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ops_test.py
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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."""
import gc
import os
import threading
import weakref
from absl.testing import parameterized
import numpy as np
from tensorflow.core.framework import attr_value_pb2
from tensorflow.core.framework import full_type_pb2
from tensorflow.core.framework import tensor_shape_pb2
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.autograph.core import ag_ctx
from tensorflow.python.client import session
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.eager import function as eager_function
from tensorflow.python.eager import wrap_function
from tensorflow.python.framework import composite_tensor
from tensorflow.python.framework import config
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 indexed_slices
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_conversion_registry
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
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 type_spec
from tensorflow.python.framework import versions
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import cond
from tensorflow.python.ops import gen_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 special_math_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.ops import while_loop
import tensorflow.python.ops.gradients # pylint: disable=unused-import
from tensorflow.python.platform import googletest
from tensorflow.python.util import compat
class ResourceTest(test_util.TensorFlowTestCase):
@test_util.run_deprecated_v1
def testBuildGraph(self):
with self.cached_session():
pt = test_ops.stub_resource_handle_op(container="a", shared_name="b")
test_ops.resource_create_op(pt).run()
@test_util.run_deprecated_v1
def testInitialize(self):
with self.cached_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.assertEqual(
len(
resources.report_uninitialized_resources(
resources.shared_resources()).eval()), 1)
resources.initialize_resources(resources.shared_resources()).run()
self.assertEqual(
len(
resources.report_uninitialized_resources(
resources.shared_resources()).eval()), 0)
class TensorAndShapeTest(test_util.TensorFlowTestCase):
def testShape(self):
op = ops.Operation.from_node_def(
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 testNdim(self):
@def_function.function
def f(a):
self.assertEqual(a.ndim, 2)
return 0
x = array_ops.zeros((3, 4))
f(x)
def testIterable(self):
if not context.executing_eagerly():
self.skipTest("Eager-mode test")
op = ops.Operation.from_node_def(
ops._NodeDef("FloatOutput", "myop"), ops.Graph(), [], [dtypes.float32]
)
t = op.outputs[0]
with self.assertRaisesRegex(TypeError, "Cannot iterate"):
iter(t)
def testIterableGraph(self):
if context.executing_eagerly():
self.skipTest("Graph-mode test")
op = ops.Operation.from_node_def(
ops._NodeDef("FloatOutput", "myop"), ops.Graph(), [], [dtypes.float32]
)
t = op.outputs[0]
with self.assertRaisesRegex(TypeError, "Iterating.*not allowed in Graph"):
next(iter(t))
with self.assertRaisesRegex(TypeError, "Iterating.*AutoGraph did convert"):
with ag_ctx.ControlStatusCtx(ag_ctx.Status.ENABLED):
next(iter(t))
with self.assertRaisesRegex(TypeError, "Iterating.*AutoGraph is disabled"):
with ag_ctx.ControlStatusCtx(ag_ctx.Status.DISABLED):
next(iter(t))
def testImplicitBool(self):
op = ops.Operation.from_node_def(
ops._NodeDef("FloatOutput", "myop"), ops.Graph(), [], [dtypes.bool]
)
t = op.outputs[0]
with self.assertRaisesRegex(TypeError,
"Using.*as a.*bool.*not allowed in Graph"):
bool(t)
with self.assertRaisesRegex(TypeError,
"Using.*as a.*bool.*AutoGraph did convert"):
with ag_ctx.ControlStatusCtx(ag_ctx.Status.ENABLED):
bool(t)
with self.assertRaisesRegex(TypeError,
"Using.*as a.*bool.*AutoGraph is disabled"):
with ag_ctx.ControlStatusCtx(ag_ctx.Status.DISABLED):
bool(t)
def testAddShape(self):
with self.cached_session():
a = array_ops.zeros([2, 3])
b = array_ops.ones([1, 3])
c = a + b
self.assertEqual([2, 3], c.shape)
@test_util.run_deprecated_v1
def testUnknownDim(self):
with self.cached_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())
@test_util.run_deprecated_v1
def testUnknownShape(self):
with self.cached_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)
@test_util.run_deprecated_v1
def testScalarShape(self):
with self.cached_session():
a = array_ops.placeholder(dtype=dtypes.float32, shape=[])
b = array_ops.ones([])
c = a + b
self.assertEqual(tensor_shape.TensorShape([]), c.shape)
@test_util.run_deprecated_v1
def testShapeFunctionError(self):
with self.cached_session():
a = array_ops.ones([1, 2, 3])
b = array_ops.ones([4, 5, 6])
with self.assertRaisesRegex(
ValueError, r"Dimensions must be equal, but are 2 and 5 for .*add"
r".*Add(V2)?.* with input shapes: \[1,2,3\], \[4,5,6\]."):
_ = a + b
def testNumpyArray(self):
with ops.Graph().as_default():
x = array_ops.ones((3, 4), name="test_ones")
with self.assertRaisesRegex(NotImplementedError,
r"Cannot convert a symbolic.+test_ones"):
np.array(x)
with self.assertRaisesRegex(TypeError, "not well defined.+test_ones"):
len(x)
