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--- |
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library_name: pytorch |
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license: mit |
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pipeline_tag: image-classification |
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tags: |
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- foundation |
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- android |
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--- |
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# OpenAI-Clip: Optimized for Mobile Deployment |
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## Multi-modal foundational model for vision and language tasks like image/text similarity and for zero-shot image classification |
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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. |
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This model is an implementation of OpenAI-Clip found [here]({source_repo}). |
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This repository provides scripts to run OpenAI-Clip 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/openai_clip). |
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### Model Details |
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- **Model Type:** Image classification |
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- **Model Stats:** |
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- Model checkpoint: ViT-B/16 |
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- Image input resolution: 224x224 |
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- Text context length: 77 |
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- Number of parameters (CLIPTextEncoder): 76.0M |
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- Model size (CLIPTextEncoder): 290 MB |
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- Number of parameters (CLIPImageEncoder): 115M |
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- Model size (CLIPImageEncoder): 437 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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| CLIPTextEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 5.779 ms | 0 - 3 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.774 ms | 0 - 16 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.so) | |
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| CLIPTextEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 35.403 ms | 0 - 130 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) | |
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| CLIPTextEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 4.079 ms | 0 - 194 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.405 ms | 0 - 66 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.so) | |
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| CLIPTextEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 26.223 ms | 0 - 534 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) | |
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| CLIPTextEncoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 5.717 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.856 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | |
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| CLIPTextEncoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 5.711 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.794 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | |
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| CLIPTextEncoder | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 5.652 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | SA8775 (Proxy) | SA8775P Proxy | QNN | 4.897 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | |
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| CLIPTextEncoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 5.683 ms | 0 - 287 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.903 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | |
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| CLIPTextEncoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 6.593 ms | 0 - 168 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 5.491 ms | 0 - 66 MB | FP16 | NPU | Use Export Script | |
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| CLIPTextEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 3.963 ms | 0 - 109 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.tflite) | |
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| CLIPTextEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.266 ms | 0 - 65 MB | FP16 | NPU | Use Export Script | |
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| CLIPTextEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 23.78 ms | 0 - 319 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) | |
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| CLIPTextEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 5.196 ms | 0 - 0 MB | FP16 | NPU | Use Export Script | |
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| CLIPTextEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 38.329 ms | 126 - 126 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPTextEncoder.onnx) | |
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| CLIPImageEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 38.384 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 27.206 ms | 0 - 56 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.so) | |
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| CLIPImageEncoder | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 174.036 ms | 0 - 194 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.onnx) | |
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| CLIPImageEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 33.247 ms | 0 - 666 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 24.164 ms | 1 - 170 MB | FP16 | NPU | [OpenAI-Clip.so](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.so) | |
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| CLIPImageEncoder | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 118.868 ms | 1 - 3571 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.onnx) | |
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| CLIPImageEncoder | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 37.343 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 22.015 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | |
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| CLIPImageEncoder | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 37.324 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | SA8255 (Proxy) | SA8255P Proxy | QNN | 22.477 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | |
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| CLIPImageEncoder | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 36.58 ms | 0 - 2 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | SA8775 (Proxy) | SA8775P Proxy | QNN | 22.644 ms | 0 - 1 MB | FP16 | NPU | Use Export Script | |
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| CLIPImageEncoder | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 36.958 ms | 0 - 3 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | SA8650 (Proxy) | SA8650P Proxy | QNN | 22.477 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | |
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| CLIPImageEncoder | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 37.123 ms | 0 - 549 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 30.382 ms | 0 - 170 MB | FP16 | NPU | Use Export Script | |
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| CLIPImageEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 25.495 ms | 0 - 460 MB | FP16 | NPU | [OpenAI-Clip.tflite](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.tflite) | |
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| CLIPImageEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 17.137 ms | 1 - 172 MB | FP16 | NPU | Use Export Script | |
