batch_size=config["batch_size"],
experiment_directory=config["load_experiment"],
load_best=config["load_best_model"])
if args.train:
experiment.analyze()
experiment.train(epochs=config["epochs"])
experiment.save_model()
experiment.bundle()
print("Training finished")
exit()
if args.benchmark:
if config["load_experiment"] is None:
exit("No model selected")
experiment.benchmark_inference()
if args.score:
experiment.score()
exit()
if args.score_generalisation:
experiment.score_generalization()
if args.predict:
input_file = args.predict
output_file_name = args.output if args.output else f"{Path(input_file).stem}-prediction"
predicted_file = experiment.predict(input_file,
output_file_name,
postprocessing=False,
save_overlay=True)
