| import sys | |
| import os | |
| sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) | |
| from utils.dataset_utils import get_cifar10_dataloaders | |
| from utils.train_utils import train_model | |
| from model import SwinTransformer | |
| def main(): | |
| # 获取数据加载器 | |
| trainloader, testloader = get_cifar10_dataloaders(batch_size=128) | |
| # 创建模型 | |
| model = SwinTransformer( | |
| img_size=32, | |
| patch_size=4, | |
| in_chans=3, | |
| num_classes=10, | |
| embed_dim=96, | |
| depths=[2, 2, 6, 2], | |
| num_heads=[3, 6, 12, 24], | |
| window_size=7, | |
| mlp_ratio=4., | |
| qkv_bias=True, | |
| drop_rate=0.0, | |
| attn_drop_rate=0.0, | |
| drop_path_rate=0.1 | |
| ) | |
| # 训练模型 | |
| train_model( | |
| model=model, | |
| trainloader=trainloader, | |
| testloader=testloader, | |
| epochs=200, | |
| lr=0.001, # Transformer类模型通常使用较小的学习率 | |
| device='cuda', | |
| save_dir='../model', | |
| model_name='swin_transformer' | |
| ) | |
| if __name__ == '__main__': | |
| main() | |