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"""
Inpainting using Generative Adversarial Networks.
The dataset can be downloaded from: https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AADIKlz8PR9zr6Y20qbkunrba/Img/img_align_celeba.zip?dl=0
(if not available there see if options are listed at http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
Instrustion on running the script:
1. Download the dataset from the provided link
2. Save the folder 'img_align_celeba' to '../../data/'
4. Run the sript using command 'python3 context_encoder.py'
"""
import argparse
import os
import numpy as np
import math
import torchvision.transforms as transforms
from torchvision.utils import save_image
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable
from datasets import *
from models import *
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=8, help="size of the batches")
parser.add_argument("--dataset_name", type=str, default="img_align_celeba", help="name of the dataset")
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=4, help="number of cpu threads to use during batch generation")
parser.add_argument("--latent_dim", type=int, default=100, help="dimensionality of the latent space")
parser.add_argument("--img_size", type=int, default=128, help="size of each image dimension")
parser.add_argument("--mask_size", type=int, default=64, help="size of random mask")
parser.add_argument("--channels", type=int, default=3, help="number of image channels")
parser.add_argument("--sample_interval", type=int, default=500, help="interval between image sampling")
opt = parser.parse_args()
print(opt)
cuda = True if torch.cuda.is_available() else False
# Calculate output of image discriminator (PatchGAN)
patch_h, patch_w = int(opt.mask_size / 2 ** 3), int(opt.mask_size / 2 ** 3)
patch = (1, patch_h, patch_w)
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)
# Loss function
adversarial_loss = torch.nn.MSELoss()
pixelwise_loss = torch.nn.L1Loss()
# Initialize generator and discriminator
generator = Generator(channels=opt.channels)
discriminator = Discriminator(channels=opt.channels)
if cuda:
generator.cuda()
discriminator.cuda()
adversarial_loss.cuda()
pixelwise_loss.cuda()
# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)
# Dataset loader
transforms_ = [
transforms.Resize((opt.img_size, opt.img_size), Image.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]
dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_cpu,
)
test_dataloader = DataLoader(
ImageDataset("../../data/%s" % opt.dataset_name, transforms_=transforms_, mode="val"),
batch_size=12,
shuffle=True,
num_workers=1,
)
# 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
def save_sample(batches_done):
samples, masked_samples, i = next(iter(test_dataloader))
samples = Variable(samples.type(Tensor))
masked_samples = Variable(masked_samples.type(Tensor))
i = i[0].item() # Upper-left coordinate of mask
# Generate inpainted image
gen_mask = generator(masked_samples)
filled_samples = masked_samples.clone()
filled_samples[:, :, i : i + opt.mask_size, i : i + opt.mask_size] = gen_mask
# Save sample
sample = torch.cat((masked_samples.data, filled_samples.data, samples.data), -2)
save_image(sample, "images/%d.png" % batches_done, nrow=6, normalize=True)
# ----------
# Training
# ----------
for epoch in range(opt.n_epochs):
for i, (imgs, masked_imgs, masked_parts) in enumerate(dataloader):
# Adversarial ground truths
valid = Variable(Tensor(imgs.shape[0], *patch).fill_(1.0), requires_grad=False)
fake = Variable(Tensor(imgs.shape[0], *patch).fill_(0.0), requires_grad=False)
# Configure input
imgs = Variable(imgs.type(Tensor))
masked_imgs = Variable(masked_imgs.type(Tensor))
masked_parts = Variable(masked_parts.type(Tensor))
# -----------------
# Train Generator
# -----------------
optimizer_G.zero_grad()
# Generate a batch of images
gen_parts = generator(masked_imgs)
# Adversarial and pixelwise loss
g_adv = adversarial_loss(discriminator(gen_parts), valid)
g_pixel = pixelwise_loss(gen_parts, masked_parts)
# Total loss
g_loss = 0.001 * g_adv + 0.999 * g_pixel
g_loss.backward()
optimizer_G.step()
# ---------------------
# Train Discriminator
# ---------------------
optimizer_D.zero_grad()
# Measure discriminator's ability to classify real from generated samples
real_loss = adversarial_loss(discriminator(masked_parts), valid)
fake_loss = adversarial_loss(discriminator(gen_parts.detach()), fake)
d_loss = 0.5 * (real_loss + fake_loss)
d_loss.backward()
optimizer_D.step()
print(
"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G adv: %f, pixel: %f]"
% (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_adv.item(), g_pixel.item())
)
# Generate sample at sample interval
batches_done = epoch * len(dataloader) + i
if batches_done % opt.sample_interval == 0:
save_sample(batches_done)
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