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1667 lines (1415 loc) · 61.3 KB
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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
from tensorflow.core.framework import attr_value_pb2
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 ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_ops
from tensorflow.python.framework import test_ops_2
from tensorflow.python.framework import test_util
from tensorflow.python.framework import versions
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_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 TensorTest(test_util.TensorFlowTestCase):
def testShape(self):
op = ops.Operation(
ops._NodeDef("noop", "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("noop", "myop"), ops.Graph(), [], [dtypes.float32])
t = op.outputs[0]
self.assertTrue(isinstance(t, ops.Tensor))
with self.assertRaisesRegexp(TypeError, "not iterable"):
for _ in t:
pass
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("noop", "bar")
self.assertProtoEquals("op: 'noop' 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)
# NOTE(mrry): Dummy shape registrations for ops used in the tests, since they
# don't have C++ op registrations on which to attach C++ shape fns.
ops.RegisterShape("a")(common_shapes.unknown_shape)
ops.RegisterShape("b")(common_shapes.unknown_shape)
ops.RegisterShape("c")(common_shapes.unknown_shape)
ops.RegisterShape("add")(common_shapes.unknown_shape)
ops.RegisterShape("an_op")(common_shapes.unknown_shape)
ops.RegisterShape("const")(common_shapes.unknown_shape)
ops.RegisterShape("copy")(common_shapes.unknown_shape)
ops.RegisterShape("foo")(common_shapes.unknown_shape)
ops.RegisterShape("identity")(common_shapes.unknown_shape)
ops.RegisterShape("mul")(common_shapes.unknown_shape)
ops.RegisterShape("nonrefop")(common_shapes.unknown_shape)
ops.RegisterShape("noop")(common_shapes.unknown_shape)
ops.RegisterShape("refop")(common_shapes.unknown_shape)
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 = ops.Operation(
ops._NodeDef("noop", "myop"),
ops.Graph(), [], [dtypes.float32, dtypes.string])
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:'noop' name:'myop'", op.node_def)
def testNoOutputs(self):
g = ops.Graph()
op1 = ops.Operation(ops._NodeDef("noop", "myop1"), g, [], [dtypes.float32])
float_t, = op1.values()
op2 = ops.Operation(ops._NodeDef("reop", "myop2"), g, [float_t], [])
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:'noop' name:'myop1'", op1.node_def)
self.assertProtoEquals("op:'reop' name:'myop2' input:'myop1'", op2.node_def)
def testInputsAndOutputs(self):
g = ops.Graph()
op1 = ops.Operation(ops._NodeDef("noop", "myop1"), g, [], [dtypes.float32])
self.assertEqual(1, len(op1.values()))
float1_t, = op1.values()
op2 = ops.Operation(
ops._NodeDef("reop", "myop2"), g, [], [dtypes.float32, dtypes.string])
self.assertEqual(2, len(op2.values()))
float2_t, label2_str_t = op2.values()
# Note that we consume label2_str_t twice here.
op3 = ops.Operation(
ops._NodeDef("add", "myop3"), g, [float1_t, label2_str_t, label2_str_t],
[dtypes.float32, dtypes.int32])
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:'add' name:'myop3'
input:'myop1' input:'myop2:1' input:'myop2:1'
""", op3.node_def)
def testDeviceObject(self):
op = ops.Operation(ops._NodeDef("noop", "myop"), ops.Graph(), [], [])
op._set_device("/job:goo/device:GPU:0")
self.assertProtoEquals(
"op:'noop' name:'myop' device:'/job:goo/device:GPU:0' ", op.node_def)
op = ops.Operation(ops._NodeDef("noop", "op2"), ops.Graph(), [], [])
op._set_device(
pydev.DeviceSpec(
job="muu", device_type="CPU", device_index=0))
self.assertProtoEquals(
"op:'noop' name:'op2' device:'/job:muu/device:CPU:0'", op.node_def)
def testReferenceInput(self):
g = ops.Graph()
op1 = ops.Operation(
ops._NodeDef("noop", "op1"), g, [],
[dtypes.float32_ref, dtypes.float32])
self.assertProtoEquals("op:'noop' name:'op1'", op1.node_def)
ref_t, nonref_t = op1.values()
