feat: add tf1 metadata builder

google/cloud/aiplatform/explain/metadata/tf/v1/__init__.py

@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2021 Google LLC
+#
+# 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.

google/cloud/aiplatform/explain/metadata/tf/v1/saved_model_metadata_builder.py

@@ -0,0 +1,165 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2021 Google LLC
+#
+# 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.
+
+from google.protobuf import json_format
+from typing import Any, Dict, List, Optional
+
+from google.cloud.aiplatform.compat.types import (
+    explanation_metadata_v1beta1 as explanation_metadata,
+)
+from google.cloud.aiplatform.explain.metadata import metadata_builder
+
+
+class SavedModelMetadataBuilder(metadata_builder.MetadataBuilder):
+    """Metadata builder class that accepts a TF1 saved model."""
+
+    def __init__(
+        self,
+        model_path: str,
+        tags: Optional[List[str]] = None,
+        signature_name: Optional[str] = None,
+        outputs_to_explain: Optional[List[str]] = None,
+    ) -> None:
+        """Initializes a SavedModelMetadataBuilder object.
+
+        Args:
+          model_path:
+              Required. Local or GCS path to load the saved model from.
+          tags:
+              Optional. Tags to identify the model graph. If None or empty,
+              TensorFlow's default serving tag will be used.
+          signature_name:
+              Optional. Name of the signature to be explained. Inputs and
+              outputs of this signature will be written in the metadata. If not
+              provided, the default signature will be used.
+          outputs_to_explain:
+              Optional. List of output names to explain. Only single output is
+              supported for now. Hence, the list should contain one element.
+              This parameter is required if the model signature (provided via
+              signature_name) specifies multiple outputs.
+
+        Raises:
+            ValueError if outputs_to_explain contains more than 1 element or
+            signature contains multiple outputs.
+        """
+        if outputs_to_explain:
+            if len(outputs_to_explain) > 1:
+                raise ValueError(
+                    "Only one output is supported at the moment. "
+                    f"Received: {outputs_to_explain}."
+                )
+            self._output_to_explain = next(iter(outputs_to_explain))
+
+        try:
+            import tensorflow.compat.v1 as tf
+        except ImportError:
+            raise ImportError(
+                "Tensorflow is not installed and is required to load saved model. "
+                'Please install the SDK using "pip install "tensorflow>=1.15,<2.0""'
+            )
+
+        if not signature_name:
+            signature_name = tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+        self._tags = tags or [tf.saved_model.tag_constants.SERVING]
+        self._graph = tf.Graph()
+
+        with self.graph.as_default():
+            self._session = tf.Session(graph=self.graph)
+            self._metagraph_def = tf.saved_model.loader.load(
+                sess=self.session, tags=self._tags, export_dir=model_path
+            )
+            if signature_name not in self._metagraph_def.signature_def:
+                raise ValueError(
+                    f"Serving sigdef key {signature_name} not in the signature def."
+                )
+            serving_sigdef = self._metagraph_def.signature_def[signature_name]
+        if not outputs_to_explain:
+            if len(serving_sigdef.outputs) > 1:
+                raise ValueError(
+                    "The signature contains multiple outputs. Specify "
+                    'an output via "outputs_to_explain" parameter.'
+                )
+            self._output_to_explain = next(iter(serving_sigdef.outputs.keys()))
+
+        self._inputs = _create_input_metadata_from_signature(serving_sigdef.inputs)
+        self._outputs = _create_output_metadata_from_signature(
+            serving_sigdef.outputs, self._output_to_explain
+        )
+
+    @property
+    def graph(self) -> "tf.Graph":  # noqa: F821
+        return self._graph
+
+    @property
+    def session(self) -> "tf.Session":  # noqa: F821
+        return self._session
+
+    def get_metadata(self) -> Dict[str, Any]:
+        """Returns the current metadata as a dictionary.
+
+        Returns:
+            Json format of the explanation metadata.
+        """
+        current_md = explanation_metadata.ExplanationMetadata(
+            inputs=self._inputs, outputs=self._outputs,
+        )
+        return json_format.MessageToDict(current_md._pb)
+
+
+def _create_input_metadata_from_signature(
+    signature_inputs: Dict[str, "tf.Tensor"]  # noqa: F821
+) -> Dict[str, explanation_metadata.ExplanationMetadata.InputMetadata]:
+    """Creates InputMetadata from signature inputs.
+
+    Args:
+      signature_inputs:
+          Required. Inputs of the signature to be explained. If not provided,
+          the default signature will be used.
+
+    Returns:
+          Inferred input metadata from the model.
+    """
+    input_mds = {}
+    for key, tensor in signature_inputs.items():
+        input_mds[key] = explanation_metadata.ExplanationMetadata.InputMetadata(
+            input_tensor_name=tensor.name
+        )
+    return input_mds
+
+
+def _create_output_metadata_from_signature(
+    signature_outputs: Dict[str, "tf.Tensor"],  # noqa: F821
+    output_to_explain: Optional[str] = None,
+) -> Dict[str, explanation_metadata.ExplanationMetadata.OutputMetadata]:
+    """Creates OutputMetadata from signature inputs.
+
+    Args:
+      signature_outputs:
+          Required. Inputs of the signature to be explained. If not provided,
+          the default signature will be used.
+      output_to_explain:
+          Optional. Output name to explain.
+
+    Returns:
+          Inferred output metadata from the model.
+    """
+    output_mds = {}
+    for key, tensor in signature_outputs.items():
+        if not output_to_explain or output_to_explain == key:
+            output_mds[key] = explanation_metadata.ExplanationMetadata.OutputMetadata(
+                output_tensor_name=tensor.name
+            )
+    return output_mds

