def testEventShapeDynamicNdims(self):
"""Check forward/inverse shape methods with dynamic ndims."""
shape_in = tensor_shape.TensorShape([
6,
])
shape_in_ph = array_ops.placeholder(dtype=dtypes.int32)
shape_out = tensor_shape.TensorShape([2, 3])
shape_out_ph = array_ops.placeholder(dtype=dtypes.int32)
bijector = Reshape(event_shape_out=shape_out_ph,
event_shape_in=shape_in_ph,
validate_args=True)
# using the _tensor methods, we should always get a fully-specified
# result since these are evaluated at graph runtime.
with self.test_session() as sess:
(shape_out_, shape_in_) = sess.run((
bijector.forward_event_shape_tensor(shape_in),
bijector.inverse_event_shape_tensor(shape_out),
),
feed_dict={
shape_in_ph: shape_in,
shape_out_ph: shape_out,
})
self.assertAllEqual(shape_out, shape_out_)
self.assertAllEqual(shape_in, shape_in_)
def testBijector(self):
"""Do a basic sanity check of forward, inverse, jacobian."""
expected_x = np.random.randn(4, 3, 2)
expected_y = np.reshape(expected_x, [4, 6])
with self.cached_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([3, 2], [6,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
(x_,
y_,
fldj_,
ildj_) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
bijector.forward_log_det_jacobian(expected_x, event_ndims=2),
bijector.inverse_log_det_jacobian(expected_y, event_ndims=2),
), feed_dict=feed_dict)
self.assertEqual("reshape", bijector.name)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
self.assertAllClose(0., fldj_, rtol=1e-6, atol=0)
self.assertAllClose(0., ildj_, rtol=1e-6, atol=0)
def testScalarReshape(self):
"""Test reshaping to and from a scalar shape ()."""
expected_x = np.random.randn(4, 3, 1)
expected_y = np.reshape(expected_x, [4, 3])
expected_x_scalar = np.random.randn(1,)
expected_y_scalar = expected_x_scalar[0]
shape_in, shape_out, feed_dict = self.build_shapes([], [1,])
with self.cached_session() as sess:
bijector = Reshape(
event_shape_out=shape_in,
event_shape_in=shape_out, validate_args=True)
(x_,
y_,
x_scalar_,
y_scalar_
) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
bijector.inverse(expected_y_scalar),
bijector.forward(expected_x_scalar),
), feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
self.assertAllClose(expected_y_scalar, y_scalar_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x_scalar, x_scalar_, rtol=1e-6, atol=0)
def testMultipleUnspecifiedDimensionsOpError(self):
with self.cached_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [4, -1, -1,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(
"elements must have at most one `-1`."):
sess.run(bijector.forward_event_shape_tensor(shape_in),
feed_dict=feed_dict)
def _testInvalidDimensionsOpError(self, expected_error_message):
with self.cached_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [1, 2, -2,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(expected_error_message):
sess.run(bijector.forward_event_shape_tensor(shape_in),
feed_dict=feed_dict)
def _testInvalidDimensionsOpError(self, expected_error_message):
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [1, 2, -2,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(expected_error_message):
sess.run(bijector.forward_event_shape_tensor(shape_in),
feed_dict=feed_dict)
def testMultipleUnspecifiedDimensionsOpError(self):
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [4, -1, -1,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(
"elements must have at most one `-1`."):
sess.run(bijector.forward_event_shape_tensor(shape_in),
feed_dict=feed_dict)
def testValidButNonMatchingInputPartiallySpecifiedOpError(self):
x = np.random.randn(4, 3, 2)
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, -1], [1, 6, 1,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(
"Input `event_shape` does not match `event_shape_in`."):
sess.run(bijector.forward(x),
feed_dict=feed_dict)
def testInvalidDimensionsOpError(self):
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [1, 2, -2,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(
"elements must be either positive integers or `-1`."):
sess.run(bijector.forward_event_shape_tensor(shape_in),
feed_dict=feed_dict)
def testValidButNonMatchingInputPartiallySpecifiedOpError(self):
x = np.random.randn(4, 3, 2)
with self.cached_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, -1], [1, 6, 1,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(
"Input `event_shape` does not match `event_shape_in`."):
sess.run(bijector.forward(x),
feed_dict=feed_dict)
def testEventShape(self):
shape_in_static = tensor_shape.TensorShape([2, 3])
shape_out_static = tensor_shape.TensorShape([6,])
bijector = Reshape(
event_shape_out=shape_out_static,
event_shape_in=shape_in_static, validate_args=True)
