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1014 lines (848 loc) · 37.2 KB
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# Copyright 2015 Google Inc. 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 tensorflow.python.platform
from tensorflow.python.framework import device as pydev
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
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 common_shapes
from tensorflow.python.ops import variables
from tensorflow.python.platform import googletest
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())
class SparseTensorTest(test_util.TensorFlowTestCase):
def testPythonConstruction(self):
sp = ops.SparseTensor([[1, 2], [2, 0], [3, 4]], ["a", "b", "c"], [4, 5])
self.assertEqual(sp.indices.dtype, dtypes.int64)
self.assertEqual(sp.values.dtype, dtypes.string)
self.assertEqual(sp.shape.dtype, dtypes.int64)
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.Device(job="j"))
self.assertProtoEquals("op:'foo' name:'bar' device:'/job:j'", nodedef)
# NOTE(mrry): Dummy shape registrations for ops used in the tests.
ops.RegisterShape("a")(None)
ops.RegisterShape("b")(None)
ops.RegisterShape("c")(None)
ops.RegisterShape("add")(None)
ops.RegisterShape("an_op")(None)
ops.RegisterShape("const")(None)
ops.RegisterShape("copy")(None)
ops.RegisterShape("foo")(None)
ops.RegisterShape("identity")(None)
ops.RegisterShape("mul")(None)
ops.RegisterShape("nonrefop")(None)
ops.RegisterShape("noop")(None)
ops.RegisterShape("refop")(None)
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.Device(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)
def testShapeFunctionAbsence(self):
def _test():
pass
g = ops.Graph()
with self.assertRaises(RuntimeError):
g.create_op("shapeless_op", [], [dtypes.float32])
def testNoShapeFunction(self):
g = ops.Graph()
op = 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())
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"):
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.assertEqual(None, op1.device)
self.assertEqual("/device:GPU", op2.device)
self.assertEqual(None, op3.device)
self.assertProtoEquals("name:'myop1' op:'const'", op1.node_def)
self.assertProtoEquals("name:'myop2' op:'add' device:'/device:GPU'",
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")
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"):
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'",
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"))
self.assertEqual("foo_1", g.unique_name("foo"))
self.assertEqual("foo_2", g.unique_name("foo"))
self.assertEqual("foo_1_1", g.unique_name("foo_1"))
self.assertEqual("foo_1_2", g.unique_name("foo_1"))
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"))
self.assertEqual("bar/foo_1", g.unique_name("foo"))
with g.name_scope(None):
self.assertEqual("foo_3", g.unique_name("foo"))
with g.name_scope("baz"):
self.assertEqual("bar/baz/foo", g.unique_name("foo"))
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"))
self.assertEqual("bar/baz_1/foo_1", g.unique_name("foo"))
with g.name_scope("quux"):
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"))
self.assertEqual("foo_4", g.unique_name("foo"))
self.assertEqual("bar_2", g.unique_name("bar"))
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"))
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.assertEqual(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.Device(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 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)
class ObjectWithName(object):
def __init__(self, name):
self._name = name
@property
def name(self):
return self._name
class CollectionTest(test_util.TensorFlowTestCase):
def testadd_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(["foo"], g.get_collection("other"))
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"))
def testDefaulGraph(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.NoGradient("an_op")
def copy_op(x):
return _apply_op(x.graph, "copy", [x], [x.dtype])
@ops.RegisterGradient("copy")
def _CopyGrad(op, x_grad):
_ = op
return x_grad
@ops.RegisterGradient("copy_override")
def _CopyOverrideGrad(op, x_grad):
_ = 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):
g = ops.Graph()
x = an_op(g)
with g.gradient_override_map({"copy": "unknown_override"}):
y = copy_op(x)
with self.assertRaisesRegexp(LookupError, "unknown_override"):
fn = ops.get_gradient_function(y.op)
class ComparisonTest(test_util.TensorFlowTestCase):
def testMembershipAllowed(self):
g = ops.Graph()
t1 = _apply_op(g, "const", [], [dtypes.float32], name="myop1")
t2 = _apply_op(g, "const", [], [dtypes.float32], name="myop2")
self.assertTrue(isinstance(t1, ops.Tensor))
self.assertTrue(isinstance(t2, ops.Tensor))
self.assertTrue(t1 in [t1])
self.assertTrue(t1 not in [t2])
class ControlDependenciesTest(test_util.TensorFlowTestCase):
def testBasic(self):
g = ops.Graph()
a = _apply_op(g, "const", [], [dtypes.float32])
b = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a]):
c = _apply_op(g, "const", [], [dtypes.float32])
d = _apply_op(g, "identity", [b], [dtypes.float32])
e = _apply_op(g, "identity", [c], [dtypes.float32])
self.assertEqual(c.op.control_inputs, [a.op])
self.assertEqual(d.op.control_inputs, [a.op])
