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Functions528 in github.com/cavalleria/cavaface

FunctionHRNet_W64
(input_size)
backbone/hrnet.py:912
FunctionIR_100
Constructs a ir-100 model.
backbone/resnet_irse.py:176
FunctionIR_101
Constructs a ir-101 model.
backbone/resnet_irse.py:184
FunctionIR_152
Constructs a ir-152 model.
backbone/resnet_irse.py:192
FunctionIR_185
Constructs a ir-185 model.
backbone/resnet_irse.py:200
FunctionIR_200
Constructs a ir-200 model.
backbone/resnet_irse.py:208
FunctionIR_50
Constructs a ir-50 model.
backbone/resnet_irse.py:168
FunctionIR_SE_100
Constructs a ir_se-100 model.
backbone/resnet_irse.py:224
FunctionIR_SE_101
Constructs a ir_se-101 model.
backbone/resnet_irse.py:232
FunctionIR_SE_152
Constructs a ir_se-152 model.
backbone/resnet_irse.py:240
FunctionIR_SE_185
Constructs a ir_se-185 model.
backbone/resnet_irse.py:248
FunctionIR_SE_200
Constructs a ir_se-200 model.
backbone/resnet_irse.py:256
FunctionIR_SE_50
Constructs a ir_se-50 model.
backbone/resnet_irse.py:216
FunctionIdentity
(img, v)
dataset/randaugment.py:148
FunctionInvert
(img, _)
dataset/randaugment.py:66
FunctionPosterize
(img, v)
dataset/randaugment.py:92
FunctionROC_by_mat
Compute ROC using a pairwise score matrix and a corresponding label matrix. A wapper of ROC function.
util/metrics.py:98
FunctionResNet_101
Constructs a ResNet-101 model.
backbone/resnet.py:188
FunctionResNet_152
Constructs a ResNet-152 model.
backbone/resnet.py:196
FunctionResNet_50
Constructs a ResNet-50 model.
backbone/resnet.py:180
FunctionRotate
(img, v)
dataset/randaugment.py:55
FunctionSharpness
(img, v)
dataset/randaugment.py:113
FunctionShearX
(img, v)
dataset/randaugment.py:11
FunctionShearY
(img, v)
dataset/randaugment.py:18
FunctionSolarize
(img, v)
dataset/randaugment.py:78
FunctionSolarizeAdd
(img, addition=0, threshold=128)
dataset/randaugment.py:83
FunctionTranslateX
(img, v)
dataset/randaugment.py:25
FunctionTranslateXabs
(img, v)
dataset/randaugment.py:33
FunctionTranslateY
(img, v)
dataset/randaugment.py:40
FunctionTranslateYabs
(img, v)
dataset/randaugment.py:48
Method__call__
(self, img)
dataset/utils.py:32
Method__call__
(self, img)
dataset/utils.py:152
Method__call__
(self, img)
dataset/randaugment.py:181
Method__enter__
(self)
evaluation/utils/pytorch_memlab/line_profiler.py:80
Method__enter__
(self)
evaluation/utils/pytorch_memlab/courtesy.py:44
Method__exit__
(self, exc_type, exc_val, exc_tb)
evaluation/utils/pytorch_memlab/line_profiler.py:83
Method__exit__
(self, *args)
evaluation/utils/pytorch_memlab/courtesy.py:48
Method__getitem__
(self, index)
dataset/datasets.py:52
Method__getitem__
(self, index)
dataset/datasets.py:107
Method__getitem__
(self, index)
dataset/datasets.py:156
Method__getitem__
(self, index)
dataset/datasets.py:185
Method__getstate__
(self)
optimizer/optimizer.py:41
Method__init__
Global average pooling over the input's spatial dimensions
backbone/resnest.py:23
Method__init__
( self, inplanes, planes, stride=1, downsample=None, radix=1,
backbone/resnest.py:38
Method__init__
( self, in_chs, se_ratio=0.25, reduced_base_chs=None, act_layer=nn.ReL
backbone/ghostnet.py:43
Method__init__
(self, in_chs, out_chs, kernel_size, stride=1, act_layer=nn.PReLU)
backbone/ghostnet.py:71
Method__init__
( self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True )
backbone/ghostnet.py:87
Method__init__
( self, in_chs, mid_chs, out_chs, dw_kernel_size=3, stride=1,
backbone/ghostnet.py:127
Method__init__
(self, input_size, num_layers, mode="ir")
backbone/resnet_irse.py:79
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
backbone/hrnet.py:376
Method__init__
( self, num_branches, blocks, num_blocks, num_inchannels, num_
backbone/hrnet.py:416
Method__init__
(self, input_size, cfg)
backbone/hrnet.py:607
Method__init__
(self, in_channels, out_channels, dropout_rate)
backbone/densenet.py:31
Method__init__
(self, in_channels, out_channels)
backbone/densenet.py:70
Method__init__
(self, inplace=True)
backbone/mobilenetv3.py:37
Method__init__
(self, channel, reduction=4)
backbone/mobilenetv3.py:55
Method__init__