# EagerTensors should still behave as numpy arrays.
with context.eager_mode():
x = array_ops.ones((3, 4))
self.assertAllEqual(x, np.ones((3, 4)))
self.assertAllEqual(np.array(x), np.ones((3, 4)))
self.assertLen(x, 3)
def testConstructor(self):
a = array_ops.ones([])
for name in ["T", "astype", "ravel", "transpose", "reshape", "clip", "size",
"tolist", "data"]:
with self.assertRaisesRegex(
AttributeError, r"If you are looking for numpy-related methods"):
getattr(a, name)
with self.assertRaisesRegex(
AttributeError, r"object has no attribute"):
a.foo_bar()
def testRef(self):
x1 = constant_op.constant(3)
x2 = x1
y = constant_op.constant(3)
z = constant_op.constant([6, 10])
w = variables.Variable(5)
self.assertEqual(x1.ref(), x1.ref())
self.assertEqual(x2.ref(), x2.ref())
self.assertEqual(x1.ref(), x2.ref())
self.assertEqual(y.ref(), y.ref())
self.assertEqual(z.ref(), z.ref())
self.assertEqual(w.ref(), w.ref())
self.assertNotEqual(x1.ref(), y.ref())
self.assertNotEqual(x1.ref(), z.ref())
self.assertNotEqual(x1.ref(), w.ref())
self.assertNotEqual(y.ref(), z.ref())
self.assertNotEqual(y.ref(), w.ref())
self.assertNotEqual(z.ref(), w.ref())
def testRefDeref(self):
x1 = constant_op.constant(3)
x2 = x1
y = constant_op.constant(3)
z = constant_op.constant([6, 10])
w = variables.Variable(5)
self.assertIs(x1, x1.ref().deref())
self.assertIs(x2, x2.ref().deref())
self.assertIs(x1, x2.ref().deref())
self.assertIs(x2, x1.ref().deref())
self.assertIs(y, y.ref().deref())
self.assertIs(z, z.ref().deref())
self.assertIsNot(x1, y.ref().deref())
self.assertIsNot(x1, z.ref().deref())
self.assertIsNot(x1, w.ref().deref())
self.assertIsNot(y, z.ref().deref())
self.assertIsNot(y, w.ref().deref())
self.assertIsNot(z, w.ref().deref())
def testRefInSet(self):
x1 = constant_op.constant(3)
x2 = x1
y = constant_op.constant(3)
z = constant_op.constant([6, 10])
w = variables.Variable(5)
self.assertEqual(x1.ref(), x2.ref())
tensor_set = {
x1.ref(),
x2.ref(),
y.ref(),
z.ref(),
w.ref(),
}
self.assertLen(tensor_set, 4)
self.assertIn(x1.ref(), tensor_set)
self.assertIn(x2.ref(), tensor_set)
self.assertIn(y.ref(), tensor_set)
self.assertIn(z.ref(), tensor_set)
self.assertIn(w.ref(), tensor_set)
def testRefInDict(self):
x1 = constant_op.constant(3)
x2 = x1
y = constant_op.constant(3)
z = constant_op.constant([6, 10])
w = variables.Variable(5)
self.assertEqual(x1.ref(), x2.ref())
tensor_dict = {
x1.ref(): "x1",
y.ref(): "y",
z.ref(): "z",
w.ref(): "w",
}
self.assertLen(tensor_dict, 4)
# Overwriting x1
tensor_dict[x2.ref()] = "x2"
self.assertLen(tensor_dict, 4)
self.assertEqual(tensor_dict[x1.ref()], "x2")
self.assertEqual(tensor_dict[x2.ref()], "x2")
self.assertEqual(tensor_dict[y.ref()], "y")
self.assertEqual(tensor_dict[z.ref()], "z")
self.assertEqual(tensor_dict[w.ref()], "w")
def testTensorRefStrong(self):
x = constant_op.constant(1.)
