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train_ltew.py
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train_ltew.py
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# modified from: https://github.com/yinboc/liif
import argparse
import os
import yaml
import torch
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
import datasets
import models
import utils
import numpy as np
from srwarp import transform, warp, crop
def make_data_loader(spec, tag=''):
if spec is None:
return None
dataset = datasets.make(spec['dataset'])
dataset = datasets.make(spec['wrapper'], args={'dataset': dataset})
log('{} dataset: size={}'.format(tag, len(dataset)))
for k, v in dataset[0].items():
log(' {}: shape={}'.format(k, tuple(v.shape)))
loader = DataLoader(dataset, batch_size=spec['batch_size'],
shuffle=(tag == 'train'), num_workers=16, pin_memory=True)
return loader
def make_data_loaders():
train_loader = make_data_loader(config.get('train_dataset'), tag='train')
# val_loader = make_data_loader(config.get('val_dataset'), tag='val')
# return train_loader, val_loader
return train_loader
def prepare_training():
# if config.get('resume') is not None:
if os.path.exists(config.get('resume')):
sv_file = torch.load(config['resume'])
model = models.make(sv_file['model'], load_sd=True).cuda()
optimizer = utils.make_optimizer(
model.parameters(), sv_file['optimizer'], load_sd=True)
epoch_start = sv_file['epoch'] + 1
if config.get('multi_step_lr') is None:
lr_scheduler = None
else:
lr_scheduler = MultiStepLR(optimizer, **config['multi_step_lr'])
for _ in range(epoch_start - 1):
lr_scheduler.step()
else:
model = models.make(config['model']).cuda()
optimizer = utils.make_optimizer(
model.parameters(), config['optimizer'])
epoch_start = 1
if config.get('multi_step_lr') is None:
lr_scheduler = None
else:
lr_scheduler = MultiStepLR(optimizer, **config['multi_step_lr'])
log('model: #params={}'.format(utils.compute_num_params(model, text=True)))
return model, optimizer, epoch_start, lr_scheduler
def train(train_loader, model, optimizer, \
epoch):
model.train()
loss_fn = nn.L1Loss()
train_loss = utils.Averager()
metric_fn = utils.calc_psnr
data_norm = config['data_norm']
t = data_norm['inp']
inp_sub = torch.FloatTensor(t['sub']).view(1, -1, 1, 1).cuda()
inp_div = torch.FloatTensor(t['div']).view(1, -1, 1, 1).cuda()
t = data_norm['gt']
gt_sub = torch.FloatTensor(t['sub']).view(1, 1, -1).cuda()
gt_div = torch.FloatTensor(t['div']).view(1, 1, -1).cuda()
num_dataset = 800 # DIV2K
iter_per_epoch = int(num_dataset / config.get('train_dataset')['batch_size'] \
* config.get('train_dataset')['dataset']['args']['repeat'])
iteration = 0
for batch in tqdm(train_loader, leave=False, desc='train'):
for k, v in batch.items():
batch[k] = v.cuda()
################ prepare LR_warp input and correspoding transform ################
hr = (batch['inp'] - inp_sub) / inp_div
# step 1. sample transformation from specific distribution
m_inv = transform.get_random_transform(hr, size_limit=256, max_iters=50)
m_inv, sizes, _ = transform.compensate_matrix(hr, m_inv)
m = transform.inverse_3x3(m_inv)
# step 2. genearate input LR images
lr = warp.warp_by_function(
hr,
m,
sizes=sizes,
kernel_type='bicubic',
adaptive_grid=True,
fill=-255,
)
# step 3. crop lr and compensate transform
lr_crop, iy, ix = crop.valid_crop(
lr,
ignore_value=-255,
patch_max=config.get('patch_size'),
stochastic=True,
)
lr_crop = utils.quantize(lr_crop)
m = transform.compensate_offset(m, ix, iy)
############### backward map (coord, cell) from Y space to X space ###############
gridx, mask = utils.gridy2gridx_homography(batch['coord'][0], \
