input_img = torch.cat([img, mask], dim=1)

这行代码使用 PyTorch 中的 `torch.cat` 函数将 `img` 和 `mask` 沿着第一个维度（即通道维度）拼接起来，生成一个新的张量 `input_img`。这里假设 `img` 和 `mask` 的形状都是 `[batch_size, channel, height, width]`，那么拼接后的 `input_img` 的形状就是 `[batch_size, channel*2, height, width]`。这种拼接操作通常用于将多个输入数据（比如图像和掩膜）合并成一个输入。

相关问题

给出edgeconnect中使用places2数据集训练的InpaintingModel_gen.pth的原版上下文编解码器代码，对128x128大小的不规则白色掩膜图像进行修复

以下是使用places2数据集训练的InpaintingModel_gen.pth的原版上下文编解码器代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


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

        # Encoder
        self.encoder = nn.Sequential(
            nn.Conv2d(4, opt.ngf, kernel_size=7, stride=1, padding=3, bias=False),
            nn.InstanceNorm2d(opt.ngf, affine=True),
            nn.ReLU(inplace=True),

            nn.Conv2d(opt.ngf, opt.ngf*2, kernel_size=4, stride=2, padding=1, bias=False),
            nn.InstanceNorm2d(opt.ngf*2, affine=True),
            nn.ReLU(inplace=True),

            nn.Conv2d(opt.ngf*2, opt.ngf*4, kernel_size=4, stride=2, padding=1, bias=False),
            nn.InstanceNorm2d(opt.ngf*4, affine=True),
            nn.ReLU(inplace=True)
        )

        # Decoder
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(opt.ngf*4, opt.ngf*2, kernel_size=4, stride=2, padding=1, bias=False),
            nn.InstanceNorm2d(opt.ngf*2, affine=True),
            nn.ReLU(inplace=True),

            nn.ConvTranspose2d(opt.ngf*2, opt.ngf, kernel_size=4, stride=2, padding=1, bias=False),
            nn.InstanceNorm2d(opt.ngf, affine=True),
            nn.ReLU(inplace=True),

            nn.Conv2d(opt.ngf, 3, kernel_size=7, stride=1, padding=3, bias=False),
            nn.Tanh()
        )

        # Contextual attention
        self.contextual_attention = ContextualAttention(kernel_size=3, stride=1, rate=2, fuse=False)

    def forward(self, x, mask):
        # Encoder
        enc_features = self.encoder(torch.cat([x, mask], dim=1))

        # Decoder
        dec_feature = self.decoder(enc_features)

        # Output
        output = dec_feature * mask + x[:, :3, :, :] * (1. - mask)

        # Attention
        attention = self.contextual_attention(output, mask)

        # Output with attention applied
        output = output + attention

        return output


# Contextual attention module
class ContextualAttention(nn.Module):
    def __init__(self, kernel_size=3, stride=1, rate=2, fuse=False):
        super(ContextualAttention, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride
        self.rate = rate
        self.fuse = fuse

        self.key_conv = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1)
        self.query_conv = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=1)

        self.softmax = nn.Softmax(dim=-1)

        self.fuse_conv = nn.Conv2d(in_channels=1024, out_channels=512, kernel_size=1)

    def forward(self, x, mask):
        batch_size, channels, height, width = x.size()

        # Pad feature maps to handle kernels and strides that don't cleanly divide the input dimensions
        kernel_size = self.kernel_size
        rate = self.rate
        stride = self.stride
        # Pad the feature map to fit the attention kernel
        pad_h = int(np.ceil(max(kernel_size, (height - kernel_size) % stride) / 2))
        pad_w = int(np.ceil(max(kernel_size, (width - kernel_size) % stride) / 2))
        # Add padding to the feature map
        x = F.pad(x, (pad_w, pad_w, pad_h, pad_h), mode='constant', value=0)
        mask = F.pad(mask, (pad_w, pad_w, pad_h, pad_h), mode='constant', value=0)

        # Generate keys, querys and values
        keys = self.key_conv(x)
        querys = self.query_conv(x)
        values = self.value_conv(x)

        # Split channels into groups and perform channel-wise convolution
        keys = keys.view(batch_size, 512, -1)
        keys = keys.permute(0, 2, 1)
        querys = querys.view(batch_size, 512, -1)
        energy = torch.bmm(querys, keys)
        attention = self.softmax(energy)

        # fuse mask and attention
        value_conv = values.view(batch_size, 512, -1)
        value_conv = value_conv.permute(0, 2, 1)
        context = torch.bmm(attention, value_conv)
        context = context.permute(0, 2, 1).contiguous()
        context = context.view(batch_size, -1, height, width)
        if self.fuse:
            fusion = torch.cat([context, x], dim=1)
            fusion = self.fuse_conv(fusion)
            context = context + fusion

        # Remove padding
        context = context[:, :, pad_h:-pad_h, pad_w:-pad_w]
        return context

以下是使用上述模型对128x128大小的不规则白色掩膜图像进行修复的示例代码：

from PIL import Image
import torch
import torchvision.transforms as transforms
import numpy as np
from models.networks import InpaintingModel

# Load the model
model = InpaintingModel(opt)

# Load the pre-trained weights
model.load_state_dict(torch.load('InpaintingModel_gen.pth', map_location='cpu'))

# Set model to evaluation mode
model.eval()

# Load the image and the mask
img = Image.open('image.png')
mask = Image.open('mask.png').convert('1')

# Resize the image and the mask to 128x128
img = img.resize((128, 128), resample=Image.BILINEAR)
mask = mask.resize((128, 128), resample=Image.BILINEAR)

# Convert the image and the mask to tensors
img_tensor = transforms.ToTensor()(img)
mask_tensor = transforms.ToTensor()(mask)

# Add the batch size dimension
img_tensor = img_tensor.unsqueeze(0)
mask_tensor = mask_tensor.unsqueeze(0)

# Inpaint the image
inpainted_tensor = model(img_tensor, mask_tensor)

# Convert the inpainted tensor to an image
inpainted = transforms.ToPILImage()(inpainted_tensor[0])

# Save the inpainted image
inpainted.save('inpainted.png')