[ONNX] reverting broadcasting fixes

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

@@ -65,19 +65,47 @@ def sample_multinomial(attrs, inputs, proto_obj):
 # Arithmetic Operations
 def add(attrs, inputs, proto_obj):
     """Adding two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_add')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_add', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_add', new_attr, inputs
 
 def subtract(attrs, inputs, proto_obj):
     """Subtracting two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_sub')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_sub', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_sub', new_attr, inputs
 
 def multiply(attrs, inputs, proto_obj):
     """Multiply two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_mul')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_mul', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_mul', new_attr, inputs
 
 def divide(attrs, inputs, proto_obj):
     """Divide two tensors"""
-    return translation_utils.broadcast_arithmetic_helper(attrs, inputs, proto_obj, 'broadcast_div')
+    new_attr = {}
+
+    if 'broadcast' in attrs and attrs['broadcast'] == 1:
+        broadcast_axis = attrs['axis']
+        op_value = translation_utils._fix_broadcast('broadcast_div', inputs,
+                                                    broadcast_axis, proto_obj)
+        return op_value, new_attr, inputs
+    return 'broadcast_div', new_attr, inputs
 
 def mean(attrs, inputs, proto_obj):
     """Mean of all the input tensors."""

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

@@ -245,17 +245,3 @@ def get_input_shape(sym, proto_obj):
     result = mod.get_outputs()[0].asnumpy()
 
     return result.shape
-
-def broadcast_arithmetic_helper(attrs, inputs, proto_obj, current_op_name):
-    """Helper function for broadcast arithmetic ops."""
-    new_attr = {}
-    op_names = ['batchnorm, convolution, deconvolution']
-    if 'broadcast' in attrs and attrs['broadcast'] == 1:
-        broadcast_axis = attrs['axis']
-        for op_name in op_names:
-            # if input is bias which comes after conv, deconv, batchnorm operators
-            # then only reshape bias term
-            if inputs[0].name.startswith(op_name):
-                op_value = _fix_broadcast(current_op_name, inputs, broadcast_axis, proto_obj)
-                return op_value, new_attr, inputs
-    return current_op_name, new_attr, inputs