PyTorch实现的Inception-v2

PyTorch实现的Inception-v2

PyTorch实现的Inception-v2

PyTorch: ​​torchimport torch.nn as nnimport torchvisiondef ConvBNReLU(in_channels,out_channels,kernel_size,stride=1,padding=0): return nn.Sequential( nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,padding=padding), nn.BatchNorm2d(out_channels), nn.ReLU6(inplace=True), )def ConvBNReLUFactorization(in_channels,out_channels,kernel_sizes,paddings): return nn.Sequential( nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_sizes, stride=1,padding=paddings), nn.BatchNorm2d(out_channels), nn.ReLU6(inplace=True) )class InceptionV2ModuleA(nn.Module): def __init__(self, in_channels,out_channels1,out_channels2reduce, out_channels2, out_channels3reduce, out_channels3, out_channels4): super(InceptionV2ModuleA, self).__init__() self.branch1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels1,kernel_size=1) self.branch2 = nn.Sequential( ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1), ConvBNReLU(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_size=3, padding=1), ) self.branch3 = nn.Sequential( ConvBNReLU(in_channels=in_channels,out_channels=out_channels3reduce,kernel_size=1), ConvBNReLU(in_channels=out_channels3reduce, out_channels=out_channels3, kernel_size=3, padding=1), ConvBNReLU(in_channels=out_channels3, out_channels=out_channels3, kernel_size=3, padding=1), ) self.branch4 = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1), ConvBNReLU(in_channels=in_channels, out_channels=out_channels4, kernel_size=1), ) def forward(self, x): out1 = self.branch1(x) out2 = self.branch2(x) out3 = self.branch3(x) out4 = self.branch4(x) out = torch.cat([out1, out2, out3, out4], dim=1) return outclass InceptionV2ModuleB(nn.Module): def __init__(self, in_channels,out_channels1,out_channels2reduce, out_channels2, out_channels3reduce, out_channels3, out_channels4): super(InceptionV2ModuleB, self).__init__() self.branch1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels1,kernel_size=1) self.branch2 = nn.Sequential( ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1), ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2reduce, kernel_sizes=[1,3],paddings=[0,1]), ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_sizes=[3,1],paddings=[1, 0]), ) self.branch3 = nn.Sequential( ConvBNReLU(in_channels=in_channels,out_channels=out_channels3reduce,kernel_size=1), ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce,kernel_sizes=[1, 3], paddings=[0, 1]), ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce,kernel_sizes=[3, 1], paddings=[1, 0]), ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3reduce, kernel_sizes=[1, 3], paddings=[0, 1]), ConvBNReLUFactorization(in_channels=out_channels3reduce, out_channels=out_channels3,kernel_sizes=[3, 1], paddings=[1, 0]), ) self.branch4 = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1), ConvBNReLU(in_channels=in_channels, out_channels=out_channels4, kernel_size=1), ) def forward(self, x): out1 = self.branch1(x) out2 = self.branch2(x) out3 = self.branch3(x) out4 = self.branch4(x) out = torch.cat([out1, out2, out3, out4], dim=1) return outclass InceptionV2ModuleC(nn.Module): def __init__(self, in_channels,out_channels1,out_channels2reduce, out_channels2, out_channels3reduce, out_channels3, out_channels4): super(InceptionV2ModuleC, self).__init__() self.branch1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels1,kernel_size=1) self.branch2_conv1 = ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1) self.branch2_conv2a = ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_sizes=[1,3],paddings=[0,1]) self.branch2_conv2b = ConvBNReLUFactorization(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_sizes=[3,1],paddings=[1,0]) self.branch3_conv1 = ConvBNReLU(in_channels=in_channels,out_channels=out_channels3reduce,kernel_size=1) self.branch3_conv2 = ConvBNReLU(in_channels=out_channels3reduce, out_channels=out_channels3, kernel_size=3,stride=1,padding=1) self.branch3_conv3a = ConvBNReLUFactorization(in_channels=out_channels3, out_channels=out_channels3, kernel_sizes=[3, 1],paddings=[1, 0]) self.branch3_conv3b = ConvBNReLUFactorization(in_channels=out_channels3, out_channels=out_channels3, kernel_sizes=[1, 3],paddings=[0, 1]) self.branch4 = nn.Sequential( nn.MaxPool2d(kernel_size=3, stride=1, padding=1), ConvBNReLU(in_channels=in_channels, out_channels=out_channels4, kernel_size=1), ) def forward(self, x): out1 = self.branch1(x) x2 = self.branch2_conv1(x) out2 = torch.cat([self.branch2_conv2a(x2), self.branch2_conv2b(x2)],dim=1) x3 = self.branch3_conv2(self.branch3_conv1(x)) out3 = torch.cat([self.branch3_conv3a(x3), self.branch3_conv3b(x3)], dim=1) out4 = self.branch4(x) out = torch.cat([out1, out2, out3, out4], dim=1) return outclass InceptionV3ModuleD(nn.Module): def __init__(self, in_channels,out_channels1reduce,out_channels1,out_channels2reduce, out_channels2): super(InceptionV3ModuleD, self).