[MO][TF FE] Do not print TF FE message in case of fallback (#16384)

* [MO][TF FE] Do not print TF FE message in case of fallback

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

* Correct test model with Switch and Merge

---------

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

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

@@ -444,6 +444,8 @@ def prepare_ir(argv: argparse.Namespace):
                     f"The detailed reason why fallback was executed: not supported {reasons_message} were used. "
                     "You can specify --use_new_frontend flag to force using the Frontend MO path to avoid additional checks. " +
                     refer_to_faq_msg(105))
+        assert not hasattr(argv, 'is_fallback'), '`is_fallback` argument must not exist.'
+        argv.is_fallback = True
 
     t.send_event("mo", "conversion_method", "mo_legacy")
     graph = unified_pipeline(argv)

tools/mo/openvino/tools/mo/main.py

@@ -47,7 +47,8 @@ def main(cli_parser: argparse.ArgumentParser, framework=None):
             print(ov_update_message)
         if ov_api20_message is not None and ngraph_function is not None:
             print(ov_api20_message)
-        if not argv.use_legacy_frontend and is_tf:
+        is_fallback = getattr(argv, 'is_fallback', False)
+        if not argv.use_legacy_frontend and is_tf and not is_fallback:
             # now TF FE is default frontend for TensorFlow models conversion
             print(get_tf_fe_message())
 

tools/mo/unit_tests/moc_tf_fe/check_info_messages.py

@@ -17,7 +17,8 @@ def arg_parse_helper(input_model,
                      use_new_frontend,
                      input_model_is_text,
                      framework,
-                     compress_to_fp16=False):
+                     compress_to_fp16=False,
+                     freeze_placeholder_with_value=None):
     path = os.path.dirname(__file__)
     input_model = os.path.join(path, "test_models", input_model)
 
@@ -46,7 +47,7 @@ def arg_parse_helper(input_model,
         layout={},
         source_layout={},
         target_layout={},
-        freeze_placeholder_with_value=None,
+        freeze_placeholder_with_value=freeze_placeholder_with_value,
         data_type=None,
         tensorflow_custom_operations_config_update=None,
         compress_to_fp16=compress_to_fp16,
@@ -67,6 +68,21 @@ class TestInfoMessagesTFFE(unittest.TestCase):
         assert tf_fe_message_found
 
 
+class TestInfoMessagesTFFEWithFallback(unittest.TestCase):
+    @patch('argparse.ArgumentParser.parse_args',
+           return_value=arg_parse_helper(input_model="model_switch_merge.pbtxt",
+                                         use_legacy_frontend=False, use_new_frontend=False,
+                                         framework=None, input_model_is_text=True,
+                                         freeze_placeholder_with_value="is_training->False"))
+    def test_tf_fe_message_fallback(self, mock_argparse):
+        f = io.StringIO()
+        with redirect_stdout(f):
+            main(argparse.ArgumentParser())
+            std_out = f.getvalue()
+        tf_fe_message_found = get_tf_fe_message() in std_out
+        assert not tf_fe_message_found, 'TF FE Info message is found for the fallback case'
+
+
 class TestInfoMessagesCompressFP16(unittest.TestCase):
     @patch('argparse.ArgumentParser.parse_args',
            return_value=arg_parse_helper(input_model="model_int32.pbtxt",

tools/mo/unit_tests/moc_tf_fe/test_models/model_switch_merge.pbtxt

@@ -0,0 +1,134 @@
+node {
+  name: "x"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 2
+        }
+        dim {
+          size: 3
+        }
+      }
+    }
+  }
+}
+node {
+  name: "y"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+        dim {
+          size: 2
+        }
+        dim {
+          size: 3
+        }
+      }
+    }
+  }
+}
+node {
+  name: "is_training"
+  op: "Placeholder"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_BOOL
+    }
+  }
+  attr {
+    key: "shape"
+    value {
+      shape {
+      }
+    }
+  }
+}
+node {
+  name: "Switch"
+  op: "Switch"
+  input: "x"
+  input: "is_training"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "Relu"
+  op: "Relu"
+  input: "Switch"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "Sigmoid"
+  op: "Sigmoid"
+  input: "Switch:1"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "Merge"
+  op: "Merge"
+  input: "Relu"
+  input: "Sigmoid"
+  attr {
+    key: "N"
+    value {
+      i: 2
+    }
+  }
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "AddV2"
+  op: "AddV2"
+  input: "Merge"
+  input: "y"
+  attr {
+    key: "T"
+    value {
+      type: DT_FLOAT
+    }
+  }
+}
+node {
+  name: "init"
+  op: "NoOp"
+}
+versions {
+  producer: 808
+}

tools/mo/unit_tests/moc_tf_fe/test_models/model_switch_merge.py

@@ -0,0 +1,20 @@
+# Copyright (C) 2018-2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import tensorflow.compat.v1 as tf
+
+tf.reset_default_graph()
+with tf.Session() as sess:
+    x = tf.placeholder(tf.float32, [2, 3], 'x')
+    y = tf.placeholder(tf.float32, [2, 3], 'y')
+    is_training = tf.placeholder(tf.bool, [], 'is_training')
+    switch = tf.raw_ops.Switch(data=x, pred=is_training)
+    relu = tf.raw_ops.Relu(features=switch[0])
+    sigmoid = tf.raw_ops.Sigmoid(x=switch[1])
+    merge = tf.raw_ops.Merge(inputs=[relu, sigmoid])
+    tf.raw_ops.AddV2(x=merge[0], y=y)
+
+    tf.global_variables_initializer()
+    tf_net = sess.graph_def
+
+tf.io.write_graph(tf_net, './', 'model_switch_merge.pbtxt', True)