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import torch.nn as nn
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import torch.nn.functional as F
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class MobileNetV1(nn.Module):
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def __init__(self, num_classes=1024):
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super(MobileNetV1, self).__init__()
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def conv_bn(inp, oup, stride):
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return nn.Sequential(
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nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
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nn.BatchNorm2d(oup),
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nn.ReLU(inplace=True)
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)
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def conv_dw(inp, oup, stride):
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return nn.Sequential(
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nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
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nn.BatchNorm2d(inp),
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nn.ReLU(inplace=True),
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nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
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nn.BatchNorm2d(oup),
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nn.ReLU(inplace=True),
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)
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self.model = nn.Sequential(
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conv_bn(3, 32, 2),
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conv_dw(32, 64, 1),
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conv_dw(64, 128, 2),
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conv_dw(128, 128, 1),
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conv_dw(128, 256, 2),
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conv_dw(256, 256, 1),
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conv_dw(256, 512, 2),
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conv_dw(512, 512, 1),
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conv_dw(512, 512, 1),
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conv_dw(512, 512, 1),
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conv_dw(512, 512, 1),
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conv_dw(512, 512, 1),
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conv_dw(512, 1024, 2),
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conv_dw(1024, 1024, 1),
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)
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self.fc = nn.Linear(1024, num_classes)
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def forward(self, x):
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x = self.model(x)
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x = F.avg_pool2d(x, 7)
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x = x.view(-1, 1024)
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x = self.fc(x)
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return x |
