resnet-dogcat/train.py

import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import models, transforms
from PIL import Image
from safetensors.torch import save_file

class CatDogDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.image_paths = []
        self.labels = []

        for filename in os.listdir(root_dir):
            if 'cat' in filename:
                self.image_paths.append(os.path.join(root_dir, filename))
                self.labels.append(0)  # cat
            elif 'dog' in filename:
                self.image_paths.append(os.path.join(root_dir, filename))
                self.labels.append(1)  # dog

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, idx):
        img_path = self.image_paths[idx]
        image = Image.open(img_path).convert('RGB')
        label = self.labels[idx]

        if self.transform:
            image = self.transform(image)

        return image, label

# 数据预处理
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'dog-cat'
image_datasets = {x: CatDogDataset(os.path.join(data_dir, x), data_transforms[x]) for x in ['train', 'val']}
dataloaders = {x: DataLoader(image_datasets[x], batch_size=32, shuffle=True, num_workers=4) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = ['cat', 'dog']

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 加载预训练的 ResNet-50 模型并进行微调
model_ft = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
num_ftrs = model_ft.fc.in_features
model_ft.fc = nn.Linear(num_ftrs, len(class_names))

model_ft = model_ft.to(device)

criterion = nn.CrossEntropyLoss()

# 优化器
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

# 学习率调度器
exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

# 训练模型
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    best_model_wts = model.state_dict()
    best_acc = 0.0

    for epoch in range(num_epochs):
        print(f'Epoch {epoch}/{num_epochs - 1}')
        print('-' * 10)

        # 每个epoch都有训练和验证阶段
        for phase in ['train', 'val']:
            if phase == 'train':
                model.train()  # 设置模型为训练模式
            else:
                model.eval()   # 设置模型为评估模式

            running_loss = 0.0
            running_corrects = 0

            # 遍历数据
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # 清零参数梯度
                optimizer.zero_grad()

                # 前向传播
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # 只有在训练阶段才反向传播+优化
                    if phase == 'train':
                        loss.backward()
                        optimizer.step()

                # 统计
                running_loss += loss.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            if phase == 'train':
                scheduler.step()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects.double() / dataset_sizes[phase]

            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')

            # 深拷贝模型
            if phase == 'val' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = model.state_dict()

        print()

    print(f'Best val Acc: {best_acc:4f}')

    # 加载最佳模型权重
    model.load_state_dict(best_model_wts)
    return model

# 训练和评估模型
model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)

# 保存模型为 .pt 文件
torch.save(model_ft.state_dict(), 'pytorch_model.bin')

metadata = {"format": "pt"}

# 保存模型为 .safetensors 文件
save_file(model_ft.state_dict(), 'model.safetensors', metadata=metadata)