mirror of
https://github.com/huggingface/lerobot.git
synced 2026-07-03 16:17:15 +00:00
052d329470
* Add Foxglove display mode for teleoperate
Add a --display_mode flag (rerun|foxglove) to lerobot-teleoperate. When set
to foxglove, stream observations/actions over a Foxglove WebSocket server:
images as RawImage/CompressedImage, scalars as typed JSON channels with
schemas generated from the feature names (sanitized so paths don't need
quoting). Adds a `foxglove` extra.
* Add Foxglove display mode to lerobot-record
Wire the --display_mode flag (rerun|foxglove) into lerobot-record, matching
lerobot-teleoperate: route init/log through the backend-agnostic dispatchers
and stop the visualization backend on exit.
* update foxglove-sdk to 0.25.1
* Use static lerobot.Scalars schema for Foxglove state topics
Replace the per-topic JSON schema derived from feature names with a single
static lerobot.Scalars schema: a scalars array of {label, value} objects. The
same schema fits any robot regardless of which observation/action features it
reports, and the label field lets Foxglove name each series automatically so
one filtered path plots every feature.
* add foxglove option to dataset viz
* Make Foxglove dataset playback loop the sole frame emitter
Address review: the listener no longer emits frames, it only mutates
playback state and queues a one-shot seek index that the playback loop
services. The loop is now the only caller of emit_frame, so concurrent
random access into the on-disk dataset / video decoder never overlaps.
Also remove the dead server_holder and tighten the _foxglove_safe_name
docstring to state what it does and why.
* Label Foxglove dataset scalars with feature dimension names
Use the dataset's per-dimension feature names (e.g. joint names) as the
Foxglove series labels for /observation/state and /action/state instead
of bare indices. LeRobot stores `names` inconsistently (flat list,
{category: [...]}, or {name: index}), so _feature_dim_names handles each
and falls back to indices on any unknown format or length mismatch.
* Make Foxglove server host bindable and refactor topic/channel handling
Pass display_ip through as the Foxglove WebSocket bind host (127.0.0.1
for local only, 0.0.0.0 for all interfaces) instead of always binding
locally. In lerobot-dataset-viz, fold the separate --port into --web-port
so one flag covers both the Rerun web viewer and the Foxglove server port.
Add a _foxglove_topic() helper and thread a per-topic channel cache
through the log helpers so dataset playback stays self-contained instead
of mutating the module-global cache. Promote SUCCESS to constants.py.
* feat(viz): add support for foxglove in rollout + add to viz tag
* fix(docs): remove misleading installation note
* fix(visualization): no duplicated prefix, consolidated norm + warnings log
* chore(viz): minor improvements
* refactor(viz): split files + autoplay + updated docs + added minimal tests
* fix(viz): right tags + warning
* feat(deprecated ws-port): removing rerun's depreacted ws-port parameter in dataset visualization
* chore(web ports): adding global variables for default foxglove/rerun web ports
* feat(depth): adding depth support to foxglove visualizer. Because of foxglove limitations (min and max values on RawImage cannot be set from the SDK), depth is normalized between [0,1] when a depth range is provided.
* fix(rerun depth range): making rerun depth range computation safe against missing stats
* chore(foxglove depth): make it simple, and make it work.
* fix(scaling): fixing depth frames scaling
---------
Co-authored-by: Roman Shtylman <roman@foxglove.dev>
Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com>
311 lines
11 KiB
Python
311 lines
11 KiB
Python
#!/usr/bin/env python
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# Copyright 2025 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 importlib
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import sys
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from types import SimpleNamespace
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import numpy as np
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import pytest
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pytest.importorskip("rerun", reason="rerun-sdk is required (install lerobot[viz])")
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from lerobot.types import TransitionKey
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from lerobot.utils.constants import OBS_STATE
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@pytest.fixture
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def mock_rerun(monkeypatch):
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"""
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Provide a mock `rerun` module (and `rerun.blueprint` submodule) so tests don't
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depend on the real library. Also reload the module-under-test so it binds to
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this mock `rr`.
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"""
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calls = []
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blueprints = []
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class DummyScalar:
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def __init__(self, value):
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# Scalars may be built from a single float or from a 1D array batch.
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self.value = value
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class DummyImage:
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def __init__(self, arr):
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self.arr = arr
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def compress(self, *a, **k):
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return self
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class DummyDepthImage:
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def __init__(self, arr, colormap=None):
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self.arr = arr
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self.colormap = colormap
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def dummy_log(key, obj=None, **kwargs):
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# Accept either positional `obj` or keyword `entity` and record remaining kwargs.
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if obj is None and "entity" in kwargs:
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obj = kwargs.pop("entity")
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calls.append((key, obj, kwargs))
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def dummy_send_blueprint(blueprint, *a, **k):
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blueprints.append(blueprint)
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# Mock the `rerun.blueprint` submodule used to build the layout.
