[TF FE] WA: Support Non-frozen Formats (#13289)

* [TF FE] WA: Support Non-frozen Formats

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

* Normalize path to the intermediate model

* Apply feedback: add path to return and correct tests

* Check that the file is not empty in the test

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>

tools/mo/openvino/tools/mo/convert_impl.py

@@ -358,6 +358,14 @@ def get_moc_frontends(argv: argparse.Namespace):
 
 
 def prepare_ir(argv: argparse.Namespace):
+    # TODO: remove this workaround once new TensorFlow frontend supports non-frozen formats: checkpoint, MetaGraph, and SavedModel
+    # Now it converts all TensorFlow formats to the frozen .pb format in case new TensorFlow frontend
+    is_tf, _, _, _, _ = deduce_legacy_frontend_by_namespace(argv)
+    path_to_aux_pb = None
+    if argv.use_new_frontend and is_tf:
+        from openvino.tools.mo.front.tf.loader import convert_to_pb
+        path_to_aux_pb = convert_to_pb(argv)
+
     argv = arguments_post_parsing(argv)
     t = tm.Telemetry()
     graph = None
@@ -380,6 +388,12 @@ def prepare_ir(argv: argparse.Namespace):
                     moc_front_end.add_extension(extension)
             ngraph_function = moc_pipeline(argv, moc_front_end)
 
+            # TODO: remove this workaround once new TensorFlow frontend supports non-frozen formats: checkpoint, MetaGraph, and SavedModel
+            # Now it converts all TensorFlow formats to the frozen .pb format in case new TensorFlow frontend
+            if argv.use_new_frontend and is_tf and path_to_aux_pb is not None:
+                if os.path.exists(path_to_aux_pb):
+                    os.remove(path_to_aux_pb)
+
             return graph, ngraph_function
         else:  # apply fallback
             reasons_message = ", ".join(fallback_reasons)
@@ -435,7 +449,7 @@ def emit_ir(graph: Graph, argv: argparse.Namespace):
     func = read_model(orig_model_name + "_tmp.xml")
 
     return_code = "not executed"
-    if not(argv.framework == 'tf' and argv.tensorflow_custom_operations_config_update):
+    if not (argv.framework == 'tf' and argv.tensorflow_custom_operations_config_update):
         try:
             from openvino.tools.mo.back.offline_transformations import apply_offline_transformations
             func = apply_offline_transformations(func, argv)

tools/mo/openvino/tools/mo/front/tf/loader.py

@@ -306,7 +306,9 @@ def convert_to_pb(argv: argparse.Namespace):
     argv.model_name = model_name
     tf_v1.io.write_graph(graph_def, argv.output_dir if argv.output_dir != '.' else os.getcwd(),
                          model_name + "_tmp.pb", as_text=False)
-    argv.input_model = model_name + "_tmp.pb"
+    path_to_pb = os.path.normpath(os.path.join(argv.output_dir, model_name + "_tmp.pb"))
+    argv.input_model = path_to_pb
+    return path_to_pb
 
 
 def protobuf_attrs(pb: tf_v1.NodeDef):

tools/mo/unit_tests/mo/front/tf/convert_to_pb_test.py

@@ -0,0 +1,54 @@
+# Copyright (C) 2018-2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import os
+import tempfile
+import unittest
+
+from openvino.tools.mo.front.tf.loader import convert_to_pb
+
+
+class ConvertToPBTests(unittest.TestCase):
+    test_directory = os.path.dirname(os.path.realpath(__file__))
+
+    def setUp(self):
+        self.argv = argparse.Namespace(input_model=None, input_model_is_text=False, input_checkpoint=None, output=None,
+                                       saved_model_dir=None, input_meta_graph=None, saved_model_tags=None,
+                                       model_name='model', output_dir=None)
+
+    def test_saved_model(self):
+        import tensorflow as tf
+        with tempfile.TemporaryDirectory(dir=self.test_directory) as tmp_dir:
+            inputs = tf.keras.Input(shape=(3,))
+            x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
+            outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
+            model = tf.keras.Model(inputs=inputs, outputs=outputs)
+            model.save(tmp_dir)
+            self.argv.saved_model_dir = tmp_dir
+            self.argv.output_dir = tmp_dir
+            path_to_pb = convert_to_pb(self.argv)
+            self.assertTrue(os.path.exists(path_to_pb), "The auxiliary .pb is not generated")
+            self.assertTrue(os.path.getsize(path_to_pb) != 0, "The auxiliary .pb is empty")
+
+    def test_meta_format(self):
+        try:
+            import tensorflow.compat.v1 as tf_v1
+            tf_v1.disable_eager_execution()
+        except ImportError:
+            import tensorflow as tf_v1
+
+        with tempfile.TemporaryDirectory(dir=self.test_directory) as tmp_dir:
+            a = tf_v1.get_variable("A", initializer=tf_v1.constant(3, shape=[2]))
+            b = tf_v1.get_variable("B", initializer=tf_v1.constant(5, shape=[2]))
+            tf_v1.add(a, b, name='Add')
+            init_op = tf_v1.global_variables_initializer()
+            saver = tf_v1.train.Saver()
+            with tf_v1.Session() as sess:
+                sess.run(init_op)
+                saver.save(sess, os.path.join(tmp_dir, 'model'))
+            self.argv.input_meta_graph = os.path.join(tmp_dir, 'model.meta')
+            self.argv.output_dir = tmp_dir
+            path_to_pb = convert_to_pb(self.argv)
+            self.assertTrue(os.path.exists(path_to_pb), "The auxiliary .pb is not generated")
+            self.assertTrue(os.path.getsize(path_to_pb) != 0, "The auxiliary .pb is empty")