185 lines
8.2 KiB
Python
185 lines
8.2 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for SavedModel utils."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.core.framework import types_pb2
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from tensorflow.core.protobuf import struct_pb2
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from tensorflow.python.eager import context
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from tensorflow.python.eager import function
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops.ragged import ragged_factory_ops
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.platform import test
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from tensorflow.python.saved_model import nested_structure_coder
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from tensorflow.python.saved_model import utils
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class UtilsTest(test.TestCase):
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@test_util.run_v1_only("b/120545219")
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def testBuildTensorInfoOp(self):
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x = constant_op.constant(1, name="x")
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y = constant_op.constant(2, name="y")
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z = control_flow_ops.group([x, y], name="op_z")
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z_op_info = utils.build_tensor_info_from_op(z)
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self.assertEqual("op_z", z_op_info.name)
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self.assertEqual(types_pb2.DT_INVALID, z_op_info.dtype)
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self.assertEqual(0, len(z_op_info.tensor_shape.dim))
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@test_util.run_v1_only("b/120545219")
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def testBuildTensorInfoDefunOp(self):
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@function.defun
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def my_init_fn(x, y):
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self.x_var = x
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self.y_var = y
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x = constant_op.constant(1, name="x")
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y = constant_op.constant(2, name="y")
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init_op_info = utils.build_tensor_info_from_op(my_init_fn(x, y))
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self.assertEqual("PartitionedCall", init_op_info.name)
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self.assertEqual(types_pb2.DT_INVALID, init_op_info.dtype)
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self.assertEqual(0, len(init_op_info.tensor_shape.dim))
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@test_util.run_v1_only("b/120545219")
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def testBuildTensorInfoDense(self):
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x = array_ops.placeholder(dtypes.float32, 1, name="x")
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x_tensor_info = utils.build_tensor_info(x)
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self.assertEqual("x:0", x_tensor_info.name)
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self.assertEqual(types_pb2.DT_FLOAT, x_tensor_info.dtype)
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self.assertEqual(1, len(x_tensor_info.tensor_shape.dim))
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self.assertEqual(1, x_tensor_info.tensor_shape.dim[0].size)
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@test_util.run_v1_only("b/120545219")
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def testBuildTensorInfoSparse(self):
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x = array_ops.sparse_placeholder(dtypes.float32, [42, 69], name="x")
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x_tensor_info = utils.build_tensor_info(x)
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self.assertEqual(x.values.name,
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x_tensor_info.coo_sparse.values_tensor_name)
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self.assertEqual(x.indices.name,
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x_tensor_info.coo_sparse.indices_tensor_name)
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self.assertEqual(x.dense_shape.name,
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x_tensor_info.coo_sparse.dense_shape_tensor_name)
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self.assertEqual(types_pb2.DT_FLOAT, x_tensor_info.dtype)
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self.assertEqual(2, len(x_tensor_info.tensor_shape.dim))
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self.assertEqual(42, x_tensor_info.tensor_shape.dim[0].size)
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self.assertEqual(69, x_tensor_info.tensor_shape.dim[1].size)
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@test_util.run_v1_only("b/120545219")
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def testBuildTensorInfoRagged(self):
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x = ragged_factory_ops.constant([[1, 2], [3]])
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x_tensor_info = utils.build_tensor_info(x)
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# Check components
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self.assertEqual(x.values.name,
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x_tensor_info.composite_tensor.components[0].name)
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self.assertEqual(types_pb2.DT_INT32,
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x_tensor_info.composite_tensor.components[0].dtype)
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self.assertEqual(x.row_splits.name,
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x_tensor_info.composite_tensor.components[1].name)
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self.assertEqual(types_pb2.DT_INT64,
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x_tensor_info.composite_tensor.components[1].dtype)
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# Check type_spec.