x_ref = x.ref()
del x
self.assertIsNotNone(x_ref.deref())
def testVariableRefStrong(self):
x = variables.Variable(1.)
x_ref = x.ref()
del x
self.assertIsNotNone(x_ref.deref())
@test_util.run_in_graph_and_eager_modes
def testBitwiseAndNumeric(self):
x = constant_op.constant([0, 1, 3])
y = constant_op.constant([1, 1, 1])
z = x & y
self.assertAllEqual(z, [0, 1, 1])
@test_util.run_in_graph_and_eager_modes
def testBitwiseAndBool(self):
x = constant_op.constant([False, False, True, True])
y = constant_op.constant([False, True, False, True])
z = x & y
self.assertAllEqual(z, [False, False, False, True])
@test_util.run_in_graph_and_eager_modes
def testBitwiseAndErrors(self):
x_int = constant_op.constant(0)
x_bool = constant_op.constant(True)
if context.executing_eagerly(): # :(
expected_errtype = errors.InvalidArgumentError
else:
expected_errtype = TypeError
with self.assertRaises(expected_errtype):
_ = x_int & x_bool
with self.assertRaises(expected_errtype):
_ = x_int & constant_op.constant("a")
with self.assertRaises(expected_errtype):
_ = x_bool & x_int
with self.assertRaises(expected_errtype):
_ = x_bool & constant_op.constant("a")
with self.assertRaises(expected_errtype):
_ = constant_op.constant("a") & constant_op.constant("b")
@test_util.run_in_graph_and_eager_modes
def testBitwiseOrNumeric(self):
x = constant_op.constant([0, 1, 2])
y = constant_op.constant([1, 1, 1])
z = x | y
self.assertAllEqual(z, [1, 1, 3])
@test_util.run_in_graph_and_eager_modes
def testBitwiseOrBool(self):
x = constant_op.constant([False, False, True, True])
y = constant_op.constant([False, True, False, True])
z = x | y
self.assertAllEqual(z, [False, True, True, True])
@test_util.run_in_graph_and_eager_modes
def testBitwiseOrErrors(self):
x_int = constant_op.constant(0)
x_bool = constant_op.constant(True)
if context.executing_eagerly(): # :(
expected_errtype = errors.InvalidArgumentError
else:
expected_errtype = TypeError
with self.assertRaises(expected_errtype):
_ = x_int | x_bool
with self.assertRaises(expected_errtype):
_ = x_int | constant_op.constant("a")
with self.assertRaises(expected_errtype):
_ = x_bool | x_int
with self.assertRaises(expected_errtype):
_ = x_bool | constant_op.constant("a")
with self.assertRaises(expected_errtype):
_ = constant_op.constant("a") | constant_op.constant("b")
@test_util.run_in_graph_and_eager_modes
def testBitwiseXorNumeric(self):
x = constant_op.constant([0, 1, 3])
y = constant_op.constant([1, 1, 1])
z = x ^ y
self.assertAllEqual(z, [1, 0, 2])
@test_util.run_in_graph_and_eager_modes
def testBitwiseXorBool(self):
x = constant_op.constant([False, False, True, True])
y = constant_op.constant([False, True, False, True])
z = x ^ y
self.assertAllEqual(z, [False, True, True, False])
@test_util.run_in_graph_and_eager_modes
def testBitwiseXorErrors(self):
x_int = constant_op.constant(0)
x_bool = constant_op.constant(True)
if context.executing_eagerly(): # :(
expected_errtype = errors.InvalidArgumentError
