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