第一步： GLCIC介绍
GLCIC-PyTorch是一个基于PyTorch的开源项目，它实现了"全局和局部一致性图像修复"方法。该方法由Iizuka等人提出，主要用于图像修复任务，能够有效地恢复图像中被遮挡或损坏的部分。项目使用Python编程语言编写，并依赖于PyTorch深度学习框架。
第二步：GLCIC网络结构
项目的核心功能是图像修复，它通过训练一个生成网络（Completion Network）和一个判别网络（Context Discriminator）来实现。生成网络负责完成图像修复任务，而判别网络则用于提高修复质量，确保修复后的图像在全局和局部上都与原始图像保持一致性。主要特点如下：
图像修复：利用生成网络对图像中缺失的部分进行修复。
全局与局部一致性：确保修复后的图像既在全局上与原图一致，又在局部细节上保持连贯。
判别网络辅助：通过判别网络对生成图像进行评估，以提升修复质量。
第三步：模型代码展示
python
import torch
import torch.nn as nn
import torch.nn.functional as F
from layers import Flatten, Concatenate
class CompletionNetwork(nn.Module):
def __init__(self):
super(CompletionNetwork, self).__init__()
# input_shape: (None, 4, img_h, img_w)
self.conv1 = nn.Conv2d(4, 64, kernel_size=5, stride=1, padding=2)
self.bn1 = nn.BatchNorm2d(64)
self.act1 = nn.ReLU()
# input_shape: (None, 64, img_h, img_w)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.act2 = nn.ReLU()
# input_shape: (None, 128, img_h//2, img_w//2)
self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(128)
self.act3 = nn.ReLU()
# input_shape: (None, 128, img_h//2, img_w//2)
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
self.bn4 = nn.BatchNorm2d(256)
self.act4 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv5 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.bn5 = nn.BatchNorm2d(256)
self.act5 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.bn6 = nn.BatchNorm2d(256)
self.act6 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv7 = nn.Conv2d(256, 256, kernel_size=3, stride=1, dilation=2, padding=2)
self.bn7 = nn.BatchNorm2d(256)
self.act7 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv8 = nn.Conv2d(256, 256, kernel_size=3, stride=1, dilation=4, padding=4)
self.bn8 = nn.BatchNorm2d(256)
self.act8 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv9 = nn.Conv2d(256, 256, kernel_size=3, stride=1, dilation=8, padding=8)
self.bn9 = nn.BatchNorm2d(256)
self.act9 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv10 = nn.Conv2d(256, 256, kernel_size=3, stride=1, dilation=16, padding=16)
self.bn10 = nn.BatchNorm2d(256)
self.act10 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv11 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.bn11 = nn.BatchNorm2d(256)
self.act11 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.conv12 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.bn12 = nn.BatchNorm2d(256)
self.act12 = nn.ReLU()
# input_shape: (None, 256, img_h//4, img_w//4)
self.deconv13 = nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1)
self.bn13 = nn.BatchNorm2d(128)
self.act13 = nn.ReLU()
# input_shape: (None, 128, img_h//2, img_w//2)
self.conv14 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.bn14 = nn.BatchNorm2d(128)
self.act14 = nn.ReLU()
# input_shape: (None, 128, img_h//2, img_w//2)
self.deconv15 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)
self.bn15 = nn.BatchNorm2d(64)
self.act15 = nn.ReLU()
# input_shape: (None, 64, img_h, img_w)
self.conv16 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1)
self.bn16 = nn.BatchNorm2d(32)
self.act16 = nn.ReLU()
# input_shape: (None, 32, img_h, img_w)
self.conv17 = nn.Conv2d(32, 3, kernel_size=3, stride=1, padding=1)
self.act17 = nn.Sigmoid()
# output_shape: (None, 3, img_h. img_w)
def forward(self, x):
x = self.bn1(self.act1(self.conv1(x)))
x = self.bn2(self.act2(self.conv2(x)))
x = self.bn3(self.act3(self.conv3(x)))
x = self.bn4(self.act4(self.conv4(x)))
x = self.bn5(self.act5(self.conv5(x)))
x = self.bn6(self.act6(self.conv6(x)))
x = self.bn7(self.act7(self.conv7(x)))
x = self.bn8(self.act8(self.conv8(x)))
x = self.bn9(self.act9(self.conv9(x)))
x = self.bn10(self.act10(self.conv10(x)))
x = self.bn11(self.act11(self.conv11(x)))
x = self.bn12(self.act12(self.conv12(x)))
x = self.bn13(self.act13(self.deconv13(x)))
x = self.bn14(self.act14(self.conv14(x)))
x = self.bn15(self.act15(self.deconv15(x)))
x = self.bn16(self.act16(self.conv16(x)))
x = self.act17(self.conv17(x))
return x
class LocalDiscriminator(nn.Module):
def __init__(self, input_shape):
super(LocalDiscriminator, self).__init__()
self.input_shape = input_shape
self.output_shape = (1024,)
self.img_c = input_shape[0]
self.img_h = input_shape[1]
self.img_w = input_shape[2]
# input_shape: (None, img_c, img_h, img_w)
self.conv1 = nn.Conv2d(self.img_c, 64, kernel_size=5, stride=2, padding=2)
self.bn1 = nn.BatchNorm2d(64)
