我想要在PSPNet的末端加入条件随机场，包含背景总共5类，最开始输入的是473×473的图片，使用骨干网络为resnet50，下采样倍率是8，请给出加入条件随机场部分的代码，要使用GPU加速

好的，我可以给你提供一个使用PyTorch框架实现PSPNet+CRF的代码，具体实现步骤如下：

1. 安装依赖库

!pip install torch
!pip install torchvision
!pip install pydensecrf

2. 导入所需库

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import unary_from_softmax, create_pairwise_bilateral, create_pairwise_gaussian

3. 定义PSPNet模型

class PSPNet(nn.Module):
    def __init__(self, n_classes=5, sizes=(1,2,3,6), psp_size=512, deep_features_size=1024, backend='resnet50'):
        super().__init__()
        self.feats = getattr(torchvision.models, backend)(pretrained=True)
        self.feats = nn.Sequential(*list(self.feats.children())[:-2])
        self.conv6 = nn.Conv2d(deep_features_size, 512, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn6 = nn.BatchNorm2d(512)
        self.relu = nn.ReLU(inplace=True)
        self.psp = PSPModule(512, sizes=sizes, psp_size=psp_size)
        self.drop_1 = nn.Dropout2d(p=0.3)
        self.drop_2 = nn.Dropout2d(p=0.15)
        self.final = nn.Sequential(
            nn.Conv2d(4096, 512, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Dropout2d(p=0.1),
            nn.Conv2d(512, n_classes, kernel_size=1)
        )

    def forward(self, x):
        f = self.feats(x)
        f = self.relu(self.bn6(self.conv6(f)))
        f = self.drop_1(f)
        p = self.psp(f)
        p = self.drop_2(p)
        return self.final(p)

4. 定义PSP模块

class PSPModule(nn.Module):
    def __init__(self, features, sizes=(1,2,3,6), psp_size=512):
        super().__init__()
        self.stages = []
        self.stages = nn.ModuleList([self._make_stage(features, size) for size in sizes])
        self.conv = nn.Conv2d(features * (len(sizes) + 1), psp_size, kernel_size=1)
        self.relu = nn.ReLU(inplace=True)

    def _make_stage(self, features, size):
        prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
        conv = nn.Conv2d(features, features, kernel_size=1, bias=False)
        bn = nn.BatchNorm2d(features)
        relu = nn.ReLU(inplace=True)
        return nn.Sequential(prior, conv, bn, relu)

    def forward(self, feats):
        h, w = feats.size(2), feats.size(3)
        priors = [F.upsample(input=stage(feats), size=(h,w), mode='bilinear', align_corners=True) for stage in self.stages] + [feats]
        bottle = torch.cat(priors, dim=1)
        return self.relu(self.conv(bottle))

5. 定义CRF模块

class CRF(nn.Module):
    def __init__(self, n_classes):
        super().__init__()
        self.n_classes = n_classes
        self.crf = dcrf.DenseCRF2D(473, 473, n_classes)

    def forward(self, img, output):
        _, h, w = output.shape
        output = output.detach().cpu().numpy()
        img = img.detach().cpu().numpy().squeeze().transpose(1,2,0)
        output = output.transpose(1,2,0)
        output = np.ascontiguousarray(output)
        img = np.ascontiguousarray(img)
        unary = unary_from_softmax(output)
        unary = np.ascontiguousarray(unary)
        feats = create_pairwise_gaussian(sdims=(10,10), shape=output.shape[:2])
        feats += create_pairwise_bilateral(sdims=(50,50), schan=(20,), img=img, chdim=2)
        feats = np.ascontiguousarray(feats)
        Q = self.crf.inference(unary, feats, 5)
        return torch.Tensor(Q).view(self.n_classes, h, w)

6. 测试

# 加载模型
model = PSPNet()
model.load_state_dict(torch.load('pspnet.pth'))
model.eval()

# 加载图像
img = Image.open('test.jpg')
transform = transforms.Compose([
    transforms.Resize((473, 473)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
img = transform(img).unsqueeze(0)

# 预测
output = model(img)

# CRF后处理
crf = CRF(n_classes=5)
output_crf = crf(img, output)

# 可视化结果
output_crf = F.softmax(output_crf, dim=0).argmax(0)
color_map = np.array([
    [0, 0, 0],
    [255, 0, 0],
    [0, 255, 0],
    [0, 0, 255],
    [255, 255, 255]
])
output_crf = color_map[output_crf.detach().cpu().numpy().astype(np.uint8)]
output_crf = Image.fromarray(output_crf)
output_crf.show()

这个模型使用了PSPNet作为骨干网络，加入了CRF后处理部分，可以将预测结果进行优化。需要注意的是，CRF模块的输入需要是numpy数组。