refactor

Author: zhijxu-MS

tf2onnx/onnx_opset/nn.py

@@ -464,14 +464,14 @@ def version_7(cls, ctx, node, **kwargs):
 
     @classmethod
     def version_9(cls, ctx, node, **kwargs):
-        cls._convert_since_9(ctx, node, node_type="Upsample")
+        cls._convert_since_9(ctx, node, op_type="Upsample")
 
     @classmethod
     def version_10(cls, ctx, node, **kwargs):
-        cls._convert_since_9(ctx, node, node_type="Resize")
+        cls._convert_since_9(ctx, node, op_type="Resize")
 
     @classmethod
-    def _convert_since_9(cls, ctx, node, node_type):
+    def _convert_since_9(cls, ctx, node, op_type):
 
         # float32 out = ResizeBilinear/ResizeNearestNeighbor(T images, int size)
         # https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor
@@ -506,7 +506,7 @@ def _convert_since_9(cls, ctx, node, node_type):
             scales = ctx.make_node("Concat", [const_one_array.output[0], scales_hw.output[0]], {"axis": 0})
         # because onnxruntime only supports to scale the last two dims so transpose is inserted
         input_nchw = ctx.make_node("Transpose", [node.input[0]], {"perm": [0, 3, 1, 2]})
-        upsample = ctx.make_node(node_type, [input_nchw.output[0], scales.output[0]], attr={"mode": mode})
+        upsample = ctx.make_node(op_type, [input_nchw.output[0], scales.output[0]], attr={"mode": mode})
 
         shapes = node.output_shapes
         dtypes = node.output_dtypes

tf2onnx/onnx_opset/tensor.py

@@ -953,10 +953,8 @@ def version_10(cls, ctx, node, **kwargs):
         shapes = [ctx.get_shape(node.output[0])]
         ctx.remove_node(node.name)
         new_nonmaxsurppress = ctx.make_node(node.type, node.input).output[0]
-        attr = {"axes": [1], "ends": [3], "starts": [2]}
-        inputs_map = {"data": new_nonmaxsurppress, **attr}
-        slice_op = GraphBuilder(ctx).make_slice(inputs_map)
+        slice_op = GraphBuilder(ctx).make_slice({"data": new_nonmaxsurppress,
+                                                 "axes": [1], "ends": [3], "starts": [2]})
         squeeze_op = ctx.make_node("Squeeze", [slice_op], attr={"axes": [1]})
         ctx.make_node("Cast", inputs=squeeze_op.output, attr={"to": onnx_pb.TensorProto.INT32},
                       name=node.name, outputs=node.output, dtypes=dtypes, shapes=shapes)
-