def test_inner_concatenate_data_should_merge_2d_with_3d():
# Given
data_inputs_3d, expected_outputs_3d = _create_data_source(SHAPE_3D)
data_inputs_2d, expected_outputs_2d = _create_data_source(SHAPE_2D)
data_container_2d = DataContainer(data_inputs=data_inputs_2d,
expected_outputs=expected_outputs_2d)
data_container_3d = DataContainer(data_inputs=data_inputs_3d, expected_outputs=expected_outputs_3d) \
.add_sub_data_container('2d', data_container_2d)
# When
p = Pipeline(
[InnerConcatenateDataContainer(sub_data_container_names=['2d'])])
data_container_3d = p.handle_transform(data_container_3d,
ExecutionContext())
# Then
assert data_container_3d.data_inputs.shape == (SHAPE_3D[0], SHAPE_3D[1],
SHAPE_3D[2] + 1)
assert data_container_3d.expected_outputs.shape == (SHAPE_3D[0],
SHAPE_3D[1],
SHAPE_3D[2] + 1)
assert np.array_equal(data_container_3d.data_inputs[..., -1],
data_container_2d.data_inputs)
assert np.array_equal(data_container_3d.expected_outputs[..., -1],
data_container_2d.expected_outputs)
def test_inner_concatenate_data_should_merge_1d_with_3d():
# Given
data_inputs_3d, expected_outputs_3d = _create_data_source(SHAPE_3D)
data_inputs_1d, expected_outputs_1d = _create_data_source(SHAPE_1D)
data_container_1d = DataContainer(data_inputs=data_inputs_1d,
expected_outputs=expected_outputs_1d)
data_container = DataContainer(data_inputs=data_inputs_3d, expected_outputs=expected_outputs_3d) \
.add_sub_data_container('1d', data_container_1d)
# When
p = Pipeline(
[InnerConcatenateDataContainer(sub_data_container_names=['1d'])])
data_container = p.handle_transform(data_container, ExecutionContext())
# Then
broadcasted_data_inputs_1d = np.broadcast_to(
np.expand_dims(data_container_1d.data_inputs, axis=-1),
shape=(SHAPE_3D[0], SHAPE_3D[1]))
broadcasted_expected_outputs_1d = np.broadcast_to(
np.expand_dims(data_container_1d.expected_outputs, axis=-1),
shape=(SHAPE_3D[0], SHAPE_3D[1]))
assert np.array_equal(data_container.data_inputs[..., -1],
broadcasted_data_inputs_1d)
assert np.array_equal(data_container.expected_outputs[..., -1],
broadcasted_expected_outputs_1d)
assert data_container.data_inputs.shape == (SHAPE_3D[0], SHAPE_3D[1],
SHAPE_3D[2] + 1)
assert data_container.expected_outputs.shape == (SHAPE_3D[0], SHAPE_3D[1],
SHAPE_3D[2] + 1)
def test_handle_predict_should_handle_transform_with_initial_is_train_mode_after_predict(
):
tape_fit = TapeCallbackFunction()
tape_transform = TapeCallbackFunction()
p = Pipeline([
TestOnlyWrapper(
CallbackWrapper(MultiplyByN(2), tape_transform, tape_fit)),
TrainOnlyWrapper(
CallbackWrapper(MultiplyByN(4), tape_transform, tape_fit))
])
data_container = DataContainer(data_inputs=np.array([1, 1]),
expected_outputs=np.array([1, 1]))
p.handle_predict(data_container=data_container.copy(),
context=ExecutionContext())
data_container = p.handle_transform(data_container, ExecutionContext())
assert np.array_equal(data_container.data_inputs, np.array([4, 4]))
def test_outer_concatenate_data_should_merge_2d_with_3d():
# Given
data_inputs_3d, expected_outputs_3d = _create_data_source(SHAPE_3D)
data_inputs_2d, expected_outputs_2d = _create_data_source(SHAPE_2D)
data_container_2d = DataContainer(data_inputs=data_inputs_2d,
expected_outputs=expected_outputs_2d)
data_container = DataContainer(data_inputs=data_inputs_3d, expected_outputs=expected_outputs_3d) \
.add_sub_data_container('2d', data_container_2d)
# When
p = Pipeline([ZipBatchDataContainer(sub_data_container_names=['2d'])])
data_container = p.handle_transform(data_container, ExecutionContext())
# Then
for i, (first_di,
second_di) in enumerate(zip(data_inputs_3d, data_inputs_2d)):
assert np.array_equal(data_container.data_inputs[i][0], first_di)
assert np.array_equal(data_container.data_inputs[i][1], second_di)
for i, (first_eo, second_eo) in enumerate(
zip(expected_outputs_3d, expected_outputs_2d)):
assert np.array_equal(data_container.expected_outputs[i][0], first_eo)
assert np.array_equal(data_container.expected_outputs[i][1], second_eo)
