Set UT to test opset 13

ci_build/azure_pipelines/templates/job_generator.yml

@@ -5,8 +5,8 @@ parameters:
   python_versions: ['3.7']
   tf_versions: ['']
   onnx_versions: ['']
-  onnx_opsets: ['12', '11', '10', '9', '8', '7']
-  onnx_backends: {onnxruntime: ['1.4.0']}
+  onnx_opsets: ['13', '12', '11', '10', '9', '8', '7']
+  onnx_backends: {onnxruntime: ['1.6.0']}
   job: {}
   run_setup: 'True'
   report_coverage: 'False'

ci_build/azure_pipelines/templates/unit_test.yml

@@ -1,7 +1,7 @@
 # Run unit test
 
 parameters:
-    onnx_opsets: ['12', '11', '10', '9', '8', '7']
+    onnx_opsets: ['13', '12', '11', '10', '9', '8', '7']
 
 steps:
 - ${{ each onnx_opset in parameters.onnx_opsets }}:

tf2onnx/constants.py

@@ -42,5 +42,5 @@
 # Mapping opset to IR version.
 # Note: opset 7 and opset 8 came out with IR3 but we need IR4 because of PlaceholderWithDefault
 OPSET_TO_IR_VERSION = {
-    1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3, 7: 4, 8: 4, 9: 4, 10: 5, 11: 6, 12: 7
+    1: 3, 2: 3, 3: 3, 4: 3, 5: 3, 6: 3, 7: 4, 8: 4, 9: 4, 10: 5, 11: 6, 12: 7, 13: 7
 }

tf2onnx/graph.py

@@ -1135,6 +1135,9 @@ def make_model(self, graph_doc, optimize=False, graph_name="tf2onnx", external_t
             kwargs["opset_imports"] = opsets
         model_proto = helper.make_model(graph, **kwargs)
 
+        utils.make_sure(self.opset in constants.OPSET_TO_IR_VERSION,
+                        "Opset %s is not supported yet. Please use a lower opset" % self.opset)
+
         # set the IR version based on opset
         try:
             model_proto.ir_version = constants.OPSET_TO_IR_VERSION.get(self.opset, model_proto.ir_version)

tf2onnx/onnx_opset/reduction.py

@@ -63,6 +63,11 @@ def version_13(cls, ctx, node, **kwargs):
             node.set_attr("noop_with_empty_axes", 1)
             if ctx.get_dtype(node.input[1]) != onnx_pb.TensorProto.INT64:
                 ctx.insert_new_node_on_input(node, "Cast", node.input[1], to=onnx_pb.TensorProto.INT64)
+            input_shape = ctx.get_shape(node.input[1])
+            input_rank = len(input_shape) if input_shape is not None else None
+            if input_rank != 1:
+                new_shape = ctx.make_const(utils.make_name("reshape_const"), np.array([-1], np.int64))
+                ctx.insert_new_node_on_input(node, "Reshape", [node.input[1], new_shape.output[0]])
         else:
             cls.version_11(ctx, node, **kwargs)
 

tf2onnx/onnx_opset/tensor.py

@@ -1192,7 +1192,8 @@ def version_1(cls, ctx, node, **kwargs):
         # for each output we need to squeeze axis
         for n in node.output:
             op_name = utils.make_name(node.name)
-            squeeze_node = ctx.insert_new_node_on_output("Squeeze", n, name=op_name, axes=[axis])
+            squeeze_node = GraphBuilder(ctx).make_squeeze({'data': n, 'axes': [axis]}, name=op_name, return_node=True)
+            ctx.insert_node_on_output(squeeze_node, n)
             ctx.copy_shape(n, squeeze_node.output[0])
             ctx.copy_dtype(n, squeeze_node.output[0])
 
@@ -1637,8 +1638,9 @@ def any_version(cls, opset, ctx, node, **kwargs):
             # add valid_outputs count
             output_idx = 2 if node.type in ["NonMaxSuppressionV5"] else 1
             shape_op = ctx.make_node("Shape", inputs=[nms_output.output[0]])
-            reduce_op = ctx.make_node("ReduceSum", inputs=shape_op.output, attr={"axes": [0], "keepdims": 0})
-            ctx.make_node("Cast", inputs=[reduce_op.output[0]], attr={"to": onnx_pb.TensorProto.INT32},
+            reduce_op = GraphBuilder(ctx).make_reduce_sum(
+                {"data": shape_op.output[0], "axes": [0], "keepdims": 0, "noop_with_empty_axes": 1})
+            ctx.make_node("Cast", inputs=[reduce_op], attr={"to": onnx_pb.TensorProto.INT32},
                           outputs=[node.output[output_idx]], dtypes=dtypes[output_idx], shapes=shapes[output_idx],
                           op_name_scope=node.name)