google/cloud/aiplatform/explain/metadata/tf/v2/saved_model_metadata_builder.py

@@ -37,7 +37,7 @@ def __init__(
 
         Args:
           model_path:
-              Required. Path to load the saved model from.
+              Required. Local or GCS path to load the saved model from.
           signature_name:
               Optional. Name of the signature to be explained. Inputs and
               outputs of this signature will be written in the metadata. If not

tests/unit/aiplatform/test_explain_saved_model_metadata_builder_tf1_test.py

@@ -0,0 +1,82 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2020 Google LLC
+#
+# 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.
+#
+
+import tensorflow.compat.v1 as tf
+
+from google.cloud.aiplatform.explain.metadata.tf.v1 import saved_model_metadata_builder
+
+
+class SavedModelMetadataBuilderTF1Test(tf.test.TestCase):
+    def _set_up(self):
+        self.sess = tf.Session(graph=tf.Graph())
+        with self.sess.graph.as_default():
+            self.x = tf.placeholder(shape=[None, 10], dtype=tf.float32, name="inp")
+            weights = tf.constant(1.0, shape=(10, 2), name="weights")
+            bias_weight = tf.constant(1.0, shape=(2,), name="bias")
+            self.linear_layer = tf.add(tf.matmul(self.x, weights), bias_weight)
+            self.prediction = tf.nn.relu(self.linear_layer)
+            # save the model
+            self.model_path = self.get_temp_dir()
+            builder = tf.saved_model.builder.SavedModelBuilder(self.model_path)
+            tensor_info_x = tf.saved_model.utils.build_tensor_info(self.x)
+            tensor_info_pred = tf.saved_model.utils.build_tensor_info(self.prediction)
+            tensor_info_lin = tf.saved_model.utils.build_tensor_info(self.linear_layer)
+            prediction_signature = tf.saved_model.signature_def_utils.build_signature_def(
+                inputs={"x": tensor_info_x},
+                outputs={"y": tensor_info_pred},
+                method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
+            )
+            double_output_signature = tf.saved_model.signature_def_utils.build_signature_def(
+                inputs={"x": tensor_info_x},
+                outputs={"y": tensor_info_pred, "lin": tensor_info_lin},
+                method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME,
+            )
+
+            builder.add_meta_graph_and_variables(
+                self.sess,
+                [tf.saved_model.tag_constants.SERVING],
+                signature_def_map={
+                    tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature,
+                    "double": double_output_signature,
+                },
+            )
+            builder.save()
+
+    def test_get_metadata_correct_inputs(self):
+        self._set_up()
+        md_builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
+            self.model_path, tags=[tf.saved_model.tag_constants.SERVING]
+        )
+        expected_md = {
+            "inputs": {"x": {"inputTensorName": "inp:0"}},
+            "outputs": {"y": {"outputTensorName": "Relu:0"}},
+        }
+
+        assert md_builder.get_metadata() == expected_md
+
+    def test_get_metadata_double_output(self):
+        self._set_up()
+        md_builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
+            self.model_path, signature_name="double", outputs_to_explain=["lin"]
+        )
+
+        expected_md = {
+            "inputs": {"x": {"inputTensorName": "inp:0"}},
+            "outputs": {"lin": {"outputTensorName": "Add:0"}},
+        }
+
+        assert md_builder.get_metadata() == expected_md

tests/unit/aiplatform/test_explain_saved_model_metadata_builder_test.py → tests/unit/aiplatform/test_explain_saved_model_metadata_builder_tf2_test.py

@@ -22,7 +22,7 @@
 from google.cloud.aiplatform.explain.metadata.tf.v2 import saved_model_metadata_builder
 
 
-class SavedModelMetadataBuilderTest(tf.test.TestCase):
+class SavedModelMetadataBuilderTF2Test(tf.test.TestCase):
     def test_get_metadata_sequential(self):
         # Set up for the sequential.
         self.seq_model = tf.keras.models.Sequential()