将一张图像切分成为四份，在训练和测试时输入网络中。当四张都没有缺陷，则被认为是正常，反之则被认定为缺陷，切片操作，图像为1024*1024，划分成512*512 代码 pytorch

下面是一个简单的 PyTorch 代码实现，用于将图像切分为四个子图像，并使用它们来进行预测：

import torch
import torch.nn as nn
import torchvision.transforms.functional as TF
from PIL import Image

def split_image(image):
    # 切分图像为四个子图像
    w, h = image.size
    sub_images = []
    for i in range(2):
        for j in range(2):
            left = i * w // 2
            top = j * h // 2
            right = (i + 1) * w // 2
            bottom = (j + 1) * h // 2
            sub_images.append(TF.crop(image, top, left, bottom-top, right-left))
    return sub_images

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.fc1 = nn.Linear(64 * 64 * 64, 256)
        self.fc2 = nn.Linear(256, 2)

    def forward(self, x):
        x = torch.cat([self.conv1(xi) for xi in x], dim=1)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = torch.cat([self.conv2(xi) for xi in x], dim=1)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = torch.cat([self.conv3(xi) for xi in x], dim=1)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 64 * 64 * 64)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 加载模型
model = Net()
model.load_state_dict(torch.load("model.pt"))

# 加载测试图像并进行预测
image = Image.open("test.jpg")
sub_images = split_image(image)
with torch.no_grad():
    inputs = [TF.to_tensor(xi).unsqueeze(0) for xi in sub_images]
    outputs = model(inputs)
    preds = torch.argmax(outputs, dim=1)
    if preds.sum() == 0:
        print("正常")
    else:
        print("有缺陷")

需要注意的是，这只是一个简单的示例代码，您可能需要根据您的具体需求进行修改。此外，还需要定义训练数据集和训练过程，以便训练模型并保存模型参数。