else:
expected_errtype = TypeError
with self.assertRaises(expected_errtype):
_ = x_int ^ x_bool
with self.assertRaises(expected_errtype):
_ = x_int ^ constant_op.constant("a")
with self.assertRaises(expected_errtype):
_ = x_bool ^ x_int
with self.assertRaises(expected_errtype):
_ = x_bool ^ constant_op.constant("a")
with self.assertRaises(expected_errtype):
_ = constant_op.constant("a") ^ constant_op.constant("b")
@test_util.run_in_graph_and_eager_modes
def testBitwiseNotNumeric(self):
x = constant_op.constant([0, dtypes.int32.min, 1])
# pylint: disable=invalid-unary-operand-type
y = ~x
self.assertAllEqual(y, [-1, dtypes.int32.max, -2])
@test_util.run_in_graph_and_eager_modes
def testBitwiseNotBool(self):
x = constant_op.constant([False, True])
# pylint: disable=invalid-unary-operand-type
y = ~x
self.assertAllEqual(y, [True, False])
@test_util.run_in_graph_and_eager_modes
def testBitwiseNotErrors(self):
if context.executing_eagerly(): # :(
expected_errtype = errors.InvalidArgumentError
else:
expected_errtype = TypeError
# pylint: disable=invalid-unary-operand-type
with self.assertRaises(expected_errtype):
_ = ~constant_op.constant("a")
@test_util.run_all_in_graph_and_eager_modes
class IndexedSlicesTest(test_util.TensorFlowTestCase):
def testToTensor(self):
values = constant_op.constant([2, 3, 5, 7], shape=[2, 2])
indices = constant_op.constant([0, 2])
x = indexed_slices.IndexedSlices(values, indices)
with self.assertRaises(ValueError):
tensor = ops.convert_to_tensor(x, name="tensor")
self.assertEqual(tensor_shape.TensorShape(None), x.shape)
dense_shape = constant_op.constant([3, 2])
y = indexed_slices.IndexedSlices(values, indices, dense_shape)
tensor = ops.convert_to_tensor(y, name="tensor")
self.assertAllEqual(tensor.shape, y.shape)
self.assertAllEqual(self.evaluate(tensor), [[2, 3], [0, 0], [5, 7]])
@test_util.run_gpu_only
def testEagerCopy(self):
with context.eager_mode():
var = variables.Variable([[0.0], [0.0], [0.0], [0.0]], name="tensor")
with backprop.GradientTape() as tape:
a = array_ops.gather(array_ops.gather(var, [0, 1]), [0, 1])
b = array_ops.gather(array_ops.gather(var, [2, 3]), [0, 1])
r = special_math_ops.einsum("ij,ij->i", a, b)
g = tape.gradient(r, [var])[0]
values = g.values if isinstance(g, indexed_slices.IndexedSlices) else g
self.assertAllEqual(values.get_shape(), [4, 1])
def testNegation(self):
values = constant_op.constant([2, 3, 5, 7], shape=[2, 2])
indices = constant_op.constant([0, 2])
x = -indexed_slices.IndexedSlices(values, indices)
self.assertAllEqual(x.values, [[-2, -3], [-5, -7]])
self.assertAllEqual(x.indices, [0, 2])
def testScalarMul(self):
values = constant_op.constant([2, 3, 5, 7], shape=[2, 2])
indices = constant_op.constant([0, 2])
x = math_ops.scalar_mul(-2, indexed_slices.IndexedSlices(values, indices))
self.assertAllEqual(x.values, [[-4, -6], [-10, -14]])
self.assertAllEqual(x.indices, [0, 2])
@test_util.run_all_in_graph_and_eager_modes
