mirror of
https://github.com/huggingface/lerobot.git
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3dd19d043e
* feat(depth): add depth quantization helpers and tests
* feat(video): add ffv1 to supported codecs
* feat(depth): persist depth metadata
* feat(depth): extend quantization tools to better fit the encoding/decoding pipeline
* feat(depth): plumb DepthEncoderConfig through LeRobotDataset and DatasetWriter
* feat(depth): wire StreamingVideoEncoder + writer to depth encoder
* feat(depth): wire DatasetReader to decode_depth_frames
* feat(cameras/realsense): expose async depth in metric meters
* feat(features): route 2D camera shapes to observation.depth.<key>
* feat(robots/so_follower): emit + populate depth keys when use_depth
* feat(record): plumb DepthEncoderConfig through lerobot-record
* feat(viz): render depth observations as rr.DepthImage in Viridis
* feat(depth maps writer): adding support for raw depth maps recording with image writer
* chore(format): format code
* feat(depth shape): ensuring depth maps shape is always including the channel
* feat(is_depth): simplifying is_depth nested name + legacy support
* fix(stop_event): fixing stop_event race condition in camera classes
* fix(plumbing): fixing missing parts in the depth maps pipeline
* chore(typos): fixing typos
* test(fix): fixing exisiting tests to still work with latest features
* tests(depth): adding new tests for depth integration validation
* feat(pix_fmt channels): use PyAv to check get pixel formats number of channels
* feat(refactor): refactor DepthEncoderConfig quantization pipeline, so that the methods do not live in the config class. Add pixel format - channels validation.Move the default pixel format for depth in the config file.
* fix(pre-commit): fixing mutable defautl value
* fix(info): fixing info metadata update when is_depth_map was set
* tests(typos): fixing typos in tests
* fix(realsense): fixing typo in realsense serial number
* fix(normalization): restricting 255 normalization to non depth/uint8 images only
* fix(typo): fixing typo
* fix(TIFF): add missing quantization and cleanup for TIFF files
* feat(batched dequantization): optimizing dequantize_depth for torch based batched dequantization
* feat(tools): adding depth support in LeRobotDataset edition tools
* test(aggregate): extending aggregation tests to depth frames
* test(cleaning): cleaning up tests
* fix(from_video_info): fixing early validation issue in from_video_info
* fix(typo): fixing typo
* fix(is_depth): adding missing doctrings and is_depth arguments in video decoding functions
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* fix(depth units): fixing depth units output for the realsense cameras
* feat(output unit): adding support for output unit specification at dataset reading/training time
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* test(depth): cleaning up depth tests
* test(depth encoding): updating and cleaning video/depth encoding tests
* chore(format): formatting code
* docs(depth): improving depth maps docs
* test(fix): fixing depth tests
* test(dataset tools): adding missing tests for new dataset edition tools features
* chore(format): formatting code
* fix(pyav check): fixing PyAV option validation for integer codec options by normalizing
numeric values before calling `is_integer()`
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* docs(mermaid): fixing mermaid diagram
* fix(rebase): rebase follow up corrections
* feat(dataset tools): adding missing docstrings and features for depth fill support in dataset edition tools
* docs(docstring): updating docstrings
* docs(dataset tools): updating docs
* fix(save images): fixing image saving in dataset tools
* fix(update video info): fixing update video info logic to match the recording and editing use cases
* test(reencode): fixing reencoding monkeypatch
* fix(review): add Claude review
* chore(format): format code
* fix(update video info): ditching the differentiated approahces for video info update - video info are always updated unless for preserved keys.
* chore(rebase): fixing rebase merge conflicts
* test(visualization): fixing visualization tests
* feat(docstrings): adding explicit docstring for encoding parameters. Docstrigns will now show up as description in the CLI --help.
