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train_degnet.py
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import os.path
import sys
import math
import argparse
import time
import random
import numpy as np
from collections import OrderedDict
import logging
import torch
import options.options as option
from utils import util
from data import create_dataloader, create_dataset
from models import create_model
def main():
opt = option.parse("options/train/train_degnet.json", is_train=True)
opt = option.dict_to_nonedict(opt) # Convert to NoneDict, which return None for missing key.
# train from scratch OR resume training
if opt['path']['resume_state']: # resuming training
resume_state = torch.load(opt['path']['resume_state'])
else: # training from scratch
resume_state = None
util.mkdir_and_rename(opt['path']['experiments_root']) # rename old 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(None, opt['path']['log'], 'train', level=logging.INFO, screen=True)
util.setup_logger('val', opt['path']['log'], 'val', level=logging.INFO)
logger = logging.getLogger('base')
if resume_state:
logger.info('Resuming training from epoch: {}, iter: {}.'.format(
resume_state['epoch'], resume_state['iter']))
option.check_resume(opt) # check resume options
logger.info(option.dict2str(opt))
# tensorboard logger
if opt['use_tb_logger'] and 'debug' not in opt['name']:
from tensorboardX import SummaryWriter
tb_logger = SummaryWriter(log_dir='./tb_logger/' + opt['name'])
# 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)
torch.backends.cudnn.benckmark = True
# torch.backends.cudnn.deterministic = True
# create train and val dataloader
for phase, dataset_opt in opt['datasets'].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(opt['train']['niter'])
total_epochs = int(math.ceil(total_iters / train_size))
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
# create model
model = create_model(opt)
# 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
# training
logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step))
for epoch in range(start_epoch, total_epochs):
for _, train_data in enumerate(train_loader):
current_step += 1
if current_step > total_iters:
break
# training
model.feed_data(train_data)
model.optimize_parameters(current_step)
# update learning rate
model.update_learning_rate()
# log
if current_step % opt['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)
if opt['use_tb_logger'] and 'debug' not in opt['name']:
tb_logger.add_scalar(k, v, current_step)
logger.info(message)
# validation
if current_step % opt['train']['val_freq'] == 0:
avg_psnr = 0.0
avg_ssim = 0.0
idx = 0
for val_data in val_loader:
idx += 1
img_name = os.path.splitext(os.path.basename(val_data['LR_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()
lr_img = util.tensor2img(visuals['FLR']) # uint8
gt_img = util.tensor2img(visuals['LR']) # uint8
# Save degradation map for reference
#save_DM_path = os.path.join(img_dir, '{:s}_{:d}.png'.format(img_name, current_step))
#degradation_img = util.tensor2img(visuals['DM']) # uint8
#util.save_img(degradation_img, save_DM_path[:-4] + '_degradation.png')
# Save fake LR
save_LR_path = opt['path']['root'] + 'validations/' + opt['name'] + '/{:d}/'.format(current_step)
util.mkdir(save_LR_path)
save_LR_img_path = os.path.join(save_LR_path, '{:s}.png'.format( \
img_name))
util.save_img(lr_img, save_LR_img_path)
# calculate PSNR
crop_size = opt['scale']
gt_img = gt_img / 255.
lr_img = lr_img / 255.
cropped_lr_img = lr_img[crop_size:-crop_size, crop_size:-crop_size, :]
cropped_gt_img = gt_img[crop_size:-crop_size, crop_size:-crop_size, :]
avg_psnr += util.calculate_psnr(cropped_lr_img * 255, cropped_gt_img * 255)
avg_ssim += util.calculate_ssim(cropped_lr_img * 255, cropped_gt_img * 255)
avg_psnr = avg_psnr / idx
avg_ssim = avg_ssim / idx
# log
logger.info('# Validation # PSNR: {:.4e} SSIM: {:.4e}'.format(avg_psnr, avg_ssim))
logger_val = logging.getLogger('val') # validation logger
logger_val.info('<epoch:{:3d}, iter:{:8,d}> psnr: {:.4e} ssim: {:.4e}'.format(
epoch, current_step, avg_psnr, avg_ssim))
if opt['use_tb_logger'] and 'debug' not in opt['name']:
tb_logger.add_scalar('psnr', avg_psnr, current_step)
tb_logger.add_scalar('ssim', avg_ssim, current_step)
# save models and training states
if current_step % opt['logger']['save_checkpoint_freq'] == 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()