自学教程：Python机器学习之基于Pytorch实现猫狗分类

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torchpip install -i https://pypi.tuna.tsinghua.edu.cn/simple torchvision

# 导入库import torch.nn.functional as Fimport torch.optim as optimimport torchimport torch.nn as nnimport torch.nn.parallel import torch.optimimport torch.utils.dataimport torch.utils.data.distributedimport torchvision.transforms as transformsimport 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}%)]/tLoss: {:.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('/nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)/n'.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')

from __future__ import print_function, divisionfrom PIL import Image from torchvision import transformsimport torch.nn.functional as F import torchimport torch.nn as nnimport torch.nn.parallel# 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# 模型存储路径model_save_path = 'E://Cat_And_Dog//kaggle//model.pth' # ------------------------ 加载数据 --------------------------- ## Data augmentation and normalization for training# Just normalization for validation# 定义预训练变换# 数据预处理transform_test = 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])])  class_names = ['cat', 'dog']  # 这个顺序很重要，要和训练时候的类名顺序一致 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # ------------------------ 载入模型并且训练 --------------------------- #model = torch.load(model_save_path)model.eval()# print(model) image_PIL = Image.open('E://Cat_And_Dog//kaggle//cats_and_dogs_small//test//cats//cat.1500.jpg')#image_tensor = transform_test(image_PIL)# 以下语句等效于 image_tensor = torch.unsqueeze(image_tensor, 0)image_tensor.unsqueeze_(0)# 没有这句话会报错image_tensor = image_tensor.to(device) out = model(image_tensor)pred = torch.tensor([[1] if num[0] >= 0.5 else [0] for num in out]).to(device)print(class_names[pred])