def testResVarInvalidOutputShape(self):
res = variables.Variable(
initial_value=lambda: array_ops.zeros(shape=[], dtype=dtypes.float32),
dtype=dtypes.float32)
with self.cached_session():
res.initializer.run()
with self.assertRaisesOpError("Output must be at least 1-D"):
state_ops.scatter_nd_update(res, [[0]], [0.22]).eval()
def testRank3InvalidShape2(self):
indices = array_ops.zeros([2, 2, 1], dtypes.int32)
updates = array_ops.zeros([2, 2], dtypes.int32)
shape = np.array([2, 2, 2])
ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
with self.assertRaisesWithPredicateMatch(
ValueError, "The inner \\d+ dimensions of input\\.shape="):
state_ops.scatter_nd_update(ref, indices, updates)
def testRank3ValidShape(self):
indices = array_ops.zeros([2, 2, 2], dtypes.int32)
updates = array_ops.zeros([2, 2, 2], dtypes.int32)
shape = np.array([2, 2, 2])
ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
self.assertAllEqual(
state_ops.scatter_nd_update(ref, indices,
updates).get_shape().as_list(), shape)
def testExtraIndicesDimensions(self):
indices = array_ops.zeros([1, 1, 2], dtypes.int32)
updates = array_ops.zeros([1, 1], dtypes.int32)
shape = np.array([2, 2])
ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
scatter_update = state_ops.scatter_nd_update(ref, indices, updates)
self.assertAllEqual(scatter_update.get_shape().as_list(), shape)
expected_result = np.zeros([2, 2], dtype=np.int32)
with self.cached_session():
ref.initializer.run()
self.assertAllEqual(expected_result, scatter_update.eval())
def testSimple(self):
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32)
ref = variables.Variable([0, 0, 0, 0, 0, 0, 0, 0], dtype=dtypes.float32)
expected = np.array([0, 11, 0, 10, 9, 0, 0, 12])
scatter = state_ops.scatter_nd_update(ref, indices, updates)
init = variables.global_variables_initializer()
with self.session(use_gpu=True) as sess:
sess.run(init)
result = sess.run(scatter)
self.assertAllClose(result, expected)
def testSimple3(self):
indices = constant_op.constant([[1]], dtype=dtypes.int32)
updates = constant_op.constant([[11., 12.]], dtype=dtypes.float32)
ref = variables.Variable(
[[0., 0.], [0., 0.], [0., 0.]], dtype=dtypes.float32)
expected = np.array([[0., 0.], [11., 12.], [0., 0.]])
scatter = state_ops.scatter_nd_update(ref, indices, updates)
init = variables.global_variables_initializer()
with self.test_session(use_gpu=True) as sess:
sess.run(init)
result = sess.run(scatter)
self.assertAllClose(result, expected)
def testSimple3(self):
indices = constant_op.constant([[1]], dtype=dtypes.int32)
updates = constant_op.constant([[11., 12.]], dtype=dtypes.float32)
ref = variables.Variable([[0., 0.], [0., 0.], [0., 0.]],
dtype=dtypes.float32)
expected = np.array([[0., 0.], [11., 12.], [0., 0.]])
scatter = state_ops.scatter_nd_update(ref, indices, updates)
init = variables.global_variables_initializer()
with self.session(use_gpu=True) as sess:
self.evaluate(init)
result = self.evaluate(scatter)
self.assertAllClose(result, expected)
def testSimpleResource(self):
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
for dtype in (dtypes.int32, dtypes.float32):
updates = constant_op.constant([9, 10, 11, 12], dtype=dtype)
ref = resource_variable_ops.ResourceVariable([0, 0, 0, 0, 0, 0, 0, 0],
dtype=dtype)
expected = np.array([0, 11, 0, 10, 9, 0, 0, 12])
scatter = state_ops.scatter_nd_update(ref, indices, updates)
with test_util.device(use_gpu=True):
self.evaluate(ref.initializer)
self.evaluate(scatter)
self.assertAllClose(ref, expected)
def testSimpleResource(self):
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
updates = constant_op.constant([9, 10, 11, 12], dtype=dtypes.float32)
ref = resource_variable_ops.ResourceVariable(
[0, 0, 0, 0, 0, 0, 0, 0], dtype=dtypes.float32)
expected = np.array([0, 11, 0, 10, 9, 0, 0, 12])
scatter = state_ops.scatter_nd_update(ref, indices, updates)
init = variables.global_variables_initializer()
with self.test_session(use_gpu=True) as sess:
sess.run(init)
sess.run(scatter)
self.assertAllClose(ref.eval(), expected)
def testSimple(self):
indices = constant_op.constant([[4], [3], [1], [7]], dtype=dtypes.int32)
for dtype in (dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64,
dtypes.complex64, dtypes.complex128):
updates = constant_op.constant([9, 10, 11, 12], dtype=dtype)
ref = variables.Variable([0, 0, 0, 0, 0, 0, 0, 0], dtype=dtype)
expected = np.array([0, 11, 0, 10, 9, 0, 0, 12])
scatter = state_ops.scatter_nd_update(ref, indices, updates)
init = variables.global_variables_initializer()
with test_util.use_gpu():
self.evaluate(init)
result = self.evaluate(scatter)
self.assertAllClose(result, expected)
def testExtraIndicesDimensions(self):
indices = array_ops.zeros([1, 1, 2], dtypes.int32)
updates = array_ops.zeros([1, 1], dtypes.int32)
shape = np.array([2, 2])
scatter = array_ops.scatter_nd(indices, updates, shape)
self.assertAllEqual(scatter.get_shape().as_list(), shape)
expected_result = np.zeros([2, 2], dtype=np.int32)
with self.test_session():
self.assertAllEqual(expected_result, scatter.eval())
ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
scatter_update = state_ops.scatter_nd_update(ref, indices, updates)
self.assertAllEqual(scatter_update.get_shape().as_list(), shape)
with self.test_session():
ref.initializer.run()
self.assertAllEqual(expected_result, scatter_update.eval())