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# 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 numbers
import os
from typing import Any
import numpy as np
import rerun as rr
from .constants import OBS_PREFIX, OBS_STR
def init_rerun(session_name: str = "lerobot_control_loop") -> None:
"""Initializes the Rerun SDK for visualizing the control loop."""
batch_size = os.getenv("RERUN_FLUSH_NUM_BYTES", "8000")
os.environ["RERUN_FLUSH_NUM_BYTES"] = batch_size
rr.init(session_name)
memory_limit = os.getenv("LEROBOT_RERUN_MEMORY_LIMIT", "10%")
rr.spawn(memory_limit=memory_limit)
def _is_scalar(x):
return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
isinstance(x, np.ndarray) and x.ndim == 0
)
def log_rerun_data(
observation: dict[str, Any] | None = None,
action: dict[str, Any] | None = None,
) -> None:
"""
Logs observation and action data to Rerun for real-time visualization.
This function iterates through the provided observation and action dictionaries and sends their contents
to the Rerun viewer. It handles different data types appropriately:
- Scalars values (floats, ints) are logged as `rr.Scalars`.
- 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
from CHW to HWC format and logged as `rr.Image`.
- 1D NumPy arrays are logged as a series of individual scalars, with each element indexed.
- Other multi-dimensional arrays are flattened and logged as individual scalars.
Keys are automatically namespaced with "observation." or "action." if not already present.
Args:
observation: An optional dictionary containing observation data to log.
action: An optional dictionary containing action data to log.
"""
if observation:
for k, v in observation.items():
if v is None:
continue
key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
if _is_scalar(v):
rr.log(key, rr.Scalars(float(v)))
elif isinstance(v, np.ndarray):
arr = v
# Convert CHW -> HWC when needed
if arr.ndim == 3 and arr.shape[0] in (1, 3, 4) and arr.shape[-1] not in (1, 3, 4):
arr = np.transpose(arr, (1, 2, 0))
if arr.ndim == 1:
for i, vi in enumerate(arr):
rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
else:
rr.log(key, rr.Image(arr), static=True)
if action:
for k, v in action.items():
if v is None:
continue
key = k if str(k).startswith("action.") else f"action.{k}"
if _is_scalar(v):
rr.log(key, rr.Scalars(float(v)))
elif isinstance(v, np.ndarray):
if v.ndim == 1:
for i, vi in enumerate(v):
rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
else:
# Fall back to flattening higher-dimensional arrays
flat = v.flatten()
for i, vi in enumerate(flat):
rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
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