写出子像素卷积应用于EDSR算法的代码朋友torch

好的,我来为您介绍子像素卷积在EDSR算法中的应用,并提供使用PyTorch实现的代码示例。 EDSR(Enhanced Deep Super-Resolution)是一种先进的图像超分辨率算法,而子像素卷积是其关键组成部分之一。子像素卷积用于将低分辨率特征图放大到高分辨率图像。 以下是使用PyTorch实现EDSR中子像素卷积的代码: ```python import torch import torch.nn as nn import torch.nn.functional as F class SubPixelConv(nn.Module): def __init__(self, in_channels, out_channels, upscale_factor): super(SubPixelConv, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels * (upscale_factor ** 2), kernel_size=3, padding=1) self.pixel_shuffle = nn.PixelShuffle(upscale_factor) def forward(self, x): x = self.conv(x) x = self.pixel_shuffle(x) return x class EDSR(nn.Module): def __init__(self, num_layers=32, num_features=256, upscale_factor=4): super(EDSR, self).__init__() self.initial_conv = nn.Conv2d(3, num_features, kernel_size=3, padding=1) self.res_blocks = nn.Sequential(*[ResidualBlock(num_features) for _ in range(num_layers)]) self.subpixel_conv = SubPixelConv(num_features, num_features, upscale_factor) self.final_conv = nn.Conv2d(num_features, 3, kernel_size=3, padding=1) def forward(self, x): x = self.initial_conv(x) residual = x x = self.res_blocks(x) x += residual x = self.subpixel_conv(x) x = self.final_conv(x) return x class ResidualBlock(nn.Module): def __init__(self, num_features): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d(num_features, num_features, kernel_size=3, padding=1) self.relu = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(num_features, num_features, kernel_size=3, padding=1) def forward(self, x): residual = x x = self.relu(self.conv1(x)) x = self.conv2(x) x += residual return x # 使用示例 model = EDSR(num_layers=32, num_features=256, upscale_factor=4) input_tensor = torch.randn(1, 3, 32, 32) # 输入低分辨率图像 output_tensor = model(input_tensor) # 输出高分辨率图像 print(output_tensor.shape) # torch.Size([1, 3, 128, 128]) ``` 这段代码实现了一个基本的EDSR模型,其中包含了子像素卷积层。子像素卷积层使用PixelShuffle操作来实现图像的放大。 主要特点: 1. 使用nn.PixelShuffle实现子像素卷积,简化了上采样过程。 2. EDSR模型包含多个残差块,用于提取图像特征。 3. 可以通过调整num_layers和num_features参数来控制模型的复杂度。 4. 该实现支持任意上采样因子(upscale_factor)。