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android
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library_name: pytorch
license: mit
pipeline_tag: image-classification
tags:
  - foundation
  - android

OpenAI-Clip: Optimized for Mobile Deployment

Multi-modal foundational model for vision and language tasks like image/text similarity and for zero-shot image classification

Contrastive Language-Image Pre-Training (CLIP) uses a ViT like transformer to get visual features and a causal language model to get the text features. Both the text and visual features can then be used for a variety of zero-shot learning tasks.

This model is an implementation of OpenAI-Clip found here. This repository provides scripts to run OpenAI-Clip on Qualcomm® devices. More details on model performance across various devices, can be found here.

Model Details

  • Model Type: Image classification
  • Model Stats:
    • Model checkpoint: ViT-B/16
    • Image input resolution: 224x224
    • Text context length: 77
    • Number of parameters (CLIPTextEncoder): 76.0M
    • Model size (CLIPTextEncoder): 290 MB
    • Number of parameters (CLIPImageEncoder): 115M
    • Model size (CLIPImageEncoder): 437 MB
Model Device Chipset Target Runtime Inference Time (ms) Peak Memory Range (MB) Precision Primary Compute Unit Target Model
CLIPTextEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 5.779 ms 0 - 3 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 4.774 ms 0 - 16 MB FP16 NPU OpenAI-Clip.so
CLIPTextEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 35.403 ms 0 - 130 MB FP16 NPU OpenAI-Clip.onnx
CLIPTextEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 4.079 ms 0 - 194 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 3.405 ms 0 - 66 MB FP16 NPU OpenAI-Clip.so
CLIPTextEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 26.223 ms 0 - 534 MB FP16 NPU OpenAI-Clip.onnx
CLIPTextEncoder QCS8550 (Proxy) QCS8550 Proxy TFLITE 5.717 ms 0 - 2 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder QCS8550 (Proxy) QCS8550 Proxy QNN 4.856 ms 0 - 1 MB FP16 NPU Use Export Script
CLIPTextEncoder SA8255 (Proxy) SA8255P Proxy TFLITE 5.711 ms 0 - 2 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder SA8255 (Proxy) SA8255P Proxy QNN 4.794 ms 0 - 1 MB FP16 NPU Use Export Script
CLIPTextEncoder SA8775 (Proxy) SA8775P Proxy TFLITE 5.652 ms 0 - 2 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder SA8775 (Proxy) SA8775P Proxy QNN 4.897 ms 0 - 1 MB FP16 NPU Use Export Script
CLIPTextEncoder SA8650 (Proxy) SA8650P Proxy TFLITE 5.683 ms 0 - 287 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder SA8650 (Proxy) SA8650P Proxy QNN 4.903 ms 0 - 1 MB FP16 NPU Use Export Script
CLIPTextEncoder QCS8450 (Proxy) QCS8450 Proxy TFLITE 6.593 ms 0 - 168 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder QCS8450 (Proxy) QCS8450 Proxy QNN 5.491 ms 0 - 66 MB FP16 NPU Use Export Script
CLIPTextEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 3.963 ms 0 - 109 MB FP16 NPU OpenAI-Clip.tflite
CLIPTextEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 3.266 ms 0 - 65 MB FP16 NPU Use Export Script
CLIPTextEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 23.78 ms 0 - 319 MB FP16 NPU OpenAI-Clip.onnx
CLIPTextEncoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 5.196 ms 0 - 0 MB FP16 NPU Use Export Script
CLIPTextEncoder Snapdragon X Elite CRD Snapdragon® X Elite ONNX 38.329 ms 126 - 126 MB FP16 NPU OpenAI-Clip.onnx
CLIPImageEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 TFLITE 38.384 ms 0 - 2 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 QNN 27.206 ms 0 - 56 MB FP16 NPU OpenAI-Clip.so
CLIPImageEncoder Samsung Galaxy S23 Snapdragon® 8 Gen 2 ONNX 174.036 ms 0 - 194 MB FP16 NPU OpenAI-Clip.onnx
CLIPImageEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 TFLITE 33.247 ms 0 - 666 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 QNN 24.164 ms 1 - 170 MB FP16 NPU OpenAI-Clip.so
CLIPImageEncoder Samsung Galaxy S24 Snapdragon® 8 Gen 3 ONNX 118.868 ms 1 - 3571 MB FP16 NPU OpenAI-Clip.onnx
CLIPImageEncoder QCS8550 (Proxy) QCS8550 Proxy TFLITE 37.343 ms 0 - 2 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder QCS8550 (Proxy) QCS8550 Proxy QNN 22.015 ms 1 - 2 MB FP16 NPU Use Export Script
CLIPImageEncoder SA8255 (Proxy) SA8255P Proxy TFLITE 37.324 ms 0 - 2 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder SA8255 (Proxy) SA8255P Proxy QNN 22.477 ms 1 - 2 MB FP16 NPU Use Export Script
CLIPImageEncoder SA8775 (Proxy) SA8775P Proxy TFLITE 36.58 ms 0 - 2 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder SA8775 (Proxy) SA8775P Proxy QNN 22.644 ms 0 - 1 MB FP16 NPU Use Export Script
CLIPImageEncoder SA8650 (Proxy) SA8650P Proxy TFLITE 36.958 ms 0 - 3 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder SA8650 (Proxy) SA8650P Proxy QNN 22.477 ms 1 - 2 MB FP16 NPU Use Export Script
CLIPImageEncoder QCS8450 (Proxy) QCS8450 Proxy TFLITE 37.123 ms 0 - 549 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder QCS8450 (Proxy) QCS8450 Proxy QNN 30.382 ms 0 - 170 MB FP16 NPU Use Export Script
CLIPImageEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite TFLITE 25.495 ms 0 - 460 MB FP16 NPU OpenAI-Clip.tflite
CLIPImageEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite QNN 17.137 ms 1 - 172 MB FP16 NPU Use Export Script
CLIPImageEncoder Snapdragon 8 Elite QRD Snapdragon® 8 Elite ONNX 17.137 ms 1 - 172 MB FP16 NPU OpenAI-Clip.onnx
CLIPImageEncoder Snapdragon X Elite CRD Snapdragon® X Elite QNN 22.135 ms 1 - 1 MB FP16 NPU Use Export Script
CLIPImageEncoder Snapdragon X Elite CRD Snapdragon® X Elite ONNX 162.155 ms 188 - 188 MB FP16 NPU OpenAI-Clip.onnx

