video swin transformer进行视频分类

### 使用Swin Transformer 实现视频分类任务 #### 构建 Video-Swin-Transformer 模型架构 为了利用 Swin Transformer 进行视频分类，模型设计上采用了三维卷积核来处理时空特征。具体来说，在输入阶段接受连续帧作为输入，并通过分层的窗口多头自注意机制（SW-MSA），有效提取时间维度上的依赖关系[^3]。 ```python import torch.nn as nn from timm.models.layers import trunc_normal_ class VideoSwinTransformer(nn.Module): """ Video Swin Transformer model. A PyTorch implementation of the Video-Swin-Transformer architecture from: 'Video Swin Transformer' (https://arxiv.org/abs/2106.13230) Args: pretrained (bool): If True, returns a model pre-trained on ImageNet num_classes (int): Number of classes for classification head """ def __init__(self, pretrained=True, num_classes=400): super().__init__() self.backbone = create_video_swin_transformer(pretrained=pretrained) self.classifier = nn.Linear(self.backbone.num_features, num_classes) # Initialize weights if not pretrained: self.apply(_init_weights) def forward(self, x): features = self.backbone(x) logits = self.classifier(features.mean([-2, -1])) return logits def _init_weights(m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=.02) if m.bias is not None: nn.init.constant_(m.bias, 0) ``` #### 数据预处理与加载 对于视频数据集而言，通常需要先将其转换成适合网络输入的形式。这涉及到读取原始视频文件、抽样固定数量的关键帧以及标准化像素值等操作。可以借助 `torchvision` 和其他工具库完成这些工作[^1]。 ```python from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize from pytorchvideo.data.encoded_video_dataset import EncodedVideoDataset from pytorchvideo.transforms import ( ApplyTransformToKey, UniformTemporalSubsample, ShortSideScale, ) transform = Compose([ ApplyTransformToKey( key="video", transform=Compose([ UniformTemporalSubsample(num_frames), Lambda(lambda x: x / 255.), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]), ), ]) dataset = EncodedVideoDataset( data_path='path/to/video/files', clip_sampler=lambda video_length, annotations: [ ([start_time, end_time], label) for start_time, end_time, label in zip( np.linspace(0, max(video_length - duration, 0), samples_per_video), np.linspace(duration, min(video_length, duration * samples_per_video), samples_per_video), itertools.repeat(label)) ], decode_audio=False, decoder="pyav", transform=transform, ) ``` #### 训练过程配置 训练过程中可以通过命令行参数灵活调整超参设置，比如学习率、批次大小等。这里展示了一个简单的解析器定义方式用于接收外部传入的配置项[^2]。 ```python import argparse parser = argparse.ArgumentParser('Swin Transformer Training Script', add_help=False) parser.add_argument('--batch_size', default=32, type=int, help='Batch size per GPU') parser.add_argument('--learning_rate', default=0.001, type=float, help='Initial learning rate') args = parser.parse_args() ``` #### 开始训练 最后一步就是编写实际执行训练循环的部分了。这部分代码会迭代整个数据集并更新权重直到满足停止条件为止。建议使用混合精度加速收敛速度，并定期保存最佳性能下的模型副本以便后续评估或部署。 ```python device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model.to(device=device) criterion = nn.CrossEntropyLoss().to(device=device) optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) for epoch in range(start_epoch, args.max_epochs): train_one_epoch(args, model, criterion, optimizer, data_loader_train, device, epoch) acc_top1, acc_top5 = evaluate(data_loader_val, model, device=device) ```