(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs)
backbone/mobilenetv3.py:87
Method__init__
(self, input_size, embedding_size=512, width_mult=1.0)
backbone/mobilenetv3.py:180
Method__init__
( self, in_channel, out_channel, mode="ir", size1=(56, 56), si
backbone/resattnet.py:26
Method__init__
( self, in_channels, out_channels, mode="ir", size1=(28, 28), size2=(14, 14) )
backbone/resattnet.py:113
Method__init__
(self, in_channels, out_channels, mode="ir", size1=(14, 14))
backbone/resattnet.py:176
Method__init__
(self, input_size, num_layers, mode="ir")
backbone/resattnet.py:298
Method__init__
(self)
backbone/rexnetv1.py:17
Method__init__
( self, in_channels, channels, t, stride, use_se=True, se_ratio=12, **kwargs )
backbone/rexnetv1.py:77
Method__init__
(self, input_size, embedding_size=512, width_mult=1.0, depth_mult=1.0)
backbone/rexnetv1.py:121
Method__init__
(self)
backbone/common.py:54
Method__init__
(self, inplace=False)
backbone/common.py:90
Method__init__
(self, scale_factor, mode="bilinear", align_corners=True)
backbone/common.py:151
Method__init__
(self, channels, reduction)
backbone/common.py:238
Method__init__
(self, in_channel, depth, stride=1)
backbone/common.py:266
Method__init__
(self, in_channel, depth, stride=1)
backbone/common.py:291
Method__init__
( self, in_channels, channels, kernel_size, stride=(1, 1), pad
backbone/common.py:323
Method__init__
(self, radix, cardinality)
backbone/common.py:424
Method__init__
(self, drop_prob, block_size, share_channel=False)
backbone/common.py:456
Method__init__
( self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1 )
backbone/common.py:591
Method__init__
(self, in_c, embedding_size)
backbone/common.py:613
Method__init__
( self, in_channels, out_channels, kernel_size, stride, paddin
backbone/common.py:654
Method__init__
( self, channels, reduction=16, round_mid=False, use_conv=True,
backbone/common.py:847
Method__init__
( self, in_channels, out_channels, kernel_size, stride, paddin
backbone/common.py:909
Method__init__
(self, channels, gamma=2, b=1)
backbone/common.py:1009
Method__init__
(self, channels, reduction_ratio=16)
backbone/common.py:1035
Method__init__
(self, channels, reduction_ratio=16)
backbone/common.py:1052
Method__init__
(self)
backbone/common.py:1073
Method__init__
(self, channels, reduction_ratio=16)
backbone/common.py:1098
Method__init__
(self, num_channels, epsilon=1e-5, mode="l2", after_relu=False)
backbone/common.py:1110
Method__init__
(self, in_channels, out_channels, stride, bn_eps, activation, tf_mode)
backbone/efficientnet.py:78
Method__init__
( self, in_channels, out_channels, kernel_size, stride, exp_fa
backbone/efficientnet.py:137
Method__init__
(self, in_channels, out_channels, bn_eps, activation, tf_mode)
backbone/efficientnet.py:227
Method__init__
(self, inp, oup, stride, expand_ratio, norm_layer=None)
backbone/mobilenetv2.py:58
Method__init__
MobileNet V2 main class Args: width_mult (float): Width multiplier - adjusts number of channels in each layer by this am
backbone/mobilenetv2.py:100
Method__init__
( self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1 )
backbone/mobilefacenet.py:37
Method__init__
( self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1 )
backbone/mobilefacenet.py:61
Method__init__
( self, in_c, out_c, attention, residual=False, kernel=(3, 3),
backbone/mobilefacenet.py:83
Method__init__
( self, c, attention, num_block, groups, kernel=(3, 3),
backbone/mobilefacenet.py:134
Method__init__
(self, embedding_size)
backbone/mobilefacenet.py:166
Method__init__
( self, input_size, embedding_size=512, output_name="GDC", attention="none" )
backbone/mobilefacenet.py:206
Method__init__
( self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=None )
backbone/mobilenext.py:36
Method__init__
MobileNeXt main class Args: width_mult (float): Width multiplier - adjusts number of channels in each layer by this amoun
backbone/mobilenext.py:149
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
backbone/resnet.py:35
Method__init__
(self, inplanes, planes, stride=1, downsample=None)
backbone/resnet.py:67
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