| pip install open_clip_torch | |
| import torch | |
| from PIL import Image | |
| import open_clip | |
| model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-32', pretrained='laion2b_s34b_b79k') | |
| model.eval() # model in train mode by default, impacts some models with BatchNorm or stochastic depth active | |
| tokenizer = open_clip.get_tokenizer('ViT-B-32') | |
| image = preprocess(Image.open("docs/CLIP.png")).unsqueeze(0) | |
| text = tokenizer(["a diagram", "a dog", "a cat"]) | |
| with torch.no_grad(), torch.cuda.amp.autocast(): | |
| image_features = model.encode_image(image) | |
| text_features = model.encode_text(text) | |
| image_features /= image_features.norm(dim=-1, keepdim=True) | |
| text_features /= text_features.norm(dim=-1, keepdim=True) | |
| text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1) | |
| print("Label probs:", text_probs) # prints: [[1., 0., 0.]] | |