asp建网站,怎么免费给自己建网站,体验营销策略,网站域名注销备案更新版本 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#xff0c;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_size3, stride1, n_inchannels64):super(ConvBlock, self).__init__()self.sequential nn.Sequential(nn.Conv2d(in_channelsn_inchannels, out_channelsn_inchannels, kernel_size(kernel_size, kernel_size),stride(stride, stride), biasFalse, padding(1, 1)),nn.BatchNorm2d(n_inchannels),nn.PReLU(),nn.Conv2d(in_channelsn_inchannels, out_channelsn_inchannels, kernel_size(kernel_size, kernel_size),stride(stride, stride), biasFalse, 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_channels3, out_channels64, 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_channels64, upscale_factor2):super(PixelShuffle, self).__init__()self.sequential nn.Sequential(nn.Conv2d(in_channelsn_channels, out_channelsn_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_factorupscale_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_channels64, out_channels64, 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_channels64, out_channels3, 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_blocks16):super(SRResNet, self).__init__()self.head Head_Conv()self.resnet list()for _ in range(n_blocks):self.resnet.append(ConvBlock(kernel_size3, stride1, n_inchannels64))self.resnet nn.Sequential(*self.resnet)self.hidden Hidden_block()self.pixelShuufe []for _ in range(2):self.pixelShuufe.append(PixelShuffle(n_channels64, upscale_factor2))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 outclass 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_channels3, out_channels64, kernel_size(3, 3), stride(1, 1), padding(3 // 2, 3 // 2)),nn.LeakyReLU(),nn.Conv2d(in_channels64, out_channels64, kernel_size(3, 3), stride(2, 2), padding(1, 1)),nn.BatchNorm2d(64),nn.LeakyReLU(),nn.Conv2d(in_channels64, out_channels128, kernel_size(3, 3), stride(1, 1), padding(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels128, out_channels128, kernel_size(3, 3), stride(2, 2), padding(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels128, out_channels256, kernel_size(3, 3), stride(1, 1), padding(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels256, out_channels256, kernel_size(3, 3), stride(2, 2), padding(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels256, out_channels512, kernel_size(3, 3), stride(1, 1), padding(0, 0)),nn.BatchNorm2d(512),nn.LeakyReLU(),nn.Conv2d(in_channels512, out_channels512, 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_slope0.2, inplaceTrue),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_channels3, out_channels64, kernel_size(3, 3), stride(1, 1), padding(3 // 2, 3 // 2)),nn.LeakyReLU(),nn.Conv2d(in_channels64, out_channels64, kernel_size(3, 3), stride(2, 2), padding(1, 1)),nn.BatchNorm2d(64),nn.LeakyReLU(),nn.Conv2d(in_channels64, out_channels128, kernel_size(3, 3), stride(1, 1), padding(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels128, out_channels128, kernel_size(3, 3), stride(2, 2), padding(0, 0)),nn.BatchNorm2d(128),nn.LeakyReLU(),nn.Conv2d(in_channels128, out_channels256, kernel_size(3, 3), stride(1, 1), padding(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels256, out_channels256, kernel_size(3, 3), stride(2, 2), padding(0, 0)),nn.BatchNorm2d(256),nn.LeakyReLU(),nn.Conv2d(in_channels256, out_channels512, kernel_size(3, 3), stride(1, 1), padding(0, 0)),nn.BatchNorm2d(512),nn.LeakyReLU(),nn.Conv2d(in_channels512, out_channels512, 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_slope0.2, inplaceTrue),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(pretrainedTrue)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
