[Paddle2ONNX] add swish op

x2paddle/convert.py

@@ -75,12 +75,6 @@ def arg_parser():
         action="store_true",
         default=False,
         help="define input shape for tf model")
-    parser.add_argument(
-        "--onnx_opset",
-        "-oo",
-        type=int,
-        default=10,
-        help="when paddle2onnx set onnx opset version to export")
     parser.add_argument(
         "--params_merge",
         "-pm",
@@ -192,12 +186,12 @@ def onnx2paddle(model_path, save_dir, params_merge=False):
     print("Paddle model and code generated.")
 
 
-def paddle2onnx(model_path, save_dir, opset):
+def paddle2onnx(model_path, save_dir):
     from x2paddle.decoder.paddle_decoder import PaddleDecoder
     from x2paddle.op_mapper.paddle_op_mapper import PaddleOpMapper
     model = PaddleDecoder(model_path, '__model__', '__params__')
     mapper = PaddleOpMapper()
-    mapper.convert(model.program, save_dir, opset)
+    mapper.convert(model.program, save_dir)
 
 
 def main():
@@ -264,7 +258,7 @@ def main():
 
     elif args.framework == "paddle2onnx":
         assert args.model is not None, "--model should be defined while translating paddle model to onnx"
-        paddle2onnx(args.model, args.save_dir, args.onnx_opset)
+        paddle2onnx(args.model, args.save_dir)
 
     else:
         raise Exception(

x2paddle/op_mapper/paddle_op_mapper.py

@@ -37,7 +37,7 @@ class PaddleOpMapper(object):
 
         self.name_counter = dict()
 
-    def convert(self, program, save_dir, opset=10):
+    def convert(self, program, save_dir):
         weight_nodes = self.convert_weights(program)
         op_nodes = list()
         input_nodes = list()
@@ -80,9 +80,7 @@ class PaddleOpMapper(object):
             initializer=[],
             inputs=input_nodes,
             outputs=output_nodes)
-        opset_imports = [helper.make_opsetid("", opset)]
-        model = helper.make_model(
-            graph, producer_name='X2Paddle', opset_imports=opset_imports)
+        model = helper.make_model(graph, producer_name='X2Paddle')
         onnx.checker.check_model(model)
 
         if not os.path.isdir(save_dir):
@@ -184,6 +182,41 @@ class PaddleOpMapper(object):
             alpha=op.attr('alpha'))
         return node
 
+    def swish(self, op, block):
+        """
+        The activation swish,  y = x / (1 + exp(-beta * x))
+        """
+        beta = op.attr('beta')
+        beta_name = self.get_name(op.type, 'beta')
+        beta_node = onnx.helper.make_node(
+            'Constant',
+            name=beta_name,
+            inputs=[],
+            outputs=[beta_name],
+            value=onnx.helper.make_tensor(
+                name=beta_name,
+                data_type=onnx.TensorProto.FLOAT,
+                dims=(),
+                vals=[beta]))
+
+        beta_x_name = self.get_name(op.type, 'beta_x')
+        beta_x_node = onnx.helper.make_node(
+            'Mul',
+            name=beta_x_name,
+            inputs=[op.input('X')[0], beta_name],
+            outputs=[beta_x_name])
+        sigmoid_name = self.get_name(op.type, 'sigmoid')
+        sigmoid_node = onnx.helper.make_node(
+            'Sigmoid',
+            name=sigmoid_name,
+            inputs=[beta_x_name],
+            outputs=[sigmoid_name])
+        swish_node = onnx.helper.make_node(
+            'Mul',
+            inputs=[op.input('X')[0], sigmoid_name],
+            outputs=op.output('Out'))
+        return [beta_node, beta_x_node, sigmoid_node, swish_node]
+
     def elementwise_add(self, op, block):
         axis = op.attr('axis')
         x_shape = block.var(op.input('X')[0]).shape