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HRNET_W48_OCR

Image Segmentation
PyTorch
LiteRT
ONNX
real_time
android
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metadata
library_name: pytorch
license: bsd-3-clause
tags:
  - real_time
  - android
pipeline_tag: image-segmentation

HRNET_W48_OCR: Optimized for Mobile Deployment

Semantic segmentation in higher resuolution

HRNET_W48_OCR is a machine learning model that can segment images from the Cityscape dataset. It has lightweight and hardware-efficient operations and thus delivers significant speedup on diverse hardware platforms

This model is an implementation of HRNET_W48_OCR found here.

This repository provides scripts to run HRNET_W48_OCR 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: hrnet_ocr_cs_8162_torch11.pth
    • Input resolution: 2048x1024
    • Number of output classes: 19
    • Number of parameters: 70.3M
    • Model size: 268 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
HRNET_W48_OCR Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 79849.148 ms 13 - 98 MB FP16 NPU HRNET_W48_OCR.tflite
HRNET_W48_OCR Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 77887.743 ms 24 - 115 MB FP16 NPU HRNET_W48_OCR.so
HRNET_W48_OCR Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 90278.624 ms 21 - 197 MB FP16 NPU HRNET_W48_OCR.onnx
HRNET_W48_OCR Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 57616.176 ms 7 - 535 MB FP16 NPU HRNET_W48_OCR.tflite
HRNET_W48_OCR Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 60639.784 ms 349 - 738 MB FP16 NPU HRNET_W48_OCR.so
HRNET_W48_OCR Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 60032.058 ms 31 - 453 MB FP16 NPU HRNET_W48_OCR.onnx
HRNET_W48_OCR Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 38509.476 ms 0 - 598 MB FP16 NPU HRNET_W48_OCR.tflite
HRNET_W48_OCR Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 40305.276 ms 26 - 484 MB FP16 NPU Use Export Script
HRNET_W48_OCR Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 45977.99 ms 24 - 512 MB FP16 NPU HRNET_W48_OCR.onnx
HRNET_W48_OCR QCS8275 (Proxy) QCS8275 Proxy TFLITE 153010.617 ms 4 - 604 MB FP16 NPU HRNET_W48_OCR.tflite
HRNET_W48_OCR QCS8275 (Proxy) QCS8275 Proxy QNN 154004.485 ms 24 - 34 MB FP16 NPU Use Export Script
HRNET_W48_OCR QCS8550 (Proxy) QCS8550 Proxy TFLITE 81548.228 ms 5 - 91 MB FP16 NPU HRNET_W48_OCR.tflite
HRNET_W48_OCR QCS8550 (Proxy) QCS8550 Proxy QNN 91229.668 ms 24 - 32 MB FP16 NPU Use Export Script
HRNET_W48_OCR QCS9075 (Proxy) QCS9075 Proxy TFLITE 97145.674 ms 5 - 604 MB FP16 NPU HRNET_W48_OCR.tflite
HRNET_W48_OCR QCS9075 (Proxy) QCS9075 Proxy QNN 97642.737 ms 24 - 33 MB FP16 NPU Use Export Script
HRNET_W48_OCR QCS8450 (Proxy) QCS8450 Proxy TFLITE 78236.611 ms 15 - 908 MB FP16 NPU HRNET_W48_OCR.tflite
HRNET_W48_OCR QCS8450 (Proxy) QCS8450 Proxy QNN 88176.859 ms 35 - 848 MB FP16 NPU Use Export Script
HRNET_W48_OCR Snapdragon X Elite CRD Snapdragon® X Elite QNN 84961.244 ms 24 - 24 MB FP16 NPU Use Export Script
HRNET_W48_OCR Snapdragon X Elite CRD Snapdragon® X Elite ONNX 82966.45 ms 132 - 132 MB FP16 NPU HRNET_W48_OCR.onnx

Installation

Install the package via pip:

pip install "qai-hub-models[hrnet-w48-ocr]"

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_w48_ocr.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_w48_ocr.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_w48_ocr.export

Profiling Results
------------------------------------------------------------
HRNET_W48_OCR
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 79849.1                
Estimated peak memory usage (MB): [13, 98]               
Total # Ops                     : 578                    
Compute Unit(s)                 : NPU (578 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_w48_ocr 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.

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 HRNET_W48_OCR's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of HRNET_W48_OCR can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community