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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, jit=True) |
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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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