switch to contrib cropandresize

Author: RandySheriffH

tf2onnx/custom_opsets/onnx_ml.py

@@ -39,3 +39,13 @@ def version_8(cls, ctx, node, **kwargs):
         customer_nodes = ctx.find_output_consumers(table_node.output[0])
         if len(customer_nodes) == 0:
             ctx.remove_node(table_node.name)
+
+@tf_op(["CropAndResize"])
+class CropAndResize:
+    @classmethod
+    def version_11(cls, ctx, node, **kwargs):
+        """ utilize contrib cropandresize """
+        node.attr['method'].name = 'mode'
+        node.domain = constants.MICROSOFT_DOMAIN
+        ctx.insert_new_node_on_input(node, "Transpose", node.input[0], perm=[0,3,1,2])
+        ctx.insert_new_node_on_output("Transpose", node.output[0], node.name + '_transposed', None, perm=[0,2,3,1])

tf2onnx/onnx_opset/nn.py

@@ -587,67 +587,6 @@ def version_11(cls, ctx, node, **kwargs):
         cls.version_1(ctx, node, **kwargs)
 
 
-@tf_op(["CropAndResize"])
-class CropAndResize:
-    @classmethod
-    def version_11(cls, ctx, node, **kwargs):
-        # create loop of resize to cater to tensorflow CropAndResize, one box one iteration
-        mode = "nearest" if node.get_attr("method") is not None and node.get_attr(
-            "method").s == b"nearest" else "linear"
-        extrapolation_value = float(node.get_attr("extrapolation_value", "0").f)
-        input_x = node.inputs[0]
-        boxes = node.inputs[1]
-        box_ind = node.inputs[2]
-        crop_size = node.inputs[3]
-        trip_name = utils.make_name(node.name + "_i")
-        cond_name = utils.make_name(node.name + "_cond")
-        cond_out_name = utils.make_name(node.name + "cond_out")
-        g = ctx.create_new_graph_with_same_config()
-        g.add_graph_input(trip_name, TensorProto.INT64, [1])
-        g.add_graph_input(cond_name, TensorProto.BOOL, [])
-        g.parent_graph = ctx
-        const_zero = g.make_const(utils.make_name(node.name + "_const_zero"), np.array([0], dtype=np.int32))
-        const_zero_long = g.make_const(utils.make_name(node.name + "_const_zero_long"), np.array([0], dtype=np.int64))
-        const_one = g.make_const(utils.make_name(node.name + "_const_one"), np.array([1], dtype=np.int32))
-        const_one_long = g.make_const(utils.make_name(node.name + "_const_one_long"), np.array([1], dtype=np.int64))
-        index_end = g.make_node("Add", [trip_name, const_one_long.output[0]])
-        box_index_from = g.make_node("Slice", [box_ind.output[0], trip_name, index_end.output[0]], name="Slice_a")
-        box_index_to = g.make_node("Add", [box_index_from.output[0], const_one.output[0]])
-        target_x = g.make_node("Slice", [input_x.output[0], box_index_from.output[0], box_index_to.output[0],
-                                         const_zero.output[0]], name="Slice_b")
-        transposed_x = g.make_node("Transpose", [target_x.output[0]], attr={'perm': constants.NHWC_TO_NCHW})
-        const_zero_zero = g.make_const(utils.make_name(node.name + "_const_zero_zero"),
-                                       np.array([0, 0], dtype=np.float32))
-        const_one_one = g.make_const(utils.make_name(node.name + "_const_one_one"),
-                                     np.array([1, 1], dtype=np.float32))
-        const_four = g.make_const(utils.make_name(node.name + "_const_four"), np.array([4], dtype=np.int64))
-        const_empty_float = g.make_const(utils.make_name("const_empty_float"), np.array([], dtype=np.float32))
-        box = g.make_node("Slice", [boxes.output[0], trip_name, index_end.output[0], const_zero_long.output[0]],
-                          name="Slice_c")
-        roi_raw = g.make_node("Reshape", [box.output[0], const_four.output[0]])
-        roi_raw_first_half = GraphBuilder(g).make_slice({"data": roi_raw.output[0], "ends": [2], "starts": [0]})
-        roi_raw_second_half = GraphBuilder(g).make_slice({"data": roi_raw.output[0], "ends": [4], "starts": [2]})
-        roi_concat_1 = g.make_node("Concat", [const_zero_zero.output[0], roi_raw_first_half], attr={'axis': 0})
-        roi_concat_2 = g.make_node("Concat", [const_one_one.output[0], roi_raw_second_half], attr={'axis': 0})
-        final_roi = g.make_node("Concat", [roi_concat_1.output[0], roi_concat_2.output[0]], attr={'axis': 0})
-        final_crop_size = build_dynamic_target_size(g, transposed_x, crop_size)
-        resized_x = g.make_node("Resize", [transposed_x.output[0], final_roi.output[0], const_empty_float.output[0],
-                                           final_crop_size.output[0]],
-                                attr={"mode": mode, "extrapolation_value": extrapolation_value,
-                                      "coordinate_transformation_mode": "tf_crop_and_resize"})
-        recovered_x = g.make_node("Transpose", [resized_x.output[0]], attr={'perm': constants.NCHW_TO_NHWC})
-        squeeze_x = g.make_node("Squeeze", inputs=[recovered_x.output[0]], attr={"axes": [0]})
-        g.make_node("Identity", [cond_name], outputs=[cond_out_name])
-        g.add_graph_output(cond_out_name, TensorProto.BOOL, [])
-        g.add_graph_output(squeeze_x.output[0], ctx.get_dtype(node.input[0]), [-1, -1, -1])
-        trip_node = ctx.make_node("Size", [box_ind.output[0]])
-        cond_const = ctx.make_const(utils.make_name("cond"), np.ones((), dtype=np.bool))
-        ctx.remove_node(node.name)
-        inner_loop = ctx.make_node("Loop", [trip_node.output[0], cond_const.output[0]], name=node.name,
-                                   outputs=node.output)
-        inner_loop.set_body_graph_as_attr("body", g)
-
-
 @tf_op(["ResizeBilinear", "ResizeNearestNeighbor"])
 class Resize:
     @classmethod