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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for tensorflow.python.framework.ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gc
import weakref
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import types_pb2
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import session
from tensorflow.python.eager import context
from tensorflow.python.eager import function as eager_function
from tensorflow.python.framework import common_shapes
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import test_ops
from tensorflow.python.framework import test_util
from tensorflow.python.framework import versions
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import resources
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
import tensorflow.python.ops.gradients # pylint: disable=unused-import
from tensorflow.python.platform import googletest
from tensorflow.python.util import compat
ops._set_call_cpp_shape_fn(common_shapes.call_cpp_shape_fn)
@test_util.with_c_api
class ResourceTest(test_util.TensorFlowTestCase):
def testBuildGraph(self):
with self.test_session():
pt = test_ops.stub_resource_handle_op(container="a", shared_name="b")
test_ops.resource_create_op(pt).run()
def testInitialize(self):
with self.test_session():
handle = test_ops.stub_resource_handle_op(container="a", shared_name="b")
resources.register_resource(
handle=handle,
create_op=test_ops.resource_create_op(handle),
is_initialized_op=test_ops.resource_initialized_op(handle))
self.assertEquals(
len(
resources.report_uninitialized_resources(
resources.shared_resources()).eval()), 1)
resources.initialize_resources(resources.shared_resources()).run()
self.assertEquals(
len(
resources.report_uninitialized_resources(
resources.shared_resources()).eval()), 0)
@test_util.with_c_api
class TensorAndShapeTest(test_util.TensorFlowTestCase):
def testShape(self):
op = ops.Operation(
ops._NodeDef("FloatOutput", "myop"), ops.Graph(), [], [dtypes.float32])
t = op.outputs[0]
self.assertEqual(tensor_shape.unknown_shape(), t.get_shape())
t.set_shape([1, 2, 3])
self.assertEqual([1, 2, 3], t.get_shape())
def testIterable(self):
op = ops.Operation(
ops._NodeDef("FloatOutput", "myop"), ops.Graph(), [], [dtypes.float32])
t = op.outputs[0]
self.assertTrue(isinstance(t, ops.Tensor))
with self.assertRaisesRegexp(TypeError, "iter"):
for _ in t:
pass
def testAddShape(self):
with self.test_session():
a = array_ops.zeros([2, 3])
b = array_ops.ones([1, 3])
c = a + b
self.assertEqual([2, 3], c.shape)
def testUnknownDim(self):
with self.test_session():
a = array_ops.placeholder(dtype=dtypes.float32, shape=[2, None, 3])
b = array_ops.placeholder(dtype=dtypes.float32, shape=[2, None, 3])
c = a + b
self.assertEqual([2, None, 3], c.shape.as_list())
def testUnknownShape(self):
with self.test_session():
a = array_ops.placeholder(dtype=dtypes.float32, shape=None)
b = array_ops.ones([1, 3])
c = a + b
self.assertEqual(tensor_shape.unknown_shape(), c.shape)
def testScalarShape(self):
with self.test_session():
a = array_ops.placeholder(dtype=dtypes.float32, shape=[])
b = array_ops.ones([])
c = a + b
self.assertEqual(tensor_shape.scalar(), c.shape)
def testShapeFunctionError(self):
with self.test_session():
a = array_ops.ones([1, 2, 3])
b = array_ops.ones([4, 5, 6])
with self.assertRaisesRegexp(
ValueError,
r"Dimensions must be equal, but are 2 and 5 for 'add' \(op: 'Add'\) "
r"with input shapes: \[1,2,3\], \[4,5,6\]."):
_ = a + b
@test_util.with_c_api
class IndexedSlicesTest(test_util.TensorFlowTestCase):
def testToTensor(self):
with self.test_session():
values = constant_op.constant([2, 3, 5, 7], shape=[2, 2])
indices = constant_op.constant([0, 2])
dense_shape = constant_op.constant([3, 2])
x = ops.IndexedSlices(values, indices, dense_shape)
tensor = ops.convert_to_tensor(x, name="tensor")
self.assertAllEqual(tensor.eval(), [[2, 3], [0, 0], [5, 7]])
def testNegation(self):
with self.test_session():
values = constant_op.constant([2, 3, 5, 7], shape=[2, 2])
indices = constant_op.constant([0, 2])
x = -ops.IndexedSlices(values, indices)
