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