# NOTE(mrry): Must specify input_types to preserve ref-typed input.
op2 = ops.Operation(
ops._NodeDef("refop", "op2"),
g, [ref_t, nonref_t], [],
input_types=[dtypes.float32_ref, dtypes.float32])
self.assertProtoEquals("op:'refop' name:'op2' input:'op1' input:'op1:1'",
op2.node_def)
op3 = ops.Operation(
ops._NodeDef("nonrefop", "op3"), g, [ref_t, nonref_t], [])
self.assertProtoEquals("op:'nonrefop' 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):
g = ops.Graph()
ops.Operation(ops._NodeDef("op", "an_op"), g, output_types=[dtypes.float32])
self.assertEqual(tensor_shape.unknown_shape(),
_apply_op(g, "an_op", [], [dtypes.float32]).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 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("noop", "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("noop", "op1"), ops.Graph(), [], [dtypes.float32])
self.assertEqual("<tf.Operation 'op1' type=noop>", repr(op))
class CreateOpTest(test_util.TensorFlowTestCase):
def testNodeDefArgs(self):
g = ops.Graph()
op1 = g.create_op("const", [], [dtypes.float32], None, name="myop1")
with g.device("/device:GPU:0"):
op2 = g.create_op(
"add", [], [dtypes.float32, dtypes.string], None, name="myop2")
op3 = g.create_op(
"foo",
[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:'const'", op1.node_def)
self.assertProtoEquals("name:'myop2' op:'add' device:'/device:GPU:0'",
op2.node_def)
self.assertProtoEquals(
"name:'myop3' input:'myop1' input:'myop2:1' input:'myop2' op:'foo'",
op3.node_def)
def testReferenceInput(self):
g = ops.Graph()
op1 = g.create_op(
"noop", [], [dtypes.float32_ref, dtypes.float32], name="op1")
self.assertProtoEquals("op:'noop' 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(
"refop", [ref_t, nonref_t], [],
input_types=[dtypes.float32_ref, dtypes.float32],
name="op2")
self.assertProtoEquals("op:'refop' name:'op2' input:'op1' input:'op1:1'",
op2.node_def)
op3 = g.create_op("nonrefop", [ref_t, nonref_t], [], name="op3")
self.assertProtoEquals("op:'nonrefop' 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("const", [], [dtypes.float32], None, name="myop1")
# Test unfinalize.
g._unsafe_unfinalize()
g.create_op("const", [], [dtypes.float32], None, name="myop1")
class ApplyOpTest(test_util.TensorFlowTestCase):
def testNodeDefArgs(self):
g = ops.Graph()
t1 = _apply_op(g, "const", [], [dtypes.float32], name="myop1")
with g.device("/device:GPU:0"):
t2 = _apply_op(
g, "add", [], [dtypes.float32, dtypes.string], name="myop2")
t3 = _apply_op(
g,
"foo", [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:'const'", t1.op.node_def)
self.assertProtoEquals("name:'myop2' op:'add' device:'/device:GPU:0'",
t2[0].op.node_def)
self.assertProtoEquals(
"name:'myop3' input:'myop1' input:'myop2:1' input:'myop2' op:'foo'",
t3[0].op.node_def)
def testReferenceInput(self):
g = ops.Graph()
ref_t, nonref_t = _apply_op(
g, "noop", [], [dtypes.float32_ref, dtypes.float32], name="op1")
self.assertProtoEquals("op:'noop' name:'op1'", ref_t.op.node_def)
# NOTE(mrry): Must specify input_types to preserve ref-typed input.
out_2 = _apply_op(
g,
"refop", [ref_t, nonref_t], [dtypes.int32],
input_types=[dtypes.float32_ref, dtypes.float32],
name="op2")
self.assertProtoEquals("op:'refop' name:'op2' input:'op1' input:'op1:1'",
out_2.op.node_def)
out_3 = _apply_op(
g, "nonrefop", [ref_t, nonref_t], [dtypes.int32], name="op3")
self.assertProtoEquals("op:'nonrefop' 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", 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 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("const", [], [dtypes.float32, dtypes.float32])
self.assertEqual("const", op0.name)
self.assertEqual("const:0", op0.outputs[0].name)
self.assertEqual("const:1", op0.outputs[1].name)
op1 = g.create_op("const", [], [dtypes.float32])
self.assertEqual("const_1", op1.name)
self.assertEqual("const_1:0", op1.outputs[0].name)
op2 = g.create_op("const", [], [dtypes.float32], name="my_op")
self.assertEqual("my_op", op2.name)
self.assertEqual("my_op:0", op2.outputs[0].name)
def testNameScope(self):
g = ops.Graph()
with g.name_scope("foo") as foo:
self.assertEqual("foo/", foo)
with g.name_scope("foo2") as foo2:
self.assertEqual("foo/foo2/", foo2)
with g.name_scope(None) as empty1:
self.assertEqual("", empty1)
with g.name_scope("foo3") as foo3:
self.assertEqual("foo3/", foo3)
with g.name_scope("") as empty2:
self.assertEqual("", empty2)
self.assertEqual("const", g.create_op("const", [], [dtypes.float32]).name)
with g.name_scope("bar") as scope:
self.assertEqual("bar/const",
g.create_op("const", [], [dtypes.float32]).name)
self.assertEqual("bar/const_1",
g.create_op("const", [], [dtypes.float32]).name)
