support resize-10

Author: zhijxu-MS

tests/test_backend.py

@@ -1529,7 +1529,10 @@ def test_resize_nearest_neighbor(self):
         _ = tf.identity(x_, name=_TFOUTPUT)
         graph = self._run_test_case([_OUTPUT], {_INPUT: x_val})
         if self.config.opset >= 9:
-            scale_node = group_nodes_by_type(graph)["Upsample"][0].inputs[1]
+            # in opset 10, upsample is removed and resize is defined.
+            node_statistic = group_nodes_by_type(graph)
+            mapped_node = (node_statistic.get("Upsample") or node_statistic.get("Resize"))[0]
+            scale_node = mapped_node.inputs[1]
             self.assertTrue(validate_const_node(scale_node, [1.0, 1.0, 2.0, 2.0]))
 
     @check_opset_min_version(9, "resize_nearest_neighbor")
@@ -1557,7 +1560,10 @@ def test_resize_bilinear(self):
         _ = tf.identity(x_, name=_TFOUTPUT)
         graph = self._run_test_case([_OUTPUT], {_INPUT: x_val})
         if self.config.opset >= 9:
-            scale_node = group_nodes_by_type(graph)["Upsample"][0].inputs[1]
+            # in opset 10, upsample is removed and resize is defined.
+            node_statistic = group_nodes_by_type(graph)
+            mapped_node = (node_statistic.get("Upsample") or node_statistic.get("Resize"))[0]
+            scale_node = mapped_node.inputs[1]
             self.assertTrue(validate_const_node(scale_node, [1.0, 1.0, 2.0, 2.0]))
 
     @check_opset_min_version(9, "resize_bilinear")
@@ -1573,6 +1579,35 @@ def test_resize_bilinear_with_non_const(self):
         _ = tf.identity(x_, name=_TFOUTPUT)
         self._run_test_case([_OUTPUT], {_INPUT: x_val, _INPUT1: x_new_size})
 
+    @check_opset_min_version(10, "resize scale can less than 1")
+    def test_resize_bilinear_with_non_const2(self):
+        # scales has an element larger than 1 and also has an element less that 1
+        x_shape = [3, 100, 8, 5]
+        x_val = np.arange(1, 1 + np.prod(x_shape), dtype=np.float32).reshape(x_shape)
+        x = tf.placeholder(tf.float32, x_shape, name=_TFINPUT)
+
+        x_new_size = np.array([20, 16]).astype(np.int32)
+        x_new_size_ = tf.placeholder(shape=[None], dtype=tf.int32, name=_TFINPUT1)
+
+        x_ = tf.image.resize_bilinear(x, x_new_size_)
+        _ = tf.identity(x_, name=_TFOUTPUT)
+        self._run_test_case([_OUTPUT], {_INPUT: x_val, _INPUT1: x_new_size})
+
+    @check_opset_min_version(10, "resize scale can less than 1")
+    def test_resize_nearest_neighbor2(self):
+        x_shape = [1, 300, 20, 2]
+        x_new_size = [30, 40]
+        x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
+        x = tf.placeholder(tf.float32, x_shape, name=_TFINPUT)
+        x_new_size_ = tf.constant(x_new_size)
+        x_ = tf.image.resize_nearest_neighbor(x, x_new_size_)
+        _ = tf.identity(x_, name=_TFOUTPUT)
+        graph = self._run_test_case([_OUTPUT], {_INPUT: x_val})
+        node_statistic = group_nodes_by_type(graph)
+        mapped_node = node_statistic.get("Resize")[0]
+        scale_node = mapped_node.inputs[1]
+        self.assertTrue(validate_const_node(scale_node, [1.0, 1.0, 0.1, 2.0]))
+
     @check_opset_min_version(9, "fill")
     def test_fill_float32(self):
         x_shape = [1, 15, 20, 2]

tf2onnx/onnx_opset/nn.py

@@ -464,14 +464,15 @@ def version_7(cls, ctx, node, **kwargs):
 
     @classmethod
     def version_9(cls, ctx, node, **kwargs):
-        cls._convert_since_9(ctx, node, **kwargs)
+        cls._convert_since_9(ctx, node, node_type="Upsample")
 
     @classmethod
     def version_10(cls, ctx, node, **kwargs):
-        cls._convert_since_9(ctx, node, **kwargs)
+        cls._convert_since_9(ctx, node, node_type="Resize")
 
     @classmethod
-    def _convert_since_9(cls, ctx, node, **kwargs):
+    def _convert_since_9(cls, ctx, node, node_type):
+
         # float32 out = ResizeBilinear/ResizeNearestNeighbor(T images, int size)
         # https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor
         # wants the input to be NHWC - adjust target_shape to this.
@@ -505,7 +506,7 @@ def _convert_since_9(cls, ctx, node, **kwargs):
             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("Upsample", [input_nchw.output[0], scales.output[0]], attr={"mode": mode})
+        upsample = ctx.make_node(node_type, [input_nchw.output[0], scales.output[0]], attr={"mode": mode})
 
         shapes = node.output_shapes
         dtypes = node.output_dtypes

tf2onnx/onnx_opset/tensor.py

@@ -948,7 +948,7 @@ def version_10(cls, ctx, node, **kwargs):
         ctx.insert_new_node_on_input(node, "Unsqueeze", node.input[0], axes=[0])
         ctx.insert_new_node_on_input(node, "Unsqueeze", node.input[1], axes=[0, 1])
         ctx.insert_new_node_on_input(node, "Cast", node.input[2], to=onnx_pb.TensorProto.INT64)
-        # replace original node with nonmaxsurppress + slice + squeeze +cast
+        # replace original node with nonmaxsurppress + slice + squeeze + cast
         dtypes = [ctx.get_dtype(node.output[0])]
         shapes = [ctx.get_shape(node.output[0])]
         ctx.remove_node(node.name)

tf2onnx/optimizer/transpose_optimizer.py

@@ -479,7 +479,7 @@ def _shape_handler(self, trans, node):
         self._g.remove_node(trans.name)
         self._g.remove_node(node.name)
         shape_node = self._g.make_node("Shape", [trans.input[0]])
-        const_node = self._g.make_const("Const", np.array(trans.get_attr("perm").ints))
+        const_node = self._g.make_const(utils.make_name("Const"), np.array(trans.get_attr("perm").ints))
         gather_node = self._g.make_node("Gather", [shape_node.output[0], const_node.output[0]], outputs=node.output)
         self._g.set_shape(gather_node.output[0], output_shape)
         self._g.set_dtype(gather_node.output[0], output_dtype)