双流网络的pytorch实现

以下是双流网络的 PyTorch 实现示例：

import torch
import torch.nn as nn

class TwoStreamNet(nn.Module):
    def __init__(self, num_classes=10):
        super(TwoStreamNet, self).__init__()
        
        # Spatial stream
        self.spatial_conv1 = nn.Conv2d(3, 96, kernel_size=7, stride=2, padding=2)
        self.spatial_pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.spatial_norm1 = nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75)
        self.spatial_conv2 = nn.Conv2d(96, 256, kernel_size=5, stride=2, padding=1)
        self.spatial_pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.spatial_norm2 = nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75)
        self.spatial_conv3 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.spatial_conv4 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.spatial_conv5 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.spatial_pool5 = nn.MaxPool2d(kernel_size=3, stride=2)
        
        # Temporal stream
        self.temporal_conv1 = nn.Conv2d(3, 96, kernel_size=7, stride=2, padding=2)
        self.temporal_pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.temporal_norm1 = nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75)
        self.temporal_conv2 = nn.Conv2d(96, 256, kernel_size=5, stride=2, padding=1)
        self.temporal_pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
        self.temporal_norm2 = nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75)
        self.temporal_conv3 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.temporal_conv4 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.temporal_conv5 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.temporal_pool5 = nn.MaxPool2d(kernel_size=3, stride=2)
        
        # Fully connected layers
        self.fc6 = nn.Linear(4096, 4096)
        self.dropout6 = nn.Dropout(p=0.5)
        self.fc7 = nn.Linear(4096, 4096)
        self.dropout7 = nn.Dropout(p=0.5)
        self.fc8 = nn.Linear(4096, num_classes)

    def forward(self, x):
        # Split the input into spatial and temporal streams
        spatial = x[:, :, :, 0:224]
        temporal = x[:, :, :, 224:448]

        # Spatial stream forward
        spatial = self.spatial_conv1(spatial)
        spatial = self.spatial_pool1(spatial)
        spatial = self.spatial_norm1(spatial)
        spatial = nn.functional.relu(spatial)
        spatial = self.spatial_conv2(spatial)
        spatial = self.spatial_pool2(spatial)
        spatial = self.spatial_norm2(spatial)
        spatial = nn.functional.relu(spatial)
        spatial = self.spatial_conv3(spatial)
        spatial = nn.functional.relu(spatial)
        spatial = self.spatial_conv4(spatial)
        spatial = nn.functional.relu(spatial)
        spatial = self.spatial_conv5(spatial)
        spatial = self.spatial_pool5(spatial)
        spatial = nn.functional.relu(spatial)
        spatial = spatial.view(spatial.size(0), -1)

        # Temporal stream forward
        temporal = self.temporal_conv1(temporal)
        temporal = self.temporal_pool1(temporal)
        temporal = self.temporal_norm1(temporal)
        temporal = nn.functional.relu(temporal)
        temporal = self.temporal_conv2(temporal)
        temporal = self.temporal_pool2(temporal)
        temporal = self.temporal_norm2(temporal)
        temporal = nn.functional.relu(temporal)
        temporal = self.temporal_conv3(temporal)
        temporal = nn.functional.relu(temporal)
        temporal = self.temporal_conv4(temporal)
        temporal = nn.functional.relu(temporal)
        temporal = self.temporal_conv5(temporal)
        temporal = self.temporal_pool5(temporal)
        temporal = nn.functional.relu(temporal)
        temporal = temporal.view(temporal.size(0), -1)

        # Concatenate the spatial and temporal streams
        out = torch.cat([spatial, temporal], dim=1)

        # Fully connected layers
        out = self.fc6(out)
        out = nn.functional.relu(out)
        out = self.dropout6(out)
        out = self.fc7(out)
        out = nn.functional.relu(out)
        out = self.dropout7(out)
        out = self.fc8(out)

        return out

这是一个简单的双流网络，其中包含一个空间流和一个时间流，每个流都有自己的卷积和池化层。两个流在最后一个全连接层之前合并。这个模型可以根据需要进行修改和扩展。