class Generator(nn.Module): def __init__(self, n_residual_blocks, upsample_factor): super(Generator, self).__init__() self.n_residual_blocks = n_residual_blocks self.upsample_factor = upsample_factor self.conv1 = nn.Conv2d(3, 64, 9, stride=1, padding=4) for i in range(self.n_residual_blocks): self.add_module('residual_block' + str(i+1), residualBlock()) self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(64) for i in range(self.upsample_factor/2): self.add_module('upsample' + str(i+1), upsampleBlock(64, 256)) self.conv3 = nn.Conv2d(64, 3, 9, stride=1, padding=4) def forward(self, x): x = swish(self.conv1(x)) y = x.clone() for i in range(self.n_residual_blocks): y = self.__getattr__('residual_block' + str(i+1))(y) x = self.bn2(self.conv2(y)) + x for i in range(self.upsample_factor/2): x = self.__getattr__('upsample' + str(i+1))(x) return self.conv3(x)
时间: 2024-04-19 09:26:52 浏览: 13
这是一个生成器模型的代码,用于图像超分辨率重建任务。该模型使用了残差块和上采样块来增加分辨率。
在初始化函数中,我们定义了模型的结构。首先是一个卷积层(conv1),将输入通道数为3的图像进行卷积操作。然后使用循环为模型添加了n_residual_blocks个残差块(residualBlock)。接着是第二个卷积层(conv2),再加上批归一化层(bn2)。随后,使用循环为模型添加了upsample_factor/2个上采样块(upsampleBlock)。最后,通过卷积层(conv3)生成输出图像。
在前向传播函数中,我们首先对输入图像进行卷积操作,并使用Swish激活函数。然后,将输入图像克隆给变量y,并通过循环使用残差块对y进行处理。接着,将y与经过卷积和批归一化操作的x相加得到新的x。最后,通过循环使用上采样块对x进行处理,并返回通过卷积操作生成的输出图像。
相关问题
运行以下Python代码:import torchimport torch.nn as nnimport torch.optim as optimfrom torchvision import datasets, transformsfrom torch.utils.data import DataLoaderfrom torch.autograd import Variableclass Generator(nn.Module): def __init__(self, input_dim, output_dim, num_filters): super(Generator, self).__init__() self.input_dim = input_dim self.output_dim = output_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters), nn.ReLU(), nn.Linear(num_filters, num_filters*2), nn.ReLU(), nn.Linear(num_filters*2, num_filters*4), nn.ReLU(), nn.Linear(num_filters*4, output_dim), nn.Tanh() ) def forward(self, x): x = self.net(x) return xclass Discriminator(nn.Module): def __init__(self, input_dim, num_filters): super(Discriminator, self).__init__() self.input_dim = input_dim self.num_filters = num_filters self.net = nn.Sequential( nn.Linear(input_dim, num_filters*4), nn.LeakyReLU(0.2), nn.Linear(num_filters*4, num_filters*2), nn.LeakyReLU(0.2), nn.Linear(num_filters*2, num_filters), nn.LeakyReLU(0.2), nn.Linear(num_filters, 1), nn.Sigmoid() ) def forward(self, x): x = self.net(x) return xclass ConditionalGAN(object): def __init__(self, input_dim, output_dim, num_filters, learning_rate): self.generator = Generator(input_dim, output_dim, num_filters) self.discriminator = Discriminator(input_dim+1, num_filters) self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate) self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate) def train(self, data_loader, num_epochs): for epoch in range(num_epochs): for i, (inputs, labels) in enumerate(data_loader): # Train discriminator with real data real_inputs = Variable(inputs) real_labels = Variable(labels) real_labels = real_labels.view(real_labels.size(0), 1) real_inputs = torch.cat((real_inputs, real_labels), 1) real_outputs = self.discriminator(real_inputs) real_loss = nn.BCELoss()(real_outputs, torch.ones(real_outputs.size())) # Train discriminator with fake data noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0, 10)) fake_labels = fake_labels.view(fake_labels.size(0), 1) fake_inputs = self.generator(torch.cat((noise, fake_labels.float()), 1)) fake_inputs = torch.cat((fake_inputs, fake_labels), 1) fake_outputs = self.discriminator(fake_inputs) fake_loss = nn.BCELoss()(fake_outputs, torch.zeros(fake_outputs.size())) # Backpropagate and update weights for discriminator discriminator_loss = real_loss + fake_loss self.discriminator.zero_grad() discriminator_loss.backward() self.optimizer_D.step() # Train generator noise = Variable(torch.randn(inputs.size(0), self.generator.input_dim)) fake_labels = Variable(torch.LongTensor(inputs.size(0)).random_(0,
这是一个用 PyTorch 实现的条件 GAN,以下是代码的简要解释:
首先引入 PyTorch 相关的库和模块:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
```
接下来定义生成器(Generator)和判别器(Discriminator)的类:
```
class Generator(nn.Module):
def __init__(self, input_dim, output_dim, num_filters):
super(Generator, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.num_filters = num_filters
self.net = nn.Sequential(
nn.Linear(input_dim, num_filters),
nn.ReLU(),
nn.Linear(num_filters, num_filters*2),
nn.ReLU(),
nn.Linear(num_filters*2, num_filters*4),
nn.ReLU(),
nn.Linear(num_filters*4, output_dim),
nn.Tanh()
)
def forward(self, x):
x = self.net(x)
return x
class Discriminator(nn.Module):
def __init__(self, input_dim, num_filters):
super(Discriminator, self).__init__()
self.input_dim = input_dim
self.num_filters = num_filters
self.net = nn.Sequential(
nn.Linear(input_dim, num_filters*4),
nn.LeakyReLU(0.2),
nn.Linear(num_filters*4, num_filters*2),
nn.LeakyReLU(0.2),
nn.Linear(num_filters*2, num_filters),
nn.LeakyReLU(0.2),
nn.Linear(num_filters, 1),
nn.Sigmoid()
)
def forward(self, x):
x = self.net(x)
return x
```
其中,生成器接受输入维度 input_dim、输出维度 output_dim 和 num_filters 个特征,采用线性层和激活函数构建多层神经网络。判别器接受输入维度 input_dim 和 num_filters 个特征,同样采用线性层和激活函数构建多层神经网络。
最后定义条件 GAN 的类 ConditionalGAN,该类包括生成器、判别器和优化器,以及 train 方法进行训练:
```
class ConditionalGAN(object):
def __init__(self, input_dim, output_dim, num_filters, learning_rate):
self.generator = Generator(input_dim, output_dim, num_filters)
self.discriminator = Discriminator(input_dim+1, num_filters)
self.optimizer_G = optim.Adam(self.generator.parameters(), lr=learning_rate)
self.optimizer_D = optim.Adam(self.discriminator.parameters(), lr=learning_rate)
def train(self, data_loader, num_epochs):
for epoch in range(num_epochs):
for i, (inputs, labels) in enumerate(data_loader):
...
```
其中,ConditionalGAN 类接受输入维度 input_dim、输出维度 output_dim、特征数 num_filters 和学习率 learning_rate。train 方法则接受数据加载器 data_loader 和训练轮数 num_epochs,用于训练模型。
class Generator(nn.Module): def __init__(self,X_shape,z_dim): super(Generator,self).__init__() self.X_shape=X_shape B,H,W=X_shape self.decoder_input = nn.Linear(z_dim,16*H*W) modules=[] hidden_dims = [256,128] for i in range(len(hidden_dims)-1): modules.append(nn.Sequential(nn.ConvTranspose2d(hidden_dims[i], hidden_dims[i + 1], kernel_size=3, stride = 2, padding=1, output_padding=1), #nn.BatchNorm2d(hidden_dims[i + 1]), nn.LeakyReLU()))
这是一个生成器的类,继承自nn.Module。在初始化时,需要传入两个参数:X_shape和z_dim。X_shape表示生成器输出的图像的形状,z_dim表示生成器输入的噪声向量的维度。在初始化函数中,首先调用父类的初始化函数,然后将X_shape保存在self.X_shape中。接着,根据X_shape计算出图像的高度H和宽度W,并将一个线性层nn.Linear(z_dim,16*H*W)保存在self.decoder_input中。接下来,定义了一个空的列表modules,用于保存生成器的各个层。然后,定义了一个包含两个隐藏层的生成器,每个隐藏层都是一个包含一个反卷积层的序列。反卷积层的输入通道数为hidden_dims[i],输出通道数为hidden_dims[i+1],卷积核大小为3,步长为2,填充为1,输出填充为1。最后,将生成器的所有层保存在modules中。