Update onnx_op_mapper.py

x2paddle/op_mapper/onnx_op_mapper.py

@@ -36,7 +36,7 @@ _logger = _logging.getLogger(__name__)
 
 
 def _const_weight_or_none(node):
-    if 'Constant' in node.layer_name:
+    if 'Constant' in node.layer_type:
         return node.value
     if isinstance(node, ONNXGraphDataNode):
         return node.weight
@@ -51,14 +51,13 @@ def get_same_padding(in_size, kernel_size, stride):
     return [pad0, pad1]
 
 
-class ONNXOpMapper(OpMapper):
+class ONNXOpMapper(OpMapper):        
     elementwise_ops = {
         'Add': 'elementwise_add',
         '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__()
@@ -71,10 +70,10 @@ class ONNXOpMapper(OpMapper):
         self.is_inference = False
         self.tmp_data_dir = os.path.join(save_dir, 'tmp_data')
         self.get_output_shapes()
-
+        
         if not self.op_checker():
             raise Exception("Model are not supported yet.")
-
+            
         #mapping op
         print("Total nodes: {}".format(
             sum([
@@ -118,7 +117,7 @@ class ONNXOpMapper(OpMapper):
     def get_results_of_inference(self, model, value_infos, data_nodes):
         if not os.path.exists(self.tmp_data_dir):
             os.makedirs(self.tmp_data_dir)
-
+            
         for data_node in data_nodes:
             value_info = value_infos[data_node]
             shape = value_info['shape']
@@ -130,7 +129,7 @@ class ONNXOpMapper(OpMapper):
             ipt = np.random.random(shape).astype(
                 value_info['dtype'])
             np.save(os.path.join(self.tmp_data_dir, data_node), ipt)
-
+            
         model = onnx.shape_inference.infer_shapes(model)
         outputs = []
         for value_info in model.graph.value_info:
@@ -166,8 +165,8 @@ 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,
@@ -264,17 +263,16 @@ 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)
         val_y_shape = val_y.out_shapes[0]
         val_x_shape = val_x.out_shapes[0]
-
-        if len(val_x_shape) < len(val_y_shape):
+        
+        if len(val_x_shape)<len(val_y_shape):
             val_x, val_y = val_y, val_x
 
         str_y_shape = ','.join(str(e) for e in val_y_shape)
@@ -312,9 +310,16 @@ class ONNXOpMapper(OpMapper):
 
     def place_holder(self, node):
         self.input_shapes.append(node.out_shapes[0])
+        
+        shape = node.out_shapes[0]
+        for i, dim_shape in enumerate(shape):
+            if dim_shape==0 and i==0:
+                shape[i]=1
+            if dim_shape==0 and i!=0:
+                assert 'shape of input is not assigned'
         attr = {
             "dtype": string(node.dtype),
-            "shape": node.out_shapes[0],
+            "shape": shape,
             "name": string(node.layer_name),
             "append_batch_size": 'False'
         }
@@ -360,28 +365,28 @@ class ONNXOpMapper(OpMapper):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_scales = self.graph.get_input_node(node, idx=1, copy=True)
         val_y = self.graph.get_node(node.layer.output[0], copy=True)
-
+        
         out_shape = val_y.out_shapes[0]
         if out_shape is not None:
             assert len(out_shape) == 4, 'only 4-D Tensor as X and Y supported'
             out_shape = out_shape[2:]
-
+            
         scales = _const_weight_or_none(val_scales)
-
+    
         if isinstance(val_scales, ONNXGraphNode):
             scales, _, _ = self.get_dynamic_shape(val_scales.layer_name)
 
