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import torch |
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import torch.nn as nn |
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from torch.nn import ( |
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Linear, |
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Conv2d, |
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BatchNorm1d, |
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BatchNorm2d, |
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PReLU, |
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ReLU, |
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Sigmoid, |
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Dropout, |
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MaxPool2d, |
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AdaptiveAvgPool2d, |
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Sequential, |
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Module, |
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) |
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from collections import namedtuple |
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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 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( |
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channels, channels // reduction, kernel_size=1, padding=0, bias=False |
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) |
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nn.init.xavier_uniform_(self.fc1.weight.data) |
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self.relu = ReLU(inplace=True) |
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self.fc2 = Conv2d( |
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channels // reduction, channels, kernel_size=1, padding=0, bias=False |
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) |
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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), |
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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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) |
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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 |
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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)] + [ |
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Bottleneck(depth, depth, 1) for i in range(num_units - 1) |
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] |
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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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return blocks |
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class Backbone(Module): |
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def __init__(self, input_size, num_layers, mode="ir"): |
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super(Backbone, self).__init__() |
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assert input_size[0] in [ |
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112, |
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224, |
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], "input_size should be [112, 112] or [224, 224]" |
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assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" |
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assert mode in ["ir", "ir_se"], "mode should be ir or ir_se" |
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blocks = get_blocks(num_layers) |
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if mode == "ir": |
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unit_module = bottleneck_IR |
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elif mode == "ir_se": |
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unit_module = bottleneck_IR_SE |
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self.input_layer = Sequential( |
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Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64) |
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) |
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if input_size[0] == 112: |
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self.output_layer = Sequential( |
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BatchNorm2d(512), |
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Dropout(), |
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Flatten(), |
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Linear(512 * 7 * 7, 512), |
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BatchNorm1d(512), |
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) |
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else: |
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self.output_layer = Sequential( |
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BatchNorm2d(512), |
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Dropout(), |
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Flatten(), |
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Linear(512 * 14 * 14, 512), |
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BatchNorm1d(512), |
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) |
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modules = [] |
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for block in blocks: |
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for bottleneck in block: |
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modules.append( |
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unit_module( |
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bottleneck.in_channel, bottleneck.depth, bottleneck.stride |
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) |
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) |
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self.body = Sequential(*modules) |
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self._initialize_weights() |
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def forward(self, x): |
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x = self.input_layer(x) |
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x = self.body(x) |
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x = self.output_layer(x) |
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return x |
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def _initialize_weights(self): |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.xavier_uniform_(m.weight.data) |
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if m.bias is not None: |
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m.bias.data.zero_() |
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elif isinstance(m, nn.BatchNorm2d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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elif isinstance(m, nn.BatchNorm1d): |
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m.weight.data.fill_(1) |
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m.bias.data.zero_() |
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elif isinstance(m, nn.Linear): |
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nn.init.xavier_uniform_(m.weight.data) |
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if m.bias is not None: |
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m.bias.data.zero_() |
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def IR_50(input_size): |
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"""Constructs a ir-50 model.""" |
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model = Backbone(input_size, 50, "ir") |
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return model |
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def IR_101(input_size): |
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"""Constructs a ir-101 model.""" |
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model = Backbone(input_size, 100, "ir") |
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return model |
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def IR_152(input_size): |
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"""Constructs a ir-152 model.""" |
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model = Backbone(input_size, 152, "ir") |
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return model |
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def IR_SE_50(input_size): |
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"""Constructs a ir_se-50 model.""" |
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model = Backbone(input_size, 50, "ir_se") |
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return model |
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def IR_SE_101(input_size): |
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"""Constructs a ir_se-101 model.""" |
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model = Backbone(input_size, 100, "ir_se") |
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return model |
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def IR_SE_152(input_size): |
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"""Constructs a ir_se-152 model.""" |
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model = Backbone(input_size, 152, "ir_se") |
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return model |
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