def main(args=None):
imed_utils.init_ivadomed()
parser = get_parser()
args = imed_utils.get_arguments(parser, args)
extract_mid_slice_and_convert_coordinates_to_heatmaps(path=args.path,
suffix=args.suffix,
aim=args.aim)
def main(args=None):
imed_utils.init_ivadomed()
parser = get_parser()
args = imed_utils.get_arguments(parser, args)
df = pd.read_csv(args.dataframe)
compute_statistics(df, int(args.n_iterations), bool(args.run_test),
args.out)
def main(args=None):
imed_utils.init_ivadomed()
parser = get_parser()
args = imed_utils.get_arguments(parser, args)
run_visualization(input=args.input,
config=args.config,
number=int(args.number),
output=args.output,
roi=args.roi)
def main(args=None):
imed_utils.init_ivadomed()
parser = get_parser()
args = imed_utils.get_arguments(parser, args)
fname_model = args.model
dimension = int(args.dimension)
gpu_id = str(args.gpu_id)
n_channels = args.n_channels
convert_pytorch_to_onnx(fname_model, dimension, n_channels, gpu_id)
def main(args=None):
imed_utils.init_ivadomed()
parser = get_parser()
args = imed_utils.get_arguments(parser, args)
if args.contrasts is not None:
contrast_list = args.contrasts.split(",")
else:
contrast_list = None
extract_small_dataset(args.input, args.output, int(args.number), contrast_list,
bool(int(args.derivatives)), int(args.seed))
def main(args=None):
imed_utils.init_ivadomed()
parser = get_parser()
args = imed_utils.get_arguments(parser, args)
y_lim_loss = [int(y) for y in args.ylim_loss.split(',')
] if args.ylim_loss else None
run_plot_training_curves(input_folder=args.input,
output_folder=args.output,
multiple_training=args.multiple,
y_lim_loss=y_lim_loss)
def main(args=None):
imed_utils.init_ivadomed()
parser = get_parser()
args = imed_utils.get_arguments(parser, args)
thr_increment = args.thr_increment if args.thr_increment else None
automate_training(
file_config=args.config,
file_config_hyper=args.config_hyper,
fixed_split=bool(args.fixed_split),
all_combin=bool(args.all_combin),
path_data=args.path_data if args.path_data is not None else None,
n_iterations=int(args.n_iterations),
run_test=bool(args.run_test),
all_logs=args.all_logs,
thr_increment=thr_increment,
multi_params=bool(args.multi_params),
output_dir=args.output_dir)
def main(args=None):
imed_utils.init_ivadomed()
# Dictionary containing list of URLs for data names.
# Mirror servers are listed in order of decreasing priority.
# If exists, favour release artifact straight from github
parser = get_parser()
arguments = imed_utils.get_arguments(parser, args)
data_name = arguments.d
if arguments.output is None:
dest_folder = os.path.join(os.path.abspath(os.curdir), data_name)
else:
dest_folder = arguments.output
url = DICT_URL[data_name]["url"]
install_data(url, dest_folder, keep=bool(arguments.keep))
return 0
