def test_predict(wrapper, data_format, tmp_path):
image = create_image(grayscale=False, data_format=data_format, tmp_path=tmp_path)
result = wrapper.predict(image)
assert "predicted_class" in result
assert "probabilities" in result
print(result)
assert isinstance(result["predicted_class"], int)
assert isinstance(result["probabilities"], np.ndarray)
assert result["probabilities"].ndim == 1
def torch_image():
return create_image(data_format="torch", seed=0, grayscale=False)
def files_image(tmp_path):
return create_image(data_format="files", seed=0, tmp_path=tmp_path)
def numpy_image():
return create_image(data_format="numpy", seed=0, grayscale=False)