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QuickSRNetLarge

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PyTorch
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ONNX
android
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QuickSRNetLarge: Optimized for Mobile Deployment

Upscale images and remove image noise

QuickSRNet Large is designed for upscaling images on mobile platforms to sharpen in real-time.

This model is an implementation of QuickSRNetLarge found here.

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

Model Details

  • Model Type: Model_use_case.super_resolution
  • Model Stats:
    • Model checkpoint: quicksrnet_large_3x_checkpoint
    • Input resolution: 128x128
    • Number of parameters: 424K
    • Model size (float): 1.63 MB
    • Model size (w8a8): 449 KB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
QuickSRNetLarge float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 14.197 ms 6 - 19 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 11.906 ms 0 - 10 MB NPU Use Export Script
QuickSRNetLarge float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 4.218 ms 6 - 31 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 3.325 ms 0 - 22 MB NPU Use Export Script
QuickSRNetLarge float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.531 ms 0 - 5 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 1.992 ms 0 - 3 MB NPU Use Export Script
QuickSRNetLarge float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 4.431 ms 6 - 22 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 3.473 ms 0 - 15 MB NPU Use Export Script
QuickSRNetLarge float SA7255P ADP Qualcomm® SA7255P TFLITE 14.197 ms 6 - 19 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float SA7255P ADP Qualcomm® SA7255P QNN 11.906 ms 0 - 10 MB NPU Use Export Script
QuickSRNetLarge float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.499 ms 0 - 5 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 1.975 ms 0 - 3 MB NPU Use Export Script
QuickSRNetLarge float SA8295P ADP Qualcomm® SA8295P TFLITE 5.187 ms 6 - 24 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float SA8295P ADP Qualcomm® SA8295P QNN 3.808 ms 0 - 18 MB NPU Use Export Script
QuickSRNetLarge float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.491 ms 0 - 5 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 1.971 ms 0 - 3 MB NPU Use Export Script
QuickSRNetLarge float SA8775P ADP Qualcomm® SA8775P TFLITE 4.431 ms 6 - 22 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float SA8775P ADP Qualcomm® SA8775P QNN 3.473 ms 0 - 15 MB NPU Use Export Script
QuickSRNetLarge float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.449 ms 0 - 5 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 2.001 ms 0 - 5 MB NPU Use Export Script
QuickSRNetLarge float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 2.589 ms 0 - 6 MB NPU QuickSRNetLarge.onnx
QuickSRNetLarge float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.7 ms 0 - 27 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 1.303 ms 0 - 26 MB NPU Use Export Script
QuickSRNetLarge float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.667 ms 0 - 28 MB NPU QuickSRNetLarge.onnx
QuickSRNetLarge float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.965 ms 0 - 19 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 1.114 ms 0 - 16 MB NPU Use Export Script
QuickSRNetLarge float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 1.466 ms 1 - 19 MB NPU QuickSRNetLarge.onnx
QuickSRNetLarge float Snapdragon X Elite CRD Snapdragon® X Elite QNN 2.163 ms 0 - 0 MB NPU Use Export Script
QuickSRNetLarge float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 2.497 ms 7 - 7 MB NPU QuickSRNetLarge.onnx
QuickSRNetLarge w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 5.239 ms 1 - 14 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 2.0 ms 0 - 10 MB NPU Use Export Script
QuickSRNetLarge w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 3.1 ms 2 - 26 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 0.967 ms 0 - 28 MB NPU Use Export Script
QuickSRNetLarge w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.219 ms 1 - 8 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 0.616 ms 0 - 3 MB NPU Use Export Script
QuickSRNetLarge w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 2.17 ms 0 - 16 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 0.84 ms 0 - 15 MB NPU Use Export Script
QuickSRNetLarge w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 4.25 ms 2 - 18 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 2.907 ms 0 - 14 MB NPU Use Export Script
QuickSRNetLarge w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 39.689 ms 1 - 4 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 5.239 ms 1 - 14 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 SA7255P ADP Qualcomm® SA7255P QNN 2.0 ms 0 - 10 MB NPU Use Export Script
QuickSRNetLarge w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.359 ms 1 - 7 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 0.617 ms 0 - 3 MB NPU Use Export Script
QuickSRNetLarge w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 2.51 ms 0 - 19 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 SA8295P ADP Qualcomm® SA8295P QNN 1.322 ms 0 - 17 MB NPU Use Export Script
QuickSRNetLarge w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 1.457 ms 0 - 6 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 0.619 ms 0 - 2 MB NPU Use Export Script
QuickSRNetLarge w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 2.17 ms 0 - 16 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 SA8775P ADP Qualcomm® SA8775P QNN 0.84 ms 0 - 15 MB NPU Use Export Script
QuickSRNetLarge w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 1.445 ms 0 - 6 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 0.623 ms 0 - 7 MB NPU Use Export Script
QuickSRNetLarge w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 14.18 ms 21 - 32 MB NPU QuickSRNetLarge.onnx
QuickSRNetLarge w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.081 ms 0 - 22 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 0.404 ms 0 - 24 MB NPU Use Export Script
QuickSRNetLarge w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 11.847 ms 23 - 48 MB NPU QuickSRNetLarge.onnx
QuickSRNetLarge w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.307 ms 0 - 21 MB NPU QuickSRNetLarge.tflite
QuickSRNetLarge w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 0.427 ms 0 - 20 MB NPU Use Export Script
QuickSRNetLarge w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 10.438 ms 20 - 41 MB NPU QuickSRNetLarge.onnx
QuickSRNetLarge w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.728 ms 0 - 0 MB NPU Use Export Script
QuickSRNetLarge w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 13.71 ms 33 - 33 MB NPU QuickSRNetLarge.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.quicksrnetlarge.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.quicksrnetlarge.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.quicksrnetlarge.export

Profiling Results
------------------------------------------------------------
QuickSRNetLarge
Device                          : cs_8275 (ANDROID 14)                
Runtime                         : TFLITE                              
Estimated inference time (ms)   : 14.2                                
Estimated peak memory usage (MB): [6, 19]                             
Total # Ops                     : 31                                  
Compute Unit(s)                 : npu (28 ops) gpu (0 ops) cpu (3 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.quicksrnetlarge 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.quicksrnetlarge.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.quicksrnetlarge.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 QuickSRNetLarge's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

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