings. Args: blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block

backbone/EfficientNets.py:435

Methodextract_endpoints

Use convolution layer to extract features from reduction levels i in [1, 2, 3, 4, 5]. Args: inputs (tensor): Input tensor

backbone/EfficientNets.py:847

Functionextract_feature

(img_root, backbone, model_root, device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu"), tta

util/extract_feature_v2.py:16

Functionextract_feature

(data_root, backbone, model_root, input_size = [112, 112], rgb_mean = [0.5, 0.5, 0.5], rgb_std = [0.5, 0.5, 0.

util/extract_feature_v1.py:39

Methodfast_det_face

paddle/Paddle-Lite-Inference-demo/MTCNN.py:235

Methodfix_basicblock_name

paddle/backbone/resnet_pp.py:644

Methodfix_bottleneck_name

paddle/backbone/resnet_pp.py:631

Methodfix_conv_norm_name

paddle/backbone/resnet_pp.py:616

Methodfix_shortcut_name

paddle/backbone/resnet_pp.py:626

backbone/EfficientNets.py:52

backbone/EfficientNets.py:59

backbone/EfficientNets.py:71

backbone/EfficientNets.py:228

backbone/EfficientNets.py:265

backbone/EfficientNets.py:298

backbone/EfficientNets.py:334

backbone/EfficientNets.py:626

MBConvBlock's forward function. Args: inputs (tensor): Input tensor. drop_connect_rate (bool): Drop connect rate (flo

backbone/EfficientNets.py:696

EfficientNet's forward function. Calls extract_features to extract features, applies final linear layer, and returns logits. Args:

backbone/EfficientNets.py:915

backbone/AttentionNets.py:12

backbone/AttentionNets.py:31

backbone/AttentionNets.py:82

backbone/AttentionNets.py:140

backbone/AttentionNets.py:184

backbone/AttentionNets.py:230

backbone/model_irse.py:12

backbone/model_irse.py:38

backbone/model_irse.py:62

backbone/model_irse.py:87

backbone/model_irse.py:167

backbone/GhostNet.py:23

backbone/GhostNet.py:60

backbone/GhostNet.py:77

backbone/GhostNet.py:103

backbone/GhostNet.py:151

backbone/GhostNet.py:237

backbone/model_resnet.py:34

backbone/model_resnet.py:68

backbone/model_resnet.py:147

backbone/MobileFaceNets.py:8

backbone/MobileFaceNets.py:17

backbone/MobileFaceNets.py:28

backbone/MobileFaceNets.py:40

backbone/MobileFaceNets.py:59

backbone/MobileFaceNets.py:81

head/metrics.py:31

(self, input, label)

head/metrics.py:95

(self, input, label)

head/metrics.py:149

(self, input, label)

head/metrics.py:218

(self, embbedings, label)

head/metrics.py:291

(self, feats, labels)

head/metrics.py:334

(self, feats, labels)

head/metrics.py:363

(self, feats, labels)

head/metrics.py:396

(self, feats, labels)

head/metrics.py:434

(self, feats, labels)

head/metrics.py:473

(self, feats, labels)

head/metrics.py:515

(self, x, label)

head/metrics.py:554

(self, x, label)

head/metrics.py:595

(self, p1, g2, p2, g1, cur_ids)

head/metrics.py:672

Arguments: x: a float tensor with shape [batch_size, c, h, w]. Returns: a float tensor with shape [batch_size

applications/align/get_nets.py:13

Arguments: x: a float tensor with shape [batch_size, 3, h, w]. Returns: b: a float tensor with shape [batch_s

applications/align/get_nets.py:59

Arguments: x: a float tensor with shape [batch_size, 3, h, w]. Returns: b: a float tensor with shape [batch_s

applications/align/get_nets.py:104

Arguments: x: a float tensor with shape [batch_size, 3, h, w]. Returns: c: a float tensor with shape [batch_s

applications/align/get_nets.py:155

paddle/backbone/resnet_pp.py:106

paddle/backbone/resnet_pp.py:144

paddle/backbone/resnet_pp.py:233

paddle/backbone/resnet_pp.py:346

paddle/backbone/resnet_pp.py:408

paddle/backbone/resnet_pp.py:561

(self, roi_feat, stage=0)

paddle/backbone/resnet_pp.py:596

paddle/backbone/model_irse.py:12

paddle/backbone/model_irse.py:35

paddle/backbone/model_irse.py:59

paddle/backbone/model_irse.py:84

paddle/backbone/model_irse.py:163

paddle/backbone/model_resnet.py:38

paddle/backbone/model_resnet.py:75

paddle/backbone/model_resnet.py:148

paddle/head/metrics.py:25

(self, input, label)

paddle/head/metrics.py:52

(self, input, label)

paddle/head/metrics.py:115

(self, input, label)

paddle/head/metrics.py:172

(self, embbedings, label)

paddle/head/metrics.py:231

Arguments: x: a float tensor with shape [batch_size, c, h, w]. Returns: a float tensor with shape [batch_size

paddle/align/get_nets.py:13

Arguments: x: a float tensor with shape [batch_size, 3, h, w]. Returns: b: a float tensor with shape [batch_s

paddle/align/get_nets.py:60

Arguments: x: a float tensor with shape [batch_size, 3, h, w]. Returns: b: a float tensor with shape [batch_s

paddle/align/get_nets.py:106

Arguments: x: a float tensor with shape [batch_size, 3, h, w]. Returns: c: a float tensor with shape [batch_s

paddle/align/get_nets.py:158

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