302 lines
14 KiB
Python
302 lines
14 KiB
Python
import os
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import math
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import argparse
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import random
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import logging
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from tqdm import tqdm
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import torch
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from data.data_sampler import DistIterSampler
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from utils import util, options as option
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from data import create_dataloader, create_dataset
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from models.ExtensibleTrainer import ExtensibleTrainer
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from time import time
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def init_dist(backend='nccl', **kwargs):
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# These packages have globals that screw with Windows, so only import them if needed.
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import torch.distributed as dist
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import torch.multiprocessing as mp
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"""initialization for distributed training"""
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if mp.get_start_method(allow_none=True) != 'spawn':
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mp.set_start_method('spawn')
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rank = int(os.environ['RANK'])
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num_gpus = torch.cuda.device_count()
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torch.cuda.set_device(rank % num_gpus)
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dist.init_process_group(backend=backend, **kwargs)
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_chained_structured_trans_invariance.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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opt = option.parse(args.opt, is_train=True)
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colab_mode = False if 'colab_mode' not in opt.keys() else opt['colab_mode']
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if colab_mode:
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# Check the configuration of the remote server. Expect models, resume_state, and val_images directories to be there.
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# Each one should have a TEST file in it.
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util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'],
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os.path.join(opt['remote_path'], 'training_state', "TEST"))
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util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'],
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os.path.join(opt['remote_path'], 'models', "TEST"))
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util.get_files_from_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'],
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os.path.join(opt['remote_path'], 'val_images', "TEST"))
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# Load the state and models needed from the remote server.
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if opt['path']['resume_state']:
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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']))
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if opt['path']['pretrain_model_G']:
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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']))
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if opt['path']['pretrain_model_D']:
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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']))
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#### distributed training settings
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if len(opt['gpu_ids']) == 1 and torch.cuda.device_count() > 1:
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gpu = input('I noticed you have multiple GPUs. Starting two jobs on the same GPU sucks. Please confirm which GPU'
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'you want to use. Press enter to use the specified one [%s]' % (opt['gpu_ids']))
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if gpu:
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opt['gpu_ids'] = [int(gpu)]
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if args.launcher == 'none': # disabled distributed training
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opt['dist'] = False
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rank = -1
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print('Disabled distributed training.')
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else:
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opt['dist'] = True
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init_dist()
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world_size = torch.distributed.get_world_size()
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rank = torch.distributed.get_rank()
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#### loading resume state if exists
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if opt['path'].get('resume_state', None):
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# distributed resuming: all load into default GPU
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device_id = torch.cuda.current_device()
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resume_state = torch.load(opt['path']['resume_state'],
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map_location=lambda storage, loc: storage.cuda(device_id))
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option.check_resume(opt, resume_state['iter']) # check resume options
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else:
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resume_state = None
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#### mkdir and loggers
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if rank <= 0: # normal training (rank -1) OR distributed training (rank 0)
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if resume_state is None:
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util.mkdir_and_rename(
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opt['path']['experiments_root']) # rename experiment folder if exists
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util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root' and path is not None
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and 'pretrain_model' not in key and 'resume' not in key))
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# config loggers. Before it, the log will not work
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util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
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screen=True, tofile=True)
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logger = logging.getLogger('base')
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logger.info(option.dict2str(opt))
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name']:
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tb_logger_path = os.path.join(opt['path']['experiments_root'], 'tb_logger')
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version = float(torch.__version__[0:3])
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if version >= 1.1: # PyTorch 1.1
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from torch.utils.tensorboard import SummaryWriter
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else:
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logger.info(
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'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version))
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from tensorboardX import SummaryWriter
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tb_logger = SummaryWriter(log_dir=tb_logger_path)
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else:
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util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True)
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logger = logging.getLogger('base')
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# convert to NoneDict, which returns None for missing keys
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opt = option.dict_to_nonedict(opt)
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#### random seed
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seed = opt['train']['manual_seed']
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if seed is None:
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seed = random.randint(1, 10000)
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if rank <= 0:
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logger.info('Random seed: {}'.format(seed))
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util.set_random_seed(seed)
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torch.backends.cudnn.benchmark = True
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# torch.backends.cudnn.deterministic = True
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# torch.autograd.set_detect_anomaly(True)
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# Save the compiled opt dict to the global loaded_options variable.
