import argparse import datetime import logging import math import random import time import torch import gc from os import path as osp from basicsr.data import create_dataloader, create_dataset from basicsr.data.data_sampler import EnlargedSampler from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher from basicsr.models import create_model from basicsr.utils import (MessageLogger, check_resume, get_env_info, get_root_logger, get_time_str, init_tb_logger, init_wandb_logger, make_exp_dirs, mkdir_and_rename, set_random_seed) from basicsr.utils.dist_util import get_dist_info, init_dist from basicsr.utils.options import dict2str, parse from basicsr.utils.nano import apply_conv_n_deconv, psf2otf import numpy as np from tqdm import tqdm def parse_options(is_train=True): parser = argparse.ArgumentParser() parser.add_argument( '-opt', type=str, required=True, help='Path to option YAML file.') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher') parser.add_argument( '--name', default=None, help='job launcher') import sys vv = sys.version_info.minor parser.add_argument('--local-rank', type=int, default=0) parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args() opt = parse(args.opt, is_train=is_train, name=args.name if args.name is not None and args.name != "" else None) # distributed settings if args.launcher == 'none': opt['dist'] = False print('Disable distributed.', flush=True) else: opt['dist'] = True if args.launcher == 'slurm' and 'dist_params' in opt: init_dist(args.launcher, **opt['dist_params']) else: init_dist(args.launcher) print('init dist .. ', args.launcher) opt['rank'], opt['world_size'] = get_dist_info() # random seed seed = opt.get('manual_seed') if seed is None: seed = random.randint(1, 10000) opt['manual_seed'] = seed set_random_seed(seed + opt['rank']) return opt def init_loggers(opt): log_file = osp.join(opt['path']['log'], f"train_{opt['name']}_{get_time_str()}.log") logger = get_root_logger( logger_name='basicsr', log_level=logging.INFO, log_file=log_file) logger.info(get_env_info()) logger.info(dict2str(opt)) # initialize wandb logger before tensorboard logger to allow proper sync: if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') is not None) and ('debug' not in opt['name']): assert opt['logger'].get('use_tb_logger') is True, ( 'should turn on tensorboard when using wandb') init_wandb_logger(opt) tb_logger = None if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']: tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name'])) return logger, tb_logger def create_train_val_dataloader(opt, logger): # create train and val dataloaders for phase, dataset_opt in opt['datasets'].items(): if phase == 'train': dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1) train_set = create_dataset(dataset_opt) train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio) train_loader = create_dataloader( train_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=train_sampler, seed=opt['manual_seed'], ) num_iter_per_epoch = math.ceil( len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size'])) total_iters = int(opt['train']['total_iter']) total_epochs = math.ceil(total_iters / (num_iter_per_epoch)) logger.info( 'Training statistics:' f'\n\tNumber of train images: {len(train_set)}' f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}' f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}' f'\n\tWorld size (gpu number): {opt["world_size"]}' f'\n\tRequire iter number per epoch: {num_iter_per_epoch}' f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.') elif phase == 'val': val_set = create_dataset(dataset_opt) val_loader = create_dataloader( val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'], ) logger.info( f'Number of val images/folders in {dataset_opt["name"]}: ' f'{len(val_set)}') else: raise ValueError(f'Dataset phase {phase} is not recognized.') return train_loader, train_sampler, val_loader, total_epochs, total_iters def main(): # parse options, set distributed setting, set ramdom seed opt = parse_options(is_train=True) torch.backends.cudnn.benchmark = True # automatic resume .. state_folder_path = 'experiments/{}/training_states/'.format(opt['name']) import os try: states = os.listdir(state_folder_path) except: states = [] resume_state = None if len(states) > 0: max_state_file = '{}.state'.format(max([int(x[0:-6]) for x in states])) resume_state = os.path.join(state_folder_path, max_state_file) opt['path']['resume_state'] = resume_state # load resume states if necessary if opt['path'].get('resume_state'): device_id = torch.cuda.current_device() resume_state = torch.load( opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id)) else: