merge elementwise op convert, supported dynamic scale for resize op

tools/check_code_style.sh

@@ -7,6 +7,7 @@ function abort(){
 
 trap 'abort' 0
 set -e
+TRAVIS_BUILD_DIR=${PWD}
 cd $TRAVIS_BUILD_DIR
 export PATH=/usr/bin:$PATH
 pre-commit install

x2paddle/op_mapper/onnx_op_mapper.py

@@ -57,7 +57,8 @@ class ONNXOpMapper(OpMapper):
         'Div': 'elementwise_div',
         'Sub': 'elementwise_sub',
         'Mul': 'elementwise_mul',
-        'Pow': 'elementwise_pow',}
+        'Pow': 'elementwise_pow',
+    }
 
     def __init__(self, decoder, save_dir):
         super(ONNXOpMapper, self).__init__()
@@ -160,8 +161,8 @@ class ONNXOpMapper(OpMapper):
             for opt in layer.output:
                 if opt in value_infos:
                     value_info = value_infos[opt]
-                    if len(value_info['shape']
-                           ) == 0 or value_info['dtype'] is None or 0 in value_info['shape']:
+                    if len(value_info['shape']) == 0 or value_info[
+                            'dtype'] is None or 0 in value_info['shape']:
                         if self.is_inference == False:
                             self.get_results_of_inference(
                                 onnx_model, value_infos,
@@ -258,13 +259,14 @@ class ONNXOpMapper(OpMapper):
                 if child_func_code is not None:
                     self.used_custom_layers[op +
                                             '_child_func'] = child_func_code
+
     def elementwise_map(self, node):
         assert node.layer_type in self.elementwise_ops
         op_type = self.elementwise_ops[node.layer_type]
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_y = self.graph.get_input_node(node, idx=1, copy=True)
 
-        if len(val_x.out_shapes[0])<len(val_y.out_shapes[0]):
+        if len(val_x.out_shapes[0]) < len(val_y.out_shapes[0]):
             val_x, val_y = val_y, val_x
 
         val_y_shape = val_y.out_shapes[0]
@@ -276,7 +278,6 @@ class ONNXOpMapper(OpMapper):
                 slice_idx += 1
             else:
                 break
-                
         attr = {"name": string(node.layer_name)}
         if slice_idx < len(val_y_shape) and slice_idx > 0:
             val_y_reshaped = val_y_shape[slice_idx:]
@@ -380,7 +381,9 @@ class ONNXOpMapper(OpMapper):
 
         fluid_op = 'resize_{}'.format(mode)
         if 'linear' in mode:
-            print('Warnning: paddle not support resize wiht mode: linear, we use bilinear replace linear')
+            print(
+                'Warnning: paddle not support resize wiht mode: linear, we use bilinear replace linear'
+            )
             fluid_op = 'resize_bilinear'
 
         if isinstance(val_scales, ONNXGraphNode):
@@ -447,7 +450,7 @@ class ONNXOpMapper(OpMapper):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         axes = node.get_attr('axes')
 
-        if len(val_x.out_shapes[0])==0:
+        if len(val_x.out_shapes[0]) == 0:
             node.fluid_code.add_layer('assign',
                                       inputs=val_x,
                                       output=node,
@@ -459,9 +462,6 @@ class ONNXOpMapper(OpMapper):
                                       output=node,
                                       param_attr=attr)
 
-
-            
-
     def Shrink(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         bias = node.get_attr('bias')
@@ -845,7 +845,6 @@ class ONNXOpMapper(OpMapper):
                                           output=node,
                                           param_attr=attr)
 
-
     def Sum(self, node):
         val_inps = node.layer.input
         inputs = {