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HRNetPose

Keypoint Detection
PyTorch
LiteRT
ONNX
android
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HRNetPose: Optimized for Mobile Deployment

Perform accurate human pose estimation

HRNet performs pose estimation in high-resolution representations.

This model is an implementation of HRNetPose found here.

This repository provides scripts to run HRNetPose on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Model_use_case.pose_estimation
  • Model Stats:
    • Model checkpoint: hrnet_posenet_FP32_state_dict
    • Input resolution: 256x192
    • Number of parameters: 28.5M
    • Model size (float): 109 MB
    • Model size (w8a8): 28 MB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
HRNetPose float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 14.4 ms 0 - 71 MB NPU HRNetPose.tflite
HRNetPose float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 14.249 ms 1 - 10 MB NPU Use Export Script
HRNetPose float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 3.877 ms 0 - 118 MB NPU HRNetPose.tflite
HRNetPose float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 5.124 ms 0 - 52 MB NPU Use Export Script
HRNetPose float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.659 ms 0 - 77 MB NPU HRNetPose.tflite
HRNetPose float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 2.649 ms 1 - 3 MB NPU Use Export Script
HRNetPose float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 4.461 ms 0 - 71 MB NPU HRNetPose.tflite
HRNetPose float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 4.344 ms 1 - 12 MB NPU Use Export Script
HRNetPose float SA7255P ADP Qualcomm® SA7255P TFLITE 14.4 ms 0 - 71 MB NPU HRNetPose.tflite
HRNetPose float SA7255P ADP Qualcomm® SA7255P QNN 14.249 ms 1 - 10 MB NPU Use Export Script
HRNetPose float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.684 ms 0 - 79 MB NPU HRNetPose.tflite
HRNetPose float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 2.677 ms 1 - 2 MB NPU Use Export Script
HRNetPose float SA8295P ADP Qualcomm® SA8295P TFLITE 4.57 ms 0 - 67 MB NPU HRNetPose.tflite
HRNetPose float SA8295P ADP Qualcomm® SA8295P QNN 4.537 ms 1 - 18 MB NPU Use Export Script
HRNetPose float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.68 ms 0 - 19 MB NPU HRNetPose.tflite
HRNetPose float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 2.663 ms 1 - 3 MB NPU Use Export Script
HRNetPose float SA8775P ADP Qualcomm® SA8775P TFLITE 4.461 ms 0 - 71 MB NPU HRNetPose.tflite
HRNetPose float SA8775P ADP Qualcomm® SA8775P QNN 4.344 ms 1 - 12 MB NPU Use Export Script
HRNetPose float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.678 ms 0 - 78 MB NPU HRNetPose.tflite
HRNetPose float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 2.646 ms 0 - 16 MB NPU Use Export Script
HRNetPose float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 2.715 ms 0 - 162 MB NPU HRNetPose.onnx
HRNetPose float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.965 ms 0 - 116 MB NPU HRNetPose.tflite
HRNetPose float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 1.997 ms 1 - 53 MB NPU Use Export Script
HRNetPose float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 2.084 ms 0 - 89 MB NPU HRNetPose.onnx
HRNetPose float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.853 ms 0 - 74 MB NPU HRNetPose.tflite
HRNetPose float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 1.808 ms 0 - 40 MB NPU Use Export Script
HRNetPose float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 1.943 ms 0 - 52 MB NPU HRNetPose.onnx
HRNetPose float Snapdragon X Elite CRD Snapdragon® X Elite QNN 2.823 ms 1 - 1 MB NPU Use Export Script
HRNetPose float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 2.699 ms 55 - 55 MB NPU HRNetPose.onnx
HRNetPose w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 2.665 ms 0 - 48 MB NPU HRNetPose.tflite
HRNetPose w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 2.888 ms 0 - 9 MB NPU Use Export Script
HRNetPose w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 1.313 ms 0 - 91 MB NPU HRNetPose.tflite
HRNetPose w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 1.92 ms 0 - 78 MB NPU Use Export Script
HRNetPose w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 0.996 ms 0 - 133 MB NPU HRNetPose.tflite
HRNetPose w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 1.146 ms 0 - 2 MB NPU Use Export Script
HRNetPose w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 1.326 ms 0 - 50 MB NPU HRNetPose.tflite
HRNetPose w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 1.422 ms 0 - 15 MB NPU Use Export Script
HRNetPose w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 3.813 ms 0 - 75 MB NPU HRNetPose.tflite
HRNetPose w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 5.224 ms 0 - 14 MB NPU Use Export Script
HRNetPose w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 17.222 ms 0 - 2 MB NPU HRNetPose.tflite
HRNetPose w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 2.665 ms 0 - 48 MB NPU HRNetPose.tflite
HRNetPose w8a8 SA7255P ADP Qualcomm® SA7255P QNN 2.888 ms 0 - 9 MB NPU Use Export Script
HRNetPose w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 0.979 ms 0 - 135 MB NPU HRNetPose.tflite
HRNetPose w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 1.152 ms 0 - 3 MB NPU Use Export Script
HRNetPose w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 1.719 ms 0 - 51 MB NPU HRNetPose.tflite
HRNetPose w8a8 SA8295P ADP Qualcomm® SA8295P QNN 1.865 ms 0 - 17 MB NPU Use Export Script
HRNetPose w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 0.992 ms 0 - 132 MB NPU HRNetPose.tflite
HRNetPose w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 1.137 ms 0 - 3 MB NPU Use Export Script
HRNetPose w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 1.326 ms 0 - 50 MB NPU HRNetPose.tflite
HRNetPose w8a8 SA8775P ADP Qualcomm® SA8775P QNN 1.422 ms 0 - 15 MB NPU Use Export Script
HRNetPose w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 0.987 ms 0 - 135 MB NPU HRNetPose.tflite
HRNetPose w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 1.16 ms 0 - 41 MB NPU Use Export Script
HRNetPose w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 4.837 ms 3 - 82 MB NPU HRNetPose.onnx
HRNetPose w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 0.743 ms 0 - 89 MB NPU HRNetPose.tflite
HRNetPose w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 0.822 ms 0 - 80 MB NPU Use Export Script
HRNetPose w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 3.744 ms 0 - 162 MB NPU HRNetPose.onnx
HRNetPose w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 0.701 ms 0 - 52 MB NPU HRNetPose.tflite
HRNetPose w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 0.735 ms 0 - 52 MB NPU Use Export Script
HRNetPose w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 3.769 ms 0 - 121 MB NPU HRNetPose.onnx
HRNetPose w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN 1.26 ms 0 - 0 MB NPU Use Export Script
HRNetPose w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 5.901 ms 27 - 27 MB NPU HRNetPose.onnx

