# 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 SavedModel utils.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.core.framework import types_pb2 from tensorflow.core.protobuf import struct_pb2 from tensorflow.python.eager import context from tensorflow.python.eager import function from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops.ragged import ragged_factory_ops from tensorflow.python.platform import test from tensorflow.python.saved_model import nested_structure_coder from tensorflow.python.saved_model import utils class UtilsTest(test.TestCase): @test_util.run_v1_only("b/120545219") def testBuildTensorInfoOp(self): x = constant_op.constant(1, name="x") y = constant_op.constant(2, name="y") z = control_flow_ops.group([x, y], name="op_z") z_op_info = utils.build_tensor_info_from_op(z) self.assertEqual("op_z", z_op_info.name) self.assertEqual(types_pb2.DT_INVALID, z_op_info.dtype) self.assertEqual(0, len(z_op_info.tensor_shape.dim)) @test_util.run_v1_only("b/120545219") def testBuildTensorInfoDefunOp(self): @function.defun def my_init_fn(x, y): self.x_var = x self.y_var = y x = constant_op.constant(1, name="x") y = constant_op.constant(2, name="y") init_op_info = utils.build_tensor_info_from_op(my_init_fn(x, y)) self.assertEqual("PartitionedCall", init_op_info.name) self.assertEqual(types_pb2.DT_INVALID, init_op_info.dtype) self.assertEqual(0, len(init_op_info.tensor_shape.dim)) @test_util.run_v1_only("b/120545219") def testBuildTensorInfoDense(self): x = array_ops.placeholder(dtypes.float32, 1, name="x") x_tensor_info = utils.build_tensor_info(x) self.assertEqual("x:0", x_tensor_info.name) self.assertEqual(types_pb2.DT_FLOAT, x_tensor_info.dtype) self.assertEqual(1, len(x_tensor_info.tensor_shape.dim)) self.assertEqual(1, x_tensor_info.tensor_shape.dim[0].size) @test_util.run_v1_only("b/120545219") def testBuildTensorInfoSparse(self): x = array_ops.sparse_placeholder(dtypes.float32, [42, 69], name="x") x_tensor_info = utils.build_tensor_info(x) self.assertEqual(x.values.name, x_tensor_info.coo_sparse.values_tensor_name) self.assertEqual(x.indices.name, x_tensor_info.coo_sparse.indices_tensor_name) self.assertEqual(x.dense_shape.name, x_tensor_info.coo_sparse.dense_shape_tensor_name) self.assertEqual(types_pb2.DT_FLOAT, x_tensor_info.dtype) self.assertEqual(2, len(x_tensor_info.tensor_shape.dim)) self.assertEqual(42, x_tensor_info.tensor_shape.dim[0].size) self.assertEqual(69, x_tensor_info.tensor_shape.dim[1].size) @test_util.run_v1_only("b/120545219") def testBuildTensorInfoRagged(self): x = ragged_factory_ops.constant([[1, 2], [3]]) x_tensor_info = utils.build_tensor_info(x) # Check components self.assertEqual(x.values.name, x_tensor_info.composite_tensor.components[0].name) self.assertEqual(types_pb2.DT_INT32, x_tensor_info.composite_tensor.components[0].dtype) self.assertEqual(x.row_splits.name, x_tensor_info.composite_tensor.components[1].name) self.assertEqual(types_pb2.DT_INT64, x_tensor_info.composite_tensor.components[1].dtype) # Check type_spec. struct_coder = nested_structure_coder.StructureCoder() spec_proto = struct_pb2.StructuredValue( type_spec_value=x_tensor_info.composite_tensor.type_spec) spec = struct_coder.decode_proto(spec_proto) self.assertEqual(spec, x._type_spec) def testBuildTensorInfoEager(self): x = constant_op.constant(1, name="x") with context.eager_mode(), self.assertRaisesRegexp( RuntimeError, "build_tensor_info is not supported in Eager mode"): utils.build_tensor_info(x) @test_util.run_v1_only("b/120545219") def testGetTensorFromInfoDense(self): expected = array_ops.placeholder(dtypes.float32, 1, name="x") tensor_info = utils.build_tensor_info(expected) actual = utils.get_tensor_from_tensor_info(tensor_info) self.assertIsInstance(actual, ops.Tensor) self.assertEqual(expected.name, actual.name) @test_util.run_v1_only("b/120545219") def testGetTensorFromInfoSparse(self): expected = array_ops.sparse_placeholder(dtypes.float32, name="x") tensor_info = utils.build_tensor_info(expected) actual = utils.get_tensor_from_tensor_info(tensor_info) self.assertIsInstance(actual, sparse_tensor.SparseTensor) self.assertEqual(expected.values.name, actual.values.name) self.assertEqual(expected.indices.name, actual.indices.name) self.assertEqual(expected.dense_shape.name, actual.dense_shape.name) def testGetTensorFromInfoInOtherGraph(self): with ops.Graph().as_default() as expected_graph: expected = array_ops.placeholder(dtypes.float32, 1, name="right") tensor_info = utils.build_tensor_info(expected) with ops.Graph().as_default(): # Some other graph. array_ops.placeholder(dtypes.float32, 1, name="other") actual = utils.get_tensor_from_tensor_info(tensor_info, graph=expected_graph) self.assertIsInstance(actual, ops.Tensor) self.assertIs(actual.graph, expected_graph) self.assertEqual(expected.name, actual.name) def testGetTensorFromInfoInScope(self): # Build a TensorInfo with name "bar/x:0". with ops.Graph().as_default(): with ops.name_scope("bar"): unscoped = array_ops.placeholder(dtypes.float32, 1, name="x") tensor_info = utils.build_tensor_info(unscoped) self.assertEqual("bar/x:0", tensor_info.name) # Build a graph with node "foo/bar/x:0", akin to importing into scope foo. with ops.Graph().as_default(): with ops.name_scope("foo"): with ops.name_scope("bar"): expected = array_ops.placeholder(dtypes.float32, 1, name="x") self.assertEqual("foo/bar/x:0", expected.name) # Test that tensor is found by prepending the import scope. actual = utils.get_tensor_from_tensor_info(tensor_info, import_scope="foo") self.assertEqual(expected.name, actual.name) @test_util.run_v1_only("b/120545219") def testGetTensorFromInfoRaisesErrors(self): expected = array_ops.placeholder(dtypes.float32, 1, name="x") tensor_info = utils.build_tensor_info(expected) tensor_info.name = "blah:0" # Nonexistent name. with self.assertRaises(KeyError): utils.get_tensor_from_tensor_info(tensor_info) tensor_info.ClearField("name") # Malformed (missing encoding). with self.assertRaises(ValueError): utils.get_tensor_from_tensor_info(tensor_info) if __name__ == "__main__": test.main()