# If you use the value from "with .. as", that values is used as-is.
self.assertEqual(
"bar", g.create_op(
"const", [], [dtypes.float32], name=scope).name)
with g.name_scope("baz") as scope:
with g.name_scope("quux"):
self.assertEqual("baz/quux/const",
g.create_op("const", [], [dtypes.float32]).name)
# If you use the value from the enclosing "with .. as", nothing is pushed.
with g.name_scope(scope):
self.assertEqual("baz/const",
g.create_op("const", [], [dtypes.float32]).name)
self.assertEqual(
"baz", g.create_op(
"const", [], [dtypes.float32], name=scope).name)
self.assertEqual(
"trailing",
g.create_op(
"const", [], [dtypes.float32], name="trailing/").name)
with g.name_scope("bar"):
self.assertEqual("bar_1/const",
g.create_op("const", [], [dtypes.float32]).name)
with g.name_scope("bar/"):
self.assertEqual("bar/const_2",
g.create_op("const", [], [dtypes.float32]).name)
class DeviceTest(test_util.TensorFlowTestCase):
def testNoDevice(self):
g = ops.Graph()
op = g.create_op("an_op", [], [dtypes.float32])
self.assertDeviceEqual(None, op.device)
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op" }
""", gd)
def testDevicePartialString(self):
g = ops.Graph()
with g.device("/job:worker/replica:2"):
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op" device: "/job:worker/replica:2" }
""", gd)
def testDeviceFull(self):
g = ops.Graph()
with g.device(
pydev.DeviceSpec(
job="worker", replica=2, task=0, device_type="CPU",
device_index=3)):
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/job:worker/replica:2/task:0/device:CPU:3" }
""", gd)
def testNesting(self):
g = ops.Graph()
with g.device("/job:worker/replica:2"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/job:worker/replica:3/task:0"):
g.create_op("an_op", [], [dtypes.float32])
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/job:worker/replica:2" }
node { name: "an_op_1" op: "an_op"
device: "/job:worker/replica:3/task:0" }
node { name: "an_op_2" op: "an_op"
device: "/job:worker/replica:2" }
""", gd)
def testNestingString(self):
g = ops.Graph()
with g.device("/job:worker/replica:2"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/job:worker/replica:3/task:0"):
g.create_op("an_op", [], [dtypes.float32])
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/job:worker/replica:2" }
node { name: "an_op_1" op: "an_op"
device: "/job:worker/replica:3/task:0" }
node { name: "an_op_2" op: "an_op"
device: "/job:worker/replica:2" }
""", gd)
def testNestingOverrideGpuCpu(self):
g = ops.Graph()
with g.device("/job:worker/replica:2/device:CPU:1"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/job:worker/replica:2/device:GPU:2"):
g.create_op("an_op", [], [dtypes.float32])
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/job:worker/replica:2/device:CPU:1" }
node { name: "an_op_1" op: "an_op"
device: "/job:worker/replica:2/device:GPU:2" }
node { name: "an_op_2" op: "an_op"
device: "/job:worker/replica:2/device:CPU:1" }
""", gd)
def testNestingWithMergeDeviceFunction(self):
g = ops.Graph()
with g.device(pydev.merge_device("/device:GPU:0")):
g.create_op("an_op", [], [dtypes.float32])
with g.device(pydev.merge_device("/job:worker")):
g.create_op("an_op", [], [dtypes.float32])
with g.device(pydev.merge_device("/device:CPU:0")):
g.create_op("an_op", [], [dtypes.float32])
with g.device(pydev.merge_device("/job:ps")):
g.create_op("an_op", [], [dtypes.float32])
with g.device(pydev.merge_device(None)):
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/device:GPU:0" }
node { name: "an_op_1" op: "an_op"
device: "/job:worker/device:GPU:0" }
node { name: "an_op_2" op: "an_op"
device: "/job:worker/device:CPU:0" }
node { name: "an_op_3" op: "an_op"
device: "/job:ps/device:CPU:0" }
node { name: "an_op_4" op: "an_op"
device: "/job:ps/device:CPU:0" }
""", gd)
def testNestingWithDeviceStrings(self):
