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(): init_ivadomed() parser = get_parser() args = parser.parse_args() # Run script extract_small_dataset(args.input, args.output, int(args.number), args.contrasts.split(","), bool(int(args.derivatives)), int(args.seed))
def main(): init_ivadomed() parser = get_parser() args = parser.parse_args() # Run automate training visualize_and_compare_models(args.ofolders, args.metric, args.metadata)
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(): init_ivadomed() parser = get_parser() args = parser.parse_args() df = pd.read_csv(args.dataframe) # Compute statistics compute_statistics(df, int(args.n_iterations), bool(args.run_test), args.out)
def main(): imed_utils.init_ivadomed() parser = get_parser() args = parser.parse_args() bids_path = args.path suffix = args.suffix aim = args.aim # Run Script extract_mid_slice_and_convert_coordinates_to_heatmaps(bids_path, suffix, aim)
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(): imed_utils.init_ivadomed() parser = get_parser() args = parser.parse_args() fname_model = args.model dimension = int(args.dimension) gpu = str(args.gpu) # Run Script convert_pytorch_to_onnx(fname_model, dimension, gpu)
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) 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(): imed_utils.init_ivadomed() parser = get_parser() args = parser.parse_args() input = args.input config = args.config number = int(args.number) output = args.output roi = args.roi # Run script run_visualization(input, config, number, output, roi)
def main(): init_ivadomed() parser = get_parser() args = parser.parse_args() # Get thr increment if available thr_increment = args.thr_increment if args.thr_increment else None # Run automate training automate_training(args.config, args.params, bool(args.fixed_split), bool(args.all_combin), int(args.n_iterations), bool(args.run_test), args.all_logs, thr_increment)
def main(): init_ivadomed() parser = get_parser() args = parser.parse_args() input_folder = args.input multiple = args.multiple output_folder = args.output y_lim_loss = [int(y) for y in args.ylim_loss.split(',') ] if args.ylim_loss else None # Run script run_plot_training_curves(input_folder, output_folder, multiple, y_lim_loss)
def run_main(): imed_utils.init_ivadomed() parser = get_parser() args = parser.parse_args() # Get context from configuration file path_config_file = args.config context = imed_config_manager.ConfigurationManager( path_config_file).get_config() # Run command run_command( context=context, n_gif=args.gif if args.gif is not None else 0, thr_increment=args.thr_increment if args.thr_increment else None, resume_training=bool(args.resume_training))
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
def run_main(): imed_utils.init_ivadomed() parser = get_parser() args = parser.parse_args() # Get context from configuration file path_config_file = args.config if not os.path.isfile(path_config_file) or not path_config_file.endswith( '.json'): print( "\nERROR: The provided configuration file path (.json) is invalid: {}\n" .format(path_config_file)) return with open(path_config_file, "r") as fhandle: context = json.load(fhandle) # Run command run_command( context=context, n_gif=args.gif if args.gif is not None else 0, thr_increment=args.thr_increment if args.thr_increment else None, resume_training=bool(args.resume_training))
import sys import os import shutil from ivadomed.utils import init_ivadomed, __ivadomed_dir__ from ivadomed.scripts import download_data as ivadomed_download_data __test_dir__ = os.path.join(__ivadomed_dir__, 'testing/unit_tests') __data_testing_dir_ref__ = "data_testing" __tmp_dir__ = "tmp" __data_testing_dir__ = os.path.join(__tmp_dir__, __data_testing_dir_ref__) init_ivadomed() class bcolors(object): """Class for different colours.""" normal = '\033[0m' red = '\033[91m' green = '\033[92m' yellow = '\033[93m' blue = '\033[94m' magenta = '\033[95m' cyan = '\033[96m' bold = '\033[1m' underline = '\033[4m' def printv(string, verbose=1, type='normal'): """Print color-coded messages, depending on verbose status.