self.assertAllEqual(x.values.eval(), [[-2, -3], [-5, -7]])
self.assertAllEqual(x.indices.eval(), [0, 2])
def testScalarMul(self):
with self.test_session():
values = constant_op.constant([2, 3, 5, 7], shape=[2, 2])
indices = constant_op.constant([0, 2])
x = math_ops.scalar_mul(-2, ops.IndexedSlices(values, indices))
self.assertAllEqual(x.values.eval(), [[-4, -6], [-10, -14]])
self.assertAllEqual(x.indices.eval(), [0, 2])
@test_util.with_c_api
class NodeDefConstructorTest(test_util.TensorFlowTestCase):
def testNoArgs(self):
nodedef = ops._NodeDef("None", "bar")
self.assertProtoEquals("op: 'None' name: 'bar'", nodedef)
def testArgs(self):
nodedef = ops._NodeDef("foo", "bar", device="/device:baz:*")
self.assertProtoEquals("op:'foo' name:'bar' device:'/device:baz:*'",
nodedef)
nodedef = ops._NodeDef("foo", "bar", device=pydev.DeviceSpec(job="j"))
self.assertProtoEquals("op:'foo' name:'bar' device:'/job:j'", nodedef)
def _apply_op(g, *args, **kwargs):
op = g.create_op(*args, **kwargs)
if len(op.outputs) == 1:
return op.outputs[0]
else:
return op.outputs
@test_util.with_c_api
class OperationTest(test_util.TensorFlowTestCase):
def testNoInputs(self):
op = test_ops.float_output_string_output(name="myop").a.op
self.assertEqual(2, len(op.values()))
self.assertEqual(0, len(op.inputs))
self.assertEqual("myop", op.name)
float_t, label_str_t = op.values()
self.assertEqual(dtypes.float32, float_t.dtype)
self.assertEqual(op, float_t.op)
self.assertEqual(0, float_t._value_index)
self.assertEqual(0, len(float_t.consumers()))
self.assertEqual("myop", float_t._as_node_def_input())
self.assertEqual(dtypes.string, label_str_t.dtype)
self.assertEqual(op, label_str_t.op)
self.assertEqual(1, label_str_t._value_index)
self.assertEqual(0, len(label_str_t.consumers()))
self.assertEqual("myop:1", label_str_t._as_node_def_input())
self.assertProtoEquals("op:'FloatOutputStringOutput' name:'myop'",
op.node_def)
def testNoOutputs(self):
op1 = test_ops.float_output(name="myop1").op
float_t, = op1.values()
op2 = test_ops.float_input(float_t, name="myop2")
self.assertEqual(0, len(op2.values()))
self.assertEqual(1, len(op2.inputs))
self.assertIs(float_t, op2.inputs[0])
self.assertEqual(1, len(float_t.consumers()))
self.assertEqual(op2, float_t.consumers()[0])
self.assertProtoEquals("op:'FloatOutput' name:'myop1'", op1.node_def)
self.assertProtoEquals("op:'FloatInput' name:'myop2' input:'myop1'",
op2.node_def)
def testInputsAndOutputs(self):
op1 = test_ops.float_output(name="myop1").op
self.assertEqual(1, len(op1.values()))
float1_t, = op1.values()
op2 = test_ops.float_output_string_output(name="myop2").a.op
self.assertEqual(2, len(op2.values()))
float2_t, label2_str_t = op2.values()
# Note that we consume label2_str_t twice here.
op3 = test_ops.foo2(float1_t, label2_str_t, label2_str_t, name="myop3").d.op
self.assertEqual(2, len(op3.values()))
self.assertEqual(1, len(float1_t.consumers()))
self.assertEqual(op3, float1_t.consumers()[0])
self.assertEqual(0, len(float2_t.consumers()))
self.assertEqual(2, len(label2_str_t.consumers()))
self.assertEqual(op3, label2_str_t.consumers()[0])
self.assertEqual(op3, label2_str_t.consumers()[1])
self.assertProtoEquals("""
op:'Foo2' name:'myop3'
input:'myop1' input:'myop2:1' input:'myop2:1'
""", op3.node_def)
def testDeviceObject(self):
op = ops.Operation(ops._NodeDef("None", "myop"), ops.Graph(), [], [])
op._set_device("/job:goo/device:GPU:0")
self.assertProtoEquals(
"op:'None' name:'myop' device:'/job:goo/device:GPU:0' ", op.node_def)
op = ops.Operation(ops._NodeDef("None", "op2"), ops.Graph(), [], [])
op._set_device(
pydev.DeviceSpec(
job="muu", device_type="CPU", device_index=0))
self.assertProtoEquals(
"op:'None' name:'op2' device:'/job:muu/device:CPU:0'", op.node_def)
def testReferenceInput(self):
g = ops.Graph()
op1 = ops.Operation(
ops._NodeDef("RefOutputFloatOutput", "op1"), g, [],
[dtypes.float32_ref, dtypes.float32])
g._add_op(op1)
self.assertProtoEquals("op:'RefOutputFloatOutput' name:'op1'", op1.node_def)
self.assertEquals([], list(op1.inputs))
ref_t, nonref_t = op1.values()
