使用pytorch猫狗

要使用 PyTorch 进行猫狗分类，首先需要收集猫狗图片数据集，然后对数据集进行预处理和划分。接下来，可以使用 PyTorch 的 torchvision 库来加载数据集，构建模型，定义损失函数和优化器，以及进行模型训练和评估。

以下是一个简单的猫狗分类示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

# 定义数据集路径和预处理操作
data_path = "/path/to/dataset"
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# 加载训练集和测试集数据
train_dataset = torchvision.datasets.ImageFolder(
    root=data_path + "/train",
    transform=transform
)
train_loader = torch.utils.data.DataLoader(
    train_dataset,
    batch_size=32,
    shuffle=True,
    num_workers=4
)
test_dataset = torchvision.datasets.ImageFolder(
    root=data_path + "/test",
    transform=transform
)
test_loader = torch.utils.data.DataLoader(
    test_dataset,
    batch_size=32,
    shuffle=False,
    num_workers=4
)

# 定义模型
model = torchvision.models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 2)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(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

# 评估模型
correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (
    100 * correct / total))

在这个示例中，我们使用了 ResNet-18 模型进行分类，使用交叉熵损失函数和随机梯度下降优化器进行训练。通过运行 10 个 epoch，我们可以得到一个简单的猫狗分类器。