def _ParseSingleSequenceExampleShape(op): # pylint: disable=invalid-name
"""Shape function for the ParseExample op."""
op.inputs[0].get_shape().with_rank(0) # input
# feature_list_dense_missing_assumed_empty
op.inputs[1].get_shape().with_rank(1)
num_context_sparse = op.get_attr("Ncontext_sparse")
num_context_dense = op.get_attr("Ncontext_dense")
num_feature_list_dense = op.get_attr("Nfeature_list_dense")
context_dense_shapes = op.get_attr("context_dense_shapes")
num_feature_list_sparse = op.get_attr("Nfeature_list_sparse")
feature_list_dense_shapes = op.get_attr("feature_list_dense_shapes")
context_sparse_index_shapes = [
tensor_shape.matrix(None, 1) for _ in range(num_context_sparse)]
context_sparse_value_shapes = [
tensor_shape.vector(None) for _ in range(num_context_sparse)]
context_sparse_shape_shapes = [
tensor_shape.vector(1) for _ in range(num_context_sparse)]
context_dense_shapes = [
tensor_shape.TensorShape(dense_shape)
for dense_shape in context_dense_shapes]
feature_list_sparse_index_shapes = [
tensor_shape.matrix(None, 2) for _ in range(num_feature_list_sparse)]
feature_list_sparse_value_shapes = [
tensor_shape.vector(None) for _ in range(num_feature_list_sparse)]
feature_list_sparse_shape_shapes = [
tensor_shape.vector(2) for _ in range(num_feature_list_sparse)]
feature_list_dense_shapes = [
tensor_shape.vector(None).concatenate(dense_shape)
for dense_shape in feature_list_dense_shapes]
assert num_context_dense == len(context_dense_shapes)
assert num_feature_list_dense == len(feature_list_dense_shapes)
return (context_sparse_index_shapes + context_sparse_value_shapes +
context_sparse_shape_shapes + context_dense_shapes +
feature_list_sparse_index_shapes + feature_list_sparse_value_shapes +
feature_list_sparse_shape_shapes + feature_list_dense_shapes)
def testShapes(self):
fdef = self._build_function_def()
g = function_def_to_graph.function_def_to_graph(fdef)
self.assertIsNone(g.inputs[0].shape.dims) # Unknown dims.
self.assertIsNone(g.inputs[1].shape.dims) # Unknown dims.
self.assertIsNone(g.outputs[0].shape.dims) # Unknown dims.
self.assertIsNone(g.outputs[1].shape.dims) # Unknown dims.
g = function_def_to_graph.function_def_to_graph(
fdef, input_shapes=[tensor_shape.vector(5),
tensor_shape.vector(5)])
self.assertSequenceEqual(g.inputs[0].shape.dims, [5])
self.assertSequenceEqual(g.inputs[1].shape.dims, [5])
self.assertSequenceEqual(g.outputs[0].shape.dims, [5])
self.assertSequenceEqual(g.outputs[1].shape.dims, [5])
g = function_def_to_graph.function_def_to_graph(
fdef, input_shapes=[None, tensor_shape.matrix(5, 7)])
self.assertIsNone(g.inputs[0].shape.dims)
self.assertSequenceEqual(g.inputs[1].shape.dims, [5, 7])
self.assertSequenceEqual(g.outputs[0].shape.dims, [5, 7])
self.assertSequenceEqual(g.outputs[1].shape.dims, [5, 7])
# Should raise a ValueError if the length of input_shapes does not match
# the number of input args in FunctionDef.signature.input_arg.
with self.assertRaises(ValueError):
g = function_def_to_graph.function_def_to_graph(
fdef, input_shapes=[tensor_shape.matrix(5, 7)])
def _ParseSingleSequenceExampleShape(op):
"""Shape function for the ParseExample op."""
op.inputs[0].get_shape().with_rank(0) # input
# feature_list_dense_missing_assumed_empty
op.inputs[1].get_shape().with_rank(1)
num_context_sparse = op.get_attr("Ncontext_sparse")
num_context_dense = op.get_attr("Ncontext_dense")
num_feature_list_dense = op.get_attr("Nfeature_list_dense")
context_dense_shapes = op.get_attr("context_dense_shapes")
num_feature_list_sparse = op.get_attr("Nfeature_list_sparse")
feature_list_dense_shapes = op.get_attr("feature_list_dense_shapes")
context_sparse_index_shapes = [tensor_shape.matrix(None, 1) for _ in range(num_context_sparse)]
context_sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_context_sparse)]
context_sparse_shape_shapes = [tensor_shape.vector(1) for _ in range(num_context_sparse)]
context_dense_shapes = [tensor_shape.TensorShape(dense_shape) for dense_shape in context_dense_shapes]
feature_list_sparse_index_shapes = [tensor_shape.matrix(None, 2) for _ in range(num_feature_list_sparse)]
feature_list_sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_feature_list_sparse)]
feature_list_sparse_shape_shapes = [tensor_shape.vector(2) for _ in range(num_feature_list_sparse)]
feature_list_dense_shapes = [
tensor_shape.vector(None).concatenate(dense_shape) for dense_shape in feature_list_dense_shapes
]
assert num_context_dense == len(context_dense_shapes)
assert num_feature_list_dense == len(feature_list_dense_shapes)
return (
context_sparse_index_shapes
+ context_sparse_value_shapes
+ context_sparse_shape_shapes
+ context_dense_shapes
+ feature_list_sparse_index_shapes
+ feature_list_sparse_value_shapes
+ feature_list_sparse_shape_shapes
+ feature_list_dense_shapes
)
def testShapes(self):
fdef = self._build_function_def()
g = function_def_to_graph.function_def_to_graph(fdef)
self.assertIsNone(g.inputs[0].shape.dims) # Unknown dims.
self.assertIsNone(g.inputs[1].shape.dims) # Unknown dims.
self.assertIsNone(g.outputs[0].shape.dims) # Unknown dims.
self.assertIsNone(g.outputs[1].shape.dims) # Unknown dims.
g = function_def_to_graph.function_def_to_graph(
fdef,
input_shapes=[tensor_shape.vector(5),
tensor_shape.vector(5)])
self.assertSequenceEqual(g.inputs[0].shape.dims, [5])
self.assertSequenceEqual(g.inputs[1].shape.dims, [5])
self.assertSequenceEqual(g.outputs[0].shape.dims, [5])
self.assertSequenceEqual(g.outputs[1].shape.dims, [5])
g = function_def_to_graph.function_def_to_graph(
fdef, input_shapes=[None, tensor_shape.matrix(5, 7)])
self.assertIsNone(g.inputs[0].shape.dims)
self.assertSequenceEqual(g.inputs[1].shape.dims, [5, 7])
self.assertSequenceEqual(g.outputs[0].shape.dims, [5, 7])
self.assertSequenceEqual(g.outputs[1].shape.dims, [5, 7])
# Should raise a ValueError if the length of input_shapes does not match
# the number of input args in FunctionDef.signature.input_arg.
with self.assertRaises(ValueError):
g = function_def_to_graph.function_def_to_graph(
fdef, input_shapes=[tensor_shape.matrix(5, 7)])
def _CTCGreedyDecoderShape(op):
"""Shape function for the CTCGreedyDecoder op."""
inputs_shape = op.inputs[0].get_shape().with_rank(3)
sequence_length_shape = op.inputs[1].get_shape().with_rank(1)
# merge batch_size
sequence_length_shape[0].merge_with(inputs_shape[1])
inputs_shape[1].merge_with(sequence_length_shape[0])
batch_size = inputs_shape[1]
# decoded_indices, decoded_values, decoded_shape, log_probability
return [tensor_shape.matrix(None, 2),
tensor_shape.vector(None),
tensor_shape.vector(2),
tensor_shape.matrix(batch_size, 1)]
def _CTCGreedyDecoderShape(op):
"""Shape function for the CTCGreedyDecoder op."""
inputs_shape = op.inputs[0].get_shape().with_rank(3)
sequence_length_shape = op.inputs[1].get_shape().with_rank(1)
# merge batch_size
sequence_length_shape[0].merge_with(inputs_shape[1])
inputs_shape[1].merge_with(sequence_length_shape[0])
batch_size = inputs_shape[1]
# decoded_indices, decoded_values, decoded_shape, log_probability
return [tensor_shape.matrix(None, 2),
tensor_shape.vector(None),
tensor_shape.vector(2),
tensor_shape.matrix(batch_size, 1)]
def testBroadcast_many_dimensions(self):
unknown = tensor_shape.unknown_shape()
shape_0 = tensor_shape.scalar()
shape_1 = tensor_shape.vector(1)
shape_4 = tensor_shape.vector(4)
shape_1x4 = tensor_shape.matrix(1, 4)
shape_4x1 = tensor_shape.matrix(4, 1)
shape_3x4 = tensor_shape.matrix(3, 4)
shape_4x3 = tensor_shape.matrix(4, 3)
# Tensors with same shape should have the same broadcast result.
for shape in (shape_0, shape_1, shape_4, shape_1x4, shape_4x1,
shape_3x4, shape_4x3):
self._assert_broadcast(expected=shape, shape1=shape, shape2=shape)
# [] and [1] act like identity.
for identity in (shape_0, shape_1):
for shape in (shape_4, shape_1x4, shape_4x1, shape_3x4, shape_4x3):
self._assert_broadcast(expected=shape,
shape1=identity,
shape2=shape)
# Unknown in, unknown out.
for shape in (shape_4, shape_1x4, shape_4x1, shape_3x4, shape_4x3):
self._assert_broadcast(expected=unknown,
shape1=shape,
shape2=unknown)
self._assert_broadcast(expected=shape_1x4,
shape1=shape_4,
shape2=shape_1x4)
shape_4x4 = tensor_shape.matrix(4, 4)
self._assert_broadcast(expected=shape_4x4,
shape1=shape_4,
shape2=shape_4x1)
self._assert_broadcast(expected=shape_3x4,
shape1=shape_4,
shape2=shape_3x4)
self._assert_incompatible_broadcast(shape1=shape_4, shape2=shape_4x3)
self._assert_broadcast(expected=shape_4x4,
shape1=shape_1x4,
shape2=shape_4x1)
self._assert_broadcast(expected=shape_3x4,
shape1=shape_1x4,
shape2=shape_3x4)
self._assert_incompatible_broadcast(shape1=shape_1x4, shape2=shape_4x3)
self._assert_incompatible_broadcast(shape1=shape_4x1, shape2=shape_3x4)
self._assert_broadcast(expected=shape_4x3,
shape1=shape_4x1,
shape2=shape_4x3)
self._assert_incompatible_broadcast(shape1=shape_3x4, shape2=shape_4x3)
def _SparseConcatShape(op):
"""Shape function for SparseConcat op."""
num_inputs = int(op.get_attr("N"))
# TF flattens and concatenates all list inputs, so reconstruct the lists here.
ind_shapes = [
ind.get_shape().with_rank(2) for ind in op.inputs[0:num_inputs]
]
val_shapes = [
val.get_shape().with_rank(1)
for val in op.inputs[num_inputs:2 * num_inputs]
]
shape_shapes = [
shape.get_shape().with_rank(1) for shape in op.inputs[2 * num_inputs:]
]
output_ind_rows = tensor_shape.Dimension(0)
output_ind_cols = tensor_shape.Dimension(None)
output_val_elems = tensor_shape.Dimension(0)
output_shape_shape = tensor_shape.TensorShape(None)
for i in xrange(num_inputs):
num_elems_i = ind_shapes[i][0].merge_with(val_shapes[i][0])
output_ind_rows += num_elems_i
output_ind_cols = output_ind_cols.merge_with(ind_shapes[i][1])
output_val_elems += num_elems_i
output_shape_shape = output_shape_shape.merge_with(shape_shapes[i])
output_ind_shape = tensor_shape.matrix(output_ind_rows, output_ind_cols)
output_val_shape = tensor_shape.vector(output_val_elems)
return [output_ind_shape, output_val_shape, output_shape_shape]
def _SerializeManySparseShape(op): # pylint: disable=invalid-name
"""Shape function for SerializeSparse op."""
op.inputs[0].get_shape().with_rank(2)
op.inputs[1].get_shape().with_rank(1)
op.inputs[2].get_shape().with_rank(1)
return [tensor_shape.matrix(None, 3)]
def _reverse_seq(input_seq, lengths):
"""Reverse a list of Tensors up to specified lengths.