# NOTE(mrry): Must specify input_types to preserve ref-typed input.
op2 = ops.Operation(
ops._NodeDef("RefInputFloatInput", "op2"),
g, [ref_t, nonref_t], [],
input_types=[dtypes.float32_ref, dtypes.float32])
g._add_op(op2)
self.assertProtoEquals(
"op:'RefInputFloatInput' name:'op2' input:'op1' input:'op1:1'",
op2.node_def)
self.assertEquals([ref_t, nonref_t], list(op2.inputs))
op3 = ops.Operation(
ops._NodeDef("TwoFloatInputs", "op3"), g, [ref_t, nonref_t], [])
g._add_op(op3)
self.assertProtoEquals(
"op:'TwoFloatInputs' name:'op3' input:'op1' input:'op1:1'",
op3.node_def)
def testInvalidNames(self):
g = ops.Graph()
with self.assertRaises(ValueError):
ops.Operation(ops._NodeDef("op", ""), g)
with self.assertRaises(ValueError):
ops.Operation(ops._NodeDef("op", "_invalid"), g)
with self.assertRaises(ValueError):
ops.Operation(ops._NodeDef("op", "-invalid"), g)
with self.assertRaises(ValueError):
ops.Operation(ops._NodeDef("op", "/invalid"), g)
with self.assertRaises(ValueError):
ops.Operation(ops._NodeDef("op", "invalid:0"), g)
def testNoShapeFunction(self):
op = test_ops.a()
self.assertEqual(tensor_shape.unknown_shape(), op.get_shape())
def testConvertToTensorNestedArray(self):
with self.test_session():
values = [[2], [3], [5], [7]]
tensor = ops.convert_to_tensor(values)
self.assertAllEqual((4, 1), tensor.get_shape().as_list())
self.assertAllEqual(values, tensor.eval())
def testShapeTuple(self):
with self.test_session():
c = constant_op.constant(1)
self.assertEqual(c._shape_tuple(), ()) # pylint: disable=protected-access
def testConvertToTensorEager(self):
with context.eager_mode():
t = constant_op.constant(1)
self.assertTrue(isinstance(t, ops.EagerTensor))
converted = ops.convert_to_tensor(t)
self.assertTrue(isinstance(converted, ops.EagerTensor))
converted = ops.convert_to_tensor(1)
self.assertTrue(isinstance(converted, ops.EagerTensor))
def testConvertToTensorNestedTuple(self):
with self.test_session():
values = ((2,), (3,), (5,), (7,))
tensor = ops.convert_to_tensor(values)
self.assertAllEqual((4, 1), tensor.get_shape().as_list())
self.assertAllEqual(values, ops.convert_to_tensor(values).eval())
def testConvertToTensorNestedTensors(self):
with self.test_session():
values = ((2,), (3,), (5,), (7,))
tensor = ops.convert_to_tensor(
[constant_op.constant(row) for row in values])
self.assertAllEqual((4, 1), tensor.get_shape().as_list())
self.assertAllEqual(values, tensor.eval())
tensor = ops.convert_to_tensor(
[[constant_op.constant(v) for v in row] for row in values])
self.assertAllEqual((4, 1), tensor.get_shape().as_list())
self.assertAllEqual(values, tensor.eval())
def testConvertToTensorNestedMix(self):
with self.test_session():
values = ([2], (3,), [constant_op.constant(5)], constant_op.constant([7]))
tensor = ops.convert_to_tensor(values)
self.assertAllEqual((4, 1), tensor.get_shape().as_list())
self.assertAllEqual(((2,), (3,), (5,), (7,)), tensor.eval())
def testConvertToTensorPreferred(self):
with self.test_session():
values = [2, 3, 5, 7]
tensor = ops.convert_to_tensor(values, preferred_dtype=dtypes.float32)
self.assertEqual(dtypes.float32, tensor.dtype)
with self.test_session():
# Convert empty tensor to anything.
values = []
tensor = ops.convert_to_tensor(values, preferred_dtype=dtypes.int64)
self.assertEqual(dtypes.int64, tensor.dtype)
with self.test_session():
# The preferred dtype is a type error and will convert to
# float32 instead.
values = [1.23]
tensor = ops.convert_to_tensor(values, preferred_dtype=dtypes.int64)
self.assertEqual(dtypes.float32, tensor.dtype)
def testConvertToInvalidTensorType(self):
with self.assertRaises(TypeError):
# Forcing an invalid dtype should fail with a type error.
values = [1.23]
_ = ops.convert_to_tensor(values, dtype=dtypes.int64)
def testNoConvert(self):
