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main_contrastive.py
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main_contrastive.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import argparse
import random
import torch
import torch.backends.cudnn as cudnn
from lib.utils.tools.configer import Configer
from lib.utils.tools.logger import Logger as Log
def str2bool(v):
""" Usage:
parser.add_argument('--pretrained', type=str2bool, nargs='?', const=True,
dest='pretrained', help='Whether to use pretrained models.')
"""
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Unsupported value encountered.')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--configs', default=None, type=str,
dest='configs', help='The file of the hyper parameters.')
parser.add_argument('--phase', default='train', type=str,
dest='phase', help='The phase of module.')
parser.add_argument('--gpu', default=[0, 1, 2, 3], nargs='+', type=int,
dest='gpu', help='The gpu list used.')
# *********** Params for data. **********
parser.add_argument('--data_dir', default=None, type=str, nargs='+',
dest='data:data_dir', help='The Directory of the data.')
parser.add_argument('--include_val', type=str2bool, nargs='?', default=False,
dest='data:include_val', help='Include validation set for training.')
# include-coarse is only provided for Cityscapes.
parser.add_argument('--include_coarse', type=str2bool, nargs='?', default=False,
dest='data:include_coarse', help='Include coarse-labeled set for training.')
parser.add_argument('--only_coarse', type=str2bool, nargs='?', default=False,
dest='data:only_coarse', help='Only include coarse-labeled set for training.')
parser.add_argument('--only_mapillary', type=str2bool, nargs='?', default=False,
dest='data:only_mapillary', help='Only include mapillary set for training.')
parser.add_argument('--only_small', type=str2bool, nargs='?', default=False,
dest='data:only_small', help='Only include small val set for testing.')
# include-atr is used to choose ATR as extra training set for LIP dataset.
parser.add_argument('--include_atr', type=str2bool, nargs='?', default=False,
dest='data:include_atr', help='Include atr set for LIP training.')
parser.add_argument('--include_cihp', type=str2bool, nargs='?', default=False,
dest='data:include_cihp', help='Include cihp set for LIP training.')
parser.add_argument('--drop_last', type=str2bool, nargs='?', default=False,
dest='data:drop_last', help='Fix bug for syncbn.')
parser.add_argument('--workers', default=None, type=int,
dest='data:workers', help='The number of workers to load data.')
parser.add_argument('--train_batch_size', default=None, type=int,
dest='train:batch_size', help='The batch size of training.')
parser.add_argument('--val_batch_size', default=None, type=int,
dest='val:batch_size', help='The batch size of validation.')
# *********** Params for checkpoint. **********
parser.add_argument('--checkpoints_root', default=None, type=str,
dest='checkpoints:checkpoints_root', help='The root dir of model save path.')
parser.add_argument('--checkpoints_name', default=None, type=str,
dest='checkpoints:checkpoints_name', help='The name of checkpoint model.')
parser.add_argument('--save_iters', default=None, type=int,
dest='checkpoints:save_iters', help='The saving iters of checkpoint model.')
parser.add_argument('--save_epoch', default=None, type=int,
dest='checkpoints:save_epoch', help='The saving epoch of checkpoint model.')
# *********** Params for model. **********
parser.add_argument('--model_name', default=None, type=str,
dest='network:model_name', help='The name of model.')
parser.add_argument('--backbone', default=None, type=str,
dest='network:backbone', help='The base network of model.')
parser.add_argument('--bn_type', default=None, type=str,
dest='network:bn_type', help='The BN type of the network.')
parser.add_argument('--multi_grid', default=None, nargs='+', type=int,
dest='network:multi_grid', help='The multi_grid for resnet backbone.')
parser.add_argument('--pretrained', type=str, default=None,
dest='network:pretrained', help='The path to pretrained model.')
parser.add_argument('--resume', default=None, type=str,
dest='network:resume', help='The path of checkpoints.')
parser.add_argument('--resume_strict', type=str2bool, nargs='?', default=True,
dest='network:resume_strict', help='Fully match keys or not.')
parser.add_argument('--resume_continue', type=str2bool, nargs='?', default=False,
dest='network:resume_continue', help='Whether to continue training.')
parser.add_argument('--finetune', type=str2bool, nargs='?', default=False,
dest='network:finetune', help='Whether to start finetune.')
parser.add_argument('--resume_eval_train', type=str2bool, nargs='?', default=True,
dest='network:resume_train', help='Whether to validate the training set during resume.')
parser.add_argument('--resume_eval_val', type=str2bool, nargs='?', default=True,
dest='network:resume_val', help='Whether to validate the val set during resume.')
parser.add_argument('--gathered', type=str2bool, nargs='?', default=True,
dest='network:gathered', help='Whether to gather the output of model.')
parser.add_argument('--loss_balance', type=str2bool, nargs='?', default=False,
dest='network:loss_balance', help='Whether to balance GPU usage.')
