| --- |
| library_name: onnx |
| license: mit |
| tags: |
| - foundation |
| - amd |
| - rocm |
| - pose-estimation |
| pipeline_tag: keypoint-detection |
| --- |
| |
|  |
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| # HRNet: Optimized for AMD ROCm |
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| HRNet (High-Resolution Network, W48 variant) is a human pose-estimation model that predicts COCO body keypoints while maintaining high-resolution feature representations throughout the network. This repository packages inference for human pose estimation using **ONNX Runtime**, 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 HRNet found [here](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch). |
| This repository contains configurations and scripts optimized for **AMD® ROCm™** platforms. You can use the [HRNet AMD scripts](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/HRNet) to reproduce results or export with custom configurations. |
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| --- |
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| ## Task Overview |
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| **Task:** Human pose estimation (keypoint detection) |
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| **Dataset:** COCO 2017 keypoints (val2017 images + person-keypoint annotations, under `dataset/coco/`) |
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| **Output metrics:** Throughput (inferences/sec), latency (mean/P95/P99 ms), per-operator profiling breakdown |
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| > **NPU note:** The NPU (VitisAI) backend accepts FP32 input and auto-quantizes internally; there is no separate FP16/BF16/INT8 NPU path. |
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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 AMD Ryzen AI NPUs. Key points: |
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| - Validated backends: **ONNX Runtime** across CPU, GPU (MIGraphX), and NPU (VitisAI) execution providers. |
| - CPU workflows run on any machine; GPU requires ROCm and a compatible AMD GPU; NPU requires an AMD Ryzen AI device. |
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| | Runtime | Precision | Backend | Hardware | Notes | |
| |---|---|---|---|---| |
| | ONNX Runtime | FP32 / FP16 / BF16 / INT8 | CPU Execution Provider | AMD CPU | — | |
| | ONNX Runtime | FP32 / FP16 / BF16 / INT8 | MIGraphX Execution Provider | AMD Instinct™ / Radeon™ GPU (ROCm) | — | |
| | ONNX Runtime | FP32 | VitisAI Execution Provider | AMD Ryzen AI NPU | Accepts FP32 input; VitisAI quantizes internally | |
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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 [HRNet on GitHub](https://github.com/AMD-PAVS/physical_ai_sdk/blob/main/models/HRNet). |
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| --- |
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| ## Model Details |
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| **Model Type:** Human pose estimation (keypoint detection), HRNet-W48 |
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| **Base Model:** `pose_hrnet_w48_384x288.pth` (HRNet W48, 384×288 input resolution) |
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| **Model Stats:** |
| - Model variant: W48, 384×288 input resolution |
| - Precision tested: FP32, FP16, BF16, INT8 (CPU/GPU); FP32 auto-quantized (NPU) |
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| --- |
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| ## Accuracy Pipeline |
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| Accuracy evaluation is not yet implemented for this model. |
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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/HRNet)** |
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| The GitHub repository includes: |
| - Benchmark and profiling scripts for CPU, GPU, and NPU |
| - Instructions for downloading pretrained weights and COCO keypoint annotations |
| - The upstream HRNet repository clone and native NMS extension build steps |
| - Manual PyTorch/ONNX accuracy validation scripts (COCO AP/AR via `pycocotools`) |
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