import torchvision

from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch
from torch import nn


# 模型文件
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



train_data = torchvision.datasets.CIFAR10("./dataset", train=True, transform=torchvision.transforms.ToTensor(),
                                          download=True)

test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),
                                         download=True)

train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集长度：{}".format(train_data_size))
print("测试数据集长度：{}".format(test_data_size))

train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)

if torch.cuda.is_available():
    tudui = Tudui().cuda()
else:
    tudui = Tudui()
# 损失函数
if torch.cuda.is_available():
    loss_fn = nn.CrossEntropyLoss().cuda()
else:
    loss_fn = nn.CrossEntropyLoss()

# 优化器
lr = 0.01
optimizer = torch.optim.SGD(tudui.parameters(), lr=lr)

# 设置训练网络的参数
# 训练次数
total_train_step = 0
# 测试次数
total_test_step = 0
# 训练轮数
epoch = 10

writer = SummaryWriter('./logs')

for i in range(epoch):
    print("--------第{}轮--------".format(i + 1))

    # 训练模式
    tudui.train()
    for data in train_dataloader:
        imgs, targets = data
        if torch.cuda.is_available():
            imgs = imgs.cuda()
            targets = targets.cuda()
        outputs = tudui(imgs)
        loss = loss_fn(outputs, targets)

        # 优化器
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_train_step += 1
        if total_train_step % 100 == 0:
            print("loss:{}".format(loss.item()))
            writer.add_scalar("train_loss", loss.item(), total_train_step)

    total_test_loss = 0
    total_acc = 0
    # 测试模式
    tudui.eval()
    with torch.no_grad():
        for data in test_dataloader:
            imgs, targets = data
            if torch.cuda.is_available():
                imgs = imgs.cuda()
                targets = targets.cuda()
            outputs = tudui(imgs)
            accuracy = (outputs.argmax(1) == targets).sum()
            total_acc += accuracy
            loss = loss_fn(outputs, targets)
            total_test_loss += loss
    print(total_test_loss)
    print(total_acc / test_data_size)
    writer.add_scalar("test_loss", total_test_loss, total_test_step)
    writer.add_scalar("test_acc", total_acc / test_data_size, total_test_step)
    total_test_step += 1

    torch.save(tudui, "tudui_{}.pth".format(i))