def testWindowDatasetPaddedBatchSparse(self, structure, shapes, dtype,
padded_shape):
"""Tests padded batching of sparse tensor windows.
Args:
structure: the input structure
shapes: the input shapes
dtype: the input data type
padded_shape: the shape to pad the output to
"""
def fn(*args):
if len(args) == 1 and not isinstance(args[0], tuple):
return batching.padded_batch_window(args[0], padded_shape)
return tuple([
fn(*arg) if isinstance(arg, tuple) else
batching.padded_batch_window(arg, padded_shape) for arg in args
])
dataset = self._structuredRaggedSparseDataset(
structure, shapes,
dtype).apply(grouping.window_dataset(len(shapes))).apply(
grouping._map_x_dataset(fn))
get_next = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
expected = sess.run(
self._structuredRaggedSparseElement(structure, shapes, dtype,
padded_shape))
actual = sess.run(get_next)
self._assertEqual(expected, actual)
def testWindowDatasetPaddedBatchSparseDynamicShape(self, shapes,
padded_shape):
"""Tests padded batching of dynamically shaped sparse tensor windows.
Args:
shapes: the input shapes
padded_shape: the shape to pad the output to
"""
shapes_t = array_ops.placeholder(dtypes.int32)
dataset = dataset_ops.Dataset.from_tensor_slices(shapes_t).map(
lambda shape: array_ops.zeros(shape, dtype=dtypes.int32)).map(
self._make_dense_to_sparse_fn(False)
).apply(grouping.window_dataset(len(shapes))).apply(
grouping._map_x_dataset(
lambda x: batching.padded_batch_window(x, padded_shape)))
iterator = dataset.make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with self.test_session() as sess:
sess.run(init_op, {shapes_t: shapes})
expected = sess.run(
self._structuredRaggedSparseElement(None, shapes, dtypes.int32,
padded_shape))
actual = sess.run(get_next)
self._assertEqual(expected, actual)
def testWindowDatasetBatchSparse(self, structure, shape, dtype):
"""Tests batching of sparse tensor windows.
Args:
structure: the input structure
shape: the input shape
dtype: the input data type
"""
def fn(*args):
if len(args) == 1 and not isinstance(args[0], tuple):
return batching.batch_window(args[0])
return tuple([
fn(*arg)
if isinstance(arg, tuple) else batching.batch_window(arg)
for arg in args
])
dataset = self._structuredSparseDataset(
structure, shape, dtype).repeat(5).apply(
grouping.window_dataset(5)).apply(grouping._map_x_dataset(fn))
get_next = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
expected = sess.run(
self._structuredSparseElement(
structure, np.concatenate(([5], shape), axis=0), dtype))
actual = sess.run(get_next)
self._assertEqual(expected, actual)
def testWindowDatasetPaddedBatchSparseDynamicShape(self, shapes,
padded_shape):
"""Tests padded batching of dynamically shaped sparse tensor windows.
Args:
shapes: the input shapes
padded_shape: the shape to pad the output to
"""
shapes_t = array_ops.placeholder(dtypes.int32)
dataset = dataset_ops.Dataset.from_tensor_slices(shapes_t).map(
lambda shape: array_ops.zeros(shape, dtype=dtypes.int32)).map(
self._make_dense_to_sparse_fn(False)
).apply(grouping.window_dataset(len(shapes))).apply(
grouping._map_x_dataset(
lambda x: batching.padded_batch_window(x, padded_shape)))
iterator = dataset.make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op, {shapes_t: shapes})
expected = sess.run(
self._structuredRaggedSparseElement(None, shapes, dtypes.int32,
padded_shape))
actual = sess.run(get_next)
self._assertEqual(expected, actual)
def testWindowDatasetPaddedBatchSparse(self, structure, shapes, dtype,
padded_shape):
"""Tests padded batching of sparse tensor windows.
Args:
structure: the input structure
shapes: the input shapes
dtype: the input data type
padded_shape: the shape to pad the output to
"""
def fn(*args):
if len(args) == 1 and not isinstance(args[0], tuple):
return batching.padded_batch_window(args[0], padded_shape)
return tuple([
fn(*arg) if isinstance(arg, tuple) else batching.padded_batch_window(
arg, padded_shape) for arg in args
])
dataset = self._structuredRaggedSparseDataset(
structure, shapes, dtype).apply(grouping.window_dataset(
len(shapes))).apply(grouping._map_x_dataset(fn))
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
expected = sess.run(
self._structuredRaggedSparseElement(structure, shapes, dtype,
padded_shape))
actual = sess.run(get_next)
self._assertEqual(expected, actual)
def testWindowDatasetBatchSparse(self, structure, shape, dtype):
"""Tests batching of sparse tensor windows.
Args:
structure: the input structure
shape: the input shape
dtype: the input data type
"""
def fn(*args):
if len(args) == 1 and not isinstance(args[0], tuple):
return batching.batch_window(args[0])
return tuple([
fn(*arg) if isinstance(arg, tuple) else batching.batch_window(arg)
for arg in args
])
dataset = self._structuredSparseDataset(
structure, shape, dtype).repeat(5).apply(
grouping.window_dataset(5)).apply(grouping._map_x_dataset(fn))
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
expected = sess.run(
self._structuredSparseElement(structure,
np.concatenate(([5], shape), axis=0),
dtype))
actual = sess.run(get_next)
self._assertEqual(expected, actual)
def testWindowDatasetPaddedBatchDenseInvalid(self, shapes, padded_shape):
"""Tests invalid padded batching of dense tensor windows.
Args:
shapes: the input shapes
padded_shape: the shape to pad the output to
"""
dataset = dataset_ops.Dataset.from_tensor_slices(shapes).map(
lambda shape: array_ops.zeros(shape, dtype=dtypes.int32)
).apply(grouping.window_dataset(len(shapes))).apply(
grouping._map_x_dataset(
lambda x: batching.padded_batch_window(x, padded_shape)))
get_next = dataset.make_one_shot_iterator().get_next()
with self.test_session() as sess:
with self.assertRaises(errors.InvalidArgumentError):
sess.run(get_next)
def testWindowDatasetPaddedBatchDenseInvalid(self, shapes, padded_shape):
"""Tests invalid padded batching of dense tensor windows.
Args:
shapes: the input shapes
padded_shape: the shape to pad the output to
"""
dataset = dataset_ops.Dataset.from_tensor_slices(shapes).map(
lambda shape: array_ops.zeros(shape, dtype=dtypes.int32)).apply(
grouping.window_dataset(len(shapes))).apply(
grouping._map_x_dataset(
lambda x: batching.padded_batch_window(x, padded_shape)))
get_next = dataset.make_one_shot_iterator().get_next()
with self.cached_session() as sess:
with self.assertRaises(errors.InvalidArgumentError):
sess.run(get_next)
def testWindowDatasetBatchDenseDynamicShape(self, shape):
"""Tests batching of dynamically shaped dense tensor windows.
Args:
shape: the input shape
"""
shape_t = array_ops.placeholder(dtypes.int32)
dataset = dataset_ops.Dataset.from_tensors(
array_ops.zeros(shape_t)).repeat(5).apply(
grouping.window_dataset(5)).apply(
grouping._map_x_dataset(batching.batch_window))
iterator = dataset.make_initializable_iterator()
init_op = iterator.initializer
get_next = iterator.get_next()
with self.cached_session() as sess:
sess.run(init_op, {shape_t: shape})
expected = sess.run(
self._structuredElement(None, np.concatenate(([5], shape), axis=0),
dtypes.int32))
actual = sess.run(get_next)
self._assertEqual(expected, actual)
