mirror of
https://github.com/huggingface/lerobot.git
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118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
# 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 numbers
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import os
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from typing import Any
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import numpy as np
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import rerun as rr
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from lerobot.processor import EnvTransition, TransitionKey
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def _init_rerun(session_name: str = "lerobot_control_loop") -> None:
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"""Initializes the Rerun SDK for visualizing the control loop."""
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batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
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os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
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rr.init(session_name)
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memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
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rr.spawn(memory_limit=memory_limit)
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def _is_scalar(x):
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return (
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isinstance(x, numbers.Real)
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or isinstance(x, (np.integer, np.floating))
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or (isinstance(x, np.ndarray) and x.ndim == 0)
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)
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def log_rerun_data(
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data: list[dict[str | Any] | EnvTransition] | dict[str | Any] | EnvTransition | None = None,
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*,
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observation: dict[str, Any] | None = None,
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action: dict[str, Any] | None = None,
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) -> None:
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items = data if isinstance(data, list) else ([data] if data is not None else [])
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obs = {} if observation is None else dict(observation)
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act = {} if action is None else dict(action)
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for idx, item in enumerate(items):
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if not isinstance(item, dict):
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continue
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if any(isinstance(k, TransitionKey) for k in item.keys()):
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o = item.get(TransitionKey.OBSERVATION) or {}
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a = item.get(TransitionKey.ACTION) or {}
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if isinstance(o, dict):
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obs.update(o)
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if isinstance(a, dict):
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act.update(a)
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continue
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keys = list(item.keys())
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has_obs = any(str(k).startswith("observation.") for k in keys)
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has_act = any(str(k).startswith("action.") for k in keys)
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if has_obs or has_act:
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if has_obs:
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obs.update(item)
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if has_act:
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act.update(item)
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else:
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# No prefixes: assume first is observation, second is action, others are observation
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if idx == 0:
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obs.update(item)
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elif idx == 1:
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act.update(item)
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else:
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obs.update(item)
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for k, v in obs.items():
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if v is None:
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continue
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key = k if str(k).startswith("observation.") else f"observation.{k}"
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if _is_scalar(v):
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rr.log(key, rr.Scalar(float(v)))
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elif isinstance(v, np.ndarray):
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arr = v
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# Convert CHW -> HWC when needed
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if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
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arr = np.transpose(arr, (1, 2, 0))
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if arr.ndim == 1:
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for i, vi in enumerate(arr):
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rr.log(f"{key}_{i}", rr.Scalar(float(vi)))
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else:
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rr.log(key, rr.Image(arr), static=True)
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for k, v in act.items():
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if v is None:
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continue
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key = k if str(k).startswith("action.") else f"action.{k}"
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if _is_scalar(v):
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rr.log(key, rr.Scalar(float(v)))
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elif isinstance(v, np.ndarray):
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if v.ndim == 1:
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for i, vi in enumerate(v):
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rr.log(f"{key}_{i}", rr.Scalar(float(vi)))
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else:
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# Fall back to flattening higher-dimensional arrays
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flat = v.flatten()
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for i, vi in enumerate(flat):
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rr.log(f"{key}_{i}", rr.Scalar(float(vi)))
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