# Operation cannot be converted to Tensor.
op = control_flow_ops.no_op()
with self.assertRaisesRegexp(TypeError,
r"Can't convert Operation '.*' to Tensor"):
ops.convert_to_tensor(op)
def testStr(self):
node_def = ops._NodeDef("None", "op1")
op = ops.Operation(node_def, ops.Graph(), [], [dtypes.float32])
self.assertEqual(str(node_def), str(op))
def testRepr(self):
op = ops.Operation(
ops._NodeDef("None", "op1"), ops.Graph(), [], [dtypes.float32])
self.assertEqual("<tf.Operation 'op1' type=None>", repr(op))
def testGetAttr(self):
op = test_ops.default_attrs()
self.assertEqual(op.get_attr("string_val"), b"abc")
self.assertEqual(op.get_attr("string_list_val"), [b"abc", b""])
self.assertEqual(op.get_attr("int_val"), 123)
self.assertEqual(op.get_attr("int_list_val"), [1, 2, 3])
self.assertEqual(op.get_attr("float_val"), 10.0)
self.assertEqual(op.get_attr("float_list_val"), [10.0])
self.assertEqual(op.get_attr("bool_val"), True)
self.assertEqual(op.get_attr("bool_list_val"), [True, False])
self.assertEqual(op.get_attr("shape_val"),
tensor_shape.as_shape([2, 1]).as_proto())
self.assertEqual(op.get_attr("shape_list_val"),
[tensor_shape.as_shape([]).as_proto(),
tensor_shape.as_shape([1]).as_proto()])
self.assertEqual(op.get_attr("tensor_val"),
tensor_util.make_tensor_proto(1, dtypes.int32))
self.assertEqual(op.get_attr("tensor_list_val"),
[tensor_util.make_tensor_proto(1, dtypes.int32)])
type_val = op.get_attr("type_val")
# First check that type_val is a DType, because the assertEquals will work
# no matter what since DType overrides __eq__
self.assertIsInstance(type_val, dtypes.DType)
self.assertEqual(type_val, dtypes.int32)
type_list_val = op.get_attr("type_list_val")
self.assertTrue(all(isinstance(x, dtypes.DType) for x in type_list_val))
self.assertEqual(type_list_val, [dtypes.int32, dtypes.float32])
@function.Defun(dtypes.float32, func_name="MyFunc")
def func(x):
return x
op = test_ops.func_attr(func)
self.assertEqual(op.get_attr("f"),
attr_value_pb2.NameAttrList(name="MyFunc"))
# Try fetching missing attr
if ops._USE_C_API:
error_msg = "Operation 'FuncAttr' has no attr named 'FakeAttr'."
else:
error_msg = "No attr named 'FakeAttr' in name: \"FuncAttr\""
with self.assertRaisesRegexp(ValueError, error_msg):
op.get_attr("FakeAttr")
# TODO(b/65162920): remove this test when users who are directly mutating the
# node_def have been updated to proper usage.
def testSetAttr(self):
op = test_ops.int_attr().op
op._set_attr("foo", attr_value_pb2.AttrValue(i=2))
# TODO(skyewm): add node_def check
self.assertEqual(op.get_attr("foo"), 2)
# TODO(nolivia): test all error cases
def testAddControlInput(self):
# The C API dedups redundant control edges, pure Python does not
if ops._USE_C_API: return
with ops.Graph().as_default():
x = constant_op.constant(1).op
y = constant_op.constant(2).op
z = constant_op.constant(3).op
z._add_control_input(x) # pylint: disable=protected-access
self.assertEqual(z.control_inputs, [x])
z._add_control_input(x) # pylint: disable=protected-access
self.assertEqual(z.control_inputs, [x, x])
z._add_control_inputs([x, y, y]) # pylint: disable=protected-access
self.assertEqual(z.control_inputs, [x, x, x, y, y])
def testAddControlInputC(self):
# The C API dedups redundant control edges, pure Python does not
if not ops._USE_C_API: return
with ops.Graph().as_default():
x = constant_op.constant(1).op
y = constant_op.constant(2).op
z = constant_op.constant(3).op
z._add_control_input(x) # pylint: disable=protected-access
self.assertEqual(z.control_inputs, [x])
z._add_control_input(x) # pylint: disable=protected-access
self.assertEqual(z.control_inputs, [x])
z._add_control_inputs([x, y, y]) # pylint: disable=protected-access
self.assertEqual(z.control_inputs, [x, y])
def testRemoveAllControlInputs(self):
a = constant_op.constant(1)
with ops.control_dependencies([a]):
b = constant_op.constant(2)
c = constant_op.constant(3)
d = constant_op.constant(4)
e = constant_op.constant(5)
with ops.control_dependencies([a, c]):
f = d + e
self.assertEqual(a.op.control_inputs, [])
self.assertEqual(b.op.control_inputs, [a.op])
self.assertEqual(f.op.control_inputs, [a.op, c.op])
