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0d80452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | # 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.
"""Rerun visualization backend.
Live control-loop streaming to the Rerun viewer (:func:`log_rerun_data`). Callers usually select a
backend at runtime through the dispatch in :mod:`lerobot.utils.visualization_utils` rather than
importing from here directly. Requires the ``viz`` extra (``pip install 'lerobot[viz]'``).
"""
import numbers
import os
import numpy as np
from lerobot.configs import DEPTH_MILLIMETER_UNIT, infer_depth_unit
from lerobot.types import RobotAction, RobotObservation
from .constants import ACTION, ACTION_PREFIX, OBS_PREFIX, OBS_STR
from .import_utils import require_package
def _is_scalar(x):
return isinstance(x, (float | numbers.Real | np.integer | np.floating)) or (
isinstance(x, np.ndarray) and x.ndim == 0
)
def init_rerun(
session_name: str = "lerobot_control_loop", ip: str | None = None, port: int | None = None
) -> None:
"""
Initializes the Rerun SDK for visualizing the control loop.
Args:
session_name: Name of the Rerun session.
ip: Optional IP for connecting to a Rerun server.
port: Optional port for connecting to a Rerun server.
"""
require_package("rerun-sdk", extra="viz", import_name="rerun")
import rerun as rr
log_rerun_data.blueprint = None # Reset blueprint cache for new session
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%")
if ip and port:
rr.connect_grpc(url=f"rerun+http://{ip}:{port}/proxy")
else:
rr.spawn(memory_limit=memory_limit)
def shutdown_rerun() -> None:
"""Shuts down the Rerun SDK gracefully."""
require_package("rerun-sdk", extra="viz", import_name="rerun")
import rerun as rr
rr.rerun_shutdown()
def _build_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]):
"""Build a Rerun blueprint laying out camera images, observation and action scalars in separate views.
Camera images, observation and action scalars are arranged in a grid.
"""
# Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
import rerun.blueprint as rrb
views = [rrb.Spatial2DView(origin=path, name=path) for path in sorted(image_paths)]
if observation_paths:
views.append(rrb.TimeSeriesView(name="observation", contents=sorted(observation_paths)))
if action_paths:
views.append(rrb.TimeSeriesView(name="action", contents=sorted(action_paths)))
return rrb.Blueprint(rrb.Grid(*views))
def _ensure_blueprint(observation_paths: set[str], action_paths: set[str], image_paths: set[str]) -> None:
"""Build and send the blueprint once, from the first observation and action data."""
if getattr(log_rerun_data, "blueprint", None) is not None:
return
if not (observation_paths or action_paths or image_paths):
return
# Safe + zero-overhead: `log_rerun_data` already ran the `require_package` guard and imported rerun.
import rerun as rr
blueprint = _build_blueprint(observation_paths, action_paths, image_paths)
log_rerun_data.blueprint = blueprint
rr.send_blueprint(blueprint)
def log_rerun_data(
observation: RobotObservation | None = None,
action: RobotAction | None = None,
compress_images: bool = False,
) -> 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, (optionally) compressed to JPEG and logged as `rr.Image` or `rr.EncodedImage`.
- 1D NumPy arrays are logged as a single `rr.Scalars` batch under one entity path, so that every
dimension shares the same view instead of being split across one view per element.
- Multi-dimensional **action** arrays are flattened and logged as a single `rr.Scalars` batch.
Keys are automatically namespaced with "observation." or "action." if not already present.
On the first call, a blueprint is built and sent so observation and action scalars get separate
time-series views and each image gets its own spatial view.
Args:
observation: An optional dictionary containing observation data to log.
action: An optional dictionary containing action data to log.
compress_images: Whether to compress images before logging to save bandwidth & memory in exchange for cpu and quality.
"""
require_package("rerun-sdk", extra="viz", import_name="rerun")
import rerun as rr
observation_paths: set[str] = set()
action_paths: set[str] = set()
image_paths: set[str] = set()
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)))
observation_paths.add(key)
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:
rr.log(key, rr.Scalars(arr.astype(float)))
observation_paths.add(key)
else:
if arr.shape[-1] == 1:
# At record time, the depth unit is inferred from the frame type.
depth_unit = infer_depth_unit(arr.dtype)
img_entity = rr.DepthImage(
arr,
meter=1000.0 if depth_unit == DEPTH_MILLIMETER_UNIT else 1.0,
colormap=rr.components.Colormap.Viridis,
)
else:
img_entity = rr.Image(arr).compress() if compress_images else rr.Image(arr)
rr.log(key, entity=img_entity, static=True)
image_paths.add(key)
if action:
for k, v in action.items():
if v is None:
continue
key = k if str(k).startswith(ACTION_PREFIX) else f"{ACTION}.{k}"
if _is_scalar(v):
rr.log(key, rr.Scalars(float(v)))
action_paths.add(key)
elif isinstance(v, np.ndarray):
# Flatten any (incl. higher-dimensional) array into a single batched Scalars
rr.log(key, rr.Scalars(v.reshape(-1).astype(float)))
action_paths.add(key)
_ensure_blueprint(observation_paths, action_paths, image_paths)
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