Installation

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

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

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.openai_clip.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.openai_clip.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.openai_clip.export

Profiling Results
------------------------------------------------------------
CLIPTextEncoder
Device                          : Samsung Galaxy S23 (13)  
Runtime                         : TFLITE                   
Estimated inference time (ms)   : 5.8                      
Estimated peak memory usage (MB): [0, 3]                   
Total # Ops                     : 660                      
Compute Unit(s)                 : NPU (658 ops) CPU (2 ops)

------------------------------------------------------------
CLIPImageEncoder
Device                          : Samsung Galaxy S23 (13)
Runtime                         : TFLITE                 
Estimated inference time (ms)   : 38.4                   
Estimated peak memory usage (MB): [0, 2]                 
Total # Ops                     : 659                    
Compute Unit(s)                 : NPU (659 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.openai_clip import CLIPTextEncoder,CLIPImageEncoder

# Load the model
text_encoder_model = CLIPTextEncoder.from_pretrained()
image_encoder_model = CLIPImageEncoder.from_pretrained()

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

# Trace model
text_encoder_input_shape = text_encoder_model.get_input_spec()
text_encoder_sample_inputs = text_encoder_model.sample_inputs()

traced_text_encoder_model = torch.jit.trace(text_encoder_model, [torch.tensor(data[0]) for _, data in text_encoder_sample_inputs.items()])

# Compile model on a specific device
text_encoder_compile_job = hub.submit_compile_job(
    model=traced_text_encoder_model ,
    device=device,
    input_specs=text_encoder_model.get_input_spec(),
)

# Get target model to run on-device
text_encoder_target_model = text_encoder_compile_job.get_target_model()
# Trace model
image_encoder_input_shape = image_encoder_model.get_input_spec()
image_encoder_sample_inputs = image_encoder_model.sample_inputs()

traced_image_encoder_model = torch.jit.trace(image_encoder_model, [torch.tensor(data[0]) for _, data in image_encoder_sample_inputs.items()])

# Compile model on a specific device
image_encoder_compile_job = hub.submit_compile_job(
    model=traced_image_encoder_model ,
    device=device,
    input_specs=image_encoder_model.get_input_spec(),
)

# Get target model to run on-device
image_encoder_target_model = image_encoder_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.

text_encoder_profile_job = hub.submit_profile_job(
    model=text_encoder_target_model,
    device=device,
)
image_encoder_profile_job = hub.submit_profile_job(
    model=image_encoder_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.

text_encoder_input_data = text_encoder_model.sample_inputs()
text_encoder_inference_job = hub.submit_inference_job(
    model=text_encoder_target_model,
    device=device,
    inputs=text_encoder_input_data,
)
text_encoder_inference_job.download_output_data()
image_encoder_input_data = image_encoder_model.sample_inputs()
image_encoder_inference_job = hub.submit_inference_job(
    model=image_encoder_target_model,
    device=device,
    inputs=image_encoder_input_data,
)
image_encoder_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 OpenAI-Clip's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

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

References

Community