edge connect网络结构

EdgeConnect网络结构是一个二阶段生成对抗网络，包括边缘生成器和图像补全网络。边缘生成器用于在图像的缺失区域生成预测边缘，而图像补全网络则利用这些预测边缘作为先验来填充缺失区域。\[3\]这个网络结构的目的是通过生成幻觉边缘来还原缺失的图像细节，首先是线条，然后是颜色。\[2\]通过对公开可用的数据集进行评估，研究者表明EdgeConnect在数量和质量上优于当前最先进的技术。\[3\]
#### 引用[.reference_title]
- *1* *2* *3* [《Generative Image Inpainting with Adversarial Edge Learning》论文阅读之edge-connect](https://blog.csdn.net/Gavinmiaoc/article/details/87873462)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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相关问题

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')

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

以下是 InpaintingModel 的上下文编解码的网络结构原版代码：

class ContextualAttention(nn.Module):
    def __init__(self, kernel_size=3):
        super(ContextualAttention, self).__init__()
        self.kernel_size = kernel_size
        self.softmax = nn.Softmax(dim=3)
        
    def forward(self, f, b, mask=None):
        # f: foreground, b: background
        # f: (n, c, h, w), b: (n, c, h, w), mask: (n, 1, h, w)
        n, c, h, w = f.size()
        kh, kw = self.kernel_size, self.kernel_size
        
        # padding the feature maps
        pad_h = int((kh - 1) / 2)
        pad_w = int((kw - 1) / 2)
        f = F.pad(f, (pad_w, pad_w, pad_h, pad_h))
        b = F.pad(b, (pad_w, pad_w, pad_h, pad_h))
        mask = F.pad(mask, (pad_w, pad_w, pad_h, pad_h))

        # convolve the padded foreground with a kernel to get the key feature map
        kernel = torch.ones(c, 1, kh, kw).to(f.device)
        key = F.conv2d(f * mask, kernel, stride=1, padding=0)
        key = key.view(n, c, -1)
        key = key.permute(0, 2, 1)

        # convolve the padded background with a kernel to get the query feature map
        query = F.conv2d(b * mask, kernel, stride=1, padding=0)
        query = query.view(n, c, -1)

        # obtain the spatial attention map
        attn = torch.bmm(key, query)
        attn = self.softmax(attn)
        
        # obtain the context feature map
        value = F.conv2d(b, kernel, stride=1, padding=0)
        value = value.view(n, c, -1)
        context = torch.bmm(value, attn.permute(0, 2, 1))
        context = context.view(n, c, kh, kw)
        
        return context

使用该原版实现载入 InpaintingModel 的预训练模型进行不规则掩膜修复的功能，可以按以下步骤进行：

1. 安装所需的 Python 库：

pip install numpy opencv-python torch torchvision

2. 下载预训练模型：

wget https://github.com/knazeri/edge-connect/releases/download/v1.0/pytorch_edge_connect.tar.gz
tar -zxvf pytorch_edge_connect.tar.gz

3. 加载预训练模型，进行不规则掩膜修复：

import cv2
import numpy as np
import torch
import torch.nn.functional as F
from models import EdgeGenerator, InpaintingModel
from utils import get_edges, tensor2im, mask_image
from PIL import Image

# Load the EdgeGenerator
edge_generator = EdgeGenerator()
edge_generator.load_state_dict(torch.load('pytorch_edge_connect/checkpoints/latest_net_G.pth'))
edge_generator.eval()

# Load the InpaintingModel
inpainting_model = InpaintingModel()
inpainting_model.load_state_dict(torch.load('pytorch_edge_connect/checkpoints/latest_net_E.pth'))
inpainting_model.eval()

# Read the input image and the mask
img = cv2.imread('input.png')
mask = cv2.imread('mask.png', 0)

# Convert the input image to a tensor
img_tensor = torch.from_numpy(np.transpose(img, (2, 0, 1))).float().unsqueeze(0) / 255.0

# Convert the mask to a tensor
mask_tensor = torch.from_numpy(mask).unsqueeze(0).unsqueeze(0).float() / 255.0

# Generate the edges
edges_tensor = get_edges(img_tensor)

# Generate the inpainted image
with torch.no_grad():
    _, _, _, _, gen_mask = edge_generator(img_tensor, edges_tensor, mask_tensor)
    inpainted_img_tensor = inpainting_model(img_tensor, gen_mask)

# Convert the inpainted image tensor to a numpy array
inpainted_img = tensor2im(inpainted_img_tensor)

# Save the inpainted image
cv2.imwrite('output.png', inpainted_img)