# e should be dominated by c.
self.assertEqual(e.op.control_inputs, [])
def testBasicWithConversion(self):
g = ops.Graph()
a = _apply_op(g, "const", [], [dtypes.float32])
class ConvertibleObj(object):
def _as_graph_element(self):
return a
with g.control_dependencies([ConvertibleObj()]):
c = _apply_op(g, "const", [], [dtypes.float32])
self.assertEqual(c.op.control_inputs, [a.op])
def testNested(self):
g = ops.Graph()
a_1 = _apply_op(g, "const", [], [dtypes.float32])
a_2 = _apply_op(g, "const", [], [dtypes.float32])
a_3 = _apply_op(g, "const", [], [dtypes.float32])
a_4 = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a_1, a_2, a_3, a_4]):
b_1 = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a_1]):
with g.control_dependencies([a_2]):
with g.control_dependencies([a_3]):
with g.control_dependencies([a_4]):
b_2 = _apply_op(g, "const", [], [dtypes.float32])
self.assertItemsEqual(
[a_1.op, a_2.op, a_3.op, a_4.op], b_1.op.control_inputs)
self.assertItemsEqual(b_1.op.control_inputs, b_2.op.control_inputs)
def testClear(self):
g = ops.Graph()
a_1 = _apply_op(g, "const", [], [dtypes.float32])
a_2 = _apply_op(g, "const", [], [dtypes.float32])
a_3 = _apply_op(g, "const", [], [dtypes.float32])
a_4 = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a_1]):
with g.control_dependencies([a_2]):
with g.control_dependencies(None):
with g.control_dependencies([a_3]):
with g.control_dependencies([a_4]):
# deps [a_3, a_4]
b_3_4 = _apply_op(g, "const", [], [dtypes.float32])
# deps = [a_3]
b_3 = _apply_op(g, "const", [], [dtypes.float32])
# deps back to None
b_none = _apply_op(g, "const", [], [dtypes.float32])
# deps back to [a_1, a_2]
b_1_2 = _apply_op(g, "const", [], [dtypes.float32])
# deps back to [a_1]
b_1 = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies(None):
# deps are None again
b_none2 = _apply_op(g, "const", [], [dtypes.float32])
self.assertItemsEqual([a_3.op, a_4.op], b_3_4.op.control_inputs)
self.assertItemsEqual([a_3.op], b_3.op.control_inputs)
self.assertItemsEqual([], b_none.op.control_inputs)
self.assertItemsEqual([a_1.op, a_2.op], b_1_2.op.control_inputs)
self.assertItemsEqual([a_1.op], b_1.op.control_inputs)
self.assertItemsEqual([], b_none2.op.control_inputs)
def testComplex(self):
g = ops.Graph()