__init__() self.branch1 = nn.Sequential( ConvBNReLU(in_channels=in_channels, out_channels=out_channels1reduce, kernel_size=1), ConvBNReLU(in_channels=out_channels1reduce, out_channels=out_channels1, kernel_size=3,stride=2,padding=1) ) self.branch2 = nn.Sequential( ConvBNReLU(in_channels=in_channels, out_channels=out_channels2reduce, kernel_size=1), ConvBNReLU(in_channels=out_channels2reduce, out_channels=out_channels2, kernel_size=3, stride=1, padding=1), ConvBNReLU(in_channels=out_channels2, out_channels=out_channels2, kernel_size=3, stride=2,padding=1), ) self.branch3 = nn.MaxPool2d(kernel_size=3,stride=2,padding=1) def forward(self, x): out1 = self.branch1(x) out2 = self.branch2(x) out3 = self.branch3(x) out = torch.cat([out1, out2, out3], dim=1) return outclass InceptionAux(nn.Module): def __init__(self, in_channels,out_channels): super(InceptionAux, self).__init__() self.auxiliary_avgpool = nn.AvgPool2d(kernel_size=5, stride=3) self.auxiliary_conv1 = ConvBNReLU(in_channels=in_channels, out_channels=128, kernel_size=1) self.auxiliary_conv2 = nn.Conv2d(in_channels=128, out_channels=768, kernel_size=5,stride=1) self.auxiliary_dropout = nn.Dropout(p=0.7) self.auxiliary_linear1 = nn.Linear(in_features=768, out_features=out_channels) def forward(self, x): x = self.auxiliary_conv1(self.auxiliary_avgpool(x)) x = self.auxiliary_conv2(x) x = x.view(x.size(0), -1) out = self.auxiliary_linear1(self.auxiliary_dropout(x)) return outclass InceptionV2(nn.Module): def __init__(self, num_classes=1000, stage='train'): super(InceptionV2, self).__init__() self.stage = stage self.block1 = nn.Sequential( ConvBNReLU(in_channels=3, out_channels=64, kernel_size=7,stride=2,padding=3), nn.MaxPool2d(kernel_size=3,stride=2,padding=1), ) self.block2 = nn.Sequential( ConvBNReLU(in_channels=64, out_channels=192, kernel_size=3, stride=1, padding=1), nn.MaxPool2d(kernel_size=3, stride=2,padding=1), ) self.block3 = nn.Sequential( InceptionV2ModuleA(in_channels=192,out_channels1=64,out_channels2reduce=64, out_channels2=64, out_channels3reduce=64, out_channels3=96, out_channels4=32), InceptionV2ModuleA(in_channels=256, out_channels1=64, out_channels2reduce=64, out_channels2=96,out_channels3reduce=64, out_channels3=96, out_channels4=64), InceptionV3ModuleD(in_channels=320, out_channels1reduce=128, out_channels1=160, out_channels2reduce=64,out_channels2=96), ) self.block4 = nn.Sequential( InceptionV2ModuleB(in_channels=576, out_channels1=224, out_channels2reduce=64, out_channels2=96,out_channels3reduce=96, out_channels3=128, out_channels4=128), InceptionV2ModuleB(in_channels=576, out_channels1=192, out_channels2reduce=96, out_channels2=128,out_channels3reduce=96, out_channels3=128, out_channels4=128), InceptionV2ModuleB(in_channels=576, out_channels1=160, out_channels2reduce=128, out_channels2=160,out_channels3reduce=128, out_channels3=128, out_channels4=128), InceptionV2ModuleB(in_channels=576, out_channels1=96, out_channels2reduce=128, out_channels2=192,out_channels3reduce=160, out_channels3=160, out_channels4=128), InceptionV3ModuleD(in_channels=576, out_channels1reduce=128, out_channels1=192, out_channels2reduce=192,out_channels2=256), ) self.block5 = nn.Sequential( InceptionV2ModuleC(in_channels=1024, out_channels1=352, out_channels2reduce=192, out_channels2=160,out_channels3reduce=160, out_channels3=112, out_channels4=128), InceptionV2ModuleC(in_channels=1024, out_channels1=352, out_channels2reduce=192, out_channels2=160, out_channels3reduce=192, out_channels3=112, out_channels4=128) ) self.max_pool = nn.MaxPool2d(kernel_size=7, stride=1) self.dropout = nn.Dropout(p=0.5) self.linear = nn.Linear(1024, num_classes) def forward(self, x): x = self.block1(x) x = self.block2(x) x = self.block3(x) x = self.block4(x) x = self.block5(x) x = self.max_pool(x) x = self.dropout(x) x = x.view(x.size(0), -1) out = self.linear(x) return outif __name__=='__main__': model = InceptionV2() print(model) input = torch.randn(1, 3, 224, 224) out = model(input) print(out.shape)

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