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struct_coder = nested_structure_coder.StructureCoder()
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spec_proto = struct_pb2.StructuredValue(
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type_spec_value=x_tensor_info.composite_tensor.type_spec)
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spec = struct_coder.decode_proto(spec_proto)
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self.assertEqual(spec, x._type_spec)
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def testBuildTensorInfoEager(self):
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x = constant_op.constant(1, name="x")
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with context.eager_mode(), self.assertRaisesRegexp(
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RuntimeError, "build_tensor_info is not supported in Eager mode"):
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utils.build_tensor_info(x)
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@test_util.run_v1_only("b/120545219")
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def testGetTensorFromInfoDense(self):
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expected = array_ops.placeholder(dtypes.float32, 1, name="x")
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tensor_info = utils.build_tensor_info(expected)
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actual = utils.get_tensor_from_tensor_info(tensor_info)
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self.assertIsInstance(actual, ops.Tensor)
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self.assertEqual(expected.name, actual.name)
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@test_util.run_v1_only("b/120545219")
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def testGetTensorFromInfoSparse(self):
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expected = array_ops.sparse_placeholder(dtypes.float32, name="x")
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tensor_info = utils.build_tensor_info(expected)
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actual = utils.get_tensor_from_tensor_info(tensor_info)
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self.assertIsInstance(actual, sparse_tensor.SparseTensor)
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self.assertEqual(expected.values.name, actual.values.name)
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self.assertEqual(expected.indices.name, actual.indices.name)
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self.assertEqual(expected.dense_shape.name, actual.dense_shape.name)
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@test_util.run_v1_only("b/120545219")
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def testGetTensorFromInfoRagged(self):
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expected = ragged_factory_ops.constant([[1, 2], [3]], name="x")
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tensor_info = utils.build_tensor_info(expected)
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actual = utils.get_tensor_from_tensor_info(tensor_info)
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self.assertIsInstance(actual, ragged_tensor.RaggedTensor)
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self.assertEqual(expected.values.name, actual.values.name)
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self.assertEqual(expected.row_splits.name, actual.row_splits.name)
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def testGetTensorFromInfoInOtherGraph(self):
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with ops.Graph().as_default() as expected_graph:
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expected = array_ops.placeholder(dtypes.float32, 1, name="right")
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tensor_info = utils.build_tensor_info(expected)
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with ops.Graph().as_default(): # Some other graph.
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array_ops.placeholder(dtypes.float32, 1, name="other")
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actual = utils.get_tensor_from_tensor_info(tensor_info,
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graph=expected_graph)
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self.assertIsInstance(actual, ops.Tensor)
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self.assertIs(actual.graph, expected_graph)
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self.assertEqual(expected.name, actual.name)
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def testGetTensorFromInfoInScope(self):
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# Build a TensorInfo with name "bar/x:0".
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with ops.Graph().as_default():
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with ops.name_scope("bar"):
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unscoped = array_ops.placeholder(dtypes.float32, 1, name="x")
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tensor_info = utils.build_tensor_info(unscoped)
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self.assertEqual("bar/x:0", tensor_info.name)
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# Build a graph with node "foo/bar/x:0", akin to importing into scope foo.
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with ops.Graph().as_default():
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with ops.name_scope("foo"):
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with ops.name_scope("bar"):
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expected = array_ops.placeholder(dtypes.float32, 1, name="x")
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self.assertEqual("foo/bar/x:0", expected.name)
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# Test that tensor is found by prepending the import scope.
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actual = utils.get_tensor_from_tensor_info(tensor_info,
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import_scope="foo")
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self.assertEqual(expected.name, actual.name)
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@test_util.run_v1_only("b/120545219")
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def testGetTensorFromInfoRaisesErrors(self):
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expected = array_ops.placeholder(dtypes.float32, 1, name="x")
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tensor_info = utils.build_tensor_info(expected)
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tensor_info.name = "blah:0" # Nonexistent name.
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with self.assertRaises(KeyError):
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utils.get_tensor_from_tensor_info(tensor_info)
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tensor_info.ClearField("name") # Malformed (missing encoding).
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with self.assertRaises(ValueError):
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utils.get_tensor_from_tensor_info(tensor_info)
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if __name__ == "__main__":
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test.main()
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