fix the onnx bug

x2paddle/decoder/onnx_decoder.py

@@ -311,7 +311,6 @@ class ONNXGraph(Graph):
                                         if new_nd_name not in node.which_child:
                                             node.which_child[new_nd_name] = idx
                                             break
-                                print(node.which_child)
                             else:
                                 first_i = node.inputs.index(nd.name)
                                 node.which_child[nd.name] = idx
@@ -334,13 +333,10 @@ class ONNXGraph(Graph):
             ipt_node = super(ONNXGraph, self).get_node(node.inputs[idx], copy)
             new_ipt_name = "{}/{}".format(ipt_node.layer_name, idx)
             if new_ipt_name in node.which_child:
-                print(new_ipt_name)
                 ipt_node.index = node.which_child[new_ipt_name]
-                print("ipt_node.index", ipt_node.index)
             else:
                 if ipt_node.layer_name in node.which_child:
                     ipt_node.index = node.which_child[ipt_node.layer_name]
-                    print("ipt_node.index", ipt_node.index)                
                 
             return ipt_node
         

x2paddle/op_mapper/dygraph/onnx2paddle/opset9/opset.py

@@ -255,10 +255,16 @@ class OpSet9():
             if len(node.layer.input) == 2:
                 # opset 10
                 val_scales = self.graph.get_input_node(node, idx=1, copy=True)
-                inputs['scale_factor'] = val_scales.name
+                # TODO(syf): paddle.nn.functional.interpolate will support the length  
+                # which is the same as the rank of input.
+#                 inputs['scale_factor'] = val_scales.name
+                attrs['scale_factor'] = self.weights[val_scales.name].tolist()[2:]
             elif len(node.layer.input) == 3:
                 # opset 11
                 val_scales = self.graph.get_input_node(node, idx=2, copy=True)
+                # TODO(syf): paddle.nn.functional.interpolate will support the length  
+                # which is the same as the rank of input.
+#                 inputs['scale_factor'] = val_scales.name
                 attrs['scale_factor'] = self.weights[val_scales.name].tolist()[2:]
             elif len(node.layer.input) == 4:
                 # opset 11
@@ -291,7 +297,7 @@ class OpSet9():
                 return
         elif node.layer_type == 'Upsample':
             val_scales = self.graph.get_input_node(node, idx=1, copy=True)
-            inputs['scale'] = val_scales
+            inputs['scale_factor'] = val_scales
 
         mode = node.get_attr('mode', 'nearest')
         attrs.update({"align_corners": False,
@@ -1097,10 +1103,6 @@ class OpSet9():
         dtypes = set()
         for i in range(len(node.layer.input)):
             ipt = self.graph.get_input_node(node, idx=i, copy=True)
-            try:
-                print(ipt.index)
-            except:
-                pass
             inputs_list.append(ipt.name)
             dtypes.add(ipt.dtype)
         if len(dtypes) > 1:

x2paddle/op_mapper/static/onnx2paddle/opset9/opset.py

@@ -245,10 +245,16 @@ class OpSet9():
             if len(node.layer.input) == 2:
                 # opset 10
                 val_scales = self.graph.get_input_node(node, idx=1, copy=True)
-                inputs['scale_factor'] = val_scales.name
+                # TODO(syf): paddle.nn.functional.interpolate will support the length  
+                # which is the same as the rank of input.
+#                 inputs['scale_factor'] = val_scales.name
+                attrs['scale_factor'] = self.params[val_scales.name].tolist()[2:]
             elif len(node.layer.input) == 3:
                 # opset 11
                 val_scales = self.graph.get_input_node(node, idx=2, copy=True)
+                # TODO(syf): paddle.nn.functional.interpolate will support the length  
+                # which is the same as the rank of input.
+#                 inputs['scale_factor'] = val_scales.name
                 attrs['scale_factor'] = self.params[val_scales.name].tolist()[2:]
             elif len(node.layer.input) == 4:
                 # opset 11