-        attr = {'name': string(node.layer_name)}
+        attr = { 'name': string(node.layer_name)}
         use_scales = True
         if scales is not None:
             try:
                 assert len(scales) == 4, 'only 4-D Tensor as X and Y supported'
                 assert scales[0] == 1 and scales[
-                    1] == 1, 'only scale on (NC)HW supported'
+                1] == 1, 'only scale on (NC)HW supported'
                 assert scales[2] == scales[
-                    3], 'only aspect-ratio-invariant scale supported'
+                3], 'only aspect-ratio-invariant scale supported'
             except:
-                use_scales = False
+                use_scales=False
         scale = scales[2] if scales else None
         if scale is None:
             assert out_shape, 'neither scales nor output shape is available'
@@ -394,24 +399,56 @@ class ONNXOpMapper(OpMapper):
                 out_shape = [in_shape[2] * scale, in_shape[3] * scale]
 
         mode = node.get_attr('mode', 'nearest')
-
+        
         fluid_op = 'resize_{}'.format(mode)
         if 'linear' in mode:
-            print(
-                'Warnning: paddle not support op:resize wiht mode: linear, we use bilinear replace linear'
-            )
+            print('Warnning: paddle not support op:resize wiht mode: linear, we use bilinear replace linear')
             fluid_op = 'resize_bilinear'
-
+        
         if use_scales and scale is not None:
             attr['scale'] = scale
-        else:
+        else:   
             attr['out_shape'] = out_shape
 
         node.fluid_code.add_layer(fluid_op,
                                   inputs=val_x,
                                   output=node,
                                   param_attr=attr)
+    def RoiAlign(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_rois = self.graph.get_input_node(node, idx=1, copy=True)
+        
+        pooled_height =  node.get_attr('output_height')
+        pooled_width =  node.get_attr('output_width')
+        spatial_scale = node.get_attr('spatial_scale')
+        sampling_ratio = node.get_attr('sampling_ratio')
+        attr = {
+                'pooled_height': pooled_height,
+                'pooled_width': pooled_width,
+                'spatial_scale': spatial_scale,
+                'sampling_ratio':sampling_ratio,
+            }
+        node.fluid_code.add_layer('roi_align',
+                                  inputs={'input':val_x,'rois':val_rois},
+                                  output=node,
+                                  param_attr=attr)
 
+    def MaxRoiPool(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_rois = self.graph.get_input_node(node, idx=1, copy=True)
+        
+        spatial_scale = node.get_attr('spatial_scale')
+        pooled_height, pooled_width = node.get_attr('pooled_shape')
+        attr = {
+                'pooled_height': pooled_height,
+                'pooled_width': pooled_width,
+                'spatial_scale': spatial_scale,
+            }
+        node.fluid_code.add_layer('roi_pool',
+                                  inputs={'input':val_x,'rois':val_rois},
+                                  output=node,
+                                  param_attr=attr)
+            
     def Pad(self, node, op_independent=True):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         pads = node.get_attr('pads')
@@ -462,11 +499,12 @@ class ONNXOpMapper(OpMapper):
     def Unsqueeze(self, node):
         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:
+        print(val_x.outputs)
+        if len(val_x.out_shapes[0])==0:
             node.fluid_code.add_layer('assign',
-                                      inputs=val_x,
-                                      output=node,
-                                      param_attr=None)
+                                  inputs=val_x,
+                                  output=node,
+                                  param_attr=None)
         else:
             attr = {'axes': axes, 'name': string(node.layer_name)}
             node.fluid_code.add_layer('unsqueeze',
@@ -493,9 +531,9 @@ class ONNXOpMapper(OpMapper):
         output_dtype = val_output.dtype
         if output_dtype:
             assert dtype == output_dtype, 'tensor dtype unmatches storage dtype'
-
+        
         shape = node.get_attr('shape', None)
-
+        
         if shape is None:
             shape = val_output.out_shapes[0]
         if shape is None:
@@ -541,26 +579,30 @@ class ONNXOpMapper(OpMapper):
     def Expand(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_shape = self.graph.get_input_node(node, idx=1, copy=True)
-
-        if len(val_shape.outputs) == 1:
+        
+        if len(val_shape.outputs)==1:
             self.omit_nodes.append(val_shape.layer_name)
 
