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#!/usr/bin/env python3
import argparse
import os
import shutil
import random
import ast
from os.path import basename, exists
import torch
import torch.backends.cudnn as cudnn
import numpy as np
import yaml
from core.trainers import CDTrainer
from utils.misc import OutPathGetter, Logger, register
def read_config(config_path):
f = open(config_path, 'r')
cfg = yaml.load(f.read(), Loader=yaml.FullLoader)
f.close()
return cfg or {}
def parse_config(cfg_name, cfg):
# Parse the name of config file
sp = cfg_name.split('.')[0].split('_')
if len(sp) >= 2:
cfg.setdefault('tag', sp[1])
cfg.setdefault('suffix', '_'.join(sp[2:]))
return cfg
def parse_args():
# Training settings
parser = argparse.ArgumentParser()
parser.add_argument('cmd', choices=['train', 'val'])
# Data
# Common
group_data = parser.add_argument_group('data')
group_data.add_argument('-d', '--dataset', type=str, default='OSCD')
group_data.add_argument('-p', '--crop-size', type=int, default=256, metavar='P',
help='patch size (default: %(default)s)')
group_data.add_argument('--num-workers', type=int, default=8)
group_data.add_argument('--repeats', type=int, default=100)
# Optimizer
group_optim = parser.add_argument_group('optimizer')
group_optim.add_argument('--optimizer', type=str, default='Adam')
group_optim.add_argument('--lr', type=float, default=1e-4, metavar='LR',
help='learning rate (default: %(default)s)')
group_optim.add_argument('--lr-mode', type=str, default='const')
group_optim.add_argument('--weight-decay', default=1e-4, type=float,
metavar='W', help='weight decay (default: %(default)s)')
group_optim.add_argument('--step', type=int, default=200)
# Training related
group_train = parser.add_argument_group('training related')
group_train.add_argument('--batch-size', type=int, default=8, metavar='B',
help='input batch size for training (default: %(default)s)')
group_train.add_argument('--num-epochs', type=int, default=1000, metavar='NE',
help='number of epochs to train (default: %(default)s)')
group_train.add_argument('--load-optim', action='store_true')
group_train.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint')
group_train.add_argument('--anew', action='store_true',
help='clear history and start from epoch 0 with the checkpoint loaded')
group_train.add_argument('--trace-freq', type=int, default=50)
group_train.add_argument('--device', type=str, default='cpu')
group_train.add_argument('--metrics', type=str, default='F1Score+Accuracy+Recall+Precision')
# Experiment
group_exp = parser.add_argument_group('experiment related')
group_exp.add_argument('--exp-dir', default='../exp/')
group_exp.add_argument('-o', '--out-dir', default='')
group_exp.add_argument('--tag', type=str, default='')
group_exp.add_argument('--suffix', type=str, default='')
group_exp.add_argument('--exp-config', type=str, default='')
group_exp.add_argument('--save-on', action='store_true')
group_exp.add_argument('--log-off', action='store_true')
group_exp.add_argument('--suffix-off', action='store_true')
# Criterion
group_critn = parser.add_argument_group('criterion related')
group_critn.add_argument('--criterion', type=str, default='NLL')
group_critn.add_argument('--weights', type=str, default=(1.0, 1.0))
# Model
group_model = parser.add_argument_group('model')
group_model.add_argument('--model', type=str, default='siamunet_conc')
group_model.add_argument('--num-feats-in', type=int, default=13)
args = parser.parse_args()
if exists(args.exp_config):
cfg = read_config(args.exp_config)
cfg = parse_config(basename(args.exp_config), cfg)
# Settings from cfg file overwrite those in args
# Note that the non-default values will not be affected
parser.set_defaults(**cfg) # Reset part of the default values
args = parser.parse_args() # Parse again
# Handle args.weights
if isinstance(args.weights, str):
args.weights = ast.literal_eval(args.weights)
args.weights = tuple(args.weights)
return args
def set_gpc_and_logger(args):
gpc = OutPathGetter(
root=os.path.join(args.exp_dir, args.tag),
suffix=args.suffix)
log_dir = '' if args.log_off else gpc.get_dir('log')
logger = Logger(
scrn=True,
log_dir=log_dir,
phase=args.cmd
)
register('GPC', gpc)
register('LOGGER', logger)
return gpc, logger
def main():
args = parse_args()
gpc, logger = set_gpc_and_logger(args)
if exists(args.exp_config):
# Make a copy of the config file
cfg_path = gpc.get_path('root', basename(args.exp_config), suffix=False)
shutil.copy(args.exp_config, cfg_path)
# Set random seed
RNG_SEED = 1
random.seed(RNG_SEED)
np.random.seed(RNG_SEED)
torch.manual_seed(RNG_SEED)
cudnn.deterministic = True
cudnn.benchmark = False
try:
trainer = CDTrainer(args.model, args.dataset, args.optimizer, args)
if args.cmd == 'train':
trainer.train()
elif args.cmd == 'val':
trainer.validate()
else:
pass
except BaseException as e:
import traceback
# Catch ALL kinds of exceptions
logger.error(traceback.format_exc())
exit(1)
if __name__ == '__main__':
main()