| 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, jit=True) |
| 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) |
|
|