         val_y = self.graph.get_node(node.layer.output[0], copy=True)
         out_shape = node.out_shapes[0]
         val_x_dtype = val_x.dtype
-
-        name_ones = node.layer_name + '_ones'
-        attr_ones = {'shape': out_shape, 'dtype': string(val_x_dtype)}
+        
+        name_ones= node.layer_name + '_ones'
+        attr_ones = {
+                'shape':out_shape,
+                'dtype':string(val_x_dtype)
+            }
         node.fluid_code.add_layer('ones',
                                   inputs=None,
                                   output=name_ones,
                                   param_attr=attr_ones)
-        inputs = {'x': name_ones, 'y': val_x}
-        attr = {'name': string(node.layer_name)}
+        inputs = {'x':name_ones,'y':val_x}
+        attr = {'name':string(node.layer_name)}
         node.fluid_code.add_layer('elementwise_mul',
                                   inputs=inputs,
                                   output=node.layer_name,
-                                  param_attr=attr)
+                                  param_attr=attr
+                                 )
 
     def Gather(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
@@ -569,7 +611,7 @@ class ONNXOpMapper(OpMapper):
         axis = node.get_attr('axis', 0)
         assert len(
             indices_shape) <= 2, "Gather op don't support dim of indice >2 "
-        if axis == 0 and len(indices_shape) <= 1:
+        if axis==0 and len(indices_shape)<=1:
             node.fluid_code.add_layer('gather',
                                       inputs={
                                           'input': val_x,
@@ -597,16 +639,14 @@ class ONNXOpMapper(OpMapper):
                                       inputs=node,
                                       output=node,
                                       param_attr=attr_trans)
-        elif len(indices_shape) > 1:
+        elif len(indices_shape)>1:
             from functools import reduce
-            reshape_shape = reduce(lambda x, y: x * y, indices_shape)
+            reshape_shape = reduce(lambda x,y:x*y, indices_shape)
             node.fluid_code.add_layer('reshape',
                                       inputs=indices,
                                       output=indices,
-                                      param_attr={'shape': [
-                                          reshape_shape,
-                                      ]})
-
+                                      param_attr={'shape':[reshape_shape,]})
+            
             perm = list(range(len(val_x.out_shapes[0])))
             perm = [axis] + perm[:axis] + perm[axis + 1:]
             attr_trans = {'perm': perm}
@@ -635,26 +675,27 @@ class ONNXOpMapper(OpMapper):
             node.fluid_code.add_layer('reshape',
                                       inputs=node,
                                       output=node,
-                                      param_attr={'shape': reshaped_shape})
+                                      param_attr={'shape':reshaped_shape})
 
     def Slice(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
-        val_starts, val_ends, val_axes, val_steps = None, None, None, None
+        starts, ends, axes, steps = None, None, None, None
         if len(node.inputs) > 1:
             starts = self.graph.get_input_node(node, idx=1, copy=True)
             ends = self.graph.get_input_node(node, idx=2, copy=True)
-            axes = self.graph.get_input_node(node, idx=3, copy=True)
-            steps = self.graph.get_input_node(node, idx=4, copy=True)
-
+            if len(node.inputs)>3:
+                axes = self.graph.get_input_node(node, idx=3, copy=True)
+                self.omit_nodes.append(axes.layer_name)
+                axes = _const_weight_or_none(axes)
+            if len(node.inputs)>4: 
+                steps = self.graph.get_input_node(node, idx=4, copy=True)
+                self.omit_nodes.append(steps.layer_name)
+                steps = _const_weight_or_none(steps)
+ 
             self.omit_nodes.append(starts.layer_name)
             self.omit_nodes.append(ends.layer_name)
-            self.omit_nodes.append(axes.layer_name)
-            self.omit_nodes.append(steps.layer_name)
-
-            starts = _const_weight_or_none(starts).copy()
-            ends = _const_weight_or_none(ends).copy()
-            axes = _const_weight_or_none(axes)
-            steps = _const_weight_or_none(steps)
+            starts = _const_weight_or_none(starts)
+            ends = _const_weight_or_none(ends)
         else:
             starts = node.get_attr('starts')
             ends = node.get_attr('ends')
@@ -715,7 +756,7 @@ class ONNXOpMapper(OpMapper):
             'dim': axis,
             'name': string(node.layer_name)
         }
-
+        
         node.fluid_code.add_layer('split',
                                   inputs=val_x,
                                   output=val_y,
@@ -729,21 +770,22 @@ class ONNXOpMapper(OpMapper):
 
