### 结合3D卷积与Transformer的代码示例 一种有效的方法是将3D卷积网络（CNN）用于提取时空特征，随后利用Transformer来建模这些特征之间的长期依赖关系。这种方法能够充分利用两种模型的优势：3D CNN擅长捕捉局部的空间和时间模式；而Transformer则能更好地处理全局上下文信息。 下面是一个简单的PyTorch实现例子： #### 3D Convolution Layer with Transformer Block Implementation ```python import torch from torch import nn import torchvision.transforms as transforms class Conv3DWithTransformers(nn.Module): def __init__(self, input_channels=3, num_classes=1000, conv_dim=64, trans_dim=768, depth=12, heads=12, mlp_dim=3072): super(Conv3DWithTransformers, self).__init__() # Define a simple 3D convolution block followed by max pooling. self.conv_layers = nn.Sequential( nn.Conv3d(input_channels, conv_dim, kernel_size=(3, 3, 3), padding=(1, 1, 1)), nn.BatchNorm3d(conv_dim), nn.ReLU(), nn.MaxPool3d(kernel_size=(1, 2, 2)) ) # Flatten output from 3D convolutions into sequence of vectors suitable for transformers. self.flatten = nn.Flatten(start_dim=2) # Positional encoding can be added here if needed. # Initialize transformer encoder blocks based on specified parameters. self.transformer_encoder = nn.TransformerEncoder( nn.TransformerEncoderLayer(d_model=trans_dim, nhead=heads, dim_feedforward=mlp_dim), num_layers=depth ) # Final classification head after processing via the transformer stack. self.fc_out = nn.Linear(trans_dim, num_classes) def forward(self, x): batch_size = x.size(0) # Pass through initial set of 3D convolutions. out_conv = self.conv_layers(x) # Reshape tensor so it fits expected shape for transformer (sequence length * feature dimension). N, C, D, H, W = out_conv.shape out_reshaped = out_conv.permute(0, 2, 1, 3, 4).reshape(N*D, C*H*W//conv_dim, -1) # Apply flattening operation across spatial dimensions. seq_input = self.flatten(out_reshaped) # Process flattened sequences using transformer layers. transformed_output = self.transformer_encoder(seq_input) # Average over temporal dimension before passing to final classifier. avg_pool = torch.mean(transformed_output, dim=1) logits = self.fc_out(avg_pool) return logits ``` 此代码片段定义了一个名为`Conv3DWithTransformers` 的类，它首先应用了几层3D卷积操作以捕获视频帧中的空间-时间特性[^1]。接着通过展平和平铺的方式准备数据供后续的变压器模块使用。最后经过多层变换器编码器之后接上一个线性分类头完成预测任务。