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import argparse
import json
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
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, CosineAnnealingLR
import datasets
import models
import utils
from test_inr_diinn_arbrcan_sadnarc_funsr_overnet import eval_psnr
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
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():
if torch.is_tensor(v):
log(' {}: shape={}'.format(k, v.shape))
elif isinstance(v, str):
pass
elif isinstance(v, dict):
for k0, v0 in v.items():
if hasattr(v0, 'shape'):
log(' {}: shape={}'.format(k0, v0.shape))
else:
raise NotImplementedError
loader = DataLoader(dataset, batch_size=spec['batch_size'],
shuffle=(tag == 'train'), num_workers=4, 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
def prepare_training():
if config.get('resume') is not None:
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
lr_scheduler = config.get('lr_scheduler')
lr_scheduler_name = lr_scheduler.pop('name')
if 'MultiStepLR' == lr_scheduler_name:
lr_scheduler = MultiStepLR(optimizer, **lr_scheduler)
elif 'CosineAnnealingLR' == lr_scheduler_name:
lr_scheduler = CosineAnnealingLR(optimizer, **lr_scheduler)
log('model: #params={}'.format(utils.compute_num_params(model, text=True)))
return model, optimizer, epoch_start, lr_scheduler
def train(train_loader, model, optimizer):
model.train()
loss_fn = nn.L1Loss()
train_loss = utils.AveragerList()
data_norm = config['data_norm']
t = data_norm['img']
img_sub = torch.FloatTensor(t['sub']).view(1, -1, 1, 1).cuda()
img_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()
for batch in tqdm(train_loader, leave=False, desc='train'):
# import pdb
# pdb.set_trace()
keys = list(batch.keys())
batch = batch[keys[torch.randint(0, len(keys), [])]]
for k, v in batch.items():
if torch.is_tensor(v):
batch[k] = v.to(device)
img = (batch['img'] - img_sub) / img_div
gt = (batch['gt'] - gt_sub) / gt_div
pred = model(img, gt.shape[-2:])
loss = loss_fn(pred, gt)
train_loss.add(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
return train_loss.item()
def main(config_, save_path):
global config, log, writer
config = config_
log, writer = utils.set_save_path(save_path)
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()
if config.get('data_norm') is None:
config['data_norm'] = {
'img': {'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_val_interval = config.get('epoch_val_interval')
epoch_save_interval = config.get('epoch_save_interval')
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)
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 device != 'cpu' and 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_interval is not None) and (epoch % epoch_save_interval == 0):
torch.save(sv_file, os.path.join(save_path, 'epoch-{}.pth'.format(epoch)))
if (epoch_val_interval is not None) and (epoch % epoch_val_interval == 0):
if device != 'cpu' and n_gpus > 1 and (config.get('eval_bsize') is not None):
model_ = model.module
else:
model_ = model
file_names = json.load(open(config['val_dataset']['dataset']['args']['split_file']))['test']
class_names = list(set([os.path.basename(os.path.dirname(x)) for x in file_names]))
val_res_psnr, val_res_ssim = eval_psnr(val_loader, class_names, model_,
data_norm=config['data_norm'],
eval_type=config.get('eval_type'),
eval_bsize=config.get('eval_bsize'),
crop_border=4)
log_info.append('val: psnr={:.4f}'.format(val_res_psnr['all']))
writer.add_scalars('psnr', {'val': val_res_psnr['all']}, epoch)
if val_res_psnr['all'] > max_val_v:
max_val_v = val_res_psnr['all']
torch.save(sv_file, os.path.join(save_path, 'epoch-best.pth'))
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', default='configs/baselines/train_UC_1x-5x_INR_diinn_arbrcan_sadnarc_overnet.yaml')
parser.add_argument('--name', default='EXP20221208_2')
parser.add_argument('--tag', default=None)
parser.add_argument('--gpu', default='0')
args = parser.parse_args()
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('./checkpoints', save_name)
main(config, save_path)
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