/
train_diff_model.py
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/
train_diff_model.py
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import torch
import data as Data
import model as Model
import argparse
import logging
import core.logger as Logger
import core.metrics as Metrics
# from tensorboardX import SummaryWriter
import os
import numpy as np
print(torch.version.cuda)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, default='config/sr_sr3_16_128.json',
help='JSON file for configuration')
parser.add_argument('-p', '--phase', type=str, choices=['train', 'val'],
help='Run either train(training) or val(generation)', default='train')
parser.add_argument('-gpu', '--gpu_ids', type=str, default=None)
parser.add_argument('-debug', '-d', action='store_true')
# parse configs
args = parser.parse_args()
opt = Logger.parse(args)
# Convert to NoneDict, which return None for missing key.
opt = Logger.dict_to_nonedict(opt)
# logging
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
Logger.setup_logger(None, opt['path']['log'],
'train', level=logging.INFO, screen=True)
Logger.setup_logger('val', opt['path']['log'], 'val', level=logging.INFO)
logger = logging.getLogger('base')
logger.info(Logger.dict2str(opt))
stage2_file = opt['stage2_file']
# dataset
for phase, dataset_opt in opt['datasets'].items():
if phase == 'train' and args.phase != 'val':
train_set = Data.create_dataset(dataset_opt, phase, stage2_file=stage2_file)
train_loader = Data.create_dataloader(
train_set, dataset_opt, phase)
elif phase == 'val':
val_set = Data.create_dataset(dataset_opt, phase, stage2_file=stage2_file)
val_loader = Data.create_dataloader(
val_set, dataset_opt, phase)
logger.info('Initial Dataset Finished')
# model
diffusion = Model.create_model(opt)
logger.info('Initial Model Finished')
# Train
current_step = diffusion.begin_step
current_epoch = diffusion.begin_epoch
n_iter = opt['train']['n_iter']
if opt['path']['resume_state']:
logger.info('Resuming training from epoch: {}, iter: {}.'.format(
current_epoch, current_step))
diffusion.set_new_noise_schedule(
opt['model']['beta_schedule'][opt['phase']], schedule_phase=opt['phase'])
if opt['phase'] == 'train':
while current_step < n_iter:
current_epoch += 1
for _, train_data in enumerate(train_loader):
current_step += 1
if current_step > n_iter:
break
diffusion.feed_data(train_data)
diffusion.optimize_parameters()
# log
if current_step % opt['train']['print_freq'] == 0:
logs = diffusion.get_current_log()
message = '<epoch:{:3d}, iter:{:8,d}> '.format(
current_epoch, current_step)
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v)
logger.info(message)
# validation
if current_step % opt['train']['val_freq'] == 0:
avg_psnr = 0.0
idx = 0
result_path = '{}/{}'.format(opt['path']
['results'], current_epoch)
os.makedirs(result_path, exist_ok=True)
diffusion.set_new_noise_schedule(
opt['model']['beta_schedule']['val'], schedule_phase='val')
for _, val_data in enumerate(val_loader):
idx += 1
diffusion.feed_data(val_data)
diffusion.test(continous=True)
visuals = diffusion.get_current_visuals()
denoised_img = Metrics.tensor2img(visuals['denoised']) # uint8
input_img = Metrics.tensor2img(visuals['X']) # uint8
Metrics.save_img(
denoised_img[:,:], '{}/{}_{}_denoised.png'.format(result_path, current_step, idx))
Metrics.save_img(
input_img[:,:], '{}/{}_{}_input.png'.format(result_path, current_step, idx))
diffusion.set_new_noise_schedule(
opt['model']['beta_schedule']['train'], schedule_phase='train')
if current_step % opt['train']['save_checkpoint_freq'] == 0:
logger.info('Saving models and training states.')
diffusion.save_network(current_epoch, current_step, save_last_only=True)
# if current_step % 1e4 == 0:
# logger.info('Saving models and training states.')
# diffusion.save_network(current_epoch, current_step, save_last_only=False)
# save model
logger.info('End of training.')
else:
logger.info('Begin Model Evaluation.')
avg_psnr = 0.0
avg_ssim = 0.0
idx = 0
result_path = '{}'.format(opt['path']['results'])
os.makedirs(result_path, exist_ok=True)
for _, val_data in enumerate(val_loader):
idx += 1
diffusion.feed_data(val_data)
diffusion.test(continous=True)
visuals = diffusion.get_current_visuals()
denoised_img = Metrics.tensor2img(visuals['denoised']) # uint8
input_img = Metrics.tensor2img(visuals['X']) # uint8
sr_img_mode = 'grid'
# grid img
sr_img = Metrics.tensor2img(visuals['denoised'], out_type=np.float) # uint8
Metrics.save_img(
sr_img, '{}/{}_{}_denoised.png'.format(result_path, current_step, idx))
Metrics.save_img(
Metrics.tensor2img(visuals['denoised'][-1], out_type=np.float), '{}/{}_{}_denoised.png'.format(result_path, current_step, idx))
Metrics.save_img(
denoised_img, '{}/{}_{}_lr.png'.format(result_path, current_step, idx))
Metrics.save_img(
input_img, '{}/{}_{}_input.png'.format(result_path, current_step, idx))
logger_val = logging.getLogger('val') # validation logger
logger_val.info('<epoch:{:3d}, iter:{:8,d}>'.format(
current_epoch, current_step))