# test that forward_ and inverse_event_shape do sensible things
# when shapes are statically known.
self.assertEqual(
bijector.forward_event_shape(shape_in_static),
shape_out_static)
self.assertEqual(
bijector.inverse_event_shape(shape_out_static),
shape_in_static)
def testEventShape(self):
shape_in_static = tensor_shape.TensorShape([2, 3])
shape_out_static = tensor_shape.TensorShape([
6,
])
bijector = Reshape(event_shape_out=shape_out_static,
event_shape_in=shape_in_static,
validate_args=True)
# test that forward_ and inverse_event_shape do sensible things
# when shapes are statically known.
self.assertEqual(bijector.forward_event_shape(shape_in_static),
shape_out_static)
self.assertEqual(bijector.inverse_event_shape(shape_out_static),
shape_in_static)
def testDefaultVectorShape(self):
expected_x = np.random.randn(4, 4)
expected_y = np.reshape(expected_x, [4, 2, 2])
with self.test_session() as sess:
_, shape_out, feed_dict = self.build_shapes([-1,], [-1, 2])
bijector = Reshape(shape_out,
validate_args=True)
(x_,
y_,
) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
), feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
def testDefaultVectorShape(self):
expected_x = np.random.randn(4, 4)
expected_y = np.reshape(expected_x, [4, 2, 2])
with self.cached_session() as sess:
_, shape_out, feed_dict = self.build_shapes([-1,], [-1, 2])
bijector = Reshape(shape_out,
validate_args=True)
(x_,
y_,
) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
), feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
def _testInputOutputMismatchOpError(self, expected_error_message):
x1 = np.random.randn(4, 2, 3)
x2 = np.random.randn(4, 1, 1, 5)
with self.test_session() as sess:
shape_in, shape_out, fd_mismatched = self.build_shapes([2, 3],
[1, 1, 5])
bijector = Reshape(event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(expected_error_message):
sess.run(bijector.forward(x1), feed_dict=fd_mismatched)
with self.assertRaisesError(expected_error_message):
sess.run(bijector.inverse(x2), feed_dict=fd_mismatched)
def _testInputOutputMismatchOpError(self, expected_error_message):
x1 = np.random.randn(4, 2, 3)
x2 = np.random.randn(4, 1, 1, 5)
with self.cached_session() as sess:
shape_in, shape_out, fd_mismatched = self.build_shapes([2, 3],
[1, 1, 5])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(expected_error_message):
sess.run(bijector.forward(x1), feed_dict=fd_mismatched)
with self.assertRaisesError(expected_error_message):
sess.run(bijector.inverse(x2), feed_dict=fd_mismatched)
def testInputOutputMismatchOpError(self):
x1 = np.random.randn(4, 2, 3)
x2 = np.random.randn(4, 1, 1, 5)
with self.test_session() as sess:
shape_in, shape_out, fd_mismatched = self.build_shapes([2, 3],
[1, 1, 5])
bijector = Reshape(event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
# test that *all* methods check basic assertions
with self.assertRaisesError("Input to reshape is a tensor with"):
sess.run(bijector.forward(x1), feed_dict=fd_mismatched)
with self.assertRaisesError("Input to reshape is a tensor with"):
sess.run(bijector.inverse(x2), feed_dict=fd_mismatched)
def testBothShapesPartiallySpecified(self):
expected_x = np.random.randn(4, 2, 3)
expected_y = np.reshape(expected_x, [4, 3, 2])
with self.cached_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([-1, 3], [-1, 2])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
(x_,
y_,
) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
), feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
def testValidButNonMatchingInputOpError(self):
x = np.random.randn(4, 3, 2)
with self.cached_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [1, 6, 1,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
