Enable preparing nested OutputSchemas for serialization

src/deepsparse/server/helpers.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from http import HTTPStatus
-from typing import Any, Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 import numpy
 from pydantic import BaseModel
@@ -75,23 +75,35 @@ def server_logger_from_config(config: ServerConfig) -> BaseLogger:
     )
 
 
-def prep_outputs_for_serialization(pipeline_outputs: Any):
+def prep_outputs_for_serialization(
+    pipeline_outputs: Union[BaseModel, numpy.ndarray, list]
+) -> Union[BaseModel, list]:
     """
     Prepares a pipeline output for JSON serialization by converting any numpy array
     field to a list. For large numpy arrays, this operation will take a while to run.
 
-    :param pipeline_outputs: output data to clean
-    :return: cleaned pipeline_outputs
+    :param pipeline_outputs: output data to that is to be processed before
+        serialisation. Nested objects are supported.
+    :return: Pipeline_outputs with potential numpy arrays
+        converted to lists
     """
     if isinstance(pipeline_outputs, BaseModel):
         for field_name in pipeline_outputs.__fields__.keys():
             field_value = getattr(pipeline_outputs, field_name)
-            if isinstance(field_value, numpy.ndarray):
-                # numpy arrays aren't JSON serializable
-                setattr(pipeline_outputs, field_name, field_value.tolist())
+            if isinstance(field_value, (numpy.ndarray, BaseModel, list)):
+                setattr(
+                    pipeline_outputs,
+                    field_name,
+                    prep_outputs_for_serialization(field_value),
+                )
+
     elif isinstance(pipeline_outputs, numpy.ndarray):
         pipeline_outputs = pipeline_outputs.tolist()
 
+    elif isinstance(pipeline_outputs, list):
+        for i, value in enumerate(pipeline_outputs):
+            pipeline_outputs[i] = prep_outputs_for_serialization(value)
+
     return pipeline_outputs