import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import torchvision
from torch.autograd import Variable
from torch.utils.data import DataLoader
import cv2

# 下载训练集
train_dataset = datasets.MNIST(root='./num/',
                               train=True,
                               transform=transforms.ToTensor(),
                               download=True)
# 下载测试集
test_dataset = datasets.MNIST(root='./num/',
                              train=False,
                              transform=transforms.ToTensor(),
                              download=True)
# dataset 参数用于指定我们载入的数据集名称
# batch_size参数设置了每个包中的图片数据个数
# 在装载的过程会将数据随机打乱顺序并进打包
batch_size = 64
# 建立一个数据迭代器
# 装载训练集
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                           batch_size=batch_size,
                                           shuffle=True)
# 装载测试集
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                          batch_size=batch_size,
                                          shuffle=True)
 #实现单张图片可视化
images, labels = next(iter(train_loader))
img = torchvision.utils.make_grid(images)

img = img.numpy().transpose(1, 2, 0)
std = [0.5, 0.5, 0.5]
mean = [0.5, 0.5, 0.5]
img = img * std + mean
print(labels)
cv2.imshow('win', img)
key_pressed = cv2.waitKey(0)

print("运行到这了1")


# 卷积层使用 torch.nn.Conv2d
# 激活层使用 torch.nn.ReLU
# 池化层使用 torch.nn.MaxPool2d
# 全连接层使用 torch.nn.Linear

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Sequential(nn.Conv2d(1, 6, 3, 1, 2), nn.ReLU(),
                                   nn.MaxPool2d(2, 2))

        self.conv2 = nn.Sequential(nn.Conv2d(6, 16, 5), nn.ReLU(),
                                   nn.MaxPool2d(2, 2))

        self.fc1 = nn.Sequential(nn.Linear(16 * 5 * 5, 120),
                                 nn.BatchNorm1d(120), nn.ReLU())

        self.fc2 = nn.Sequential(
            nn.Linear(120, 84),
            nn.BatchNorm1d(84),
            nn.ReLU(),
            nn.Linear(84, 10))  # 最后的结果一定要变为 10，因为数字的选项是 0 ~ 9

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size()[0], -1)
        x = self.fc1(x)
        x = self.fc2(x)
        return x


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
LR = 0.001

net = LeNet().to(device)
# 损失函数使用交叉熵
criterion = nn.CrossEntropyLoss()
# 优化函数使用 Adam 自适应优化算法
optimizer = optim.Adam(
    net.parameters(),
    lr=LR,
)
epoch = 1
if __name__ == '__main__':
    for epoch in range(epoch):
        sum_loss = 0.0
        for i, data in enumerate(train_loader):
            inputs, labels = data
            inputs, labels = Variable(inputs).cuda(), Variable(labels).cuda()
            optimizer.zero_grad()  # 将梯度归零
            outputs = net(inputs)  # 将数据传入网络进行前向运算
            loss = criterion(outputs, labels)  # 得到损失函数
            loss.backward()  # 反向传播
            optimizer.step()  # 通过梯度做一步参数更新
            # print(loss)
            sum_loss += loss.item()
            if i % 100 == 99:
                print('[%d,%d] loss:%.03f' %
                      (epoch + 1, i + 1, sum_loss / 100))
                sum_loss = 0.0
    #测试集
    net.eval()  # 将模型变换为测试模式
    correct = 0
    total = 0
    for data_test in test_loader:
        images, labels = data_test
        images, labels = Variable(images).cuda(), Variable(labels).cuda()
        output_test = net(images)
        _, predicted = torch.max(output_test, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum()
    print("correct1: ", correct)
    print("Test acc: {0}".format(correct.item() /
                                  len(test_dataset)))