[Refactor] use new attribute system in MultiThreshold

src/finn/custom_op/multithreshold.py

@@ -2,7 +2,6 @@ import numpy as np
 import onnx.helper as helper
 
 from finn.core.datatype import DataType
-from finn.core.utils import get_by_name
 from finn.custom_op import CustomOp
 
 
@@ -71,15 +70,8 @@ class MultiThreshold(CustomOp):
 
     def infer_node_datatype(self, model):
         node = self.onnx_node
-        try:
-            odt = get_by_name(node.attribute, "out_dtype").s.decode("utf-8")
-            model.set_tensor_datatype(node.output[0], DataType[odt])
-        except AttributeError:
-            # number of thresholds decides # output bits
-            # use get_smallest_possible, assuming unsigned
-            n_thres = model.get_tensor_shape(node.input[1])[1]
-            odtype = DataType.get_smallest_possible(n_thres)
-            model.set_tensor_datatype(node.output[0], odtype)
+        odt = self.get_nodeattr("out_dtype")
+        model.set_tensor_datatype(node.output[0], DataType[odt])
 
     def execute_node(self, context, graph):
         node = self.onnx_node
@@ -87,14 +79,8 @@ class MultiThreshold(CustomOp):
         v = context[node.input[0]]
         thresholds = context[node.input[1]]
         # retrieve attributes if output scaling is used
-        try:
-            out_scale = get_by_name(node.attribute, "out_scale").f
-        except AttributeError:
-            out_scale = None
-        try:
-            out_bias = get_by_name(node.attribute, "out_bias").f
-        except AttributeError:
-            out_bias = None
+        out_scale = self.get_nodeattr("out_scale")
+        out_bias = self.get_nodeattr("out_bias")
         # calculate output
         output = multithreshold(v, thresholds, out_scale, out_bias)
         # setting context according to output