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