Enhanced conv and pad functions in onnx opset9 (#707)

* Enhanced conv and pad functions in onnx opset9

* update code style

* code style

* fix style

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -60,7 +60,7 @@ def _rename_or_remove_weight(weights,
         None
     '''
     if origin_name not in weights:
-        raise KeyError('{} not a key in {}'.format(origin_name, weights))
+        raise KeyError('{} not a key in {}'.format(origin_name, weights.keys()))
     if is_remove:
         # remove weight
         data = weights.pop(origin_name)
@@ -585,7 +585,21 @@ class OpSet9():
                         layer_attrs["pad"] = paddings
                         paddle_op = "custom_layer:PadAllDim4WithOneInput"
             else:
-                raise Exception("The padding value {} is wrong!".format(pads))
+                pad_data = node.get_attr('pads')
+                pad_data1 = pad_data[0::2]
+                pad_data_all = []
+                for i in range(len(pad_data1)):
+                    pad_data_all.append(pad_data[i])
+                    pad_data_all.append(pad_data[len(pad_data1) + i])
+
+                layer_attrs["pad"] = pad_data_all
+                self.paddle_graph.add_layer(
+                    'paddle.nn.functional.pad',
+                    inputs={'x': val_x.name},
+                    outputs=layer_outputs[1:],
+                    **layer_attrs)
+                return
+
             self.paddle_graph.add_layer(
                 paddle_op,
                 inputs={'x': val_x.name},
@@ -1982,11 +1996,17 @@ class OpSet9():
 
     @print_mapping_info
     def Conv(self, node):
-        op_name = name_generator("conv", self.nn_name2id)
         output_name = node.name
-        layer_outputs = [op_name, output_name]
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_w = self.graph.get_input_node(node, idx=1, copy=True)
+
+        if val_w.name in self.weights.keys():
+            op_name = name_generator("conv", self.nn_name2id)
+        else:
+            op_name = output_name
+
+        layer_outputs = [op_name, output_name]
+
         has_bias = len(node.layer.input) == 3
         if has_bias:
             val_b = self.graph.get_input_node(node, idx=2, copy=True)
@@ -2015,6 +2035,25 @@ class OpSet9():
             paddings = pad_h + pad_w
 
         layer_inputs = {'x': val_x if isinstance(val_x, str) else val_x.name}
+        if val_w.name not in self.weights.keys():
+            layer_attrs = {
+                "stride": strides,
+                "padding": paddings,
+                "dilation": dilations,
+                "groups": num_groups,
+            }
+            layer_inputs['weight'] = val_w.name
+            if has_bias:
+                layer_inputs['bias'] = val_b.name
+
+            paddle_op = 'paddle.nn.functional.conv{}d'.format(convnd)
+            self.paddle_graph.add_layer(
+                paddle_op,
+                inputs=layer_inputs,
+                outputs=[node.name],
+                **layer_attrs)
+            return
+
         layer_attrs = {
             "in_channels": num_in_channels * num_groups,
             "out_channels": num_out_channels,
@@ -2055,11 +2094,17 @@ class OpSet9():
 
     @print_mapping_info
     def ConvTranspose(self, node):
-        op_name = name_generator("conv_trans", self.nn_name2id)
         output_name = node.name
-        layer_outputs = [op_name, output_name]
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_w = self.graph.get_input_node(node, idx=1, copy=True)
+
+        if val_w.name in self.weights.keys():
+            op_name = name_generator("conv_trans", self.nn_name2id)
+        else:
+            op_name = output_name
+
+        layer_outputs = [op_name, output_name]
+
         val_b = None
         if len(node.layer.input) > 2:
             val_b = self.graph.get_input_node(node, idx=2, copy=True)
@@ -2092,6 +2137,27 @@ class OpSet9():
 
         # Conv2DTranspose缺少output_size，只能在forward里头传进output_size
         inputs_dict = {'x': val_x if isinstance(val_x, str) else val_x.name}
+        if val_w.name not in self.weights.keys():
+            layer_attrs = {
+                "stride": strides,
+                "dilation": dilations,
+                "padding": paddings,
+                "groups": num_groups,
+                "output_padding": out_padding
+            }
+            paddle_op = 'paddle.nn.functional.conv{}d_transpose'.format(convnd)
+
+            inputs_dict['weight'] = val_w.name
+            if len(node.layer.input) > 2:
+                inputs_dict['bias'] = val_b.name
+
+            self.paddle_graph.add_layer(
+                paddle_op,
+                inputs=inputs_dict,
+                outputs=[node.name],
+                **layer_attrs)
+            return
+
         layer_attrs = {
             "in_channels": num_in_channels,
             "out_channels": num_out_channels * num_groups,