def testBroadcastDimension(self, axis, row_length, original_dim_sizes,
broadcast_dim_sizes):
"""Tests for the broadcast_dimension method.
Verifies that:
* `original.broadcast_dimension(axis, row_length) == broadcast`
* `broadcast.broadcast_dimension(axis, row_length) == broadcast`
* `broadcast.broadcast_dimension(axis, 1) == broadcast`
Args:
axis: The axis to broadcast
row_length: The slice lengths to broadcast to.
original_dim_sizes: The dimension sizes before broadcasting.
original_dim_sizes[axis] should be equal to `1` or `row_length`.
broadcast_dim_sizes: THe dimension sizes after broadcasting.
"""
original_shape = RaggedTensorDynamicShape.from_dim_sizes(
original_dim_sizes)
bcast_shape = RaggedTensorDynamicShape.from_dim_sizes(
broadcast_dim_sizes)
self.assertEqual(original_shape.rank, bcast_shape.rank)
# shape[axis].value == 1 and row_length > 1:
bcast1 = original_shape.broadcast_dimension(axis, row_length)
# shape[axis].value > 1 and row_length == shape[axis].value:
bcast2 = bcast_shape.broadcast_dimension(axis, row_length)
# shape[axis].value > 1 and row_length == 1:
bcast3 = bcast_shape.broadcast_dimension(axis, 1)
self.assertShapeEq(bcast1, bcast_shape)
self.assertShapeEq(bcast2, bcast_shape)
self.assertShapeEq(bcast3, bcast_shape)
def testBroadcastDimension(self, axis, row_length, original_dim_sizes,
broadcast_dim_sizes):
"""Tests for the broadcast_dimension method.
Verifies that:
* `original.broadcast_dimension(axis, row_length) == broadcast`
* `broadcast.broadcast_dimension(axis, row_length) == broadcast`
* `broadcast.broadcast_dimension(axis, 1) == broadcast`
Args:
axis: The axis to broadcast
row_length: The slice lengths to broadcast to.
original_dim_sizes: The dimension sizes before broadcasting.
original_dim_sizes[axis] should be equal to `1` or `row_length`.
broadcast_dim_sizes: THe dimension sizes after broadcasting.
"""
original_shape = RaggedTensorDynamicShape.from_dim_sizes(original_dim_sizes)
bcast_shape = RaggedTensorDynamicShape.from_dim_sizes(broadcast_dim_sizes)
self.assertEqual(original_shape.rank, bcast_shape.rank)
# shape[axis].value == 1 and row_length > 1:
bcast1 = original_shape.broadcast_dimension(axis, row_length)
# shape[axis].value > 1 and row_length == shape[axis].value:
bcast2 = bcast_shape.broadcast_dimension(axis, row_length)
# shape[axis].value > 1 and row_length == 1:
bcast3 = bcast_shape.broadcast_dimension(axis, 1)
self.assertShapeEq(bcast1, bcast_shape)
self.assertShapeEq(bcast2, bcast_shape)
self.assertShapeEq(bcast3, bcast_shape)
def testBroadcastDynamicShape(self, x_dims, y_dims, expected_dims):
x_shape = RaggedTensorDynamicShape.from_dim_sizes(x_dims)
y_shape = RaggedTensorDynamicShape.from_dim_sizes(y_dims)
expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dims)
result1 = ragged_tensor_shape.broadcast_dynamic_shape(x_shape, y_shape)
result2 = ragged_tensor_shape.broadcast_dynamic_shape(y_shape, x_shape)
self.assertShapeEq(expected, result1)
self.assertShapeEq(expected, result2)
def testBroadcastDynamicShape(self, x_dims, y_dims, expected_dims): x_shape = RaggedTensorDynamicShape.from_dim_sizes(x_dims) y_shape = RaggedTensorDynamicShape.from_dim_sizes(y_dims) expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dims) result1 = ragged_tensor_shape.broadcast_dynamic_shape(x_shape, y_shape) result2 = ragged_tensor_shape.broadcast_dynamic_shape(y_shape, x_shape) self.assertShapeEq(expected, result1) self.assertShapeEq(expected, result2)
def testRepr(self):
shape = RaggedTensorDynamicShape.from_dim_sizes([2, (2, 1), 2, 1])
self.assertRegexpMatches(
repr(shape),
r'RaggedTensorDynamicShape\('
r'partitioned_dim_sizes=\(<[^>]+>, <[^>]+>\), '
r'inner_dim_sizes=<[^>]+>\)')
def testRepr(self):
shape = RaggedTensorDynamicShape.from_dim_sizes([2, (2, 1), 2, 1])
self.assertRegexpMatches(
repr(shape),
r'RaggedTensorDynamicShape\('
r'partitioned_dim_sizes=\(<[^>]+>, <[^>]+>\), '
r'inner_dim_sizes=<[^>]+>\)')
def testBroadcastToRank(self, dim_sizes, rank, expected_dim_sizes):
shape = RaggedTensorDynamicShape.from_dim_sizes(dim_sizes)
expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dim_sizes)
broadcasted_shape = shape.broadcast_to_rank(rank)
self.assertShapeEq(broadcasted_shape, expected)
self.assertEqual(broadcasted_shape.rank, rank)
def testFromTensor(self, value, expected_dim_sizes):
shape = RaggedTensorDynamicShape.from_tensor(value)
expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dim_sizes)
self.assertShapeEq(shape, expected)
def testRaggedBroadcastTo(self, x, dim_sizes, expected):
shape = RaggedTensorDynamicShape.from_dim_sizes(dim_sizes)
result = ragged_tensor_shape.broadcast_to(x, shape)
self.assertEqual(getattr(result, 'ragged_rank', 0),
getattr(expected, 'ragged_rank', 0))
self.assertAllEqual(result, expected)
def testFromTensor(self, value, expected_dim_sizes): shape = RaggedTensorDynamicShape.from_tensor(value) expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dim_sizes) self.assertShapeEq(shape, expected)
def testRaggedBroadcastTo(self, x, dim_sizes, expected):
shape = RaggedTensorDynamicShape.from_dim_sizes(dim_sizes)
result = ragged_tensor_shape.broadcast_to(x, shape)
self.assertEqual(
getattr(result, 'ragged_rank', 0), getattr(expected, 'ragged_rank', 0))
self.assertRaggedEqual(result, expected)
def testBroadcastToRank(self, dim_sizes, rank, expected_dim_sizes): shape = RaggedTensorDynamicShape.from_dim_sizes(dim_sizes) expected = RaggedTensorDynamicShape.from_dim_sizes(expected_dim_sizes) broadcasted_shape = shape.broadcast_to_rank(rank) self.assertShapeEq(broadcasted_shape, expected) self.assertEqual(broadcasted_shape.rank, rank)
