onnx: export model constants as outputs, instead of in the initializer list (#4073)

* export model constants as outputs, instead of in the initializer list

* changelog

com.unity.ml-agents/CHANGELOG.md

 ### Bug Fixes
 - Fixed an issue where SAC would perform too many model updates when resuming from a
   checkpoint, and too few when using `buffer_init_steps`. (#4038)
+- Fixed a bug in the onnx export that would cause constants needed for inference to not be visible to some versions of
+  the Barracuda importer. (#4073)
 #### com.unity.ml-agents (C#)
 #### ml-agents / ml-agents-envs / gym-unity (Python)
 

ml-agents/mlagents/model_serialization.py

 from distutils.version import LooseVersion
 
 try:
-    import onnx
     from tf2onnx.tfonnx import process_tf_graph, tf_optimize
     from tf2onnx import optimizer
 
 ) -> Any:
     # This is basically https://github.com/onnx/tensorflow-onnx/blob/master/tf2onnx/convert.py
 
-    # Some constants in the graph need to be read by the inference system.
-    # These aren't used by the model anywhere, so trying to make sure they propagate
-    # through conversion and import is a losing battle. Instead, save them now,
-    # so that we can add them back later.
-    constant_values = {}
-    for n in frozen_graph_def.node:
-        if n.name in MODEL_CONSTANTS:
-            val = n.attr["value"].tensor.int_val[0]
-            constant_values[n.name] = val
-
     inputs = _get_input_node_names(frozen_graph_def)
     outputs = _get_output_node_names(frozen_graph_def)
     logger.info(f"onnx export - inputs:{inputs} outputs:{outputs}")
     onnx_graph = optimizer.optimize_graph(g)
     model_proto = onnx_graph.make_model(settings.brain_name)
 
-    # Save the constant values back the graph initializer.
-    # This will ensure the importer gets them as global constants.
-    constant_nodes = []
-    for k, v in constant_values.items():
-        constant_node = _make_onnx_node_for_constant(k, v)
-        constant_nodes.append(constant_node)
-    model_proto.graph.initializer.extend(constant_nodes)
-
-
-def _make_onnx_node_for_constant(name: str, value: int) -> Any:
-    tensor_value = onnx.TensorProto(
-        data_type=onnx.TensorProto.INT32,
-        name=name,
-        int32_data=[value],
-        dims=[1, 1, 1, 1],
-    )
-    return tensor_value
 
 
 def _get_input_node_names(frozen_graph_def: Any) -> List[str]:
 def _get_output_node_names(frozen_graph_def: Any) -> List[str]:
     """
     Get the list of output node names from the graph.
+    Also include constants, so that they will be readable by the
+    onnx importer.
-    output_names = node_names & POSSIBLE_OUTPUT_NODES
+    output_names = node_names & (POSSIBLE_OUTPUT_NODES | MODEL_CONSTANTS)
     # Append the port
     return [f"{n}:0" for n in output_names]