example.py

from all_clip import load_clip
import torch
from PIL import Image
import pathlib


model, preprocess, tokenizer = load_clip("open_clip:ViT-B-32/laion2b_s34b_b79k", device="cpu", use_jit=False)


image = preprocess(Image.open(str(pathlib.Path(__file__).parent.resolve()) + "/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.]]