Args:
input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
lengths: A tensor of dimension batch_size, containing lengths for each
sequence in the batch. If "None" is specified, simply reverses
the list.
Returns:
time-reversed sequence
"""
if lengths is None:
return list(reversed(input_seq))
input_shape = tensor_shape.matrix(None, None)
for input_ in input_seq:
input_shape.merge_with(input_.get_shape())
input_.set_shape(input_shape)
# Join into (time, batch_size, depth)
s_joined = array_ops.pack(input_seq)
# TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
if lengths is not None:
lengths = math_ops.to_int64(lengths)
# Reverse along dimension 0
s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
# Split again into list
result = array_ops.unpack(s_reversed)
for r in result:
r.set_shape(input_shape)
return result
def _reverse_seq(input_seq, lengths):
"""Reverse a list of Tensors up to specified lengths.
Args:
input_seq: Sequence of seq_len tensors of dimension (batch_size, depth)
lengths: A tensor of dimension batch_size, containing lengths for each
sequence in the batch. If "None" is specified, simply reverses
the list.
Returns:
time-reversed sequence
"""
if lengths is None:
return list(reversed(input_seq))
input_shape = tensor_shape.matrix(None, None)
for input_ in input_seq:
input_shape.merge_with(input_.get_shape())
input_.set_shape(input_shape)
# Join into (time, batch_size, depth)
s_joined = array_ops.pack(input_seq)
# TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
if lengths is not None:
lengths = math_ops.to_int64(lengths)
# Reverse along dimension 0
s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
# Split again into list
result = array_ops.unpack(s_reversed)
for r in result:
r.set_shape(input_shape)
return result
def _ComputeAccidentalHitsShape(op):
num_true = op.get_attr("num_true")
# Validate that the input shape matches the attrs, even though it
# does not influence the shape of the output.
true_candidates_shape = op.inputs[0].get_shape().merge_with(tensor_shape.matrix(None, num_true))
output_shape = tensor_shape.vector(None)
return [output_shape] * 3
def _SparseConcatShape(op):
"""Shape function for SparseConcat op."""
num_inputs = int(op.get_attr("N"))
# TF flattens and concatenates all list inputs, so reconstruct the lists here.
ind_shapes = [ind.get_shape().with_rank(2) for ind in op.inputs[0:num_inputs]]
val_shapes = [val.get_shape().with_rank(1)
for val in op.inputs[num_inputs:2 * num_inputs]]
shape_shapes = [shape.get_shape().with_rank(1)
for shape in op.inputs[2 * num_inputs:]]
output_ind_rows = tensor_shape.Dimension(0)
output_ind_cols = tensor_shape.Dimension(None)
output_val_elems = tensor_shape.Dimension(0)
output_shape_shape = tensor_shape.TensorShape(None)
for i in xrange(num_inputs):
num_elems_i = ind_shapes[i][0].merge_with(val_shapes[i][0])
output_ind_rows += num_elems_i
output_ind_cols = output_ind_cols.merge_with(ind_shapes[i][1])
output_val_elems += num_elems_i
output_shape_shape = output_shape_shape.merge_with(shape_shapes[i])
output_ind_shape = tensor_shape.matrix(output_ind_rows, output_ind_cols)
output_val_shape = tensor_shape.vector(output_val_elems)
return [output_ind_shape, output_val_shape, output_shape_shape]
def _SerializeManySparseShape(op): # pylint: disable=invalid-name """Shape function for SerializeSparse op.""" op.inputs[0].get_shape().with_rank(2) op.inputs[1].get_shape().with_rank(1) op.inputs[2].get_shape().with_rank(1) return [tensor_shape.matrix(None, 3)]
def testHelpers(self):
tensor_shape.TensorShape([]).assert_is_compatible_with(
tensor_shape.scalar())
tensor_shape.TensorShape([37]).assert_is_compatible_with(
tensor_shape.vector(37))
tensor_shape.TensorShape(
[94, 43]).assert_is_compatible_with(tensor_shape.matrix(94, 43))
def testHelpers(self):
tensor_shape.TensorShape([]).assert_is_compatible_with(
tensor_shape.scalar())
tensor_shape.TensorShape([37]).assert_is_compatible_with(
tensor_shape.vector(37))
tensor_shape.TensorShape([94, 43]).assert_is_compatible_with(
tensor_shape.matrix(94, 43))
def _CandidateSamplerShape(op):
true_classes_shape = op.inputs[0].get_shape().with_rank(2)
batch_size = true_classes_shape[0]
num_sampled = op.get_attr("num_sampled")
num_true = op.get_attr("num_true")
return [tensor_shape.vector(num_sampled),
tensor_shape.matrix(batch_size, num_true),
tensor_shape.vector(num_sampled)]
def _ComputeAccidentalHitsShape(op):
num_true = op.get_attr("num_true")
# Validate that the input shape matches the attrs, even though it
# does not influence the shape of the output.
true_candidates_shape = op.inputs[0].get_shape().merge_with(
tensor_shape.matrix(None, num_true))
output_shape = tensor_shape.vector(None)
return [output_shape] * 3
def _DeserializeSparseShape(op): # pylint: disable=invalid-name
"""Shape function for DeserializeManySparse op."""
serialized_sparse_shape = op.inputs[0].get_shape().with_rank(2)
serialized_sparse_shape.merge_with(
tensor_shape.TensorShape([None, 3]))
return [tensor_shape.matrix(None, None),
tensor_shape.vector(None),
tensor_shape.vector(None)]
def _SparseTensorDenseMatMulShape(op): # pylint: disable=invalid-name
"""Shape function for SparseTensorDenseMatMul op."""
adjoint_b = op.get_attr("adjoint_b")
op.inputs[0].get_shape().assert_has_rank(2) # a_indices
op.inputs[1].get_shape().assert_has_rank(1) # a_values
op.inputs[2].get_shape().merge_with(tensor_shape.vector(2)) # a_shape
b_shape = op.inputs[3].get_shape().with_rank(2)
output_shape_right = b_shape[0] if adjoint_b else b_shape[1]
return [tensor_shape.matrix(None, output_shape_right)]
def _DeserializeSparseShape(op): # pylint: disable=invalid-name
"""Shape function for DeserializeManySparse op."""
serialized_sparse_shape = op.inputs[0].get_shape().with_rank(2)
serialized_sparse_shape.merge_with(
tensor_shape.TensorShape([None, 3]))
return [tensor_shape.matrix(None, None),
tensor_shape.vector(None),
tensor_shape.vector(None)]
def _SparseTensorDenseMatMulShape(op): # pylint: disable=invalid-name
"""Shape function for SparseTensorDenseMatMul op."""
adjoint_b = op.get_attr("adjoint_b")
op.inputs[0].get_shape().assert_has_rank(2) # a_indices
op.inputs[1].get_shape().assert_has_rank(1) # a_values
op.inputs[2].get_shape().merge_with(tensor_shape.vector(2)) # a_shape
b_shape = op.inputs[3].get_shape().with_rank(2)
output_shape_right = b_shape[0] if adjoint_b else b_shape[1]
return [tensor_shape.matrix(None, output_shape_right)]
def _CTCBeamSearchDecoderShape(op):
"""Shape function for the CTCBeamSearchDecoder op."""
inputs_shape = op.inputs[0].get_shape().with_rank(3)
sequence_length_shape = op.inputs[1].get_shape().with_rank(1)
# merge batch size
sequence_length_shape[0].merge_with(inputs_shape[1])
inputs_shape[1].merge_with(sequence_length_shape[0])
batch_size = inputs_shape[1]
top_paths = op.get_attr("top_paths")
# first the decoded indices
output_shapes = [tensor_shape.matrix(None, 2) for _ in range(top_paths)]
# next the decoded values
output_shapes.extend([tensor_shape.vector(None) for _ in range(top_paths)])
# the shapes of the decoded values
output_shapes.extend([tensor_shape.vector(2)] * top_paths)
# the log_probability matrix
output_shapes.append(tensor_shape.matrix(batch_size, top_paths))
return output_shapes
def _CTCBeamSearchDecoderShape(op):
"""Shape function for the CTCBeamSearchDecoder op."""
inputs_shape = op.inputs[0].get_shape().with_rank(3)
sequence_length_shape = op.inputs[1].get_shape().with_rank(1)
# merge batch size
sequence_length_shape[0].merge_with(inputs_shape[1])
inputs_shape[1].merge_with(sequence_length_shape[0])
batch_size = inputs_shape[1]
top_paths = op.get_attr("top_paths")
# first the decoded indices
output_shapes = [tensor_shape.matrix(None, 2) for _ in range(top_paths)]
# next the decoded values
output_shapes.extend([tensor_shape.vector(None) for _ in range(top_paths)])
# the shapes of the decoded values
output_shapes.extend([tensor_shape.vector(2)] * top_paths)
# the log_probability matrix
output_shapes.append(tensor_shape.matrix(batch_size, top_paths))
return output_shapes
def testStr(self):
self.assertEqual("<unknown>", str(tensor_shape.unknown_shape()))
self.assertEqual("(?,)", str(tensor_shape.unknown_shape(ndims=1)))
self.assertEqual("(?, ?)", str(tensor_shape.unknown_shape(ndims=2)))
self.assertEqual("(?, ?, ?)", str(tensor_shape.unknown_shape(ndims=3)))
self.assertEqual("()", str(tensor_shape.scalar()))
self.assertEqual("(7,)", str(tensor_shape.vector(7)))
self.assertEqual("(3, 8)", str(tensor_shape.matrix(3, 8)))
self.assertEqual("(4, 5, 2)", str(tensor_shape.TensorShape([4, 5, 2])))
self.assertEqual("(32, ?, 1, 9)",
str(tensor_shape.TensorShape([32, None, 1, 9])))
def testStr(self):
self.assertEqual("<unknown>", str(tensor_shape.unknown_shape()))
self.assertEqual("(?,)", str(tensor_shape.unknown_shape(ndims=1)))
self.assertEqual("(?, ?)", str(tensor_shape.unknown_shape(ndims=2)))
self.assertEqual("(?, ?, ?)", str(tensor_shape.unknown_shape(ndims=3)))
self.assertEqual("()", str(tensor_shape.scalar()))
self.assertEqual("(7,)", str(tensor_shape.vector(7)))
self.assertEqual("(3, 8)", str(tensor_shape.matrix(3, 8)))
self.assertEqual("(4, 5, 2)", str(tensor_shape.TensorShape([4, 5, 2])))
self.assertEqual("(32, ?, 1, 9)",
str(tensor_shape.TensorShape([32, None, 1, 9])))
def _ParseExampleShape(op):
"""Shape function for the ParseExample op."""