# Usage pattern:
# * Nodes a_i are constants defined at the outermost scope, and are used
# as control inputs for the ith nested scope.
# * Nodes b_i are defined as Mul(a_3, a_4) at each scope.
# * Nodes c_i are defined as Mul(a_1, b_1) at each scope.
# * Nodes d_i are defined as Mul(b_i, c_i) at each scope.
# * Nodes e_i are defined as Mul(e_i-1, e_i-1) at each scope i > 1.
a_1 = _apply_op(g, "const", [], [dtypes.float32])
a_2 = _apply_op(g, "const", [], [dtypes.float32])
a_3 = _apply_op(g, "const", [], [dtypes.float32])
a_4 = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a_1]):
b_1 = _apply_op(g, "mul", [a_3, a_4], [dtypes.float32])
c_1 = _apply_op(g, "mul", [a_1, b_1], [dtypes.float32])
d_1 = _apply_op(g, "mul", [b_1, c_1], [dtypes.float32])
e_1 = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a_2]):
b_2 = _apply_op(g, "mul", [a_3, a_4], [dtypes.float32])
c_2 = _apply_op(g, "mul", [a_1, b_1], [dtypes.float32])
d_2 = _apply_op(g, "mul", [b_2, c_2], [dtypes.float32])
e_2 = _apply_op(g, "mul", [e_1, e_1], [dtypes.float32])
with g.control_dependencies([a_3]):
b_3 = _apply_op(g, "mul", [a_3, a_4], [dtypes.float32])
c_3 = _apply_op(g, "mul", [a_1, b_1], [dtypes.float32])
d_3 = _apply_op(g, "mul", [b_3, c_3], [dtypes.float32])
e_3 = _apply_op(g, "mul", [e_2, e_2], [dtypes.float32])
with g.control_dependencies([a_4]):
b_4 = _apply_op(g, "mul", [a_3, a_4], [dtypes.float32])
c_4 = _apply_op(g, "mul", [a_1, b_1], [dtypes.float32])
d_4 = _apply_op(g, "mul", [b_4, c_4], [dtypes.float32])
e_4 = _apply_op(g, "mul", [e_3, e_3], [dtypes.float32])
self.assertItemsEqual([a_1.op], b_1.op.control_inputs)
self.assertItemsEqual([a_1.op, a_2.op], b_2.op.control_inputs)
self.assertItemsEqual([a_1.op, a_2.op], b_3.op.control_inputs)
self.assertItemsEqual([a_1.op, a_2.op], b_4.op.control_inputs)
self.assertItemsEqual([], c_1.op.control_inputs)
self.assertItemsEqual([a_2.op], c_2.op.control_inputs)
self.assertItemsEqual([a_2.op, a_3.op], c_3.op.control_inputs)
self.assertItemsEqual([a_2.op, a_3.op, a_4.op], c_4.op.control_inputs)
self.assertItemsEqual([], d_1.op.control_inputs)
self.assertItemsEqual([], d_2.op.control_inputs)
self.assertItemsEqual([], d_3.op.control_inputs)
self.assertItemsEqual([], d_4.op.control_inputs)
self.assertItemsEqual([a_1.op], e_1.op.control_inputs)
self.assertItemsEqual([a_2.op], e_2.op.control_inputs)
self.assertItemsEqual([a_3.op], e_3.op.control_inputs)
self.assertItemsEqual([a_4.op], e_4.op.control_inputs)
def testRepeatedDependency(self):
g = ops.Graph()
a = g.create_op("foo", [], [dtypes.float32, dtypes.float32])
a_0, a_1 = a.outputs
with g.control_dependencies([a_0]):
b = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a_1]):
c = _apply_op(g, "const", [], [dtypes.float32])
self.assertEqual(b.op.control_inputs, [a])
self.assertEqual(c.op.control_inputs, [a])
def testNoControlDependencyWithDataDependency(self):
g = ops.Graph()
a = _apply_op(g, "const", [], [dtypes.float32])
with g.control_dependencies([a]):
b = _apply_op(g, "identity", [a], [dtypes.float32])
self.assertEqual(b.op.control_inputs, [])
class OpScopeTest(test_util.TensorFlowTestCase):
def testNoScopeName(self):
g0 = ops.Graph()
values = [
g0.create_op("a", [], [dtypes.float32]),
g0.create_op("b", [], [dtypes.float32])]
with self.assertRaises(ValueError):
with ops.op_scope(values, None):
pass
with self.assertRaises(ValueError):
with ops.op_scope(values, None, None):
pass
def testEmptyScopeName(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
with ops.op_scope([a, b], "") as scope:
self.assertEqual("", scope)
self.assertEqual(g0, ops.get_default_graph())
with ops.op_scope([a, b], "", "my_default_scope") as scope:
self.assertEqual("", scope)
self.assertEqual(g0, ops.get_default_graph())
def testDefaultScopeName(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
scope_name = "my_scope"
default_scope_name = "my_default_scope"
with ops.op_scope([a, b], scope_name, default_scope_name) as scope:
self.assertEqual("%s/" % scope_name, scope)
self.assertEqual(g0, ops.get_default_graph())
with ops.op_scope([a, b], None, default_scope_name) as scope:
self.assertEqual("%s/" % default_scope_name, scope)
self.assertEqual(g0, ops.get_default_graph())
def _testGraphElements(self, graph_elements):
scope_name = "my_scope"
with ops.op_scope(graph_elements, scope_name) as scope:
self.assertEqual("%s/" % scope_name, scope)
self.assertEqual(graph_elements[0].graph, ops.get_default_graph())
g1 = ops.Graph()
c = g1.create_op("c", [], [dtypes.float32])
