def test_unknown_size(self):
reshape_lt = ops.reshape(self.original_lt, ['channel', 'z', 'probs'],
['new_dim'])
golden_lt = core.LabeledTensor(
array_ops.reshape(self.original_lt.tensor, [self.x_size, -1]),
[self.original_lt.axes['x'], 'new_dim'])
self.assertLabeledTensorsEqual(reshape_lt, golden_lt)
def decode(self, labeled_tensor):
"""Reshape the input to the original shape.
This is the inverse of encode.
Encode must have been called at least once prior to this method being
called.
Args:
labeled_tensor: The input tensor.
Returns:
The input reshaped to the original shape.
Raises:
ValueError: If this method was called before encode was called.
"""
if self._existing_axes is None:
raise ValueError('decode called before encode')
with tf_ops.name_scope(self._name, 'lt_reshape_decode',
[labeled_tensor]) as scope:
labeled_tensor = core.convert_to_labeled_tensor(labeled_tensor)
new_axis_names = [
axis
if isinstance(axis, string_types) else core.as_axis(axis).name
for axis in self._new_axes
]
return ops.reshape(labeled_tensor,
new_axis_names,
self._existing_axes,
name=scope)
def encode(self, labeled_tensor):
"""Reshape the input to the target shape.
If called several times, the axes named in existing_axis_names must be
identical.
Args:
labeled_tensor: The input tensor.
Returns:
The input reshaped to the target shape.
Raises:
ValueError: If the axes in existing_axis_names don't match the axes of
a tensor in a previous invocation of this method.
"""
with tf_ops.name_scope(self._name, 'lt_reshape_encode',
[labeled_tensor]) as scope:
labeled_tensor = core.convert_to_labeled_tensor(labeled_tensor)
reshape_lt = ops.reshape(labeled_tensor,
self._existing_axis_names,
self._new_axes,
name=scope)
axes = [labeled_tensor.axes[n] for n in self._existing_axis_names]
if self._existing_axes is not None and self._existing_axes != axes:
raise ValueError(
'input axes %r do not match axes from previous method call %r'
% (axes, self._existing_axes))
else:
self._existing_axes = axes
return reshape_lt
def test_with_labels(self):
new_dim_size = self.channel_size * self.z_size * self.probs_size
reshape_lt = ops.reshape(self.original_lt, ['channel', 'z', 'probs'],
[('new_dim', range(new_dim_size))])
golden_lt = core.LabeledTensor(
array_ops.reshape(self.original_lt.tensor, [self.x_size, -1]),
[self.original_lt.axes['x'], ('new_dim', range(new_dim_size))])
self.assertLabeledTensorsEqual(reshape_lt, golden_lt)
def test_unknown_dimension(self):
orig_lt = core.LabeledTensor(
array_ops.placeholder(dtypes.float32, [None]), ['x'])
reshape_lt = ops.reshape(orig_lt, ['x'], ['y', ('z', 1)])
self.assertEqual(reshape_lt.axes, core.Axes([('y', None), ('z', 1)]))
with self.cached_session() as sess:
result = sess.run(reshape_lt, feed_dict={orig_lt.tensor: [1, 2]})
np.testing.assert_array_equal(result, [[1], [2]])
def test_invalid_input(self):
with self.assertRaisesRegexp(ValueError, 'not contained in the set'):
ops.reshape(self.original_lt, ['foo'], ['bar'])
with self.assertRaisesRegexp(core.AxisOrderError,
'not a slice of axis names'):
ops.reshape(self.original_lt, ['probs', 'z'], ['bar'])
with self.assertRaisesRegexp(ValueError,
'at most one axis in new_axes'):
ops.reshape(self.original_lt, ['probs'], ['foo', 'bar'])
def test_identity(self):
reshape_lt = ops.reshape(self.original_lt,
self.original_lt.axes.keys(),
self.original_lt.axes.values())
self.assertLabeledTensorsEqual(reshape_lt, self.original_lt)
def test_name(self):
reshape_lt = ops.reshape(self.original_lt, ['channel'], ['foo'])
self.assertIn('lt_reshape', reshape_lt.name)