我需要一个基于pytorch完成全景图像拼接的代码 要求输入图片，并且生成拼接之后的图片

以下是一个基于PyTorch的全景图像拼接代码示例：

import torch
import numpy as np
from PIL import Image

# 定义模型
class PanoramaNet(torch.nn.Module):
    def __init__(self):
        super(PanoramaNet, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv4 = torch.nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.conv5 = torch.nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.conv6 = torch.nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1)
        self.upconv1 = torch.nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.upconv2 = torch.nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.upconv3 = torch.nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.upconv4 = torch.nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.upconv5 = torch.nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2)
        self.final_conv = torch.nn.Conv2d(32, 3, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        x = torch.nn.functional.relu(self.conv1(x))
        x = torch.nn.functional.relu(self.conv2(x))
        x = torch.nn.functional.relu(self.conv3(x))
        x = torch.nn.functional.relu(self.conv4(x))
        x = torch.nn.functional.relu(self.conv5(x))
        x = torch.nn.functional.relu(self.conv6(x))
        x = torch.nn.functional.relu(self.upconv1(x))
        x = torch.nn.functional.relu(self.upconv2(x))
        x = torch.nn.functional.relu(self.upconv3(x))
        x = torch.nn.functional.relu(self.upconv4(x))
        x = torch.nn.functional.relu(self.upconv5(x))
        x = self.final_conv(x)
        return x

# 加载模型
model = PanoramaNet()
model.load_state_dict(torch.load('panorama_model.pth'))

# 加载输入图片
image1 = Image.open('image1.jpg')
image2 = Image.open('image2.jpg')
images = [image1, image2]

# 将输入图片转换为PyTorch张量
images_tensors = []
for image in images:
    image_tensor = torch.tensor(np.array(image))
    image_tensor = image_tensor.permute(2, 0, 1).float().unsqueeze(0)
    images_tensors.append(image_tensor)

# 将输入张量传入模型，生成全景图像
with torch.no_grad():
    output = model(torch.cat(images_tensors, dim=0))
output = output.squeeze().permute(1, 2, 0).numpy()

# 将全景图像转换为PIL图像，保存结果
output_image = Image.fromarray((output * 255).astype(np.uint8))
output_image.save('panorama_image.jpg')

该代码使用PyTorch实现了一个基于卷积神经网络的全景图像拼接模型，通过加载已经训练好的模型，输入两张图片，并生成拼接之后的全景图像。