P274书上的代码，这里是从ipynb文件中按顺序复制来的：
使用到的图片如下：

代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import PIL
import torch
from torchvision import transforms
import torchvision
model = torchvision.models.segmentation.fcn_resnet101(pretrained=True)
model.eval()

## 读取照片
image = PIL.Image.open("Zdata/img1.jpg")
# 显示图像
plt.imshow(image)
plt.show()
## 图像预处理，转为0-1之间，标准化处理
image_transf = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean = [0.485, 0.456,0.406],
                        std = [0.229, 0.224, 0.225])
])
image_tensor = image_transf(image).unsqueeze(0)

# 将图像丢入模型进行处理
output = model(image_tensor)["out"]

# 查看输出张量的形状
print("Output shape:", output.shape)

## 将输出转化为二维图像
outputarg = torch.argmax(output.squeeze(),dim=0).numpy()
outputarg

#对得到的输出结果进行编码
def decode_segmaps(image,label_colors,nc=21):
    """函数将输出的2D图像，会将不同的类编码为不同的颜色"""
    r = np.zeros_like(image).astype(np.uint8)
    g = np.zeros_like(image).astype(np.uint8)
    b = np.zeros_like(image).astype(np.uint8)
    for cla in range(0,nc):
        idx = image ==cla
        r[idx] = label_colors[cla ,0]
        g[idx] = label_colors[cla ,1]
        b[idx] = label_colors[cla ,2]
    rgbimage = np.stack([r,g,b],axis=2)
    return rgbimage

label_colors = np.array([(0,0,0),
                        (128,0,0),(0,128,0),(128,128,0),(0,0,128),(128,0,128),
                        (0,128,128),(128,128,128),(64,0,0),(192,0,0),(64,128,0),
                        (192,128,0),(64,0,128),(192,0,128),(64,128,128),(192,128,128),
                        (0,64,0),(128,64,0),(0,192,0),(128,192,0),(0,64,128)])
outputrgb = decode_segmaps(outputarg,label_colors)
plt.figure(figsize=(20,8))
plt.subplot(1,2,1)
plt.imshow(image)
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(outputrgb)
plt.axis("off")
plt.subplots_adjust(wspace=0.5)
plt.show()

output_probs = torch.tensor([[0.1, 0.6, 0.3],
                            [0.4, 0.2, 0.7]])

# 获取每个样本预测的类别索引
predicted_classes = torch.argmax(output_probs, dim=1)

print(predicted_classes)

tensor([1, 2])

注：

此处使用torch.argmax()得到的是21个类别