时空注意力机制研究

系列博客目录

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代码

下面是一个简化的示例代码，演示了如何使用 时空注意力机制 结合 Vision Transformer (ViT) 来处理视频数据。在这个例子中，我们假设你有一段视频，并且希望通过 ViT 提取每一帧图像的空间特征，再通过时空注意力机制来处理视频帧之间的时序信息。

步骤：

1. 提取视频帧：从视频中提取每一帧作为图像。
2. 使用 ViT 提取图像特征：每一帧图像通过 ViT 模型提取空间特征。
3. 时空注意力机制：使用时空注意力机制来捕捉视频帧之间的时序关系。

代码实现：

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import ViTModel, ViTConfig

# Step 1: Video Frame Feature Extraction using Vision Transformer (ViT)
class VideoFeatureExtractor(nn.Module):
    def __init__(self, vit_model_name="google/vit-base-patch16-224-in21k"):
        super(VideoFeatureExtractor, self).__init__()
        # Load pre-trained ViT model
        self.vit = ViTModel.from_pretrained(vit_model_name)
    
    def forward(self, video_frames):
        """
        :param video_frames: A tensor of shape (batch_size, num_frames, channels, height, width)
        :return: Extracted features of shape (batch_size, num_frames, feature_dim)
        """
        batch_size, num_frames, _, _, _ = video_frames.shape
        frame_features = []
        
        # Process each video frame through ViT model
        for i in range(num_frames):
            frame = video_frames[:, i, :, :, :]  # Get the i-th frame
            frame = frame.view(-1, 3, 224, 224)  # Adjust shape for ViT input
            with torch.no_grad():
                vit_output = self.vit(frame)
            frame_features.append(vit_output.last_hidden_state[:, 0, :])  # Use [CLS] token embedding
        
        # Stack frame features (shape: batch_size, num_frames, feature_dim)
        return torch.stack(frame_features, dim=1)


# Step 2: Spatio-Temporal Attention Mechanism
class SpatioTemporalAttention(nn.Module):
    def __init__(self, feature_dim, num_heads=4):
        super(SpatioTemporalAttention, self).__init__()
        self.num_heads = num_heads
        self.feature_dim = feature_dim
        self.attn = nn.MultiheadAttention(embed_dim=feature_dim, num_heads=num_heads)
    
    def forward(self, frame_features):
        """
        :param frame_features: A tensor of shape (batch_size, num_frames, feature_dim)
        :return: Attention-weighted frame features
        """
        # Transpose for multi-head attention input shape (num_frames, batch_size, feature_dim)
        frame_features = frame_features.permute(1, 0, 2)
        
        # Apply multi-head attention
        attn_output, _ = self.attn(frame_features, frame_features, frame_features)
        
        # Transpose back (batch_size, num_frames, feature_dim)
        return attn_output.permute(1, 0, 2)


# Step 3: Complete Model for Video Fake News Detection
class VideoFakeNewsModel(nn.Module):
    def __init__(self, vit_model_name="google/vit-base-patch16-224-in21k", feature_dim=768, num_heads=4):
        super(VideoFakeNewsModel, self).__init__()
        self.feature_extractor = VideoFeatureExtractor(vit_model_name)
        self.temporal_attention = SpatioTemporalAttention(feature_dim, num_heads)
        self.fc = nn.Linear(feature_dim, 2)  # Assuming binary classification (rumor or non-rumor)
    
    def forward(self, video_frames):
        """
        :param video_frames: A tensor of shape (batch_size, num_frames, channels, height, width)
        :return: Predicted class probabilities
        """
        # Extract features from each video frame using ViT
        frame_features = self.feature_extractor(video_frames)
        
        # Apply spatio-temporal attention
        attended_features = self.temporal_attention(frame_features)
        
        # Aggregate features across frames (e.g., by averaging)
        aggregated_features = attended_features.mean(dim=1)  # (batch_size, feature_dim)
        
        # Make prediction
        logits = self.fc(aggregated_features)
        return F.softmax(logits, dim=-1)


# Example usage
if __name__ == "__main__":
    # Generate random video data: (batch_size, num_frames, channels, height, width)
    batch_size = 8
    num_frames =