import torch
import torchvision
import torchvision.transforms as transforms
from torch import nn
from torch.utils.data import DataLoader

#数据预处理
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

#加载数据集
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64)

test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64)

#定义网络结构
class re_cifar(nn.Module):
    def __init__(self):
        super(re_cifar, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=5, padding=2, stride=1),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, kernel_size=5, padding=2, stride=1),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, kernel_size=5, padding=2, stride=1),
            nn.MaxPool2d(2),
            nn.Flatten()

        )

    def forward(self, input):
        return self.model(input)
input = torch.ones(64, 1, 32, 32)
input = torch.tensor(input, dtype=torch.float32)
input = torch.reshape(input, [-1, 3, 32, 32])
model = re_cifar()
output = model(input)
print(output)