import numpy as np import pytest import torch from PIL import Image import clip @pytest.mark.parametrize('model_name', clip.available_models()) def test_consistency(model_name): device = "cpu" jit_model, transform = clip.load(model_name, device=device) py_model, _ = clip.load(model_name, device=device, jit=False) image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device) text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device) with torch.no_grad(): logits_per_image, _ = jit_model(image, text) jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy() logits_per_image, _ = py_model(image, text) py_probs = logits_per_image.softmax(dim=-1).cpu().numpy() assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)