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train_hmdb51.py
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train_hmdb51.py
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import os
import json
import socket
import logging
import argparse
import torch
import torch.nn.parallel
import torch.distributed as dist
import dataset
from train_model import train_model
from network.symbol_builder import get_symbol
parser = argparse.ArgumentParser(description="PyTorch Video Classification Parser")
# debug
parser.add_argument('--debug-mode', type=bool, default=True,
help="print all setting for debugging.")
# io
parser.add_argument('--dataset', default='HMDB51', choices=['UCF101', 'HMDB51', 'Kinetics'],
help="path to dataset")
parser.add_argument('--clip-length', default=16,
help="define the length of each input sample.")
parser.add_argument('--train-frame-interval', type=int, default=2,
help="define the sampling interval between frames.")
parser.add_argument('--val-frame-interval', type=int, default=2,
help="define the sampling interval between frames.")
parser.add_argument('--task-name', type=str, default='',
help="name of current task, leave it empty for using folder name")
parser.add_argument('--model-dir', type=str, default="./exps/models",
help="set logging file.")
parser.add_argument('--log-file', type=str, default="",
help="set logging file.")
# device
parser.add_argument('--gpus', type=str, default="0,1,2,3,4,5,6,7",
help="define gpu id")
# algorithm
parser.add_argument('--network', type=str, default='MFNet_3D',
choices=['MFNet_3D'],
help="chose the base network")
# initialization with priority (the next step will overwrite the previous step)
# - step 1: random initialize
# - step 2: load the 2D pretrained model if `pretrained_2d' is True
# - step 3: load the 3D pretrained model if `pretrained_3d' is defined
# - step 4: resume if `resume_epoch' >= 0
parser.add_argument('--pretrained_2d', type=bool, default=True,
help="load default 2D pretrained model.")
parser.add_argument('--pretrained_3d', type=str,
default='./network/pretrained/vY5_866M_Kinetics_v47_fm16-it123_ep-0020.pth',
help="load default 3D pretrained model.")
parser.add_argument('--resume-epoch', type=int, default=-1,
help="resume train")
# optimization
parser.add_argument('--fine-tune', type=bool, default=True,
help="apply different learning rate for different layers")
parser.add_argument('--batch-size', type=int, default=32,
help="batch size")
parser.add_argument('--lr-base', type=float, default=0.005,
help="learning rate")
parser.add_argument('--lr-steps', type=list, default=[int(1e4*x) for x in [5, 10, 15]],
help="number of samples to pass before changing learning rate") # 1e6 million
parser.add_argument('--lr-factor', type=float, default=0.1,
help="reduce the learning with factor")
parser.add_argument('--save-frequency', type=float, default=1,
help="save once after N epochs")
parser.add_argument('--end-epoch', type=int, default=100,
help="maxmium number of training epoch")
parser.add_argument('--random-seed', type=int, default=1,
help='random seed (default: 1)')
# distributed training
parser.add_argument('--backend', default='nccl', type=str, choices=['gloo', 'nccl'],
help='Name of the backend to use')
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--dist-url', default='tcp://192.168.0.11:23456', type=str,
help='url used to set up distributed training')
def autofill(args):
# customized
if not args.task_name:
args.task_name = os.path.basename(os.getcwd())
if not args.log_file:
if os.path.exists("./exps/logs"):
args.log_file = "./exps/logs/{}_at-{}.log".format(args.task_name, socket.gethostname())
else:
args.log_file = ".{}_at-{}.log".format(args.task_name, socket.gethostname())
# fixed
args.model_prefix = os.path.join(args.model_dir, args.task_name)
return args
def set_logger(log_file='', debug_mode=False):
if log_file:
if not os.path.exists("./"+os.path.dirname(log_file)):
os.makedirs("./"+os.path.dirname(log_file))
handlers = [logging.FileHandler(log_file), logging.StreamHandler()]
else:
handlers = [logging.StreamHandler()]
""" add '%(filename)s:%(lineno)d %(levelname)s:' to format show source file """
logging.basicConfig(level=logging.DEBUG if debug_mode else logging.INFO,
format='%(asctime)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
handlers = handlers)
if __name__ == "__main__":
# set args
args = parser.parse_args()
args = autofill(args)
set_logger(log_file=args.log_file, debug_mode=args.debug_mode)
logging.info("Using pytorch {} ({})".format(torch.__version__, torch.__path__))
logging.info("Start training with args:\n" +
json.dumps(vars(args), indent=4, sort_keys=True))
# set device states
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus # before using torch
assert torch.cuda.is_available(), "CUDA is not available"
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
# distributed training
args.distributed = args.world_size > 1
if args.distributed:
import re, socket
rank = int(re.search('192.168.0.(.*)', socket.gethostname()).group(1))
logging.info("Distributed Training (rank = {}), world_size = {}, backend = `{}'".format(
rank, args.world_size, args.backend))
dist.init_process_group(backend=args.backend, init_method=args.dist_url, rank=rank,
group_name=args.task_name, world_size=args.world_size)
# load dataset related configuration
dataset_cfg = dataset.get_config(name=args.dataset)
# creat model with all parameters initialized
net, input_conf = get_symbol(name=args.network,
pretrained=args.pretrained_2d if args.resume_epoch < 0 else None,
print_net=True if args.distributed else False,
**dataset_cfg)
# training
kwargs = {}
kwargs.update(dataset_cfg)
kwargs.update({'input_conf': input_conf})
kwargs.update(vars(args))
train_model(sym_net=net, **kwargs)