import torch.optim
import torchvision.datasets
from torch.nn import CrossEntropyLoss, Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoader
from torch import nn
dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),
                                       download=True)
dataloader = DataLoader(dataset, batch_size=1)
class TD(nn.Module):
    def __init__(self):
        super(TD, self).__init__()

        # # 方法1，使用Sequential，将所有操作放到一个model里，
        # self.model = Sequential(
        #     Conv2d(3, 32, 5, padding=2),
        #     MaxPool2d(2),
        #     Conv2d(32, 32, 5, padding=2),
        #     MaxPool2d(2),
        #     Conv2d(32, 64, 5, padding=2),
        #     MaxPool2d(2),
        #     Flatten(),
        #     Linear(1024, 64),
        #     Linear(64, 10),
        # )

        # 方法2，自己定义每一层，在forward里自行调用
        self.conv1 = Conv2d(3, 32, 5, padding=2)
        self.maxpool1 = MaxPool2d(2)
        self.conv2 = Conv2d(32, 32, 5, padding=2)
        self.maxpool2 = MaxPool2d(2)
        self.conv3 = Conv2d(32, 64, 5, padding=2)
        self.maxpool3 = MaxPool2d(2)
        self.flaten = Flatten()
        self.linear1 = Linear(1024, 64)
        self.linear2 = Linear(64, 10)

    def forward(self, x):

        # # 方法1，直接调用即可
        # x = self.model(x)

        # 方法2，每一层都需要自行调用
        x = self.conv1(x)
        x = self.maxpool1(x)
        x = self.conv2(x)
        x = self.maxpool2(x)
        x = self.conv3(x)
        x = self.maxpool3(x)
        x = self.flaten(x)
        x = self.linear1(x)
        x = self.linear2(x)
        return x

loss = CrossEntropyLoss()
td = TD()
optim = torch.optim.SGD(td.parameters(), lr=0.01)
for epoch in range(20):
    running_loss = 0.0
    for data in dataloader:
        imgs, targets = data
        outputs = td(imgs)
        res_loss = loss(outputs, targets)
        optim.zero_grad()
        res_loss.backward()
        optim.step()
        running_loss += res_loss
    print(running_loss)