| | |
| | |
| | |
| | |
| |
|
| | import enum |
| | import importlib |
| | import os |
| | from collections.abc import Callable |
| |
|
| | import rich.console |
| | import rich.table |
| | from common import ROOT_DIR |
| | from generator import generate, get_algorithms_per_rl_library |
| | from InquirerPy import inquirer, separator |
| |
|
| |
|
| | class CLIHandler: |
| | """CLI handler for the Isaac Lab template.""" |
| |
|
| | def __init__(self): |
| | self.console = rich.console.Console() |
| |
|
| | @staticmethod |
| | def get_choices(choices: list[str], default: list[str]) -> list[str]: |
| | return default if "all" in choices or "both" in choices else choices |
| |
|
| | def output_table(self, table: rich.table.Table, new_line_start: bool = True) -> None: |
| | """Print a rich table to the console. |
| | |
| | Args: |
| | table: The table to print. |
| | new_line_start: Whether to print a new line before the table. |
| | """ |
| | self.console.print(table, new_line_start=new_line_start) |
| |
|
| | def input_select( |
| | self, message: str, choices: list[str], default: str | None = None, long_instruction: str = "" |
| | ) -> str: |
| | """Prompt the user to select an option from a list of choices. |
| | |
| | Args: |
| | message: The message to display to the user. |
| | choices: The list of choices to display to the user. |
| | default: The default choice. |
| | long_instruction: The long instruction to display to the user. |
| | |
| | Returns: |
| | str: The selected choice. |
| | """ |
| | return inquirer.select( |
| | message=message, |
| | choices=choices, |
| | cycle=True, |
| | default=default, |
| | style=None, |
| | wrap_lines=True, |
| | long_instruction=long_instruction, |
| | ).execute() |
| |
|
| | def input_checkbox(self, message: str, choices: list[str], default: str | None = None) -> list[str]: |
| | """Prompt the user to select one or more options from a list of choices. |
| | |
| | Args: |
| | message: The message to display to the user. |
| | choices: The list of choices to display to the user. |
| | default: The default choice. |
| | |
| | Returns: |
| | The selected choices. |
| | """ |
| |
|
| | def transformer(result: list[str]) -> str: |
| | if "all" in result or "both" in result: |
| | token = "all" if "all" in result else "both" |
| | return f"{token} ({', '.join(choices[: choices.index('---')])})" |
| | return ", ".join(result) |
| |
|
| | return inquirer.checkbox( |
| | message=message, |
| | choices=[separator.Separator() if "---" in item else item for item in choices], |
| | cycle=True, |
| | default=default, |
| | style=None, |
| | wrap_lines=True, |
| | validate=lambda result: len(result) >= 1, |
| | invalid_message="No option selected (SPACE: select/deselect an option, ENTER: confirm selection)", |
| | transformer=transformer, |
| | ).execute() |
| |
|
| | def input_path( |
| | self, |
| | message: str, |
| | default: str | None = None, |
| | validate: Callable[[str], bool] | None = None, |
| | invalid_message: str = "", |
| | ) -> str: |
| | """Prompt the user to input a path. |
| | |
| | Args: |
| | message: The message to display to the user. |
| | default: The default path. |
| | validate: A callable to validate the path. |
| | invalid_message: The message to display to the user if the path is invalid. |
| | |
| | Returns: |
| | The input path. |
| | """ |
| | return inquirer.filepath( |
| | message=message, |
| | default=default if default is not None else "", |
| | validate=validate, |
| | invalid_message=invalid_message, |
| | ).execute() |
| |
|
| | def input_text( |
| | self, |
| | message: str, |
| | default: str | None = None, |
| | validate: Callable[[str], bool] | None = None, |
| | invalid_message: str = "", |
| | ) -> str: |
| | """Prompt the user to input a text. |
| | |
| | Args: |
| | message: The message to display to the user. |
| | default: The default text. |
| | validate: A callable to validate the text. |
| | invalid_message: The message to display to the user if the text is invalid. |
| | |
| | Returns: |
| | The input text. |
| | """ |
| | return inquirer.text( |
| | message=message, |
| | default=default if default is not None else "", |
| | validate=validate, |
| | invalid_message=invalid_message, |
| | ).execute() |
| |
|
| |
|
| | class State(str, enum.Enum): |
| | Yes = "[green]yes[/green]" |
| | No = "[red]no[/red]" |
| |
|
| |
|
| | def main() -> None: |
| | """Main function to run template generation from CLI.""" |
| | cli_handler = CLIHandler() |
| |
|
| | lab_module = importlib.import_module("isaaclab") |
| | lab_path = os.path.realpath(getattr(lab_module, "__file__", "") or (getattr(lab_module, "__path__", [""])[0])) |
| | is_lab_pip_installed = ("site-packages" in lab_path) or ("dist-packages" in lab_path) |
| |
|
| | if not is_lab_pip_installed: |
| | |
| | is_external_project = ( |
