# 导入库
import torch.nn.functional as F
import torch.optim as optim
import torch
import torch.nn as nn
import torch.nn.parallel
 
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 设置超参数
#每次的个数
BATCH_SIZE = 20
#迭代次数
EPOCHS = 10
#采用cpu还是gpu进行计算
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 数据预处理
 
transform = transforms.Compose([
    transforms.Resize(100),
    transforms.RandomVerticalFlip(),
    transforms.RandomCrop(50),
    transforms.RandomResizedCrop(150),
    transforms.ColorJitter(brightness=0.5, contrast=0.5, hue=0.5),
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])

# 读取数据
 
dataset_train = datasets.ImageFolder('E:\Cat_And_Dog\kaggle\cats_and_dogs_small\train', transform)
 
print(dataset_train.imgs)
 
# 对应文件夹的label
 
print(dataset_train.class_to_idx)
 
dataset_test = datasets.ImageFolder('E:\Cat_And_Dog\kaggle\cats_and_dogs_small\validation', transform)
 
# 对应文件夹的label
 
print(dataset_test.class_to_idx)
 
# 导入数据
 
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True)
 
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=True)

# 定义网络
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3)
        self.max_pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(32, 64, 3)
        self.max_pool2 = nn.MaxPool2d(2)
        self.conv3 = nn.Conv2d(64, 64, 3)
        self.conv4 = nn.Conv2d(64, 64, 3)
        self.max_pool3 = nn.MaxPool2d(2)
        self.conv5 = nn.Conv2d(64, 128, 3)
        self.conv6 = nn.Conv2d(128, 128, 3)
        self.max_pool4 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(4608, 512)
        self.fc2 = nn.Linear(512, 1)
  
    def forward(self, x):
        in_size = x.size(0)
        x = self.conv1(x)
        x = F.relu(x)
        x = self.max_pool1(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.max_pool2(x)
        x = self.conv3(x)
        x = F.relu(x)
        x = self.conv4(x)
        x = F.relu(x)
        x = self.max_pool3(x)
        x = self.conv5(x)
        x = F.relu(x)
        x = self.conv6(x)
        x = F.relu(x)
        x = self.max_pool4(x)
        # 展开
        x = x.view(in_size, -1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = torch.sigmoid(x)
        return x
 
modellr = 1e-4
 
# 实例化模型并且移动到GPU
 
model = ConvNet().to(DEVICE)
 
# 选择简单暴力的Adam优化器，学习率调低
 
optimizer = optim.Adam(model.parameters(), lr=modellr)

def adjust_learning_rate(optimizer, epoch):
 
    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
    modellrnew = modellr * (0.1 ** (epoch // 5))
    print("lr:",modellrnew)
    for param_group in optimizer.param_groups:
        param_group['lr'] = modellrnew

# 定义训练过程
def train(model, device, train_loader, optimizer, epoch):
 
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
 
        data, target = data.to(device), target.to(device).float().unsqueeze(1)
 
        optimizer.zero_grad()
 
        output = model(data)
 
        # print(output)
 
        loss = F.binary_cross_entropy(output, target)
 
        loss.backward()
 
        optimizer.step()
 
        if (batch_idx + 1) % 10 == 0:
 
            print('Train Epoch: {} [{}/{} ({:.0f}%)]	Loss: {:.6f}'.format(
 
                epoch, (batch_idx + 1) * len(data), len(train_loader.dataset),
 
                    100. * (batch_idx + 1) / len(train_loader), loss.item()))
# 定义测试过程
 
def val(model, device, test_loader):
 
    model.eval()
 
    test_loss = 0
 
    correct = 0
 
    with torch.no_grad():
 
        for data, target in test_loader:
 
            data, target = data.to(device), target.to(device).float().unsqueeze(1)
 
            output = model(data)
            # print(output)
            test_loss += F.binary_cross_entropy(output, target, reduction='mean').item()
            pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in output]).to(device)
            correct += pred.eq(target.long()).sum().item()
 
        print('
Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)
'.format(
            test_loss, correct, len(test_loader.dataset),
            100. * correct / len(test_loader.dataset)))

# 训练
for epoch in range(1, EPOCHS + 1):
 
    adjust_learning_rate(optimizer, epoch)
    train(model, DEVICE, train_loader, optimizer, epoch)
    val(model, DEVICE, test_loader)
 
torch.save(model, 'E:\Cat_And_Dog\kaggle\model.pth')