diff --git a/mindspore/ccsrc/transform/op_declare.cc b/mindspore/ccsrc/transform/op_declare.cc
index bb57a630fe..ec6e15d6c7 100644
--- a/mindspore/ccsrc/transform/op_declare.cc
+++ b/mindspore/ccsrc/transform/op_declare.cc
@@ -610,7 +610,8 @@ OUTPUT_MAP(ROIAlign) = {{0, OUTPUT_DESC(y)}};
 ATTR_MAP(ROIAlign) = {{"pooled_height", ATTR_DESC(pooled_height, AnyTraits<int>())},
                       {"pooled_width", ATTR_DESC(pooled_width, AnyTraits<int>())},
                       {"spatial_scale", ATTR_DESC(spatial_scale, AnyTraits<float>())},
-                      {"sample_num", ATTR_DESC(sample_num, AnyTraits<int>())}};
+                      {"sample_num", ATTR_DESC(sample_num, AnyTraits<int>())},
+                      {"roi_end_mode", ATTR_DESC(roi_end_mode, AnyTraits<int>())}};
 
 // ROIAlignGrad
 INPUT_MAP(ROIAlignGrad) = {{1, INPUT_DESC(ydiff)}, {2, INPUT_DESC(rois)}};
diff --git a/mindspore/ops/_op_impl/tbe/roi_align.py b/mindspore/ops/_op_impl/tbe/roi_align.py
index bc4eed80ce..d392651217 100644
--- a/mindspore/ops/_op_impl/tbe/roi_align.py
+++ b/mindspore/ops/_op_impl/tbe/roi_align.py
@@ -27,7 +27,7 @@ roi_align_op_info = TBERegOp("ROIAlign") \
     .attr("pooled_height", "required", "int", "all") \
     .attr("pooled_width", "required", "int", "all") \
     .attr("sample_num", "optional", "int", "all", "2") \
-    .attr("roi_end_mode", "optional", "0,1", "1") \
+    .attr("roi_end_mode", "optional", "int", "0,1", "1") \
     .input(0, "features", False, "required", "all") \
     .input(1, "rois", False, "required", "all") \
     .input(2, "rois_n", False, "optional", "all") \
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 80b877765c..0d2499c0a3 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -2695,6 +2695,7 @@ class ROIAlign(PrimitiveWithInfer):
             feature map coordinates. Suppose the height of a RoI is `ori_h` in the raw image and `fea_h` in the
             input feature map, the `spatial_scale` should be `fea_h / ori_h`.
         sample_num (int): Number of sampling points. Default: 2.
+        roi_end_mode (int): Number must be 0 or 1. Default: 1.
 
     Inputs:
         - **features** (Tensor) - The input features, whose shape should be `(N, C, H, W)`.
@@ -2717,16 +2718,19 @@ class ROIAlign(PrimitiveWithInfer):
     """
 
     @prim_attr_register
-    def __init__(self, pooled_height, pooled_width, spatial_scale, sample_num=2):
+    def __init__(self, pooled_height, pooled_width, spatial_scale, sample_num=2, roi_end_mode=1):
         """init ROIAlign"""
         validator.check_value_type("pooled_height", pooled_height, [int], self.name)
         validator.check_value_type("pooled_width", pooled_width, [int], self.name)
         validator.check_value_type("spatial_scale", spatial_scale, [float], self.name)
         validator.check_value_type("sample_num", sample_num, [int], self.name)
+        validator.check_value_type("roi_end_mode", roi_end_mode, [int], self.name)
+        validator.check_int_range("roi_end_mode", roi_end_mode, 0, 1, Rel.INC_BOTH, self.name)
         self.pooled_height = pooled_height
         self.pooled_width = pooled_width
         self.spatial_scale = spatial_scale
         self.sample_num = sample_num
+        self.roi_end_mode = roi_end_mode
 
     def infer_shape(self, inputs_shape, rois_shape):
         return [rois_shape[0], inputs_shape[1], self.pooled_height, self.pooled_width]