| | cli_handler.input_select( |
| | "Task type:", |
| | choices=["External", "Internal"], |
| | long_instruction=( |
| | "External (recommended): task/project is in its own folder/repo outside the Isaac Lab project.\n" |
| | "Internal: the task is implemented within the Isaac Lab project (in source/isaaclab_tasks)." |
| | ), |
| | ).lower() |
| | == "external" |
| | ) |
| | else: |
| | is_external_project = True |
| |
|
| | |
| | project_path = None |
| | if is_external_project: |
| | project_path = cli_handler.input_path( |
| | "Project path:", |
| | default=os.path.dirname(ROOT_DIR) + os.sep, |
| | validate=lambda path: not os.path.abspath(path).startswith(os.path.abspath(ROOT_DIR)), |
| | invalid_message="External project path cannot be within the Isaac Lab project", |
| | ) |
| |
|
| | |
| | project_name = cli_handler.input_text( |
| | "Project name:" if is_external_project else "Task's folder name:", |
| | validate=lambda name: name.isidentifier(), |
| | invalid_message=( |
| | "Project/task name must be a valid identifier (Letters, numbers and underscores only. No spaces, etc.)" |
| | ), |
| | ) |
| |
|
| | |
| | |
| | workflow_table = rich.table.Table(title="RL environment features support according to Isaac Lab workflows") |
| | workflow_table.add_column("Environment feature", no_wrap=True) |
| | workflow_table.add_column("Direct", justify="center") |
| | workflow_table.add_column("Manager-based", justify="center") |
| | workflow_table.add_row("Single-agent", State.Yes, State.Yes) |
| | workflow_table.add_row("Multi-agent", State.Yes, State.No) |
| | workflow_table.add_row("Fundamental/composite spaces (apart from 'Box')", State.Yes, State.No) |
| | cli_handler.output_table(workflow_table) |
| | |
| | supported_workflows = ["Direct | single-agent", "Direct | multi-agent", "Manager-based | single-agent"] |
| | workflow = cli_handler.get_choices( |
| | cli_handler.input_checkbox("Isaac Lab workflow:", choices=[*supported_workflows, "---", "all"]), |
| | default=supported_workflows, |
| | ) |
| | workflow = [{"name": item.split(" | ")[0].lower(), "type": item.split(" | ")[1].lower()} for item in workflow] |
| | single_agent_workflow = [item for item in workflow if item["type"] == "single-agent"] |
| | multi_agent_workflow = [item for item in workflow if item["type"] == "multi-agent"] |
| |
|
| | |
| | rl_library_algorithms = [] |
| | algorithms_per_rl_library = get_algorithms_per_rl_library() |
| | |
| | rl_library_table = rich.table.Table(title="Supported RL libraries") |
| | rl_library_table.add_column("RL/training feature", no_wrap=True) |
| | rl_library_table.add_column("rl_games") |
| | rl_library_table.add_column("rsl_rl") |
| | rl_library_table.add_column("skrl") |
| | rl_library_table.add_column("sb3") |
| | rl_library_table.add_row("ML frameworks", "PyTorch", "PyTorch", "PyTorch, JAX", "PyTorch") |
| | rl_library_table.add_row("Relative performance", "~1X", "~1X", "~1X", "~0.03X") |
| | rl_library_table.add_row( |
| | "Algorithms", |
| | ", ".join(algorithms_per_rl_library.get("rl_games", [])), |
| | ", ".join(algorithms_per_rl_library.get("rsl_rl", [])), |
| | ", ".join(algorithms_per_rl_library.get("skrl", [])), |
| | ", ".join(algorithms_per_rl_library.get("sb3", [])), |
| | ) |
| | rl_library_table.add_row("Multi-agent support", State.No, State.No, State.Yes, State.No) |
| | rl_library_table.add_row("Distributed training", State.Yes, State.No, State.Yes, State.No) |
| | rl_library_table.add_row("Vectorized training", State.Yes, State.Yes, State.Yes, State.No) |
| | rl_library_table.add_row("Fundamental/composite spaces", State.No, State.No, State.Yes, State.No) |
| | cli_handler.output_table(rl_library_table) |
| | |
| | supported_rl_libraries = ["rl_games", "rsl_rl", "skrl", "sb3"] if len(single_agent_workflow) else ["skrl"] |
| | selected_rl_libraries = cli_handler.get_choices( |
| | cli_handler.input_checkbox("RL library:", choices=[*supported_rl_libraries, "---", "all"]), |
| | default=supported_rl_libraries, |
| | ) |
| | |
| | algorithms_per_rl_library = get_algorithms_per_rl_library(len(single_agent_workflow), len(multi_agent_workflow)) |
| | for rl_library in selected_rl_libraries: |
| | algorithms = algorithms_per_rl_library.get(rl_library, []) |
| | if len(algorithms) > 1: |
| | algorithms = cli_handler.get_choices( |
| | cli_handler.input_checkbox(f"RL algorithms for {rl_library}:", choices=[*algorithms, "---", "all"]), |
| | default=algorithms, |
| | ) |
| | rl_library_algorithms.append({"name": rl_library, "algorithms": [item.lower() for item in algorithms]}) |
| |
|
| | specification = { |
| | "external": is_external_project, |
| | "path": project_path, |
| | "name": project_name, |
| | "workflows": workflow, |
| | "rl_libraries": rl_library_algorithms, |
| | } |
| | generate(specification) |
| |
|
| |
|
| | if __name__ == "__main__": |
| | main() |
| |
|