a.op._remove_all_control_inputs() # pylint: disable=protected-access
self.assertEqual(a.op.control_inputs, [])
b.op._remove_all_control_inputs() # pylint: disable=protected-access
self.assertEqual(b.op.control_inputs, [])
f.op._remove_all_control_inputs() # pylint: disable=protected-access
self.assertEqual(f.op.control_inputs, [])
self.assertEqual(list(f.op.inputs), [d, e])
def testControlInputCycle(self):
# Non-C API path has a different error message
if not ops._USE_C_API: return
graph = ops.Graph()
with graph.as_default():
z = constant_op.constant(0)
x = constant_op.constant(1)
y = constant_op.constant(2)
y.op._add_control_input(z.op) # pylint: disable=protected-access
y.op._add_control_input(x.op) # pylint: disable=protected-access
x.op._add_control_input(y.op) # pylint: disable=protected-access
with self.test_session(graph=graph) as sess:
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"Graph is invalid, contains a cycle with 2 nodes"):
sess.run(x)
def testUpdateInput(self):
g = ops.Graph()
with g.as_default():
x = constant_op.constant(1)
y = constant_op.constant(2)
z = x + y
z.op._update_input(0, y) # pylint: disable=protected-access
self.assertEquals(list(z.op.inputs), [y, y])
self.assertEquals(x.consumers(), [])
self.assertEquals(y.consumers(), [z.op, z.op])
with session.Session(graph=g) as sess:
self.assertEquals(sess.run(z), 4)
z.op._update_input(0, x) # pylint: disable=protected-access
self.assertEquals(list(z.op.inputs), [x, y])
self.assertEquals(x.consumers(), [z.op])
self.assertEquals(y.consumers(), [z.op])
with session.Session(graph=g) as sess:
self.assertEquals(sess.run(z), 3)
z.op._update_input(1, y) # pylint: disable=protected-access
self.assertEquals(list(z.op.inputs), [x, y])
self.assertEquals(x.consumers(), [z.op])
self.assertEquals(y.consumers(), [z.op])
with session.Session(graph=g) as sess:
self.assertEquals(sess.run(z), 3)
def testUpdateInputGraphError(self):
g_0 = ops.Graph()
g_1 = ops.Graph()
with g_0.as_default():
x = constant_op.constant(1)
with g_1.as_default():
y = constant_op.constant(2)
z = y * 2
with self.assertRaisesRegexp(ValueError, "must be from the same graph"):
z.op._update_input(0, x) # pylint: disable=protected-access
def testUpdateInputTypeError(self):
g = ops.Graph()
with g.as_default():
w = constant_op.constant(0)
x = constant_op.constant("")
y = constant_op.constant(1)
z = y + w
z.op._update_input(0, x) # pylint: disable=protected-access
with session.Session(graph=g) as sess:
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
"Input 0 of node add was passed string from Const_1:0 incompatible "
"with expected int32"):
sess.run(z)
def testUpdateInputShapeError(self):
# C-API throws the error differently.
if ops._USE_C_API:
return
g = ops.Graph()
with g.as_default():
w = constant_op.constant(2, shape=[3, 1])
x = constant_op.constant(0, shape=[3, 1])
y = constant_op.constant(1, shape=[2, 2])
z = w + x
z.op._update_input(0, y) # pylint: disable=protected-access
with session.Session(graph=g) as sess:
with self.assertRaisesRegexp(errors.InvalidArgumentError,
r"Incompatible shapes: \[2,2\] vs. \[3,1\]"):
sess.run(z)
def testUpdateInputShapeErrorC(self):
if not ops._USE_C_API:
return
g = ops.Graph()
with g.as_default():
w = constant_op.constant(2, shape=[3, 1])
x = constant_op.constant(0, shape=[3, 1])
y = constant_op.constant(1, shape=[2, 2])
z = w + x
with self.assertRaisesRegexp(
errors.InvalidArgumentError,
r"Cannot update edge, incompatible shapes: \[2,2\] and \[3,1\]"):
z.op._update_input(0, y) # pylint: disable=protected-access
def testUpdateInputOutOfRange(self):
# C-API throws the error differently.
if ops._USE_C_API: return
g = ops.Graph()
with g.as_default():
x = constant_op.constant(1)
with self.assertRaisesRegexp(IndexError, "list index out of range"):
x.op._update_input(1, x) # pylint: disable=protected-access
def testUpdateInputOutOfRangeC(self):
# C-API throws the error differently.
if not ops._USE_C_API: return
g = ops.Graph()
with g.as_default():
x = constant_op.constant(1)
with self.assertRaisesRegexp(
errors.OutOfRangeError,
r"Cannot update edge. Input index \[1\] is greater than the number of "
r"total inputs \[0\]."