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dummy_rrb = SimpleNamespace(
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Spatial2DView=lambda origin=None, name=None: SimpleNamespace(
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kind="Spatial2DView", origin=origin, name=name
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),
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TimeSeriesView=lambda name=None, contents=None: SimpleNamespace(
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kind="TimeSeriesView", name=name, contents=contents
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),
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Grid=lambda *views: SimpleNamespace(kind="Grid", views=list(views)),
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Blueprint=lambda root: SimpleNamespace(kind="Blueprint", root=root),
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)
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dummy_rr = SimpleNamespace(
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__name__="rerun",
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__package__="rerun",
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__spec__=SimpleNamespace(name="rerun", submodule_search_locations=None),
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Scalars=DummyScalar,
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Image=DummyImage,
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DepthImage=DummyDepthImage,
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components=SimpleNamespace(Colormap=SimpleNamespace(Viridis="viridis")),
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log=dummy_log,
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send_blueprint=dummy_send_blueprint,
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init=lambda *a, **k: None,
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spawn=lambda *a, **k: None,
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blueprint=dummy_rrb,
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)
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# Inject fake modules into sys.modules (both `rerun` and `rerun.blueprint`).
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monkeypatch.setitem(sys.modules, "rerun", dummy_rr)
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monkeypatch.setitem(sys.modules, "rerun.blueprint", dummy_rrb)
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# Now import and reload the module under test, to bind to our rerun mock
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import lerobot.utils.rerun_visualization as rv
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importlib.reload(rv)
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# Expose the reloaded module, the call recorder and the captured blueprints
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yield rv, calls, blueprints
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def _keys(calls):
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"""Helper to extract just the keys logged to rr.log"""
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return [k for (k, _obj, _kw) in calls]
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def _obj_for(calls, key):
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"""Find the first object logged under a given key."""
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for k, obj, _kw in calls:
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if k == key:
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return obj
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raise KeyError(f"Key {key} not found in calls: {calls}")
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def _kwargs_for(calls, key):
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for k, _obj, kw in calls:
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if k == key:
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return kw
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raise KeyError(f"Key {key} not found in calls: {calls}")
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def _views_by_kind(blueprint, kind):
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"""Return the views of a given kind from the (single) blueprint's grid."""
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return [v for v in blueprint.root.views if v.kind == kind]
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def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
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rv, calls, blueprints = mock_rerun
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# Build EnvTransition dict
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obs = {
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f"{OBS_STATE}.temperature": np.float32(25.0),
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# CHW image should be converted to HWC for rr.Image
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"observation.camera": np.zeros((3, 10, 20), dtype=np.uint8),
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}
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act = {
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"action.throttle": 0.7,
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# 1D array should be logged as a single Scalars batch under one entity path
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"action.vector": np.array([1.0, 2.0], dtype=np.float32),
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}
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transition = {
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TransitionKey.OBSERVATION: obs,
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TransitionKey.ACTION: act,
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}
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# Extract observation and action data from transition like in the real call sites
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obs_data = transition.get(TransitionKey.OBSERVATION, {})
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action_data = transition.get(TransitionKey.ACTION, {})
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rv.log_rerun_data(observation=obs_data, action=action_data)
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# We expect:
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# - observation.state.temperature -> Scalars
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# - observation.camera -> Image (HWC) with static=True
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# - action.throttle -> Scalars
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# - action.vector -> single Scalars batch (no per-element suffix)
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expected_keys = {
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f"{OBS_STATE}.temperature",
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"observation.camera",
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"action.throttle",
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"action.vector",
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}
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assert set(_keys(calls)) == expected_keys
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# Check scalar types and values
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temp_obj = _obj_for(calls, f"{OBS_STATE}.temperature")
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assert type(temp_obj).__name__ == "DummyScalar"
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assert float(temp_obj.value) == pytest.approx(25.0)
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throttle_obj = _obj_for(calls, "action.throttle")
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assert type(throttle_obj).__name__ == "DummyScalar"
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assert float(throttle_obj.value) == pytest.approx(0.7)
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# 1D vector logged as a single batched Scalars under one entity path
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vec = _obj_for(calls, "action.vector")
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assert type(vec).__name__ == "DummyScalar"
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np.testing.assert_allclose(np.asarray(vec.value), [1.0, 2.0])
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# Check image handling: CHW -> HWC
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img_obj = _obj_for(calls, "observation.camera")
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assert type(img_obj).__name__ == "DummyImage"
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assert img_obj.arr.shape == (10, 20, 3) # transposed
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assert _kwargs_for(calls, "observation.camera").get("static", False) is True # static=True for images
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# A blueprint should have been built and sent exactly once, and cached on the function.
