def _gather_1d_on_axis(labeled_tensor, indexer, axis, name=None):
with ops.name_scope(name, 'lt_take', [labeled_tensor]) as scope:
temp_axes = core.Axes([axis] + list(
labeled_tensor.axes.remove(axis.name).values()))
transposed = core.transpose(labeled_tensor, temp_axes.keys())
indexed = core.LabeledTensor(
array_ops.gather(transposed.tensor, indexer), temp_axes)
return core.transpose(indexed, labeled_tensor.axes.keys(), name=scope)
def _gather_1d_on_axis(labeled_tensor, indexer, axis, name=None):
with ops.name_scope(name, 'lt_take', [labeled_tensor]) as scope:
temp_axes = core.Axes(
[axis] + list(labeled_tensor.axes.remove(axis.name).values()))
transposed = core.transpose(labeled_tensor, temp_axes.keys())
indexed = core.LabeledTensor(
array_ops.gather(transposed.tensor, indexer), temp_axes)
return core.transpose(indexed, labeled_tensor.axes.keys(), name=scope)
def test_reverse(self):
axis_order = ['w', 'x', 'y', 'z']
lt = core.LabeledTensor(tf.reshape(tf.range(24), (1, 2, 3, 4)), axis_order)
actual = core.impose_axis_order(lt, axis_order[::-1])
expected = core.transpose(lt, axis_order[::-1])
self.assertLabeledTensorsEqual(expected, actual)
lt = core.LabeledTensor(tf.reshape(tf.range(6), (1, 2, 3)), axis_order[:3])
actual = core.impose_axis_order(lt, axis_order[::-1])
expected = core.transpose(lt, ['y', 'x', 'w'])
self.assertLabeledTensorsEqual(expected, actual)
def test_reverse(self):
axis_order = ['w', 'x', 'y', 'z']
lt = core.LabeledTensor(
array_ops.reshape(math_ops.range(24), (1, 2, 3, 4)), axis_order)
actual = core.impose_axis_order(lt, axis_order[::-1])
expected = core.transpose(lt, axis_order[::-1])
self.assertLabeledTensorsEqual(expected, actual)
lt = core.LabeledTensor(
array_ops.reshape(math_ops.range(6), (1, 2, 3)), axis_order[:3])
actual = core.impose_axis_order(lt, axis_order[::-1])
expected = core.transpose(lt, ['y', 'x', 'w'])
self.assertLabeledTensorsEqual(expected, actual)
def test_default_axis_order(self):
transpose_lt = core.transpose(self.original_lt)
golden_lt = core.LabeledTensor(
array_ops.transpose(self.tensor, [3, 2, 1, 0]),
list(reversed(list(self.original_lt.axes.values()))))
self.assertLabeledTensorsEqual(transpose_lt, golden_lt)
def test(self):
transpose_lt = core.transpose(self.original_lt,
['z', 'channel', 'x', 'probs'])
golden_lt = core.LabeledTensor(tf.transpose(self.tensor, [2, 1, 0, 3]),
[self.a2, self.a1, self.a0, self.a3])
self.assertLabeledTensorsEqual(transpose_lt, golden_lt)
def test_default_axis_order(self):
transpose_lt = core.transpose(self.original_lt)
golden_lt = core.LabeledTensor(
tf.transpose(self.tensor, [3, 2, 1, 0]),
list(reversed(list(self.original_lt.axes.values()))))
self.assertLabeledTensorsEqual(transpose_lt, golden_lt)
def test_scope(self):
axis_order = ['w', 'x', 'y', 'z']
lt = core.LabeledTensor(tf.reshape(tf.range(24), (1, 2, 3, 4)), axis_order)
expected = core.transpose(lt, axis_order[::-1])
with core.axis_order_scope(axis_order[::-1]):
actual = core.impose_axis_order(lt)
self.assertLabeledTensorsEqual(expected, actual)
def test(self):
transpose_lt = core.transpose(self.original_lt, ['z', 'channel', 'x',
'probs'])
golden_lt = core.LabeledTensor(
tf.transpose(self.tensor, [2, 1, 0, 3]),
[self.a2, self.a1, self.a0, self.a3])
self.assertLabeledTensorsEqual(transpose_lt, golden_lt)
def test_scope(self):
axis_order = ['w', 'x', 'y', 'z']
lt = core.LabeledTensor(
array_ops.reshape(math_ops.range(24), (1, 2, 3, 4)), axis_order)
expected = core.transpose(lt, axis_order[::-1])
with core.axis_order_scope(axis_order[::-1]):
actual = core.impose_axis_order(lt)
self.assertLabeledTensorsEqual(expected, actual)
def test_matrix_matrix(self):
xy_lt = core.LabeledTensor(tf.reshape(tf.range(6), (2, 3)), ['x', 'y'])
yz_lt = core.LabeledTensor(tf.reshape(tf.range(12), (3, 4)), ['y', 'z'])
matmul_lt = ops.matmul(xy_lt, yz_lt)
golden_lt = core.LabeledTensor(
tf.matmul(xy_lt.tensor, yz_lt.tensor), ['x', 'z'])
self.assertLabeledTensorsEqual(matmul_lt, golden_lt)
transpose = lambda x: core.transpose(x, list(x.axes.keys())[::-1])
matmul_lt = ops.matmul(xy_lt, transpose(yz_lt))
self.assertLabeledTensorsEqual(matmul_lt, golden_lt)
matmul_lt = ops.matmul(transpose(xy_lt), yz_lt)
self.assertLabeledTensorsEqual(matmul_lt, golden_lt)
matmul_lt = ops.matmul(transpose(xy_lt), transpose(yz_lt))
self.assertLabeledTensorsEqual(matmul_lt, golden_lt)
matmul_lt = ops.matmul(yz_lt, xy_lt)
self.assertLabeledTensorsEqual(matmul_lt, transpose(golden_lt))
def test_invalid_input(self):
with self.assertRaises(ValueError):
core.transpose(self.original_lt, ['channel', 'x', 'probs'])
with self.assertRaises(ValueError):
core.transpose(self.original_lt, ['z', 'foo', 'x', 'probs'])
def test_identity(self):
transpose_lt = core.transpose(self.original_lt,
self.original_lt.axes.keys())
golden_lt = self.original_lt
self.assertLabeledTensorsEqual(transpose_lt, golden_lt)
def test_name(self):
transpose_lt = core.transpose(self.original_lt,
self.original_lt.axes.keys())
self.assertIn('lt_transpose', transpose_lt.name)
def test_identity(self):
transpose_lt = core.transpose(self.original_lt,
self.original_lt.axes.keys())
golden_lt = self.original_lt
self.assertLabeledTensorsEqual(transpose_lt, golden_lt)
def test_invalid_input(self):
with self.assertRaises(ValueError):
core.transpose(self.original_lt, ['channel', 'x', 'probs'])
with self.assertRaises(ValueError):
core.transpose(self.original_lt, ['z', 'foo', 'x', 'probs'])
def test_transposed(self):
green_transposed = core.transpose(self.green_lt,
['probs', 'channel', 'z', 'x'])
with self.assertRaises(ValueError):
ops.concat([self.red_lt, green_transposed], 'channel')
def test_transposed(self):
green_transposed = core.transpose(self.green_lt,
['probs', 'channel', 'z', 'x'])
with self.assertRaises(ValueError):
ops.concat([self.red_lt, green_transposed], 'channel')
def test_name(self):
transpose_lt = core.transpose(self.original_lt,
self.original_lt.axes.keys())
self.assertIn('lt_transpose', transpose_lt.name)