):
x.op._update_input(1, x) # pylint: disable=protected-access
def testOpDef(self):
x = constant_op.constant(0)
y = constant_op.constant(1)
z = x + y
# Pure Python mode doesn't create OpDefs for constants
if ops._USE_C_API:
self.assertEqual(x.op.op_def.name, "Const")
self.assertEqual(len(x.op.op_def.input_arg), 0)
self.assertEqual(len(x.op.op_def.output_arg), 1)
self.assertEqual(z.op.op_def.name, "Add")
self.assertEqual(len(z.op.op_def.input_arg), 2)
self.assertEqual(len(z.op.op_def.output_arg), 1)
def testInputFromDifferentGraphError(self):
g_0 = ops.Graph()
g_1 = ops.Graph()
with g_0.as_default():
x = constant_op.constant(1)
with g_1.as_default():
y = constant_op.constant(2)
with self.assertRaisesRegexp(ValueError, "must be from the same graph"):
y * x # pylint: disable=pointless-statement
def testInputsAreImmutable(self):
g = ops.Graph()
with g.as_default():
x = test_ops.int_output()
op = test_ops.int_input_int_output(x, name="myop").op
with self.assertRaisesRegexp(
AttributeError, "'_InputList' object has no attribute 'append'"):
op.inputs.append(None)
@test_util.with_c_api
class CreateOpTest(test_util.TensorFlowTestCase):
def testNodeDefArgs(self):
g = ops.Graph()
op1 = g.create_op("FloatOutput", [], [dtypes.float32], None, name="myop1")
with g.device("/device:GPU:0"):
op2 = g.create_op(
"FloatOutputStringOutput", [], [dtypes.float32, dtypes.string], None,
name="myop2")
op3 = g.create_op(
"Foo3",
[list(op1.values())[0], list(op2.values())[1], list(op2.values())[0]],
[dtypes.float32, dtypes.int32],
None,
name="myop3")
self.assertDeviceEqual(None, op1.device)
self.assertDeviceEqual("/device:GPU:0", op2.device)
self.assertDeviceEqual(None, op3.device)
self.assertProtoEquals("name:'myop1' op:'FloatOutput'", op1.node_def)
self.assertProtoEquals(
"name:'myop2' op:'FloatOutputStringOutput' device:'/device:GPU:0'",
op2.node_def)
self.assertProtoEquals(
"name:'myop3' input:'myop1' input:'myop2:1' input:'myop2' op:'Foo3'",
op3.node_def)
def testReferenceInput(self):
g = ops.Graph()
op1 = g.create_op(
"RefOutputFloatOutput", [], [dtypes.float32_ref, dtypes.float32],
name="op1")
self.assertProtoEquals("op:'RefOutputFloatOutput' name:'op1'", op1.node_def)
ref_t, nonref_t = op1.values()
# NOTE(mrry): Must specify input_types to preserve ref-typed input.
op2 = g.create_op(
"RefInputFloatInput", [ref_t, nonref_t], [],
input_types=[dtypes.float32_ref, dtypes.float32],
name="op2")
self.assertProtoEquals(
"op:'RefInputFloatInput' name:'op2' input:'op1' input:'op1:1'",
op2.node_def)
op3 = g.create_op("TwoFloatInputs", [ref_t, nonref_t], [], name="op3")
self.assertProtoEquals(
"op:'TwoFloatInputs' name:'op3' input:'op1' input:'op1:1'",
op3.node_def)
def testFinalized(self):
g = ops.Graph()
g.finalize()
with self.assertRaises(RuntimeError):
g.create_op("FloatOutput", [], [dtypes.float32], None, name="myop1")
# Test unfinalize.
g._unsafe_unfinalize()
g.create_op("FloatOutput", [], [dtypes.float32], None, name="myop1")
# NOTE(skyewm): these cases test the private Graph._create_op_from_tf_operation
# method. Arguably we should only test the public APIs that depend on this
# method. However, this logic is complex and tricky, and it can be difficult to
# ascertain if we have adequate coverage (e.g. a graph may run successfully if
# the control flow context isn't set properly, but a more complicated use case
# that might not be obvious to test will fail). Thus we instead explicitly test
# the low-level behavior.
@test_util.with_c_api
class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase):
def testBasic(self):
g = ops.Graph()
with g.as_default():
x = test_ops.int_output()
if ops._USE_C_API:
c_op = ops._create_c_op(
g, ops._NodeDef("IntInputIntOutput", "myop"), [x], [])
op = g._create_op_from_tf_operation(c_op)
else:
# Test pure-Python version to make sure C API has same behavior.
op = test_ops.int_input_int_output(x, name="myop").op
self.assertEqual(op.name, "myop")
self.assertEqual(op.type, "IntInputIntOutput")
self.assertEqual(len(op.outputs), 1)
self.assertEqual(op.outputs[0].shape, tensor_shape.unknown_shape())
self.assertEqual(list(op.inputs), [x])
self.assertEqual(op.control_inputs, [])
self.assertEqual(op.graph, g)
self.assertEqual(x.consumers(), [op])
self.assertIsNotNone(op.traceback)
self.assertEqual(g.get_operation_by_name("myop"), op)
self.assertEqual(g.get_tensor_by_name("myop:0"), op.outputs[0])
def testShape(self):
g = ops.Graph()
with g.as_default():
x = constant_op.constant([[1, 2, 3], [4, 5, 6]])
if ops._USE_C_API:
c_op = ops._create_c_op(g, ops._NodeDef("Identity", "myop"), [x], [])
op = g._create_op_from_tf_operation(c_op)
else:
# Test pure-Python version to make sure C API has same behavior.
op = array_ops.identity(x, name="myop").op
self.assertEqual(op.name, "myop")
self.assertEqual(op.type, "Identity")
self.assertEqual(len(op.outputs), 1)
self.assertEqual(op.outputs[0].shape, tensor_shape.matrix(2, 3))
def testUniqueName(self):
g = ops.Graph()
with g.as_default():
if ops._USE_C_API:
c_op = ops._create_c_op(g, ops._NodeDef("IntOutput", "myop"), [], [])
c_op2 = ops._create_c_op(g, ops._NodeDef("IntOutput", "myop_1"), [], [])
op = g._create_op_from_tf_operation(c_op)
op2 = g._create_op_from_tf_operation(c_op2)
else:
# Test pure-Python version to make sure C API has same behavior.
op = test_ops.int_output(name="myop").op
op2 = test_ops.int_output(name="myop_1").op
# Create ops with same names as op1 and op2. We expect the new names to be
# uniquified.
op3 = test_ops.int_output(name="myop").op
op4 = test_ops.int_output(name="myop_1").op
self.assertEqual(op.name, "myop")
self.assertEqual(op2.name, "myop_1")
self.assertEqual(op3.name, "myop_2")
self.assertEqual(op4.name, "myop_1_1")
def testCond(self):
g = ops.Graph()
with g.as_default():
x = test_ops.int_output()
def true_fn():
if ops._USE_C_API:
ops._create_c_op(ops.get_default_graph(),
ops._NodeDef("IntInput", "cond/myop"), [x], [])
new_ops = g._add_new_tf_operations()
self.assertEqual(len(new_ops), 1)
else:
# Test pure-Python version to make sure C API has same behavior.
test_ops.int_input(x, name="myop")
return x
control_flow_ops.cond(x < 10, true_fn, lambda: x)
op = g.get_operation_by_name("cond/myop")
self.assertIsNotNone(op)
self.assertEqual(op.name, "cond/myop")
self.assertEqual(op.type, "IntInput")
self.assertEqual(op.outputs, [])
op_input = op.inputs[0].op
self.assertEqual(op_input.type, "Switch")
self.assertEqual(op_input.inputs[0], x)
self.assertEqual(op.graph, g)
# pylint: disable=protected-access
self.assertIsNotNone(op._get_control_flow_context())
self.assertEqual(op._get_control_flow_context().name,
"cond/cond_text")
# pylint: enable=protected-access
def testWhileLoop(self):
g = ops.Graph()
with g.as_default():
x = test_ops.int_output()
def body(i):
if ops._USE_C_API:
ops._create_c_op(ops.get_default_graph(),
ops._NodeDef("IntInput", "myloop/myop"), [x], [])
new_ops = g._add_new_tf_operations()
self.assertEqual(len(new_ops), 1)
else:
# Test pure-Python version to make sure C API has same behavior.
test_ops.int_input(x, name="myop")
return i
control_flow_ops.while_loop(lambda i: i < 10, body, [0], name="myloop")
op = g.get_operation_by_name("myloop/myop")
self.assertIsNotNone(op)
self.assertEqual(op.name, "myloop/myop")
self.assertEqual(op.type, "IntInput")
self.assertEqual(op.outputs, [])
op_input = op.inputs[0].op
self.assertEqual(op_input.type, "Enter")
self.assertEqual(list(op_input.inputs), [x])
self.assertEqual(op.graph, g)
# pylint: disable=protected-access
self.assertIsNotNone(op._get_control_flow_context())
self.assertEqual(op._get_control_flow_context().name,
"myloop/while_context")
# pylint: enable=protected-access
def testWhileLoopWithInternalControlDep(self):
g = ops.Graph()
with g.as_default():
x = test_ops.int_output()
def body(i):
c = constant_op.constant(1.0, name="c")
if ops._USE_C_API:
ops._create_c_op(ops.get_default_graph(),
ops._NodeDef("IntInput", "myloop/myop"), [x], [])
with ops.control_dependencies([c]):
new_ops = g._add_new_tf_operations()
self.assertEqual(len(new_ops), 1)
else:
with ops.control_dependencies([c]):
test_ops.int_input(x, name="myop")
return i
control_flow_ops.while_loop(lambda i: i < 10, body, [0], name="myloop")
op = g.get_operation_by_name("myloop/myop")
self.assertIsNotNone(op)
c = g.get_operation_by_name("myloop/c")
self.assertIsNotNone(c)
# Internal control dep is preserved
self.assertEqual(op.control_inputs, [c])
def testWhileLoopWithExternalControlDep(self):
