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train.py
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train.py
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import argparse
import random
from importlib import import_module
from pathlib import Path
import os
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.utils.data as data
from PIL import Image, ImageFile
from tensorboardX import SummaryWriter
from torchvision import transforms
from torchvision.utils import make_grid
from tqdm import tqdm
from sampler import InfiniteSamplerWrapper
from test import style_transfer
cudnn.benchmark = True
Image.MAX_IMAGE_PIXELS = None # Disable DecompressionBombError
ImageFile.LOAD_TRUNCATED_IMAGES = True # Disable OSError: image file is truncated
def prepare_seed(rand_seed):
random.seed(rand_seed)
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
torch.cuda.manual_seed_all(rand_seed)
def train_transform():
transform_list = [
transforms.Resize(size=(512, 512)),
transforms.RandomCrop(256),
transforms.ToTensor()
]
return transforms.Compose(transform_list)
class FlatFolderDataset(data.Dataset):
def __init__(self, root, transform):
super(FlatFolderDataset, self).__init__()
self.root = root
self.paths = list(Path(self.root).glob('*'))
self.transform = transform
def __getitem__(self, index):
path = self.paths[index]
img = Image.open(str(path)).convert('RGB')
img = self.transform(img)
return img
def __len__(self):
return len(self.paths)
def name(self):
return 'FlatFolderDataset'
def adjust_learning_rate(optimizer, iteration_count):
"""Imitating the original implementation"""
lr = args.lr / (1.0 + args.lr_decay * iteration_count)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Basic options
parser.add_argument('--content_dir', type=str, required=True,
help='Directory path to a batch of content images')
parser.add_argument('--style_dir', type=str, required=True,
help='Directory path to a batch of style images')
parser.add_argument('--vgg', type=str, default='models/vgg_normalised.pth')
# training options
parser.add_argument('--save_dir',required=True,
help='Directory to save the model')
parser.add_argument('--log_dir', default='./logs',
help='Directory to save the log')
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--lr_decay', type=float, default=5e-5)
parser.add_argument('--max_iter', type=int, default=160000)
parser.add_argument('--batch_size', type=int, default=8)
parser.add_argument('--style_weight', type=float, default=10.0)
parser.add_argument('--content_weight', type=float, default=1.0)
parser.add_argument('--n_threads', type=int, default=16)
parser.add_argument('--save_model_interval', type=int, default=10000)
parser.add_argument('--rand_seed', type=int, default=777, help='manual seed')
parser.add_argument('--net_file', type=str,
choices=['wave_net'],
required=True,
help='net file')
parser.add_argument('--start_iter', type=int, default=0)
args = parser.parse_args()
prepare_seed(args.rand_seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
save_dir = Path(str(args.save_dir) + str(args.net_file) + '_' + str(args.max_iter))
save_dir.mkdir(exist_ok=True, parents=True)
print(f'=> save_dir: {str(save_dir)}')
# log_dir = Path(args.log_dir)
# log_dir.mkdir(exist_ok=True, parents=True)
writer = SummaryWriter(log_dir=str(save_dir))
net = import_module(args.net_file)
decoder = net.WaveDecoder()
vgg = net.WaveEncoder()
vgg.load_state_dict(torch.load(args.vgg))
network = net.Net(vgg, decoder)
if args.start_iter > 0:
print("Loading state after {:d} iterations".format(args.start_iter + 0))
states = torch.load(save_dir / 'ckpt_iter_{:d}.pth.tar'.format(args.start_iter))
network.decoder.load_state_dict(states['decoder_state_dict'])
network.safin4.load_state_dict(states['safin4_state_dict'])
network.safin3.load_state_dict(states['safin3_state_dict'])
network.train()
network.to(device)
content_tf = train_transform()
style_tf = train_transform()
assert os.path.exists(args.content_dir), args.content_dir
assert os.path.exists(args.style_dir), args.style_dir
content_dataset = FlatFolderDataset(args.content_dir, content_tf)
style_dataset = FlatFolderDataset(args.style_dir, style_tf)
print(f"length of content_dataset:{len(content_dataset)}")
print(f"length of style_dataset: {len(style_dataset)}")
content_iter = iter(data.DataLoader(
content_dataset, batch_size=args.batch_size,
sampler=InfiniteSamplerWrapper(content_dataset),
num_workers=args.n_threads))
style_iter = iter(data.DataLoader(
style_dataset, batch_size=args.batch_size,
sampler=InfiniteSamplerWrapper(style_dataset),
num_workers=args.n_threads))
if hasattr(network, 'safin4'):
params = list(network.decoder.parameters())+list(network.safin4.parameters())+\
list(network.safin3.parameters())
optimizer = torch.optim.Adam(params, lr=args.lr)
print('=> training safin')
for i in tqdm(range(args.max_iter)):
adjust_learning_rate(optimizer, iteration_count=i)
content_images = next(content_iter).to(device)
style_images = next(style_iter).to(device)
network.train()
loss_c, loss_s = network(content_images, style_images)
loss_c = args.content_weight * loss_c
loss_s = args.style_weight * loss_s
loss = loss_c + loss_s
optimizer.zero_grad()
loss.backward()
optimizer.step()
writer.add_scalar('loss_content', loss_c.item(), i + 1)
writer.add_scalar('loss_style', loss_s.item(), i + 1)
if (i + 1) % args.save_model_interval == 0 or (i + 1) == args.max_iter or i == 0:
if hasattr(network, 'safin4'):
states = {
'decoder_state_dict': network.decoder.state_dict(),
'safin4_state_dict': network.safin4.state_dict(),
'safin3_state_dict': network.safin3.state_dict()
}
else:
states = {
'decoder_state_dict': network.decoder.state_dict(),
}
torch.save(states, save_dir /
'ckpt_iter_{:d}.pth.tar'.format(i + 1))
with torch.no_grad():
network.eval()
if hasattr(network, 'safin4'):
safin_list = [network.safin3, network.safin4]
output = style_transfer(vgg, network.decoder, content_images, style_images, \
1.0, safin_list)
else :
output = style_transfer(vgg, network.decoder, content_images, style_images, \
1.0, None)
styled_img_grid = make_grid(output, nrow=4, normalize=True, scale_each=True)
reference_img_grid = make_grid(style_images, nrow=4, normalize=True, scale_each=True)
content_img_grid = make_grid(content_images, nrow=4, normalize=True, scale_each=True)
writer.add_image('styled_images', styled_img_grid, i)
writer.add_image('reference_images', reference_img_grid, i)
writer.add_image('content_images', content_img_grid, i)
writer.close()