complete GEMM op

x2paddle/op_mapper/onnx_op_mapper.py

@@ -464,6 +464,7 @@ class ONNXOpMapper(OpMapper):
                                   inputs=matmul_inputs,
                                   output=val_mm,
                                   param_attr=attr_matmul)
+
         if beta != 0:
             if beta == 1.:
                 add_inputs = {"x": val_mm, "y": val_c}
@@ -473,7 +474,19 @@ class ONNXOpMapper(OpMapper):
                                           output=node,
                                           param_attr=attr)
             else:
-                pass
+                var_beta = node.layer_name + '_beta'
+                matmul_beta_inputs = {"x": val_c, "y": var_beta}
+                node.fluid_code.add_layer("Constant",
+                                          inputs=matmul_beta_inputs,
+                                          output=var_beta,
+                                          param_attr={'value': beta})
+
+                add_inputs = {"x": val_mm, "y": var_beta}
+                attr = {"name": string(node.layer_name)}
+                node.fluid_code.add_layer("elementwise_add",
+                                          inputs=add_inputs,
+                                          output=node,
+                                          param_attr=attr)
 
     def Add(self, node):
         val_x = self.graph.get_node(node.layer.input[0], copy=True)
@@ -504,25 +517,6 @@ class ONNXOpMapper(OpMapper):
                                   output=node,
                                   param_attr=attr)
 
-    def LRN(self, node):
-        val_x = self.graph.get_node(node.layer.input[0], copy=True)
-        size = node.get_attr('size')  # required
-        alpha = node.get_attr('alpha', 0.0001)  # optional
-        beta = node.get_attr('beta', 0.75)  # optional
-        bias = node.get_attr('bias', 1.0)  # optional
-
-        attr = {
-            "n": max(1, size),
-            "k": bias,
-            "alpha": alpha,
-            'beta': beta,
-            "name": string(node.layer_name)
-        }
-        node.fluid_code.add_layer("lrn",
-                                  inputs=val_x,
-                                  output=node,
-                                  param_attr=attr)
-
     def BatchNormalization(self, node):
         val_x = self.graph.get_node(node.layer.input[0], copy=True)
         val_scale = self.graph.get_node(node.layer.input[1], copy=True)