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| """ 基于MNIST 实现对抗生成网络 (GAN) """
import torch
import torchvision
import torch.nn as nn
import numpy as np
image_size = [1, 28, 28]
latent_dim = 96
batch_size = 64
use_gpu = torch.cuda.is_available()
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.model = nn.Sequential(
nn.Linear(latent_dim, 128),
torch.nn.BatchNorm1d(128),
torch.nn.GELU(),
nn.Linear(128, 256),
torch.nn.BatchNorm1d(256),
torch.nn.GELU(),
nn.Linear(256, 512),
torch.nn.BatchNorm1d(512),
torch.nn.GELU(),
nn.Linear(512, 1024),
torch.nn.BatchNorm1d(1024),
torch.nn.GELU(),
nn.Linear(1024, np.prod(image_size, dtype=np.int32)),
nn.Sigmoid(),
)
def forward(self, z):
output = self.model(z)
image = output.reshape(z.shape[0], *image_size)
return image
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
self.model = nn.Sequential(
nn.Linear(np.prod(image_size, dtype=np.int32), 512),
torch.nn.GELU(),
nn.Linear(512, 256),
torch.nn.GELU(),
nn.Linear(256, 128),
torch.nn.GELU(),
nn.Linear(128, 64),
torch.nn.GELU(),
nn.Linear(64, 32),
torch.nn.GELU(),
nn.Linear(32, 1),
nn.Sigmoid(),
)
def forward(self, image):
prob = self.model(image.reshape(image.shape[0], -1))
return prob
dataset = torchvision.datasets.MNIST("data", train=True, download=True,
transform=torchvision.transforms.Compose(
[
torchvision.transforms.Resize(28),
torchvision.transforms.ToTensor(),
]
)
)
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size, shuffle=True, drop_last=True)
generator = Generator()
discriminator = Discriminator()
g_optimizer = torch.optim.Adam(generator.parameters(
), lr=0.0003, betas=(0.4, 0.8), weight_decay=0.0001)
d_optimizer = torch.optim.Adam(discriminator.parameters(
), lr=0.0003, betas=(0.4, 0.8), weight_decay=0.0001)
loss_fn = nn.BCELoss()
labels_one = torch.ones(batch_size, 1)
labels_zero = torch.zeros(batch_size, 1)
if use_gpu:
print("use gpu for training")
generator = generator.cuda()
discriminator = discriminator.cuda()
loss_fn = loss_fn.cuda()
labels_one = labels_one.to("cuda")
labels_zero = labels_zero.to("cuda")
num_epoch = 200
for epoch in range(num_epoch):
for i, mini_batch in enumerate(dataloader):
gt_images, _ = mini_batch
z = torch.randn(batch_size, latent_dim)
if use_gpu:
gt_images = gt_images.to("cuda")
z = z.to("cuda")
pred_images = generator(z)
g_optimizer.zero_grad()
recons_loss = torch.abs(pred_images-gt_images).mean()
g_loss = recons_loss*0.05 + \
loss_fn(discriminator(pred_images), labels_one)
g_loss.backward()
g_optimizer.step()
d_optimizer.zero_grad()
real_loss = loss_fn(discriminator(gt_images), labels_one)
fake_loss = loss_fn(discriminator(pred_images.detach()), labels_zero)
d_loss = (real_loss + fake_loss)
d_loss.backward()
d_optimizer.step()
if i % 50 == 0:
print(f"step:{len(dataloader)*epoch+i}, recons_loss:{recons_loss.item()}, g_loss:{g_loss.item()}, d_loss:{d_loss.item()}, real_loss:{real_loss.item()}, fake_loss:{fake_loss.item()}")
if i % 400 == 0:
image = pred_images[:16].data
torchvision.utils.save_image(
image, f"image_{len(dataloader)*epoch+i}.png", nrow=4)
|