class IndexedSlicesSpecTest(test_util.TensorFlowTestCase,
parameterized.TestCase):
def assertAllTensorsEqual(self, list1, list2):
self.assertLen(list1, len(list2))
for (t1, t2) in zip(list1, list2):
self.assertAllEqual(t1, t2)
def testConstruction(self):
spec1 = indexed_slices.IndexedSlicesSpec()
self.assertIsNone(spec1._shape.rank)
self.assertEqual(spec1._values_dtype, dtypes.float32)
self.assertEqual(spec1._indices_dtype, dtypes.int64)
self.assertIsNone(spec1._dense_shape_dtype)
self.assertEqual(spec1._indices_shape.as_list(), [None])
spec2 = indexed_slices.IndexedSlicesSpec([None, None], dtypes.string,
dtypes.int32, dtypes.int64, [10])
self.assertEqual(spec2._shape.as_list(), [None, None])
self.assertEqual(spec2._values_dtype, dtypes.string)
self.assertEqual(spec2._indices_dtype, dtypes.int32)
self.assertEqual(spec2._dense_shape_dtype, dtypes.int64)
self.assertEqual(spec2._indices_shape.as_list(), [10])
def testValueType(self):
spec1 = indexed_slices.IndexedSlicesSpec()
self.assertEqual(spec1.value_type, indexed_slices.IndexedSlices)
@parameterized.parameters([
(indexed_slices.IndexedSlicesSpec(),
(tensor_shape.TensorShape(None), dtypes.float32, dtypes.int64, None,
tensor_shape.TensorShape([None]))),
(indexed_slices.IndexedSlicesSpec(shape=[5, None, None]),
(tensor_shape.TensorShape([5, None, None]), dtypes.float32,
dtypes.int64, None, tensor_shape.TensorShape([None]))),
(indexed_slices.IndexedSlicesSpec(
dtype=dtypes.int32, dense_shape_dtype=dtypes.int64),
(tensor_shape.TensorShape(None), dtypes.int32, dtypes.int64,
dtypes.int64, tensor_shape.TensorShape([None]))),
(indexed_slices.IndexedSlicesSpec(indices_shape=[100]),
(tensor_shape.TensorShape(None), dtypes.float32, dtypes.int64, None,
tensor_shape.TensorShape([100]))),
]) # pyformat: disable
def testSerialize(self, spec, expected):
serialization = spec._serialize()
# TensorShape has an unconventional definition of equality, so we can't use
# assertEqual directly here. But repr() is deterministic and lossless for
# the expected values, so we can use that instead.
self.assertEqual(repr(serialization), repr(expected))
@parameterized.parameters([
(indexed_slices.IndexedSlicesSpec(dtype=dtypes.string), (
tensor_spec.TensorSpec(None, dtypes.string),
tensor_spec.TensorSpec([None], dtypes.int64),
)),
(indexed_slices.IndexedSlicesSpec(
dtype=dtypes.string, dense_shape_dtype=dtypes.int32), (
tensor_spec.TensorSpec(None, dtypes.string),
tensor_spec.TensorSpec([None], dtypes.int64),
tensor_spec.TensorSpec([None], dtypes.int32),
)),
(indexed_slices.IndexedSlicesSpec(
shape=[5, 10, 15], dense_shape_dtype=dtypes.int32), (
tensor_spec.TensorSpec([None, 10, 15], dtypes.float32),
tensor_spec.TensorSpec([None], dtypes.int64),
tensor_spec.TensorSpec([3], dtypes.int32),
)),
(indexed_slices.IndexedSlicesSpec(
shape=[5, 10, 15], dense_shape_dtype=dtypes.int32,
indices_shape=[20]), (
tensor_spec.TensorSpec([20, 10, 15], dtypes.float32),
tensor_spec.TensorSpec([20], dtypes.int64),
