def test_ragged_value_concatenation(self):
tensor_1 = ragged_tensor_value.RaggedTensorValue(
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))
tensor_2 = ragged_tensor_value.RaggedTensorValue(
np.array([3, 4, 5]), np.array([0, 2, 3], dtype=np.int64))
concatenated_tensor = composite_tensor_utils.append_composite_tensor(
tensor_1, tensor_2)
self.assertAllEqual(concatenated_tensor.values, [0, 1, 2, 3, 4, 5])
self.assertAllEqual(concatenated_tensor.row_splits, [0, 1, 3, 5, 6])
def _eval_tensor(self, tensor):
if ragged_tensor.is_ragged(tensor):
return ragged_tensor_value.RaggedTensorValue(
self._eval_tensor(tensor.values),
self._eval_tensor(tensor.row_splits))
else:
return test_util.TensorFlowTestCase._eval_tensor(self, tensor)
def _eager_value(self):
"""Returns a RaggedTensorValue for self. Requires self._is_eager()=true."""
value = self.inner_values.numpy()
for row_splits in reversed(self.nested_row_splits):
value = ragged_tensor_value.RaggedTensorValue(
value, row_splits.numpy())
return value
def testPaddedBatchRaggedError(self):
rt = ragged_tensor_value.RaggedTensorValue(
np.array([0, 42]), np.array([0, 2], dtype=np.int64))
with self.assertRaises(TypeError):
_ = dataset_ops.Dataset.from_tensors(rt).repeat(10).padded_batch(10)
def _append_ragged_tensor_value(target, to_append):
"""Append ragged tensor value objects."""
# Make sure the ragged tensors are of the same size (save for the 0th dim).
if len(target.shape) != len(to_append.shape):
raise RuntimeError('Unable to concatenate %s and %s' %
(target, to_append))
if target.shape[1:] != to_append.shape[1:]:
raise RuntimeError('Unable to concatenate %s and %s' %
(target, to_append))
adjusted_row_splits = to_append.row_splits[1:] + target.row_splits[-1]
new_row_splits = np.append(target.row_splits, adjusted_row_splits)
if isinstance(target.values, ragged_tensor_value.RaggedTensorValue):
new_values = _append_ragged_tensor_value(target.values,
to_append.values)
else:
new_values = np.concatenate((target.values, to_append.values), axis=0)
return ragged_tensor_value.RaggedTensorValue(new_values, new_row_splits)
def test_is_composite(self):
# Validate that all composite tensor and value types return true.
self.assertTrue(
training_utils_v1.is_composite_or_composite_value(
sparse_tensor.SparseTensor([[0, 0]], [1], [1, 1])))
self.assertTrue(
training_utils_v1.is_composite_or_composite_value(
sparse_tensor.SparseTensorValue([[0, 0]], [1], [1, 1])))
self.assertTrue(
training_utils_v1.is_composite_or_composite_value(
ragged_tensor.RaggedTensor.from_row_splits(
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))))
self.assertTrue(
training_utils_v1.is_composite_or_composite_value(
ragged_tensor_value.RaggedTensorValue(
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64))))
# Test that numpy arrays and tensors return false.
self.assertFalse(
training_utils_v1.is_composite_or_composite_value(np.ndarray([0, 1])))
self.assertFalse(
training_utils_v1.is_composite_or_composite_value(
ops.convert_to_tensor_v2_with_dispatch([3, 1])))
def _eval_tensor(self, tensor):
if tensor is None:
return None
elif callable(tensor):
return self._eval_helper(tensor())
else:
try:
if sparse_tensor.is_sparse(tensor):
return sparse_tensor.SparseTensorValue(
tensor.indices.numpy(), tensor.values.numpy(),
tensor.dense_shape.numpy())
elif ragged_tensor.is_ragged(tensor):
return ragged_tensor_value.RaggedTensorValue(
self._eval_tensor(tensor.values),
self._eval_tensor(tensor.row_splits))
elif isinstance(tensor, ops.IndexedSlices):
return ops.IndexedSlicesValue(
values=tensor.values.numpy(),
indices=tensor.indices.numpy(),
dense_shape=tensor.dense_shape.numpy())
return tensor.numpy()
except AttributeError as e:
ValueError("Unsupported type %s." % type(tensor)), e
def _ragged_factory(values, row_splits):
row_splits = np.array(row_splits, dtype=row_splits_dtype)
return ragged_tensor_value.RaggedTensorValue(values, row_splits)
def testRaggedElement(self):
ds = dataset_ops.Dataset.from_tensors(
ragged_tensor_value.RaggedTensorValue(
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64)))
with self.assertRaises(TypeError):
ds.as_numpy_iterator()
def testRaggedElement(self):
self._testInvalidElement(
ragged_tensor_value.RaggedTensorValue(
np.array([0, 1, 2]), np.array([0, 1, 3], dtype=np.int64)))
def _ragged_tensor_value_from_components(components):
components = list(components)
value = components.pop()
while components:
value = ragged_tensor_value.RaggedTensorValue(value, components.pop())
return value
