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began.py
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began.py
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import argparse
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch
os.makedirs("images", exist_ok=True)
parser = argparse.ArgumentParser()
parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
parser.add_argument("--batch_size", type=int, default=64, help="size of the batches")
parser.add_argument("--lr", type=float, default=0.0002, help="adam: learning rate")
parser.add_argument("--b1", type=float, default=0.5, help="adam: decay of first order momentum of gradient")
parser.add_argument("--b2", type=float, default=0.999, help="adam: decay of first order momentum of gradient")
parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=62, help="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=32, help="size of each image dimension")
parser.add_argument("--channels", type=int, default=1, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=400, help="number of image channels")
opt = parser.parse_args()
print(opt)
img_shape = (opt.channels, opt.img_size, opt.img_size)
cuda = True if torch.cuda.is_available() else False
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
class Generator(nn.Module):
def __init__(self):
super(Generator, self).__init__()
self.init_size = opt.img_size // 4
self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128 * self.init_size ** 2))
self.conv_blocks = nn.Sequential(
nn.BatchNorm2d(128),
nn.Upsample(scale_factor=2),
nn.Conv2d(128, 128, 3, stride=1, padding=1),
nn.BatchNorm2d(128, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Upsample(scale_factor=2),
nn.Conv2d(128, 64, 3, stride=1, padding=1),
nn.BatchNorm2d(64, 0.8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(64, opt.channels, 3, stride=1, padding=1),
nn.Tanh(),
)
def forward(self, noise):
out = self.l1(noise)
out = out.view(out.shape[0], 128, self.init_size, self.init_size)
img = self.conv_blocks(out)
return img
class Discriminator(nn.Module):
def __init__(self):
super(Discriminator, self).__init__()
# Upsampling
self.down = nn.Sequential(nn.Conv2d(opt.channels, 64, 3, 2, 1), nn.ReLU())
# Fully-connected layers
self.down_size = opt.img_size // 2
down_dim = 64 * (opt.img_size // 2) ** 2
self.fc = nn.Sequential(
nn.Linear(down_dim, 32),
nn.BatchNorm1d(32, 0.8),
nn.ReLU(inplace=True),
nn.Linear(32, down_dim),
nn.BatchNorm1d(down_dim),
nn.ReLU(inplace=True),
)
# Upsampling
self.up = nn.Sequential(nn.Upsample(scale_factor=2), nn.Conv2d(64, opt.channels, 3, 1, 1))
def forward(self, img):
out = self.down(img)
out = self.fc(out.view(out.size(0), -1))
out = self.up(out.view(out.size(0), 64, self.down_size, self.down_size))
return out
# Initialize generator and discriminator
generator = Generator()
discriminator = Discriminator()
if cuda:
generator.cuda()
discriminator.cuda()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Configure data loader
os.makedirs("../../data/mnist", exist_ok=True)
dataloader = torch.utils.data.DataLoader(
datasets.MNIST(
"../../data/mnist",
train=True,
download=True,
transform=transforms.Compose(
[transforms.Resize(opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]
),
),
batch_size=opt.batch_size,
shuffle=True,
)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
# ----------
# Training
# ----------
# BEGAN hyper parameters
gamma = 0.75
lambda_k = 0.001
k = 0.0
for epoch in range(opt.n_epochs):
for i, (imgs, _) in enumerate(dataloader):
# Configure input
real_imgs = Variable(imgs.type(Tensor))
# -----------------
# Train Generator
# -----------------
optimizer_G.zero_grad()
# Sample noise as generator input
z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))
# Generate a batch of images
gen_imgs = generator(z)
# Loss measures generator's ability to fool the discriminator
g_loss = torch.mean(torch.abs(discriminator(gen_imgs) - gen_imgs))
g_loss.backward()
optimizer_G.step()
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Measure discriminator's ability to classify real from generated samples
d_real = discriminator(real_imgs)
d_fake = discriminator(gen_imgs.detach())
d_loss_real = torch.mean(torch.abs(d_real - real_imgs))
d_loss_fake = torch.mean(torch.abs(d_fake - gen_imgs.detach()))
d_loss = d_loss_real - k * d_loss_fake
d_loss.backward()
optimizer_D.step()
# ----------------
# Update weights
# ----------------
diff = torch.mean(gamma * d_loss_real - d_loss_fake)
# Update weight term for fake samples
k = k + lambda_k * diff.item()
k = min(max(k, 0), 1) # Constraint to interval [0, 1]
# Update convergence metric
M = (d_loss_real + torch.abs(diff)).data[0]
# --------------
# Log Progress
# --------------
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f] -- M: %f, k: %f"
% (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item(), M, k)
)
batches_done = epoch * len(dataloader) + i
if batches_done % opt.sample_interval == 0:
save_image(gen_imgs.data[:25], "images/%d.png" % batches_done, nrow=5, normalize=True)