利用已经训练好的模型，拿出训练集中的部分数据进行测试
下面给出完整的示例代码：

# -*- coding: utf-8 -*-
# 作者：小土堆
# 公众号：土堆碎念
import torch
import torchvision
from PIL import Image
from torch import nn

image_path = "imgs/dog.jpg"
image = Image.open(image_path)
print(image)
image = image.convert('RGB')
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
                                            torchvision.transforms.ToTensor()])

image = transform(image)
print(image.shape)

class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(64*4*4, 64),
            nn.Linear(64, 10)
        )

    def forward(self, x):
        x = self.model(x)
        return x

model = torch.load("tudui_9.pth", map_location=torch.device('cpu'), weights_only=False)
# model = torch.load("tudui_9.pth")
print(model)
image = torch.reshape(image, (1, 3, 32, 32))
model.eval()
with torch.no_grad():
    output = model(image)
print(output)

print(output.argmax(1))

运行输出结果如下：

/home/decre/miniconda3/envs/pytorch/bin/python /home/decre/work/ybb/base_pytorch/06_verify_model.py 
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x726 at 0x777290ABD040>
torch.Size([3, 32, 32])
Tudui(
  (model): Sequential(
    (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Flatten(start_dim=1, end_dim=-1)
    (7): Linear(in_features=1024, out_features=64, bias=True)
    (8): Linear(in_features=64, out_features=10, bias=True)
  )
)
tensor([[-1.4207, -7.0271,  1.5175,  3.6118,  1.4886,  5.2744,  1.2743,  3.9919,
         -5.6039, -2.7634]])
tensor([5])

Process finished with exit code 0

结果分析：最终的输出结果通过print(output.argmax(1))，找到数组 output 中每行的最大值的索引，最终结果是tensor[5]。CIFAR10数据集对应的结果和下标如下：

也即我们放了狗的图片，识别出来的是狗，模型识别正确。