Functions — github.com/ZhaoJ9014/face.evoLVe

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Functions423 in github.com/ZhaoJ9014/face.evoLVe

⨍ Functions 423◇ Types & classes 103

↓ 1 callersMethodreset

util/utils.py:228

↓ 1 callersMethodreset_parameters

head/metrics.py:392

↓ 1 callersMethodrotate_with_fill

(self, img, magnitude)

data_processing/randaugment.py:93

↓ 1 callersFunctionround_repeats

Calculate module's repeat number of a block based on depth multiplier. Use depth_coefficient of global_params. Args: repeats (int)

backbone/EfficientNets.py:103

↓ 1 callersMethodset_swish

Sets swish function as memory efficient (for training) or standard (for export). Args: memory_efficient (bool): Whether to use me

backbone/EfficientNets.py:739

↓ 1 callersFunctionsoftmax_o

paddle/PaddleInference-demo/utils.py:322

↓ 1 callersFunctionsoftmax_o

paddle/Paddle-Lite-Inference-demo/MTCNN.py:270

↓ 1 callersFunctionsoftmax_p

paddle/PaddleInference-demo/utils.py:303

↓ 1 callersFunctionsoftmax_p

paddle/Paddle-Lite-Inference-demo/MTCNN.py:251

↓ 1 callersFunctionsoftmax_r

paddle/PaddleInference-demo/utils.py:312

↓ 1 callersFunctionsoftmax_r

paddle/Paddle-Lite-Inference-demo/MTCNN.py:260

↓ 1 callersMethodtest_single_operation

:param image: image :param op_name: operation name in self.transforms :param M: -1 stands for the max Magnitude in t

data_processing/randaugment.py:98

↓ 1 callersFunctiontforminv

Function: ---------- apply the inverse of affine transform 'trans' to uv Parameters: ---------- @trans: 3x3 np.array

applications/align/matlab_cp2tform.py:37

↓ 1 callersMethodupdate_face_data

用于更新人脸数据库中的特征值 :return:

paddle/Paddle-Lite-Inference-demo/main.py:20

↓ 1 callersFunctionwarm_up_lr

(batch, num_batch_warm_up, init_lr, optimizer)

util/utils.py:107

↓ 1 callersFunctionwarp_and_crop_face

Function: ---------- apply affine transform 'trans' to uv Parameters: ---------- @src_img: 3x3 np.array i

applications/align/align_trans.py:202

↓ 1 callersFunctionwarp_and_crop_face

Function: ---------- apply affine transform 'trans' to uv Parameters: ---------- @src_img: 3x3 np.array i

paddle/align/align_trans.py:202

data_processing/randaugment.py:84

Method__getitem__

paddle/dataload.py:26

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

backbone/EfficientNets.py:224

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

backbone/EfficientNets.py:247

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

backbone/EfficientNets.py:292

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

backbone/EfficientNets.py:315

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

backbone/EfficientNets.py:770

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

backbone/AttentionNets.py:49

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

backbone/AttentionNets.py:112

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

backbone/AttentionNets.py:161

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

backbone/AttentionNets.py:196

(self, in_channel, depth, stride)

backbone/model_irse.py:50

(self, in_channel, depth, stride)

backbone/model_irse.py:70

(self, input_size, num_layers, mode='ir')

backbone/model_irse.py:130

(self, in_chs, se_ratio=0.25, reduced_base_chs=None, act_layer=nn.ReLU, gate_fn=hard_sigmoid,

backbone/GhostNet.py:50

(self, in_chs, out_chs, kernel_size, stride=1, act_layer=nn.ReLU)

backbone/GhostNet.py:70

(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True)

backbone/GhostNet.py:85

(self, in_chs, mid_chs, out_chs, dw_kernel_size=3, stride=1, act_layer=nn.ReLU, se_ratio=0.)