input_shape = op.inputs[0].get_shape().with_rank(1)
op.inputs[1].get_shape().with_rank(1) # names
num_sparse = op.get_attr("Nsparse")
num_dense = op.get_attr("Ndense")
dense_shapes = op.get_attr("dense_shapes")
sparse_index_shapes = [tensor_shape.matrix(None, 2) for _ in range(num_sparse)]
sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_sparse)]
sparse_shape_shapes = [tensor_shape.vector(2) for _ in range(num_sparse)]
assert num_dense == len(dense_shapes)
dense_shapes = [input_shape.concatenate(dense_shape) for dense_shape in dense_shapes]
return sparse_index_shapes + sparse_value_shapes + sparse_shape_shapes + dense_shapes
def testBroadcast_many_dimensions(self):
unknown = tensor_shape.unknown_shape()
shape_0 = tensor_shape.scalar()
shape_1 = tensor_shape.vector(1)
shape_4 = tensor_shape.vector(4)
shape_1x4 = tensor_shape.matrix(1, 4)
shape_4x1 = tensor_shape.matrix(4, 1)
shape_3x4 = tensor_shape.matrix(3, 4)
shape_4x3 = tensor_shape.matrix(4, 3)
# Tensors with same shape should have the same broadcast result.
for shape in (
shape_0, shape_1, shape_4, shape_1x4, shape_4x1, shape_3x4, shape_4x3):
self._assert_broadcast(expected=shape, shape1=shape, shape2=shape)
# [] and [1] act like identity.
for identity in (shape_0, shape_1):
for shape in (shape_4, shape_1x4, shape_4x1, shape_3x4, shape_4x3):
self._assert_broadcast(expected=shape, shape1=identity, shape2=shape)
# Unknown in, unknown out.
for shape in (shape_4, shape_1x4, shape_4x1, shape_3x4, shape_4x3):
self._assert_broadcast(expected=unknown, shape1=shape, shape2=unknown)
self._assert_broadcast(expected=shape_1x4, shape1=shape_4, shape2=shape_1x4)
shape_4x4 = tensor_shape.matrix(4, 4)
self._assert_broadcast(expected=shape_4x4, shape1=shape_4, shape2=shape_4x1)
self._assert_broadcast(expected=shape_3x4, shape1=shape_4, shape2=shape_3x4)
self._assert_incompatible_broadcast(shape1=shape_4, shape2=shape_4x3)
self._assert_broadcast(
expected=shape_4x4, shape1=shape_1x4, shape2=shape_4x1)
self._assert_broadcast(
expected=shape_3x4, shape1=shape_1x4, shape2=shape_3x4)
self._assert_incompatible_broadcast(shape1=shape_1x4, shape2=shape_4x3)
self._assert_incompatible_broadcast(shape1=shape_4x1, shape2=shape_3x4)
self._assert_broadcast(
expected=shape_4x3, shape1=shape_4x1, shape2=shape_4x3)
self._assert_incompatible_broadcast(shape1=shape_3x4, shape2=shape_4x3)
def _ParseExampleShape(op):
"""Shape function for the ParseExample op."""
input_shape = op.inputs[0].get_shape().with_rank(1)
num_sparse = op.get_attr("Nsparse")
num_dense = op.get_attr("Ndense")
dense_shapes = op.get_attr("dense_shapes")
sparse_index_shapes = [
tensor_shape.matrix(None, 2) for _ in range(num_sparse)]
sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_sparse)]
sparse_shape_shapes = [tensor_shape.vector(2) for _ in range(num_sparse)]
assert num_dense == len(dense_shapes)
dense_shapes = [
input_shape.concatenate((d.size for d in dense_shape.dim))
for dense_shape in dense_shapes]
return (sparse_index_shapes + sparse_value_shapes + sparse_shape_shapes +
dense_shapes)
def _ParseExampleShape(op): # pylint: disable=invalid-name
"""Shape function for the ParseExample op."""
input_shape = op.inputs[0].get_shape().with_rank(1)
op.inputs[1].get_shape().with_rank(1) # names
num_sparse = op.get_attr("Nsparse")
num_dense = op.get_attr("Ndense")
dense_shapes = op.get_attr("dense_shapes")
sparse_index_shapes = [
tensor_shape.matrix(None, 2) for _ in range(num_sparse)]
sparse_value_shapes = [tensor_shape.vector(None) for _ in range(num_sparse)]
sparse_shape_shapes = [tensor_shape.vector(2) for _ in range(num_sparse)]
assert num_dense == len(dense_shapes)
dense_shapes = [
input_shape.concatenate(dense_shape)
for dense_shape in dense_shapes]
return (sparse_index_shapes + sparse_value_shapes + sparse_shape_shapes +
dense_shapes)
def testStr(self):
self.assertEqual("<unknown>", str(tensor_shape.unknown_shape()))
self.assertEqual(
"(None,)",
str(tensor_shape.unknown_shape(rank=1)).replace("?", "None"))
self.assertEqual(
"(None, None)",
str(tensor_shape.unknown_shape(rank=2)).replace("?", "None"))
self.assertEqual(
"(None, None, None)",
str(tensor_shape.unknown_shape(rank=3)).replace("?", "None"))
self.assertEqual(
"(32, None, 1, 9)",
str(tensor_shape.TensorShape([32, None, 1, 9])).replace("?", "None"))
self.assertEqual("()", str(tensor_shape.scalar()))
self.assertEqual("(7,)", str(tensor_shape.vector(7)))
self.assertEqual("(3, 8)", str(tensor_shape.matrix(3, 8)))
self.assertEqual("(4, 5, 2)", str(tensor_shape.TensorShape([4, 5, 2])))
def testStr(self):
self.assertEqual("<unknown>", str(tensor_shape.unknown_shape()))
self.assertEqual(
"(None,)",
str(tensor_shape.unknown_shape(rank=1)).replace("?", "None"))
self.assertEqual(
"(None, None)",
str(tensor_shape.unknown_shape(rank=2)).replace("?", "None"))
self.assertEqual(
"(None, None, None)",
str(tensor_shape.unknown_shape(rank=3)).replace("?", "None"))
self.assertEqual(
"(32, None, 1, 9)",
str(tensor_shape.TensorShape([32, None, 1,
9])).replace("?", "None"))
self.assertEqual("()", str(tensor_shape.scalar()))
self.assertEqual("(7,)", str(tensor_shape.vector(7)))
self.assertEqual("(3, 8)", str(tensor_shape.matrix(3, 8)))
self.assertEqual("(4, 5, 2)", str(tensor_shape.TensorShape([4, 5, 2])))
def testBroadcast_unknown_dims(self):
unknown = tensor_shape.unknown_shape()
shape_0 = tensor_shape.scalar()
shape_1 = tensor_shape.vector(1)
# pylint: disable=invalid-name
shape_U = tensor_shape.vector(None)
shape_1xU = tensor_shape.matrix(1, None)
shape_Ux1 = tensor_shape.matrix(None, 1)
shape_4xU = tensor_shape.matrix(4, None)
shape_Ux4 = tensor_shape.matrix(None, 4)
# pylint: enable=invalid-name
# Tensors with same shape should have the same broadcast result.
for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
self._assert_broadcast_with_unknown_dims(
expected=shape, shape1=shape, shape2=shape)
# [] and [1] act like identity.
for identity in (shape_0, shape_1):
for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
self._assert_broadcast_with_unknown_dims(
expected=shape, shape1=identity, shape2=shape)
# Unknown in, unknown out.
for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
self._assert_broadcast_with_unknown_dims(
expected=unknown, shape1=shape, shape2=unknown)
self._assert_broadcast_with_unknown_dims(
expected=shape_1xU, shape1=shape_U, shape2=shape_1xU)
shape_UxU = tensor_shape.matrix(None, None) # pylint: disable=invalid-name
self._assert_broadcast_with_unknown_dims(
expected=shape_UxU, shape1=shape_U, shape2=shape_Ux1)
self._assert_broadcast_with_unknown_dims(
expected=shape_4xU, shape1=shape_U, shape2=shape_4xU)
self._assert_broadcast_with_unknown_dims(
expected=shape_Ux4, shape1=shape_U, shape2=shape_Ux4)
self._assert_broadcast_with_unknown_dims(
expected=shape_UxU, shape1=shape_1xU, shape2=shape_Ux1)
self._assert_broadcast_with_unknown_dims(
expected=shape_4xU, shape1=shape_1xU, shape2=shape_4xU)
self._assert_broadcast_with_unknown_dims(
expected=shape_Ux4, shape1=shape_1xU, shape2=shape_Ux4)
self._assert_broadcast_with_unknown_dims(
expected=shape_4xU, shape1=shape_Ux1, shape2=shape_4xU)
self._assert_broadcast_with_unknown_dims(
expected=shape_Ux4, shape1=shape_Ux1, shape2=shape_Ux4)
shape_4x4 = tensor_shape.matrix(4, 4)
self._assert_broadcast_with_unknown_dims(
expected=shape_4x4, shape1=shape_4xU, shape2=shape_Ux4)
def testBroadcast_unknown_dims(self):
unknown = tensor_shape.unknown_shape()
shape_0 = tensor_shape.scalar()
shape_1 = tensor_shape.vector(1)
# pylint: disable=invalid-name
shape_U = tensor_shape.vector(None)
shape_1xU = tensor_shape.matrix(1, None)
shape_Ux1 = tensor_shape.matrix(None, 1)
shape_4xU = tensor_shape.matrix(4, None)
shape_Ux4 = tensor_shape.matrix(None, 4)
# pylint: enable=invalid-name
# Tensors with same shape should have the same broadcast result.
for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
self._assert_broadcast_with_unknown_dims(
expected=shape, shape1=shape, shape2=shape)
# [] and [1] act like identity.
for identity in (shape_0, shape_1):
for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
self._assert_broadcast_with_unknown_dims(
expected=shape, shape1=identity, shape2=shape)
# Unknown in, unknown out.
for shape in (shape_U, shape_1xU, shape_Ux1, shape_4xU, shape_Ux4):
self._assert_broadcast_with_unknown_dims(
expected=unknown, shape1=shape, shape2=unknown)
self._assert_broadcast_with_unknown_dims(
expected=shape_1xU, shape1=shape_U, shape2=shape_1xU)
shape_UxU = tensor_shape.matrix(None, None) # pylint: disable=invalid-name
self._assert_broadcast_with_unknown_dims(
expected=shape_UxU, shape1=shape_U, shape2=shape_Ux1)
self._assert_broadcast_with_unknown_dims(
expected=shape_4xU, shape1=shape_U, shape2=shape_4xU)
self._assert_broadcast_with_unknown_dims(
expected=shape_Ux4, shape1=shape_U, shape2=shape_Ux4)
self._assert_broadcast_with_unknown_dims(
expected=shape_UxU, shape1=shape_1xU, shape2=shape_Ux1)
self._assert_broadcast_with_unknown_dims(
expected=shape_4xU, shape1=shape_1xU, shape2=shape_4xU)
self._assert_broadcast_with_unknown_dims(
expected=shape_Ux4, shape1=shape_1xU, shape2=shape_Ux4)
self._assert_broadcast_with_unknown_dims(
expected=shape_4xU, shape1=shape_Ux1, shape2=shape_4xU)
self._assert_broadcast_with_unknown_dims(
expected=shape_Ux4, shape1=shape_Ux1, shape2=shape_Ux4)
shape_4x4 = tensor_shape.matrix(4, 4)
self._assert_broadcast_with_unknown_dims(
expected=shape_4x4, shape1=shape_4xU, shape2=shape_Ux4)
def _PadShape(op):
"""Shape function for the Pad op.
This op has two inputs:
* input: A rank-N tensor.