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assert len(blueprints) == 1
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assert rv.log_rerun_data.blueprint is blueprints[0]
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bp = blueprints[0]
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# One spatial view per image path
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spatial_views = _views_by_kind(bp, "Spatial2DView")
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assert {v.origin for v in spatial_views} == {"observation.camera"}
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# One time-series view each for observation and action scalars
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ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
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assert set(ts_views) == {"observation", "action"}
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assert ts_views["observation"].contents == [f"{OBS_STATE}.temperature"]
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assert ts_views["action"].contents == ["action.throttle", "action.vector"]
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def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
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rv, calls, blueprints = mock_rerun
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# First dict without prefixes treated as observation
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# Second dict without prefixes treated as action
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obs_plain = {
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"temp": 1.5,
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# Already HWC image => should stay as-is
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"img": np.zeros((5, 6, 3), dtype=np.uint8),
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"none": None, # should be skipped
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}
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act_plain = {
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"throttle": 0.3,
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"vec": np.array([9, 8, 7], dtype=np.float32),
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}
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# Extract observation and action data from list like the old function logic did
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# First dict was treated as observation, second as action
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rv.log_rerun_data(observation=obs_plain, action=act_plain)
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# Expected keys with auto-prefixes. The 1D vector is a single batched Scalars.
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expected = {
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"observation.temp",
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"observation.img",
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"action.throttle",
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"action.vec",
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}
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logged = set(_keys(calls))
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assert logged == expected
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# Scalars
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t = _obj_for(calls, "observation.temp")
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assert type(t).__name__ == "DummyScalar"
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assert float(t.value) == pytest.approx(1.5)
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throttle = _obj_for(calls, "action.throttle")
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assert type(throttle).__name__ == "DummyScalar"
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assert float(throttle.value) == pytest.approx(0.3)
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# Image stays HWC
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img = _obj_for(calls, "observation.img")
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assert type(img).__name__ == "DummyImage"
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assert img.arr.shape == (5, 6, 3)
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assert _kwargs_for(calls, "observation.img").get("static", False) is True
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# Vector logged as a single batched Scalars under one entity path
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vec = _obj_for(calls, "action.vec")
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assert type(vec).__name__ == "DummyScalar"
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np.testing.assert_allclose(np.asarray(vec.value), [9, 8, 7])
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# Blueprint sent once with the expected view layout
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assert len(blueprints) == 1
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bp = blueprints[0]
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spatial_views = _views_by_kind(bp, "Spatial2DView")
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assert {v.origin for v in spatial_views} == {"observation.img"}
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ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
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assert ts_views["observation"].contents == ["observation.temp"]
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assert ts_views["action"].contents == ["action.throttle", "action.vec"]
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def test_log_rerun_data_kwargs_only(mock_rerun):
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rv, calls, blueprints = mock_rerun
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rv.log_rerun_data(
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observation={"observation.temp": 10.0, "observation.gray": np.zeros((8, 8, 1), dtype=np.uint8)},
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action={"action.a": 1.0},
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)
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keys = set(_keys(calls))
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assert "observation.temp" in keys
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assert "observation.gray" in keys
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assert "action.a" in keys
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temp = _obj_for(calls, "observation.temp")
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assert type(temp).__name__ == "DummyScalar"
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assert float(temp.value) == pytest.approx(10.0)
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img = _obj_for(calls, "observation.gray")
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assert type(img).__name__ == "DummyDepthImage" # single-channel -> DepthImage
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assert img.arr.shape == (8, 8, 1) # remains HWC
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assert _kwargs_for(calls, "observation.gray").get("static", False) is True
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a = _obj_for(calls, "action.a")
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assert type(a).__name__ == "DummyScalar"
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assert float(a.value) == pytest.approx(1.0)
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# Blueprint sent once, with a spatial view for the image and time-series views for scalars
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assert len(blueprints) == 1
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bp = blueprints[0]
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assert {v.origin for v in _views_by_kind(bp, "Spatial2DView")} == {"observation.gray"}
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ts_views = {v.name: v for v in _views_by_kind(bp, "TimeSeriesView")}
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assert ts_views["observation"].contents == ["observation.temp"]
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assert ts_views["action"].contents == ["action.a"]
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def test_log_rerun_data_blueprint_sent_only_once(mock_rerun):
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"""The blueprint is built from the first call and not resent on subsequent calls."""
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rv, calls, blueprints = mock_rerun
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rv.log_rerun_data(observation={"temp": 1.0}, action={"a": 2.0})
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assert len(blueprints) == 1
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first_blueprint = rv.log_rerun_data.blueprint
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rv.log_rerun_data(observation={"temp": 3.0}, action={"a": 4.0})
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# Still only one blueprint, and the cached one is unchanged.
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assert len(blueprints) == 1
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assert rv.log_rerun_data.blueprint is first_blueprint
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