加载InpaintingModel_gen.pth预训练模型时出现：RuntimeError: Error(s) in loading state_dict for ContextEncoder: Missing key(s) in state_dict: "encoder.0.weight", "encoder.0.bias", "encoder.2.weight", "encoder.2.bias", "encoder.3.weight", "encoder.3.bias", "encoder.3.running_mean", "encoder.3.running_var", "encoder.5.weight", "encoder.5.bias", "encoder.6.weight", "encoder.6.bias", "encoder.6.running_mean", "encoder.6.running_var", "encoder.8.weight", "encoder.8.bias", "encoder.9.weight", "encoder.9.bias", "encoder.9.running_mean", "encoder.9.running_var", "encoder.11.weight", "encoder.11.bias", "encoder.12.weight", "encoder.12.bias", "encoder.12.running_mean", "encoder.12.running_var", "encoder.14.weight", "encoder.14.bias", "encoder.15.weight", "encoder.15.bias", "encoder.15.running_mean", "encoder.15.running_var", "encoder.17.weight", "encoder.17.bias", "encoder.18.weight", "encoder.18.bias", "encoder.18.running_mean", "encoder.18.running_var", "encoder.20.weight", "encoder.20.bias", "encoder.21.weight", "encoder.21.bias", "encoder.21.running_mean", "encoder.21.running_var", "encoder.23.weight", "encoder.23.bias", "encoder.24.weight", "encoder.24.bias", "encoder.24.running_mean", "encoder.24.running_var", "decoder.0.weight", "decoder.0.bias", "decoder.1.weight", "decoder.1.bias", "decoder.1.running_mean", "decoder.1.running_var", "decoder.3.weight", "decoder.3.bias", "decoder.4.weight", "decoder.4.bias", "decoder.4.running_mean", "decoder.4.running_var", "decoder.6.weight", "decoder.6.bias", "decoder.7.weight", "decoder.7.bias", "decoder.7.running_mean", "decoder.7.running_var", "decoder.9.weight", "decoder.9.bias", "decoder.10.weight", "decoder.10.bias", "decoder.10.running_mean", "decoder.10.running_var", "decoder.12.weight", "decoder.12.bias", "decoder.13.weight", "decoder.13.bias", "decoder.13.running_mean", "decoder.13.running_var", "decoder.15.weight", "decoder.15.bias", "decoder.16.weight", "decoder.16.bias", "decoder.16.running_mean", "decoder.16.running_var", "decoder.18.weight", "decoder.18.bias", "decoder.19.weight", "decoder.19.bias", "decoder.19.running_mean", "decoder.19.running_var", "decoder.21.weight", "decoder.21.bias". Unexpected key(s) in state_dict: "iteration", "generator". 怎么解决

这个错误的原因是预训练模型的state_dict中的键值与你的模型不匹配。你需要检查一下你的模型和预训练模型的结构是否一致。如果不一致，你需要修改你的模型结构或者加载对应的预训练模型。

另外，你的错误信息中还显示了一些意外的键值，这可能是因为你在加载模型时使用了错误的函数或参数。你可以检查一下你的代码，确保你正确地加载了预训练模型并将其赋值给了你的模型。如果你仍然无法解决错误，你可以参考其他人的实现或者询问模型作者或开发者。

相关问题

edge connect的InpaintingModel_gen.pth预训练模型怎么用

InpaintingModel_gen.pth是EdgeConnect模型的预训练权重文件，可以用于图像修复（inpainting）任务。使用该模型需要进行以下步骤：

1. 安装PyTorch框架，并下载EdgeConnect的源代码。可以在GitHub上搜索EdgeConnect，选择自己需要的版本进行下载。

2. 将InpaintingModel_gen.pth文件放到EdgeConnect源代码中的checkpoints目录下。

3. 运行inference.py文件进行推理。在该文件中，需要进行以下配置：

（1）指定输入图像的路径。

（2）指定输出图像的路径和名称。

（3）设置其他参数，如模型名称、模型路径、输入图像大小、GPU编号等。

4. 运行inference.py文件，即可得到修复后的图像。

需要注意的是，InpaintingModel_gen.pth是预训练权重文件，如果需要在特定任务上进行fine-tuning，可以使用EdgeConnect源代码中提供的train.py文件进行训练。在该文件中，需要指定训练数据集、验证数据集、训练轮数、学习率等参数。

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。