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main.py
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main.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Main Scripts for computer vision.
import os
import json
import random
import argparse
import torch
import torch.backends.cudnn as cudnn
from lib.runner.controller import Controller
from lib.tools.util.configer import Configer
from lib.tools.util.logger import Logger as Log
from tools.fcn_segmentor import FCNSegmentor
from tools.fcn_segmentor_test import FCNSegmentorTest
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('--config_file', default=None, type=str,
dest='config_file', 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,
dest='data.data_dir', help='The Directory of the data.')
parser.add_argument('--tag', default=None, type=str,
dest='data.tag', help='The Tag of the data.')
parser.add_argument('--include_val', type=str2bool, nargs='?', default=False,
dest='data.include_val', help='Include validation set for final 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.')
parser.add_argument('--test_batch_size', default=None, type=int,
dest='test.batch_size', help='The batch size of testing.')
# *********** Params for model. **********
parser.add_argument('--model_name', default=None, type=str,
dest='network.model_name', help='The name of model.')
parser.add_argument('--checkpoints_root', default=None, type=str,
dest='network.checkpoints_root', help='The root dir of model save path.')
parser.add_argument('--checkpoints_name', default=None, type=str,
dest='network.checkpoints_name', help='The name of checkpoint model.')
parser.add_argument('--backbone', default=None, type=str,
dest='network.backbone', help='The base network of model.')
parser.add_argument('--norm_type', default=None, type=str,
dest='network.norm_type', help='The BN type of the network.')
parser.add_argument('--syncbn', type=str2bool, nargs='?', default=False,
dest='network.syncbn', help='Whether to sync BN.')
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('--resume_val', type=str2bool, nargs='?', default=False,
dest='network.resume_val', help='Whether to validate during resume.')
parser.add_argument('--gather', type=str2bool, nargs='?', default=True,
dest='network.gather', help='Whether to gather the output of model.')
parser.add_argument('--dist', type=str2bool, nargs='?', default=False,
dest='network.distributed', help='Whether to gather the output of model.')
# *********** Params for solver. **********
parser.add_argument('--optim_method', default=None, type=str,
dest='solver.optim.optim_method', help='The optim method that used.')
parser.add_argument('--base_lr', default=None, type=float,
dest='solver.lr.base_lr', help='The learning rate.')
parser.add_argument('--bb_lr_scale', default=1.0, type=float,
dest='solver.lr.bb_lr_scale', help='The learning rate.')
parser.add_argument('--nbb_mult', default=1.0, type=float,
dest='solver.lr.nbb_mult', help='The not backbone mult ratio of learning rate.')
parser.add_argument('--lr_policy', default=None, type=str,
dest='solver.lr.lr_policy', help='The policy of lr during training.')
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.')
parser.add_argument('--save_iters', default=None, type=int,
dest='solver.save_iters', help='The saving iters of checkpoint model.')
parser.add_argument('--save_epoch', default=None, type=int,
dest='solver.save_epoch', help='The saving epoch of checkpoint model.')
# *********** Params for loss. **********
parser.add_argument('--loss_type', default=None, type=str,
dest='loss.loss_type', help='The loss type of the network.')
# *********** Params for logging. **********
parser.add_argument('--log_level', default="info", type=str,
dest='logging.log_level', help='To set the level to print to screen.')
parser.add_argument('--log_format', default="%(asctime)s %(levelname)-7s %(message)s", type=str,
dest='logging.log_format', help='To set the format to print to screen.')
# *********** Params for test or submission. **********
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.')
# *********** Params for env. **********
parser.add_argument('--seed', default=None, type=int, help='manual seed')
parser.add_argument('--cudnn', type=str2bool, nargs='?', default=True, help='Use CUDNN.')
parser.add_argument("--local_rank", default=0, type=int)
args = parser.parse_args()
configer = Configer(args_parser=args)
if args.seed is not None:
random.seed(args.seed + args.local_rank)
torch.manual_seed(args.seed + args.local_rank)
cudnn.enabled = True
cudnn.benchmark = args.cudnn
abs_data_dir = os.path.expanduser(configer.get('data', 'data_dir'))
configer.update('data.data_dir', abs_data_dir)
if configer.get('gpu') is not None and not configer.get('network.distributed', default=False):
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(gpu_id) for gpu_id in configer.get('gpu'))
if configer.get('network', 'norm_type') is None:
configer.update('network.norm_type', 'batchnorm')
if torch.cuda.device_count() <= 1 or configer.get('network.distributed', default=False):
configer.update('network.gather', True)
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add('project_dir', project_dir)
Log.init(log_level=configer.get('logging', 'log_level'),
log_format=configer.get('logging', 'log_format'),
distributed_rank=configer.get('local_rank'))
Log.info('BN Type is {}.'.format(configer.get('network', 'norm_type')))
Log.info('Config Dict: {}'.format(json.dumps(configer.to_dict(), indent=2)))
if configer.get('phase') == 'train':
runner = FCNSegmentor(configer)
if configer.get('network', 'resume') is None or not configer.get('network.resume_continue'):
Controller.init(runner)
Controller.train(runner)
elif configer.get('phase') == 'test' and configer.get('network', 'resume') is not None:
runner = FCNSegmentorTest(configer)
Controller.test(runner)
else:
Log.error('Phase: {} is not valid.'.format(configer.get('phase')))
exit(1)