esrgan代码复现pytorch

ESRGAN是一种基于深度学习的图像超分辨率重建方法，可以将低分辨率图像转换为高分辨率图像。以下是ESRGAN的PyTorch代码复现：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += residual
        out = self.relu(out)
        return out

class UpsampleBlock(nn.Module):
    def __init__(self, in_channels, up_scale):
        super(UpsampleBlock, self).__init__()
        self.conv = nn.Conv2d(in_channels, in_channels * up_scale ** 2, kernel_size=3, stride=1, padding=1)
        self.pixel_shuffle = nn.PixelShuffle(up_scale)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        out = self.conv(x)
        out = self.pixel_shuffle(out)
        out = self.relu(out)
        return out

class Generator(nn.Module):
    def __init__(self, scale_factor):
        super(Generator, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=9, stride=1, padding=4)
        self.relu = nn.ReLU(inplace=True)
        self.residual_blocks = nn.Sequential(
            ResidualBlock(64),
            ResidualBlock(64),
            ResidualBlock(64),            ResidualBlock(64),
            ResidualBlock(64)
        )
        self.conv2 = nn.Conv2d(64,64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.upsample_blocks = nn.Sequential(
            UpsampleBlock(64, scale_factor),
            UpsampleBlock(64, scale_factor),
            UpsampleBlock(64, scale_factor)
        )
        self.conv3 = nn.Conv2d(64, 3, kernel_size=9, stride=1, padding=4)

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu(out)
        residual = out
        out = self.residual_blocks(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += residual
        out = self.upsample_blocks(out)
        out = self.conv3(out)
        return out

这是一个简单的ESRGAN生成器模型，包含了残差块和上采样块。可以通过调整残差块和上采样块的数量和大小来改进模型的性能。