114 lines
5.1 KiB
Python
114 lines
5.1 KiB
Python
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import os
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import sys
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import argparse
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import importlib
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import cv2 as cv
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import torch.backends.cudnn
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import torch.distributed as dist
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import torch
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import random
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import numpy as np
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torch.backends.cudnn.benchmark = False
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import _init_paths
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import lib.train.admin.settings as ws_settings
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def init_seeds(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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torch.set_num_threads(4)
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cv.setNumThreads(1)
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cv.ocl.setUseOpenCL(False)
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def run_training(script_name, config_name, cudnn_benchmark=True, local_rank=-1, save_dir=None, base_seed=None,
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use_lmdb=False, script_name_prv=None, config_name_prv=None, use_wandb=False,
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distill=None, script_teacher=None, config_teacher=None):
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"""Run the train script.
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args:
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script_name: Name of emperiment in the "experiments/" folder.
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config_name: Name of the yaml file in the "experiments/<script_name>".
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cudnn_benchmark: Use cudnn benchmark or not (default is True).
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"""
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if save_dir is None:
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print("save_dir dir is not given. Use the default dir instead.")
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# This is needed to avoid strange crashes related to opencv
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torch.set_num_threads(4)
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cv.setNumThreads(4)
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torch.backends.cudnn.benchmark = cudnn_benchmark
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print('script_name: {}.py config_name: {}.yaml'.format(script_name, config_name))
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'''2021.1.5 set seed for different process'''
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if base_seed is not None:
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if local_rank != -1:
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init_seeds(base_seed + local_rank)
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else:
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init_seeds(base_seed)
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settings = ws_settings.Settings()
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settings.script_name = script_name
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settings.config_name = config_name
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settings.project_path = 'train/{}/{}'.format(script_name, config_name)
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if script_name_prv is not None and config_name_prv is not None:
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settings.project_path_prv = 'train/{}/{}'.format(script_name_prv, config_name_prv)
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settings.local_rank = local_rank
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settings.save_dir = os.path.abspath(save_dir)
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settings.use_lmdb = use_lmdb
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prj_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
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settings.cfg_file = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_name, config_name))
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settings.use_wandb = use_wandb
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if distill:
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settings.distill = distill
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settings.script_teacher = script_teacher
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settings.config_teacher = config_teacher
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if script_teacher is not None and config_teacher is not None:
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settings.project_path_teacher = 'train/{}/{}'.format(script_teacher, config_teacher)
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settings.cfg_file_teacher = os.path.join(prj_dir, 'experiments/%s/%s.yaml' % (script_teacher, config_teacher))
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expr_module = importlib.import_module('lib.train.train_script_distill')
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else:
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expr_module = importlib.import_module('lib.train.train_script')
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expr_func = getattr(expr_module, 'run')
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expr_func(settings)
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def main():
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parser = argparse.ArgumentParser(description='Run a train scripts in train_settings.')
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parser.add_argument('--script', type=str, required=True, help='Name of the train script.')
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parser.add_argument('--config', type=str, required=True, help="Name of the config file.")
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parser.add_argument('--cudnn_benchmark', type=bool, default=False, help='Set cudnn benchmark on (1) or off (0) (default is on).')
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parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training')
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parser.add_argument('--save_dir', type=str, help='the directory to save checkpoints and logs')
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parser.add_argument('--seed', type=int, default=42, help='seed for random numbers')
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parser.add_argument('--use_lmdb', type=int, choices=[0, 1], default=0) # whether datasets are in lmdb format
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parser.add_argument('--script_prv', type=str, default=None, help='Name of the train script of previous model.')
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parser.add_argument('--config_prv', type=str, default=None, help="Name of the config file of previous model.")
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parser.add_argument('--use_wandb', type=int, choices=[0, 1], default=0) # whether to use wandb
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# for knowledge distillation
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parser.add_argument('--distill', type=int, choices=[0, 1], default=0) # whether to use knowledge distillation
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parser.add_argument('--script_teacher', type=str, help='teacher script name')
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parser.add_argument('--config_teacher', type=str, help='teacher yaml configure file name')
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args = parser.parse_args()
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if args.local_rank != -1:
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dist.init_process_group(backend='nccl')
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torch.cuda.set_device(args.local_rank)
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else:
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torch.cuda.set_device(0)
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run_training(args.script, args.config, cudnn_benchmark=args.cudnn_benchmark,
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local_rank=args.local_rank, save_dir=args.save_dir, base_seed=args.seed,
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use_lmdb=args.use_lmdb, script_name_prv=args.script_prv, config_name_prv=args.config_prv,
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use_wandb=args.use_wandb,
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distill=args.distill, script_teacher=args.script_teacher, config_teacher=args.config_teacher)
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if __name__ == '__main__':
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main()
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