幫我寫用pytorch，vgg16，gpu訓練，實現two stream early fusion，我發現之前跑得時候出現rgb圖像channel數與光流圖像channel不同而造成無法融合，有建議方法？

首先，确保你的RGB图像和光流图像具有相同数量的通道，例如都是3通道。如果不是，则需要通过转换来使它们具有相同数量的通道。

其次，实现Two-Stream Early Fusion，可以按照以下步骤进行：

1. 定义两个分支网络：一个处理RGB图像，另一个处理光流图像。在这里，我们使用VGG16作为我们的分支网络。

2. 对于每个分支网络，将其前几层冻结，并添加一个全局平均池化层和一个全连接层。这些层将作为特征提取器，以提取RGB图像和光流图像的特征。

3. 将两个分支网络的输出连接起来，并添加一个全连接层和一个softmax层，以进行分类。

4. 定义您的优化器和损失函数。在这里，我们使用交叉熵损失函数和Adam优化器。

5. 在GPU上训练模型。在PyTorch中，您可以使用.cuda()方法将模型和数据移动到GPU上。

下面是一个简单的代码示例，可以帮助您开始：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.models import vgg16

# Define the Two-Stream Early Fusion model
class TwoStreamEarlyFusion(nn.Module):
    def __init__(self, num_classes):
        super(TwoStreamEarlyFusion, self).__init__()
        # Define the RGB branch
        self.rgb = vgg16(pretrained=True)
        self.rgb.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(25088, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )
        # Define the optical flow branch
        self.flow = vgg16(pretrained=True)
        self.flow.classifier = nn.Sequential(
            nn.Dropout(),
            nn.Linear(25088, 4096),
            nn.ReLU(inplace=True),
            nn.Dropout(),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, num_classes),
        )
        # Define the fusion layer
        self.fusion = nn.Sequential(
            nn.Linear(num_classes*2, num_classes),
            nn.Softmax(dim=1)
        )

    def forward(self, rgb, flow):
        rgb_feat = self.rgb.features(rgb)
        rgb_feat = self.rgb.avgpool(rgb_feat)
        rgb_feat = torch.flatten(rgb_feat, 1)
        rgb_feat = self.rgb.classifier(rgb_feat)

        flow_feat = self.flow.features(flow)
        flow_feat = self.flow.avgpool(flow_feat)
        flow_feat = torch.flatten(flow_feat, 1)
        flow_feat = self.flow.classifier(flow_feat)

        fusion_feat = torch.cat((rgb_feat, flow_feat), dim=1)
        fusion_feat = self.fusion(fusion_feat)

        return fusion_feat

# Move the model and data to GPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = TwoStreamEarlyFusion(num_classes=10)
model.to(device)

# Define the optimizer and loss function
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the model
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # Get the inputs
        rgb, flow, labels = data
        rgb, flow, labels = rgb.to(device), flow.to(device), labels.to(device)

        # Zero the parameter gradients
        optimizer.zero_grad()

        # Forward + backward + optimize
        outputs = model(rgb, flow)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # Print statistics
        running_loss += loss.item()
        if i % 100 == 99:    # Print every 100 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')