# *********** Params for solver. **********
parser.add_argument('--optim_method', default=None, type=str,
dest='optim:optim_method', help='The optim method that used.')
parser.add_argument('--group_method', default=None, type=str,
dest='optim:group_method', help='The group method that used.')
parser.add_argument('--base_lr', default=None, type=float,
dest='lr:base_lr', help='The learning rate.')
parser.add_argument('--nbb_mult', default=1.0, type=float,
dest='lr:nbb_mult', help='The not backbone mult ratio of learning rate.')
parser.add_argument('--lr_policy', default=None, type=str,
dest='lr:lr_policy', help='The policy of lr during training.')
parser.add_argument('--loss_type', default=None, type=str,
dest='loss:loss_type', help='The loss type of the network.')
parser.add_argument('--is_warm', type=str2bool, nargs='?', default=False,
dest='lr:is_warm', help='Whether to warm training.')
# *********** Params for display. **********
parser.add_argument('--max_epoch', default=None, type=int,
dest='solver:max_epoch', help='The max epoch of training.')
parser.add_argument('--max_iters', default=None, type=int,
dest='solver:max_iters', help='The max iters of training.')
parser.add_argument('--display_iter', default=None, type=int,
dest='solver:display_iter', help='The display iteration of train logs.')
parser.add_argument('--test_interval', default=None, type=int,
dest='solver:test_interval', help='The test interval of validation.')
# *********** Params for logging. **********
parser.add_argument('--logfile_level', default=None, type=str,
dest='logging:logfile_level', help='To set the log level to files.')
parser.add_argument('--stdout_level', default=None, type=str,
dest='logging:stdout_level', help='To set the level to print to screen.')
parser.add_argument('--log_file', default=None, type=str,
dest='logging:log_file', help='The path of log files.')
parser.add_argument('--rewrite', type=str2bool, nargs='?', default=True,
dest='logging:rewrite', help='Whether to rewrite files.')
parser.add_argument('--log_to_file', type=str2bool, nargs='?', default=True,
dest='logging:log_to_file', help='Whether to write logging into files.')
# *********** Params for test or submission. **********
parser.add_argument('--test_img', default=None, type=str,
dest='test:test_img', help='The test path of image.')
parser.add_argument('--test_dir', default=None, type=str,
dest='test:test_dir', help='The test directory of images.')
parser.add_argument('--out_dir', default='none', type=str,
dest='test:out_dir', help='The test out directory of images.')
parser.add_argument('--save_prob', type=str2bool, nargs='?', default=False,
dest='test:save_prob', help='Save the logits map during testing.')
# *********** Params for env. **********
parser.add_argument('--seed', default=304, type=int, help='manual seed')
parser.add_argument('--cudnn', type=str2bool, nargs='?', default=True, help='Use CUDNN.')
# *********** Params for distributed training. **********
parser.add_argument('--local_rank', type=int, default=-1, dest='local_rank', help='local rank of current process')
parser.add_argument('--distributed', action='store_true', dest='distributed', help='Use multi-processing training.')
parser.add_argument('--use_ground_truth', action='store_true', dest='use_ground_truth', help='Use ground truth for training.')
parser.add_argument('REMAIN', nargs='*')
args_parser = parser.parse_args()
from lib.utils.distributed import handle_distributed
handle_distributed(args_parser, os.path.expanduser(os.path.abspath(__file__)))
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
data_dir = configer.get('data', 'data_dir')
if isinstance(data_dir, str):
data_dir = [data_dir]
abs_data_dir = [os.path.expanduser(x) for x in data_dir]
configer.update(['data', 'data_dir'], abs_data_dir)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
Log.init(logfile_level=configer.get('logging', 'logfile_level'),
stdout_level=configer.get('logging', 'stdout_level'),
log_file=configer.get('logging', 'log_file'),
log_format=configer.get('logging', 'log_format'),
rewrite=configer.get('logging', 'rewrite'))
Log.info('batch size: {}'.format(configer.get('train', 'batch_size')))
model = None
if configer.get('method') == 'fcn_segmentor':
if configer.get('phase') == 'train':
from segmentor.trainer_contrastive import Trainer
model = Trainer(configer)
elif configer.get('phase') == 'test':
from segmentor.tester import Tester
model = Tester(configer)
elif configer.get('phase') == 'test_offset':
from segmentor.tester_offset import Tester
model = Tester(configer)
else:
Log.error('Method: {} is not valid.'.format(configer.get('task')))
exit(1)
if configer.get('phase') == 'train':
model.train()
elif configer.get('phase').startswith('test') and configer.get('network', 'resume') is not None:
model.test()
else:
Log.error('Phase: {} is not valid.'.format(configer.get('phase')))
exit(1)