g = ops.Graph()
with g.device("/device:GPU:0"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/job:worker"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/device:CPU:0"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/job:ps"):
g.create_op("an_op", [], [dtypes.float32])
with g.device(""):
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/device:GPU:0" }
node { name: "an_op_1" op: "an_op"
device: "/job:worker/device:GPU:0" }
node { name: "an_op_2" op: "an_op"
device: "/job:worker/device:CPU:0" }
node { name: "an_op_3" op: "an_op"
device: "/job:ps/device:CPU:0" }
node { name: "an_op_4" op: "an_op"
device: "/job:ps/device:CPU:0" }
""", gd)
def testNestingWithDeviceStringWildcard(self):
g = ops.Graph()
with g.device("/device:GPU:7"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/device:GPU:*"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/device:CPU:*"):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/device:CPU:5"):
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/device:GPU:7" }
node { name: "an_op_1" op: "an_op"
device: "/device:GPU:7" }
node { name: "an_op_2" op: "an_op"
device: "/device:CPU:*" }
node { name: "an_op_3" op: "an_op"
device: "/device:CPU:5" }
""", gd)
def testNoneClearsDefault(self):
g = ops.Graph()
with g.device("/job:worker/replica:2/device:CPU:1"):
g.create_op("an_op", [], [dtypes.float32])
with g.device(None):
g.create_op("an_op", [], [dtypes.float32])
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/job:worker/replica:2/device:CPU:1" }
node { name: "an_op_1" op: "an_op" }
node { name: "an_op_2" op: "an_op"
device: "/job:worker/replica:2/device:CPU:1" }
""", gd)
def testNoneIgnoresOuterDeviceFunction(self):
g = ops.Graph()
with g.device(lambda op: "/job:worker/replica:2/device:CPU:1"):
g.create_op("an_op", [], [dtypes.float32])
with g.device(None):
g.create_op("an_op", [], [dtypes.float32])
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/job:worker/replica:2/device:CPU:1" }
node { name: "an_op_1" op: "an_op" }
node { name: "an_op_2" op: "an_op"
device: "/job:worker/replica:2/device:CPU:1" }
""", gd)
def _overwritingDeviceFunction(self, unused_op):
# This device function unconditionally overwrites the device of ops.
#
# NOTE(mrry): Writing device functions like this is not
# recommended. Instead, in most cases you should use
# `pydev.merge_device("/job:ps")` or simply `"/job:ps"` as the
# argument to `tf.device()` and the device component will be merged in.
return "/job:overwrite"
def testOverwritingBehavior(self):
g = ops.Graph()
with g.device(self._overwritingDeviceFunction):
g.create_op("an_op", [], [dtypes.float32])
with g.device("/job:ps"): # Will be overwritten.
g.create_op("an_op", [], [dtypes.float32])
with g.device(pydev.merge_device("/job:ps")): # Will be overwritten.
g.create_op("an_op", [], [dtypes.float32])
with g.device(None): # Disables overwriting device function
with g.device("/job:ps"):
g.create_op("an_op", [], [dtypes.float32])
with g.device(None): # Disables overwriting device function
with g.device(pydev.merge_device("/job:ps")):
g.create_op("an_op", [], [dtypes.float32])
gd = g.as_graph_def()
self.assertProtoEqualsVersion("""
node { name: "an_op" op: "an_op"
device: "/job:overwrite" }
node { name: "an_op_1" op: "an_op"
device: "/job:overwrite" }
node { name: "an_op_2" op: "an_op"
device: "/job:overwrite" }
node { name: "an_op_3" op: "an_op"
device: "/job:ps" }
node { name: "an_op_4" op: "an_op"
device: "/job:ps" }
""", gd)
class ObjectWithName(object):
def __init__(self, name):
self._name = name
@property
def name(self):
return self._name
class CollectionTest(test_util.TensorFlowTestCase):
def test_add_to_collection(self):
g = ops.Graph()
g.add_to_collection("key", 12)
g.add_to_collection("other", "foo")
g.add_to_collection("key", 34)