* paddings: An N-by-2 matrix, in which the i^th row contains the
number of padding elements to add before and after `input` in the
i^th dimension.
It has one output, which has the same rank as input, and additional
elements according to the values in paddings.
Args:
op: A Pad Operation.
Returns:
A single-element list containing the shape of the output.
Raises:
ValueError: If the input shapes are incompatible.
"""
paddings_shape = op.inputs[1].get_shape().with_rank(2)
input_shape = op.inputs[0].get_shape()
if input_shape.ndims == 0 and paddings_shape[0].value == 1:
# TODO(irving): Remove once !kAllowLegacyScalars.
input_shape = tensor_shape.TensorShape([1])
else:
input_shape = input_shape.with_rank(paddings_shape[0].value)
paddings_shape = paddings_shape.merge_with(
tensor_shape.matrix(input_shape.ndims, 2))
paddings = tensor_util.ConstantValue(op.inputs[1])
if paddings is None:
return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
else:
output_dims = []
for i, dim in enumerate(input_shape.dims):
if paddings[i, 0] < 0 or paddings[i, 1] < 0:
raise ValueError("paddings must be non-negative")
output_dims.append(dim + paddings[i, 0] + paddings[i, 1])
return [tensor_shape.TensorShape(output_dims)]
def _PadShape(op):
"""Shape function for the Pad op.
This op has two inputs:
* input: A rank-N tensor.
* paddings: An N-by-2 matrix, in which the i^th row contains the
number of padding elements to add before and after `input` in the
i^th dimension.
It has one output, which has the same rank as input, and additional
elements according to the values in paddings.
Args:
op: A Pad Operation.
Returns:
A single-element list containing the shape of the output.
Raises:
ValueError: If the input shapes are incompatible.
"""
paddings_shape = op.inputs[1].get_shape().with_rank(2)
input_shape = op.inputs[0].get_shape()
if input_shape.ndims == 0 and paddings_shape[0].value == 1:
# TODO(irving): Remove once !kAllowLegacyScalars.
input_shape = tensor_shape.TensorShape([1])
else:
input_shape = input_shape.with_rank(paddings_shape[0].value)
paddings_shape = paddings_shape.merge_with(
tensor_shape.matrix(input_shape.ndims, 2))
paddings = tensor_util.ConstantValue(op.inputs[1])
if paddings is None:
return [tensor_shape.unknown_shape(ndims=input_shape.ndims)]
else:
output_dims = []
for i, dim in enumerate(input_shape.dims):
if paddings[i, 0] < 0 or paddings[i, 1] < 0:
raise ValueError("paddings must be non-negative")
output_dims.append(dim + paddings[i, 0] + paddings[i, 1])
return [tensor_shape.TensorShape(output_dims)]
def reverse_seq(input_seq, lengths):
if lengths is None:
return list(reversed(input_seq))
input_shape = tensor_shape.matrix(None, None)
for input_ in input_seq:
input_shape.merge_with(input_.get_shape())
input_.set_shape(input_shape)
# Join into (time, batch_size, depth)
s_joined = array_ops.pack(input_seq)
# TODO(schuster, ebrevdo): Remove cast when reverse_sequence takes int32
if lengths is not None:
lengths = math_ops.to_int64(lengths)
# Reverse along dimension 0
s_reversed = array_ops.reverse_sequence(s_joined, lengths, 0, 1)
# Split again into list
result = array_ops.unpack(s_reversed)
for r in result:
r.set_shape(input_shape)
return result
def _MultinomialShape(op): # pylint: disable=invalid-name
logits_shape = op.inputs[0].get_shape().with_rank(2)
batch_size = logits_shape[0]
num_samples_or_none = tensor_util.constant_value(op.inputs[1])
return [tensor_shape.matrix(batch_size, num_samples_or_none)]
def _SparseFeatureCrossShape(unused_op): # pylint: disable=invalid-name
return [
tensor_shape.matrix(None, 2),
tensor_shape.vector(None),
tensor_shape.vector(2)
]
class StructureTest(test_base.DatasetTestBase, parameterized.TestCase,
test_util.TensorFlowTestCase):
# pylint: disable=g-long-lambda,protected-access
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0), tensor_spec.TensorSpec,
[dtypes.float32], [[]]),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArraySpec, [dtypes.variant], [[]]),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
sparse_tensor.SparseTensorSpec, [dtypes.variant], [None]),
("RaggedTensor",
lambda: ragged_factory_ops.constant([[1, 2], [], [4]]),
ragged_tensor.RaggedTensorSpec, [dtypes.variant], [None]),
("Nested_0", lambda:
(constant_op.constant(37.0), constant_op.constant([1, 2, 3])), tuple,
[dtypes.float32, dtypes.int32], [[], [3]]),
("Nested_1", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, dict, [dtypes.float32, dtypes.int32], [[], [3]]),
("Nested_2", lambda: {
"a":
constant_op.constant(37.0),
"b":
(sparse_tensor.
SparseTensor(indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
}, dict, [dtypes.float32, dtypes.variant, dtypes.variant], [[], None,
None]),
)
def testFlatStructure(self, value_fn, expected_structure, expected_types,
expected_shapes):
value = value_fn()
s = structure.type_spec_from_value(value)
self.assertIsInstance(s, expected_structure)
flat_types = structure.get_flat_tensor_types(s)
self.assertEqual(expected_types, flat_types)
flat_shapes = structure.get_flat_tensor_shapes(s)
self.assertLen(flat_shapes, len(expected_shapes))
for expected, actual in zip(expected_shapes, flat_shapes):
if expected is None:
self.assertEqual(actual.ndims, None)
else:
self.assertEqual(actual.as_list(), expected)
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0), lambda: [
constant_op.constant(38.0),
array_ops.placeholder(dtypes.float32),
variables.Variable(100.0), 42.0,
np.array(42.0, dtype=np.float32)
],
lambda: [constant_op.constant([1.0, 2.0]),
constant_op.constant(37)]),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0), lambda: [
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=10)
], lambda: [
tensor_array_ops.TensorArray(
dtype=dtypes.int32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(), size=0)
]),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]), lambda: [
sparse_tensor.SparseTensor(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
sparse_tensor.SparseTensorValue(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
array_ops.sparse_placeholder(dtype=dtypes.int32),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None])
], lambda: [
constant_op.constant(37, shape=[4, 5]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[5, 6]),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None, None]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1.0], dense_shape=[4, 5])
]),
("RaggedTensor",
lambda: ragged_factory_ops.constant([[1, 2], [], [3]]), lambda: [
ragged_factory_ops.constant([[1, 2], [3, 4], []]),
ragged_factory_ops.constant([[1], [2, 3, 4], [5]]),
], lambda: [
ragged_factory_ops.constant(1),
ragged_factory_ops.constant([1, 2]),
ragged_factory_ops.constant([[1], [2]]),
ragged_factory_ops.constant([["a", "b"]]),
]),
("Nested", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6])
}], lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6, 7])
}, {
"a": constant_op.constant(15),
"b": constant_op.constant([4, 5, 6])
}, {
"a":
constant_op.constant(15),
"b":
sparse_tensor.SparseTensor(
indices=[[0], [1], [2]], values=[4, 5, 6], dense_shape=[3])
}, (constant_op.constant(15.0), constant_op.constant([4, 5, 6]))]),
)
@test_util.run_deprecated_v1
def testIsCompatibleWithStructure(self, original_value_fn,
compatible_values_fn,
incompatible_values_fn):
original_value = original_value_fn()
compatible_values = compatible_values_fn()
incompatible_values = incompatible_values_fn()
s = structure.type_spec_from_value(original_value)
for compatible_value in compatible_values:
self.assertTrue(
structure.are_compatible(
s, structure.type_spec_from_value(compatible_value)))
for incompatible_value in incompatible_values:
self.assertFalse(
structure.are_compatible(
s, structure.type_spec_from_value(incompatible_value)))
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0),
lambda: constant_op.constant(42.0),
lambda: constant_op.constant([5])),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.int32, element_shape=(), size=0)),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[1, 2]], values=[42], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[3]], values=[-1], dense_shape=[5]),
lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[1.0], dense_shape=[4, 5])),
("RaggedTensor",
lambda: ragged_factory_ops.constant([[[1, 2]], [[3]]]),
lambda: ragged_factory_ops.constant([[[5]], [[8], [3, 2]]]), lambda:
ragged_factory_ops.constant([[[1]], [[2], [3]]], ragged_rank=1),
lambda: ragged_factory_ops.constant([[[1.0, 2.0]], [[3.0]]]),
lambda: ragged_factory_ops.constant([[[1]], [[2]], [[3]]])),
("Nested", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: {
"a": constant_op.constant(42.0),
"b": constant_op.constant([4, 5, 6])
}, lambda: {
"a": constant_op.constant([1, 2, 3]),
"b": constant_op.constant(37.0)
}),
) # pyformat: disable
def testStructureFromValueEquality(self, value1_fn, value2_fn,
*not_equal_value_fns):
# pylint: disable=g-generic-assert
s1 = structure.type_spec_from_value(value1_fn())
s2 = structure.type_spec_from_value(value2_fn())
self.assertEqual(s1, s1) # check __eq__ operator.
self.assertEqual(s1, s2) # check __eq__ operator.
self.assertFalse(s1 != s1) # check __ne__ operator.
self.assertFalse(s1 != s2) # check __ne__ operator.
for c1, c2 in zip(nest.flatten(s1), nest.flatten(s2)):
self.assertEqual(hash(c1), hash(c1))
self.assertEqual(hash(c1), hash(c2))
for value_fn in not_equal_value_fns:
s3 = structure.type_spec_from_value(value_fn())
self.assertNotEqual(s1, s3) # check __ne__ operator.
self.assertNotEqual(s2, s3) # check __ne__ operator.
self.assertFalse(s1 == s3) # check __eq_ operator.
self.assertFalse(s2 == s3) # check __eq_ operator.
@parameterized.named_parameters(
("RaggedTensor_RaggedRank",
structure.RaggedTensorStructure(dtypes.int32, None, 1),
structure.RaggedTensorStructure(dtypes.int32, None, 2)),
("RaggedTensor_Shape",
structure.RaggedTensorStructure(dtypes.int32, [3, None], 1),
structure.RaggedTensorStructure(dtypes.int32, [5, None], 1)),
("RaggedTensor_DType",
structure.RaggedTensorStructure(dtypes.int32, None, 1),
structure.RaggedTensorStructure(dtypes.float32, None, 1)),
)
def testRaggedStructureInequality(self, s1, s2):
# pylint: disable=g-generic-assert
self.assertNotEqual(s1, s2) # check __ne__ operator.
self.assertFalse(s1 == s2) # check __eq__ operator.