g = ops.Graph()
with g.as_default():
x = test_ops.int_output()
c = constant_op.constant(1.0)
def body(i):
if ops._USE_C_API:
ops._create_c_op(ops.get_default_graph(),
ops._NodeDef("IntInput", "myloop/myop"), [x], [])
with ops.control_dependencies([c]):
new_ops = g._add_new_tf_operations()
self.assertEqual(len(new_ops), 1)
else:
with ops.control_dependencies([c]):
test_ops.int_input(x, name="myop")
return i
control_flow_ops.while_loop(lambda i: i < 10, body, [0], name="myloop")
op = g.get_operation_by_name("myloop/myop")
self.assertIsNotNone(op)
self.assertEqual(len(op.control_inputs), 1)
# External control dep is removed and replaced with internal control dep
self.assertNotEqual(op.control_inputs[0], c.op)
self.assertIsNotNone(op.control_inputs[0]._get_control_flow_context())
@test_util.with_c_api
class ApplyOpTest(test_util.TensorFlowTestCase):
def testNodeDefArgs(self):
g = ops.Graph()
t1 = _apply_op(g, "FloatOutput", [], [dtypes.float32], name="myop1")
with g.device("/device:GPU:0"):
t2 = _apply_op(
g, "TwoIntOutputs", [], [dtypes.int32, dtypes.int32], name="myop2")
t3 = _apply_op(
g,
"Foo1", [t1, t2[1], t2[0]], [dtypes.float32, dtypes.int32],
name="myop3")
self.assertTrue(isinstance(t1, ops.Tensor))
self.assertTrue(isinstance(t2, list))
self.assertTrue(isinstance(t3, list))
self.assertTrue(isinstance(t3[0], ops.Tensor))
self.assertEqual("myop1", t1._as_node_def_input())
self.assertEqual("myop2", t2[0]._as_node_def_input())
self.assertEqual("myop2:1", t2[1]._as_node_def_input())
self.assertEqual("myop3", t3[0]._as_node_def_input())
# Validate that we got the right ops as well
self.assertProtoEquals("name:'myop1' op:'FloatOutput'", t1.op.node_def)
self.assertProtoEquals(
"name:'myop2' op:'TwoIntOutputs' device:'/device:GPU:0'",
t2[0].op.node_def)
self.assertProtoEquals(
"name:'myop3' input:'myop1' input:'myop2:1' input:'myop2' op:'Foo1'",
t3[0].op.node_def)
def testReferenceInput(self):
g = ops.Graph()
ref_t, nonref_t = _apply_op(
g, "RefOutputFloatOutput", [], [dtypes.float32_ref, dtypes.float32],
name="op1")
self.assertProtoEquals("op:'RefOutputFloatOutput' name:'op1'",
ref_t.op.node_def)
# NOTE(mrry): Must specify input_types to preserve ref-typed input.
out_2 = _apply_op(
g,
"RefInputFloatInputIntOutput", [ref_t, nonref_t], [dtypes.int32],
input_types=[dtypes.float32_ref, dtypes.float32],
name="op2")
self.assertProtoEquals(
"op:'RefInputFloatInputIntOutput' name:'op2' input:'op1' input:'op1:1'",
out_2.op.node_def)
out_3 = _apply_op(
g, "TwoFloatInputsIntOutput", [ref_t, nonref_t], [dtypes.int32],
name="op3")
self.assertProtoEquals(
"op:'TwoFloatInputsIntOutput' name:'op3' input:'op1' input:'op1:1'",
out_3.op.node_def)
@test_util.with_c_api
class NameStackTest(test_util.TensorFlowTestCase):
def testBasics(self):
g = ops.Graph()
self.assertEqual("foo", g.unique_name("foo", mark_as_used=False))
self.assertEqual("foo", g.unique_name("foo", mark_as_used=False))
self.assertEqual("foo", g.unique_name("foo"))
self.assertEqual("foo_1", g.unique_name("foo", mark_as_used=False))
self.assertEqual("foo_1", g.unique_name("foo"))
self.assertEqual("foo_2", g.unique_name("foo", mark_as_used=False))
self.assertEqual("foo_2", g.unique_name("foo"))
self.assertEqual("foo_1_1", g.unique_name("foo_1", mark_as_used=False))
self.assertEqual("foo_1_1", g.unique_name("foo_1"))
self.assertEqual("foo_1_2", g.unique_name("foo_1", mark_as_used=False))
self.assertEqual("foo_1_2", g.unique_name("foo_1"))
self.assertEqual("foo_1_2_1", g.unique_name("foo_1_2", mark_as_used=False))
self.assertEqual("foo_1_2_1", g.unique_name("foo_1_2"))
with g.name_scope("bar"):
self.assertEqual("bar/foo", g.unique_name("foo", mark_as_used=False))
self.assertEqual("bar/foo", g.unique_name("foo"))
self.assertEqual("bar/foo_1", g.unique_name("foo", mark_as_used=False))