# Here we pass in a tensor (x) whose shape is compatible with
# the output shape, so tf.reshape will throw no error, but
# doesn't match the expected input shape.
with self.assertRaisesError(
"Input `event_shape` does not match `event_shape_in`."):
sess.run(bijector.forward(x),
feed_dict=feed_dict)
def testValidButNonMatchingInputOpError(self):
x = np.random.randn(4, 3, 2)
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [1, 6, 1,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
# Here we pass in a tensor (x) whose shape is compatible with
# the output shape, so tf.reshape will throw no error, but
# doesn't match the expected input shape.
with self.assertRaisesError(
"Input `event_shape` does not match `event_shape_in`."):
sess.run(bijector.forward(x),
feed_dict=feed_dict)
def testBothShapesPartiallySpecified(self):
expected_x = np.random.randn(4, 2, 3)
expected_y = np.reshape(expected_x, [4, 3, 2])
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([-1, 3], [-1, 2])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
(x_,
y_,
) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
), feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
def testBijectiveAndFinite(self):
x = np.random.randn(4, 2, 3)
y = np.reshape(x, [4, 1, 2, 3])
with self.test_session():
bijector = Reshape(event_shape_in=[2, 3],
event_shape_out=[1, 2, 3],
validate_args=True)
assert_bijective_and_finite(bijector, x, y, rtol=1e-6, atol=0)
def testInvalidDimensionsOpError(self):
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([2, 3], [
1,
2,
-2,
])
bijector = Reshape(event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
with self.assertRaisesError(
"elements must be either positive integers or `-1`."):
sess.run(bijector.forward_event_shape_tensor(shape_in),
feed_dict=feed_dict)
def testInputOutputMismatchOpError(self):
x1 = np.random.randn(4, 2, 3)
x2 = np.random.randn(4, 1, 1, 5)
with self.test_session() as sess:
shape_in, shape_out, fd_mismatched = self.build_shapes([2, 3],
[1, 1, 5])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
# test that *all* methods check basic assertions
with self.assertRaisesError(
"Input to reshape is a tensor with"):
sess.run(bijector.forward(x1), feed_dict=fd_mismatched)
with self.assertRaisesError(
"Input to reshape is a tensor with"):
sess.run(bijector.inverse(x2), feed_dict=fd_mismatched)
def testOneShapePartiallySpecified(self):
expected_x = np.random.randn(4, 6)
expected_y = np.reshape(expected_x, [4, 2, 3])
with self.test_session() as sess:
# one of input/output shapes is partially specified
shape_in, shape_out, feed_dict = self.build_shapes([-1,], [2, 3])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
(x_,
y_,
) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
), feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
def testEventShapeTensor(self):
"""Test event_shape_tensor methods when even ndims may be dynamic."""
shape_in_static = [2, 3]
shape_out_static = [6,]
shape_in, shape_out, feed_dict = self.build_shapes(shape_in_static,
shape_out_static)
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in, validate_args=True)
# using the _tensor methods, we should always get a fully-specified
# result since these are evaluated at graph runtime.
with self.cached_session() as sess:
(shape_out_,
shape_in_) = sess.run((
bijector.forward_event_shape_tensor(shape_in),
bijector.inverse_event_shape_tensor(shape_out),
), feed_dict=feed_dict)
self.assertAllEqual(shape_out_static, shape_out_)
self.assertAllEqual(shape_in_static, shape_in_)
def testEventShapeTensor(self):
"""Test event_shape_tensor methods when even ndims may be dynamic."""
