add Or and Xor

docs/inference_model_convertor/op_list.md

@@ -76,7 +76,7 @@
 | 81 | Add | 82 | Concat | 83 | Max | 84 | Min |
 | 85 | GreaterOrEqual | 86 | GatherND | 87 | And | 88 | cos |
 | 89 | Neg | 90 | SpaceToDepth | 91 | GatherElement | 92 | Sin |
-| 93 | CumSum |  |  |  |  |  |  |
+| 93 | CumSum | 94 | Or | 95 | Xor |  |  |
 
 ## PyTorch
 

tests/onnx/test_auto_scan_logical_ops.py

@@ -21,6 +21,8 @@ import unittest
 
 min_opset_version_map = {
     "And": 7,
+    "Or": 7,
+    "Xor": 7,
 }
 
 
@@ -47,7 +49,7 @@ class TestLogicalopsConvert(OPConvertAutoScanTest):
         input_dtype = draw(st.sampled_from(["bool"]))
 
         config = {
-            "op_names": ["And"],
+            "op_names": ["And", "Or", "Xor"],
             "test_data_shapes": [input1_shape, input2_shape],
             "test_data_types": [[input_dtype], [input_dtype]],
             "inputs_shape": [],

x2paddle/op_mapper/onnx2paddle/opset7.py

@@ -33,6 +33,26 @@ class OpSet7(OpSet):
     def __init__(self, decoder, paddle_graph):
         super(OpSet7, self).__init__(decoder, paddle_graph)
 
+    @print_mapping_info
+    def Or(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)
+        self.paddle_graph.add_layer(
+            "paddle.logical_or",
+            inputs={"x": val_x.name,
+                    "y": val_y.name},
+            outputs=[node.name])
+
+    @print_mapping_info
+    def Xor(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)
+        self.paddle_graph.add_layer(
+            "paddle.logical_xor",
+            inputs={"x": val_x.name,
+                    "y": val_y.name},
+            outputs=[node.name])
+
     @print_mapping_info
     def Unsqueeze(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)