Used common method to convert types.

src/bindings/python/src/openvino/frontend/tensorflow/utils.py

@@ -11,7 +11,7 @@
 from typing import List, Dict, Union
 
 import numpy as np
-from openvino.runtime import PartialShape, Dimension
+from openvino.runtime import PartialShape, Dimension, Type
 
 
 # TODO: reuse this method in ovc and remove duplication
@@ -435,3 +435,26 @@ def model_is_graph_iterator(model):
     except:
         return False
     return isinstance(model, GraphIteratorTFGraph)
+
+
+def tf_type_to_ov_type(val):
+    import tensorflow as tf  # pylint: disable=import-error
+    if not isinstance(val, tf.dtypes.DType):
+        raise Exception("The provided type is not a TF type {}.".format(val))
+
+    tf_to_ov_type = {
+        tf.float32: Type.f32,
+        tf.float16: Type.f16,
+        tf.float64: Type.f64,
+        tf.bfloat16: Type.bf16,
+        tf.uint8: Type.u8,
+        tf.int8: Type.i8,
+        tf.int16: Type.i16,
+        tf.int32: Type.i32,
+        tf.int64: Type.i64,
+        tf.bool: Type.boolean,
+        tf.string: Type.string
+    }
+    if val not in tf_to_ov_type:
+        raise Exception("The provided data type is not supported by OpenVino {}.".format(val))
+    return tf_to_ov_type[val]

tools/ovc/openvino/tools/ovc/convert_impl.py

@@ -12,7 +12,6 @@
 from pathlib import Path
 from typing import Iterable, Callable
 
-import numpy as np
 
 try:
     import openvino_telemetry as tm
@@ -23,6 +22,7 @@
 from openvino.tools.ovc.moc_frontend.check_config import any_extensions_used
 from openvino.tools.ovc.moc_frontend.pipeline import moc_pipeline
 from openvino.tools.ovc.moc_frontend.moc_emit_ir import moc_emit_ir
+from openvino.tools.ovc.moc_frontend.type_utils import to_ov_type
 from openvino.tools.ovc.cli_parser import get_available_front_ends, get_common_cli_options, depersonalize, \
     get_mo_convert_params, input_to_input_cut_info, parse_inputs
 from openvino.tools.ovc.help import get_convert_model_help_specifics
@@ -40,7 +40,7 @@
 # pylint: disable=no-name-in-module,import-error
 from openvino.frontend import FrontEndManager, OpConversionFailure, TelemetryExtension
 from openvino.runtime import get_version as get_rt_version
-from openvino.runtime import Type, PartialShape
+from openvino.runtime import PartialShape
 
 try:
     from openvino.frontend.tensorflow.utils import create_tf_graph_iterator, type_supported_by_tf_fe, \
@@ -343,11 +343,8 @@ def normalize_inputs(argv: argparse.Namespace):
             else:
                 shape_dict[inp.name] = None
             if inp.type is not None:
-                # Convert type to numpy type for uniformity of stored values
-                if isinstance(inp.type, (np.dtype, str)):
-                    data_type_dict[inp.name] = Type(inp.type)
-                else:
-                    data_type_dict[inp.name] = inp.type
+                # Convert type to ov.Type for uniformity of stored values
+                data_type_dict[inp.name] = to_ov_type(inp.type)
         argv.placeholder_shapes = shape_dict if shape_dict else None
         argv.placeholder_data_types = data_type_dict if data_type_dict else {}
     else:
@@ -359,11 +356,8 @@ def normalize_inputs(argv: argparse.Namespace):
                 # Wrap shape to PartialShape for uniformity of stored values
                 shape_list.append(PartialShape(inp.shape))
             if inp.type is not None:
-                # Convert type to numpy type for uniformity of stored values
-                if isinstance(inp.type, (np.dtype, str)):
-                    data_type_list.append(Type(inp.type))
-                else:
-                    data_type_list.append(inp.type)
+                # Convert type to ov.Type for uniformity of stored values
+                data_type_list.append(to_ov_type(inp.type))
         argv.placeholder_shapes = shape_list if shape_list else None
         argv.placeholder_data_types = data_type_list if data_type_list else {}
     if hasattr(argv, "framework") and argv.framework == "pytorch" and getattr(argv, "example_input", None) is not None:

tools/ovc/openvino/tools/ovc/moc_frontend/type_utils.py

@@ -3,6 +3,8 @@
 
 import sys
 
+import numpy as np
+
 import openvino as ov
 from openvino.runtime import Type
 
@@ -28,27 +30,13 @@ def is_type(val):
 def to_ov_type(val):
     if isinstance(val, Type):
         return val
-    if isinstance(val, type):
+    if isinstance(val, (type, str, np.dtype)):
         return Type(val)
     if 'tensorflow' in sys.modules:
         import tensorflow as tf # pylint: disable=import-error
         if isinstance(val, tf.dtypes.DType):
-            tf_to_ov_type = {
-                tf.float32: ov.Type.f32,
-                tf.float16: ov.Type.f16,
-                tf.float64: ov.Type.f64,
-                tf.bfloat16: ov.Type.bf16,
-                tf.uint8: ov.Type.u8,
-                tf.int8: ov.Type.i8,
-                tf.int16: ov.Type.i16,
-                tf.int32: ov.Type.i32,
-                tf.int64: ov.Type.i64,
-                tf.bool: ov.Type.boolean,
-                tf.string: ov.Type.string
-            }
-            if val not in tf_to_ov_type:
-                raise Exception("The provided data time is not supported {}.".format(val))
-            return tf_to_ov_type[val]
+            from openvino.frontend.tensorflow.utils import tf_type_to_ov_type  # pylint: disable=no-name-in-module,import-error
+            return tf_type_to_ov_type(val)
     if 'torch' in sys.modules:
         import torch