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train.py
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train.py
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# System libs
import os
from collections import OrderedDict
import logging
import argparse
import random
import math
from engfmt import Quantity
# Numerical libs
import numpy as np
import torch
import torch.utils.data
import torch.backends.cudnn
from data import create_dataloader, create_dataset
import options.options as option
from utils import util
from models import create_model
try:
from tensorboardX import SummaryWriter
is_tensorboard_available = True
except Exception:
is_tensorboard_available = False
def main():
# parse command line arguments
parser = argparse.ArgumentParser(description="PyTorch LapSRN")
opt_p = 'experiments/001_Train_SR-RRDB-3d_SynomagD_scale4.json'
parser.add_argument('-opt', default=opt_p, type=str, required=False, help='Path to option JSON file.')
config = option.parse(parser.parse_args().opt, True, is_tensorboard_available)
config = option.dict_to_nonedict(config)
run_config = config['run_config']
optim_config = config['optim_config']
data_config = config['data_config']
# train from scratch OR resume training
if run_config['path']['resume_state']: # resuming training
resume_state = torch.load(run_config['path']['resume_state'])
else: # training from scratch
resume_state = None
util.mkdir_and_rename(run_config['path']['experiments_root']) # rename old folder if exists
util.mkdirs((path for key, path in run_config['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(None, run_config['path']['log'], 'train', level=logging.INFO, screen=True)
util.setup_logger('val', run_config['path']['log'], 'val', level=logging.INFO)
logger = logging.getLogger('base')
logger.info(option.dict2str(config))
if resume_state:
# TODO: not implemented just copied, update check_resume
# logger.info('Resuming training from epoch: {}, iter: {}.'.format(
# resume_state['epoch'], resume_state['iter']))
# option.check_resume(config) # check resume options
raise NotImplementedError
# tensorboard logger
if run_config['use_tb_logger'] and 'debug' not in run_config['id']:
util.mkdir_and_rename(
os.path.join(run_config['path']['root'], 'tb_logger', run_config['id'])) # rename old folder if exists
tb_logger = SummaryWriter(log_dir=os.path.join(run_config['path']['root'], 'tb_logger', run_config['id']))
# set random seed
logger.info("===> Set seed")
seed = run_config['manual_seed']
if seed is None:
seed = random.randint(1, 10000)
logger.info("=> Random seed: {}".format(seed))
else:
seed = int(seed, 16)
logger.info("=> Manual seed: {}".format(seed))
seed = int(run_config['manual_seed'], 16)
util.set_random_seed(seed)
torch.backends.cudnn.benckmark = True
logger.info("===> Loading datasets")
for phase, dataset_opt in data_config.items():
if phase == 'train':
train_set = create_dataset(dataset_opt)
train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size']))
logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
len(train_set), train_size))
total_iters = int(optim_config['niter'])
total_epochs = int(math.ceil(total_iters / train_size))
if 'debug' in run_config['id']:
total_epochs = 10
logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
total_epochs, total_iters))
train_loader = create_dataloader(train_set, dataset_opt)
elif phase == 'val':
val_set = create_dataset(dataset_opt)
val_loader = create_dataloader(val_set, dataset_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
logger.info("===> Building model")
# create model
model = create_model(config)
if is_tensorboard_available and 'debug' not in run_config['id']:
# TODO: fix problem
# Save graph to tensorboard
# dummy_input = Variable(torch.rand((10,) + config['model_config']['input_shape']))
# tb_logger.add_graph(model.netG, (dummy_input,))
pass
# resume training
if resume_state:
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
logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step))
best_psnr = OrderedDict([])
is_newBest = True
for epoch in range(start_epoch, total_epochs):
for _, train_data in enumerate(train_loader):
current_step += 1
if current_step > total_iters:
break
# update learning rate
model.update_learning_rate()
