mirror of https://github.com/JDAI-CV/fast-reid.git
117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
"""
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Author: Guan'an Wang
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Contact: guan.wang0706@gmail.com
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"""
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import torch
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import torch.nn as nn
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from .blocks import ShuffleV2Block
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class ShuffleNetV2(nn.Module):
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"""
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Reference:
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https://github.com/megvii-model/ShuffleNet-Series/tree/master/ShuffleNetV2
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"""
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def __init__(self, input_size=224, n_class=1000, model_size='1.5x'):
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super(ShuffleNetV2, self).__init__()
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print('model size is ', model_size)
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self.stage_repeats = [4, 8, 4]
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self.model_size = model_size
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if model_size == '0.5x':
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self.stage_out_channels = [-1, 24, 48, 96, 192, 1024]
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elif model_size == '1.0x':
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self.stage_out_channels = [-1, 24, 116, 232, 464, 1024]
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elif model_size == '1.5x':
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self.stage_out_channels = [-1, 24, 176, 352, 704, 1024]
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elif model_size == '2.0x':
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self.stage_out_channels = [-1, 24, 244, 488, 976, 2048]
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else:
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raise NotImplementedError
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# building first layer
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input_channel = self.stage_out_channels[1]
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self.first_conv = nn.Sequential(
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nn.Conv2d(3, input_channel, 3, 2, 1, bias=False),
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nn.BatchNorm2d(input_channel),
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nn.ReLU(inplace=True),
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)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.features = []
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for idxstage in range(len(self.stage_repeats)):
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numrepeat = self.stage_repeats[idxstage]
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output_channel = self.stage_out_channels[idxstage + 2]
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for i in range(numrepeat):
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if i == 0:
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self.features.append(ShuffleV2Block(input_channel, output_channel,
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mid_channels=output_channel // 2, ksize=3, stride=2))
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else:
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self.features.append(ShuffleV2Block(input_channel // 2, output_channel,
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mid_channels=output_channel // 2, ksize=3, stride=1))
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input_channel = output_channel
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self.features = nn.Sequential(*self.features)
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self.conv_last = nn.Sequential(
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nn.Conv2d(input_channel, self.stage_out_channels[-1], 1, 1, 0, bias=False),
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nn.BatchNorm2d(self.stage_out_channels[-1]),
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nn.ReLU(inplace=True)
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)
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self.globalpool = nn.AvgPool2d(7)
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if self.model_size == '2.0x':
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self.dropout = nn.Dropout(0.2)
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self.classifier = nn.Sequential(nn.Linear(self.stage_out_channels[-1], n_class, bias=False))
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self._initialize_weights()
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def forward(self, x):
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x = self.first_conv(x)
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x = self.maxpool(x)
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x = self.features(x)
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x = self.conv_last(x)
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x = self.globalpool(x)
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if self.model_size == '2.0x':
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x = self.dropout(x)
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x = x.contiguous().view(-1, self.stage_out_channels[-1])
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x = self.classifier(x)
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return x
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def _initialize_weights(self):
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for name, m in self.named_modules():
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if isinstance(m, nn.Conv2d):
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if 'first' in name:
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nn.init.normal_(m.weight, 0, 0.01)
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else:
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nn.init.normal_(m.weight, 0, 1.0 / m.weight.shape[1])
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0.0001)
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nn.init.constant_(m.running_mean, 0)
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elif isinstance(m, nn.BatchNorm1d):
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nn.init.constant_(m.weight, 1)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0.0001)
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nn.init.constant_(m.running_mean, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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if __name__ == "__main__":
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model = ShuffleNetV2()
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# print(model)
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test_data = torch.rand(5, 3, 224, 224)
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test_outputs = model(test_data)
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print(test_outputs.size())
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