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