tiny imagenet 训练_Dataloader读取图片集并训练网络python实现

以下是使用PyTorch实现Dataloader读取Tiny ImageNet数据集并训练网络的示例代码：

import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler

# 定义自定义数据集类
class TinyImageNetDataset(Dataset):
    def __init__(self, data_dir, transform=None):
        self.data_dir = data_dir
        self.transform = transform
        self.image_paths = []
        self.labels = []
        with open(data_dir + '/wnids.txt', 'r') as f:
            self.classes = [line.strip() for line in f.readlines()]
        for i, cls in enumerate(self.classes):
            for img_file in os.listdir(data_dir + '/train/' + cls + '/images/'):
                self.image_paths.append(data_dir + '/train/' + cls + '/images/' + img_file)
                self.labels.append(i)
    
    def __len__(self):
        return len(self.labels)
    
    def __getitem__(self, idx):
        image_path = self.image_paths[idx]
        image = Image.open(image_path).convert('RGB')
        label = self.labels[idx]
        if self.transform:
            image = self.transform(image)
        return image, label

# 定义数据增强和预处理操作
transform_train = transforms.Compose([
    transforms.RandomCrop(64, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

transform_test = transforms.Compose([
    transforms.Resize(64),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 创建训练、验证和测试数据集的实例
train_dataset = TinyImageNetDataset(data_dir='/path/to/tiny-imagenet-200', transform=transform_train)
val_dataset = TinyImageNetDataset(data_dir='/path/to/tiny-imagenet-200', transform=transform_test)
test_dataset = TinyImageNetDataset(data_dir='/path/to/tiny-imagenet-200', transform=transform_test)

# 创建随机子采样器
train_sampler = SubsetRandomSampler(range(100000))
val_sampler = SubsetRandomSampler(range(10000))
test_sampler = SubsetRandomSampler(range(10000))

# 创建Dataloader
train_loader = DataLoader(train_dataset, batch_size=128, sampler=train_sampler, num_workers=4)
val_loader = DataLoader(val_dataset, batch_size=128, sampler=val_sampler, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=128, sampler=test_sampler, num_workers=4)

# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(256 * 8 * 8, 1024)
        self.fc2 = nn.Linear(1024, 200)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        x = F.relu(self.conv3(x))
        x = F.max_pool2d(x, kernel_size=2, stride=2)
        x = x.view(-1, 256 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 实例化神经网络模型、损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# 训练循环
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# 验证和测试循环
correct = 0
total = 0
with torch.no_grad():
    for data in val_loader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

val_accuracy = correct / total
print('Validation accuracy: %.2f %%' % (100 * val_accuracy))

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

test_accuracy = correct / total
print('Test accuracy: %.2f %%' % (100 * test_accuracy))

注意：上述代码仅供参考，实际使用时需要根据自己的需求进行修改。