CLIP-tf2

OpenAI CLIP converted to Tensorflow 2/Keras

Official Repository: https://github.com/openai/CLIP

Model conversion

$ python convert_clip.py --help

       USAGE: convert_clip.py [flags]
flags:

convert_clip.py:
  --[no]all: Export all versions. (will use output location if image_output or
    text_output are not present)
    (default: 'false')
  --image_output: Image encoder Keras SavedModel output destination (optional)
  --model: <RN50|RN101|RN50x4|ViT-B/32>: CLIP model architecture to convert
    (default: 'RN50')
  --output: CLIP Keras SavedModel Output destination
    (default: 'models/CLIP_{model}')
  --text_output: Text encoder Keras SavedModel output destination (optional)

Example:

$ python convert_clip.py --model RN50 --output models/CLIP_{model}

Output:

Copying weights: 100%|██████████| 482/482 [00:00<00:00, 674.13it/s]
I0523 18:18:40.867926 4600192512 builder_impl.py:774] Assets written to: CLIP_RN50/assets

Model: "clip"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
visual (ModifiedResNet)      multiple                  38370144  
_________________________________________________________________
transformer (Transformer)    multiple                  37828608  
_________________________________________________________________
ln_final (LayerNorm)         multiple                  1024      
=================================================================
Total params: 102,060,385
Trainable params: 102,007,137
Non-trainable params: 53,248
_________________________________________________________________
Classify image: https://github.com/openai/CLIP/blob/main/CLIP.png?raw=true
Text options: ['a diagram', 'a dog', 'a cat', 'a neural network']
Pytorch: [[0.24351287 0.00320374 0.00082513 0.7524583 ]]
Tensorflow: [[0.24351244 0.00320391 0.0008252  0.7524584 ]]

Process finished with exit code 0

Exporting standalone encoders:

Image encoder:

$ python convert_clip.py --model RN50 --image_output models/CLIP_image_{model}

Text encoder:

$ python convert_clip.py --model RN50 --text_output models/CLIP_image_{model}

Currently supported models:

• RN50
• RN101
• RN50x4
• RN50x16
• RN50x64
• ViT-B/32
• ViT-B/16
• ViT-L/14
• ViT-L/14@336px

Tasks

• Convert PyTorch to Tensorflow model (RN)
• Export as Tensorflow SavedModel
• ViT conversion
• Export standalone image and text encoders
• Installable pip package
• Improve API: loading model, usage
• Float16 support
• Make PyTorch dependency optional (only for updating model from official weights)
• Implement training