# Autogenerated via: # python ~/code/watch/dev/maintain/mirror_package_geowatch.py from geowatch.tasks.rutgers_material_change_detection.predict import parser, parser, parser, parser, parser, parser, parser, parser, parser, evaluator def __getattr__(key): import geowatch.tasks.rutgers_material_change_detection.predict as mirror return getattr(mirror, key) def __dir__(): import geowatch.tasks.rutgers_material_change_detection.predict as mirror return dir(mirror) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("model_path", type=str, help="Path to the .pth.tar file ") parser.add_argument( "input_kwcoco_dir", type=str, help="Path to a directory that contains a vali.kwcoco.json file to get input data from.", ) parser.add_argument( "output_kwcoco_file_path", type=str, help="Output predictions placed in a .kwcoco.json at this path." ) parser.add_argument( "--device", type=str, default="cuda", choices=["cuda", "cpu"], help="The type of hardware to process data with model.", ) parser.add_argument("--n_workers", type=int, default=4, help="Number of CPU processes to load data into model.") parser.add_argument("--batch_size", type=int, default=8) parser.add_argument("--split", default="valid", help="TODO: Remove this") parser.add_argument("--stride", type=int, default=None, help="Set the step that crops are created.") parser.add_argument( "--heatmap_pred_channel_names", type=str, default="not_salient|salient", help="Overwrite the name of heatmap predictions in the produced kwcoco file.", ) # parser.add_argument( # "--ignore_material_features", # default=False, # action="store_true", # help="Do not add material features to output kwcoco file.", # ) # parser.add_argument( # "--ignore_heatmaps", # default=False, # action="store_true", # help="Do not add confidence heatmaps to output kwcoco file.", # ) # parser.add_argument( # "--ignore_material_change_mask", # default=False, # action="store_true", # help="Do not add material change mask to output kwcoco file.", # ) args = parser.parse_args() evaluator = Evaluator(args) evaluator.gen_kwcoco_file()