qualcomm / 3D-Deep-BOX

3D-Deep-BOX: Optimized for Mobile Deployment

Real-time 3D object detection

3D Deep Box is a machine learning model that predicts 3D bounding boxes and classes of objects in an image.

This model is an implementation of 3D-Deep-BOX found here .

This repository provides scripts to run 3D-Deep-BOX on Qualcomm® devices. More details on model performance across various devices, can be found here .

Model Details

• Model Type: Object detection
• Model Stats:
  • Model checkpoint: YOLOv3-tiny
  • Input resolution(YOLO): 224x640
  • Number of parameters(YOLO): 8.85M
  • Model size(YOLO): 37.3 MB
  • Input resolution(VGG): 224x224
  • Number of parameters(VGG): 144M
  • Model size(VGG): 175.9 MB

Installation

Install the package via pip:

pip install "qai-hub-models[deepbox]"

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.deepbox.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.deepbox.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.deepbox.export

Profiling Results
------------------------------------------------------------
Yolo
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 22.2                   
Estimated peak memory usage (MB): [0, 59]                
Total # Ops                     : 128                    
Compute Unit(s)                 : NPU (128 ops)          

------------------------------------------------------------
VGG
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 4.8                    
Estimated peak memory usage (MB): [0, 616]               
Total # Ops                     : 40                     
Compute Unit(s)                 : NPU (40 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.deepbox import Model

# Load the model
model = Model.from_pretrained()
bbox2D_dectector_model = model.bbox2D_dectector
bbox3D_dectector_model = model.bbox3D_dectector

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

# Trace model
bbox2D_dectector_input_shape = bbox2D_dectector_model.get_input_spec()
bbox2D_dectector_sample_inputs = bbox2D_dectector_model.sample_inputs()

traced_bbox2D_dectector_model = torch.jit.trace(bbox2D_dectector_model, [torch.tensor(data[0]) for _, data in bbox2D_dectector_sample_inputs.items()])

# Compile model on a specific device
bbox2D_dectector_compile_job = hub.submit_compile_job(
    model=traced_bbox2D_dectector_model ,
    device=device,
    input_specs=bbox2D_dectector_model.get_input_spec(),
)

# Get target model to run on-device
bbox2D_dectector_target_model = bbox2D_dectector_compile_job.get_target_model()
# Trace model
bbox3D_dectector_input_shape = bbox3D_dectector_model.get_input_spec()
bbox3D_dectector_sample_inputs = bbox3D_dectector_model.sample_inputs()

traced_bbox3D_dectector_model = torch.jit.trace(bbox3D_dectector_model, [torch.tensor(data[0]) for _, data in bbox3D_dectector_sample_inputs.items()])

# Compile model on a specific device
bbox3D_dectector_compile_job = hub.submit_compile_job(
    model=traced_bbox3D_dectector_model ,
    device=device,
    input_specs=bbox3D_dectector_model.get_input_spec(),
)

# Get target model to run on-device
bbox3D_dectector_target_model = bbox3D_dectector_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.

bbox2D_dectector_profile_job = hub.submit_profile_job(
    model=bbox2D_dectector_target_model,
    device=device,
)
bbox3D_dectector_profile_job = hub.submit_profile_job(
    model=bbox3D_dectector_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.

bbox2D_dectector_input_data = bbox2D_dectector_model.sample_inputs()
bbox2D_dectector_inference_job = hub.submit_inference_job(
    model=bbox2D_dectector_target_model,
    device=device,
    inputs=bbox2D_dectector_input_data,
)
bbox2D_dectector_inference_job.download_output_data()
bbox3D_dectector_input_data = bbox3D_dectector_model.sample_inputs()
bbox3D_dectector_inference_job = hub.submit_inference_job(
    model=bbox3D_dectector_target_model,
    device=device,
    inputs=bbox3D_dectector_input_data,
)
bbox3D_dectector_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 3D-Deep-BOX's performance across various devices here . Explore all available models on Qualcomm® AI Hub

License

• The license for the original implementation of 3D-Deep-BOX can be found here .
• The license for the compiled assets for on-device deployment can be found here

References

• 3D Bounding Box Estimation Using Deep Learning and Geometry
• 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 .