test_models_keras.py 文件源码

def test_keras_backward(num_classes):

    bounds = (0, 255)
    channels = num_classes

    model = Sequential()
    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", category=DeprecationWarning)
        model.add(GlobalAveragePooling2D(
            data_format='channels_last', input_shape=(5, 5, channels)))

        model = KerasModel(
            model,
            bounds=bounds,
            predicts='logits')

    test_image = np.random.rand(5, 5, channels).astype(np.float32)
    test_grad_pre = np.random.rand(num_classes).astype(np.float32)

    test_grad = model.backward(test_grad_pre, test_image)
    assert test_grad.shape == test_image.shape

    manual_grad = np.repeat(np.repeat(
        (test_grad_pre / 25.).reshape((1, 1, -1)),
        5, axis=0), 5, axis=1)

    np.testing.assert_almost_equal(
        test_grad,
        manual_grad)