# Note that only blank1 is returned.
g.add_to_collection("blah", 27)
blank1 = ObjectWithName("prefix/foo")
g.add_to_collection("blah", blank1)
blank2 = ObjectWithName("junk/foo")
g.add_to_collection("blah", blank2)
self.assertEqual([12, 34], g.get_collection("key"))
self.assertEqual([], g.get_collection("nothing"))
self.assertEqual([27, blank1, blank2], g.get_collection("blah"))
self.assertEqual([blank1], g.get_collection("blah", "prefix"))
self.assertEqual([blank1], g.get_collection("blah", ".*x"))
# Make sure that get_collection() returns a first-level
# copy of the collection, while get_collection_ref() returns
# the original list.
other_collection_snapshot = g.get_collection("other")
other_collection_ref = g.get_collection_ref("other")
self.assertEqual(["foo"], other_collection_snapshot)
self.assertEqual(["foo"], other_collection_ref)
g.add_to_collection("other", "bar")
self.assertEqual(["foo"], other_collection_snapshot)
self.assertEqual(["foo", "bar"], other_collection_ref)
self.assertEqual(["foo", "bar"], g.get_collection("other"))
self.assertTrue(other_collection_ref is g.get_collection_ref("other"))
# Verify that getting an empty collection ref returns a modifiable list.
empty_coll_ref = g.get_collection_ref("empty")
self.assertEqual([], empty_coll_ref)
empty_coll = g.get_collection("empty")
self.assertEqual([], empty_coll)
self.assertFalse(empty_coll is empty_coll_ref)
empty_coll_ref2 = g.get_collection_ref("empty")
self.assertTrue(empty_coll_ref2 is empty_coll_ref)
# Add to the collection.
empty_coll_ref.append("something")
self.assertEqual(["something"], empty_coll_ref)
self.assertEqual(["something"], empty_coll_ref2)
self.assertEqual([], empty_coll)
self.assertEqual(["something"], g.get_collection("empty"))
empty_coll_ref3 = g.get_collection_ref("empty")
self.assertTrue(empty_coll_ref3 is empty_coll_ref)
def test_add_to_collections_uniquify(self):
g = ops.Graph()
g.add_to_collections([1, 2, 1], "key")
# Make sure "key" is not added twice
self.assertEqual(["key"], g.get_collection(1))
def test_add_to_collections_from_list(self):
g = ops.Graph()
g.add_to_collections(["abc", "123"], "key")
self.assertEqual(["key"], g.get_collection("abc"))
self.assertEqual(["key"], g.get_collection("123"))
def test_add_to_collections_from_tuple(self):
g = ops.Graph()
g.add_to_collections(("abc", "123"), "key")
self.assertEqual(["key"], g.get_collection("abc"))
self.assertEqual(["key"], g.get_collection("123"))
def test_add_to_collections_from_generator(self):
g = ops.Graph()
def generator():
yield "abc"
yield "123"
g.add_to_collections(generator(), "key")
self.assertEqual(["key"], g.get_collection("abc"))
self.assertEqual(["key"], g.get_collection("123"))
def test_add_to_collections_from_set(self):
g = ops.Graph()
g.add_to_collections(set(["abc", "123"]), "key")
self.assertEqual(["key"], g.get_collection("abc"))
self.assertEqual(["key"], g.get_collection("123"))
def test_add_to_collections_from_string(self):
g = ops.Graph()
g.add_to_collections("abc", "key")
self.assertEqual(["key"], g.get_collection("abc"))
def test_default_graph(self):
with ops.Graph().as_default():
ops.add_to_collection("key", 90)
ops.add_to_collection("key", 100)
# Collections are ordered.
self.assertEqual([90, 100], ops.get_collection("key"))
def an_op(g):
return _apply_op(g, "an_op", [], [dtypes.float32])
ops.NotDifferentiable("an_op")
def copy_op(x):
return _apply_op(x.graph, "copy", [x], [x.dtype])
@ops.RegisterGradient("copy")
def _CopyGrad(op, x_grad): # pylint: disable=invalid-name
_ = op
return x_grad
@ops.RegisterGradient("copy_override")
def _CopyOverrideGrad(op, x_grad): # pylint: disable=invalid-name
_ = op
return x_grad
class RegistrationTest(test_util.TensorFlowTestCase):
def testRegisterGradients(self):
g = ops.Graph()
x = an_op(g)
y = copy_op(x)
fn = ops.get_gradient_function(y.op)
self.assertEqual(_CopyGrad, fn)
def testOverrideGradients(self):
g = ops.Graph()
x = an_op(g)
with g.gradient_override_map({"copy": "copy_override"}):
y = copy_op(x)
fn = ops.get_gradient_function(y.op)
self.assertEqual(_CopyOverrideGrad, fn)
def testNonExistentOverride(self):