anirudhacharya commented on a change in pull request #12646: ONNX export: Fully
connected operator w/o bias, ReduceSum, Square
URL: https://github.com/apache/incubator-mxnet/pull/12646#discussion_r224596258
##########
File path: python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
##########
@@ -2077,3 +2112,79 @@ def convert_sqrt(node, **kwargs):
name=name,
)
return [node]
+
+@mx_op.register("square")
+def convert_square(node, **kwargs):
+ """Map MXNet's square operator attributes to onnx's Pow operator
+ and return the created node.
+ """
+ onnx = import_onnx_modules()
+ name = node["name"]
+ proc_nodes = kwargs["proc_nodes"]
+ inputs = node["inputs"]
+
+ input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
+ input_node_a = proc_nodes[input_node_a_id].name
+
+ initializer = kwargs["initializer"]
+ np_arr = np.array([2])
+ data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np_arr.dtype]
+ dims = np.shape(np_arr)
+
+ power2_name = "square_tensor" + str(kwargs["idx"])
+ tensor_node = onnx.helper.make_tensor_value_info(power2_name, data_type,
dims)
+ initializer.append(
+ onnx.helper.make_tensor(
+ name=power2_name,
+ data_type=data_type,
+ dims=dims,
+ vals=[2],
+ raw=False,
+ )
+ )
+
+ node = onnx.helper.make_node(
+ "Pow",
+ [input_node_a, power2_name],
+ [name],
+ name=name
+ )
+ return [tensor_node, node]
+
+@mx_op.register("sum")
+def convert_sum(node, **kwargs):
+ """Map MXNet's sum operator attributes to onnx's ReduceSum operator
+ and return the created node.
+ """
+ onnx = import_onnx_modules()
+ name = node["name"]
+ proc_nodes = kwargs["proc_nodes"]
+ inputs = node["inputs"]
+ attrs = node["attrs"]
+
+ mx_axis = attrs.get("axis", None)
+ axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else
None
+
+ keepdims = get_boolean_attribute_value(attrs, "keepdims")
+
+ input_node_id = kwargs["index_lookup"][inputs[0][0]]
+ input_node = proc_nodes[input_node_id].name
+
+ if axes is not None:
Review comment:
can this be `if axes`
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