         if isinstance(val_shape, ONNXGraphDataNode):
             self.omit_nodes.append(val_shape.layer_name)
-
+            
         attr = {'name': string(node.layer_name)}
         # catch dynamic graph shape
         if isinstance(val_shape, ONNXGraphNode):
             shape, _, _ = self.get_dynamic_shape(val_shape.layer_name)
             if val_shape.dtype == 'int64':
-                val_shape_cast = val_shape.layer_name + '_cast'
+                val_shape_cast  = val_shape.layer_name+'_cast'
                 node.fluid_code.add_layer('cast',
-                                          inputs=val_shape,
-                                          output=val_shape_cast,
-                                          param_attr={'dtype': string('int32')})
-
+                                  inputs=val_shape,
+                                  output=val_shape_cast,
+                                  param_attr={'dtype':string('int32')})
+            
                 attr['actual_shape'] = val_shape_cast
             else:
                 attr['actual_shape'] = val_shape
+
         if shape is None:
             shape = val_reshaped.out_shapes[0]
 
@@ -754,7 +796,7 @@ class ONNXOpMapper(OpMapper):
                 'input "shape" not inferred, use [1, -1] as dummy value, '
                 'the behavior of Paddle fluid maybe undefined', node.layer_name,
                 val_x.layer_name, val_reshaped.layer_name)
-
+        
         attr['shape'] = shape
         node.fluid_code.add_layer('reshape',
                                   inputs=val_x,
@@ -991,11 +1033,73 @@ class ONNXOpMapper(OpMapper):
                                   inputs=val_x,
                                   output=node,
                                   param_attr=attr)
-
+        
+    def Equal(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        node.fluid_code.add_layer("equal",
+                                  inputs={'x':val_x, 'y':val_y},
+                                  output=node,
+                                  param_attr=None)
+    def Where(self, node):
+        condition = self.graph.get_input_node(node, idx=0, copy=True)
+        val_x = self.graph.get_input_node(node, idx=1, copy=True)
+        val_y = self.graph.get_input_node(node, idx=2, copy=True)
+        
+        not_condition = condition.layer_name + '_not'
+        node.fluid_code.add_layer("logical_not",
+                                  inputs=condition,
+                                  output=not_condition,
+                                  param_attr=None)
+        cast_not_condition = not_condition+'_cast' 
+        node.fluid_code.add_layer("cast",
+                                  inputs=not_condition,
+                                  output=cast_not_condition,
+                                  param_attr={'dtype':string(val_x.dtype)})
+        cast_condition = condition.layer_name + '_cast'
+        node.fluid_code.add_layer("cast",
+                                  inputs=condition,
+                                  output=cast_condition,
+                                  param_attr={'dtype':string(val_x.dtype)})
+        mul_val_x = val_x.layer_name + '_mul' 
+        node.fluid_code.add_layer("elementwise_mul",
+                                  inputs={'x':val_x,'y':cast_condition},
+                                  output=mul_val_x,
+                                  param_attr=None)
+        
+        mul_val_y = val_y.layer_name + '_mul'
+        node.fluid_code.add_layer("elementwise_mul",
+                                  inputs={'x':val_y,'y':cast_not_condition},
+                                  output=mul_val_y,
+                                  param_attr=None)
+        
+        node.fluid_code.add_layer("elementwise_add",
+                                  inputs={'x':mul_val_x,'y':mul_val_y},
+                                  output=node,
+                                  param_attr=None)
+        
     def Identity(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         node.fluid_code.add_layer("assign", inputs=val_x, output=node)
-
+        
+    def Tile(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_repeats = self.graph.get_input_node(node, idx=1, copy=True)
+        repeats = _const_weight_or_none(val_repeats)
+        assert repeats is not None, 'for OP:Tile, only const repeats supported'
+        
+        if isinstance(repeats, int):
+            repeats = [repeats]
+            
+        attr = {
+            'expand_times':repeats,
+            "name": string(node.layer_name),
+        }
+        node.fluid_code.add_layer("expand", 
+                                  inputs=val_x, 
+                                  output=node,
+                                  param_attr=attr)
+        
     def MaxPool(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
 