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant(37.0),
lambda: constant_op.constant(42.0),
lambda: constant_op.constant([5])),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
lambda: tensor_array_ops.TensorArray(
dtype=dtypes.int32, element_shape=(), size=0)),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[1, 2]], values=[42], dense_shape=[4, 5]),
lambda: sparse_tensor.SparseTensor(
indices=[[3]], values=[-1], dense_shape=[5])),
("Nested", lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: {
"a": constant_op.constant(42.0),
"b": constant_op.constant([4, 5, 6])
}, lambda: {
"a": constant_op.constant([1, 2, 3]),
"b": constant_op.constant(37.0)
}),
)
def testHash(self, value1_fn, value2_fn, value3_fn):
s1 = structure.type_spec_from_value(value1_fn())
s2 = structure.type_spec_from_value(value2_fn())
s3 = structure.type_spec_from_value(value3_fn())
for c1, c2, c3 in zip(nest.flatten(s1), nest.flatten(s2),
nest.flatten(s3)):
self.assertEqual(hash(c1), hash(c1))
self.assertEqual(hash(c1), hash(c2))
self.assertNotEqual(hash(c1), hash(c3))
self.assertNotEqual(hash(c2), hash(c3))
@parameterized.named_parameters(
(
"Tensor",
lambda: constant_op.constant(37.0),
),
(
"SparseTensor",
lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
),
("TensorArray", lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(), size=1).write(0, 7)),
(
"RaggedTensor",
lambda: ragged_factory_ops.constant([[1, 2], [], [3]]),
),
(
"Nested_0",
lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
},
),
(
"Nested_1",
lambda: {
"a":
constant_op.constant(37.0),
"b": (sparse_tensor.SparseTensor(
indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
},
),
)
def testRoundTripConversion(self, value_fn):
value = value_fn()
s = structure.type_spec_from_value(value)
def maybe_stack_ta(v):
if isinstance(v, tensor_array_ops.TensorArray):
return v.stack()
else:
return v
before = self.evaluate(maybe_stack_ta(value))
after = self.evaluate(
maybe_stack_ta(
structure.from_tensor_list(s,
structure.to_tensor_list(s,
value))))
flat_before = nest.flatten(before)
flat_after = nest.flatten(after)
for b, a in zip(flat_before, flat_after):
if isinstance(b, sparse_tensor.SparseTensorValue):
self.assertAllEqual(b.indices, a.indices)
self.assertAllEqual(b.values, a.values)
self.assertAllEqual(b.dense_shape, a.dense_shape)
elif isinstance(b, (ragged_tensor.RaggedTensor,
ragged_tensor_value.RaggedTensorValue)):
self.assertAllEqual(b, a)
else:
self.assertAllEqual(b, a)
# pylint: enable=g-long-lambda
def preserveStaticShape(self):
rt = ragged_factory_ops.constant([[1, 2], [], [3]])
rt_s = structure.type_spec_from_value(rt)
rt_after = structure.from_tensor_list(
rt_s, structure.to_tensor_list(rt_s, rt))
self.assertEqual(rt_after.row_splits.shape.as_list(),
rt.row_splits.shape.as_list())
self.assertEqual(rt_after.values.shape.as_list(), [None])
st = sparse_tensor.SparseTensor(indices=[[3, 4]],
values=[-1],
dense_shape=[4, 5])
st_s = structure.type_spec_from_value(st)
st_after = structure.from_tensor_list(
st_s, structure.to_tensor_list(st_s, st))
self.assertEqual(st_after.indices.shape.as_list(), [None, 2])
self.assertEqual(st_after.values.shape.as_list(), [None])
self.assertEqual(st_after.dense_shape.shape.as_list(),
st.dense_shape.shape.as_list())
def testIncompatibleStructure(self):
# Define three mutually incompatible values/structures, and assert that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructre a flattened value with an
# incompatible structure fails.
value_tensor = constant_op.constant(42.0)
s_tensor = structure.type_spec_from_value(value_tensor)
flat_tensor = structure.to_tensor_list(s_tensor, value_tensor)
value_sparse_tensor = sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
s_sparse_tensor = structure.type_spec_from_value(value_sparse_tensor)
flat_sparse_tensor = structure.to_tensor_list(s_sparse_tensor,
value_sparse_tensor)
value_nest = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_nest = structure.type_spec_from_value(value_nest)
flat_nest = structure.to_tensor_list(s_nest, value_nest)
with self.assertRaisesRegexp(
ValueError,
r"SparseTensor.* is not convertible to a tensor with "
r"dtype.*float32.* and shape \(\)"):
structure.to_tensor_list(s_tensor, value_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_tensor, value_nest)
with self.assertRaisesRegexp(
TypeError, "Neither a SparseTensor nor SparseTensorValue"):
structure.to_tensor_list(s_sparse_tensor, value_tensor)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_sparse_tensor, value_nest)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_nest, value_tensor)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_nest, value_sparse_tensor)
with self.assertRaisesRegexp(ValueError, r"Incompatible input:"):
structure.from_tensor_list(s_tensor, flat_sparse_tensor)
with self.assertRaisesRegexp(ValueError,
"Expected 1 tensors but got 2."):
structure.from_tensor_list(s_tensor, flat_nest)
with self.assertRaisesRegexp(ValueError, "Incompatible input: "):
structure.from_tensor_list(s_sparse_tensor, flat_tensor)
with self.assertRaisesRegexp(ValueError,
"Expected 1 tensors but got 2."):
structure.from_tensor_list(s_sparse_tensor, flat_nest)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 1."):
structure.from_tensor_list(s_nest, flat_tensor)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 1."):
structure.from_tensor_list(s_nest, flat_sparse_tensor)
def testIncompatibleNestedStructure(self):
# Define three mutually incompatible nested values/structures, and assert
# that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructure a flattened value with an
# incompatible structure fails.
value_0 = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_0 = structure.type_spec_from_value(value_0)
flat_s_0 = structure.to_tensor_list(s_0, value_0)
# `value_1` has compatible nested structure with `value_0`, but different
# classes.
value_1 = {
"a":
constant_op.constant(37.0),
"b":
sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
}
s_1 = structure.type_spec_from_value(value_1)
flat_s_1 = structure.to_tensor_list(s_1, value_1)
# `value_2` has incompatible nested structure with `value_0` and `value_1`.
value_2 = {
"a":
constant_op.constant(37.0),
"b": (sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1]),
sparse_tensor.SparseTensor(indices=[[3, 4]],
values=[-1],
dense_shape=[4, 5]))
}
s_2 = structure.type_spec_from_value(value_2)
flat_s_2 = structure.to_tensor_list(s_2, value_2)
with self.assertRaisesRegexp(
ValueError,
r"SparseTensor.* is not convertible to a tensor with "
r"dtype.*int32.* and shape \(3,\)"):
structure.to_tensor_list(s_0, value_1)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_0, value_2)
with self.assertRaisesRegexp(
TypeError, "Neither a SparseTensor nor SparseTensorValue"):
structure.to_tensor_list(s_1, value_0)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_1, value_2)
# NOTE(mrry): The repr of the dictionaries is not sorted, so the regexp
# needs to account for "a" coming before or after "b". It might be worth
# adding a deterministic repr for these error messages (among other
# improvements).
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_2, value_0)
with self.assertRaisesRegexp(
ValueError,
"The two structures don't have the same nested structure."):
structure.to_tensor_list(s_2, value_1)
with self.assertRaisesRegexp(ValueError, r"Incompatible input:"):
structure.from_tensor_list(s_0, flat_s_1)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 3."):
structure.from_tensor_list(s_0, flat_s_2)
with self.assertRaisesRegexp(ValueError, "Incompatible input: "):
structure.from_tensor_list(s_1, flat_s_0)
with self.assertRaisesRegexp(ValueError,
"Expected 2 tensors but got 3."):
structure.from_tensor_list(s_1, flat_s_2)
with self.assertRaisesRegexp(ValueError,
"Expected 3 tensors but got 2."):
structure.from_tensor_list(s_2, flat_s_0)
with self.assertRaisesRegexp(ValueError,
"Expected 3 tensors but got 2."):
structure.from_tensor_list(s_2, flat_s_1)
@parameterized.named_parameters(
("Tensor", dtypes.float32, tensor_shape.scalar(), ops.Tensor,
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor", dtypes.int32, tensor_shape.matrix(
2, 2), sparse_tensor.SparseTensor,
structure.SparseTensorStructure(dtypes.int32, [2, 2])),
("TensorArray_0", dtypes.int32,
tensor_shape.as_shape([None, True, 2, 2
]), tensor_array_ops.TensorArray,
structure.TensorArrayStructure(
dtypes.int32, [2, 2], dynamic_size=None, infer_shape=True)),
("TensorArray_1", dtypes.int32,
tensor_shape.as_shape([True, None, 2, 2
]), tensor_array_ops.TensorArray,
structure.TensorArrayStructure(
dtypes.int32, [2, 2], dynamic_size=True, infer_shape=None)),
("TensorArray_2", dtypes.int32,
tensor_shape.as_shape([True, False, 2, 2
]), tensor_array_ops.TensorArray,
structure.TensorArrayStructure(
dtypes.int32, [2, 2], dynamic_size=True, infer_shape=False)),
("RaggedTensor", dtypes.int32, tensor_shape.matrix(2, None),
structure.RaggedTensorStructure(dtypes.int32, [2, None], 1),
structure.RaggedTensorStructure(dtypes.int32, [2, None], 1)),
("Nested", {
"a": dtypes.float32,
"b": (dtypes.int32, dtypes.string)
}, {
"a": tensor_shape.scalar(),
"b": (tensor_shape.matrix(2, 2), tensor_shape.scalar())
}, {
"a": ops.Tensor,
"b": (sparse_tensor.SparseTensor, ops.Tensor)
}, {
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]),
structure.TensorStructure(dtypes.string, []))
}),
)
def testConvertLegacyStructure(self, output_types, output_shapes,
output_classes, expected_structure):
actual_structure = structure.convert_legacy_structure(
output_types, output_shapes, output_classes)
self.assertEqual(actual_structure, expected_structure)
def testNestedNestedStructure(self):
s = (structure.TensorStructure(dtypes.int64, []),
(structure.TensorStructure(dtypes.float32, []),
structure.TensorStructure(dtypes.string, [])))
int64_t = constant_op.constant(37, dtype=dtypes.int64)
float32_t = constant_op.constant(42.0)
string_t = constant_op.constant("Foo")
nested_tensors = (int64_t, (float32_t, string_t))
tensor_list = structure.to_tensor_list(s, nested_tensors)
for expected, actual in zip([int64_t, float32_t, string_t],
tensor_list):
self.assertIs(expected, actual)
(actual_int64_t,
(actual_float32_t,
actual_string_t)) = structure.from_tensor_list(s, tensor_list)
self.assertIs(int64_t, actual_int64_t)
self.assertIs(float32_t, actual_float32_t)
self.assertIs(string_t, actual_string_t)
(actual_int64_t,
(actual_float32_t,
actual_string_t)) = (structure.from_compatible_tensor_list(
s, tensor_list))
self.assertIs(int64_t, actual_int64_t)
self.assertIs(float32_t, actual_float32_t)
self.assertIs(string_t, actual_string_t)
@parameterized.named_parameters(
("Tensor", structure.TensorStructure(dtypes.float32, []), 32,
structure.TensorStructure(dtypes.float32, [32])),
("TensorUnknown", structure.TensorStructure(dtypes.float32, []), None,
structure.TensorStructure(dtypes.float32, [None])),
("SparseTensor", structure.SparseTensorStructure(
dtypes.float32, [None]), 32,
structure.SparseTensorStructure(dtypes.float32, [32, None])),
("SparseTensorUnknown",
structure.SparseTensorStructure(dtypes.float32, [4]), None,
structure.SparseTensorStructure(dtypes.float32, [None, 4])),
("RaggedTensor",
structure.RaggedTensorStructure(dtypes.float32, [2, None], 1), 32,
structure.RaggedTensorStructure(dtypes.float32, [32, 2, None], 2)),
("RaggedTensorUnknown",
structure.RaggedTensorStructure(dtypes.float32, [4, None], 1), None,
structure.RaggedTensorStructure(dtypes.float32, [None, 4, None], 2)),
("Nested", {
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]),
structure.TensorStructure(dtypes.string, []))
}, 128, {
"a":
structure.TensorStructure(dtypes.float32, [128]),
"b": (structure.SparseTensorStructure(dtypes.int32, [128, 2, 2]),
structure.TensorStructure(dtypes.string, [128]))
}),
)
def testBatch(self, element_structure, batch_size,
expected_batched_structure):
batched_structure = nest.map_structure(
lambda component_spec: component_spec._batch(batch_size),
element_structure)
self.assertEqual(batched_structure, expected_batched_structure)
@parameterized.named_parameters(
("Tensor", structure.TensorStructure(dtypes.float32, [32]),
structure.TensorStructure(dtypes.float32, [])),
("TensorUnknown", structure.TensorStructure(dtypes.float32, [None]),
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor",
structure.SparseTensorStructure(dtypes.float32, [32, None]),
structure.SparseTensorStructure(dtypes.float32, [None])),
("SparseTensorUnknown",
structure.SparseTensorStructure(dtypes.float32, [None, 4]),
structure.SparseTensorStructure(dtypes.float32, [4])),
("RaggedTensor",
structure.RaggedTensorStructure(dtypes.float32, [32, None, None], 2),
structure.RaggedTensorStructure(dtypes.float32, [None, None], 1)),
("RaggedTensorUnknown",
structure.RaggedTensorStructure(dtypes.float32, [None, None, None],
2),
structure.RaggedTensorStructure(dtypes.float32, [None, None], 1)),
("Nested", {
"a":
structure.TensorStructure(dtypes.float32, [128]),
"b": (structure.SparseTensorStructure(dtypes.int32, [128, 2, 2]),
structure.TensorStructure(dtypes.string, [None]))
}, {
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]),
structure.TensorStructure(dtypes.string, []))
}),
)
def testUnbatch(self, element_structure, expected_unbatched_structure):
unbatched_structure = nest.map_structure(
lambda component_spec: component_spec._unbatch(),
element_structure)
self.assertEqual(unbatched_structure, expected_unbatched_structure)
# pylint: disable=g-long-lambda
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant([[1.0, 2.0], [3.0, 4.0]]),
lambda: constant_op.constant([1.0, 2.0])),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2]),
lambda: sparse_tensor.SparseTensor(
indices=[[0]], values=[13], dense_shape=[2])),
("RaggedTensor", lambda: ragged_factory_ops.constant([[[1]], [[2]]]),
lambda: ragged_factory_ops.constant([[1]])),
("Nest", lambda:
(constant_op.constant([[1.0, 2.0], [3.0, 4.0]]),
sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2])),
lambda: (constant_op.constant([1.0, 2.0]),
sparse_tensor.SparseTensor(
indices=[[0]], values=[13], dense_shape=[2]))),
)
def testToBatchedTensorList(self, value_fn, element_0_fn):
batched_value = value_fn()
s = structure.type_spec_from_value(batched_value)
batched_tensor_list = structure.to_batched_tensor_list(
s, batched_value)
