import os import math import argparse import random import logging from tqdm import tqdm import torch from data.data_sampler import DistIterSampler from utils import util, options as option from data import create_dataloader, create_dataset from models.ExtensibleTrainer import ExtensibleTrainer from time import time def init_dist(backend, **kwargs): # These packages have globals that screw with Windows, so only import them if needed. import torch.distributed as dist import torch.multiprocessing as mp """initialization for distributed training""" if mp.get_start_method(allow_none=True) != 'spawn': mp.set_start_method('spawn') rank = int(os.environ['RANK']) num_gpus = torch.cuda.device_count() torch.cuda.set_device(rank % num_gpus) dist.init_process_group(backend=backend, **kwargs) class Trainer: def init(self, opt, launcher, all_networks={}): self._profile = False self.val_compute_psnr = opt['eval']['compute_psnr'] if 'compute_psnr' in opt['eval'] else True self.val_compute_fea = opt['eval']['compute_fea'] if 'compute_fea' in opt['eval'] else True #### loading resume state if exists if opt['path'].get('resume_state', None): # distributed resuming: all load into default GPU device_id = torch.cuda.current_device() resume_state = torch.load(opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id)) option.check_resume(opt, resume_state['iter']) # check resume options else: resume_state = None #### mkdir and loggers if self.rank <= 0: # normal training (self.rank -1) OR distributed training (self.rank 0) if resume_state is None: 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 path is not None 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) self.logger = logging.getLogger('base') self.logger.info(option.dict2str(opt)) # tensorboard logger if opt['use_tb_logger'] and 'debug' not in opt['name']: self.tb_logger_path = os.path.join(opt['path']['experiments_root'], 'tb_logger') version = float(torch.__version__[0:3]) if version >= 1.1: # PyTorch 1.1 from torch.utils.tensorboard import SummaryWriter else: self.self.logger.info( 'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version)) from tensorboardX import SummaryWriter self.tb_logger = SummaryWriter(log_dir=self.tb_logger_path) else: util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True) self.logger = logging.getLogger('base') # convert to NoneDict, which returns None for missing keys opt = option.dict_to_nonedict(opt) self.opt = opt #### random seed seed = opt['train']['manual_seed'] if seed is None: seed = random.randint(1, 10000) if self.rank <= 0: self.logger.info('Random seed: {}'.format(seed)) util.set_random_seed(seed) torch.backends.cudnn.benchmark = True # torch.backends.cudnn.deterministic = True # torch.autograd.set_detect_anomaly(True) # Save the compiled opt dict to the global loaded_options variable. util.loaded_options = opt #### create train and val dataloader dataset_ratio = 1 # enlarge the size of each epoch for phase, dataset_opt in opt['datasets'].items(): if phase == 'train': self.train_set = create_dataset(dataset_opt) train_size = int(math.ceil(len(self.train_set) / dataset_opt['batch_size'])) total_iters = int(opt['train']['niter']) self.total_epochs = int(math.ceil(total_iters / train_size)) if opt['dist']: self.train_sampler = DistIterSampler(self.train_set, self.world_size, self.rank, dataset_ratio) self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio))) else: self.train_sampler = None self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, self.train_sampler) if self.rank <= 0: self.logger.info('Number of train images: {:,d}, iters: {:,d}'.format( len(self.train_set), train_size)) self.logger.info('Total epochs needed: {:d} for iters {:,d}'.format( self.total_epochs, total_iters)) elif phase == 'val': self.val_set = create_dataset(dataset_opt) self.val_loader = create_dataloader(self.val_set, dataset_opt, opt, None) if self.rank <= 0: self.logger.info('Number of val images in [{:s}]: {:d}'.format( dataset_opt['name'], len(self.val_set))) else: raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase)) assert self.train_loader is not None #### create model self.model = ExtensibleTrainer(opt, cached_networks=all_networks) #### resume training if resume_state: self.logger.info('Resuming training from epoch: {}, iter: {}.'