Use `tensorflow_lite_support` (#13042)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/engine/exporter.py

@@ -1015,12 +1015,17 @@ class Exporter:
 
     def _add_tflite_metadata(self, file):
         """Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata."""
-        from tflite_support import flatbuffers  # noqa
-        from tflite_support import metadata as _metadata  # noqa
-        from tflite_support import metadata_schema_py_generated as _metadata_fb  # noqa
+        import flatbuffers
+
+        if MACOS:  # TFLite Support bug https://github.com/tensorflow/tflite-support/issues/954#issuecomment-2108570845
+            from tflite_support import metadata  # noqa
+            from tflite_support import metadata_schema_py_generated as schema  # noqa
+        else:
+            from tensorflow_lite_support.metadata import metadata_schema_py_generated as schema  # noqa
+            from tensorflow_lite_support.metadata.python import metadata  # noqa
 
         # Create model info
-        model_meta = _metadata_fb.ModelMetadataT()
+        model_meta = schema.ModelMetadataT()
         model_meta.name = self.metadata["description"]
         model_meta.version = self.metadata["version"]
         model_meta.author = self.metadata["author"]
@@ -1031,41 +1036,41 @@ class Exporter:
         with open(tmp_file, "w") as f:
             f.write(str(self.metadata))
 
-        label_file = _metadata_fb.AssociatedFileT()
+        label_file = schema.AssociatedFileT()
         label_file.name = tmp_file.name
-        label_file.type = _metadata_fb.AssociatedFileType.TENSOR_AXIS_LABELS
+        label_file.type = schema.AssociatedFileType.TENSOR_AXIS_LABELS
 
         # Create input info
-        input_meta = _metadata_fb.TensorMetadataT()
+        input_meta = schema.TensorMetadataT()
         input_meta.name = "image"
         input_meta.description = "Input image to be detected."
-        input_meta.content = _metadata_fb.ContentT()
-        input_meta.content.contentProperties = _metadata_fb.ImagePropertiesT()
-        input_meta.content.contentProperties.colorSpace = _metadata_fb.ColorSpaceType.RGB
-        input_meta.content.contentPropertiesType = _metadata_fb.ContentProperties.ImageProperties
+        input_meta.content = schema.ContentT()
+        input_meta.content.contentProperties = schema.ImagePropertiesT()
+        input_meta.content.contentProperties.colorSpace = schema.ColorSpaceType.RGB
+        input_meta.content.contentPropertiesType = schema.ContentProperties.ImageProperties
 
         # Create output info
-        output1 = _metadata_fb.TensorMetadataT()
+        output1 = schema.TensorMetadataT()
         output1.name = "output"
         output1.description = "Coordinates of detected objects, class labels, and confidence score"
         output1.associatedFiles = [label_file]
         if self.model.task == "segment":
-            output2 = _metadata_fb.TensorMetadataT()
+            output2 = schema.TensorMetadataT()
             output2.name = "output"
             output2.description = "Mask protos"
             output2.associatedFiles = [label_file]
 
         # Create subgraph info
-        subgraph = _metadata_fb.SubGraphMetadataT()
+        subgraph = schema.SubGraphMetadataT()
         subgraph.inputTensorMetadata = [input_meta]
         subgraph.outputTensorMetadata = [output1, output2] if self.model.task == "segment" else [output1]
         model_meta.subgraphMetadata = [subgraph]
 
         b = flatbuffers.Builder(0)
-        b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
+        b.Finish(model_meta.Pack(b), metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
         metadata_buf = b.Output()
 
-        populator = _metadata.MetadataPopulator.with_model_file(str(file))
+        populator = metadata.MetadataPopulator.with_model_file(str(file))
         populator.load_metadata_buffer(metadata_buf)
         populator.load_associated_files([str(tmp_file)])
         populator.populate()