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train_hacs.py
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train_hacs.py
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'''
--- I M P O R T S T A T E M E N T S ---
'''
import os
import json
import socket
import coloredlogs, logging
coloredlogs.install()
import argparse
import torch
import torch.nn.parallel
import dataset
from train_model import train_model
from network.symbol_builder import get_symbol
# Create main parser
parser = argparse.ArgumentParser(description="PyTorch Video Classification Parser")
# debug
parser.add_argument('--debug-mode', type=bool, default=True,
help="print all setting for debugging.")
parser.add_argument('--dataset', default='HACS',
help="path to dataset")
parser.add_argument('--clip-length', default=16,
help="define the length of each input sample.")
parser.add_argument('--clip-size', default=284,
help="define the size of each input sample.")
parser.add_argument('--train-frame-interval', type=int, default=[1,2],
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="./results/HACS",
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='r3d_50',
help="chose the base network")
'''
Initialisation steps :
- step 1: random initialize
- step 2: load the 3D pretrained model if `pretrained_3d' is defined
- step 3: resume if `resume_epoch' >= 0
'''
parser.add_argument('--pretrained_3d', type=str, default=None,
help="load default 3D pretrained model.")
# optimization
parser.add_argument('--fine-tune', type=bool, default=False,
help="resume training and then fine tune the classifier")
parser.add_argument('--resume-epoch', type=int, default=-1,
help="resume train")
parser.add_argument('--batch-size', type=int, default=48,
help="batch size")
parser.add_argument('--long-cycles', type=bool, default=True,
help="Enebling long cycles for batches")
parser.add_argument('--short-cycles', type=bool, default=True,
help="Enebling short cycles for batches")
parser.add_argument('--lr-base', type=float, default=0.1,
help="learning rate")
parser.add_argument('--lr-steps', type=list, default=[40,60,75,90],
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=120,
help="maxmium number of training epoch")
parser.add_argument('--random-seed', type=int, default=1,
help='random seed (default: 1)')
'''
--- S T A R T O F F U N C T I O N A U T O F I L L ---
[About]
Function for creating log directories based on the parser arguments
[Args]
- args: ArgumentParser object containg both the name of task (if empty a default folder is created) and the log file to be created.
[Returns]
- args: ArgumentParser object with additionally including the model directory and the model prefix.
'''
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_dir = os.path.join(args.model_dir,args.network)
args.model_prefix = os.path.join(args.model_dir, args.network)
return args
'''
--- E N D O F F U N C T I O N A U T O F I L L ---
'''
'''
--- S T A R T O F F U N C T I O N S E T _ L O G G E R ---
[About]
Function for creating logger for displaying and storing all events.
[Args]
- log_file: String for the log file to store all session info through logging.
- debug_mode: Boolean for additional information while logging.
[Returns]
- None
'''
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)
'''
--- E N D O F F U N C T I O N S E T _ L O G G E R ---
'''
'''
--- S T A R T O F M A I N F U N C T I O N ---
'''
if __name__ == "__main__":
# set args
args = parser.parse_args()
args = autofill(args)
# Use logger
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
print('Can use cuda: '+str(torch.cuda.is_available()))
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)
# Load dataset related configuration
dataset_cfg = dataset.get_config(name=args.dataset)
# Creat model with all parameters initialised
assert (not args.fine_tune or not args.resume_epoch < 0), \
"args: `resume_epoch' must be defined for fine tuning"
net, input_conf = get_symbol(name=args.network,
print_net=False, # True if args.distributed else False,
**dataset_cfg)
# training parameters intialisation
kwargs = {}
kwargs.update(dataset_cfg)
kwargs.update({'input_conf': input_conf})
kwargs.update(vars(args))
# Call main train script
train_model(sym_net=net, name='HACS', net_name=args.network, dataset_location=args.dataset, **kwargs)
'''
--- E N D O F M A I N F U N C T I O N ---
'''