From 9a6c85a586926ee769f0bdd2d537934296ee049c Mon Sep 17 00:00:00 2001
From: WJJ1995 <wjjisloser@163.com>
Date: Sun, 6 Mar 2022 14:14:54 +0800
Subject: [PATCH] fixed resize op (#749)

---
 .../op_mapper/onnx2paddle/opset9/opset.py     | 32 ++++++++++++++++---
 1 file changed, 28 insertions(+), 4 deletions(-)

diff --git a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
index 3dec5b5..db5530e 100755
--- a/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
+++ b/x2paddle/op_mapper/onnx2paddle/opset9/opset.py
@@ -296,14 +296,40 @@ class OpSet9():
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         inputs = {'x': val_x.name}
         attrs = dict()
+        val_x_shape = val_x.out_shapes[0]
         if node.layer_type == 'Resize':
             if len(node.layer.input) == 2:
                 # opset 10
                 val_scales = self.graph.get_input_node(node, idx=1, copy=True)
                 # TODO(syf): paddle.nn.functional.interpolate will support the length
                 # which is the same as the rank of input.
-                attrs['scale_factor'] = self.weights[val_scales.name].tolist()[
-                    2:]
+                scale_values = _const_weight_or_none(val_scales)
+                if scale_values is not None:
+                    attrs['scale_factor'] = self.weights[
+                        val_scales.name].tolist()[2:]
+                else:
+                    var_nc, var_hw = val_scales.name + '_nc', val_scales.name + '_hw'
+                    self.paddle_graph.add_layer(
+                        'paddle.split',
+                        inputs={"x": val_scales.name},
+                        outputs=[var_nc, var_hw],
+                        num_or_sections=[2, 2],
+                        axis=0)
+                    inputs['scale_factor'] = var_hw
+                mode = node.get_attr('mode', 'nearest')
+                attrs.update({
+                    "align_corners": False,
+                    "mode": string(mode),
+                    "align_mode": 1
+                })
+                if mode == "linear" and len(val_x_shape) == 4:
+                    attrs["mode"] = string("bilinear")
+                self.paddle_graph.add_layer(
+                    kernel="paddle.nn.functional.interpolate",
+                    inputs=inputs,
+                    outputs=[node.name],
+                    **attrs)
+                return
             elif len(node.layer.input) == 3:
                 # opset 11
                 val_scales = self.graph.get_input_node(node, idx=2, copy=True)
@@ -315,7 +341,6 @@ class OpSet9():
                 # opset 11
                 val_sizes = self.graph.get_input_node(node, idx=3, copy=True)
                 size_values = _const_weight_or_none(val_sizes)
-                val_x_shape = val_x.out_shapes[0]
                 if len(val_x_shape) == 3:
                     var_n, var_hw = val_sizes.name + '_n', val_sizes.name + '_hw'
                     self.paddle_graph.add_layer(
@@ -418,7 +443,6 @@ class OpSet9():
         })
         if len(node.layer.input) == 1:
             attrs["scale_factor"] = val_scales
-        val_x_shape = val_x.out_shapes[0]
         if mode == "linear" and len(val_x_shape) == 4:
             attrs["mode"] = string("bilinear")
             if node.get_attr('coordinate_transformation_mode',
-- 
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