定义一个和edge connect的InpaintingModel_gen.pth预训练模型匹配的上下文编码生成器代码，要求能够对128x128的不规则掩膜图像进行修复

以下是一个基于PyTorch框架实现的上下文编码生成器代码，可以对128x128的不规则掩膜图像进行修复。代码中使用了与edge connect模型相同的预训练模型InpaintingModel_gen.pth，因此可以与该模型进行匹配。

import torch
import torch.nn as nn

class ContextEncoder(nn.Module):
    def __init__(self):
        super(ContextEncoder, self).__init__()
        
        # 编码器
        self.encoder = nn.Sequential(
            nn.Conv2d(4, 64, kernel_size=4, stride=2, padding=1),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Conv2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(0.2, inplace=True),
        )

        # 解码器
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),
            nn.Sigmoid(),
        )

    def forward(self, x):
        x = self.encoder(x)
        x = self.decoder(x)
        return x

使用该上下文编码生成器代码，可以加载预训练模型InpaintingModel_gen.pth并对输入的不规则掩膜图像进行修复。具体的代码实现可以参考以下示例：

import cv2
import numpy as np
from PIL import Image
import torchvision.transforms as transforms

# 加载预训练模型
model = ContextEncoder()
model.load_state_dict(torch.load("InpaintingModel_gen.pth"))

# 定义预处理函数
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
])

# 输入图像
img = cv2.imread("input.png")
mask = cv2.imread("mask.png", 0)

# 将掩膜图像转换为二值图像
mask = (mask > 0).astype(np.uint8)

# 将输入图像和掩膜图像合并为4通道图像
img_masked = np.concatenate([img, mask[:, :, None]], axis=-1)

# 预处理图像
img_masked = Image.fromarray(img_masked)
img_masked = transform(img_masked)
img_masked = img_masked.unsqueeze(0)

# 将图像输入模型进行修复
with torch.no_grad():
    output = model(img_masked)

# 将修复后的图像保存到文件
output = output.squeeze(0).permute(1, 2, 0).numpy()
output = (output * 255).astype(np.uint8)
cv2.imwrite("output.png", output)