pytorch FCN8s代码编写

FCN8s（Fully Convolutional Network）是一个经典的语义分割模型，下面是使用PyTorch实现FCN8s的代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class FCN8s(nn.Module):
    def __init__(self, num_classes):
        super(FCN8s, self).__init__()

        # conv1
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=100)
        self.relu1_1 = nn.ReLU(inplace=True)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.relu1_2 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)

        # conv2
        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.relu2_1 = nn.ReLU(inplace=True)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.relu2_2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)

        # conv3
        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.relu3_1 = nn.ReLU(inplace=True)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.relu3_2 = nn.ReLU(inplace=True)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.relu3_3 = nn.ReLU(inplace=True)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)

        # conv4
        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.relu4_1 = nn.ReLU(inplace=True)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.relu4_2 = nn.ReLU(inplace=True)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.relu4_3 = nn.ReLU(inplace=True)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)

        # conv5
        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.relu5_1 = nn.ReLU(inplace=True)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.relu5_2 = nn.ReLU(inplace=True)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.relu5_3 = nn.ReLU(inplace=True)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)

        # fc6
        self.fc6 = nn.Conv2d(512, 4096, kernel_size=7)
        self.relu6 = nn.ReLU(inplace=True)
        self.drop6 = nn.Dropout2d()

        # fc7
        self.fc7 = nn.Conv2d(4096, 4096, kernel_size=1)
        self.relu7 = nn.ReLU(inplace=True)
        self.drop7 = nn.Dropout2d()

        # score_fr
        self.score_fr = nn.Conv2d(4096, num_classes, kernel_size=1)

        # upscore2
        self.upscore2 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=4, stride=2, bias=False)
        self.upscore_pool4 = nn.ConvTranspose2d(512, num_classes, kernel_size=4, stride=2, bias=False)

        # upscore8
        self.upscore8 = nn.ConvTranspose2d(num_classes, num_classes, kernel_size=16, stride=8, bias=False)

    def forward(self, x):
        # conv1
        x = self.conv1_1(x)
        x = self.relu1_1(x)
        x = self.conv1_2(x)
        x = self.relu1_2(x)
        x = self.pool1(x)

        # conv2
        x = self.conv2_1(x)
        x = self.relu2_1(x)
        x = self.conv2_2(x)
        x = self.relu2_2(x)
        x = self.pool2(x)

        # conv3
        x = self.conv3_1(x)
        x = self.relu3_1(x)
        x = self.conv3_2(x)
        x = self.relu3_2(x)
        x = self.conv3_3(x)
        x = self.relu3_3(x)
        x = self.pool3(x)

        # conv4
        x = self.conv4_1(x)
        x = self.relu4_1(x)
        x = self.conv4_2(x)
        x = self.relu4_2(x)
        x = self.conv4_3(x)
        x = self.relu4_3(x)
        pool4 = self.pool4(x)

        # conv5
        x = self.conv5_1(pool4)
        x = self.relu5_1(x)
        x = self.conv5_2(x)
        x = self.relu5_2(x)
        x = self.conv5_3(x)
        x = self.relu5_3(x)
        pool5 = self.pool5(x)

        # fc6
        x = self.fc6(pool5)
        x = self.relu6(x)
        x = self.drop6(x)

        # fc7
        x = self.fc7(x)
        x = self.relu7(x)
        x = self.drop7(x)

        # score_fr
        x = self.score_fr(x)

        # upscore2
        upscore2 = self.upscore2(x)

        # upscore_pool4
        pool4 = 0.01 * self.upscore_pool4(pool4[:, :, 5:5 + upscore2.size()[2], 5:5 + upscore2.size()[3]])
        fuse_pool4 = upscore2 + pool4

        # upscore8
        upscore8 = self.upscore8(fuse_pool4)

        return upscore8[:, :, 31:31 + x.size()[2], 31:31 + x.size()[3]]

以上就是FCN8s的PyTorch代码实现。需要注意的是，在实现过程中，我们使用了反卷积（ConvTranspose2d）来进行上采样，同时还需要注意输入输出张量的大小。