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学习网站制作,免费发广告的网站,做一钓鱼网站吗,网站建设seo需求文档更新版本实现了生成对抗网络在超分辨率上的使用更新了损失函数，增加先验函数 SRresnet实现 import torch import torchvision from torch import nnclass ConvBlock(nn.Module):def __init__(self, kernel_size3, stride1, n_inchannels64):super(ConvBlock…

更新版本
实现了生成对抗网络在超分辨率上的使用
更新了损失函数，增加先验函数
SRresnet实现

import torch
import torchvision
from torch import nnclass ConvBlock(nn.Module):def __init__(self, kernel_size=3, stride=1, n_inchannels=64):super(ConvBlock, self).__init__()self.sequential = nn.Sequential(nn.Conv2d(in_channels=n_inchannels, out_channels=n_inchannels, kernel_size=(kernel_size, kernel_size),stride=(stride, stride), bias=False, padding=(1, 1)),nn.BatchNorm2d(n_inchannels),nn.PReLU(),nn.Conv2d(in_channels=n_inchannels, out_channels=n_inchannels, kernel_size=(kernel_size, kernel_size),stride=(stride, stride), bias=False, padding=(1, 1)),nn.BatchNorm2d(n_inchannels),nn.PReLU(),)def forward(self, x):redisious = xout = self.sequential(x)return redisious + outclass Head_Conv(nn.Module):def __init__(self):super(Head_Conv, self).__init__()self.sequential = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(9, 9), stride=(1, 1), padding=(9 // 2, 9 // 2)),nn.PReLU(),)def forward(self, x):return self.sequential(x)class PixelShuffle(nn.Module):def __init__(self, n_channels=64, upscale_factor=2):super(PixelShuffle, self).__init__()self.sequential = nn.Sequential(nn.Conv2d(in_channels=n_channels, out_channels=n_channels * (upscale_factor ** 2), kernel_size=(3, 3),stride=(1, 1), padding=(3 // 2, 3 // 2)),nn.BatchNorm2d(n_channels * (upscale_factor ** 2)),nn.PixelShuffle(upscale_factor=upscale_factor))def forward(self, x):return self.sequential(x)class Hidden_block(nn.Module):def __init__(self):super(Hidden_block, self).__init__()self.sequential = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(3 // 2, 3 // 2)),nn.BatchNorm2d(64),)def forward(self, x):return self.sequential(x)class TailConv(nn.Module):def __init__(self):super(TailConv, self).__init__()self.sequential = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=3, kernel_size=(9, 9), stride=(1, 1), padding=(9 // 2, 9 // 2)),nn.Tanh(),)def forward(self, x):return self.sequential(x)class SRResNet(nn.Module):def __init__(self, n_blocks=16):super(SRResNet, self).__init__()self.head = Head_Conv()self.resnet = list()for _ in range(n_blocks):self.resnet.append(ConvBlock(kernel_size=3, stride=1, n_inchannels=64))self.resnet = nn.Sequential(*self.resnet)self.hidden = Hidden_block()self.pixelShuufe = []for _ in range(2):self.pixelShuufe.append(PixelShuffle(n_channels=64, upscale_factor=2))self.pixelShuufe = nn.Sequential(*self.pixelShuufe)self.tail_conv = TailConv()def forward(self, x):head_out = self.head(x)resnet_out = self.resnet(head_out)out = head_out + resnet_outresult = self.pixelShuufe(out)out = self.tail_conv(result)return out


class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.model = SRResNet()def forward(self, x):''':param x:lr_img:return: '''return self.model(x)class Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.hidden = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(3 // 2, 3 // 2)),nn.LeakyReLU(),nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),nn.BatchNorm2d(64),nn.LeakyReLU(),nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)),nn.BatchNorm2d(512),nn.LeakyReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)),nn.BatchNorm2d(512),nn.LeakyReLU(),nn.AdaptiveAvgPool2d((6, 6)))self.out_layer = nn.Sequential(nn.Linear(512 * 6 * 6, 1024),nn.LeakyReLU(negative_slope=0.2, inplace=True),nn.Linear(1024, 1),nn.Sigmoid())def forward(self, x):result = self.hidden(x)# print(result.shape)result = result.reshape(result.shape[0], -1)out = self.out_layer(result)return out

SRGAN模型的生成器与判别器的实现


class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.model = SRResNet()def forward(self, x):''':param x:lr_img:return: '''return self.model(x)class Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.hidden = nn.Sequential(nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(3, 3), stride=(1, 1), padding=(3 // 2, 3 // 2)),nn.LeakyReLU(),nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),nn.BatchNorm2d(64),nn.LeakyReLU(),nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3, 3), stride=(1, 1), padding=(0, 0)),nn.BatchNorm2d(512),nn.LeakyReLU(),nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(2, 2), padding=(0, 0)),nn.BatchNorm2d(512),nn.LeakyReLU(),nn.AdaptiveAvgPool2d((6, 6)))self.out_layer = nn.Sequential(nn.Linear(512 * 6 * 6, 1024),nn.LeakyReLU(negative_slope=0.2, inplace=True),nn.Linear(1024, 1),nn.Sigmoid())def forward(self, x):result = self.hidden(x)# print(result.shape)result = result.reshape(result.shape[0], -1)out = self.out_layer(result)return out```
- 针对VGG19 的层数截取
```python
class TruncatedVGG19(nn.Module):"""truncated VGG19网络，用于计算VGG特征空间的MSE损失"""def __init__(self, i, j):""":参数 i: 第 i 个池化层:参数 j: 第 j 个卷积层"""super(TruncatedVGG19, self).__init__()# 加载预训练的VGG模型vgg19 = torchvision.models.vgg19(pretrained=True)print(vgg19)maxpool_counter = 0conv_count = 0truncate_at = 0# 迭代搜索for layer in vgg19.features.children():truncate_at += 1# 统计if isinstance(layer, nn.Conv2d):conv_count += 1if isinstance(layer, nn.MaxPool2d):maxpool_counter += 1conv_counter = 0# 截断位置在第(i-1)个池化层之后（第 i 个池化层之前）的第 j 个卷积层if maxpool_counter == i - 1 and conv_count == j:break# 检查是否满足条件assert maxpool_counter == i - 1 and conv_count == j, "当前 i=%d 、 j=%d 不满足 VGG19 模型结构" % (i, j)# 截取网络self.truncated_vgg19 = nn.Sequential(*list(vgg19.features.children())[:truncate_at + 1])def forward(self, input):output = self.truncated_vgg19(input)  # (N, channels, _w,h)return output
```