# The batch dimension is 2 for all of the test cases.
# NOTE(mrry): `tf.shape()` does not currently work for the DT_VARIANT
# tensors in which we store sparse tensors.
for t in batched_tensor_list:
if t.dtype != dtypes.variant:
self.assertEqual(2, self.evaluate(array_ops.shape(t)[0]))
# Test that the 0th element from the unbatched tensor is equal to the
# expected value.
expected_element_0 = self.evaluate(element_0_fn())
unbatched_s = nest.map_structure(
lambda component_spec: component_spec._unbatch(), s)
actual_element_0 = structure.from_tensor_list(
unbatched_s, [t[0] for t in batched_tensor_list])
for expected, actual in zip(nest.flatten(expected_element_0),
nest.flatten(actual_element_0)):
self.assertValuesEqual(expected, actual)
def output_shapes(self):
num_elements = tensor_shape.Dimension(None)
return (tensor_shape.matrix(num_elements, self._row_shape.shape[0] + 1),
tensor_shape.vector(num_elements),
tensor_shape.vector(self._row_shape.shape[0] + 1))
def _to_legacy_output_shapes(self):
# Sneak the dynamic_size and infer_shape values into the legacy shape.
return (tensor_shape.matrix(self._dynamic_size,
self._infer_shape).concatenate(
self._element_shape))
class StructureTest(test.TestCase, parameterized.TestCase):
# NOTE(mrry): The arguments must be lifted into lambdas because otherwise they
# will be executed before the (eager- or graph-mode) test environment has been
# set up.
# pylint: disable=g-long-lambda,protected-access
@parameterized.parameters(
(lambda: constant_op.constant(37.0), structure.TensorStructure,
[dtypes.float32], [[]]),
(lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
structure.SparseTensorStructure, [dtypes.variant], [None]),
(lambda: (constant_op.constant(37.0), constant_op.constant([1, 2, 3])),
structure.NestedStructure, [dtypes.float32, dtypes.int32], [[], [3]]),
(lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, structure.NestedStructure, [dtypes.float32, dtypes.int32], [[], [3]
]),
(lambda: {
"a":
constant_op.constant(37.0),
"b":
(sparse_tensor.
SparseTensor(indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
}, structure.NestedStructure,
[dtypes.float32, dtypes.variant, dtypes.variant], [[], None, None]))
def testFlatStructure(self, value_fn, expected_structure, expected_types,
expected_shapes):
value = value_fn()
s = structure.Structure.from_value(value)
self.assertIsInstance(s, expected_structure)
self.assertEqual(expected_types, s._flat_types)
for expected, actual in zip(expected_shapes, s._flat_shapes):
self.assertTrue(actual.is_compatible_with(expected))
self.assertTrue(
tensor_shape.as_shape(expected).is_compatible_with(actual))
@parameterized.parameters(
(lambda: constant_op.constant(37.0), lambda: [
constant_op.constant(38.0),
array_ops.placeholder(dtypes.float32),
variables.Variable(100.0), 42.0,
np.array(42.0, dtype=np.float32)
],
lambda: [constant_op.constant([1.0, 2.0]),
constant_op.constant(37)]),
(lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]), lambda: [
sparse_tensor.SparseTensor(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
sparse_tensor.SparseTensorValue(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
array_ops.sparse_placeholder(dtype=dtypes.int32),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None])
], lambda: [
constant_op.constant(37, shape=[4, 5]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[5, 6]),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None, None]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1.0], dense_shape=[4, 5])
]),
(lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6])
}], lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6, 7])
}, {
"a": constant_op.constant(15),
"b": constant_op.constant([4, 5, 6])
}, {
"a":
constant_op.constant(15),
"b":
sparse_tensor.SparseTensor(
indices=[[0], [1], [2]], values=[4, 5, 6], dense_shape=[3])
}, (constant_op.constant(15.0), constant_op.constant([4, 5, 6]))]),
)
def testIsCompatibleWithStructure(self, original_value_fn,
compatible_values_fn,
incompatible_values_fn):
original_value = original_value_fn()
compatible_values = compatible_values_fn()
incompatible_values = incompatible_values_fn()
s = structure.Structure.from_value(original_value)
for compatible_value in compatible_values:
self.assertTrue(
s.is_compatible_with(
structure.Structure.from_value(compatible_value)))
for incompatible_value in incompatible_values:
self.assertFalse(
s.is_compatible_with(
structure.Structure.from_value(incompatible_value)))
@parameterized.parameters(
(lambda: constant_op.constant(37.0), ),
(lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]), ),
(lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, ),
(lambda: {
"a":
constant_op.constant(37.0),
"b":
(sparse_tensor.
SparseTensor(indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
}, ),
)
def testRoundTripConversion(self, value_fn):
value = value_fn()
s = structure.Structure.from_value(value)
before = self.evaluate(value)
after = self.evaluate(s._from_tensor_list(s._to_tensor_list(value)))
flat_before = nest.flatten(before)
flat_after = nest.flatten(after)
for b, a in zip(flat_before, flat_after):
if isinstance(b, sparse_tensor.SparseTensorValue):
self.assertAllEqual(b.indices, a.indices)
self.assertAllEqual(b.values, a.values)
self.assertAllEqual(b.dense_shape, a.dense_shape)
else:
self.assertAllEqual(b, a)
# pylint: enable=g-long-lambda
def testIncompatibleStructure(self):
# Define three mutually incompatible values/structures, and assert that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructre a flattened value with an
# incompatible structure fails.
value_tensor = constant_op.constant(42.0)
s_tensor = structure.Structure.from_value(value_tensor)
flat_tensor = s_tensor._to_tensor_list(value_tensor)
value_sparse_tensor = sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
s_sparse_tensor = structure.Structure.from_value(value_sparse_tensor)
flat_sparse_tensor = s_sparse_tensor._to_tensor_list(
value_sparse_tensor)
value_nest = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_nest = structure.Structure.from_value(value_nest)
flat_nest = s_nest._to_tensor_list(value_nest)
with self.assertRaisesRegexp(
ValueError,
r"SparseTensor.* is not convertible to a tensor with "
r"dtype.*float32.* and shape \(\)"):
s_tensor._to_tensor_list(value_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
r"Value \{.*\} is not convertible to a tensor with "
r"dtype.*float32.* and shape \(\)"):
s_tensor._to_tensor_list(value_nest)
with self.assertRaisesRegexp(TypeError,
"Input must be a SparseTensor"):
s_sparse_tensor._to_tensor_list(value_tensor)
with self.assertRaisesRegexp(TypeError,
"Input must be a SparseTensor"):
s_sparse_tensor._to_tensor_list(value_nest)
with self.assertRaisesRegexp(
ValueError,
"Tensor.* not compatible with the nested structure "
".*TensorStructure.*TensorStructure"):
s_nest._to_tensor_list(value_tensor)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.* not compatible with the nested structure "
".*TensorStructure.*TensorStructure"):
s_nest._to_tensor_list(value_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
r"Cannot convert.*with dtype.*float32.* and shape \(\)"):
s_tensor._from_tensor_list(flat_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
"TensorStructure corresponds to a single tf.Tensor."):
s_tensor._from_tensor_list(flat_nest)
with self.assertRaisesRegexp(
ValueError,
"SparseTensorStructure corresponds to a single tf.variant "
"vector of length 3."):
s_sparse_tensor._from_tensor_list(flat_tensor)
with self.assertRaisesRegexp(
ValueError,
"SparseTensorStructure corresponds to a single tf.variant "
"vector of length 3."):
s_sparse_tensor._from_tensor_list(flat_nest)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 1."):
s_nest._from_tensor_list(flat_tensor)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 1."):
s_nest._from_tensor_list(flat_sparse_tensor)
def testIncompatibleNestedStructure(self):
# Define three mutually incompatible nested values/structures, and assert
# that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructre a flattened value with an
# incompatible structure fails.
value_0 = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_0 = structure.Structure.from_value(value_0)
flat_s_0 = s_0._to_tensor_list(value_0)
# `value_1` has compatible nested structure with `value_0`, but different
# classes.
value_1 = {
"a":
constant_op.constant(37.0),
"b":
sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
}
s_1 = structure.Structure.from_value(value_1)
flat_s_1 = s_1._to_tensor_list(value_1)
# `value_2` has incompatible nested structure with `value_0` and `value_1`.
value_2 = {
"a":
constant_op.constant(37.0),
"b": (sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1]),
sparse_tensor.SparseTensor(indices=[[3, 4]],
values=[-1],
dense_shape=[4, 5]))
}
s_2 = structure.Structure.from_value(value_2)
flat_s_2 = s_2._to_tensor_list(value_2)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.* not compatible with the nested structure "
".*TensorStructure"):
s_0._to_tensor_list(value_1)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.*SparseTensor.* not compatible with the "
"nested structure .*TensorStructure"):
s_0._to_tensor_list(value_2)
with self.assertRaisesRegexp(
ValueError,
"Tensor.* not compatible with the nested structure "
".*SparseTensorStructure"):
s_1._to_tensor_list(value_0)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.*SparseTensor.* not compatible with the "
"nested structure .*TensorStructure"):
s_0._to_tensor_list(value_2)