backbone/GhostNet.py:113

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

backbone/model_resnet.py:24

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

backbone/model_resnet.py:56

(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1)

backbone/MobileFaceNets.py:12

(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1)

backbone/MobileFaceNets.py:24

(self, in_c, out_c, residual = False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1)

backbone/MobileFaceNets.py:34

(self, embedding_size, out_h, out_w)

backbone/MobileFaceNets.py:63

(self, in_features, out_features, device_id)

head/metrics.py:20

(self, in_features, out_features, device_id, s = 64.0, m = 0.50, easy_margin = False)

head/metrics.py:77

(self, in_features, out_features, device_id, s = 64.0, m = 0.35)

head/metrics.py:138

(self, in_features, out_features, device_id, m = 4)

head/metrics.py:193

(self, in_features, out_features, device_id, m = 0.35, s = 30.0)

head/metrics.py:280

(self, feat_dim, num_class, margin=0.35, scale=32)

head/metrics.py:357

(self, feat_dim, num_class, margin=0.5, scale=64)

head/metrics.py:380

(self, feat_dim, num_class, margin=0.25, gamma=256)

head/metrics.py:422

(self, feat_dim, num_class, m = 0.5, s = 64.)

head/metrics.py:461

(self, feat_dim, num_class, margin_am=0.0, scale=32, l_a=10, u_a=110, l_margin=0.45, u_margin=0.8, lamda=20)

head/metrics.py:498

(self, feat_dim, num_class, is_am, margin=0.35, mv_weight=1.12, scale=32)

head/metrics.py:541

(self, feat_dim=512, num_class=86876, margin=0.5, scale=64)

head/metrics.py:579

(self, feat_dim=512, queue_size=16384, scale=30.0, loss_type='softmax', margin=0.0)

head/metrics.py:624

applications/align/get_nets.py:10

applications/align/get_nets.py:76

applications/align/get_nets.py:121

paddle/utils.py:57

(self, data_root, input_size, mean, std)

paddle/dataload.py:9

(self, ch_in, ch_out, filter_size, stride,

paddle/backbone/resnet_pp.py:28

(self, ch, reduction_ratio=16)

paddle/backbone/resnet_pp.py:126

(self, ch_in, ch_out, stride, shortcut,

paddle/backbone/resnet_pp.py:159

(self, ch_in, ch_out, stride, shortcut,

paddle/backbone/resnet_pp.py:253

Residual Network, see https://arxiv.org/abs/1512.03385 Args: depth (int): ResNet depth, should be 18, 34, 50, 101, 152.

paddle/backbone/resnet_pp.py:418

(self, depth=50)

paddle/backbone/resnet_pp.py:579

paddle/backbone/resnet_pp.py:604

(self, in_channel, depth, stride)

paddle/backbone/model_irse.py:47

(self, in_channel, depth, stride)

paddle/backbone/model_irse.py:67

(self, input_size, num_layers, mode='ir')

paddle/backbone/model_irse.py:127

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

paddle/backbone/model_resnet.py:25

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

paddle/backbone/model_resnet.py:60

(self,embedding_size,class_dim,margin=0.50,scale=64.0,easy_margin=False)

paddle/head/metrics.py:39

(self, in_features, out_features, s=64.0, m=0.35)

paddle/head/metrics.py:106

(self, in_features, out_features, m=4)

paddle/head/metrics.py:148

(self, in_features, out_features, m=0.35, s=30.0)

paddle/head/metrics.py:220

paddle/PaddleInference-demo/utils.py:22

paddle/PaddleInference-demo/main.py:11

paddle/align/get_nets.py:10

paddle/align/get_nets.py:77

paddle/align/get_nets.py:123

(self, gamma = 2, eps = 1e-7)

paddle/loss/focal.py:9

paddle/Paddle-Lite-Inference-demo/MTCNN.py:10

paddle/Paddle-Lite-Inference-demo/main.py:11

backup/utils.py:203

(self, in_features, out_features, s = 64.0, m = 0.50, easy_margin = False)

backup/metrics.py:59

(self, in_features, out_features, s = 64.0, m = 0.35)

backup/metrics.py:103

(self, in_features, out_features, m = 4.0)

backup/metrics.py:142

(self, in_features, out_features, m = 0.35, s = 30.0)

backup/metrics.py:212

(self, gamma = 2, eps = 1e-7)

loss/focal.py:9

(self, Numbers=None, max_Magnitude=None)

data_processing/randaugment.py:10

util/utils.py:225

paddle/dataload.py:32

paddle/dataload.py:94

(cls, channels=None, height=None, width=None, stride=None)

paddle/backbone/resnet_pp.py:685

Function__reader__

paddle/quant/quant_post_static.py:10

head/metrics.py:176

head/metrics.py:256

paddle/head/metrics.py:131

paddle/head/metrics.py:197

backup/metrics.py:126

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