@@ -1036,7 +1140,7 @@ class ONNXOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
-    def GlobalAveragePool(self, node):
+    def _global_pool(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_y = self.graph.get_node(node.layer.output[0], copy=True)
         input_shape = val_x.out_shapes[0]
@@ -1048,8 +1152,15 @@ class ONNXOpMapper(OpMapper):
             poolnd = len(output_shape) - 2  # NC...
         assert 2 <= poolnd <= 3, 'only pool2d and pool3d is supported'
         fluid_op = 'pool{}d'.format(poolnd)
+        
+        pool_type = None
+        if node.layer.op_type == 'GlobalMaxPool':
+            pool_type = 'max'
+        elif node.layer.op_type == 'GlobalAveragePool':
+            pool_type = 'avg'
+
         attr = {
-            "pool_type": string("avg"),
+            "pool_type": string(pool_type),
             "global_pooling": True,
             "name": string(node.layer_name)
         }
@@ -1057,7 +1168,13 @@ class ONNXOpMapper(OpMapper):
                                   inputs=val_x,
                                   output=node,
                                   param_attr=attr)
-
+        
+    def GlobalMaxPool(self, node):
+        self._global_pool(node)
+        
+    def GlobalAveragePool(self, node):
+        self._global_pool(node)
+        
     def Conv(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)
         val_w = self.graph.get_input_node(node, idx=1, copy=True)
@@ -1162,3 +1279,149 @@ class ONNXOpMapper(OpMapper):
                                   inputs=val_x,
                                   output=node,
                                   param_attr=attr)
+
+    def GRU(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_w = self.graph.get_input_node(node, idx=1, copy=True)
+        val_r = self.graph.get_input_node(node, idx=2, copy=True)
+        
+        val_b = None
+        val_len = None
+        val_xh = None
+        miss_arg_num = 0
+        num_ipt = len(node.layer.input)
+        if num_ipt>3 and node.layer.input[3] != '':
+            val_b = self.graph.get_input_node(node, idx=3, copy=True)
+        else:
+            miss_arg_num += 1
+        if num_ipt>4 and node.layer.input[4] != '':
+            val_len = self.graph.get_input_node(node, idx=4-miss_arg_num, copy=True)
+        else:
+            miss_arg_num += 1
+        if num_ipt>5 and node.layer.input[5] != '':
+            val_xh = self.graph.get_input_node(node, idx=5-miss_arg_num, copy=True)
+            
+        data, dtype, shape = self.get_dynamic_shape(val_x.layer_name)
+        
+        x_shape = val_x.out_shapes[0]
+        
+        assert x_shape[1] == 1, 'only X with batch_size = 1 supported'
+        assert node.get_attr('clip', None) is None, 'clipping not supported'
+
+        hidden_size = node.get_attr('hidden_size', None)
+        if hidden_size is None:
+            r_shape = val_r.out_shapes[0]
+            if r_shape:
+                hidden_size = r_shape[-1]
+        if hidden_size is None:
+            w_shape = var_w.out_shapes[0]
+            if w_shape:
+                hidden_size = w_shape[-2] // 3
+        if hidden_size is None and val_b:
+            b_shape = val_b.out_shapes[0]
+            if b_shape:
+                hidden_size = b_shape[-1] // 6
+        if hidden_size is None and val_xh:
+            xh_shape = val_xh.out_shapes[0]
+            if xh_shape:
+                hidden_size = xh_shape[-1]
+            
+        direction = node.get_attr('direction', 'forward') 
+        assert direction != 'bidirectional', 'direction = bidirectional not supported'
+        
+        activations = node.get_attr('activations', ['Sigmoid', 'Tanh'])
+        assert len(activations) == 2, 'bidirectional operation not supported'
+        
+        assert node.get_attr(
+        'linear_before_reset',
+        0) == 0, 'only linear_before_reset = 0 supported'
+        
+        activations = [s.lower() for s in activations]
+        gate_activation, candidate_activation = activations
+        is_reverse = direction == 'reverse'
+        
+        var_x0 = node.layer_name + '_x0'
+        node.fluid_code.add_layer('squeeze',
