chore: import upstream snapshot with attribution

tensorflow/python/saved_model/utils_test.py

@@ -0,0 +1,173 @@
+# 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 tensorflow.core.framework import types_pb2
+from tensorflow.core.protobuf import struct_pb2
+from tensorflow.python.eager import context
+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 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.ops.ragged import ragged_tensor
+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: `build_tensor_info` is only available in graph mode.")
+  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: `build_tensor_info` is only available in graph mode.")
+  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: `build_tensor_info` is only available in graph mode.")
+  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: `build_tensor_info` is only available in graph mode.")
+  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.
+    spec_proto = struct_pb2.StructuredValue(
+        type_spec_value=x_tensor_info.composite_tensor.type_spec)
+    spec = nested_structure_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.assertRaisesRegex(
+        RuntimeError, "`build_tensor_info` is not supported"):
+      utils.build_tensor_info(x)
+
+  @test_util.run_v1_only(
+      "b/120545219: `build_tensor_info` is only available in graph mode.")
+  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, tensor.Tensor)
+    self.assertEqual(expected.name, actual.name)
+
+  @test_util.run_v1_only(
+      "b/120545219: `build_tensor_info` is only available in graph mode.")
+  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)
+
+  @test_util.run_v1_only(
+      "b/120545219: `build_tensor_info` is only available in graph mode.")
+  def testGetTensorFromInfoRagged(self):
+    expected = ragged_factory_ops.constant([[1, 2], [3]], name="x")
+    tensor_info = utils.build_tensor_info(expected)
+    actual = utils.get_tensor_from_tensor_info(tensor_info)
+    self.assertIsInstance(actual, ragged_tensor.RaggedTensor)
+    self.assertEqual(expected.values.name, actual.values.name)
+    self.assertEqual(expected.row_splits.name, actual.row_splits.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, tensor.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: `build_tensor_info` is only available in graph mode.")
+  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()