fix for params with no dims in onnx

python/mxnet/contrib/onnx/onnx2mx/import_onnx.py

@@ -19,6 +19,7 @@
 # pylint: disable=invalid-name,too-many-locals,no-self-use
 """ Support import export formats."""
 from __future__ import absolute_import as _abs
+import numpy as np
 from .... import symbol
 from .... import ndarray as nd
 from ....base import string_types
@@ -87,7 +88,7 @@ def from_onnx(self, graph):
         params : dict
             A dict of name: nd.array pairs, used as pretrained weights
         """
-        #get input, output shapes
+        # get input, output shapes
         self.model_metadata = self.get_graph_metadata(graph)
         # parse network inputs, aka parameters
         for init_tensor in graph.initializer:
@@ -196,7 +197,11 @@ def _parse_array(self, tensor_proto):
         except ImportError:
             raise ImportError("Onnx and protobuf need to be installed. "
                               + "Instructions to install - https://github.com/onnx/onnx")
-        np_array = to_array(tensor_proto).reshape(tuple(tensor_proto.dims))
+        if len(tuple(tensor_proto.dims)) > 0:
+            np_array = to_array(tensor_proto).reshape(tuple(tensor_proto.dims))
+        else:
+            # If onnx's params are scalar values without dims mentioned.
+            np_array = np.array([to_array(tensor_proto)])
         return nd.array(np_array)
 
     def _parse_attr(self, attr_proto):

tests/python-pytest/onnx/test_models.py

@@ -51,6 +51,7 @@
         'https://s3.amazonaws.com/download.onnx/models/opset_8/inception_v2.tar.gz'
 }
 
+test_model_path = "https://s3.amazonaws.com/onnx-mxnet/test_model.onnx"
 
 def get_test_files(name):
     """Extract tar file and returns model path and input, output data"""
@@ -152,6 +153,16 @@ def get_model_results(modelpath):
 
                 logging.info(model_name + " conversion successful")
 
+    def test_nodims_import(self):
+        # Download test model without dims mentioned in params
+        test_model = download(test_model_path, dirname=CURR_PATH.__str__())
+        input_data = np.array([0.2, 0.5])
+        nd_data = mx.nd.array(input_data).expand_dims(0)
+        sym, arg_params, aux_params = onnx_mxnet.import_model(test_model)
+        model_metadata = onnx_mxnet.get_model_metadata(test_model)
+        input_names = [inputs[0] for inputs in model_metadata.get('input_tensor_data')]
+        output_data = forward_pass(sym, arg_params, aux_params, input_names, nd_data)
+        assert(output_data.shape == (1,1))
 
 # test_case = ("model name", input shape, output shape)
 test_cases = [