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.]]