fix bug & optimize code struct

x2paddle/op_mapper/onnx2paddle/onnx_op_mapper.py

@@ -12,9 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from x2paddle.op_mapper.onnx2paddle.opsets.opset9 import OpSet9
+from x2paddle.op_mapper.onnx2paddle.opset9 import OpSet9, custom_layers
 from x2paddle.core.op_mapper import OpMapper
-from x2paddle.op_mapper.onnx_opsets.custom_layer import *
 from x2paddle.decoder.onnx_decoder import ONNXGraph, ONNXGraphNode, ONNXGraphDataNode
 
 

x2paddle/op_mapper/onnx2paddle/opset9/__init__.py

+from .opset import OpSet9
+from .custom_layer import custom_layers

x2paddle/op_mapper/onnx2paddle/opsets/opset9.py → x2paddle/op_mapper/onnx2paddle/opset9/opset.py

@@ -17,6 +17,7 @@ from x2paddle.core.graph import GraphNode
 from x2paddle.core.fluid_code import Layer
 from x2paddle.core.fluid_code import FluidCode
 from x2paddle.core.util import string
+from x2paddle.op_mapper.onnx2paddle.opset9.custom_layer import *
 from functools import reduce
 import numpy as np
 import onnx
@@ -1379,8 +1380,8 @@ class OpSet9():
                 node, idx=5 - miss_arg_num, copy=True)
 
         x_shape = val_x.out_shapes[0]
-
-        assert x_shape[1] == 1, 'only X with batch_size = 1 supported'
+        print(x_shape)
+        #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)
@@ -1467,8 +1468,8 @@ class OpSet9():
                 inputs=val_b,
                 output=var_bi + ',' + var_bh,
                 param_attr={
-                    'axis': 1,
-                    'split': [hidden_size * 3, hidden_size * 3],
+                    'dim': 1,
+                    'num_or_sections': [hidden_size * 3, hidden_size * 3],
                     'name': string(node.layer_name + '.b/split')
                 })
             var_bi0 = node.layer_name + '_bi0'
@@ -1480,7 +1481,7 @@ class OpSet9():
                             'name': string(var_bi0)})
 
             node.fluid_code.add_layer(
-                'elmentwise_add',
+                'elementwise_add',
                 inputs=[var_mm, var_bi0],
                 output=var_fc,
                 param_attr={