def test_is_static(self, tensor_shape, is_static):
"""Tests that is_static correctly checks if shape is static."""
if tf.executing_eagerly():
return
tensor = tf.compat.v1.placeholder(shape=tensor_shape, dtype=tf.float32)
with self.subTest(name="tensor_shape_is_list"):
self.assertEqual(shape.is_static(tensor_shape), is_static)
with self.subTest(name="tensor_shape"):
self.assertEqual(shape.is_static(tensor.shape), is_static)
def test_compare_batch_dimensions_raises_exceptions(
self, error_msg, tensor_shapes, last_axes, broadcast_compatible):
"""Tests that compare_batch_dimensions raises expected exceptions."""
if not tensor_shapes:
tensors = 0
else:
if all(
shape.is_static(tensor_shape)
for tensor_shape in tensor_shapes):
tensors = [
tf.ones(tensor_shape) for tensor_shape in tensor_shapes
]
else:
# Dynamic shapes are not supported in eager mode.
if tf.executing_eagerly():
return
tensors = [
tf.compat.v1.placeholder(shape=tensor_shape,
dtype=tf.float32)
for tensor_shape in tensor_shapes
]
self.assert_exception_is_raised(
shape.compare_batch_dimensions,
error_msg,
shapes=[],
tensors=tensors,
last_axes=last_axes,
broadcast_compatible=broadcast_compatible)
def _is_dynamic_shape(tensors):
"""Helper function to test if any tensor in a list has a dynamic shape.
Args:
tensors: A list or tuple of tensors with shapes to test.
Returns:
True if any tensor in the list has a dynamic shape, False otherwise.
"""
if not isinstance(tensors, (list, tuple)):
raise ValueError("'tensors' must be list of tuple.")
return not all([shape.is_static(tensor.shape) for tensor in tensors])
def test_compare_batch_dimensions_raises_no_exceptions(
self, tensor_shapes, last_axes, broadcast_compatible, initial_axes):
"""Tests that compare_batch_dimensions works for various inputs."""
if all(shape.is_static(tensor_shape) for tensor_shape in tensor_shapes):
tensors = [tf.ones(tensor_shape) for tensor_shape in tensor_shapes]
else:
# Dynamic shapes are not supported in eager mode.
if tf.executing_eagerly():
return
tensors = [
tf.compat.v1.placeholder(shape=tensor_shape, dtype=tf.float32)
for tensor_shape in tensor_shapes
]
self.assert_exception_is_not_raised(
shape.compare_batch_dimensions,
shapes=[],
tensors=tensors,
last_axes=last_axes,
broadcast_compatible=broadcast_compatible,
initial_axes=initial_axes)
