diff --git a/codes/train.py b/codes/train.py index 45b6da28..c7232118 100644 --- a/codes/train.py +++ b/codes/train.py @@ -278,7 +278,7 @@ class Trainer: if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_chained_structured_trans_invariance.yml') + parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_prog_imgset_multifaceted_chained.yml') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') args = parser.parse_args() opt = option.parse(args.opt, is_train=True) diff --git a/codes/train2.py b/codes/train2.py index 90933d43..c7232118 100644 --- a/codes/train2.py +++ b/codes/train2.py @@ -13,288 +13,275 @@ from data import create_dataloader, create_dataset from models.ExtensibleTrainer import ExtensibleTrainer from time import time +class Trainer: + def init_dist(self, 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 -def init_dist(backend='nccl', **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') + self.rank = int(os.environ['RANK']) + num_gpus = torch.cuda.device_count() + torch.cuda.set_device(self.rank % num_gpus) + dist.init_process_group(backend=backend, **kwargs) - """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) + def init(self, opt, launcher, all_networks={}): + self._profile = False -def main(): - #### options - parser = argparse.ArgumentParser() - parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_mi1_multifaceted_chained.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) + #### distributed training settings + if len(opt['gpu_ids']) == 1 and torch.cuda.device_count() > 1: + gpu = input( + 'I noticed you have multiple GPUs. Starting two jobs on the same GPU sucks. Please confirm which GPU' + 'you want to use. Press enter to use the specified one [%s]' % (opt['gpu_ids'])) + if gpu: + opt['gpu_ids'] = [int(gpu)] + if launcher == 'none': # disabled distributed training + opt['dist'] = False + self.rank = -1 + print('Disabled distributed training.') - colab_mode = False if 'colab_mode' not in opt.keys() else opt['colab_mode'] - if colab_mode: - # Check the configuration of the remote server. Expect models, resume_state, and val_images directories to be there. - # Each one should have a TEST file in it. - util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'], - os.path.join(opt['remote_path'], 'training_state', "TEST")) - util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'], - os.path.join(opt['remote_path'], 'models', "TEST")) - util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'], - os.path.join(opt['remote_path'], 'val_images', "TEST")) - # Load the state and models needed from the remote server. - if opt['path']['resume_state']: - util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'], os.path.join(opt['remote_path'], 'training_state', opt['path']['resume_state'])) - if opt['path']['pretrain_model_G']: - util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'], os.path.join(opt['remote_path'], 'models', opt['path']['pretrain_model_G'])) - if opt['path']['pretrain_model_D']: - util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'], os.path.join(opt['remote_path'], 'models', opt['path']['pretrain_model_D'])) - - #### distributed training settings - if len(opt['gpu_ids']) == 1 and torch.cuda.device_count() > 1: - gpu = input('I noticed you have multiple GPUs. Starting two jobs on the same GPU sucks. Please confirm which GPU' - 'you want to use. Press enter to use the specified one [%s]' % (opt['gpu_ids'])) - if gpu: - opt['gpu_ids'] = [int(gpu)] - if args.launcher == 'none': # disabled distributed training - opt['dist'] = False - rank = -1 - print('Disabled distributed training.') - - else: - opt['dist'] = True - init_dist() - world_size = torch.distributed.get_world_size() - rank = torch.distributed.get_rank() - - #### 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 rank <= 0: # normal training (rank -1) OR distributed training (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) - logger = logging.getLogger('base') - logger.info(option.dict2str(opt)) - # tensorboard logger - if opt['use_tb_logger'] and 'debug' not in opt['name']: - 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: - logger.info( - 'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version)) - from tensorboardX import SummaryWriter - tb_logger = SummaryWriter(log_dir=tb_logger_path) - else: - util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True) - logger = logging.getLogger('base') - - # convert to NoneDict, which returns None for missing keys - opt = option.dict_to_nonedict(opt) - - #### random seed - seed = opt['train']['manual_seed'] - if seed is None: - seed = random.randint(1, 10000) - if rank <= 0: - 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': - train_set = create_dataset(dataset_opt) - train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size'])) - total_iters = int(opt['train']['niter']) - total_epochs = int(math.ceil(total_iters / train_size)) - if opt['dist']: - train_sampler = DistIterSampler(train_set, world_size, rank, dataset_ratio) - total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio))) - else: - train_sampler = None - train_loader = create_dataloader(train_set, dataset_opt, opt, train_sampler) - if rank <= 0: - logger.info('Number of train images: {:,d}, iters: {:,d}'.format( - len(train_set), train_size)) - logger.info('Total epochs needed: {:d} for iters {:,d}'.format( - total_epochs, total_iters)) - elif phase == 'val': - val_set = create_dataset(dataset_opt) - val_loader = create_dataloader(val_set, dataset_opt, opt, None) - if rank <= 0: - 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 + opt['dist'] = True + self.init_dist() + world_size = torch.distributed.get_world_size() + self.rank = torch.distributed.get_rank() - #### create model - model = ExtensibleTrainer(opt) + #### 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 - #### resume training - if resume_state: - logger.info('Resuming training from epoch: {}, iter: {}.'