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| class FCN_VGG16(nn.Module):
'''
FCN 的 backbone，由 VGG16 修改而来，舍弃最后的全连接层
以池化层为区分，一个池化层到上一个池化层之间的部分认为一个卷积块。
'''
def __init__(self):
super(FCN_VGG16, self).__init__()
self.features = nn.Sequential(
# 第一个卷积块: 输入通道数：3，输出通道数：64，卷积核大小：3*3，步长：1，填充：1
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
# 第二个卷积块
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
# 第三个卷积块
nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
# 第四个卷积块
nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512,out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
# 第五个卷积块
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
)
# 每一层在 features 中的范围，{0，1，2，3，4} 为第一个卷积块，{5，6，7，8，9} 为第二个卷积块...
self.range = ((0, 5), (5, 10), (10, 17), (17, 24), (24, 31))
def forward(self, input):
output = {}
# 每一块的输出
for idx, (start, end) in enumerate(self.range):
for layer in range(start, end):
input = self.features[layer](input)
output["x%d" % (idx + 1)] = input
return output
|
