tensorflow/tensorflow/python/framework/ops_test.py at master · codeplaysoftware/tensorflow · GitHub

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# 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 threading

import weakref

from tensorflow.core.framework import attr_value_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 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)

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)

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

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])

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

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

with self.assertRaisesRegexp(

ValueError, "Operation 'FuncAttr' has no attr named 'FakeAttr'."):

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):

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])

self.assertEqual(x._control_outputs, [z])

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):

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):

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):

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

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)

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.

class CreateOpFromTFOperationTest(test_util.TensorFlowTestCase):

def testBasic(self):

g = ops.Graph()

with g.as_default():

x = test_ops.int_output()

c_op = ops._create_c_op(

g, ops._NodeDef("IntInputIntOutput", "myop"), [x], [])

op = g._create_op_from_tf_operation(c_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])

@test_util.enable_c_shapes

def testShape(self):

g = ops.Graph()

with g.as_default():

x = constant_op.constant([[1, 2, 3], [4, 5, 6]])

c_op = ops._create_c_op(g, ops._NodeDef("Identity", "myop"), [x], [])

op = g._create_op_from_tf_operation(c_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():

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)

# 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():

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)

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):

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)

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")

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)

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):

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)

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)

# 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())

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(

"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(

"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)

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"))

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))

self.assertEqual("bar/foo_1", g.unique_name("foo"))

with g.name_scope(None):

self.assertEqual("foo_3", g.unique_name("foo", mark_as_used=False))

self.assertEqual("foo_3", g.unique_name("foo"))

with g.name_scope("baz"):

self.assertEqual(

"bar/baz/foo", g.unique_name(

"foo", mark_as_used=False))

self.assertEqual("bar/baz/foo", g.unique_name("foo"))

self.assertEqual(

"bar/baz/foo_1", g.unique_name(

"foo", mark_as_used=False))

self.assertEqual("bar/baz/foo_1", g.unique_name("foo"))

with g.name_scope("baz"):

self.assertEqual(

"bar/baz_1/foo", g.unique_name(

"foo", mark_as_used=False))

self.assertEqual("bar/baz_1/foo", g.unique_name("foo"))

self.assertEqual(

"bar/baz_1/foo_1", g.unique_name(

"foo", mark_as_used=False))

self.assertEqual("bar/baz_1/foo_1", g.unique_name("foo"))

with g.name_scope("quux"):

self.assertEqual("quux/foo", g.unique_name("foo", mark_as_used=False))

self.assertEqual("quux/foo", g.unique_name("foo"))

with g.name_scope("bar"):

with g.name_scope("baz"):

self.assertEqual(

"bar_1/baz/foo", g.unique_name(

"foo", mark_as_used=False))

self.assertEqual("bar_1/baz/foo", g.unique_name("foo"))

self.assertEqual("foo_4", g.unique_name("foo", mark_as_used=False))

self.assertEqual("foo_4", g.unique_name("foo"))

self.assertEqual("bar_2", g.unique_name("bar", mark_as_used=False))

self.assertEqual("bar_2", g.unique_name("bar"))

def testNameAndVariableScope(self):

with self.test_session() as sess:

with sess.graph.name_scope("l0"):

with variable_scope.variable_scope("l1"):

with sess.graph.name_scope("l1") as scope:

self.assertEqual("l0/l1/l1/", scope)

self.assertEqual(

"l0/l1/l1/foo",

sess.graph.unique_name(

"foo", mark_as_used=False))

self.assertEqual("l0/l1/l1/foo", sess.graph.unique_name("foo"))

with sess.graph.name_scope("l2") as scope:

self.assertEqual("l0/l1/l2/", scope)

self.assertEqual(

"l0/l1/l2/foo",

sess.graph.unique_name(

"foo", mark_as_used=False))

self.assertEqual("l0/l1/l2/foo", sess.graph.unique_name("foo"))

def testOutOfOrderUniqueName(self):

g = ops.Graph()

self.assertEqual("foo_2", g.unique_name("foo_2"))

self.assertEqual("foo", g.unique_name("foo"))

self.assertEqual("foo_1", g.unique_name("foo"))

self.assertEqual("foo_3", g.unique_name("foo"))

def testUniqueNameCaseInsensitivity(self):

g = ops.Graph()

self.assertEqual("foo", g.unique_name("foo"))

self.assertEqual("Foo_1", g.unique_name("Foo"))

with g.name_scope("bar"):

self.assertEqual("bar/foo", g.unique_name("foo"))

with g.name_scope("Bar"):

self.assertEqual("Bar_1/foo", g.unique_name("foo"))

def testInvalidNameRaisesError(self):

g = ops.Graph()

with g.name_scope(""): # Should not raise

pass

with g.name_scope("foo/"): # Should not raise

with g.name_scope("_bar"): # Should not raise

pass

with self.assertRaises(ValueError):

with g.name_scope("foo:0"):

pass

with self.assertRaises(ValueError):

with g.name_scope("_bar"):

pass

class NameTest(test_util.TensorFlowTestCase):

def testGenerateName(self):

g = ops.Graph()

op0 = g.create_op("TwoFloatOutputs", [], [dtypes.float32, dtypes.float32])

self.assertEqual("TwoFloatOutputs", op0.name)

View remainder of file in raw view

tensorflow/tensorflow/python/framework/ops_test.py at master · codeplaysoftware/tensorflow · GitHub

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