update Reshape&elementwise_map

x2paddle/decoder/onnx_decoder.py

@@ -350,7 +350,6 @@ class ONNXGraph(Graph):
                     node.out_shapes.append(value_info['shape'])
                 else:
                     node.out_shapes.append([])
-                print(layer.name, node.out_shapes)
 
 
 class ONNXDecoder(object):

x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -40,6 +40,21 @@ def _const_weight_or_none(node):
     return None
 
 
+def _is_static_shape(shape):
+    negtive_dims = 0
+    error_dims = 0
+    for dim in shape:
+        if dim < 0:
+            negtive_dims += 1
+        if dim != -1:
+            error_dims += 1
+    if negtive_dims > 1:
+        return False
+    if error_dims > 0:
+        return False
+    return True
+
+
 def _get_same_padding(in_size, kernel_size, stride):
     new_size = int(math.ceil(in_size * 1.0 / stride))
     pad_size = (new_size - 1) * stride + kernel_size - in_size
@@ -230,11 +245,35 @@ class OpSet9():
         val_y = self.graph.get_input_node(node, idx=1, copy=True)
         val_y_shape = val_y.out_shapes[0]
         val_x_shape = val_x.out_shapes[0]
-
+        inputs = {}
         if len(val_x_shape) < len(val_y_shape):
+            if node.layer_type in ['Mul', 'Add']:
                 val_x, val_y = val_y, val_x
                 val_y_shape, val_x_shape = val_x_shape, val_y_shape
-
+                inputs = {'x': val_x, 'y': val_y}
+            elif node.layer_type in ['Sub', 'Div', 'Pow']:
+                val_x_expand = val_x.layer_name + '_expand'
+                x_value = _const_weight_or_none(val_x)
+                if (val_x_shape == [1] or len(val_x_shape) == 0) and x_value:
+                    attr = {
+                        'shape': val_y_shape,
+                        'dtype': string(val_x.dtype),
+                        'value': x_value
+                        if len(val_x_shape) == 0 else x_value[0]
+                    }
+                    node.fluid_code.add_layer(
+                        'fill_constant',
+                        inputs=None,
+                        output=val_x_expand,
+                        param_attr=attr)
+                    val_x_shape = val_y_shape
+                    inputs = {'x': val_x_expand, 'y': val_y}
+                else:
+                    assert 'Unsupported situation happened.'
+        else:
+            inputs = {'x': val_x, 'y': val_y}
+        print(node.layer_name)
+        print(val_x_shape, val_y_shape)
         str_y_shape = ','.join(str(e) for e in val_y_shape)
         str_x_shape = ','.join(str(e) for e in val_x_shape)
         slice_idx = 0
@@ -244,7 +283,6 @@ class OpSet9():
                     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:]
             var_y_reshaped = val_y.layer_name + '_reshaped'
@@ -257,13 +295,12 @@ class OpSet9():
                 inputs=val_y,
                 output=var_y_reshaped,
                 param_attr=attr_reshaped)
-            inputs = {'x': val_x, 'y': var_y_reshaped}
+            inputs['y'] = var_y_reshaped
             node.fluid_code.add_layer(
-                op_type, inputs=inputs, output=node, param_attr=attr)
+                op_type, inputs=inputs, output=node, param_attr=None)
         else:
-            inputs = {'x': val_x, 'y': val_y}
             node.fluid_code.add_layer(
-                op_type, inputs=inputs, output=node, param_attr=attr)
+                op_type, inputs=inputs, output=node, param_attr=None)
 
     @print_mapping_info
     def place_holder(self, node):
@@ -941,7 +978,8 @@ class OpSet9():
                 inputs={'x': val_x},
                 output=node,
                 param_attr={'shape': shape_value.tolist()})
-        elif len(node.out_shapes[0]) > 0:
+        elif len(node.out_shapes[0]) > 0 and _is_static_shape(node.out_shapes[
+                0]):
             node.fluid_code.add_layer(
                 'reshape',
                 inputs={'x': val_x,