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| #!/usr/bin/env python | |
| # Copyright 2024 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import logging | |
| import os | |
| import re | |
| from glob import glob | |
| from pathlib import Path | |
| from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE | |
| from termcolor import colored | |
| from lerobot.common.constants import PRETRAINED_MODEL_DIR | |
| from lerobot.configs.train import TrainPipelineConfig | |
| def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[str] | str: | |
| """Return a group name for logging. Optionally returns group name as list.""" | |
| lst = [ | |
| f"policy:{cfg.policy.type}", | |
| f"dataset:{cfg.dataset.repo_id}", | |
| f"seed:{cfg.seed}", | |
| ] | |
| if cfg.env is not None: | |
| lst.append(f"env:{cfg.env.type}") | |
| return lst if return_list else "-".join(lst) | |
| def get_wandb_run_id_from_filesystem(log_dir: Path) -> str: | |
| # Get the WandB run ID. | |
| paths = glob(str(log_dir / "wandb/latest-run/run-*")) | |
| if len(paths) != 1: | |
| raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.") | |
| match = re.search(r"run-([^\.]+).wandb", paths[0].split("/")[-1]) | |
| if match is None: | |
| raise RuntimeError("Couldn't get the previous WandB run ID for run resumption.") | |
| wandb_run_id = match.groups(0)[0] | |
| return wandb_run_id | |
| def get_safe_wandb_artifact_name(name: str): | |
| """WandB artifacts don't accept ":" or "/" in their name.""" | |
| return name.replace(":", "_").replace("/", "_") | |
| class WandBLogger: | |
| """A helper class to log object using wandb.""" | |
| def __init__(self, cfg: TrainPipelineConfig): | |
| self.cfg = cfg.wandb | |
| self.log_dir = cfg.output_dir | |
| self.job_name = cfg.job_name | |
| self.env_fps = cfg.env.fps if cfg.env else None | |
| self._group = cfg_to_group(cfg) | |
| # Set up WandB. | |
| os.environ["WANDB_SILENT"] = "True" | |
| import wandb | |
| wandb_run_id = ( | |
| cfg.wandb.run_id | |
| if cfg.wandb.run_id | |
| else get_wandb_run_id_from_filesystem(self.log_dir) | |
| if cfg.resume | |
| else None | |
| ) | |
| wandb.init( | |
| id=wandb_run_id, | |
| project=self.cfg.project, | |
| entity=self.cfg.entity, | |
| name=self.job_name, | |
| notes=self.cfg.notes, | |
| tags=cfg_to_group(cfg, return_list=True), | |
| dir=self.log_dir, | |
| config=cfg.to_dict(), | |
| # TODO(rcadene): try set to True | |
| save_code=False, | |
| # TODO(rcadene): split train and eval, and run async eval with job_type="eval" | |
| job_type="train_eval", | |
| resume="must" if cfg.resume else None, | |
| mode=self.cfg.mode if self.cfg.mode in ["online", "offline", "disabled"] else "online", | |
| ) | |
| print(colored("Logs will be synced with wandb.", "blue", attrs=["bold"])) | |
| logging.info(f"Track this run --> {colored(wandb.run.get_url(), 'yellow', attrs=['bold'])}") | |
| self._wandb = wandb | |
| def log_policy(self, checkpoint_dir: Path): | |
| """Checkpoints the policy to wandb.""" | |
| if self.cfg.disable_artifact: | |
| return | |
| step_id = checkpoint_dir.name | |
| artifact_name = f"{self._group}-{step_id}" | |
| artifact_name = get_safe_wandb_artifact_name(artifact_name) | |
| artifact = self._wandb.Artifact(artifact_name, type="model") | |
| artifact.add_file(checkpoint_dir / PRETRAINED_MODEL_DIR / SAFETENSORS_SINGLE_FILE) | |
| self._wandb.log_artifact(artifact) | |
| def log_dict(self, d: dict, step: int, mode: str = "train"): | |
| if mode not in {"train", "eval"}: | |
| raise ValueError(mode) | |
| for k, v in d.items(): | |
| if not isinstance(v, (int, float, str)): | |
| logging.warning( | |
| f'WandB logging of key "{k}" was ignored as its type is not handled by this wrapper.' | |
| ) | |
| continue | |
| self._wandb.log({f"{mode}/{k}": v}, step=step) | |
| def log_video(self, video_path: str, step: int, mode: str = "train"): | |
| if mode not in {"train", "eval"}: | |
| raise ValueError(mode) | |
| wandb_video = self._wandb.Video(video_path, fps=self.env_fps, format="mp4") | |
| self._wandb.log({f"{mode}/video": wandb_video}, step=step) | |