hr.shape[-2], hr.shape[-1], lr_crop.shape[-2], lr_crop.shape[-1], m.cuda(), cpu=False) # backward map coord from Y to X
cell = utils.celly2cellx_homography(batch['cell'][0], \
hr.shape[-2], hr.shape[-1], lr_crop.shape[-2], lr_crop.shape[-1], m.cuda(), cpu=False) # backward map cell from Y to X
######################## sample query point to train LTEW ########################
num_samples = lr_crop.shape[-2] * lr_crop.shape[-1]
sample_lst = np.random.choice(np.nonzero(mask.cpu())[:, 0],\
min(num_samples, int(torch.sum(mask).item())), replace=False)
gridx = gridx[sample_lst].unsqueeze(0).expand(hr.shape[0], -1, -1) # coord sample
cell = cell[sample_lst].unsqueeze(0).expand(hr.shape[0], -1, -1) # cell sample
gt = (batch['gt'][:, sample_lst].cuda() - gt_sub) / gt_div # gt sample
pred = model(lr_crop, gridx, cell)
loss = loss_fn(pred, gt)
# tensorboard
writer.add_scalars('loss', {'train': loss.item()}, (epoch-1)*iter_per_epoch + iteration)
iteration += 1
train_loss.add(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
pred = None; loss = None
return train_loss.item()
def main(config_, save_path):
global config, log, writer
config = config_
log, writer = utils.set_save_path(save_path, remove=False)
with open(os.path.join(save_path, 'config.yaml'), 'w') as f:
yaml.dump(config, f, sort_keys=False)
# train_loader, val_loader = make_data_loaders()
train_loader = make_data_loaders()
if config.get('data_norm') is None:
config['data_norm'] = {
'inp': {'sub': [0], 'div': [1]},
'gt': {'sub': [0], 'div': [1]}
}
model, optimizer, epoch_start, lr_scheduler = prepare_training()
n_gpus = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))
if n_gpus > 1:
model = nn.parallel.DataParallel(model)
epoch_max = config['epoch_max']
epoch_save = config.get('epoch_save')
max_val_v = -1e18
timer = utils.Timer()
for epoch in range(epoch_start, epoch_max + 1):
t_epoch_start = timer.t()
log_info = ['epoch {}/{}'.format(epoch, epoch_max)]
writer.add_scalar('lr', optimizer.param_groups[0]['lr'], epoch)
train_loss = train(train_loader, model, optimizer, \
epoch)
if lr_scheduler is not None:
lr_scheduler.step()
log_info.append('train: loss={:.4f}'.format(train_loss))
# writer.add_scalars('loss', {'train': train_loss}, epoch)
if n_gpus > 1:
model_ = model.module
else:
model_ = model
model_spec = config['model']
model_spec['sd'] = model_.state_dict()
optimizer_spec = config['optimizer']
optimizer_spec['sd'] = optimizer.state_dict()
sv_file = {
'model': model_spec,
'optimizer': optimizer_spec,
'epoch': epoch
}
torch.save(sv_file, os.path.join(save_path, 'epoch-last.pth'))
if (epoch_save is not None) and (epoch % epoch_save == 0):
torch.save(sv_file,
os.path.join(save_path, 'epoch-{}.pth'.format(epoch)))
t = timer.t()
prog = (epoch - epoch_start + 1) / (epoch_max - epoch_start + 1)
t_epoch = utils.time_text(t - t_epoch_start)
t_elapsed, t_all = utils.time_text(t), utils.time_text(t / prog)
log_info.append('{} {}/{}'.format(t_epoch, t_elapsed, t_all))
log(', '.join(log_info))
writer.flush()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config')
parser.add_argument('--name', default=None)
parser.add_argument('--tag', default=None)
parser.add_argument('--gpu', default='0')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
with open(args.config, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
print('config loaded.')
save_name = args.name
if save_name is None:
save_name = '_' + args.config.split('/')[-1][:-len('.yaml')]
if args.tag is not None:
save_name += '_' + args.tag
save_path = os.path.join('./save', save_name)
main(config, save_path)