# NOTE(mrry): The repr of the dictionaries is not sorted, so the regexp
# needs to account for "a" coming before or after "b". It might be worth
# adding a deterministic repr for these error messages (among other
# improvements).
with self.assertRaisesRegexp(
ValueError,
"Tensor.*Tensor.* not compatible with the nested structure "
".*(TensorStructure.*SparseTensorStructure.*SparseTensorStructure|"
"SparseTensorStructure.*SparseTensorStructure.*TensorStructure)"
):
s_2._to_tensor_list(value_0)
with self.assertRaisesRegexp(
ValueError, "(Tensor.*SparseTensor|SparseTensor.*Tensor).* "
"not compatible with the nested structure .*"
"(TensorStructure.*SparseTensorStructure.*SparseTensorStructure|"
"SparseTensorStructure.*SparseTensorStructure.*TensorStructure)"
):
s_2._to_tensor_list(value_1)
with self.assertRaisesRegexp(
ValueError,
r"Cannot convert.*with dtype.*int32.* and shape \(3,\)"):
s_0._from_tensor_list(flat_s_1)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 3."):
s_0._from_tensor_list(flat_s_2)
with self.assertRaisesRegexp(
ValueError,
"SparseTensorStructure corresponds to a single tf.variant "
"vector of length 3."):
s_1._from_tensor_list(flat_s_0)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 3."):
s_1._from_tensor_list(flat_s_2)
with self.assertRaisesRegexp(
ValueError,
"Expected 3 flat values in NestedStructure but got 2."):
s_2._from_tensor_list(flat_s_0)
with self.assertRaisesRegexp(
ValueError,
"Expected 3 flat values in NestedStructure but got 2."):
s_2._from_tensor_list(flat_s_1)
@parameterized.named_parameters(
("Tensor", dtypes.float32, tensor_shape.scalar(), ops.Tensor,
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor", dtypes.int32, tensor_shape.matrix(
2, 2), sparse_tensor.SparseTensor,
structure.SparseTensorStructure(dtypes.int32, [2, 2])),
("Nest", {
"a": dtypes.float32,
"b": (dtypes.int32, dtypes.string)
}, {
"a": tensor_shape.scalar(),
"b": (tensor_shape.matrix(2, 2), tensor_shape.scalar())
}, {
"a": ops.Tensor,
"b": (sparse_tensor.SparseTensor, ops.Tensor)
},
structure.NestedStructure({
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]),
structure.TensorStructure(dtypes.string, []))
})),
)
def testFromLegacyStructure(self, output_types, output_shapes,
output_classes, expected_structure):
actual_structure = structure.Structure._from_legacy_structure(
output_types, output_shapes, output_classes)
self.assertTrue(
expected_structure.is_compatible_with(actual_structure))
self.assertTrue(
actual_structure.is_compatible_with(expected_structure))
def _WhereShape(op): """Shape function for the Where op.""" input_shape = op.inputs[0].get_shape() return [tensor_shape.matrix(None, input_shape.ndims)]
def _MultinomialShape(op): # pylint: disable=invalid-name logits_shape = op.inputs[0].get_shape().with_rank(2) batch_size = logits_shape[0] num_samples_or_none = tensor_util.constant_value(op.inputs[1]) return [tensor_shape.matrix(batch_size, num_samples_or_none)]
def output_shapes(self):
num_elements = tensor_shape.Dimension(None)
return (tensor_shape.matrix(num_elements, self._row_shape.shape[0] + 1),
tensor_shape.vector(num_elements),
tensor_shape.vector(self._row_shape.shape[0] + 1))
def _to_legacy_output_shapes(self):
# Sneak the dynamic_size and infer_shape values into the legacy shape.
return (tensor_shape.matrix(self._dynamic_size, self._infer_shape)
.concatenate(self._element_shape))
def _SparseFeatureCrossShape(unused_op): # pylint: disable=invalid-name
return [
tensor_shape.matrix(None, 2),
tensor_shape.vector(None),
tensor_shape.vector(2)
]
def _WhereShape(op): """Shape function for the Where op.""" input_shape = op.inputs[0].get_shape() return [tensor_shape.matrix(None, input_shape.ndims)]
class StructureTest(test_base.DatasetTestBase, parameterized.TestCase):
# NOTE(mrry): The arguments must be lifted into lambdas because otherwise they
# will be executed before the (eager- or graph-mode) test environment has been
# set up.
# pylint: disable=g-long-lambda,protected-access
@parameterized.parameters(
(lambda: constant_op.constant(37.0), structure.TensorStructure,
[dtypes.float32], [[]]),
(lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
structure.TensorArrayStructure, [dtypes.variant], [None, 3]),
(lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]),
structure.SparseTensorStructure, [dtypes.variant], [None]),
(lambda: (constant_op.constant(37.0), constant_op.constant([1, 2, 3])),
structure.NestedStructure, [dtypes.float32, dtypes.int32], [[], [3]]),
(lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, structure.NestedStructure, [dtypes.float32, dtypes.int32], [[], [3]
]),
(lambda: {
"a":
constant_op.constant(37.0),
"b":
(sparse_tensor.
SparseTensor(indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
}, structure.NestedStructure,
[dtypes.float32, dtypes.variant, dtypes.variant], [[], None, None]))
def testFlatStructure(self, value_fn, expected_structure, expected_types,
expected_shapes):
value = value_fn()
s = structure.Structure.from_value(value)
self.assertIsInstance(s, expected_structure)
self.assertEqual(expected_types, s._flat_types)
for expected, actual in zip(expected_shapes, s._flat_shapes):
self.assertTrue(actual.is_compatible_with(expected))
self.assertTrue(
tensor_shape.as_shape(expected).is_compatible_with(actual))
@parameterized.parameters(
(lambda: constant_op.constant(37.0), lambda: [
constant_op.constant(38.0),
array_ops.placeholder(dtypes.float32),
variables.Variable(100.0), 42.0,
np.array(42.0, dtype=np.float32)
],
lambda: [constant_op.constant([1.0, 2.0]),
constant_op.constant(37)]),
(lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0), lambda: [
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(3, ), size=10)
], lambda: [
tensor_array_ops.TensorArray(
dtype=dtypes.int32, element_shape=(3, ), size=0),
tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(), size=0)
]),
(lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]), lambda: [
sparse_tensor.SparseTensor(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
sparse_tensor.SparseTensorValue(indices=[[1, 1], [3, 4]],
values=[10, -1],
dense_shape=[4, 5]),
array_ops.sparse_placeholder(dtype=dtypes.int32),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None])
], lambda: [
constant_op.constant(37, shape=[4, 5]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[5, 6]),
array_ops.sparse_placeholder(dtype=dtypes.int32,
shape=[None, None, None]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1.0], dense_shape=[4, 5])
]),
(lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6])
}], lambda: [{
"a": constant_op.constant(15.0),
"b": constant_op.constant([4, 5, 6, 7])
}, {
"a": constant_op.constant(15),
"b": constant_op.constant([4, 5, 6])
}, {
"a":
constant_op.constant(15),
"b":
sparse_tensor.SparseTensor(
indices=[[0], [1], [2]], values=[4, 5, 6], dense_shape=[3])
}, (constant_op.constant(15.0), constant_op.constant([4, 5, 6]))]),
)
@test_util.run_deprecated_v1
def testIsCompatibleWithStructure(self, original_value_fn,
compatible_values_fn,
incompatible_values_fn):
original_value = original_value_fn()
compatible_values = compatible_values_fn()
incompatible_values = incompatible_values_fn()
s = structure.Structure.from_value(original_value)
for compatible_value in compatible_values:
self.assertTrue(
s.is_compatible_with(
structure.Structure.from_value(compatible_value)))
for incompatible_value in incompatible_values:
self.assertFalse(
s.is_compatible_with(
structure.Structure.from_value(incompatible_value)))
@parameterized.parameters(
(lambda: constant_op.constant(37.0), ),
(lambda: sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]), ),
(lambda: tensor_array_ops.TensorArray(
dtype=dtypes.float32, element_shape=(), size=1).write(0, 7)),
(lambda: {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}, ),
(lambda: {
"a":
constant_op.constant(37.0),
"b":
(sparse_tensor.
SparseTensor(indices=[[0, 0]], values=[1], dense_shape=[1, 1]),
sparse_tensor.SparseTensor(
indices=[[3, 4]], values=[-1], dense_shape=[4, 5]))
}, ),
)
def testRoundTripConversion(self, value_fn):
value = value_fn()
s = structure.Structure.from_value(value)
def maybe_stack_ta(v):
if isinstance(v, tensor_array_ops.TensorArray):
return v.stack()
else:
return v
before = self.evaluate(maybe_stack_ta(value))
after = self.evaluate(
maybe_stack_ta(s._from_tensor_list(s._to_tensor_list(value))))
flat_before = nest.flatten(before)
flat_after = nest.flatten(after)
for b, a in zip(flat_before, flat_after):
if isinstance(b, sparse_tensor.SparseTensorValue):
self.assertAllEqual(b.indices, a.indices)
self.assertAllEqual(b.values, a.values)
self.assertAllEqual(b.dense_shape, a.dense_shape)
else:
self.assertAllEqual(b, a)
# pylint: enable=g-long-lambda
def testIncompatibleStructure(self):
# Define three mutually incompatible values/structures, and assert that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructre a flattened value with an
# incompatible structure fails.
value_tensor = constant_op.constant(42.0)
s_tensor = structure.Structure.from_value(value_tensor)
flat_tensor = s_tensor._to_tensor_list(value_tensor)
value_sparse_tensor = sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
s_sparse_tensor = structure.Structure.from_value(value_sparse_tensor)
flat_sparse_tensor = s_sparse_tensor._to_tensor_list(
value_sparse_tensor)
value_nest = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_nest = structure.Structure.from_value(value_nest)
flat_nest = s_nest._to_tensor_list(value_nest)
with self.assertRaisesRegexp(
ValueError,
r"SparseTensor.* is not convertible to a tensor with "
r"dtype.*float32.* and shape \(\)"):
s_tensor._to_tensor_list(value_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
r"Value \{.*\} is not convertible to a tensor with "
r"dtype.*float32.* and shape \(\)"):
s_tensor._to_tensor_list(value_nest)
with self.assertRaisesRegexp(TypeError,
"Input must be a SparseTensor"):
s_sparse_tensor._to_tensor_list(value_tensor)
with self.assertRaisesRegexp(TypeError,
"Input must be a SparseTensor"):
s_sparse_tensor._to_tensor_list(value_nest)
with self.assertRaisesRegexp(
ValueError,
"Tensor.* not compatible with the nested structure "
".*TensorStructure.*TensorStructure"):
s_nest._to_tensor_list(value_tensor)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.* not compatible with the nested structure "
".*TensorStructure.*TensorStructure"):
s_nest._to_tensor_list(value_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
r"Cannot convert.*with dtype.*float32.* and shape \(\)"):
s_tensor._from_tensor_list(flat_sparse_tensor)
with self.assertRaisesRegexp(
ValueError,
"TensorStructure corresponds to a single tf.Tensor."):
s_tensor._from_tensor_list(flat_nest)
with self.assertRaisesRegexp(
ValueError,
"SparseTensorStructure corresponds to a single tf.variant "
"vector of length 3."):
s_sparse_tensor._from_tensor_list(flat_tensor)
with self.assertRaisesRegexp(
ValueError,
"SparseTensorStructure corresponds to a single tf.variant "
"vector of length 3."):
s_sparse_tensor._from_tensor_list(flat_nest)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 1."):
s_nest._from_tensor_list(flat_tensor)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 1."):
s_nest._from_tensor_list(flat_sparse_tensor)
def testIncompatibleNestedStructure(self):
