fix

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

@@ -248,15 +248,22 @@ class OpSet9():
     def _interpolate(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         inputs = {'x': val_x.name}
+        attrs = dict()
         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)
-                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)
-                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) == 4:
                 # opset 11
                 val_sizes = self.graph.get_input_node(node, idx=3, copy=True)
@@ -280,27 +287,15 @@ class OpSet9():
                     inputs=inputs,
                     outputs=[node.name],
                     **attrs)
-                
-#                 # TODO(syf): all use 
-#                 inputs['out_shape'] = var_hw
-#                 ipt = inputs.pop("x")
-#                 inputs["input"] = ipt
-#                 mode = node.get_attr('mode', 'nearest')
-#                 attrs = {"align_corners": False}
-#                 self.paddle_graph.add_layer(
-#                     kernel="fluid.layers.resize_nearest",
-#                     inputs=inputs,
-#                     outputs=[node.name],
-#                     **attrs)
                 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 = {"align_corners": False,
-                 "mode": string(mode),
-                 "align_mode": 1}
+        attrs.update({"align_corners": False,
+                      "mode": string(mode),
+                      "align_mode": 1})
         self.paddle_graph.add_layer(
             kernel="paddle.nn.functional.interpolate",
             inputs=inputs,
@@ -403,7 +398,7 @@ class OpSet9():
             layer_attrs['data_format'] = string('NCHW')
             layer_attrs['value'] = value
         else:
-            paddle_op = 'fluid.layers.pad'
+            paddle_op = 'paddle.fluid.layers.pad'
             layer_attrs["pad_value"] = value
         if len(pads) == 4:
             paddings = np.array(pads).reshape(
@@ -931,16 +926,17 @@ class OpSet9():
                 'max': max_value,
                 'min': min_value,
             }
+            
             self.paddle_graph.add_layer(
                 'paddle.clip', 
                 inputs={"x": val_x.name}, 
                 outputs=[node.name], 
                 **layer_attrs)
         else:
-            max_ipt = self.graph.get_input_node(node, idx=1, copy=True)
-            min_ipt = self.graph.get_input_node(node, idx=2, copy=True)
-            max_value = _const_weight_or_none(max_ipt)
+            min_ipt = self.graph.get_input_node(node, idx=1, copy=True)
+            max_ipt = self.graph.get_input_node(node, idx=2, copy=True)
             min_value = _const_weight_or_none(min_ipt)
+            max_value = _const_weight_or_none(max_ipt)
             if max_value.shape == (1, ):
                 max_value = max_value[0]
             if min_value.shape == (1, ):
@@ -1625,3 +1621,16 @@ class OpSet9():
             inputs=inputs_dict,
             outputs=[node.name],
             **layer_attrs)
+        
+    @print_mapping_info
+    def ArgMax(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        axis = node.get_attr('axis')
+        keepdims = False if node.get_attr('keepdims') == 0 else True
+        layer_attrs = {'axis': axis,
+                      'keepdim': keepdims}
+        self.paddle_graph.add_layer(
+            'paddle.argmax', 
+            inputs={"x": val_x.name}, 
+            outputs=[node.name],
+            **layer_attrs)
\ No newline at end of file

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

@@ -86,7 +86,7 @@ class OpSet9():
     elementwise_ops = {
         'Add': 'paddle.add',
         'Div': 'paddle.divide',
-        'Sub': 'paddle.substract',
+        'Sub': 'paddle.subtract',
         'Mul': 'paddle.multiply',
         'Pow': 'paddle.pow',
     }
@@ -238,15 +238,22 @@ class OpSet9():
     def _interpolate(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         inputs = {'x': val_x.name}
+        attrs = dict()
         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)
-                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)
-                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) == 4:
                 # opset 11
                 val_sizes = self.graph.get_input_node(node, idx=3, copy=True)
@@ -270,27 +277,15 @@ class OpSet9():
                     inputs=inputs,
                     outputs=[node.name],
                     **attrs)
-                
-#                 # TODO(syf): all use 
-#                 inputs['out_shape'] = var_hw
-#                 ipt = inputs.pop("x")
-#                 inputs["input"] = ipt
-#                 mode = node.get_attr('mode', 'nearest')
-#                 attrs = {"align_corners": False}
-#                 self.paddle_graph.add_layer(
-#                     kernel="fluid.layers.resize_nearest",
-#                     inputs=inputs,
-#                     outputs=[node.name],
-#                     **attrs)
                 return
         elif node.layer_type == 'Upsample':
             val_scales = self.graph.get_input_node(node, idx=1, copy=True)
             inputs['scale'] = val_scales
 
         mode = node.get_attr('mode', 'nearest')
-        attrs = {"align_corners": False,
+        attrs.update({"align_corners": False,
                  "mode": string(mode),
-                 "align_mode": 1}
+                 "align_mode": 1})
         self.paddle_graph.add_layer(
             kernel="paddle.nn.functional.interpolate",
             inputs=inputs,
@@ -922,10 +917,10 @@ class OpSet9():
                 outputs=[node.name], 
                 **layer_attrs)
         else:
-            max_ipt = self.graph.get_input_node(node, idx=1, copy=True)
-            min_ipt = self.graph.get_input_node(node, idx=2, copy=True)
-            max_value = _const_weight_or_none(max_ipt)
+            min_ipt = self.graph.get_input_node(node, idx=1, copy=True)
+            max_ipt = self.graph.get_input_node(node, idx=2, copy=True)
             min_value = _const_weight_or_none(min_ipt)
+            max_value = _const_weight_or_none(max_ipt)
             if max_value.shape == (1, ):
                 max_value = max_value[0]
             if min_value.shape == (1, ):
@@ -1059,7 +1054,6 @@ class OpSet9():
             inputs={'x': val_x if isinstance(val_x, str) else val_x.name}, 
             outputs=[node.name], 
             **layer_attrs)
-        # TODO(syf): op has diff
 
     @print_mapping_info
     def Concat(self, node):
@@ -1580,4 +1574,17 @@ class OpSet9():
             kernel=paddle_op,
             inputs=layer_inputs,
             outputs=[node.name],
+            **layer_attrs)
+        
+    @print_mapping_info
+    def ArgMax(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        axis = node.get_attr('axis')
+        keepdims = False if node.get_attr('keepdims') == 0 else True
+        layer_attrs = {'axis': axis,
+                      'keepdim': keepdims}
+        self.paddle_graph.add_layer(
+            'paddle.argmax', 
+            inputs={"x": val_x.name}, 
+            outputs=[node.name],
             **layer_attrs)
\ No newline at end of file