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metadata 
library_name: pytorch
license: bsd-3-clause
pipeline_tag: video-classification
tags:
  - backbone
  - android

ResNet-2Plus1D: Optimized for Mobile Deployment

Sports and human action recognition in videos

ResNet (2+1)D Convolutions is a network which explicitly factorizes 3D convolution into two separate and successive operations, a 2D spatial convolution and a 1D temporal convolution. It used for video understanding applications.

This model is an implementation of ResNet-2Plus1D found here.

This repository provides scripts to run ResNet-2Plus1D on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Video classification
  • Model Stats:
    • Model checkpoint: Kinetics-400
    • Input resolution: 112x112
    • Number of parameters: 31.5M
    • Model size: 120 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
ResNet-2Plus1D Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 116.337 ms 4 - 601 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 14.065 ms 0 - 200 MB FP16 NPU ResNet-2Plus1D.onnx
ResNet-2Plus1D Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 87.809 ms 28 - 69 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 10.458 ms 2 - 61 MB FP16 NPU ResNet-2Plus1D.onnx
ResNet-2Plus1D Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 104.072 ms 27 - 64 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 10.015 ms 2 - 59 MB FP16 NPU ResNet-2Plus1D.onnx
ResNet-2Plus1D QCS8550 (Proxy) QCS8550 Proxy TFLITE 116.778 ms 4 - 604 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D SA7255P ADP SA7255P TFLITE 826.709 ms 28 - 63 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D SA8255 (Proxy) SA8255P Proxy TFLITE 115.504 ms 4 - 604 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D SA8295P ADP SA8295P TFLITE 143.259 ms 28 - 66 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D SA8650 (Proxy) SA8650P Proxy TFLITE 115.149 ms 4 - 614 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D SA8775P ADP SA8775P TFLITE 156.336 ms 28 - 63 MB FP16 NPU ResNet-2Plus1D.tflite
ResNet-2Plus1D QCS8450 (Proxy) QCS8450 Proxy TFLITE 127.438 ms 28 - 69 MB FP16 NPU ResNet-2Plus1D.tflite

Installation

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

pip install "qai-hub-models[resnet_2plus1d]"

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.resnet_2plus1d.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.resnet_2plus1d.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.resnet_2plus1d.export

Profiling Results
------------------------------------------------------------
ResNet-2Plus1D
Device                          : Samsung Galaxy S23 (13) 
Runtime                         : TFLITE                  
Estimated inference time (ms)   : 116.3                   
Estimated peak memory usage (MB): [4, 601]                
Total # Ops                     : 92                      
Compute Unit(s)                 : NPU (84 ops) CPU (8 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.resnet_2plus1d 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 ResNet-2Plus1D's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of ResNet-2Plus1D can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

Community