resume_state = None # mkdir for experiments and logger if resume_state is None: make_exp_dirs(opt) if opt['logger'].get('use_tb_logger') and 'debug' not in opt[ 'name'] and opt['rank'] == 0: mkdir_and_rename(osp.join('tb_logger', opt['name'])) # initialize loggers logger, tb_logger = init_loggers(opt) # define ks for Wiener filters ks_params = opt['train'].get('ks', None) if not ks_params: raise NotImplementedError M = ks_params['num'] ks = torch.logspace(ks_params['start'], ks_params['end'], M) ks = ks.view(1,M,1,1,1,1).to("cuda") # create model if resume_state: # resume training check_resume(opt, resume_state['iter']) model = create_model(opt) model.resume_training(resume_state) # handle optimizers and schedulers logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.") start_epoch = resume_state['epoch'] current_iter = resume_state['iter'] else: model = create_model(opt) start_epoch = 0 current_iter = 0 # create train and validation dataloaders result = create_train_val_dataloader(opt, logger) train_loader, train_sampler, val_loader, total_epochs, total_iters = result # create message logger (formatted outputs) msg_logger = MessageLogger(opt, current_iter, tb_logger) # dataloader prefetcher prefetch_mode = opt['datasets']['train'].get('prefetch_mode') if prefetch_mode is None or prefetch_mode == 'cpu': prefetcher = CPUPrefetcher(train_loader) elif prefetch_mode == 'cuda': prefetcher = CUDAPrefetcher(train_loader, opt) logger.info(f'Use {prefetch_mode} prefetch dataloader') if opt['datasets']['train'].get('pin_memory') is not True: raise ValueError('Please set pin_memory=True for CUDAPrefetcher.') else: raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.") # training logger.info( f'Start training from epoch: {start_epoch}, iter: {current_iter}') data_time, iter_time = time.time(), time.time() start_time = time.time() epoch = start_epoch pbar = tqdm(total = total_iters+1) pbar.update(current_iter) # load psf psf = torch.tensor(np.load("./psf.npy")).to("cuda") psf_n,psf_h,psf_w,_ = psf.shape psf_n_row = int(psf_n ** 0.5) sensor_h = opt['datasets']['train'].get('sensor_size') otf = psf2otf(psf, h=psf_h*3, w=psf_w*3, permute=True)[None] gt_size = opt['datasets']['train']['gt_size'] val_conv = opt['val'].get("apply_conv", True) while current_iter <= total_iters: train_sampler.set_epoch(epoch) prefetcher.reset() train_data = prefetcher.next() while train_data is not None: data_time = time.time() - data_time gt = train_data['gt'].to("cuda") # B,C,H,H padding = train_data['padding'] padding = torch.stack(padding).T lq, gt = apply_conv_n_deconv(gt, otf, padding, M, gt_size, ks=ks, ph=psf_h, num_psf=psf_n_row, sensor_h=sensor_h) # 3 H W . conv -> crop current_iter += 1 if current_iter > total_iters: break # update learning rate model.update_learning_rate( current_iter, warmup_iter=opt['train'].get('warmup_iter', -1)) model.feed_train_data({'lq': lq, 'gt':gt}) model.optimize_parameters(current_iter) iter_time = time.time() - iter_time # log if current_iter % opt['logger']['print_freq'] == 0: log_vars = {'epoch': epoch, 'iter': current_iter} log_vars.update({'lrs': model.get_current_learning_rate()}) log_vars.update({'time': iter_time, 'data_time': data_time}) log_vars.update(model.get_current_log()) msg_logger(log_vars) # save models and training states if current_iter % opt['logger']['save_checkpoint_freq'] == 0: logger.info('Saving models and training states.') model.save(epoch, current_iter) # validation if opt.get('val') is not None and ((current_iter % opt['val']['val_freq'] == 0)): rgb2bgr = opt['val'].get('rgb2bgr', True) # wheather use uint8 image to compute metrics use_image = opt['val'].get('use_image', True) model.validation(val_loader, current_iter, tb_logger, False, rgb2bgr, use_image, psf=otf, ks=ks, val_conv=val_conv) gc.collect() torch.cuda.empty_cache() data_time = time.time() iter_time = time.time() train_data = prefetcher.next() pbar.update(1) # end of iter epoch += 1 # end of epoch consumed_time = str( datetime.timedelta(seconds=int(time.time() - start_time))) logger.info(f'End of training. Time consumed: {consumed_time}') logger.info('Save the latest model.') model.save(epoch=-1, current_iter=-1) # -1 stands for the latest if opt.get('val') is not None: rgb2bgr = opt['val'].get('rgb2bgr', True) use_image = opt['val'].get('use_image', True) psnr, others = model.validation(val_loader, current_iter, tb_logger, True, rgb2bgr, use_image, psf=otf, ks=ks, val_conv=val_conv) print("==================") print(f"Test results: PSNR: {psnr:.2f}, SSIM: {others['ssim']:.4f}, LPIPS: {others['lpips']:.4f}\n") if tb_logger: tb_logger.close() if __name__ == '__main__': main()