import torch
from torch import nn, real, select
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data.dataloader import DataLoader
from torchvision import transforms
from torchvision import utils as vutils
import argparse
from tqdm import tqdm
from models import weights_init, Discriminator, Generator, SimpleDecoder
from operation import copy_G_params, load_params, get_dir
from operation import ImageFolder, InfiniteSamplerWrapper
from diffaug import DiffAugment
policy = 'color,translation'
import lpips
percept = lpips.PerceptualLoss(model='net-lin', net='vgg', use_gpu=True)
#torch.backends.cudnn.benchmark = True
def crop_image_by_part(image, part):
hw = image.shape[2]//2
if part==0:
return image[:,:,:hw,:hw]
if part==1:
return image[:,:,:hw,hw:]
if part==2:
return image[:,:,hw:,:hw]
if part==3:
return image[:,:,hw:,hw:]
def train_d(net, data, label="real"):
"""Train function of discriminator"""
if label=="real":
#pred, [rec_all, rec_small, rec_part], part = net(data, label)
pred = net(data, label)
err = F.relu( torch.rand_like(pred) * 0.2 + 0.8 - pred).mean() #+ \
#percept( rec_all, F.interpolate(data, rec_all.shape[2]) ).sum() +\
#percept( rec_small, F.interpolate(data, rec_small.shape[2]) ).sum() +\
#percept( rec_part, F.interpolate(crop_image_by_part(data, part), rec_part.shape[2]) ).sum()
err.backward()
return pred.mean().item()#, rec_all, rec_small, rec_part
else:
pred = net(data, label)
err = F.relu( torch.rand_like(pred) * 0.2 + 0.8 + pred).mean()
err.backward()
return pred.mean().item()
@torch.no_grad()
def interpolate(z1, z2, netG, img_name, step=8):
z = [ a*z2 + (1-a)*z1 for a in torch.linspace(0, 1, steps=step) ]
z = torch.cat(z).view(step, -1)
g_image = netG(z)[0]
vutils.save_image( g_image.add(1).mul(0.5), img_name , nrow=step)
def train(args):
data_root = args.path
total_iterations = args.iter
checkpoint = args.ckpt
batch_size = args.batch_size
im_size = args.im_size
ndf = 64
ngf = 64
nz = 256
nlr = 0.0002
nbeta1 = 0.5
use_cuda = True
multi_gpu = False
dataloader_workers = 8
current_iteration = 0
save_interval = 100
saved_model_folder, saved_image_folder = get_dir(args)
device = torch.device("cpu")
if use_cuda:
device = torch.device("cuda:0")
transform_list = [
transforms.Resize((int(im_size),int(im_size))),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]
trans = transforms.Compose(transform_list)
dataset = ImageFolder(root=data_root, transform=trans, return_idx=True)
dataloader = iter(DataLoader(dataset, batch_size=batch_size, shuffle=False,
sampler=InfiniteSamplerWrapper(dataset), num_workers=dataloader_workers, pin_memory=True))
total_iterations = int(len(dataset)*100/batch_size)
netG = Generator(ngf=ngf, nz=nz, im_size=im_size)
ckpt = torch.load(checkpoint)
load_params( netG , ckpt['g_ema'] )
#netG.eval()
netG.to(device)
fixed_noise = torch.randn(len(dataset), nz, requires_grad=True, device=device)
optimizerG = optim.Adam([fixed_noise], lr=0.1, betas=(nbeta1, 0.999))
log_rec_loss = 0
for iteration in tqdm(range(current_iteration, total_iterations+1)):
real_image, noise_idx = next(dataloader)
real_image = real_image.to(device)
optimizerG.zero_grad()
select_noise = fixed_noise[noise_idx]
g_image = netG(select_noise)[0]
rec_loss = percept( F.avg_pool2d( g_image, 2, 2), F.avg_pool2d(real_image,2,2) ).sum() + 0.2*F.mse_loss(g_image, real_image)
rec_loss.backward()
optimizerG.step()
log_rec_loss += rec_loss.item()
if iteration % 100 == 0:
print("lpips loss g: %.5f"%(log_rec_loss/100))
log_rec_loss = 0
if iteration % (save_interval*10) == 0:
with torch.no_grad():
vutils.save_image( torch.cat([
real_image, g_image]).add(1).mul(0.5), saved_image_folder+'/rec_%d.jpg'%iteration , nrow=batch_size)
interpolate(fixed_noise[0], fixed_noise[1], netG, saved_image_folder+'/interpolate_0_1_%d.jpg'%iteration)
if iteration % (save_interval*10) == 0 or iteration == total_iterations:
torch.save(fixed_noise, saved_model_folder+'/%d.pth'%iteration)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=dataloader_workers, pin_memory=True)
mean_lpips = 0
for idx, data in enumerate(dataloader):
real_image, noise_idx = data
real_image = real_image.to(device)
select_noise = fixed_noise[noise_idx]
g_image = netG(select_noise)[0]
rec_loss = percept( F.avg_pool2d( g_image, 2, 2), F.avg_pool2d(real_image,2,2) ).sum()
mean_lpips += rec_loss.sum()
mean_lpips /= len(dataset)
print(mean_lpips)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='region gan')
parser.add_argument('--path', type=str, default='../lmdbs/art_landscape_1k', help='path of resource dataset, should be a folder that has one or many sub image folders inside')
parser.add_argument('--cuda', type=int, default=0, help='index of gpu to use')
parser.add_argument('--name', type=str, default='test1', help='experiment name')
parser.add_argument('--iter', type=int, default=50000, help='number of iterations')
parser.add_argument('--start_iter', type=int, default=0, help='the iteration to start training')
parser.add_argument('--batch_size', type=int, default=4, help='mini batch number of images')
parser.add_argument('--im_size', type=int, default=1024, help='image resolution')
parser.add_argument('--ckpt', type=str, default='None', help='checkpoint weight path')
args = parser.parse_args()
print(args)
train(args)