Skip converting resize nodes.

onnxconverter_common/float16.py

@@ -180,6 +180,10 @@ def convert_float_to_float16(model, min_positive_val=1e-7, max_finite_val=1e4,
                 value_info_list.append(new_value_info)
                 io_casts.add(node_name)
 
+    for node in model.graph.node:
+        if node.op_type == "Resize":
+            graph_io_to_skip = graph_io_to_skip.union([n for n in node.input[1:] if n])
+
     while queue:
         next_level = []
         for q in queue:
@@ -222,8 +226,9 @@ def convert_float_to_float16(model, min_positive_val=1e-7, max_finite_val=1e4,
             if isinstance(q, onnx_proto.GraphProto):
                 for n in q.initializer:  # TensorProto type
                     if n.data_type == onnx_proto.TensorProto.FLOAT:
-                        n = convert_tensor_float_to_float16(n, min_positive_val, max_finite_val)
-                        value_info_list.append(make_value_info_from_tensor(n))
+                        if n.name not in graph_io_to_skip:
+                            n = convert_tensor_float_to_float16(n, min_positive_val, max_finite_val)
+                            value_info_list.append(make_value_info_from_tensor(n))
                 # for all ValueInfoProto with tensor(float) type in input, output and value_info, convert them to
                 # tensor(float16) except map and seq(map). And save them in value_info_list for further processing
                 for n in itertools.chain(q.input, q.output, q.value_info):