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| from collections import namedtuple | |
| import torch | |
| from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module | |
| """ | |
| ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) | |
| """ | |
| class Flatten(Module): | |
| def forward(self, input): | |
| return input.view(input.size(0), -1) | |
| def l2_norm(input, axis=1): | |
| norm = torch.norm(input, 2, axis, True) | |
| output = torch.div(input, norm) | |
| return output | |
| class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): | |
| """ A named tuple describing a ResNet block. """ | |
| def get_block(in_channel, depth, num_units, stride=2): | |
| return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] | |
| def get_blocks(num_layers): | |
| if num_layers == 50: | |
| blocks = [ | |
| get_block(in_channel=64, depth=64, num_units=3), | |
| get_block(in_channel=64, depth=128, num_units=4), | |
| get_block(in_channel=128, depth=256, num_units=14), | |
| get_block(in_channel=256, depth=512, num_units=3) | |
| ] | |
| elif num_layers == 100: | |
| blocks = [ | |
| get_block(in_channel=64, depth=64, num_units=3), | |
| get_block(in_channel=64, depth=128, num_units=13), | |
| get_block(in_channel=128, depth=256, num_units=30), | |
| get_block(in_channel=256, depth=512, num_units=3) | |
| ] | |
| elif num_layers == 152: | |
| blocks = [ | |
| get_block(in_channel=64, depth=64, num_units=3), | |
| get_block(in_channel=64, depth=128, num_units=8), | |
| get_block(in_channel=128, depth=256, num_units=36), | |
| get_block(in_channel=256, depth=512, num_units=3) | |
| ] | |
| else: | |
| raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) | |
| return blocks | |
| class SEModule(Module): | |
| def __init__(self, channels, reduction): | |
| super(SEModule, self).__init__() | |
| self.avg_pool = AdaptiveAvgPool2d(1) | |
| self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) | |
| self.relu = ReLU(inplace=True) | |
| self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) | |
| self.sigmoid = Sigmoid() | |
| def forward(self, x): | |
| module_input = x | |
| x = self.avg_pool(x) | |
| x = self.fc1(x) | |
| x = self.relu(x) | |
| x = self.fc2(x) | |
| x = self.sigmoid(x) | |
| return module_input * x | |
| class bottleneck_IR(Module): | |
| def __init__(self, in_channel, depth, stride): | |
| super(bottleneck_IR, self).__init__() | |
| if in_channel == depth: | |
| self.shortcut_layer = MaxPool2d(1, stride) | |
| else: | |
| self.shortcut_layer = Sequential( | |
| Conv2d(in_channel, depth, (1, 1), stride, bias=False), | |
| BatchNorm2d(depth) | |
| ) | |
| self.res_layer = Sequential( | |
| BatchNorm2d(in_channel), | |
| Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), | |
| Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) | |
| ) | |
| def forward(self, x): | |
| shortcut = self.shortcut_layer(x) | |
| res = self.res_layer(x) | |
| return res + shortcut | |
| class bottleneck_IR_SE(Module): | |
| def __init__(self, in_channel, depth, stride): | |
| super(bottleneck_IR_SE, self).__init__() | |
| if in_channel == depth: | |
| self.shortcut_layer = MaxPool2d(1, stride) | |
| else: | |
| self.shortcut_layer = Sequential( | |
| Conv2d(in_channel, depth, (1, 1), stride, bias=False), | |
| BatchNorm2d(depth) | |
| ) | |
| self.res_layer = Sequential( | |
| BatchNorm2d(in_channel), | |
| Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), | |
| PReLU(depth), | |
| Conv2d(depth, depth, (3, 3), stride, 1, bias=False), | |
| BatchNorm2d(depth), | |
| SEModule(depth, 16) | |
| ) | |
| def forward(self, x): | |
| shortcut = self.shortcut_layer(x) | |
| res = self.res_layer(x) | |
| return res + shortcut | |