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| CLIPImageEncoder | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 17.137 ms | 1 - 172 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.onnx) | |
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| CLIPImageEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 22.135 ms | 1 - 1 MB | FP16 | NPU | Use Export Script | |
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| CLIPImageEncoder | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 162.155 ms | 188 - 188 MB | FP16 | NPU | [OpenAI-Clip.onnx](https://huggingface.co/qualcomm/OpenAI-Clip/blob/main/CLIPImageEncoder.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[openai_clip]" |
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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.openai_clip.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.openai_clip.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.openai_clip.export |
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``` |
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``` |
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Profiling Results |
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------------------------------------------------------------ |
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CLIPTextEncoder |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 5.8 |
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Estimated peak memory usage (MB): [0, 3] |
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Total # Ops : 660 |
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Compute Unit(s) : NPU (658 ops) CPU (2 ops) |
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------------------------------------------------------------ |
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CLIPImageEncoder |
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Device : Samsung Galaxy S23 (13) |
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Runtime : TFLITE |
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Estimated inference time (ms) : 38.4 |
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Estimated peak memory usage (MB): [0, 2] |
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Total # Ops : 659 |
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Compute Unit(s) : NPU (659 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/openai_clip/qai_hub_models/models/OpenAI-Clip/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.openai_clip import CLIPTextEncoder,CLIPImageEncoder |
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# Load the model |
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text_encoder_model = CLIPTextEncoder.from_pretrained() |
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image_encoder_model = CLIPImageEncoder.from_pretrained() |
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# Device |
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device = hub.Device("Samsung Galaxy S23") |
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# Trace model |
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text_encoder_input_shape = text_encoder_model.get_input_spec() |
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text_encoder_sample_inputs = text_encoder_model.sample_inputs() |
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traced_text_encoder_model = torch.jit.trace(text_encoder_model, [torch.tensor(data[0]) for _, data in text_encoder_sample_inputs.items()]) |
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# Compile model on a specific device |
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text_encoder_compile_job = hub.submit_compile_job( |
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model=traced_text_encoder_model , |
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device=device, |
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input_specs=text_encoder_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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text_encoder_target_model = text_encoder_compile_job.get_target_model() |
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# Trace model |
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image_encoder_input_shape = image_encoder_model.get_input_spec() |
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image_encoder_sample_inputs = image_encoder_model.sample_inputs() |
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traced_image_encoder_model = torch.jit.trace(image_encoder_model, [torch.tensor(data[0]) for _, data in image_encoder_sample_inputs.items()]) |
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# Compile model on a specific device |
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image_encoder_compile_job = hub.submit_compile_job( |
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model=traced_image_encoder_model , |
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device=device, |
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input_specs=image_encoder_model.get_input_spec(), |
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) |
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# Get target model to run on-device |
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image_encoder_target_model = image_encoder_compile_job.get_target_model() |
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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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text_encoder_profile_job = hub.submit_profile_job( |
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model=text_encoder_target_model, |
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device=device, |
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) |
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image_encoder_profile_job = hub.submit_profile_job( |
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model=image_encoder_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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text_encoder_input_data = text_encoder_model.sample_inputs() |
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text_encoder_inference_job = hub.submit_inference_job( |
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model=text_encoder_target_model, |
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device=device, |
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inputs=text_encoder_input_data, |
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) |
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text_encoder_inference_job.download_output_data() |
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image_encoder_input_data = image_encoder_model.sample_inputs() |
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image_encoder_inference_job = hub.submit_inference_job( |
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model=image_encoder_target_model, |
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device=device, |
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inputs=image_encoder_input_data, |
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) |
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image_encoder_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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## 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 OpenAI-Clip's performance across various devices [here](https://aihub.qualcomm.com/models/openai_clip). |
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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 OpenAI-Clip can be found [here](https://github.com/openai/CLIP/blob/main/LICENSE). |
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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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* [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) |
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* [Source Model Implementation](https://github.com/openai/CLIP/) |
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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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