利用pytorch实现PSPNet语义分割。

好的，下面是一个简单的利用 PyTorch 实现 PSPNet 语义分割的示例代码：

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

class PSPModule(nn.Module):
    def __init__(self, in_channels, pool_sizes=(1, 2, 3, 6)):
        super(PSPModule, self).__init__()
        self.stages = nn.ModuleList([nn.Sequential(
            nn.AdaptiveAvgPool2d(pool_size),
            nn.Conv2d(in_channels, in_channels // len(pool_sizes), kernel_size=1),
            nn.BatchNorm2d(in_channels // len(pool_sizes)),
            nn.ReLU(inplace=True)
        ) for pool_size in pool_sizes])
        self.conv = nn.Conv2d(in_channels * 2, in_channels, kernel_size=1)
        self.bn = nn.BatchNorm2d(in_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        h, w = x.size(2), x.size(3)
        out = [x]
        for stage in self.stages:
            out.append(F.interpolate(stage(x), size=(h, w), mode='bilinear', align_corners=True))
        out = torch.cat(out, 1)
        out = self.conv(out)
        out = self.bn(out)
        out = self.relu(out)
        return out

class PSPNet(nn.Module):
    def __init__(self, num_classes=21):
        super(PSPNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn4 = nn.BatchNorm2d(256)
        self.conv5 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(512)
        self.psp = PSPModule(512)
        self.final = nn.Sequential(
            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Dropout(0.1),
            nn.Conv2d(256, num_classes, kernel_size=1)
        )

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)
        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu(x)
        x = self.conv5(x)
        x = self.bn5(x)
        x = self.relu(x)
        x = self.psp(x)
        x = self.final(x)
        x = F.interpolate(x, size=(x.size(2)*8, x.size(3)*8), mode='bilinear', align_corners=True)
        return x

这里定义了一个 PSPNet 的 PyTorch 模型，包括了 PSPModule 和 PSPNet 两个类。其中 PSPModule 是一个池化模块，用于提取不同尺度的特征信息，PSPNet 是一个完整的 PSPNet 模型，包含了多个卷积层和 PSPModule，最后输出语义分割结果。在 forward() 方法中，我们按照 PSPNet 的结构依次进行卷积操作和 PSPModule 池化操作，最终通过插值方法将输出的结果还原到原始尺寸大小。

你可以使用这个模型进行图像的语义分割任务，具体使用方法和训练方法可以参考 PyTorch 官方文档和论文中的描述。