with self.assertRaises(ValueError):
with ops.op_scope(graph_elements + [c], scope_name):
pass
def testTensor(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
self._testGraphElements([a, b])
def testSparseTensor(self):
g0 = ops.Graph()
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
sparse = ops.SparseTensor(
_apply_op(g0, "const", [], [dtypes.int64]),
_apply_op(g0, "const", [], [dtypes.float32]),
_apply_op(g0, "const", [], [dtypes.int64]))
self._testGraphElements([a, sparse, b])
def testVariable(self):
g0 = ops.Graph()
with g0.as_default():
variable = variables.Variable([1.0])
a = g0.create_op("a", [], [dtypes.float32])
b = g0.create_op("b", [], [dtypes.float32])
self._testGraphElements([a, variable, b])
class GraphTest(test_util.TensorFlowTestCase):
def setUp(self):
ops.reset_default_graph()
def _AssertDefault(self, expected):
self.assertIs(expected, ops.get_default_graph())
def testGraphContextManager(self):
g0 = ops.Graph()
with g0.as_default() as g1:
self.assertIs(g0, g1)
def testDefaultGraph(self):
orig = ops.get_default_graph()
self._AssertDefault(orig)
g0 = ops.Graph()
self._AssertDefault(orig)
context_manager_0 = g0.as_default()
self._AssertDefault(orig)
with context_manager_0 as g0:
self._AssertDefault(g0)
with ops.Graph().as_default() as g1:
self._AssertDefault(g1)
self._AssertDefault(g0)
self._AssertDefault(orig)
def testAsGraphElementConversions(self):
class ConvertibleObj(object):
def _as_graph_element(self):
return "const:0"
class NonConvertibleObj(object):
pass
g = ops.Graph()
a = _apply_op(g, "const", [], [dtypes.float32])
self.assertEqual(a, g.as_graph_element(ConvertibleObj()))
with self.assertRaises(TypeError):
g.as_graph_element(NonConvertibleObj())
ops.RegisterShape("KernelLabel")(common_shapes.scalar_shape)
class KernelLabelTest(test_util.TensorFlowTestCase):
def testNoLabel(self):
with self.test_session():
self.assertAllEqual(b"My label is: default",
test_ops.kernel_label().eval())
def testLabelMap(self):
with self.test_session() as sess:
default_1 = test_ops.kernel_label()
# pylint: disable=protected-access
with sess.graph._kernel_label_map({"KernelLabel": "overload_1"}):
overload_1_1 = test_ops.kernel_label()
with sess.graph._kernel_label_map({"KernelLabel": "overload_2"}):
overload_2 = test_ops.kernel_label()
with sess.graph._kernel_label_map({"KernelLabel": ""}):
default_2 = test_ops.kernel_label()
overload_1_2 = test_ops.kernel_label()
# pylint: enable=protected-access
default_3 = test_ops.kernel_label()
self.assertAllEqual(b"My label is: default", default_1.eval())
self.assertAllEqual(b"My label is: default", default_2.eval())
self.assertAllEqual(b"My label is: default", default_3.eval())
self.assertAllEqual(b"My label is: overload_1", overload_1_1.eval())
self.assertAllEqual(b"My label is: overload_1", overload_1_2.eval())
self.assertAllEqual(b"My label is: overload_2", overload_2.eval())
class GraphDefVersionTest(test_util.TensorFlowTestCase):
def testGraphDefVersion(self):
"""Test that the graphdef version is plumbed through to kernels."""
for version in range(versions.GRAPH_DEF_VERSION_MIN,
versions.GRAPH_DEF_VERSION_MAX + 1):
with ops.Graph().as_default() as g:
g.graph_def_version = version
with self.test_session(graph=g):
v = test_ops.graph_def_version().eval()
self.assertEqual(version, v)
# NOTE(petewarden): Dummy stats registrations for ops used in the tests.
@ops.RegisterStatistics("a", "weight_parameters")
def _calc_a_weight_params(unused_graph, unused_node):
return ops.OpStats("weight_parameters", 10)
@ops.RegisterStatistics("a", "flops")
def _calc_a_forward_flops(unused_graph, unused_node):
return ops.OpStats("flops", 20)
class StatisticsTest(test_util.TensorFlowTestCase):
def testRegisteredNode(self):
graph = ops.Graph()
node = ops._NodeDef("a", "an_a")
weight_params = ops.get_stats_for_node_def(graph, node, "weight_parameters")
self.assertEqual(10, weight_params.value)
flops = ops.get_stats_for_node_def(graph, node, "flops")
self.assertEqual(20, flops.value)
missing_stat = ops.get_stats_for_node_def(graph, node, "missing_stat")
self.assertEqual(None, missing_stat.value)
def testUnregisteredNode(self):
graph = ops.Graph()
node = ops._NodeDef("b", "a_b")
weight_params = ops.get_stats_for_node_def(graph, node, "weight_params")
self.assertEqual(None, weight_params.value)
def testAccumulateStatistics(self):
weight_params_total = ops.OpStats("weight_parameters")
self.assertEqual(None, weight_params_total.value)
flops_total = ops.OpStats("flops")