shape_in_static = [2, 3]
shape_out_static = [6,]
shape_in, shape_out, feed_dict = self.build_shapes(shape_in_static,
shape_out_static)
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in, validate_args=True)
# using the _tensor methods, we should always get a fully-specified
# result since these are evaluated at graph runtime.
with self.test_session() as sess:
(shape_out_,
shape_in_) = sess.run((
bijector.forward_event_shape_tensor(shape_in),
bijector.inverse_event_shape_tensor(shape_out),
), feed_dict=feed_dict)
self.assertAllEqual(shape_out_static, shape_out_)
self.assertAllEqual(shape_in_static, shape_in_)
def testScalarReshape(self):
"""Test reshaping to and from a scalar shape ()."""
expected_x = np.random.randn(4, 3, 1)
expected_y = np.reshape(expected_x, [4, 3])
expected_x_scalar = np.random.randn(1, )
expected_y_scalar = expected_x_scalar[0]
shape_in, shape_out, feed_dict = self.build_shapes([], [
1,
])
with self.test_session() as sess:
bijector = Reshape(event_shape_out=shape_in,
event_shape_in=shape_out,
validate_args=True)
(x_, y_, x_scalar_, y_scalar_) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
bijector.inverse(expected_y_scalar),
bijector.forward(expected_x_scalar),
),
feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
self.assertAllClose(expected_y_scalar,
y_scalar_,
rtol=1e-6,
atol=0)
self.assertAllClose(expected_x_scalar,
x_scalar_,
rtol=1e-6,
atol=0)
def testBijector(self):
"""Do a basic sanity check of forward, inverse, jacobian."""
expected_x = np.random.randn(4, 3, 2)
expected_y = np.reshape(expected_x, [4, 6])
with self.test_session() as sess:
shape_in, shape_out, feed_dict = self.build_shapes([3, 2], [6,])
bijector = Reshape(
event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
(x_,
y_,
fldj_,
ildj_) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
bijector.forward_log_det_jacobian(expected_x),
bijector.inverse_log_det_jacobian(expected_y),
), feed_dict=feed_dict)
self.assertEqual("reshape", bijector.name)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
self.assertAllClose(0., fldj_, rtol=1e-6, atol=0)
self.assertAllClose(0., ildj_, rtol=1e-6, atol=0)
def testEventShapeStatic(self):
"""Check shape methods when shape is statically known."""
shape_in = tensor_shape.TensorShape([
6,
])
shape_out = tensor_shape.TensorShape([2, 3])
bijector_static = Reshape(event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
