Changed tf_utils pass1 to mostly use attribute type not name to determine how to convert.

Signed-off-by: Tom Wildenhain <[email protected]>

Author: TomWildenhain-Microsoft

tf2onnx/tf_utils.py

@@ -318,7 +318,7 @@ def tflist_to_onnx(g, shape_override, const_node_values=None):
                     "T_threshold", "element_dtype", "shape_type", "_lower_using_switch_merge",
                     "parallel_iterations", "_num_original_outputs", "output_types", "output_shapes",
                     "key_dtype", "value_dtype", "Tin", "Tout", "capacity", "component_types", "shapes",
-                    "Toutput_types", "dense_shapes", "Tdense", "Tidx", "Tsegmentids", "Tshift", "Tnumsegments",
+                    "Toutput_types", "dense_shapes", "Tdense", "Tsegmentids", "Tshift", "Tnumsegments", "SrcT",
                     "Tcomplex", "Treal",  # For RFFT, Tcomplex is ignored because
                                           # onnx.helper.make_node fails,
                                           # TODO: it should be added back.
@@ -353,43 +353,32 @@ def tflist_to_onnx(g, shape_override, const_node_values=None):
         op_cnt[node.type] += 1
         for a in node.node_def.attr:
             attr_cnt[a] += 1
-            if a == "dtype":
-                attr[a] = map_tf_dtype(get_tf_node_attr(node, "dtype"))
+            value = get_tf_node_attr(node, a)
+            if a in ignored_attr:
+                pass
             elif a == "T":
-                dtype = get_tf_node_attr(node, a)
-                if dtype and not isinstance(dtype, list):
-                    dtypes[node.name] = map_tf_dtype(dtype)
-            elif a in {"output_type", "output_dtype", "out_type", "Tidx", "out_idx", "out_type", "internal_type",
-                       "Tsegmentids"}:
-                # Tidx is used by Range
-                # out_idx is used by ListDiff
-                attr[a] = map_tf_dtype(get_tf_node_attr(node, a))
-            elif a == "sparse_types":
-                attr[a] = [map_tf_dtype(d) for d in get_tf_node_attr(node, a)]
+                if value and not isinstance(value, list):
+                    dtypes[node.name] = map_tf_dtype(value)
             elif a == "shape":
                 shape = get_tf_shape_attr(node)
                 if shape is not None:
                     attr[a] = shape
-            elif a == "output_shapes":
-                # we should not need it since we pull the shapes above already
-                pass
             elif a in {"body", "cond", "then_branch", "else_branch", "f"}:
                 input_shapes = [inp.get_shape() for inp in node.inputs]
                 nattr = get_tf_node_attr(node, a)
                 attr[a] = nattr.name
                 functions[nattr.name] = input_shapes
-            elif a == "value":
-                tensor = get_tf_node_attr(node, a)
+            elif a == "DstT":
+                attr["to"] = map_tf_dtype(value)
+            elif isinstance(value, tensor_pb2.TensorProto):
                 if const_node_values and node.name in const_node_values:
-                    tensor.tensor_content = const_node_values[node.name]
-                onnx_tensor = tf_to_onnx_tensor(tensor, name=port_name(node.name))
+                    value.tensor_content = const_node_values[node.name]
+                onnx_tensor = tf_to_onnx_tensor(value, name=port_name(node.name))
                 attr[a] = onnx_tensor
-            elif a == "DstT":
-                attr["to"] = map_tf_dtype(get_tf_node_attr(node, "DstT"))
-            elif a == "SrcT":
-                continue
-            elif a in ignored_attr:
-                continue
+            elif isinstance(value, tf.DType):
+                attr[a] = map_tf_dtype(value)
+            elif isinstance(value, list) and len(value) > 0 and isinstance(value[0], tf.DType):
+                attr[a] = [map_tf_dtype(v) for v in value]
             else:
                 attr[a] = get_tf_node_attr(node, a)