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from pathlib import Path |
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import typing as t |
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import click |
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from ._demo import create_algo_runner as init_algo_infra |
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class WithStateAttribute(t.Protocol): |
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"""Protocol for classes that have a state attribute.""" |
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state: t.Any |
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class HandleAlgorithmProgressUpdatesAble(t.Protocol): |
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def update(self, subject: WithStateAttribute) -> None: ... |
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# Ask user what to do depending on whether the output folder already exists |
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def validate_and_normalize_path(ctx, param, value): |
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"""Custom function to validate and normalize a path.""" |
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if value is None: |
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return None |
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path = Path(value) |
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if path.is_absolute(): |
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abs_path = path |
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else: |
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current_directory = Path.cwd() |
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abs_path = current_directory / path |
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if not abs_path.exists(): |
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abs_path.mkdir() |
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click.echo(f'Folder "{abs_path}" created') |
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else: |
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# get files inside the folder |
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folder_files = [f for f in abs_path.iterdir() if f.is_file()] |
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if len(folder_files) > 0: |
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# ask user whether to delete everything, process as it is or exit |
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click.echo(f'Folder "{abs_path}" already exists and is not empty.') |
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click.echo('What do you want to do?') |
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click.echo('1. Delete everything and start from scratch') |
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click.echo('2. Process the existing files') |
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click.echo('3. Exit') |
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choice = click.prompt('Enter your choice', type=int) |
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if choice == 1: |
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click.echo('Deleting everything...') |
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for file in folder_files: |
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file.unlink() |
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elif choice == 2: |
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click.echo('Processing existing files...') |
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elif choice == 3: |
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click.echo('Exiting...') |
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ctx.exit() |
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else: |
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raise click.BadParameter(f'Invalid choice "{choice}".') |
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return abs_path |
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# DEMO CMD |
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@click.command() |
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@click.option('-it', '--iterations', type=int, default=100, show_default=True, |
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help='Number of iterations to run the algorithm.') |
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@click.option('-o', '--output', 'output_folder', |
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# type=click.Path(exists=True), |
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type=click.Path( |
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# exists=True, |
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file_okay=False, dir_okay=True, resolve_path=True), |
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default='demo-output', show_default=True, |
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help='Location to save the generated images.', |
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callback=validate_and_normalize_path, |
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) |
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def demo(iterations, output_folder): |
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print("[DEBUG] output type: {}".format(type(output_folder))) |
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click.echo(f"Running demo with {iterations} iterations and location {output_folder}.") |
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# By default the backend adds 2 Listeners/Observers to the Subject/State object |
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# The Subject being an object representing the current state of the Algorithm |
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# Progress (ie current Cost Values for Jc, Js, Jt, current iteration, etc) |
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# The backend does it by suscribing the Listeners to the Subject (see below |
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# the subscribe_to_algorithm_progress method in case you want to add more) |
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# One Observer configured by the backend receives updates more frequently |
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# and it prints the Progress stats/metrics as a formatted message to the |
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# console |
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# The other Observer configured by the backend receives updates less frequently |
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# and it saves a snapshot of the Generated image during the current iteration |
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# to the output folder |
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backend_objects = init_algo_infra( |
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iterations=iterations, |
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output_folder=output_folder |
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) |
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# destructuring the backend objects |
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run_algorithm: t.Callable[[], None] = backend_objects['run'] |
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# subscribe_to_algorithm_progress: t.Callable[[HandleAlgorithmProgressUpdatesAble], None] = \ |
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# backend_objects['subscribe'] |
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run_algorithm() |
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# print a stylized message to console informing that program finished suuccessfully :) |
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click.secho('Demo finished successfull :)', fg='green', bold=True) |
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click.secho('Check the output folder for the generated images.', fg='green', bold=True) |
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click.secho('xdg-open {}'.format(output_folder), fg='green', bold=True) |
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click.secho('Bye!', fg='green', bold=True) |
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boromir674 /
neural-style-transfer