# test that forward_ and inverse_event_shape do sensible things
# when shapes are statically known.
self.assertEqual(bijector_static.forward_event_shape(shape_in),
shape_out)
self.assertEqual(bijector_static.inverse_event_shape(shape_out),
shape_in)
with self.test_session() as sess:
(
shape_out_static_,
shape_in_static_,
) = sess.run((
bijector_static.forward_event_shape_tensor(shape_in),
bijector_static.inverse_event_shape_tensor(shape_out),
))
self.assertAllEqual(shape_out, shape_out_static_)
self.assertAllEqual(shape_in, shape_in_static_)
def testRaisesOpError(self):
x1 = np.random.randn(4, 2, 3)
x2 = np.random.randn(4, 3, 2)
x3 = np.random.randn(4, 5, 1, 1)
with self.test_session() as sess:
shape_in_ph = array_ops.placeholder(shape=[
2,
],
dtype=dtypes.int32)
shape_out_ph = array_ops.placeholder(shape=[
3,
],
dtype=dtypes.int32)
bijector = Reshape(event_shape_out=shape_out_ph,
event_shape_in=shape_in_ph,
validate_args=True)
with self.assertRaisesOpError(
"Input `event_shape` does not match `event_shape_in`."):
sess.run(bijector.forward(x2),
feed_dict={
shape_out_ph: [1, 6, 1],
shape_in_ph: [2, 3]
})
with self.assertRaisesOpError(
"event_shape_out entries must be positive."):
sess.run(bijector.forward(x1),
feed_dict={
shape_out_ph: [-1, -1, 6],
shape_in_ph: [2, 3]
})
# test that *all* methods check basic assertions
fd_mismatched = {shape_out_ph: [1, 1, 5], shape_in_ph: [2, 3]}
with self.assertRaisesOpError(
"Input/output `event_size`s do not match."):
sess.run(bijector.forward(x1), feed_dict=fd_mismatched)
with self.assertRaisesOpError(
"Input/output `event_size`s do not match."):
sess.run(bijector.inverse(x3), feed_dict=fd_mismatched)
with self.assertRaisesOpError(
"Input/output `event_size`s do not match."):
sess.run(bijector.inverse_log_det_jacobian(x3),
feed_dict=fd_mismatched)
with self.assertRaisesOpError(
"Input/output `event_size`s do not match."):
sess.run(bijector.forward_log_det_jacobian(x1),
feed_dict=fd_mismatched)
def testOneShapePartiallySpecified(self):
expected_x = np.random.randn(4, 6)
expected_y = np.reshape(expected_x, [4, 2, 3])
with self.cached_session() as sess:
# one of input/output shapes is partially specified
shape_in, shape_out, feed_dict = self.build_shapes([
-1,
], [2, 3])
bijector = Reshape(event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
(
x_,
y_,
) = sess.run((
bijector.inverse(expected_y),
bijector.forward(expected_x),
),
feed_dict=feed_dict)
self.assertAllClose(expected_y, y_, rtol=1e-6, atol=0)
self.assertAllClose(expected_x, x_, rtol=1e-6, atol=0)
def _testInvalidDimensionsStatic(self, expected_error_message):
"""Version of _testInvalidDimensionsOpError for errors detected statically.
Statically means at graph construction time.
Args:
expected_error_message: String that should be present in the error
message that `Reshape` raises for invalid shapes.
"""
shape_in, shape_out, _ = self.build_shapes([2, 3], [
1,
2,
-2,
])
with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError,
expected_error_message):
_ = Reshape(event_shape_out=shape_out,
event_shape_in=shape_in,
validate_args=True)
def testEventShapeDynamic(self):
"""Check shape methods with static ndims but dynamic shape."""
shape_in = tensor_shape.TensorShape([
6,
])
shape_in_partial = tensor_shape.TensorShape([
None,
])
shape_in_ph = array_ops.placeholder(shape=[
1,
], dtype=dtypes.int32)
shape_out = tensor_shape.TensorShape([2, 3])
shape_out_partial = tensor_shape.TensorShape([None, None])
shape_out_ph = array_ops.placeholder(shape=[
2,
], dtype=dtypes.int32)
bijector = Reshape(event_shape_out=shape_out_ph,
event_shape_in=shape_in_ph,
validate_args=True)
# if event shapes are not statically available, should
# return partially-specified TensorShapes.
self.assertAllEqual(
bijector.forward_event_shape(shape_in).as_list(),
shape_out_partial.as_list())
self.assertAllEqual(
bijector.inverse_event_shape(shape_out).as_list(),
shape_in_partial.as_list())
# using the _tensor methods, we should always get a fully-specified
# result since these are evaluated at graph runtime.
with self.test_session() as sess:
(shape_out_, shape_in_) = sess.run((
bijector.forward_event_shape_tensor(shape_in),
bijector.inverse_event_shape_tensor(shape_out),
),
feed_dict={
shape_in_ph: shape_in,
shape_out_ph: shape_out,
})
self.assertAllEqual(shape_out, shape_out_)
self.assertAllEqual(shape_in, shape_in_)
