gan圖檔生成

import torch
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
from torchvision.utils import save_image

# 生成器
class Generator(nn.Module):
    def __init__(self, image_size=320, latent_dim=100, output_channel=1):
        """
        image_size: image with and height
        latent dim: the dimension of random noise z
        output_channel: the channel of generated image, for example, 1 for gray image, 3 for RGB image
        """
        super(Generator, self).__init__()
        self.latent_dim = latent_dim
        self.output_channel = output_channel
        self.image_size = image_size
        
        # Linear layer: latent_dim -> 128 -> 256 -> 512 -> 1024 -> output_channel * image_size * image_size -> Tanh
        self.model = nn.Sequential(
            nn.Linear(latent_dim, 128),
            nn.BatchNorm1d(128),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(128, 256),
            nn.BatchNorm1d(256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 512),
            nn.BatchNorm1d(512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024),
            nn.LeakyReLU(0.2, inplace=True),
            
            nn.Linear(1024, output_channel * image_size * image_size),
            nn.Tanh()
        )

    def forward(self, z):
        img = self.model(z)
        img = img.view(img.size(0), self.output_channel, self.image_size, self.image_size)
        save_image(img, "/pic/111.png", nrow=2, normalize=True)
        return img

# 判别器
class Discriminator(nn.Module):
    def __init__(self, image_size=320, input_channel=1):
        """
        image_size: image with and height
        input_channel: the channel of input image, for example, 1 for gray image, 3 for RGB image
        """
        super(Discriminator, self).__init__()
        self.image_size = image_size
        self.input_channel = input_channel
        
        # Linear layer: input_channel * image_size * image_size -> 1024 -> 512 -> 256 -> 1 -> Sigmoid
        self.model = nn.Sequential(
            nn.Linear(input_channel * image_size * image_size, 1024),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(1024, 512),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(512, 256),
            nn.LeakyReLU(0.2, inplace=True),
            nn.Linear(256, 1),
            nn.Sigmoid(),
        )

    def forward(self, img):
        img_flat = img.view(img.size(0), -1)
        out = self.model(img_flat)
        return out
        
def load_mnist_data():
    """
    load mnist(0,1,2) dataset 
    """
    
    transform = torchvision.transforms.Compose([
        # transform to 1-channel gray image since we reading image in RGB mode
        transforms.Grayscale(1),
        # resize image from 28 * 28 to 32 * 32
        transforms.Resize(320),
        transforms.ToTensor(),
        # normalize with mean=0.5 std=0.5
        transforms.Normalize(mean=(0.5, ), 
                             std=(0.5, ))
        ])
    
    train_dataset = torchvision.datasets.ImageFolder(root='/pic', transform=transform)
    return train_dataset

def load_furniture_data():
    """
    load furniture dataset 
    """
    transform = torchvision.transforms.Compose([
        transforms.ToTensor(),
        # normalize with mean=0.5 std=0.5
        transforms.Normalize(mean=(0.5, 0.5, 0.5), 
                             std=(0.5, 0.5, 0.5))
        ])
    train_dataset = torchvision.datasets.ImageFolder(root='/pic', transform=transform)
    return train_dataset

def train(trainloader, G, D, G_optimizer, D_optimizer, loss_func, device, z_dim):
    """
    train a GAN with model G and D in one epoch
    Args:
        trainloader: data loader to train
        G: model Generator
        D: model Discriminator
        G_optimizer: optimizer of G(etc. Adam, SGD)
        D_optimizer: optimizer of D(etc. Adam, SGD)
        loss_func: loss function to train G and D. For example, Binary Cross Entropy(BCE) loss function
        device: cpu or cuda device
        z_dim: the dimension of random noise z
    """
    # set train mode
    D.train()
    G.train()
    
    D_total_loss = 0
    G_total_loss = 0
    
    for i, (x, _) in enumerate(trainloader):
        # real label and fake label
        y_real = torch.ones(x.size(0), 1).to(device)
        y_fake = torch.zeros(x.size(0), 1).to(device)
        # batch_size個真實資料
        x = x.to(device)
        # batch_size個z_dim維的随機噪聲
        z = torch.rand(x.size(0), z_dim).to(device)

        # update D network
        # D optimizer zero grads
        D_optimizer.zero_grad()
        
        # D real loss from real images
        d_real = D(x)
        d_real_loss = loss_func(d_real, y_real)
        
        # D fake loss from fake images generated by G
        g_z = G(z)
        d_fake = D(g_z)
        d_fake_loss = loss_func(d_fake, y_fake)
        
        # D backward and step
        d_loss = d_real_loss + d_fake_loss
        d_loss.backward()
        D_optimizer.step()

        # update G network
        # G optimizer zero grads
        G_optimizer.zero_grad()
        
        # G loss
        g_z = G(z)
        d_fake = D(g_z)
        g_loss = loss_func(d_fake, y_real)
        
        # G backward and step
        g_loss.backward()
        G_optimizer.step()
        
        D_total_loss += d_loss.item()
        G_total_loss += g_loss.item()
    
    return D_total_loss / len(trainloader), G_total_loss / len(trainloader)
def run_gan(trainloader, G, D, G_optimizer, D_optimizer, loss_func, n_epochs, device, latent_dim):
    d_loss_hist = []
    g_loss_hist = []

    for epoch in range(n_epochs):
        d_loss, g_loss = train(trainloader, G, D, G_optimizer, D_optimizer, loss_func, device, 
                               z_dim=latent_dim)
        print('Epoch {}: Train D loss: {:.4f}, G loss: {:.4f}'.format(epoch, d_loss, g_loss))

        d_loss_hist.append(d_loss)
        g_loss_hist.append(g_loss)

    
    return d_loss_hist, g_loss_hist
# hyper params

# z dim
latent_dim = 100

# image size and channel
image_size=320
image_channel=1

# Adam lr and betas
learning_rate = 0.0002
betas = (0.5, 0.999)

# epochs and batch size
n_epochs = 100
batch_size = 9

# device : cpu or cuda:0/1/2/3
device = torch.device('cpu')

# mnist dataset and dataloader
train_dataset = load_mnist_data()

trainloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# use BCELoss as loss function
bceloss = nn.BCELoss().to(device)

# G and D model
G = Generator(image_size=image_size, latent_dim=latent_dim, output_channel=image_channel).to(device)
D = Discriminator(image_size=image_size, input_channel=image_channel).to(device)

# G and D optimizer, use Adam or SGD
G_optimizer = optim.Adam(G.parameters(), lr=learning_rate, betas=betas)
D_optimizer = optim.Adam(D.parameters(), lr=learning_rate, betas=betas)
d_loss_hist, g_loss_hist = run_gan(trainloader, G, D, G_optimizer, D_optimizer, bceloss, 
                                   n_epochs, device, latent_dim)


test_input = torch.rand(2,100).to(device)
prediction = G(test_input)
print(prediction.shape)
save_image(prediction, "/pic/1.png", nrow=2, normalize=True)