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Image Segmentation
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LiteRT
ONNX
real_time
android
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metadata
library_name: pytorch
license: mit
tags:
  - real_time
  - android
pipeline_tag: image-segmentation

PidNet: Optimized for Mobile Deployment

Segment images or video by class in real-time on device

PIDNet (Proportional-Integral-Derivative Network) is a real-time semantic segmentation model based on PID controllers

This model is an implementation of PidNet found here.

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

Model Details

  • Model Type: Semantic segmentation
  • Model Stats:
    • Model checkpoint: PIDNet_S_Cityscapes_val.pt
    • Inference latency: RealTime
    • Input resolution: 1024x2048
    • Number of output classes: 19
    • Number of parameters: 7.62M
    • Model size: 29.1 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
PidNet Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 42.979 ms 2 - 17 MB FP16 NPU PidNet.tflite
PidNet Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 31.41 ms 25 - 27 MB FP16 NPU PidNet.so
PidNet Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 44.905 ms 29 - 95 MB FP16 NPU PidNet.onnx
PidNet Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 29.652 ms 2 - 61 MB FP16 NPU PidNet.tflite
PidNet Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 21.971 ms 24 - 42 MB FP16 NPU PidNet.so
PidNet Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 30.579 ms 28 - 87 MB FP16 NPU PidNet.onnx
PidNet Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 27.568 ms 1 - 50 MB FP16 NPU PidNet.tflite
PidNet Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 28.823 ms 10 - 53 MB FP16 NPU Use Export Script
PidNet Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 30.097 ms 10 - 57 MB FP16 NPU PidNet.onnx
PidNet SA7255P ADP SA7255P TFLITE 670.759 ms 2 - 50 MB FP16 NPU PidNet.tflite
PidNet SA7255P ADP SA7255P QNN 653.99 ms 24 - 34 MB FP16 NPU Use Export Script
PidNet SA8255 (Proxy) SA8255P Proxy TFLITE 43.958 ms 2 - 16 MB FP16 NPU PidNet.tflite
PidNet SA8255 (Proxy) SA8255P Proxy QNN 31.758 ms 24 - 26 MB FP16 NPU Use Export Script
PidNet SA8295P ADP SA8295P TFLITE 58.863 ms 2 - 49 MB FP16 NPU PidNet.tflite
PidNet SA8295P ADP SA8295P QNN 44.036 ms 24 - 42 MB FP16 NPU Use Export Script
PidNet SA8650 (Proxy) SA8650P Proxy TFLITE 43.065 ms 2 - 17 MB FP16 NPU PidNet.tflite
PidNet SA8650 (Proxy) SA8650P Proxy QNN 32.431 ms 24 - 27 MB FP16 NPU Use Export Script
PidNet SA8775P ADP SA8775P TFLITE 61.855 ms 0 - 48 MB FP16 NPU PidNet.tflite
PidNet SA8775P ADP SA8775P QNN 46.8 ms 23 - 33 MB FP16 NPU Use Export Script
PidNet QCS8275 (Proxy) QCS8275 Proxy TFLITE 670.759 ms 2 - 50 MB FP16 NPU PidNet.tflite
PidNet QCS8275 (Proxy) QCS8275 Proxy QNN 653.99 ms 24 - 34 MB FP16 NPU Use Export Script
PidNet QCS8550 (Proxy) QCS8550 Proxy TFLITE 44.531 ms 2 - 17 MB FP16 NPU PidNet.tflite
PidNet QCS8550 (Proxy) QCS8550 Proxy QNN 31.392 ms 24 - 27 MB FP16 NPU Use Export Script
PidNet QCS9075 (Proxy) QCS9075 Proxy TFLITE 61.855 ms 0 - 48 MB FP16 NPU PidNet.tflite
PidNet QCS9075 (Proxy) QCS9075 Proxy QNN 46.8 ms 23 - 33 MB FP16 NPU Use Export Script
PidNet QCS8450 (Proxy) QCS8450 Proxy TFLITE 54.017 ms 2 - 61 MB FP16 NPU PidNet.tflite
PidNet QCS8450 (Proxy) QCS8450 Proxy QNN 54.115 ms 24 - 67 MB FP16 NPU Use Export Script
PidNet Snapdragon X Elite CRD Snapdragon® X Elite QNN 31.297 ms 24 - 24 MB FP16 NPU Use Export Script
PidNet Snapdragon X Elite CRD Snapdragon® X Elite ONNX 42.839 ms 24 - 24 MB FP16 NPU PidNet.onnx

Installation

Install the 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.pidnet.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.pidnet.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.pidnet.export

Profiling Results
------------------------------------------------------------
PidNet
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 43.0                   
Estimated peak memory usage (MB): [2, 17]                
Total # Ops                     : 169                    
Compute Unit(s)                 : NPU (169 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.pidnet 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.pidnet.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.pidnet.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 PidNet's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

Community