qualcomm / MediaPipe-Pose-Estimation

MediaPipe-Pose-Estimation: Optimized for Mobile Deployment

Detect and track human body poses in real-time images and video streams

The MediaPipe Pose Landmark Detector is a machine learning pipeline that predicts bounding boxes and pose skeletons of poses in an image.

This model is an implementation of MediaPipe-Pose-Estimation found here . This repository provides scripts to run MediaPipe-Pose-Estimation on Qualcomm® devices. More details on model performance across various devices, can be found here .

Model Details

• Model Type: Pose estimation
• Model Stats:
  • Input resolution: 256x256
  • Number of parameters (MediaPipePoseDetector): 815K
  • Model size (MediaPipePoseDetector): 3.14 MB
  • Number of parameters (MediaPipePoseLandmarkDetector): 3.37M
  • Model size (MediaPipePoseLandmarkDetector): 12.9 MB

Installation

This model can be installed as a Python package via pip.

pip install qai-hub-models

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.mediapipe_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.mediapipe_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.mediapipe_pose.export

Profile Job summary of MediaPipePoseDetector
--------------------------------------------------
Device: SA8255 (Proxy) (13)
Estimated Inference Time: 0.89 ms
Estimated Peak Memory Range: 0.02-5.94 MB
Compute Units: NPU (140) | Total (140)

Profile Job summary of MediaPipePoseLandmarkDetector
--------------------------------------------------
Device: SA8255 (Proxy) (13)
Estimated Inference Time: 1.08 ms
Estimated Peak Memory Range: 0.02-11.66 MB
Compute Units: NPU (292) | Total (292)

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.mediapipe_pose import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S23")

# 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 .

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 MediaPipe-Pose-Estimation's performance across various devices here . Explore all available models on Qualcomm® AI Hub

License

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

References

• BlazePose: On-device Real-time Body Pose tracking
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us .