Fix dimensionality handling issues in TextVectorization.
PiperOrigin-RevId: 313206360 Change-Id: I5929e83b26011c975561e525b90aef6949a185b2
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A. Unique TensorFlower
TensorFlower Gardener
parent
00664cef68
commit
a92ff929b8
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@ -87,6 +87,8 @@ class TableHandler(object):
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self.table.lookup, inputs)
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indexed_data = ragged_functional_ops.map_flat_values(
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self._replace_oov_buckets, inputs, indexed_data)
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# table.lookup is not shape-preserving, so we need to set the shape here.
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indexed_data._set_shape(inputs.shape) # pylint: disable=protected-access
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# Composite tensors can pass tensor values through, which will cause
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# errors if all operations in the TF graph do so. We can break this chain
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# with an identity here.
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@ -490,11 +490,12 @@ class TextVectorization(CombinerPreprocessingLayer):
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# in None for undefined shape axes. If using 'and !=', this causes the
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# expression to evaluate to False instead of True if the shape is undefined;
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# the expression needs to evaluate to True in that case.
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if self._split is not None and not input_shape[1] == 1: # pylint: disable=g-comparison-negation
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raise RuntimeError(
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"When using TextVectorization to tokenize strings, the first "
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"dimension of the input array must be 1, got shape "
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"{}".format(input_shape))
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if self._split is not None:
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if input_shape.ndims > 1 and not input_shape[-1] == 1: # pylint: disable=g-comparison-negation
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raise RuntimeError(
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"When using TextVectorization to tokenize strings, the innermost "
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"dimension of the input array must be 1, got shape "
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"{}".format(input_shape))
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super(TextVectorization, self).build(input_shape)
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@ -536,7 +537,8 @@ class TextVectorization(CombinerPreprocessingLayer):
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# If we are splitting, we validate that the 1st axis is of dimension 1 and
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# so can be squeezed out. We do this here instead of after splitting for
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# performance reasons - it's more expensive to squeeze a ragged tensor.
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inputs = array_ops.squeeze(inputs, axis=1)
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if inputs.shape.ndims > 1:
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inputs = array_ops.squeeze(inputs, axis=-1)
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if self._split == SPLIT_ON_WHITESPACE:
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# This treats multiple whitespaces as one whitespace, and strips leading
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# and trailing whitespace.
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@ -561,8 +563,6 @@ class TextVectorization(CombinerPreprocessingLayer):
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def call(self, inputs):
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if isinstance(inputs, (list, tuple, np.ndarray)):
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inputs = ops.convert_to_tensor(inputs)
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if inputs.shape.rank == 1:
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inputs = array_ops.expand_dims(inputs, axis=-1)
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self._called = True
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inputs = self._preprocess(inputs)
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@ -570,9 +570,7 @@ class TextVectorization(CombinerPreprocessingLayer):
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# If we're not doing any output processing, return right away.
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if self._output_mode is None:
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return inputs
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indexed_data = self._index_lookup_layer(inputs)
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if self._output_mode == INT:
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# Once we have the dense tensor, we can return it if we weren't given a
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# fixed output sequence length. If we were, though, we have to dynamically
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@ -585,7 +583,6 @@ class TextVectorization(CombinerPreprocessingLayer):
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dense_data = indexed_data
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if self._output_sequence_length is None:
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dense_data.set_shape(tensor_shape.TensorShape((None, None)))
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return dense_data
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else:
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sequence_len = K.shape(dense_data)[1]
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@ -596,8 +593,9 @@ class TextVectorization(CombinerPreprocessingLayer):
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sequence_len < self._output_sequence_length,
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true_fn=pad_fn,
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false_fn=slice_fn)
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output_tensor.set_shape(
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tensor_shape.TensorShape((None, self._output_sequence_length)))
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output_shape = output_tensor.shape.as_list()
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output_shape[-1] = self._output_sequence_length
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output_tensor.set_shape(tensor_shape.TensorShape(output_shape))
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return output_tensor
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# If we're not returning integers here, we rely on the vectorization layer
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@ -355,6 +355,59 @@ class TextVectorizationLayerTest(keras_parameterized.TestCase,
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if context.executing_eagerly():
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self.assertAllClose(out.numpy(), [[2, 3], [4, 5]])
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@parameterized.named_parameters(
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{
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"testcase_name": "1d",
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"data": ["0", "a", "b", "c", "d", "e", "a", "b", "c", "d", "f"],
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"expected": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1]
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},
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{
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"testcase_name": "2d",
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"data": [["0", "a", "b", "c", "d"], ["e", "a", "b", "c", "d"], ["f"]],
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"expected": [[1, 2, 3, 4, 5], [1, 2, 3, 4, 5], [1, 0, 0, 0, 0]]
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},
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{
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"testcase_name":
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"3d",
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"data": [[["0", "a", "b"], ["c", "d"]], [["e", "a"], ["b", "c", "d"]],
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[["f"]]],
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"expected": [[[1, 2, 3], [4, 5, 0]], [[1, 2, 0], [3, 4, 5]],
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[[1, 0, 0], [0, 0, 0]]]
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},
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)
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def test_layer_dimensionality_handling(self, data, expected):
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vocab = ["a", "b", "c", "d"]
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vectorization = get_layer_class()(
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max_tokens=None, standardize=None, split=None, pad_to_max_tokens=False)
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vectorization.set_vocabulary(vocab)
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output = vectorization(ragged_factory_ops.constant(data))
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self.assertAllEqual(expected, output)
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@parameterized.named_parameters(
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{
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"testcase_name": "1d",
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"data": ["0 a b c d e a b c d f"],
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"expected": [[1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1]]
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},
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{
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"testcase_name":
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"3d",
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"data": [[["0 a b"], ["c d"]], [["e a"], ["b c d"]], [["f"]]],
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"expected": [[[1, 2, 3], [4, 5, 0]], [[1, 2, 0], [3, 4, 5]],
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[[1, 0, 0], [0, 0, 0]]]
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},
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)
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def test_layer_dimensionality_handling_with_split(self, data, expected):
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vocab = ["a", "b", "c", "d"]
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vectorization = get_layer_class()(
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max_tokens=None,
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standardize=None,
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split=text_vectorization.SPLIT_ON_WHITESPACE,
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pad_to_max_tokens=False)
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vectorization.set_vocabulary(vocab)
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output = vectorization(ragged_factory_ops.constant(data, inner_shape=(1,)))
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self.assertAllEqual(expected, output)
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@keras_parameterized.run_all_keras_modes
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class TextVectorizationPreprocessingTest(
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@ -580,7 +633,7 @@ class TextVectorizationPreprocessingTest(
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split=text_vectorization.SPLIT_ON_WHITESPACE,
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output_mode=None)
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with self.assertRaisesRegex(RuntimeError,
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".*tokenize strings, the first dimension.*"):
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".*tokenize strings, the innermost dime.*"):
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_ = layer(input_data)
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def test_string_splitting_with_non_1d_raggedarray_fails(self):
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@ -591,7 +644,7 @@ class TextVectorizationPreprocessingTest(
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split=text_vectorization.SPLIT_ON_WHITESPACE,
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output_mode=None)
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with self.assertRaisesRegex(RuntimeError,
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".*tokenize strings, the first dimension.*"):
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".*tokenize strings, the innermost dime.*"):
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_ = layer(input_data)
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def test_standardization_with_invalid_standardize_arg(self):
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