[GitHub] [incubator-mxnet] waytrue17 commented on a change in pull request #20001: Onnx Dynamic Shapes Support

[GitBox <gi...@apache.org>]

waytrue17 commented on a change in pull request #20001:
URL: https://github.com/apache/incubator-mxnet/pull/20001#discussion_r592886498



##########
File path: python/mxnet/contrib/onnx/mx2onnx/export_model.py
##########
@@ -62,42 +74,57 @@ def export_model(sym, params, input_shape, input_type=np.float32,
     """
 
     try:
-        from onnx import helper, mapping
+        from onnx import helper, mapping, shape_inference
         from onnx.defs import onnx_opset_version
     except ImportError:
         raise ImportError("Onnx and protobuf need to be installed. "
                           + "Instructions to install - https://github.com/onnx/onnx")
 
+    if input_type is not None:
+        in_types = input_type
+
+    if input_shape is not None:
+        in_shapes = input_shape
+
     converter = MXNetGraph()
     if opset_version is None:
         # default is to use latest opset version the onnx package supports
         opset_version = onnx_opset_version()
 
-    if not isinstance(input_type, list):
-        input_type = [input_type for _ in range(len(input_shape))]
-    input_dtype = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(inp_type)] for inp_type in input_type]
+    if not isinstance(in_types, list):
+        in_types = [in_types for _ in range(len(in_shapes))]
+    in_types_t = [mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(i_t)] for i_t in in_types]
     # if input parameters are strings(file paths), load files and create symbol parameter objects
     if isinstance(sym, string_types) and isinstance(params, string_types):
         logging.info("Converting json and weight file to sym and params")
         sym_obj, params_obj = load_module(sym, params)
-        onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, input_shape,
-                                                       input_dtype,
-                                                       verbose=verbose, opset_version=opset_version)
+        onnx_graph = converter.create_onnx_graph_proto(sym_obj, params_obj, in_shapes,
+                                                       in_types_t,
+                                                       verbose=verbose, opset_version=opset_version,
+                                                       dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
     elif isinstance(sym, symbol.Symbol) and isinstance(params, dict):
-        onnx_graph = converter.create_onnx_graph_proto(sym, params, input_shape,
-                                                       input_dtype,
-                                                       verbose=verbose, opset_version=opset_version)
+        onnx_graph = converter.create_onnx_graph_proto(sym, params, in_shapes,
+                                                       in_types_t,
+                                                       verbose=verbose, opset_version=opset_version,
+                                                       dynamic=dynamic, dynamic_input_shapes=dynamic_input_shapes)
     else:
         raise ValueError("Input sym and params should either be files or objects")
 
     # Create the model (ModelProto)
     onnx_model = helper.make_model(onnx_graph)
 
+    # Run shape inference on the model. Due to ONNX bug/incompatibility this may or may not crash

Review comment:
       Why do we run shape inference here? Seems previously we don't have it




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