| --- |
| library_name: onnx |
| license: mit |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - object-detection |
| pipeline_tag: object-detection |
| --- |
| |
|  |
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| # CenterPoint: Optimized for AMD ROCm |
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| CenterPoint is a center-based 3D object detector for LIDAR point clouds, widely used in autonomous-driving perception pipelines. This repository packages evaluation/inference for 3D object detection using ONNX Runtime and PyTorch (OpenPCDet), exported and validated for **AMD ROCm** so it runs efficiently on AMD GPUs, CPUs, and NPUs. |
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| This is based on the implementation of CenterPoint found [here](https://github.com/tianweiy/CenterPoint/tree/master). |
| This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [CenterPoint AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/CenterPoint) to reproduce results or export with custom configurations. More details on model performance can be found [here](#performance-summary). |
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| --- |
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| ## Task Overview |
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| **Task:** 3D object detection (LIDAR point clouds) |
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| **Dataset:** nuScenes mini (v1.0-mini — 81 val samples, 10 detection classes) |
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| **Output metrics:** mAP, NDS (nuScenes Detection Score), per-class AP, TP errors (ATE/ASE/AOE/AVE/AAE), FPS |
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| > **GPU backend note:** GPU inference uses OpenPCDet (PyTorch on AMD ROCm) rather than ONNX Runtime, since MIGraphX cannot lower the Scatter/Gather patterns in `pointpillars.onnx`. |
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| --- |
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| ## AMD ROCm Optimization |
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| This model export has been adapted and validated for **AMD Instinct™ / Radeon™ GPUs** running **ROCm**, as well as AMD CPUs and NPUs. Key points: |
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| - Validated backends: **ONNX Runtime** (CPU, FP32; NPU via VitisAI) and **PyTorch/OpenPCDet** (GPU, native ROCm). |
| - CPU fallback path supported for environments without a ROCm-capable GPU or NPU. |
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| | Runtime | Precision | Backend | Hardware | Notes | |
| |---|---|---|---|---| |
| | ONNX Runtime | FP32 | CPU Execution Provider | AMD CPU | — | |
| | PyTorch | Native | OpenPCDet | AMD Instinct™ / Radeon™ GPU (ROCm) | Runs natively via OpenPCDet instead of ONNX Runtime, since MIGraphX cannot lower the Scatter/Gather patterns in `pointpillars.onnx` | |
| | ONNX Runtime | Auto (BF16) | VitisAI Execution Provider | AMD NPU | Quantization handled internally by VitisAI | |
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| --- |
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| ## Getting Started |
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| For setup instructions, evaluation scripts, and custom configuration options, see the [CenterPoint on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/CenterPoint). |
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| --- |
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| ## Model Details |
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| **Model Type:** 3D object detection (PointPillars variant of CenterPoint) |
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| **Model Stats:** |
| - Input tensors: features `(1, 10, 30000, 20)` float32, indices `(1, 30000, 2)` int64 |
| - Output: 128 x 128 BEV feature maps (per-task detection heads), float32 |
| - Precision tested: FP32 (CPU, ONNX Runtime), native (GPU, PyTorch/OpenPCDet), auto BF16 quantization (NPU, VitisAI) |
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| --- |
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| ## Performance Summary |
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| Higher mAP means the model's predicted boxes and classes agree more closely with ground truth across the dataset — 1.0 would be perfect detection, 0.0 means no correct detections. NDS combines mAP with localization and attribute errors into a single score; higher is better. |
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| ### Metrics Explained |
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| | Metric | Description | |
| |--------|-------------| |
| | mAP | Mean Average Precision over the 10 nuScenes detection classes at center-distance thresholds 0.5/1.0/2.0/4.0 m. The primary accuracy number — higher means more objects are correctly detected and classified. | |
| | NDS | nuScenes Detection Score — combines mAP (50%) with five True-Positive error terms (ATE, ASE, AOE, AVE, AAE) into a single number. Higher means the model is both finding objects and localizing them accurately (position, size, orientation, velocity, attributes). | |
| | ATE (Translation Error) | Average center-distance error in meters for true positives — lower means the model's 3D box centers are closer to ground truth. | |
| | ASE (Scale Error) | Average IoU-based size error (1 - IoU) for true positives — lower means predicted box dimensions more closely match ground truth. | |
| | AOE (Orientation Error) | Average angular error in radians for true positives — lower means heading predictions are more accurate. Matters for downstream planning. | |
| | AVE (Velocity Error) | Average velocity error in m/s for true positives — lower means better motion estimation. Only computed for moving classes (car, truck, bus, etc.). | |
| | AAE (Attribute Error) | Average attribute classification error (1 - accuracy) for true positives — lower means the model better predicts secondary labels (e.g. parked vs. moving). | |
| | Per-class AP | mAP broken down per class (car, truck, bus, trailer, construction_vehicle, pedestrian, motorcycle, bicycle, traffic_cone, barrier) — exposes class-specific weaknesses the aggregate mAP would hide. | |
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| --- |
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| ## Dig Deeper |
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| Want to explore the full evaluation scripts, config options, and other AMD-optimized model examples? |
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| 📂 **[View the full project on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/CenterPoint)** |
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| The GitHub repository includes: |
| - Setup and prerequisites for ROCm environments |
| - Scripts for the supported runners |
| - Additional model variants and datasets |
| - Benchmarking and reproduction instructions |
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