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| ''' | |
| Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py | |
| Original author cavalleria | |
| ''' | |
| import torch.nn as nn | |
| from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module | |
| import torch | |
| class Flatten(Module): | |
| def forward(self, x): | |
| return x.view(x.size(0), -1) | |
| class ConvBlock(Module): | |
| def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): | |
| super(ConvBlock, self).__init__() | |
| self.layers = nn.Sequential( | |
| Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False), | |
| BatchNorm2d(num_features=out_c), | |
| PReLU(num_parameters=out_c) | |
| ) | |
| def forward(self, x): | |
| return self.layers(x) | |
| class LinearBlock(Module): | |
| def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1): | |
| super(LinearBlock, self).__init__() | |
| self.layers = nn.Sequential( | |
| Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False), | |
| BatchNorm2d(num_features=out_c) | |
| ) | |
| def forward(self, x): | |
| return self.layers(x) | |
| class DepthWise(Module): | |
| def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1): | |
| super(DepthWise, self).__init__() | |
| self.residual = residual | |
| self.layers = nn.Sequential( | |
| ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)), | |
| ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride), | |
| LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1)) | |
| ) | |
| def forward(self, x): | |
| short_cut = None | |
| if self.residual: | |
| short_cut = x | |
| x = self.layers(x) | |
| if self.residual: | |
| output = short_cut + x | |
| else: | |
| output = x | |
| return output | |
| class Residual(Module): | |
| def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)): | |
| super(Residual, self).__init__() | |
| modules = [] | |
| for _ in range(num_block): | |
| modules.append(DepthWise(c, c, True, kernel, stride, padding, groups)) | |
| self.layers = Sequential(*modules) | |
| def forward(self, x): | |
| return self.layers(x) | |
| class GDC(Module): | |
| def __init__(self, embedding_size): | |
| super(GDC, self).__init__() | |
| self.layers = nn.Sequential( | |
| LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)), | |
| Flatten(), | |
| Linear(512, embedding_size, bias=False), | |
| BatchNorm1d(embedding_size)) | |
| def forward(self, x): | |
| return self.layers(x) | |
| class MobileFaceNet(Module): | |
| def __init__(self, fp16=False, num_features=512): | |
| super(MobileFaceNet, self).__init__() | |
| scale = 2 | |
| self.fp16 = fp16 | |
| self.layers = nn.Sequential( | |
| ConvBlock(3, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1)), | |
| ConvBlock(64 * scale, 64 * scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64), | |
| DepthWise(64 * scale, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128), | |
| Residual(64 * scale, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), | |
| DepthWise(64 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256), | |
| Residual(128 * scale, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), | |
| DepthWise(128 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512), | |
| Residual(128 * scale, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)), | |
| ) | |
| self.conv_sep = ConvBlock(128 * scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0)) | |
| self.features = GDC(num_features) | |
| self._initialize_weights() | |
| def _initialize_weights(self): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.Linear): | |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| def forward(self, x): | |
| with torch.cuda.amp.autocast(self.fp16): | |
| x = self.layers(x) | |
| x = self.conv_sep(x.float() if self.fp16 else x) | |
| x = self.features(x) | |
| return x | |
| def get_mbf(fp16, num_features): | |
| return MobileFaceNet(fp16, num_features) |