def test_image_input_with_three_dim():
x = utils.generate_data(shape=(32, 32))
adapter = input_adapter.ImageInputAdapter()
x = adapter.transform(x)
assert isinstance(x, tf.data.Dataset)
for a in x:
assert a.shape[1:] == (32, 32, 1)
break
def test_image_input_numerical():
x = np.array([[["unknown"]]])
adapter = input_adapter.ImageInputAdapter()
with pytest.raises(TypeError) as info:
x = adapter.transform(x)
assert "Expect the data to ImageInput to be numerical" in str(info.value)
def test_image_input_unsupported_type():
x = "unknown"
adapter = input_adapter.ImageInputAdapter()
with pytest.raises(TypeError) as info:
x = adapter.transform(x)
assert "Expect the data to ImageInput to be numpy" in str(info.value)
def test_image_input_with_illegal_dim():
x = utils.generate_data(shape=(32, ))
adapter = input_adapter.ImageInputAdapter()
with pytest.raises(ValueError) as info:
x = adapter.transform(x)
assert "Expect the data to ImageInput to have 3" in str(info.value)
def test_image_input_adapter_shape_is_list():
x = utils.generate_data()
adapter = input_adapter.ImageInputAdapter()
adapter.fit_transform(x)
assert isinstance(adapter.shape, list)
assert all(map(lambda x: isinstance(x, int), adapter.shape))
def test_image_input_adapter_transform_to_dataset():
x = utils.generate_data()
adapter = input_adapter.ImageInputAdapter()
assert isinstance(adapter.transform(x), tf.data.Dataset)
def test_image_input():
x = utils.generate_data()
input_node = input_adapter.ImageInputAdapter()
assert isinstance(input_node.transform(x), tf.data.Dataset)
def test_image_input_numerical():
x = np.array([[['unknown']]])
input_node = input_adapter.ImageInputAdapter()
with pytest.raises(TypeError) as info:
x = input_node.transform(x)
assert 'Expect the data to ImageInput to be numerical' in str(info.value)