+                                  inputs=val_x,
+                                  output=var_x0,
+                                  param_attr={'axes': [1],'name':string(var_x0)})
+        
+        var_w0 = node.layer_name + '_w0'
+        node.fluid_code.add_layer('squeeze',
+                                  inputs=val_w,
+                                  output=var_w0,
+                                  param_attr={'axes': [0],'name':string(var_w0)})
+        
+        var_fc = node.layer_name + '_fc'
+        var_mm = (node.layer_name + '_mm') if val_b else var_fc
+        node.fluid_code.add_layer('matmul',
+                                  inputs={'x':var_x0, 'y':var_w0},
+                                  output=var_mm,
+                                  param_attr={'transpose_x': 0,'transpose_y': 1,'name':string(var_mm)})
+        
+        var_r0 = node.layer_name + '_r0'
+        node.fluid_code.add_layer('squeeze',
+                                  inputs=val_r,
+                                  output=var_r0,
+                                  param_attr={'axes': [0],'name':string(var_r0)})
+        
+        var_r0t = node.layer_name + '_r0t' 
+        
+        node.fluid_code.add_layer('transpose',
+                                  inputs=var_r0,
+                                  output=var_r0t,
+                                  param_attr={'perm': [1, 0],'name':string(var_r0t)})
+        if val_b:
+            var_bi = node.layer_name + '_bi'
+            var_bh = node.layer_name + '_bh'
+            node.fluid_code.add_layer('split',
+                                  inputs=val_b,
+                                  output=var_bi+','+var_bh,
+                                  param_attr={'axis': 1,
+                                              'split': [hidden_size * 3, hidden_size * 3],
+                                              'name':string(node.layer_name+'.b/split')})
+            var_bi0 = node.layer_name + '_bi0'
+            node.fluid_code.add_layer('squeeze',
+                                  inputs=var_bi,
+                                  output=var_bi0,
+                                  param_attr={'axes': [0],'name':string(var_bi0)})
+            
+            node.fluid_code.add_layer('elmentwise_add',
+                                  inputs=[var_mm, var_bi0],
+                                  output=var_fc,
+                                  param_attr={'axes': 1,'name':string(node.layer_name+'.i/bias')})
+
+        if val_xh:
+            var_xh0 = node.layer_name + '_xh0'
+            node.fluid_code.add_layer('squeeze',
+                                  inputs=val_xh,
+                                  output=var_xh0,
+                                  param_attr={'axes': [1],'name':string(var_xh0)})
+        var_y00 = node.layer_name + '_y00'
+        
+        attr={
+            'origin_mode':True,
+            'h_0': var_xh0 if val_xh else None,
+            'is_reverse':is_reverse,
+            'gate_activation':string(gate_activation),
+            'candidate_activation':string(candidate_activation),
+            'param_attr':string(var_r0t),
+            'bias_attr':string(var_bh) if val_b else False,
+        }
+        node.fluid_code.add_layer('dynamic_gru',
+                                  inputs=var_fc +','+ str(hidden_size),
+                                  output=var_y00,
+                                  param_attr=attr)
+        
+        num_opt = len(node.layer.output)
+        
+        if num_opt>0 and node.layer.output[0] != '':
+            node.fluid_code.add_layer('unsqueeze',
+                                  inputs=var_y00,
+                                  output=node.layer.output[0],
+                                  param_attr={'axes': [1, 1],'name':string(node.layer.output[0])})
+        if num_opt>1 and node.layer.output[1] != '':
+            node.fluid_code.add_layer('unsqueeze',
+                                  inputs=var_y00,
+                                  output=node.layer.output[1],
+                                  param_attr={'axes': [1, 1],'name':string(node.layer.output[1])})