给出edge-connect中的InpaintingModel的上下文编解码的网络结构极其相似的生成器代码，要求该生成器可以载入InpaintingModel的预训练模型，正常的进行不规则掩膜图像修复

以下是基于PyTorch框架的代码实现：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import vgg16


class InpaintGenerator(nn.Module):
    def __init__(self, in_channels=4, out_channels=3, ngf=64):
        super(InpaintGenerator, self).__init__()

        self.conv1 = nn.Conv2d(in_channels, ngf, 5, stride=1, padding=2)
        self.conv2 = nn.Conv2d(ngf, 2 * ngf, 3, stride=2, padding=1)
        self.conv3 = nn.Conv2d(2 * ngf, 4 * ngf, 3, stride=2, padding=1)

        self.res1 = ResidualBlock(4 * ngf)
        self.res2 = ResidualBlock(4 * ngf)
        self.res3 = ResidualBlock(4 * ngf)
        self.res4 = ResidualBlock(4 * ngf)
        self.res5 = ResidualBlock(4 * ngf)

        self.deconv1 = nn.ConvTranspose2d(4 * ngf, 2 * ngf, 3, stride=2, padding=1, output_padding=1)
        self.deconv2 = nn.ConvTranspose2d(2 * ngf, ngf, 3, stride=2, padding=1, output_padding=1)
        self.deconv3 = nn.Conv2d(ngf, out_channels, 5, stride=1, padding=2)

        self.tanh = nn.Tanh()

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)

        x = self.conv2(x)
        x = F.relu(x)

        x = self.conv3(x)
        x = F.relu(x)

        x = self.res1(x)
        x = self.res2(x)
        x = self.res3(x)
        x = self.res4(x)
        x = self.res5(x)

        x = self.deconv1(x)
        x = F.relu(x)

        x = self.deconv2(x)
        x = F.relu(x)

        x = self.deconv3(x)
        x = self.tanh(x)

        return x


class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()

        self.conv1 = nn.Conv2d(channels, channels, 3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(channels, channels, 3, stride=1, padding=1)

        self.bn1 = nn.BatchNorm2d(channels)
        self.bn2 = nn.BatchNorm2d(channels)

        self.relu = nn.ReLU()

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out += residual
        out = self.relu(out)

        return out

这里我们采用了类似于edge-connect中InpaintingModel的上下文编解码的网络结构，代码中的ResidualBlock为残差块，使用了Batch Normalization进行归一化，激活函数使用ReLU。其中，输入通道为4，输出通道为3，ngf为64，可以根据需要进行调整。

载入InpaintingModel的预训练模型：

model = InpaintGenerator()
model.load_state_dict(torch.load('pretrained_model.pth'))

其中pretrained_model.pth是已经训练好的模型的参数文件。在正常的进行不规则掩膜图像修复时，可以将待修复的图像和掩膜拼接为4通道输入，然后将输出的3通道图像作为修复结果。