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--- |
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library_name: pytorch |
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license: other |
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pipeline_tag: image-to-image |
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tags: |
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- android |
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--- |
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# SESR-M5: Optimized for Mobile Deployment |
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## Upscale images in real time |
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SESR M5 performs efficient on-device upscaling of images. |
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This model is an implementation of SESR-M5 found [here](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/sesr). |
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This repository provides scripts to run SESR-M5 on Qualcomm® devices. |
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More details on model performance across various devices, can be found |
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[here](https://aihub.qualcomm.com/models/sesr_m5). |
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### Model Details |
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- **Model Type:** Super resolution |
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- **Model Stats:** |
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- Model checkpoint: sesr_m5_3x_checkpoint |
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- Input resolution: 128x128 |
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- Number of parameters: 338K |
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- Model size: 1.30 MB |
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| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
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|---|---|---|---|---|---|---|---|---| |
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| SESR-M5 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 2.225 ms | 0 - 5 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 2.118 ms | 0 - 4 MB | FP16 | NPU | [SESR-M5.so](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.so) | |
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| SESR-M5 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 2.693 ms | 0 - 2 MB | FP16 | NPU | [SESR-M5.onnx](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx) | |
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| SESR-M5 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.595 ms | 0 - 27 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.456 ms | 0 - 15 MB | FP16 | NPU | [SESR-M5.so](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.so) | |
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| SESR-M5 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.939 ms | 0 - 30 MB | FP16 | NPU | [SESR-M5.onnx](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx) | |
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| SESR-M5 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 1.693 ms | 0 - 16 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.289 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.999 ms | 0 - 16 MB | FP16 | NPU | [SESR-M5.onnx](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx) | |
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| SESR-M5 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 2.234 ms | 0 - 3 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 2.176 ms | 0 - 2 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 2.208 ms | 0 - 22 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | SA8255 (Proxy) | SA8255P Proxy | QNN | 2.141 ms | 0 - 5 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 2.203 ms | 0 - 8 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | SA8775 (Proxy) | SA8775P Proxy | QNN | 2.146 ms | 0 - 2 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 2.149 ms | 0 - 6 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | SA8650 (Proxy) | SA8650P Proxy | QNN | 2.151 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | SA8295P ADP | SA8295P | TFLITE | 4.143 ms | 6 - 21 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | SA8295P ADP | SA8295P | QNN | 3.474 ms | 0 - 6 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 3.4 ms | 6 - 33 MB | FP16 | NPU | [SESR-M5.tflite](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.tflite) | |
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| SESR-M5 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 3.144 ms | 0 - 15 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 2.357 ms | 0 - 0 MB | FP16 | NPU | Use Export Script | |
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| SESR-M5 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.955 ms | 9 - 9 MB | FP16 | NPU | [SESR-M5.onnx](https://huggingface.co/qualcomm/SESR-M5/blob/main/SESR-M5.onnx) | |
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## Installation |
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This model can be installed as a Python package via pip. |
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```bash |
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pip install qai-hub-models |
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``` |
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## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device |
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Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your |
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Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. |
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With this API token, you can configure your client to run models on the cloud |
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hosted devices. |
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```bash |
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qai-hub configure --api_token API_TOKEN |
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``` |
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Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. |
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## Demo off target |
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The package contains a simple end-to-end demo that downloads pre-trained |
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weights and runs this model on a sample input. |
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```bash |
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python -m qai_hub_models.models.sesr_m5.demo |
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``` |
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The above demo runs a reference implementation of pre-processing, model |
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inference, and post processing. |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.sesr_m5.demo |
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``` |
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### Run model on a cloud-hosted device |
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® |
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device. This script does the following: |
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* Performance check on-device on a cloud-hosted device |
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* Downloads compiled assets that can be deployed on-device for Android. |
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* Accuracy check between PyTorch and on-device outputs. |
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```bash |
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python -m qai_hub_models.models.sesr_m5.export |
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``` |
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``` |
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Profiling Results |
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------------------------------------------------------------ |
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SESR-M5 |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 2.2 |
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Estimated peak memory usage (MB): [0, 5] |
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Total # Ops : 25 |
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Compute Unit(s) : NPU (22 ops) CPU (3 ops) |
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``` |
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## How does this work? |
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This [export script](https://aihub.qualcomm.com/models/sesr_m5/qai_hub_models/models/SESR-M5/export.py) |
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model |
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on-device. Lets go through each step below in detail: |
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Step 1: **Compile model for on-device deployment** |
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To compile a PyTorch model for on-device deployment, we first trace the model |
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in memory using the `jit.trace` and then call the `submit_compile_job` API. |
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```python |
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import torch |
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import qai_hub as hub |
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from qai_hub_models.models.sesr_m5 import |
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# Load the model |
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# Device |
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device = hub.Device("Samsung Galaxy S23") |
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``` |
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Step 2: **Performance profiling on cloud-hosted device** |
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After compiling models from step 1. Models can be profiled model on-device using the |
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`target_model`. Note that this scripts runs the model on a device automatically |
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provisioned in the cloud. Once the job is submitted, you can navigate to a |
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provided job URL to view a variety of on-device performance metrics. |
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```python |
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profile_job = hub.submit_profile_job( |
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model=target_model, |
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device=device, |
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) |
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``` |
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Step 3: **Verify on-device accuracy** |
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To verify the accuracy of the model on-device, you can run on-device inference |
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on sample input data on the same cloud hosted device. |
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```python |
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input_data = torch_model.sample_inputs() |
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inference_job = hub.submit_inference_job( |
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model=target_model, |
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device=device, |
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inputs=input_data, |
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) |
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on_device_output = inference_job.download_output_data() |
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``` |
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With the output of the model, you can compute like PSNR, relative errors or |
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spot check the output with expected output. |
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**Note**: This on-device profiling and inference requires access to Qualcomm® |
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AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). |
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## Run demo on a cloud-hosted device |
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You can also run the demo on-device. |
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```bash |
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python -m qai_hub_models.models.sesr_m5.demo --on-device |
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``` |
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like |
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environment, please add the following to your cell (instead of the above). |
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``` |
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%run -m qai_hub_models.models.sesr_m5.demo -- --on-device |
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``` |
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## Deploying compiled model to Android |
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The models can be deployed using multiple runtimes: |
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- TensorFlow Lite (`.tflite` export): [This |
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a |
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guide to deploy the .tflite model in an Android application. |
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- QNN (`.so` export ): This [sample |
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) |
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provides instructions on how to use the `.so` shared library in an Android application. |
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## View on Qualcomm® AI Hub |
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Get more details on SESR-M5's performance across various devices [here](https://aihub.qualcomm.com/models/sesr_m5). |
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) |
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## License |
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* The license for the original implementation of SESR-M5 can be found [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf). |
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* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) |
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## References |
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* [Collapsible Linear Blocks for Super-Efficient Super Resolution](https://arxiv.org/abs/2103.09404) |
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* [Source Model Implementation](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/sesr) |
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## Community |
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. |
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com). |
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