tensor_spec.TensorSpec([3], dtypes.int32),
)),
])
def testComponentSpecs(self, spec, expected):
self.assertEqual(spec._component_specs, expected)
@parameterized.parameters([
{
"spec": indexed_slices.IndexedSlicesSpec(),
"values": [3.0, 5.0],
"indices": [5, 10]
},
{
"spec":
indexed_slices.IndexedSlicesSpec(dense_shape_dtype=dtypes.int32),
"values": [3.0, 5.0],
"indices": [5, 10],
"dense_shape": [100]
},
])
def testToFromComponents(self, spec, indices, values, dense_shape=None):
x = indexed_slices.IndexedSlices(indices, values, dense_shape)
actual_components = spec._to_components(x)
if dense_shape is None:
self.assertAllTensorsEqual(actual_components, [indices, values])
else:
self.assertAllTensorsEqual(actual_components,
[indices, values, dense_shape])
st_reconstructed = spec._from_components(actual_components)
self.assertAllEqual(x.indices, st_reconstructed.indices)
self.assertAllEqual(x.values, st_reconstructed.values)
if dense_shape is None:
self.assertIsNone(st_reconstructed.dense_shape)
else:
self.assertAllEqual(x.dense_shape, st_reconstructed.dense_shape)
@test_util.run_v1_only("IndexedSlicesValue is deprecated in v2")
def testFromNumpyComponents(self):
indices = np.array([3, 8])
values = np.array([1.0, 9.0])
dense_shape = np.array([100])
spec1 = indexed_slices.IndexedSlicesSpec(dense_shape_dtype=dtypes.int32)
st1 = spec1._from_components((values, indices, dense_shape))
self.assertIsInstance(st1, indexed_slices.IndexedSlicesValue)
self.assertAllEqual(st1.indices, indices)
self.assertAllEqual(st1.values, values)
self.assertAllEqual(st1.dense_shape, dense_shape)
spec2 = indexed_slices.IndexedSlicesSpec()
st2 = spec2._from_components((values, indices))
self.assertIsInstance(st2, indexed_slices.IndexedSlicesValue)
self.assertAllEqual(st2.indices, indices)
self.assertAllEqual(st2.values, values)
self.assertIsNone(st2.dense_shape)
class NodeDefConstructorTest(test_util.TensorFlowTestCase):
def testNoArgs(self):
nodedef = ops._NodeDef("None", "bar")
self.assertProtoEquals("op: 'None' name: 'bar'", 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 testTraceback(self):
g = ops.Graph()
op1 = ops.Operation.from_node_def(
ops._NodeDef("None", "op1"), g, [], [dtypes.float32_ref, dtypes.float32]
)
self.assertIn("testTraceback", op1.traceback[-1])
@test_util.run_deprecated_v1
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)
@test_util.run_deprecated_v1
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)
@test_util.run_deprecated_v1
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.from_node_def(
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.from_node_def(
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.from_node_def(
ops._NodeDef("RefOutputFloatOutput", "op1"),
g,
[],
[dtypes.float32_ref, dtypes.float32],
)
self.assertProtoEquals("op:'RefOutputFloatOutput' name:'op1'", op1.node_def)
self.assertEqual([], list(op1.inputs))
ref_t, nonref_t = op1.values()