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util.loaded_options = opt
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#### create train and val dataloader
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dataset_ratio = 1 # enlarge the size of each epoch
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for phase, dataset_opt in opt['datasets'].items():
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if phase == 'train':
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train_set = create_dataset(dataset_opt)
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train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size']))
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total_iters = int(opt['train']['niter'])
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total_epochs = int(math.ceil(total_iters / train_size))
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if opt['dist']:
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train_sampler = DistIterSampler(train_set, world_size, rank, dataset_ratio)
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total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
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else:
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train_sampler = None
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train_loader = create_dataloader(train_set, dataset_opt, opt, train_sampler)
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if rank <= 0:
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logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
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len(train_set), train_size))
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logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
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total_epochs, total_iters))
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elif phase == 'val':
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val_set = create_dataset(dataset_opt)
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val_loader = create_dataloader(val_set, dataset_opt, opt, None)
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if rank <= 0:
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logger.info('Number of val images in [{:s}]: {:d}'.format(
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dataset_opt['name'], len(val_set)))
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else:
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raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
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assert train_loader is not None
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#### create model
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model = ExtensibleTrainer(opt)
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#### resume training
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if resume_state:
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logger.info('Resuming training from epoch: {}, iter: {}.'.format(
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resume_state['epoch'], resume_state['iter']))
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start_epoch = resume_state['epoch']
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current_step = resume_state['iter']
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model.resume_training(resume_state, 'amp_opt_level' in opt.keys()) # handle optimizers and schedulers
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else:
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current_step = -1 if 'start_step' not in opt.keys() else opt['start_step']
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start_epoch = 0
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if 'force_start_step' in opt.keys():
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current_step = opt['force_start_step']
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#### training
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logger.info('Start training from epoch: {:d}, iter: {:d}'.format(start_epoch, current_step))
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for epoch in range(start_epoch, total_epochs + 1):
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if opt['dist']:
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train_sampler.set_epoch(epoch)
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tq_ldr = tqdm(train_loader)
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_t = time()
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_profile = False
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for train_data in tq_ldr:
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if _profile:
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print("Data fetch: %f" % (time() - _t))
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_t = time()
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current_step += 1
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if current_step > total_iters:
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break
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#### update learning rate
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model.update_learning_rate(current_step, warmup_iter=opt['train']['warmup_iter'])
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#### training
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if _profile:
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print("Update LR: %f" % (time() - _t))
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_t = time()
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model.feed_data(train_data)
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model.optimize_parameters(current_step)
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if _profile:
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print("Model feed + step: %f" % (time() - _t))
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_t = time()
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#### log
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if current_step % opt['logger']['print_freq'] == 0:
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logs = model.get_current_log(current_step)
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message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(epoch, current_step)
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for v in model.get_current_learning_rate():
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message += '{:.3e},'.format(v)
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message += ')] '
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for k, v in logs.items():
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if 'histogram' in k:
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if rank <= 0:
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tb_logger.add_histogram(k, v, current_step)
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else:
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message += '{:s}: {:.4e} '.format(k, v)
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name']:
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if rank <= 0:
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tb_logger.add_scalar(k, v, current_step)
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if rank <= 0:
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logger.info(message)
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#### save models and training states
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if current_step % opt['logger']['save_checkpoint_freq'] == 0:
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if rank <= 0:
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logger.info('Saving models and training states.')
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model.save(current_step)
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model.save_training_state(epoch, current_step)
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if 'alt_path' in opt['path'].keys():
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import shutil
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print("Synchronizing tb_logger to alt_path..")
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alt_tblogger = os.path.join(opt['path']['alt_path'], "tb_logger")
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shutil.rmtree(alt_tblogger, ignore_errors=True)
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shutil.copytree(tb_logger_path, alt_tblogger)
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#### validation
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if opt['datasets'].get('val', None) and current_step % opt['train']['val_freq'] == 0:
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if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan', 'extensibletrainer'] and rank <= 0: # image restoration validation
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avg_psnr = 0.
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avg_fea_loss = 0.
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idx = 0
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colab_imgs_to_copy = []
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val_tqdm = tqdm(val_loader)
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for val_data in val_tqdm:
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idx += 1
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for b in range(len(val_data['LQ_path'])):
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img_name = os.path.splitext(os.path.basename(val_data['LQ_path'][b]))[0]
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img_dir = os.path.join(opt['path']['val_images'], img_name)
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util.mkdir(img_dir)
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model.feed_data(val_data)
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model.test()
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visuals = model.get_current_visuals()
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if visuals is None:
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continue
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if colab_mode:
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colab_imgs_to_copy.append(save_img_path)
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# calculate PSNR
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sr_img = util.tensor2img(visuals['rlt'][b]) # uint8
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gt_img = util.tensor2img(visuals['GT'][b]) # uint8
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sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
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avg_psnr += util.calculate_psnr(sr_img, gt_img)
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# calculate fea loss
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avg_fea_loss += model.compute_fea_loss(visuals['rlt'][b], visuals['GT'][b])
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# Save SR images for reference
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img_base_name = '{:s}_{:d}.png'.format(img_name, current_step)
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save_img_path = os.path.join(img_dir, img_base_name)
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util.save_img(sr_img, save_img_path)
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if colab_mode:
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util.copy_files_to_server(opt['ssh_server'], opt['ssh_username'], opt['ssh_password'],
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colab_imgs_to_copy,
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os.path.join(opt['remote_path'], 'val_images', img_base_name))
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avg_psnr = avg_psnr / idx
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avg_fea_loss = avg_fea_loss / idx
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# log
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logger.info('# Validation # PSNR: {:.4e} Fea: {:.4e}'.format(avg_psnr, avg_fea_loss))
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# tensorboard logger
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if opt['use_tb_logger'] and 'debug' not in opt['name'] and rank <= 0:
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tb_logger.add_scalar('val_psnr', avg_psnr, current_step)
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tb_logger.add_scalar('val_fea', avg_fea_loss, current_step)
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if rank <= 0:
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logger.info('Saving the final model.')
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model.save('latest')
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logger.info('End of training.')
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tb_logger.close()
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if __name__ == '__main__':
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main()
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