Merge pull request #2 from PaddlePaddle/develop

Update

x2paddle/op_mapper/onnx_opsets/opset9.py

@@ -107,6 +107,10 @@ class OpSet9():
             'reduce_min', ['X'], ['Out'], dict(
                 axes='dim', keepdims='keep_dim'), dict(keep_dim=1)
         ],
+        'ReduceMax': [
+            'reduce_max', ['X'], ['Out'], dict(
+                axes='dim', keepdims='keep_dim'), dict(keep_dim=1)
+        ],
         #active function
         'Relu': ['relu', ['X'], ['Out']],
         'LeakyRelu': ['leaky_relu', ['X'], ['Out'], dict(), dict(alpha=.01)],
@@ -131,10 +135,7 @@ class OpSet9():
         'Abs': ['abs', ['X'], ['Out']],
     }
 
-    default_ioa_constraint = {
-        'Gather':
-        [(lambda i, o, a: a.get('axis', 0) == 0, 'only axis = 0 is supported')],
-    }
+    default_ioa_constraint = {}
 
     def __init__(self, decoder):
         super(OpSet9, self).__init__()
@@ -1082,6 +1083,17 @@ class OpSet9():
             output=node,
             param_attr=None)
 
+    @print_mapping_info
+    def Greater(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(
+            "greater_than",
+            inputs={'x': val_x,
+                    'y': val_y},
+            output=node,
+            param_attr=None)
+
     @print_mapping_info
     def Where(self, node):
         condition = self.graph.get_input_node(node, idx=0, copy=True)

x2paddle/op_mapper/paddle_op_mapper.py

@@ -42,7 +42,6 @@ class PaddleOpMapper(object):
         op_nodes = list()
         input_nodes = list()
         output_nodes = list()
-
         unsupported_ops = set()
 
         print("Translating PaddlePaddle to ONNX...\n")
@@ -183,6 +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