.format( resume_state['epoch'], resume_state['iter'])) self.start_epoch = resume_state['epoch'] self.current_step = resume_state['iter'] self.model.resume_training(resume_state, 'amp_opt_level' in opt.keys()) # handle optimizers and schedulers else: self.current_step = -1 if 'start_step' not in opt.keys() else opt['start_step'] self.start_epoch = 0 if 'force_start_step' in opt.keys(): self.current_step = opt['force_start_step'] def do_step(self, train_data): if self._profile: print("Data fetch: %f" % (time() - _t)) _t = time() opt = self.opt self.current_step += 1 #### update learning rate self.model.update_learning_rate(self.current_step, warmup_iter=opt['train']['warmup_iter']) #### training if self._profile: print("Update LR: %f" % (time() - _t)) _t = time() self.model.feed_data(train_data) self.model.optimize_parameters(self.current_step) if self._profile: print("Model feed + step: %f" % (time() - _t)) _t = time() #### log if self.current_step % opt['logger']['print_freq'] == 0 and self.rank <= 0: logs = self.model.get_current_log(self.current_step) message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(self.epoch, self.current_step) for v in self.model.get_current_learning_rate(): message += '{:.3e},'.format(v) message += ')] ' for k, v in logs.items(): if 'histogram' in k: self.tb_logger.add_histogram(k, v, self.current_step) elif isinstance(v, dict): self.tb_logger.add_scalars(k, v, self.current_step) else: message += '{:s}: {:.4e} '.format(k, v) # tensorboard logger if opt['use_tb_logger'] and 'debug' not in opt['name']: self.tb_logger.add_scalar(k, v, self.current_step) self.logger.info(message) #### save models and training states if self.current_step % opt['logger']['save_checkpoint_freq'] == 0: if self.rank <= 0: self.logger.info('Saving models and training states.') self.model.save(self.current_step) self.model.save_training_state(self.epoch, self.current_step) if 'alt_path' in opt['path'].keys(): import shutil print("Synchronizing tb_logger to alt_path..") alt_tblogger = os.path.join(opt['path']['alt_path'], "tb_logger") shutil.rmtree(alt_tblogger, ignore_errors=True) shutil.copytree(self.tb_logger_path, alt_tblogger) #### validation if opt['datasets'].get('val', None) and self.current_step % opt['train']['val_freq'] == 0: if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan', 'extensibletrainer'] and self.rank <= 0: # image restoration validation avg_psnr = 0. avg_fea_loss = 0. idx = 0 val_tqdm = tqdm(self.val_loader) for val_data in val_tqdm: idx += 1 for b in range(len(val_data['GT_path'])): img_name = os.path.splitext(os.path.basename(val_data['GT_path'][b]))[0] img_dir = os.path.join(opt['path']['val_images'], img_name) util.mkdir(img_dir) self.model.feed_data(val_data) self.model.test() visuals = self.model.get_current_visuals() if visuals is None: continue sr_img = util.tensor2img(visuals['rlt'][b]) # uint8 # calculate PSNR if self.val_compute_psnr: gt_img = util.tensor2img(visuals['GT'][b]) # uint8 sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale']) avg_psnr += util.calculate_psnr(sr_img, gt_img) # calculate fea loss if self.val_compute_fea: avg_fea_loss += self.model.compute_fea_loss(visuals['rlt'][b], visuals['GT'][b]) # Save SR images for reference img_base_name = '{:s}_{:d}.png'.format(img_name, self.current_step) save_img_path = os.path.join(img_dir, img_base_name) util.save_img(sr_img, save_img_path) avg_psnr = avg_psnr / idx avg_fea_loss = avg_fea_loss / idx # log self.logger.info('# Validation # PSNR: {:.4e} Fea: {:.4e}'.format(avg_psnr, avg_fea_loss)) # tensorboard logger if opt['use_tb_logger'] and 'debug' not in opt['name'] and self.rank <= 0: self.tb_logger.add_scalar('val_psnr', avg_psnr, self.current_step) self.tb_logger.add_scalar('val_fea', avg_fea_loss, self.current_step) def do_training(self): self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step)) for epoch in range(self.start_epoch, self.total_epochs + 1): self.epoch = epoch if opt['dist']: self.train_sampler.set_epoch(epoch) tq_ldr = tqdm(self.train_loader) _t = time() for train_data in tq_ldr: self.do_step(train_data) def create_training_generator(self, index): self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step)) for epoch in range(self.start_epoch, self.total_epochs + 1): self.epoch = epoch if self.opt['dist']: self.train_sampler.set_epoch(epoch) tq_ldr = tqdm(self.train_loader, position=index) _t = time() for train_data in tq_ldr: yield self.model self.do_step(train_data) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_mi1_rrdb4x_6bl_bypass.yml') 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) trainer = Trainer() #### distributed training settings if args.launcher == 'none': # disabled distributed training opt['dist'] = False trainer.rank = -1 print('Disabled distributed training.') else: opt['dist'] = True init_dist('nccl') trainer.world_size = torch.distributed.get_world_size() trainer.rank = torch.distributed.get_rank() trainer.init(opt, args.launcher) trainer.do_training()