* feat(mm as default): adding a global DEFAULT_DEPTH_UNIT variable setting mm as default depth unit
* fix(RGB <-> camera): renaming camera_encoder to rgb_encoder for clarity
* chore(TODO): removing deprecated TODO
* doc(write_u16_plane): improving docstrings for write_u16_plane
* feat(units): adding constants for depth frames units (m and mm)
* fix(spam): replacing spamming warning but a debug log
* feat(leagcy metadata): adding automatic metadata update for legacy 'video.is_depth_map' feature
* fix(copy&reindex): fixing metadat reshaping for single channel frames
* fix(ImageNet): excluding dpeth frames from ImageNet stats
* fix(PyAV container seek): fixing initial PyAV container seek to be robust againsy codec choice
* feat(lerobot-dataset-viz): adding support for depth in lerobot-dataset-viz
* fix(compress): removing rerun compression for DepthImages
* fix(signle channel squeeze): fixing single channel squeezing
* chore(format): format code
* fix(streaming): adding support for dequantization in streaming_dataset.py
* refactor(read depth): factorizing depth reading methods for realsense camera and adding support for depth-only usage
* chore(renaming): fixing missed RGBEncoderConfig renamings
* docs(renaming): reflecting renamings in a clearer way in the docs
* chore(annotation): excluding depth from the annotation pipeline
* feat(robots): adding depth support in compatible follower robots
* feat(LeSadKiwi): excluding LeKiwi from depth support (for now)
* chore(fail): removing misplaced file
* chore(fail): removing misplaced file
* fix(remove ffv1): removing ffv1 as it does not support MP4
* docs(cheat sheet): adding depth and video encoding to the cheat sheet
* fix(lossless): tuning depth encoding parameters for lossless depth storage
* test(fix): fixing failing tests
* depth(ZMQ): excluding ZMQ from depth support
* Revert "depth(ZMQ): excluding ZMQ from depth support"
This reverts commit b95cf4e4c2.
* fix(image transforms): excluding depth frames from images transforms
* fix(typo): typo
* fix(stats): fixing stats computation for depth frames
* fix(TIFF vs. pytorch): adding an extra uint16 to float32 conversion for depth maps stored as raw TIFF images
* fix(typos): fixing typos
* test(dtype): fixing stats computation typing tests
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi Ai <wsai@stanford.edu>
242 lines
7.7 KiB
Python
242 lines
7.7 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 so tests don't depend on the real library.
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Also reload the module-under-test so it binds to this mock `rr`.
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"""
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calls = []
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class DummyScalar:
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def __init__(self, value):
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self.value = float(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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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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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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init=lambda *a, **k: None,
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spawn=lambda *a, **k: None,
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)
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# Inject fake module into sys.modules
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monkeypatch.setitem(sys.modules, "rerun", dummy_rr)
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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.visualization_utils as vu
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importlib.reload(vu)
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# Expose both the reloaded module and the call recorder
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yield vu, calls
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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 test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
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vu, calls = 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 log individual Scalars with suffix _i
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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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vu.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_0, action.vector_1 -> Scalars
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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_0",
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"action.vector_1",
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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 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 throttle_obj.value == pytest.approx(0.7)
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v0 = _obj_for(calls, "action.vector_0")
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v1 = _obj_for(calls, "action.vector_1")
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assert type(v0).__name__ == "DummyScalar"
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assert type(v1).__name__ == "DummyScalar"
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assert v0.value == pytest.approx(1.0)
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assert v1.value == pytest.approx(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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def test_log_rerun_data_plain_list_ordering_and_prefixes(mock_rerun):
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vu, calls = 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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vu.log_rerun_data(observation=obs_plain, action=act_plain)
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# Expected keys with auto-prefixes
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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_0",
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"action.vec_1",
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"action.vec_2",
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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 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 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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# Vectors
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for i, val in enumerate([9, 8, 7]):
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o = _obj_for(calls, f"action.vec_{i}")
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assert type(o).__name__ == "DummyScalar"
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assert o.value == pytest.approx(val)
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def test_log_rerun_data_kwargs_only(mock_rerun):
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vu, calls = mock_rerun
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vu.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 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 a.value == pytest.approx(1.0)
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