self.act1 = nn.ReLU()
# input_shape: (None, 64, img_h//2, img_w//2)
self.conv2 = nn.Conv2d(64, 128, kernel_size=5, stride=2, padding=2)
self.bn2 = nn.BatchNorm2d(128)
self.act2 = nn.ReLU()
# input_shape: (None, 128, img_h//4, img_w//4)
self.conv3 = nn.Conv2d(128, 256, kernel_size=5, stride=2, padding=2)
self.bn3 = nn.BatchNorm2d(256)
self.act3 = nn.ReLU()
# input_shape: (None, 256, img_h//8, img_w//8)
self.conv4 = nn.Conv2d(256, 512, kernel_size=5, stride=2, padding=2)
self.bn4 = nn.BatchNorm2d(512)
self.act4 = nn.ReLU()
# input_shape: (None, 512, img_h//16, img_w//16)
self.conv5 = nn.Conv2d(512, 512, kernel_size=5, stride=2, padding=2)
self.bn5 = nn.BatchNorm2d(512)
self.act5 = nn.ReLU()
# input_shape: (None, 512, img_h//32, img_w//32)
in_features = 512 * (self.img_h//32) * (self.img_w//32)
self.flatten6 = Flatten()
# input_shape: (None, 512 * img_h//32 * img_w//32)
self.linear6 = nn.Linear(in_features, 1024)
self.act6 = nn.ReLU()
# output_shape: (None, 1024)
def forward(self, x):
x = self.bn1(self.act1(self.conv1(x)))
x = self.bn2(self.act2(self.conv2(x)))
x = self.bn3(self.act3(self.conv3(x)))
x = self.bn4(self.act4(self.conv4(x)))
x = self.bn5(self.act5(self.conv5(x)))
x = self.act6(self.linear6(self.flatten6(x)))
return x
class GlobalDiscriminator(nn.Module):
def __init__(self, input_shape, arc='celeba'):
super(GlobalDiscriminator, self).__init__()
self.arc = arc
self.input_shape = input_shape
self.output_shape = (1024,)
self.img_c = input_shape[0]
self.img_h = input_shape[1]
self.img_w = input_shape[2]
# input_shape: (None, img_c, img_h, img_w)
self.conv1 = nn.Conv2d(self.img_c, 64, kernel_size=5, stride=2, padding=2)
self.bn1 = nn.BatchNorm2d(64)
self.act1 = nn.ReLU()
# input_shape: (None, 64, img_h//2, img_w//2)
self.conv2 = nn.Conv2d(64, 128, kernel_size=5, stride=2, padding=2)
self.bn2 = nn.BatchNorm2d(128)
self.act2 = nn.ReLU()
# input_shape: (None, 128, img_h//4, img_w//4)
self.conv3 = nn.Conv2d(128, 256, kernel_size=5, stride=2, padding=2)
self.bn3 = nn.BatchNorm2d(256)
self.act3 = nn.ReLU()
# input_shape: (None, 256, img_h//8, img_w//8)
self.conv4 = nn.Conv2d(256, 512, kernel_size=5, stride=2, padding=2)
self.bn4 = nn.BatchNorm2d(512)
self.act4 = nn.ReLU()
# input_shape: (None, 512, img_h//16, img_w//16)
self.conv5 = nn.Conv2d(512, 512, kernel_size=5, stride=2, padding=2)
self.bn5 = nn.BatchNorm2d(512)
self.act5 = nn.ReLU()
# input_shape: (None, 512, img_h//32, img_w//32)
if arc == 'celeba':
in_features = 512 * (self.img_h//32) * (self.img_w//32)
self.flatten6 = Flatten()
self.linear6 = nn.Linear(in_features, 1024)
self.act6 = nn.ReLU()
elif arc == 'places2':
self.conv6 = nn.Conv2d(512, 512, kernel_size=5, stride=2, padding=2)
self.bn6 = nn.BatchNorm2d(512)
self.act6 = nn.ReLU()
# input_shape (None, 512, img_h//64, img_w//64)
in_features = 512 * (self.img_h//64) * (self.img_w//64)
self.flatten7 = Flatten()
self.linear7 = nn.Linear(in_features, 1024)
self.act7 = nn.ReLU()
else:
raise ValueError('Unsupported architecture \'%s\'.' % self.arc)
# output_shape: (None, 1024)
def forward(self, x):
x = self.bn1(self.act1(self.conv1(x)))
x = self.bn2(self.act2(self.conv2(x)))
x = self.bn3(self.act3(self.conv3(x)))
x = self.bn4(self.act4(self.conv4(x)))
x = self.bn5(self.act5(self.conv5(x)))
if self.arc == 'celeba':
x = self.act6(self.linear6(self.flatten6(x)))
elif self.arc == 'places2':
x = self.bn6(self.act6(self.conv6(x)))
x = self.act7(self.linear7(self.flatten7(x)))
return x
class ContextDiscriminator(nn.Module):
def __init__(self, local_input_shape, global_input_shape, arc='celeba'):
super(ContextDiscriminator, self).__init__()
self.arc = arc
self.input_shape = [local_input_shape, global_input_shape]
self.output_shape = (1,)
self.model_ld = LocalDiscriminator(local_input_shape)
self.model_gd = GlobalDiscriminator(global_input_shape, arc=arc)
# input_shape: [(None, 1024), (None, 1024)]
in_features = self.model_ld.output_shape[-1] + self.model_gd.output_shape[-1]
self.concat1 = Concatenate(dim=-1)
# input_shape: (None, 2048)
self.linear1 = nn.Linear(in_features, 1)
self.act1 = nn.Sigmoid()
# output_shape: (None, 1)
def forward(self, x):
x_ld, x_gd = x
x_ld = self.model_ld(x_ld)
x_gd = self.model_gd(x_gd)
out = self.act1(self.linear1(self.concat1([x_ld, x_gd])))
return out第四步：运行交互代码
第五步：整个工程的内容
项目完整文件下载请见演示与介绍视频的简介处给出：➷➷➷
图像修复：深度学习GLCIC神经网络实现老照片划痕修复_哔哩哔哩_bilibili
