Compiled BPU models for RDK S100/S100P
Browse files- .gitattributes +2 -0
- README.md +86 -0
- las2_m_crop_nashm.hbm +3 -0
- las2_m_int16_nashm.hbm +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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las2_m_crop_nashm.hbm filter=lfs diff=lfs merge=lfs -text
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las2_m_int16_nashm.hbm filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: mit
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library_name: bcdl
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tags:
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- rdk-s100
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- rdk-s100p
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- d-robotics
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- bpu
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- hbm
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- depth-estimation
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- stereo
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---
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# Lite Any Stereo V2 for RDK S100/S100P β zero-shot stereo disparity
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Compiled BPU models (`.hbm`) for the **D-Robotics RDK S100 / S100P**, ready to
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load β no ONNX export, no calibration, no `hb_compile`. Built and measured with
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[**BCDL**](https://github.com/ruisv/bcdl), a C++17 inference and media library
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for the RDK S-series with Python bindings.
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Upstream: [Lite Any Stereo V2](https://github.com/Tinker-Twins/LiteAnyStereo) (M)
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> [!TIP]
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> **Redistributable, including commercially.** The licence chain was checked on
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> the code, the pretrained weights it started from, and the data it was trained
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> on β all three, because a permissive repository badge does not by itself say
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> anything about the weights. See [Licence](#licence).
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## Files
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| file | what it is |
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|---|---|
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| `las2_m_crop_nashm.hbm` | centre-crop-fit calibration, 640x480 β 38.8 MB |
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| `las2_m_int16_nashm.hbm` | resize-fit calibration, 640x480 β 39.3 MB |
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## Measured on an S100P
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| stage | latency | throughput |
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|---|---|---|
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| either build | 13.70 ms | 73 FPS |
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`hrt_model_exec perf`, one thread, minimum of three runs, on a board first gated
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against its own previously recorded numbers. **BPU time only** β CPU
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pre/post-processing is on top and is listed per task in BCDL's
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[benchmark results](https://github.com/ruisv/bcdl/blob/main/benchmarks/RESULTS.md).
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## Use it
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```bash
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conda install -c https://mirrors.ruis.ai/conda -c conda-forge bcdl
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```
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```python
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import bcdl
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engine = bcdl.Engine("las2_m_crop_nashm.hbm")
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print(engine.input_shape(0), engine.output_shape(0))
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```
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Each task has a decoder in BCDL that turns those raw outputs into boxes,
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keypoints, masks, disparity or text β see the
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[Python API](https://github.com/ruisv/bcdl/blob/main/docs/API.md)
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([δΈζ](https://github.com/ruisv/bcdl/blob/main/docs/API.zh.md)).
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## What to know before deploying
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**Two calibration modes, and you must match the one you letterbox with.** `_crop`
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was calibrated on centre-cropped pairs and `_int16` on resize-fitted ones; feed a
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build the geometry it was not calibrated for and the disparity degrades quietly
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rather than failing.
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All-2D and feed-forward β no 3-D cost volume β which is exactly why it runs at
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73 FPS here. Cost-volume stereo of comparable quality took ~40 s/frame on this
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part and was abandoned.
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## Licence
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MIT on the code and the weights. One thing worth knowing rather than a term passed down: the release was distilled from FoundationStereo, whose NVIDIA licence is research-only. That is upstream's compliance question, but look at it before a commercial build.
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**BCDL itself is Apache-2.0 and is unrelated to these terms** β it is a
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general-purpose runtime that loads any `.hbm`. The licence above constrains
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*these weights and this compiled artefact*.
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The conversion recipe β ONNX export, calibration, `hb_compile` config and the
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acceptance numbers β is public in
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[**bcdl-model-zoo**](https://github.com/ruisv/bcdl-model-zoo), so this build can
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be reproduced or retargeted rather than taken on trust.
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las2_m_crop_nashm.hbm
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version https://git-lfs.github.com/spec/v1
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oid sha256:323f567365f70ab1ea59e42364560293a8b8a2eb057df52e02fa8d67d1b7d06d
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size 40700800
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las2_m_int16_nashm.hbm
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version https://git-lfs.github.com/spec/v1
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oid sha256:78caecfa9d31a954dbd77c45fd2acd06e98fa5efe779e2a064634ba4e01e7908
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size 41197264
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