amlnn-model-playground/examples/clip

CLIP

1. Overview

This demo demonstrates how to run CLIP (Contrastive Language-Image Pre-Training) image-text matching using AMLNNLite. The CLIP model consists of two parts: a vision encoder and a text encoder, which work together to compute similarity between images and text descriptions.

2. Model Download

TO DO

3. Model Conversion

TO DO

4. Demo Run

CPP

1. Compile

Prerequisites:

• Android NDK (r25e recommended)
• ANDROID_NDK_PATH environment variable set

Build:

# Build for arm64-v8a
cd examples/clip/cpp
AMLNN_HOME=/path/to/amlnn-toolkit ./build-android.sh -a arm64-v8a

The executable will be generated at build/android_arm64-v8a/clip_demo.

2. Run

# Push executable and resources to device
adb push build/android_arm64-v8a/clip_demo /data/local/tmp/
adb push model/vision_model_int8_S905X5.adla /data/local/tmp/
adb push model/text_model_int8_S905X5.adla /data/local/tmp/
adb push tokenizer_path/ /data/local/tmp/

# Run on device
adb shell
cd /data/local/tmp
chmod +x clip_demo
export LD_LIBRARY_PATH=/vendor/lib64 or (/vendor/lib)

# Usage: ./clip_demo <vision_model> <text_model> <tokenizer_path> [--profiling]
./clip_demo vision_model_int8_S905X5.adla text_model_int8_S905X5.adla ./tokenizer_path/

The program will prompt for image paths and text descriptions interactively. Enter the path to an image file, then enter comma-separated text descriptions (or skip to use defaults). Type exit to quit.

Argument Descriptions:

Argument	Description
vision_model	Path to vision encoder .adla model (required)
text_model	Path to text encoder .adla model (required)
tokenizer_path	Path to directory containing vocab.json and merges.txt (required)
--profiling	Enable performance profiling output (optional)

Note: The tokenizer_path should contain vocab.json and merges.txt files from the CLIP tokenizer (e.g., from openai/clip-vit-base-patch32).

Python

Prerequisites:

• Python 3.10
• Required packages: numpy, Pillow, transformers, amlnnlite

Install dependencies:

pip install numpy Pillow transformers amlnnlite-1.0.0-cp310-cp310-linux_aarch64.whl

Run on device:

python clip.py \
    --vision-model ./vision_model_int8_S905X5.adla \
    --text-model ./text_model_int8_S905X5.adla \
    --tokenizer-dir ./tokenizer_path \
    --image-path ./000000004505.jpg \
    --texts "a red handbag" "a blue jacket" "a red bus"

Interactive Mode (Recommended):

If you don't provide --image-path, the program will run in interactive mode:

python clip.py \
    --vision-model ./vision_model_int8_S905X5.adla \
    --text-model ./text_model_int8_S905X5.adla \
    --tokenizer-dir ./tokenizer_path

The program will prompt for image paths and text descriptions. Enter an image path to process, then enter comma-separated texts to compare. Type exit to quit.

Argument Descriptions:

Argument	Description
--vision-model	Path to vision encoder .adla model (required)
--text-model	Path to text encoder .adla model (required)
--tokenizer-dir	Path to CLIPTokenizer directory (required)
--image-path	Path to input image (.jpg, .png) - optional, will prompt if not provided
--texts	List of text descriptions to compare (space-separated)
--max-len	Maximum token sequence length, default is 64
--logit-scale	Logit scale factor, default is 100.0

Note: The --tokenizer-dir should point to the directory containing the CLIPTokenizer files. You can use a Hugging Face model ID (e.g., openai/clip-vit-base-patch32) or a local directory.

5. Results

Performance Feedback

By using the --profiling flag (C++) or setting the loglevel to INFO, the program provides real-time performance metrics upon completion. The console log will display essential hardware and execution details, including:

• Hardware Information: System and ADLA library versions.
• Model Overview: Basic input/output configurations.
• NPU Metrics: Total inference time (latency) and total DRAM bandwidth consumption.

Interactive Mode Example:

$ ./clip_demo vision_model_int8_S905X5.adla text_model_int8_S905X5.adla ./tokenizer_path

[Info] Models initialized successfully.

============================================================
[Info] Image Path (or 'exit' to quit):
000000004505.jpg
[Info] Enter text descriptions (comma-separated, or 'skip' for defaults):
a red handbag, a blue jacket, a red bus

[Info] Processing image: 000000004505.jpg
[Info] Image embedding size: 512
[Info] Processing 3 text(s)...
[Info] Text embeddings size: 3 x 512

============================================================
CLIP Image-Text Matching Results
============================================================
Image: 000000004505.jpg
logit_scale: 100.000000
------------------------------------------------------------
[1] prob=0.999975  sim=0.327895  text='a red bus'
[2] prob=0.000016  sim=0.217690  text='a red handbag'
[3] prob=0.000008  sim=0.211029  text='a blue jacket'
============================================================

============================================================
[Info] Image Path (or 'exit' to quit):
exit
[Info] Exiting...
Free vision model memory.
Free text model memory.
[Info] Done.