幫我寫一個基於pytorch環境，有使用pretrain，two stream early fusion的架構，包含載入rgb圖片與光流圖片路徑

好的，以下是一个基于PyTorch环境，使用pretrain的Two Stream Early Fusion网络，并包含载入RGB图像和光流图像路径的代码：

import torch
import torch.nn as nn
import torchvision.models as models
import cv2
import numpy as np

class TwoStreamEarlyFusion(nn.Module):
    def __init__(self, num_classes):
        super(TwoStreamEarlyFusion, self).__init__()
        self.rgb_vgg16 = models.vgg16(pretrained=True)
        self.opt_vgg16 = models.vgg16(pretrained=True)
        self.rgb_fc = nn.Linear(4096, num_classes)
        self.opt_fc = nn.Linear(4096, num_classes)
        self.fusion_fc = nn.Linear(num_classes * 2, num_classes)
        
    def forward(self, rgb, opt):
        rgb_feats = self.rgb_vgg16.features(rgb)
        rgb_feats = self.rgb_vgg16.avgpool(rgb_feats)
        rgb_feats = torch.flatten(rgb_feats, 1)
        rgb_feats = self.rgb_vgg16.classifier(rgb_feats)
        rgb_feats = self.rgb_fc(rgb_feats)
        
        opt_feats = self.opt_vgg16.features(opt)
        opt_feats = self.opt_vgg16.avgpool(opt_feats)
        opt_feats = torch.flatten(opt_feats, 1)
        opt_feats = self.opt_vgg16.classifier(opt_feats)
        opt_feats = self.opt_fc(opt_feats)
        
        fusion_feats = torch.cat([rgb_feats, opt_feats], dim=1)
        output = self.fusion_fc(fusion_feats)
        
        return output

def load_rgb_image(filename):
    img = cv2.imread(filename)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (224, 224))
    img = img.astype(np.float32) / 255.0
    img = (img - np.array([0.485, 0.456, 0.406])) / np.array([0.229, 0.224, 0.225])
    img = np.transpose(img, (2, 0, 1))
    img = np.expand_dims(img, axis=0)
    img = torch.from_numpy(img)
    return img

def load_optical_flow(filename):
    flow = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
    flow = cv2.resize(flow, (224, 224))
    flow = flow.astype(np.float32) / 255.0
    flow = (flow - 0.5) / 0.226
    flow = np.expand_dims(flow, axis=0)
    flow = np.expand_dims(flow, axis=0)
    flow = torch.from_numpy(flow)
    return flow

这个架构的基本思想是：将RGB图像和光流图像分别输入两个VGG16网络中，然后将RGB网络和光流网络提取出来的特征拼接起来，再通过一个全连接层进行融合，最终输出分类结果。在这个架构中，我们使用了预训练的VGG16网络，并且定义了两个全连接层(rgb_fc和opt_fc)和一个融合层(fusion_fc)。

下面是载入RGB图像和光流图像路径的代码：

rgb_path = 'path/to/rgb/image.jpg'
opt_path = 'path/to/optical/flow/image.jpg'

rgb_image = load_rgb_image(rgb_path)
opt_image = load_optical_flow(opt_path)

model = TwoStreamEarlyFusion(num_classes=10)
output = model(rgb_image, opt_image)

这里我们使用了OpenCV库来读取图像，并将RGB图像和光流图像都resize到224x224的大小。对于RGB图像，我们对其进行了标准化处理，使其像素值的范围在[0, 1]之间，并进行了零均值归一化处理，使其像素值的均值为0，方差为1。对于光流图像，我们只进行了像素值的归一化处理，使其像素值的范围在[-1, 1]之间。最后，我们将图像转换为PyTorch张量，并将其作为输入传递给TwoStreamEarlyFusion模型。