pytorch复现经典生成对抗式的超分辨率网络
论文原文：Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
论文的中文翻译：翻译：Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
网络结构如下图所示：
上面和下面分别是生成网络和判别网络：
废话不多说，直接看代码。比较不喜欢一堆废话的博客，懂得都懂，最有用的就是代码！
代码的实现参考pytorch torchvision中的网络实现优点：模块化、简洁易读、而且容易修改网络宽度和深度（方便调整网络架构做对比实验，消融实验）。
实现生成网络：
# -*- coding: utf-8 -*-
# @Use :
# @Time : 2022/8/17 21:32
# @FileName: models.py
# @Software: PyCharm
# @inference：https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
import torch
from torch import nn
import torchvision
from torch import Tensor
class GeneratorBasicBlock(nn.Module):
"""
生成器重复的部分
"""
def __init__(self, channel, kernel_size) -> None:
super(GeneratorBasicBlock, self).__init__()
self.channel = channel
self.conv1 = nn.Conv2d(in_channels=channel, out_channels=channel,
kernel_size=(kernel_size, kernel_size),
stride=(1, 1), padding=(1, 1))
self.bn1 = nn.BatchNorm2d(num_features=channel)
self.p_relu1 = nn.PReLU()
self.conv2 = nn.Conv2d(in_channels=channel, out_channels=channel,
kernel_size=(kernel_size, kernel_size),
stride=(1, 1), padding=(1, 1))
self.bn2 = nn.BatchNorm2d(num_features=channel)
def forward(self, x: Tensor) -> Tensor:
"""
前向推断
:param x:
:return:
"""
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.p_relu1(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
return out
class PixelShufflerBlock(nn.Module):
"""
生成器最后的pixelshuffler
"""
def __init__(self, in_channel, out_channel) -> None:
super(PixelShufflerBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,
kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
self.pixels_shuffle = nn.PixelShuffle(upscale_factor=2)
self.prelu = nn.PReLU()
def forward(self, x: Tensor) -> Tensor:
"""
前向
"""
out = self.conv1(x)
out = self.pixels_shuffle(out)
out = self.prelu(out)
return out
class Generator(nn.Module):
"""
生成器
"""
def __init__(self, config) -> None:
# Generator parameters
super(Generator, self).__init__()
large_kernel_size = config.G.large_kernel_size # = 9
small_kernel_size = config.G.small_kernel_size # = 3
n_channels = config.G.n_channels # = 64
n_blocks = config.G.n_blocks # = 16
base_block_type = config.G.base_block_type # 'depthwise_conv_residual' # 'conv_residual' or 'depthwise_conv_residual'
# base block
if base_block_type == 'depthwise_conv_residual':
self.repeat_block = GeneratorDepthwiseBlock
if base_block_type == 'conv_residual':
self.repeat_block = GeneratorBasicBlock
self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_channels,
kernel_size=(large_kernel_size, large_kernel_size),
stride=(1, 1), padding=(4, 4))
self.prelu1 = nn.PReLU()
self.B_residul_block = self._make_layer(self.repeat_block, n_channels,
n_blocks, small_kernel_size)
self.conv2 = nn.Conv2d(in_channels=n_channels, out_channels=n_channels,
kernel_size=(small_kernel_size, small_kernel_size),
stride=(1, 1), padding=(1, 1))
self.bn1 = nn.BatchNorm2d(n_channels)
self.pixel_shuffle_block1 = PixelShufflerBlock(n_channels, 4 * n_channels)
self.pixel_shuffle_block2 = PixelShufflerBlock(n_channels, 4 * n_channels)
self.conv3 = nn.Conv2d(in_channels=n_channels, out_channels=3,
kernel_size=(large_kernel_size, large_kernel_size),
stride=(1, 1), padding=(4, 4))
def _make_layer(self, base_block, n_channels, n_block, kernel_size) -> nn.Sequential:
"""
构建重复的B个基本块
:param base_block: 基本块
:param n_channels: 块里面的通道数
:param n_block: 块数
:return:
"""
layers = []
self.base_block = base_block
for _ in range(n_block):
layers.append(self.base_block(n_channels, kernel_size))
return nn.Sequential(*layers)
def _forward_impl(self, x: Tensor) -> Tensor:
"""
前向的实现
"""
out = self.conv1(x)
out = self.prelu1(out)
identity = out
out = self.B_residul_block(out)
out = self.conv2(out)
out = self.bn1(out)
out += identity
out = self.pixel_shuffle_block1(out)
out = self.pixel_shuffle_block2(out)
out = self.conv3(out)
return out
def forward(self, x: Tensor) -> Tensor:
"""
前向
"""
return self._forward_impl(x)
判别网络实现：
class DiscriminatorBaseblock(nn.Module):
"""
判别器的基本块
"""
def __init__(self, inchannel, out_chanel, kernel_size, stride):
super(DiscriminatorBaseblock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=inchannel, out_channels=out_chanel,
kernel_size=kernel_size, stride=stride, padding=(1, 1))
self.bn1 = nn.BatchNorm2d(out_chanel)
self.act1 = nn.LeakyReLU(0.2)
def forward(self, x: Tensor) -> Tensor:
"""
前向
"""
out = self.conv1(x)
out = self.bn1(out)
out = self.act1(out)
return out
class Discriminator(nn.Module):
"""
判别器
"""
def __init__(self, config):
super(Discriminator, self).__init__()
# Discriminator parameters
kernel_size = config.D.kernel_size = 3
n_channels = config.D.n_channels = 64
n_blocks = config.D.n_blocks = 8
fc_size = config.D.fc_size = 1024
self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_channels,
kernel_size=(kernel_size, kernel_size), stride=(1, 1), padding=(1, 1))
self.leaky_relu1 = nn.LeakyReLU(0.2)
conv_configs = [[3, 64, 2], # 配置二维数组
[3, 128, 1],
[3, 128, 2],
[3, 256, 1],
[3, 256, 2],
[3, 512, 1],
[3, 512, 2]]
self.base_blocks = self._make_layer(n_channels, DiscriminatorBaseblock, conv_configs)
self.dense1 = nn.Linear(512 * 6 * 6, 1024)
self.leaky_relu2 = nn.LeakyReLU(0.2)
self.dense2 = nn.Linear(1024, 1)
self.sigmod1 = nn.Sigmoid()
def _make_layer(self, in_channel, base_block, conv_configs: list) -> nn.Sequential:
"""
:param base_block: DiscriminatorBaseblock
:param conv_configs: (kernel, channel, stride)
:return:
"""
layers = []
self.base_block = base_block
self.in_channel = in_channel
for i in range(len(conv_configs)):
# in_channel, out_chanel, kernel_size, stride
kernel_size = (conv_configs[i][0], conv_configs[i][0])
stride = (conv_configs[i][2], conv_configs[i][2])
out_channel = conv_configs[i][1]
layers.append(self.base_block(self.in_channel, out_channel, kernel_size, stride))
self.in_channel = out_channel
return nn.Sequential(*layers)
def _forward_impl(self, x: Tensor) -> Tensor:
"""
前向
"""
out = self.conv1(x)
out = self.leaky_relu1(out)
out = self.base_blocks(out)
out = torch.flatten(out, 1)
out = self.dense1(out)
out = self.leaky_relu2(out)
out = self.dense2(out)
out = self.sigmod1(out)
return out
def forward(self, x: Tensor) -> Tensor:
"""
前向
"""
return self._forward_impl(x)