# Define three mutually incompatible nested values/structures, and assert
# that:
# 1. Using one structure to flatten a value with an incompatible structure
# fails.
# 2. Using one structure to restructre a flattened value with an
# incompatible structure fails.
value_0 = {
"a": constant_op.constant(37.0),
"b": constant_op.constant([1, 2, 3])
}
s_0 = structure.Structure.from_value(value_0)
flat_s_0 = s_0._to_tensor_list(value_0)
# `value_1` has compatible nested structure with `value_0`, but different
# classes.
value_1 = {
"a":
constant_op.constant(37.0),
"b":
sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1])
}
s_1 = structure.Structure.from_value(value_1)
flat_s_1 = s_1._to_tensor_list(value_1)
# `value_2` has incompatible nested structure with `value_0` and `value_1`.
value_2 = {
"a":
constant_op.constant(37.0),
"b": (sparse_tensor.SparseTensor(indices=[[0, 0]],
values=[1],
dense_shape=[1, 1]),
sparse_tensor.SparseTensor(indices=[[3, 4]],
values=[-1],
dense_shape=[4, 5]))
}
s_2 = structure.Structure.from_value(value_2)
flat_s_2 = s_2._to_tensor_list(value_2)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.* not compatible with the nested structure "
".*TensorStructure"):
s_0._to_tensor_list(value_1)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.*SparseTensor.* not compatible with the "
"nested structure .*TensorStructure"):
s_0._to_tensor_list(value_2)
with self.assertRaisesRegexp(
ValueError,
"Tensor.* not compatible with the nested structure "
".*SparseTensorStructure"):
s_1._to_tensor_list(value_0)
with self.assertRaisesRegexp(
ValueError,
"SparseTensor.*SparseTensor.* not compatible with the "
"nested structure .*TensorStructure"):
s_0._to_tensor_list(value_2)
# NOTE(mrry): The repr of the dictionaries is not sorted, so the regexp
# needs to account for "a" coming before or after "b". It might be worth
# adding a deterministic repr for these error messages (among other
# improvements).
with self.assertRaisesRegexp(
ValueError,
"Tensor.*Tensor.* not compatible with the nested structure "
".*(TensorStructure.*SparseTensorStructure.*SparseTensorStructure|"
"SparseTensorStructure.*SparseTensorStructure.*TensorStructure)"
):
s_2._to_tensor_list(value_0)
with self.assertRaisesRegexp(
ValueError, "(Tensor.*SparseTensor|SparseTensor.*Tensor).* "
"not compatible with the nested structure .*"
"(TensorStructure.*SparseTensorStructure.*SparseTensorStructure|"
"SparseTensorStructure.*SparseTensorStructure.*TensorStructure)"
):
s_2._to_tensor_list(value_1)
with self.assertRaisesRegexp(
ValueError,
r"Cannot convert.*with dtype.*int32.* and shape \(3,\)"):
s_0._from_tensor_list(flat_s_1)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 3."):
s_0._from_tensor_list(flat_s_2)
with self.assertRaisesRegexp(
ValueError,
"SparseTensorStructure corresponds to a single tf.variant "
"vector of length 3."):
s_1._from_tensor_list(flat_s_0)
with self.assertRaisesRegexp(
ValueError,
"Expected 2 flat values in NestedStructure but got 3."):
s_1._from_tensor_list(flat_s_2)
with self.assertRaisesRegexp(
ValueError,
"Expected 3 flat values in NestedStructure but got 2."):
s_2._from_tensor_list(flat_s_0)
with self.assertRaisesRegexp(
ValueError,
"Expected 3 flat values in NestedStructure but got 2."):
s_2._from_tensor_list(flat_s_1)
@parameterized.named_parameters(
("Tensor", dtypes.float32, tensor_shape.scalar(), ops.Tensor,
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor", dtypes.int32, tensor_shape.matrix(
2, 2), sparse_tensor.SparseTensor,
structure.SparseTensorStructure(dtypes.int32, [2, 2])),
("TensorArray0", dtypes.int32, tensor_shape.as_shape(
[None, True, 2, 2]), tensor_array_ops.TensorArray,
structure.TensorArrayStructure(
dtypes.int32, [2, 2], dynamic_size=None, infer_shape=True)),
("TensorArray1", dtypes.int32, tensor_shape.as_shape(
[True, None, 2, 2]), tensor_array_ops.TensorArray,
structure.TensorArrayStructure(
dtypes.int32, [2, 2], dynamic_size=True, infer_shape=None)),
("TensorArray2", dtypes.int32,
tensor_shape.as_shape([True, False, 2, 2
]), tensor_array_ops.TensorArray,
structure.TensorArrayStructure(
dtypes.int32, [2, 2], dynamic_size=True, infer_shape=False)),
("Nest", {
"a": dtypes.float32,
"b": (dtypes.int32, dtypes.string)
}, {
"a": tensor_shape.scalar(),
"b": (tensor_shape.matrix(2, 2), tensor_shape.scalar())
}, {
"a": ops.Tensor,
"b": (sparse_tensor.SparseTensor, ops.Tensor)
},
structure.NestedStructure({
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]),
structure.TensorStructure(dtypes.string, []))
})),
)
def testConvertLegacyStructure(self, output_types, output_shapes,
output_classes, expected_structure):
actual_structure = structure.convert_legacy_structure(
output_types, output_shapes, output_classes)
self.assertTrue(
expected_structure.is_compatible_with(actual_structure))
self.assertTrue(
actual_structure.is_compatible_with(expected_structure))
def testNestedNestedStructure(self):
# Although `Structure.from_value()` will not construct one, a nested
# structure containing nested `NestedStructure` objects can occur if a
# structure is constructed manually.
s = structure.NestedStructure(
(structure.TensorStructure(dtypes.int64, []),
structure.NestedStructure(
(structure.TensorStructure(dtypes.float32, []),
structure.TensorStructure(dtypes.string, [])))))
int64_t = constant_op.constant(37, dtype=dtypes.int64)
float32_t = constant_op.constant(42.0)
string_t = constant_op.constant("Foo")
nested_tensors = (int64_t, (float32_t, string_t))
tensor_list = s._to_tensor_list(nested_tensors)
for expected, actual in zip([int64_t, float32_t, string_t],
tensor_list):
self.assertIs(expected, actual)
(actual_int64_t, (actual_float32_t,
actual_string_t)) = s._from_tensor_list(tensor_list)
self.assertIs(int64_t, actual_int64_t)
self.assertIs(float32_t, actual_float32_t)
self.assertIs(string_t, actual_string_t)
(actual_int64_t,
(actual_float32_t,
actual_string_t)) = (s._from_compatible_tensor_list(tensor_list))
self.assertIs(int64_t, actual_int64_t)
self.assertIs(float32_t, actual_float32_t)
self.assertIs(string_t, actual_string_t)
@parameterized.named_parameters(
("Tensor", structure.TensorStructure(dtypes.float32, []), 32,
structure.TensorStructure(dtypes.float32, [32])),
("TensorUnknown", structure.TensorStructure(dtypes.float32, []), None,
structure.TensorStructure(dtypes.float32, [None])),
("SparseTensor", structure.SparseTensorStructure(
dtypes.float32, [None]), 32,
structure.SparseTensorStructure(dtypes.float32, [32, None])),
("SparseTensorUnknown",
structure.SparseTensorStructure(dtypes.float32, [4]), None,
structure.SparseTensorStructure(dtypes.float32, [None, 4])),
("Nest",
structure.NestedStructure({
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]),
structure.TensorStructure(dtypes.string, []))
}), 128,
structure.NestedStructure({
"a":
structure.TensorStructure(dtypes.float32, [128]),
"b": (structure.SparseTensorStructure(dtypes.int32, [128, 2, 2]),
structure.TensorStructure(dtypes.string, [128]))
})),
)
def testBatch(self, element_structure, batch_size,
expected_batched_structure):
batched_structure = element_structure._batch(batch_size)
self.assertTrue(
batched_structure.is_compatible_with(expected_batched_structure))
self.assertTrue(
expected_batched_structure.is_compatible_with(batched_structure))
@parameterized.named_parameters(
("Tensor", structure.TensorStructure(dtypes.float32, [32]),
structure.TensorStructure(dtypes.float32, [])),
("TensorUnknown", structure.TensorStructure(dtypes.float32, [None]),
structure.TensorStructure(dtypes.float32, [])),
("SparseTensor",
structure.SparseTensorStructure(dtypes.float32, [32, None]),
structure.SparseTensorStructure(dtypes.float32, [None])),
("SparseTensorUnknown",
structure.SparseTensorStructure(dtypes.float32, [None, 4]),
structure.SparseTensorStructure(dtypes.float32, [4])),
("Nest",
structure.NestedStructure({
"a":
structure.TensorStructure(dtypes.float32, [128]),
"b": (structure.SparseTensorStructure(dtypes.int32, [128, 2, 2]),
structure.TensorStructure(dtypes.string, [None]))
}),
structure.NestedStructure({
"a":
structure.TensorStructure(dtypes.float32, []),
"b": (structure.SparseTensorStructure(dtypes.int32, [2, 2]),
structure.TensorStructure(dtypes.string, []))
})),
)
def testUnbatch(self, element_structure, expected_unbatched_structure):
unbatched_structure = element_structure._unbatch()
self.assertTrue(
unbatched_structure.is_compatible_with(
expected_unbatched_structure))
self.assertTrue(
expected_unbatched_structure.is_compatible_with(
unbatched_structure))
# pylint: disable=g-long-lambda
@parameterized.named_parameters(
("Tensor", lambda: constant_op.constant([[1.0, 2.0], [3.0, 4.0]]),
lambda: constant_op.constant([1.0, 2.0])),
("SparseTensor", lambda: sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2]),
lambda: sparse_tensor.SparseTensor(
indices=[[0]], values=[13], dense_shape=[2])),
("Nest", lambda:
(constant_op.constant([[1.0, 2.0], [3.0, 4.0]]),
sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 1]], values=[13, 27], dense_shape=[2, 2])),
lambda: (constant_op.constant([1.0, 2.0]),
sparse_tensor.SparseTensor(
indices=[[0]], values=[13], dense_shape=[2]))),
)
def testToBatchedTensorList(self, value_fn, element_0_fn):
batched_value = value_fn()
s = structure.Structure.from_value(batched_value)
batched_tensor_list = s._to_batched_tensor_list(batched_value)
# The batch dimension is 2 for all of the test cases.
# NOTE(mrry): `tf.shape()` does not currently work for the DT_VARIANT
# tensors in which we store sparse tensors.
for t in batched_tensor_list:
if t.dtype != dtypes.variant:
self.assertEqual(2, self.evaluate(array_ops.shape(t)[0]))
# Test that the 0th element from the unbatched tensor is equal to the
# expected value.
expected_element_0 = self.evaluate(element_0_fn())
unbatched_s = s._unbatch()
actual_element_0 = unbatched_s._from_tensor_list(
[t[0] for t in batched_tensor_list])
for expected, actual in zip(nest.flatten(expected_element_0),
nest.flatten(actual_element_0)):
if sparse_tensor.is_sparse(expected):
self.assertSparseValuesEqual(expected, actual)
else:
self.assertAllEqual(expected, actual)