# NOTE(mrry): Must specify input_types to preserve ref-typed input.
op2 = ops.Operation.from_node_def(
ops._NodeDef("RefInputFloatInput", "op2"),
g,
[ref_t, nonref_t],
[],
input_types=[dtypes.float32_ref, dtypes.float32],
)
self.assertProtoEquals(
"op:'RefInputFloatInput' name:'op2' input:'op1' input:'op1:1'",
op2.node_def)
self.assertEqual([ref_t, nonref_t], list(op2.inputs))
op3 = ops.Operation.from_node_def(
ops._NodeDef("TwoFloatInputs", "op3"), g, [ref_t, nonref_t], []
)
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.from_node_def(ops._NodeDef("op", ""), g)
with self.assertRaises(ValueError):
ops.Operation.from_node_def(ops._NodeDef("op", "_invalid"), g)
with self.assertRaises(ValueError):
ops.Operation.from_node_def(ops._NodeDef("op", "-invalid"), g)
with self.assertRaises(ValueError):
ops.Operation.from_node_def(ops._NodeDef("op", "/invalid"), g)
with self.assertRaises(ValueError):
ops.Operation.from_node_def(ops._NodeDef("op", "invalid:0"), g)
@test_util.run_deprecated_v1
def testNoShapeFunction(self):
op = test_ops.a()
self.assertEqual(tensor_shape.unknown_shape(), op.get_shape())
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorNestedArray(self):
values = [[2], [3], [5], [7]]
tensor = ops.convert_to_tensor(values)
self.assertAllEqual((4, 1), tensor.get_shape().as_list())
self.assertAllEqual(values, self.evaluate(tensor))
def testShapeTuple(self):
with self.cached_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))
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorNestedTuple(self):
values = ((2,), (3,), (5,), (7,))
tensor = ops.convert_to_tensor(values)
self.assertAllEqual((4, 1), tensor.get_shape().as_list())
self.assertAllEqual(values, self.evaluate(ops.convert_to_tensor(values)))
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorNestedTensors(self):
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, self.evaluate(tensor))
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, self.evaluate(tensor))
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorNestedMix(self):
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,)), self.evaluate(tensor))
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorPreferred(self):
values = [2, 3, 5, 7]
tensor = ops.convert_to_tensor(values, preferred_dtype=dtypes.float32)
self.assertEqual(dtypes.float32, tensor.dtype)
# Convert empty tensor to anything.
values = []
tensor = ops.convert_to_tensor(values, preferred_dtype=dtypes.int64)
self.assertEqual(dtypes.int64, tensor.dtype)
# 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)
@test_util.run_in_graph_and_eager_modes
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)
@test_util.run_in_graph_and_eager_modes
def testConvertToLongLongTensorType(self):
tensor = ops.convert_to_tensor(
# Get a numpy array of dtype NPY_LONGLONG
np.prod(constant_op.constant([1])._shape_tuple()),
dtype=dtypes.int64)
self.assertEqual(dtypes.int64, tensor.dtype)
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorFromValidTensor(self):
tensor = constant_op.constant(413, dtype=dtypes.int64)
converted = ops.convert_to_tensor(tensor, dtype=dtypes.int64)
# If dtype is compatible, the returned tensor should be the same instance.
self.assertEqual(tensor, converted)
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorFromInvalidTensor(self):
tensor = constant_op.constant(42.0, dtype=dtypes.float32)
with self.assertRaises(ValueError):
ops.convert_to_tensor(tensor, dtype=dtypes.int32)
@test_util.run_in_graph_and_eager_modes
def testConvertToTensorProtocol(self):
class TensorCompatible:
def __tf_tensor__(self, dtype=None, name=None):
return constant_op.constant((1, 2, 3), dtype=dtype, name=name)
tc = TensorCompatible()
tensor = ops.convert_to_tensor(tc, dtype=dtypes.int32)
self.assertEqual(tensor.dtype, dtypes.int32)
self.assertAllEqual((1, 2, 3), self.evaluate(tensor))
@test_util.run_deprecated_v1
def testNoConvert(self):
# Operation cannot be converted to Tensor.
op = gen_control_flow_ops.no_op()
with self.assertRaisesRegex(TypeError,
"can't convert Operation '.+' to Tensor"):
ops.convert_to_tensor(op)
def testStr(self):
node_def = ops._NodeDef("None", "op1")
op = ops.Operation.from_node_def(
node_def, ops.Graph(), [], [dtypes.float32]
)
self.assertEqual(str(node_def), str(op))
def testRepr(self):
op = ops.Operation.from_node_def(
ops._NodeDef("None", "op1"), ops.Graph(), [], [dtypes.float32]
)
self.assertEqual("<tf.Operation 'op1' type=None>", repr(op))
@test_util.run_deprecated_v1
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 assertEqual 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.assertRaisesRegex(
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.
@test_util.run_deprecated_v1
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)
@test_util.run_v2_only