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train_ClassSR.py
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import os
import math
import argparse
import random
import logging
import options.options as option
from utils import util
from data.LQGT_rcan_dataset import create_dataloader
from data.LQGT_rcan_dataset import LQGTDataset_rcan
# from data import create_dataloader, create_dataset
from models import create_model
#1
#import paddle.distributed as dist
import paddle
def main():
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt',default='options/train/train_ClassSR_RCAN.yml', type=str, help='Path to option YAML file.')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
opt = option.parse(args.opt, is_train=True)
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
util.mkdir_and_rename(opt['path']['experiments_root']) # rename experiment folder if exists
util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root'
and 'pretrain_model' not in key and 'resume' not in key))
# config loggers. Before it, the log will not work
util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
screen=True, tofile=True)
logger = logging.getLogger('base')
logger.info(option.dict2str(opt))
# convert to NoneDict, which returns None for missing keys
opt = option.dict_to_nonedict(opt)
#### random seed
seed = opt['train']['manual_seed']
if seed is None:
seed = random.randint(1, 10000)
logger.info('Random seed: {}'.format(seed))
util.set_random_seed(seed)
#2
#dist.init_parallel_env()
#### create train and val dataloader
dataset_ratio = 200 # enlarge the size of each epoch
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train':
train_set = LQGTDataset_rcan(dataset_opt)
train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size']))
total_iters = int(opt['train']['niter'])
total_epochs = int(math.ceil(total_iters / train_size))
train_sampler = None
train_loader = create_dataloader(train_set, dataset_opt, opt)
logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
len(train_set), train_size))
logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
total_epochs, total_iters))
elif phase == 'val':
val_set = LQGTDataset_rcan(dataset_opt)
val_loader = create_dataloader(val_set, dataset_opt, opt)
logger.info('Number of val images in [{:s}]: {:d}'.format(
dataset_opt['name'], len(val_set)))
else:
raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
assert train_loader is not None
#### create model
model = create_model(opt)
#3
#model = paddle.DataParallel(model)
if opt['path'].get('resume_state', None):
# distributed resuming: all load into default GPU
resume_state = paddle.load(opt['path']['resume_state'])
option.check_resume(opt, resume_state['iter']) # check resume options
else:
resume_state = None
#resume_state = paddle.load(opt['path']['resume_state'])
### resume training
if resume_state:
logger.info('Resuming training from epoch: {}, iter: {}.'.format(
resume_state['epoch'], resume_state['iter']))
start_epoch = resume_state['epoch']
current_step = resume_state['iter']
model.resume_training(resume_state) # handle optimizers and schedulers
else:
current_step = 0
start_epoch = 0
# current_step = 0
# start_epoch = 0
#### training
logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step))
for epoch in range(start_epoch, total_epochs + 1):
for train_data in train_loader:
current_step += 1
if current_step > total_iters:
break
#### update learning rate
model.update_learning_rate(current_step, warmup_iter=opt['train']['warmup_iter'])
#### training
#print(train_data)
model.feed_data(train_data)
model.optimize_parameters(current_step)
#### log
if current_step % opt['logger']['print_freq'] == 0:
logs = model.get_current_log()
message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(epoch, current_step)
for v in model.get_current_learning_rate():
message += '{:.3e},'.format(v)
message += ')] '
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
logger.info(message)
### validation
if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0:
if opt['model'] in ['vsr']: # video restoration validation
pbar = util.ProgressBar(len(val_loader))
psnr_rlt = {} # with border and center frames
psnr_rlt_avg = {}
psnr_total_avg = 0.
for val_data in val_loader:
folder = val_data['folder'][0]
idx_d = val_data['idx'].item()
if psnr_rlt.get(folder, None) is None:
psnr_rlt[folder] = []
model.feed_data(val_data)
model.test()
visuals = model.get_current_visuals()
rlt_img = util.tensor2img(visuals['rlt']) # uint8
gt_img = util.tensor2img(visuals['GT']) # uint8
# calculate PSNR
psnr = util.calculate_psnr(rlt_img, gt_img)
psnr_rlt[folder].append(psnr)
pbar.update('Test {} - {}'.format(folder, idx_d))
for k, v in psnr_rlt.items():
psnr_rlt_avg[k] = sum(v) / len(v)
psnr_total_avg += psnr_rlt_avg[k]
psnr_total_avg /= len(psnr_rlt)
log_s = '# Validation # PSNR: {:.4e}:'.format(psnr_total_avg)
for k, v in psnr_rlt_avg.items():
log_s += ' {}: {:.4e}'.format(k, v)
logger.info(log_s)
else:
# does not support multi-GPU validation
pbar = util.ProgressBar(len(val_loader))
avg_psnr = 0.
idx = 0
num_ress = [0, 0,0]
psnr_ress=[0, 0,0]
for val_data in val_loader:
idx += 1
img_name = os.path.splitext(os.path.basename(val_data['LQ_path'][0]))[0]
img_dir = os.path.join(opt['path']['val_images'], img_name)
util.mkdir(img_dir)
model.feed_data(val_data)
model.test()
visuals = model.get_current_visuals()
sr_img = visuals['rlt'] # uint8
gt_img = visuals['GT'] # uint8
num_res = visuals['num_res']
psnr_res=visuals['psnr_res']
num_ress[0] += num_res[0]
num_ress[1] += num_res[1]
num_ress[2] += num_res[2]
psnr_ress[0] += psnr_res[0]
psnr_ress[1] += psnr_res[1]
psnr_ress[2] += psnr_res[2]
# Save SR images for reference
save_img_path = os.path.join(img_dir,
'{:s}_{:d}.png'.format(img_name, current_step))
util.save_img(sr_img, save_img_path)
# calculate PSNR
sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
avg_psnr += util.calculate_psnr(sr_img, gt_img)
pbar.update('Test {}'.format(img_name))
flops,percent=util.cal_FLOPs(which_model,num_ress)
if num_ress[0]==0:
num_ress[0]=1
if num_ress[1]==0:
num_ress[1]=1
if num_ress[2]==0:
num_ress[2]=1
# log
logger.info('# Validation # PSNR: {:.4e}'.format(avg_psnr))
logger.info('# Validation # FLOPs: {:.4e}'.format(flops))
logger.info('# Validation # Percent: {:.4e}'.format(percent))
logger.info('# Validation # TYPE num: {0} {1} {2} '.format(num_ress[0], num_ress[1],num_ress[2]))
logger.info('# Validation # PSNR Class: {0} {1} {2}'.format(psnr_ress[0]/num_ress[0],psnr_ress[1]/num_ress[1],psnr_ress[2]/num_ress[2]))
#### save models and training states
if current_step % opt['logger']['save_checkpoint_freq'] == 0:
# if rank <= 0:
logger.info('Saving models and training states.')
model.save(current_step)
model.save_training_state(epoch, current_step)
logger.info('Saving the final model.')
model.save('latest')
logger.info('End of training.')
if __name__ == '__main__':
main()