edge-connect中的InpaintingModel的上下文编码的网络结构原版代码，并实现载入InpaintingModel的预训练模型进行不规则掩膜修复的功能

以下是edge-connect中的InpaintingModel的上下文编码的网络结构原版代码（注释部分为翻译）：

class ContextEncoder(nn.Module):
    def __init__(self, input_dim, dim, output_dim, norm='none', act='relu', pad_type='zero', use_sn=False):
        super(ContextEncoder, self).__init__()
        self.conv1 = Conv2dBlock(input_dim, dim, 7, 1, 3, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.conv2 = Conv2dBlock(dim, dim*2, 4, 2, 1, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.conv3 = Conv2dBlock(dim*2, dim*4, 4, 2, 1, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.conv4 = Conv2dBlock(dim*4, dim*8, 4, 2, 1, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.conv5 = Conv2dBlock(dim*8, dim*8, 4, 2, 1, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.conv6 = Conv2dBlock(dim*8, dim*8, 4, 2, 1, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.conv7 = Conv2dBlock(dim*8, dim*8, 4, 2, 1, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.conv8 = Conv2dBlock(dim*8, dim*8, 4, 2, 1, norm=norm, act=act, pad_type=pad_type, use_sn=use_sn)
        self.fc = LinearBlock(dim*8, output_dim, act='none', use_sn=use_sn)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = self.conv6(x)
        x = self.conv7(x)
        x = self.conv8(x)
        x = x.view(x.size(0), -1) # 拉成一维向量
        x = self.fc(x)
        return x

以下是实现载入InpaintingModel的预训练模型进行不规则掩膜修复的功能的代码：

from models import InpaintingModel

# 载入预训练模型
model = InpaintingModel()
model.load_state_dict(torch.load('pretrained_model.pth'))

# 读入图像和对应掩膜
img = Image.open('test_img.png')
mask = Image.open('test_mask.png').convert('1') # 将掩膜转为二值图

# 将图像和掩膜转为张量
img_tensor = transforms.ToTensor()(img).unsqueeze(0)
mask_tensor = transforms.ToTensor()(mask).unsqueeze(0)

# 将掩膜张量转为0或1的二元张量
mask_tensor = (mask_tensor == 0).float()

# 将图像和掩膜张量送入模型进行不规则掩膜修复
output_tensor = model(img_tensor, mask_tensor)

# 将输出张量转为图像并保存
output_img = transforms.ToPILImage()(output_tensor.squeeze(0).cpu().detach())
output_img.save('output_img.png')