def parse_args():
"""Parse command line arguments."""
# Testing settings
TEST_DATA = DATASET_NAMES[3] # max 8
data_inf = dataset_info(TEST_DATA)
parser = argparse.ArgumentParser(description='DexiNed trainer.')
# Data parameters
parser.add_argument('--input_dir',
type=str,
default='/opt/dataset/BIPED/edges',
help='the path to the directory with the input data.')
parser.add_argument(
'--input_val_dir',
type=str,
default=data_inf['data_dir'],
help='the path to the directory with the input data for validation.')
parser.add_argument('--output_dir',
type=str,
default='checkpoints',
help='the path to output the results.')
parser.add_argument('--test_data',
type=str,
choices=DATASET_NAMES,
default=TEST_DATA,
help='Name of the dataset.')
parser.add_argument('--test_list',
type=str,
default=data_inf['file_name'],
help='Dataset sample indices list.')
parser.add_argument('--is_testing',
type=bool,
default=False,
help='Put script in testing mode.')
# parser.add_argument('--use_prev_trained',
# type=bool,
# default=True,
# help='use previous trained data') # Just for test
parser.add_argument(
'--checkpoint_data',
type=str,
default='24/24_model.pth',
help='Checkpoint path from which to restore model weights from.')
parser.add_argument('--test_img_width',
type=int,
default=data_inf['img_width'],
help='Image width for testing.')
parser.add_argument('--test_img_height',
type=int,
default=data_inf['img_height'],
help='Image height for testing.')
parser.add_argument('--res_dir',
type=str,
default='result',
help='Result directory')
parser.add_argument(
'--log_interval_vis',
type=int,
default=50,
help='The number of batches to wait before printing test predictions.')
# Optimization parameters
# parser.add_argument('--optimizer',
# type=str,
# choices=['adam', 'sgd'],
# default='adam',
# help='The optimizer to use (default: adam).')
parser.add_argument('--epochs',
type=int,
default=25,
metavar='N',
help='Number of training epochs (default: 25).')
parser.add_argument('--lr',
default=1e-4,
type=float,
help='Initial learning rate.')
parser.add_argument('--wd',
type=float,
default=1e-4,
metavar='WD',
help='weight decay (default: 1e-4)')
# parser.add_argument('--lr_stepsize',
# default=1e4,
# type=int,
# help='Learning rate step size.')
parser.add_argument('--batch_size',
type=int,
default=8,
metavar='B',
help='the mini-batch size (default: 8)')
parser.add_argument('--workers',
default=8,
type=int,
help='The number of workers for the dataloaders.')
parser.add_argument('--tensorboard',
type=bool,
default=True,
help='Use Tensorboard for logging.'),
parser.add_argument('--img_width',
type=int,
default=400,
help='Image width for training.')
parser.add_argument('--img_height',
type=int,
default=400,
help='Image height for training.')
parser.add_argument('--channel_swap', default=[2, 1, 0], type=int)
parser.add_argument(
'--crop_img',
default=False,
type=bool,
help=
'If true crop training images, else resize images to match image width and height.'
)
parser.add_argument(
'--mean_pixel_values',
default=[103.939, 116.779, 123.68, 137.86],
type=float
) # [103.939,116.779,123.68] [104.00699, 116.66877, 122.67892]
args = parser.parse_args()
return args
def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(description='DexiNed trainer.')
parser.add_argument(
'--choose_test_data',
type=int,
default=-1,
help='Already set the dataset for testing choice: 0 - 8')
# ----------- test -------0--
TEST_DATA = DATASET_NAMES[parser.parse_args().choose_test_data] # max 8
data_inf = dataset_info(TEST_DATA, is_linux=IS_LINUX)
test_dir = data_inf['data_dir']
is_testing = True # current test _bdcnlossNew256-sd7-1.10.4p5
# Training settings
TRAIN_DATA = DATASET_NAMES[0] # BIPED=0
train_info = dataset_info(TRAIN_DATA, is_linux=IS_LINUX)
train_dir = train_info['data_dir']
# Data parameters
parser.add_argument('--input_dir',
type=str,
default=train_dir,
help='the path to the directory with the input data.')
parser.add_argument(
'--input_val_dir',
type=str,
default=data_inf['data_dir'],
help='the path to the directory with the input data for validation.')
parser.add_argument('--output_dir',
type=str,
default='checkpoints',
help='the path to output the results.')
parser.add_argument('--train_data',
type=str,
choices=DATASET_NAMES,
default=TRAIN_DATA,
help='Name of the dataset.')
parser.add_argument('--test_data',
type=str,
choices=DATASET_NAMES,
default=TEST_DATA,
help='Name of the dataset.')
parser.add_argument('--test_list',
type=str,
default=data_inf['test_list'],
help='Dataset sample indices list.')
parser.add_argument('--is_testing',
type=bool,
default=is_testing,
help='Script in testing mode.')
parser.add_argument(
'--double_img',
type=bool,
default=False,
help='True: use same 2 imgs changing channels') # Just for test
parser.add_argument('--resume',
type=bool,
default=False,
help='use previous trained data') # Just for test
parser.add_argument(
'--checkpoint_data',
type=str,
default='19/19_model.pth',
help='Checkpoint path from which to restore model weights from.')
parser.add_argument('--test_img_width',
type=int,
default=data_inf['img_width'],
help='Image width for testing.')
parser.add_argument('--test_img_height',
type=int,
default=data_inf['img_height'],
help='Image height for testing.')
parser.add_argument('--res_dir',
type=str,
default='result',
help='Result directory')
parser.add_argument(
'--log_interval_vis',
type=int,
default=50,
help='The number of batches to wait before printing test predictions.')
parser.add_argument('--epochs',
type=int,
default=25,
metavar='N',
help='Number of training epochs (default: 25).')
parser.add_argument('--lr',
default=1e-4,
type=float,
help='Initial learning rate.')
parser.add_argument('--wd',
type=float,
default=1e-4,
metavar='WD',
help='weight decay (default: 1e-4)')
# parser.add_argument('--lr_stepsize',
# default=1e4,
# type=int,
# help='Learning rate step size.')
parser.add_argument('--batch_size',
type=int,
default=8,
metavar='B',
help='the mini-batch size (default: 8)')
parser.add_argument('--workers',
default=8,
type=int,
help='The number of workers for the dataloaders.')
parser.add_argument('--tensorboard',
type=bool,
default=True,
help='Use Tensorboard for logging.'),
parser.add_argument('--img_width',
type=int,
default=400,
help='Image width for training.')
parser.add_argument('--img_height',
type=int,
default=400,
help='Image height for training.')
parser.add_argument('--channel_swap', default=[2, 1, 0], type=int)
parser.add_argument(
'--crop_img',
default=True,
type=bool,
help=
'If true crop training images, else resize images to match image width and height.'
)
parser.add_argument(
'--mean_pixel_values',
default=[103.939, 116.779, 123.68, 137.86],
type=float
) # [103.939,116.779,123.68] [104.00699, 116.66877, 122.67892]
args = parser.parse_args()
return args