# training
model.feed_data(train_data)
model.optimize_parameters(current_step)
# log
if current_step % run_config['logger']['print_freq'] == 0:
logs = model.get_current_log()
message = '<epoch:{:3d}, iter:{:8,d}, lr:{:.3e}> '.format(
epoch, current_step, model.get_current_learning_rate())
for k, v in logs.items():
message += '{:s}: {:.4e} '.format(k, v.val) if v.val is not None else ''
# tensorboard logger
if run_config['use_tb_logger'] and 'debug' not in run_config['id']:
tb_logger.add_scalar('Train/running/{}'.format(k), v.val, current_step)
logger.info(message)
# validation
if current_step % optim_config['val_freq'] == 0:
avg_metric = OrderedDict([])
idx = 0
total_images = 0
img_dir = os.path.join(run_config['path']['val_images'])
util.mkdir(img_dir)
for val_data in val_loader:
idx += 1
model.feed_data(val_data)
model.test()
visuals = model.get_current_visuals()
visuals['hz'] = visuals['hz'].numpy()
sr_imgs = OrderedDict([])
lr_imgs = OrderedDict([])
for k in visuals.keys():
if 'SR' in k:
sr_imgs[k] = (util.tensor2img(visuals[k], min_max=None, out_type=np.float32,
as_grid=False, data_format=data_config['val']['data_format'])) # float32
if sr_imgs[k].ndim == 4:
sr_imgs[k] = sr_imgs[k][np.newaxis, :, :, :, :]
if 'LR' in k:
lr_imgs[k] = (util.tensor2img(visuals[k], min_max=None, out_type=np.float32,
as_grid=False, data_format=data_config['val']['data_format'])) # float32
if lr_imgs[k].ndim == 4:
lr_imgs[k] = lr_imgs[k][np.newaxis, :, :, :, :]
gt_img = util.tensor2img(visuals['HR'], min_max=None, out_type=np.float32,
as_grid=False, data_format=data_config['val']['data_format'])
if gt_img.ndim == 4:
gt_img = gt_img[np.newaxis, :, :, :, :]
# calculate PSNR
for sr_k in sr_imgs.keys():
if 'x' in sr_k: # find correct key
for lr_k in lr_imgs.keys():
if sr_k.replace('SR', '') in lr_k:
tmp_hr = lr_imgs[lr_k]
break
else:
tmp_hr = gt_img
for sr_vol, lr_vol in zip(sr_imgs[sr_k], tmp_hr):
mse, rmse, psnr = util.calculate_mse_rmse_psnr(sr_vol, lr_vol)
if sr_k in avg_metric:
avg_metric[sr_k]['mse'] += mse
avg_metric[sr_k]['rmse'] += rmse
avg_metric[sr_k]['psnr'] += psnr
else:
avg_metric[sr_k] = OrderedDict([])
avg_metric[sr_k]['mse'] = mse
avg_metric[sr_k]['rmse'] = rmse
avg_metric[sr_k]['psnr'] = psnr
# Save SR images for reference
for img_num in range(len(visuals['hz'])):
if total_images % 40 == 0:
img_name = "{0:d}_{1:s}_{2:d}.png".format(total_images,
str(Quantity(visuals['hz'][img_num], 'hz')),
current_step)
save_img_path = os.path.join(img_dir, img_name)
util.showAndSaveSlice(sr_imgs, lr_imgs, gt_img, save_img_path,
scale=config['model_config']['scale'], index=img_num,
data_format=data_config['val']['data_format'],
data_mean=data_config['val']['data_mean'],
data_std=data_config['val']['data_std'])
total_images += 1
log_str = '# Validation #'
log_str2 = '<epoch:{:3d}, iter:{:8,d}>'.format(epoch, current_step)
for k in avg_metric.keys():
for metric_k in avg_metric[k]:
avg_metric[k][metric_k] = avg_metric[k][metric_k] / idx
if 'rmse' in metric_k:
if k not in best_psnr:
best_psnr[k] = 10e6
if avg_metric[k][metric_k] < best_psnr[k]:
is_newBest = True
best_psnr[k] = avg_metric[k][metric_k]
log_str += '\tBEST'
log_str += ' {}-{}: {:.4e} * {}'.format(k, metric_k, avg_metric[k][metric_k], idx)
log_str2 += ' {}-{}: {:.4e} * {}'.format(k, metric_k, avg_metric[k][metric_k], idx)
# tensorboard logger
if run_config['use_tb_logger'] and 'debug' not in run_config['id']:
tb_logger.add_scalar('val/{}_{}'.format(k, metric_k), avg_metric[k][metric_k], current_step)
# log
logger.info(log_str)
logger_val = logging.getLogger('val') # validation logger
logger_val.info(log_str2)
# save models and training states
if current_step % run_config['logger']['save_checkpoint_freq'] == 0 or is_newBest:
logger.info('Saving models and training states.')
model.save(current_step)
model.save_training_state(epoch, current_step)
is_newBest = False
# log
logs = model.get_current_log()
for k, v in logs.items():
# tensorboard logger
if run_config['use_tb_logger'] and 'debug' not in run_config['id']:
if v.avg is not None:
tb_logger.add_scalar('Train/{}'.format(k), v.avg, current_step)
model.reset_log()
logger.info('Saving the final model.')
model.save('latest')
logger.info('End of training.')
if __name__ == "__main__":
main()