def test_pack_flat_sequence_to_spec_structure(self):
subset_placeholders = utils.make_placeholders(mock_nested_subset_spec)
flattened_subset_placeholders = utils.flatten_spec_structure(
subset_placeholders)
packed_subset_placeholders = utils.pack_flat_sequence_to_spec_structure(
mock_nested_subset_spec, flattened_subset_placeholders)
utils.assert_equal(subset_placeholders, packed_subset_placeholders)
utils.assert_equal(mock_nested_subset_spec,
packed_subset_placeholders,
ignore_batch=True)
placeholders = utils.make_placeholders(mock_nested_spec)
flattened_placeholders = utils.flatten_spec_structure(placeholders)
packed_placeholders = utils.pack_flat_sequence_to_spec_structure(
mock_nested_subset_spec, flattened_placeholders)
# We only subselect what we need in pack_flat_sequence_to_spec_structure,
# hence, we should recover what we wanted.
utils.assert_equal(mock_nested_subset_spec,
packed_placeholders,
ignore_batch=True)
utils.assert_equal(subset_placeholders, packed_placeholders)
packed_optional_placeholders = utils.pack_flat_sequence_to_spec_structure(
mock_nested_optional_spec, flattened_placeholders)
# Although mock_nested_optional_spec would like more tensors
# flattened_placeholders cannot provide them, fortunately they are optional.
utils.assert_required(packed_optional_placeholders, placeholders)
utils.assert_required(mock_nested_spec,
packed_optional_placeholders,
ignore_batch=True)
def test_validate_flatten_and_pack(self):
# An example data pipeline.
# Some input generator creates input features according to some spec.
input_features = utils.make_placeholders(mock_nested_spec)
# Assume a preprocessor has altered these input_features and we want
# to pass the data on to the next stage, then we simply assure that
# our output is according to our spec and flatten.
flat_input_features = utils.validate_and_flatten(
mock_nested_optional_spec, input_features, ignore_batch=True)
utils.assert_required(
mock_nested_optional_spec, input_features, ignore_batch=True)
# Then e.g. the model_fn receives the flat_input_spec and validates
# that it is according to it's requirements and packs it back into the
# spec structure.
output_features = utils.validate_and_pack(
mock_nested_subset_spec, flat_input_features, ignore_batch=True)
utils.assert_required(
mock_nested_subset_spec, output_features, ignore_batch=True)
def test_assert_required(self):
# A subset of the required spec is asked for, that should work.
utils.assert_required(mock_nested_subset_spec, mock_nested_spec)
# We would like to have more, but all additional specs are optional.
utils.assert_required(mock_nested_optional_spec, mock_nested_spec)
# We ask for more required tensor_specs than we actually have.
with self.assertRaises(ValueError):
utils.assert_required(mock_nested_spec, mock_nested_subset_spec)
