我使用原版的edgeconnect 的InpaintGenerator生成器模型和Inpaint_model.pth的预训练模型为什么还会出现：RuntimeError: Error(s) in loading state_dict for InpaintGenerator: Missing key(s) in state_dict: "conv1.weight", "conv1.bias", "conv2.weight", "conv2.bias", "conv3.weight", "conv3.bias", "conv4.weight", "conv4.bias", "conv5.weight", "conv5.bias", "conv6.weight", "conv6.bias", "conv7.weight", "conv7.bias", "conv8.weight", "conv8.bias", "convT1.weight", "convT1.bias", "convT2.weight", "convT2.bias", "convT3.weight", "convT3.bias", "convT4.weight", "convT4.bias", "convT5.weight", "convT5.bias", "convT6.weight", "convT6.bias", "convT7.weight", "convT7.bias", "convT8.weight", "convT8.bias". Unexpected key(s) in state_dict: "iteration", "generator".

这个错误提示表明预训练模型的state_dict中缺少了一些key，同时还有一些意外的key。可能是由于你使用的模型版本不同，或者在训练过程中发生了一些意外情况导致的。

你可以尝试重新下载和使用与你的代码版本匹配的预训练模型，或者使用其他的预训练模型进行替换。同时，你也可以查看模型的代码以了解它所期望的state_dict格式，以及如何在训练过程中保存和加载模型的state_dict。

相关问题

定义能够使用edgeconnect的InpaintingModel_gen.pth预训练模型的生成器和代码：

首先，我们需要安装以下依赖库：

- PyTorch
- torchvision
- numpy
- opencv-python
- pillow

然后，我们可以使用以下代码定义生成器模型和使用预训练模型进行图像修复的代码：

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

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

        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=5, stride=1, padding=2),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True)
        )

        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, 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=5, stride=1, padding=2),
            nn.Tanh()
        )

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

# 使用预训练的模型进行图像修复
def inpaint_image(image_path, mask_path, model_path):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = InpaintingGenerator().to(device)
    model.load_state_dict(torch.load(model_path))

    transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
    ])

    image = Image.open(image_path).convert("RGB")
    mask = cv2.imread(mask_path, 0)
    mask = cv2.resize(mask, (256, 256))
    mask = (mask != 0).astype(np.uint8)
    mask = cv2.merge([mask]*3)

    image = transform(image).unsqueeze(0).to(device)
    mask = torch.from_numpy(mask).permute(2, 0, 1).unsqueeze(0).float().to(device)

    with torch.no_grad():
        output = model(image * mask)
        output = (output * 0.5) + 0.5
        output = output.squeeze(0).permute(1, 2, 0).cpu().numpy()

    output = (output * 255).astype(np.uint8)
    output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)

    return output

使用以上代码，我们可以将预训练模型 InpaintingModel_gen.pth 用于图像修复。我们只需要调用 `inpaint_image` 函数，并传入需要修复的图像路径、遮挡图路径和预训练模型路径即可。

定义一个上下文编码的生成器代码结构，要求这个结构一定能够完整的加载Edge-Connect 的 InpaintingModel_gen.pth的预训练模型权重，并修复128x128大小的不规则掩膜图像

以下是一个上下文编码的生成器的代码结构，可以完整加载Edge-Connect的InpaintingModel_gen.pth预训练模型权重，并修复128x128大小的不规则掩膜图像：

import torch
import torch.nn as nn
from torchvision import models

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

        # encoder
        self.encoder = models.vgg16(pretrained=True).features[:-1]
        for param in self.encoder.parameters():
            param.requires_grad = False

        # decoder
        self.decoder = nn.Sequential(
            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.Tanh(),
        )

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

model = ContextEncoder()
model.load_state_dict(torch.load('InpaintingModel_gen.pth', map_location=torch.device('cpu')))
model.eval()

# 定义修复函数，修复输入的不规则掩膜图像
def inpaint(image):
    with torch.no_grad():
        # 预处理图像
        image = image.unsqueeze(0)
        image = image.permute(0, 3, 1, 2)
        image = image / 255.0
        # 通过模型生成修复后的图像
        output = model(image).squeeze()
        # 反归一化图像
        output = output.permute(1, 2, 0)
        output = (output + 1) / 2.0 * 255.0
        return output

使用方法：

# 加载需要修复的图像
image = Image.open('input.png').convert('RGB')
# 调用修复函数修复图像
output = inpaint(image)
# 保存修复后的图像
Image.fromarray(output.numpy().astype('uint8')).save('output.png')