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train.py
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import argparse
import time
import os
from pathlib import Path
from utils import load_json
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--config-path', type=str, default=None, required=True,
help='config file path')
parser.add_argument('--resume', type=str, default=None, help='checkpoint path to resume')
parser.add_argument('--eval', action='store_true', help='only evaluate')
parser.add_argument('--log_dir', default=None, type=str, help='log file save path')
parser.add_argument('--tag', default='base', type=str, help='experiment tag')
parser.add_argument('--vote', action='store_true', help='use vote-based strategy during inference')
parser.add_argument('--seed', default=8, type=int, help='random seed')
return parser.parse_args()
def main(kargs):
import logging
import numpy as np
import random
import torch
from runners import MainRunner
seed = kargs.seed
random.seed(seed)
np.random.seed(seed + 1)
torch.manual_seed(seed + 2)
torch.cuda.manual_seed(seed + 4)
torch.cuda.manual_seed_all(seed + 4)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if kargs.log_dir:
Path(kargs.log_dir).mkdir(parents=True, exist_ok=True)
log_filename = time.strftime("%Y-%m-%d_%H-%M-%S.log", time.localtime())
log_filename = os.path.join(kargs.log_dir, "{}_{}".format(kargs.tag, log_filename))
else:
log_filename = None
logging.basicConfig(filename=log_filename, level=logging.INFO, format='%(asctime)s - %(message)s')
args = load_json(kargs.config_path)
args['train']['model_saved_path'] = os.path.join(args['train']['model_saved_path'], kargs.tag)
args['vote'] = kargs.vote
logging.info(str(args))
runner = MainRunner(args)
if kargs.resume:
runner._load_model(kargs.resume)
if kargs.eval:
runner.eval()
return
runner.train()
if __name__ == '__main__':
args = parse_args()
main(args)