def parse_args(description):
parser = argparse.ArgumentParser(description=description)
parser.add_argument('--cfg',
dest='cfg_file',
action='append',
help='an optional config file',
default=None,
type=str)
parser.add_argument('--batch',
dest='batch_size',
help='batch size',
default=None,
type=int)
parser.add_argument('--epoch',
dest='epoch',
help='epoch number',
default=None,
type=int)
parser.add_argument('--model',
dest='model',
help='model name',
default=None,
type=str)
parser.add_argument('--dataset',
dest='dataset',
help='dataset name',
default=None,
type=str)
args = parser.parse_args()
# load cfg from file
if args.cfg_file is not None:
for f in args.cfg_file:
cfg_from_file(f)
# load cfg from arguments
if args.batch_size is not None:
cfg_from_list(['BATCH_SIZE', args.batch_size])
if args.epoch is not None:
cfg_from_list([
'TRAIN.START_EPOCH', args.epoch, 'EVAL.EPOCH', args.epoch,
'VISUAL.EPOCH', args.epoch
])
if args.model is not None:
cfg_from_list(['MODEL_NAME', args.model])
if args.dataset is not None:
cfg_from_list(['DATASET_NAME', args.dataset])
if len(cfg.MODEL_NAME) != 0 and len(cfg.DATASET_NAME) != 0:
outp_path = get_output_dir(cfg.MODEL_NAME, cfg.DATASET_NAME)
cfg_from_list(['OUTPUT_PATH', outp_path])
assert len(
cfg.OUTPUT_PATH
) != 0, 'Invalid OUTPUT_PATH! Make sure model name and dataset name are specified.'
if not Path(cfg.OUTPUT_PATH).exists():
Path(cfg.OUTPUT_PATH).mkdir(parents=True)
return args
def test_parse_args(description):
parser = argparse.ArgumentParser()
parser.add_argument('--cfg',
dest='cfg_file',
type=str,
help='an optional config file',
default="experiments/vgg16_scannet.yaml")
parser.add_argument(
'--model_path',
dest='model_path',
help='model name',
default='output/vgg16_linematching_wire/params/params_0010.pt',
type=str)
parser.add_argument('--left_img',
dest='left_img',
help='left image name',
default='test_data/000800.jpg',
type=str)
parser.add_argument('--right_img',
dest='right_img',
help='right image name',
default='test_data/000900.jpg',
type=str)
parser.add_argument('--left_lines',
dest='left_lines',
help='left lines name',
default='test_data/000800.txt',
type=str)
parser.add_argument('--right_lines',
dest='right_lines',
help='right lines name',
default='test_data/000900.txt',
type=str)
parser.add_argument('--output_path',
dest='output_path',
help='output path name',
default='./test_data/',
type=str)
args = parser.parse_args()
# load cfg from file
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if len(cfg.MODEL_NAME) != 0 and len(cfg.DATASET_NAME) != 0:
outp_path = get_output_dir(cfg.MODEL_NAME, cfg.DATASET_NAME)
cfg_from_list(['OUTPUT_PATH', outp_path])
assert len(
cfg.OUTPUT_PATH
) != 0, 'Invalid OUTPUT_PATH! Make sure model name and dataset name are specified.'
if not Path(cfg.OUTPUT_PATH).exists():
Path(cfg.OUTPUT_PATH).mkdir(parents=True)
return args
def parse_args(description):
parser = argparse.ArgumentParser(description=description)
parser.add_argument('--cfg',
dest='cfg_file',
action='append',
help='an optional config file',
default=['experiments/net1.yaml'],
type=str)
parser.add_argument('--batch',
dest='batch_size',
help='batch size',
default=None,
type=int)
parser.add_argument('--epoch',
dest='epoch',
help='epoch number',
default=None,
type=int)
parser.add_argument('--model',
dest='model',
help='model name',
default=None,
type=str)
parser.add_argument('--dataset',
dest='dataset',
help='dataset name',
default=None,
type=str)
args = parser.parse_args()
# load cfg from file
if args.cfg_file is not None:
for f in args.cfg_file:
cfg_from_file(f)
# load cfg from arguments
if args.batch_size is not None:
cfg_from_list(['BATCH_SIZE', args.batch_size])
if args.epoch is not None:
cfg_from_list(
['TRAIN.START_EPOCH', args.epoch, 'EVAL.EPOCH', args.epoch])
if args.model is not None:
cfg_from_list(['MODEL_NAME', args.model])
if args.dataset is not None:
cfg_from_list(['DATASET_NAME', args.dataset])
return args
# train sizes: train, smalltrain, minitrain
# train scale: ['150-50-20', '150-50-50', '500-150-80', '750-250-150', '1750-700-450', '1600-400-20']
args.imdb_name = "vg_150-50-50_minitrain"
args.imdbval_name = "vg_150-50-50_minival"
args.set_cfgs = [
'ANCHOR_SCALES', '[4, 8, 16, 32]', 'ANCHOR_RATIOS', '[0.5,1,2]',
'MAX_NUM_GT_BOXES', '50'
]
args.cfg_file = "cfgs/{}_ls.yml".format(
args.net) if args.large_scale else "cfgs/{}.yml".format(args.net)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
#torch.backends.cudnn.benchmark = True
if torch.cuda.is_available() and not args.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
# train set
# -- Note: Use validation set and disable the flipped to enable faster loading.
cfg.TRAIN.USE_FLIPPED = True
cfg.USE_GPU_NMS = args.cuda