Installation

Install the package via pip:

pip install "qai-hub-models[hrnet-pose]" torch==2.4.1 -f https://download.openmmlab.com/mmcv/dist/cpu/torch2.4/index.html -f https://qaihub-public-python-wheels.s3.us-west-2.amazonaws.com/index.html

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub with your Qualcomm® ID. Once signed in navigate to Account -> Settings -> API Token.

With this API token, you can configure your client to run models on the cloud hosted devices.

qai-hub configure --api_token API_TOKEN

Navigate to docs for more information.

Demo off target

The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input.

python -m qai_hub_models.models.hrnet_pose.demo

The above demo runs a reference implementation of pre-processing, model inference, and post processing.

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.hrnet_pose.demo

Run model on a cloud-hosted device

In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following:

  • Performance check on-device on a cloud-hosted device
  • Downloads compiled assets that can be deployed on-device for Android.
  • Accuracy check between PyTorch and on-device outputs.
python -m qai_hub_models.models.hrnet_pose.export

Profiling Results
------------------------------------------------------------
HRNetPose
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : TFLITE                               
Estimated inference time (ms)   : 14.4                                 
Estimated peak memory usage (MB): [0, 71]                              
Total # Ops                     : 516                                  
Compute Unit(s)                 : npu (516 ops) gpu (0 ops) cpu (0 ops)

How does this work?

This export script leverages Qualcomm® AI Hub to optimize, validate, and deploy this model on-device. Lets go through each step below in detail:

Step 1: Compile model for on-device deployment

To compile a PyTorch model for on-device deployment, we first trace the model in memory using the jit.trace and then call the submit_compile_job API.

import torch

import qai_hub as hub
from qai_hub_models.models.hrnet_pose import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S24")

# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()

pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])

# Compile model on a specific device
compile_job = hub.submit_compile_job(
    model=pt_model,
    device=device,
    input_specs=torch_model.get_input_spec(),
)

# Get target model to run on-device
target_model = compile_job.get_target_model()

Step 2: Performance profiling on cloud-hosted device

After compiling models from step 1. Models can be profiled model on-device using the target_model. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics.

profile_job = hub.submit_profile_job(
    model=target_model,
    device=device,
)

Step 3: Verify on-device accuracy

To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device.

input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
    model=target_model,
    device=device,
    inputs=input_data,
)
    on_device_output = inference_job.download_output_data()

With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output.

Note: This on-device profiling and inference requires access to Qualcomm® AI Hub. Sign up for access.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.hrnet_pose.demo --on-device

NOTE: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above).

%run -m qai_hub_models.models.hrnet_pose.demo -- --on-device

Deploying compiled model to Android

The models can be deployed using multiple runtimes:

  • TensorFlow Lite (.tflite export): This tutorial provides a guide to deploy the .tflite model in an Android application.

  • QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on HRNetPose's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of HRNetPose can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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