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SESR-M5

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SESR-M5: Optimized for Mobile Deployment

Upscale images in real time

SESR M5 performs efficient on-device upscaling of images.

This model is an implementation of SESR-M5 found here.

This repository provides scripts to run SESR-M5 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: sesr_m5_3x_checkpoint
    • Input resolution: 128x128
    • Number of parameters: 338K
    • Model size (float): 1.30 MB
    • Model size (w8a8): 389 KB
Model Precision Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Primary Compute Unit Target Model
SESR-M5 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 11.776 ms 6 - 17 MB NPU SESR-M5.tflite
SESR-M5 float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 10.533 ms 0 - 10 MB NPU Use Export Script
SESR-M5 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 3.717 ms 6 - 35 MB NPU SESR-M5.tflite
SESR-M5 float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 2.993 ms 0 - 21 MB NPU Use Export Script
SESR-M5 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.24 ms 0 - 6 MB NPU SESR-M5.tflite
SESR-M5 float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 1.966 ms 0 - 4 MB NPU Use Export Script
SESR-M5 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 3.737 ms 0 - 15 MB NPU SESR-M5.tflite
SESR-M5 float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 3.139 ms 0 - 15 MB NPU Use Export Script
SESR-M5 float SA7255P ADP Qualcomm® SA7255P TFLITE 11.776 ms 6 - 17 MB NPU SESR-M5.tflite
SESR-M5 float SA7255P ADP Qualcomm® SA7255P QNN 10.533 ms 0 - 10 MB NPU Use Export Script
SESR-M5 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.305 ms 6 - 11 MB NPU SESR-M5.tflite
SESR-M5 float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 1.99 ms 0 - 2 MB NPU Use Export Script
SESR-M5 float SA8295P ADP Qualcomm® SA8295P TFLITE 4.299 ms 6 - 27 MB NPU SESR-M5.tflite
SESR-M5 float SA8295P ADP Qualcomm® SA8295P QNN 3.338 ms 0 - 16 MB NPU Use Export Script
SESR-M5 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.271 ms 0 - 6 MB NPU SESR-M5.tflite
SESR-M5 float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 1.979 ms 0 - 2 MB NPU Use Export Script
SESR-M5 float SA8775P ADP Qualcomm® SA8775P TFLITE 3.737 ms 0 - 15 MB NPU SESR-M5.tflite
SESR-M5 float SA8775P ADP Qualcomm® SA8775P QNN 3.139 ms 0 - 15 MB NPU Use Export Script
SESR-M5 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.341 ms 0 - 4 MB NPU SESR-M5.tflite
SESR-M5 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 1.97 ms 0 - 5 MB NPU Use Export Script
SESR-M5 float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 2.598 ms 0 - 7 MB NPU SESR-M5.onnx
SESR-M5 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.565 ms 0 - 25 MB NPU SESR-M5.tflite
SESR-M5 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 1.277 ms 0 - 28 MB NPU Use Export Script
SESR-M5 float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 1.624 ms 0 - 23 MB NPU SESR-M5.onnx
SESR-M5 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 2.265 ms 0 - 16 MB NPU SESR-M5.tflite
SESR-M5 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 1.307 ms 0 - 21 MB NPU Use Export Script
SESR-M5 float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 1.691 ms 1 - 17 MB NPU SESR-M5.onnx
SESR-M5 float Snapdragon X Elite CRD Snapdragon® X Elite QNN 2.139 ms 0 - 0 MB NPU Use Export Script
SESR-M5 float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 2.59 ms 8 - 8 MB NPU SESR-M5.onnx
SESR-M5 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 3.647 ms 2 - 15 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 1.947 ms 0 - 10 MB NPU Use Export Script
SESR-M5 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 3.094 ms 2 - 29 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 1.026 ms 0 - 29 MB NPU Use Export Script
SESR-M5 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 1.34 ms 0 - 6 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 0.626 ms 0 - 3 MB NPU Use Export Script
SESR-M5 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 2.059 ms 0 - 16 MB NPU SESR-M5.tflite
SESR-M5 w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 0.858 ms 0 - 14 MB NPU Use Export Script
SESR-M5 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 3.856 ms 2 - 18 MB NPU SESR-M5.tflite
SESR-M5 w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 2.82 ms 0 - 14 MB NPU Use Export Script
SESR-M5 w8a8 RB5 (Proxy) Qualcomm® QCS8250 (Proxy) TFLITE 21.329 ms 2 - 9 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 3.647 ms 2 - 15 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA7255P ADP Qualcomm® SA7255P QNN 1.947 ms 0 - 10 MB NPU Use Export Script
SESR-M5 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 1.558 ms 2 - 8 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 0.65 ms 0 - 2 MB NPU Use Export Script
SESR-M5 w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 3.006 ms 2 - 24 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8295P ADP Qualcomm® SA8295P QNN 1.345 ms 0 - 18 MB NPU Use Export Script
SESR-M5 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 1.34 ms 0 - 6 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 0.646 ms 0 - 11 MB NPU Use Export Script
SESR-M5 w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 2.059 ms 0 - 16 MB NPU SESR-M5.tflite
SESR-M5 w8a8 SA8775P ADP Qualcomm® SA8775P QNN 0.858 ms 0 - 14 MB NPU Use Export Script
SESR-M5 w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.038 ms 0 - 6 MB NPU SESR-M5.tflite
SESR-M5 w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 0.625 ms 0 - 10 MB NPU Use Export Script
SESR-M5 w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 0.973 ms 0 - 8 MB NPU SESR-M5.onnx
SESR-M5 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.112 ms 0 - 21 MB NPU SESR-M5.tflite
SESR-M5 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 0.446 ms 0 - 26 MB NPU Use Export Script
SESR-M5 w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 0.629 ms 25 - 53 MB NPU SESR-M5.onnx
SESR-M5 w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.334 ms 0 - 19 MB NPU SESR-M5.tflite
SESR-M5 w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 0.471 ms 0 - 19 MB NPU Use Export Script
SESR-M5 w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 0.716 ms 0 - 18 MB NPU SESR-M5.onnx
SESR-M5 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN 0.762 ms 0 - 0 MB NPU Use Export Script
SESR-M5 w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 0.967 ms 3 - 3 MB NPU SESR-M5.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.sesr_m5.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.sesr_m5.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.sesr_m5.export

Profiling Results
------------------------------------------------------------
SESR-M5
Device                          : cs_8275 (ANDROID 14)                
Runtime                         : TFLITE                              
Estimated inference time (ms)   : 11.8                                
Estimated peak memory usage (MB): [6, 17]                             
Total # Ops                     : 25                                  
Compute Unit(s)                 : npu (22 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.sesr_m5 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.sesr_m5.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.sesr_m5.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 SESR-M5's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

  • The license for the original implementation of SESR-M5 can be found here.
  • The license for the compiled assets for on-device deployment can be found here

References

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