.format( - resume_state['epoch'], resume_state['iter'])) + #### 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)) - start_epoch = resume_state['epoch'] - current_step = resume_state['iter'] - model.resume_training(resume_state, 'amp_opt_level' in opt.keys()) # handle optimizers and schedulers - else: - current_step = -1 if 'start_step' not in opt.keys() else opt['start_step'] - start_epoch = 0 - if 'force_start_step' in opt.keys(): - current_step = opt['force_start_step'] + # 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') - #### training - logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step)) - for epoch in range(start_epoch, total_epochs + 1): - if opt['dist']: - train_sampler.set_epoch(epoch) - tq_ldr = tqdm(train_loader) + # convert to NoneDict, which returns None for missing keys + opt = option.dict_to_nonedict(opt) + self.opt = opt - _t = time() - _profile = False - for train_data in tq_ldr: - if _profile: - print("Data fetch: %f" % (time() - _t)) - _t = time() + #### 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) - current_step += 1 - if current_step > total_iters: - break - #### update learning rate - model.update_learning_rate(current_step, warmup_iter=opt['train']['warmup_iter']) + torch.backends.cudnn.benchmark = True + # torch.backends.cudnn.deterministic = True + # torch.autograd.set_detect_anomaly(True) - #### training - if _profile: - print("Update LR: %f" % (time() - _t)) - _t = time() - model.feed_data(train_data) - model.optimize_parameters(current_step) - if _profile: - print("Model feed + step: %f" % (time() - _t)) - _t = time() + # Save the compiled opt dict to the global loaded_options variable. + util.loaded_options = opt - #### log - if current_step % opt['logger']['print_freq'] == 0 and rank <= 0: - logs = model.get_current_log(current_step) - message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(epoch, current_step) - for v in model.get_current_learning_rate(): - message += '{:.3e},'.format(v) - message += ')] ' - for k, v in logs.items(): - if 'histogram' in k: - tb_logger.add_histogram(k, v, current_step) - elif isinstance(v, dict): - tb_logger.add_scalars(k, v, current_step) - else: - message += '{:s}: {:.4e} '.format(k, v) - # tensorboard logger - if opt['use_tb_logger'] and 'debug' not in opt['name']: - tb_logger.add_scalar(k, v, current_step) - logger.info(message) + #### 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']: + train_sampler = DistIterSampler(self.train_set, world_size, self.rank, dataset_ratio) + self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio))) + else: + train_sampler = None + self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, 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 - #### save models and training states - if current_step % opt['logger']['save_checkpoint_freq'] == 0: - if rank <= 0: - logger.info('Saving models and training states.') - model.save(current_step) - model.save_training_state(epoch, 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(tb_logger_path, alt_tblogger) + #### create model + self.model = ExtensibleTrainer(opt, cached_networks=all_networks) - #### validation - if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0: - if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan', 'extensibletrainer'] and rank <= 0: # image restoration validation - avg_psnr = 0. - avg_fea_loss = 0. - idx = 0 - colab_imgs_to_copy = [] - val_tqdm = tqdm(val_loader) - for val_data in val_tqdm: - idx += 1 - for b in range(len(val_data['LQ_path'])): - img_name = os.path.splitext(os.path.basename(val_data['LQ_path'][b]))[0] - img_dir = os.path.join(opt['path']['val_images'], img_name) - util.mkdir(img_dir) + #### resume training + if resume_state: + self.logger.info('Resuming training from epoch: {}, iter: {}.'.format( + resume_state['epoch'], resume_state['iter'])) - model.feed_data(val_data) - model.test() + 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'] - visuals = model.get_current_visuals() - if visuals is None: - continue + def do_step(self, train_data): + if self._profile: + print("Data fetch: %f" % (time() - _t)) + _t = time() - if colab_mode: - colab_imgs_to_copy.append(save_img_path) + opt = self.opt + self.current_step += 1 + #### update learning rate + self.model.update_learning_rate(self.current_step, warmup_iter=opt['train']['warmup_iter']) - # calculate PSNR - sr_img = util.tensor2img(visuals['rlt'][b]) # uint8 - 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) + #### 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() - # calculate fea loss - avg_fea_loss += model.compute_fea_loss(visuals['rlt'][b], visuals['GT'][b]) - - # Save SR images for reference - img_base_name = '{:s}_{:d}.png'.format(img_name, current_step) - save_img_path = os.path.join(img_dir, img_base_name) - util.save_img(sr_img, save_img_path) - - if colab_mode: - util.copy_files_to_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'], - colab_imgs_to_copy, - os.path.join(opt['remote_path'], 'val_images', img_base_name)) - - avg_psnr = avg_psnr / idx - avg_fea_loss = avg_fea_loss / idx - - # log - logger.info('# Validation # PSNR: {:.4e} Fea: {:.4e}'.format(avg_psnr, avg_fea_loss)) + #### 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'] and rank <= 0: - tb_logger.add_scalar('val_psnr', avg_psnr, current_step) - tb_logger.add_scalar('val_fea', avg_fea_loss, current_step) + 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) - if rank <= 0: - logger.info('Saving the final model.') - model.save('latest') - logger.info('End of training.') - tb_logger.close() + #### 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['LQ_path'])): + img_name = os.path.splitext(os.path.basename(val_data['LQ_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 + + # calculate PSNR + sr_img = util.tensor2img(visuals['rlt'][b]) # uint8 + 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 + 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__': - main() + parser = argparse.ArgumentParser() + parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_prog_imgset_multifaceted_chained.yml') + parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') + args = parser.parse_args() + opt = option.parse(args.opt, is_train=True) + trainer = Trainer() + trainer.init(opt, args.launcher) + trainer.do_training()