diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/roi_align_impl.cu b/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/roi_align_impl.cu
index 6eeb2e533c7..477c227e347 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/roi_align_impl.cu
+++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/roi_align_impl.cu
@@ -18,15 +18,14 @@
 #include "util.cuh"
 #include "runtime/device/gpu/cuda_common.h"
 
-inline __device__ int roi_cast_int(float x) { return static_cast<int>(x); }
+inline __device__ int roi_cast_int(float x) { return __float2int_rd(x); }
 inline __device__ int roi_cast_int(half x) { return __half2int_rd(x); }
 
 template <typename T>
 __device__ void bilinear_interpolate(const int height, const int width, T y, T x, int *x_low, int *y_low, int *x_high,
                                      int *y_high, T *w1, T *w2, T *w3, T *w4) {
   // return 0 if out of map boundary
-  if (y <= static_cast<T>(-1.0) || y >= static_cast<T>(height) || x <= static_cast<T>(-1.0) ||
-      x >= static_cast<T>(width)) {
+  if (y < static_cast<T>(-1.0) || y > static_cast<T>(height) || x < static_cast<T>(-1.0) || x > static_cast<T>(width)) {
     *w1 = *w2 = *w3 = *w4 = 0;
     *x_low = *x_high = *y_low = *y_high = -1;
     return;
@@ -137,16 +136,18 @@ __global__ void ROIAlignKernel(size_t size, const T *input, const T *roi_boxes,
                     static_cast<T>(ix + .5f) * bin_size_w / static_cast<T>(roi_bin_grid_w);
         // bilinear interpolate by shifted y / x
         // calculate bilinear interpolation
-        int x_low, y_low, x_high, y_high;
+        int x_low = 0, y_low = 0, x_high = 0, y_high = 0;
         T w1, w2, w3, w4;
         bilinear_interpolate(height, width, y, x, &x_low, &y_low, &x_high, &y_high, &w1, &w2, &w3, &w4);
-        T v1 = input[y_low * width + x_low + offset];
-        T v2 = input[y_low * width + x_high + offset];
-        T v3 = input[y_high * width + x_low + offset];
-        T v4 = input[y_high * width + x_high + offset];
+        if (x_low != -1 || x_high != -1 || y_low != -1 || y_high != -1) {
+          T v1 = input[offset + y_low * width + x_low];
+          T v2 = input[offset + y_low * width + x_high];
+          T v3 = input[offset + y_high * width + x_low];
+          T v4 = input[offset + y_high * width + x_high];
 
-        T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
-        accumulate_val += val;
+          T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+          accumulate_val += val;
+        }
       }
     }
     accumulate_val /= count_points_in_grid_cell;
@@ -205,23 +206,25 @@ __global__ void ROIAlignGradKernel(size_t size, const T *dy, const T *roi_boxes,
                     static_cast<T>(ix + .5f) * bin_size_w / static_cast<T>(roi_bin_grid_w);
         // bilinear interpolate by shifted y / x
         // calculate bilinear interpolation
-        int x_low, y_low, x_high, y_high;
+        int x_low = 0, y_low = 0, x_high = 0, y_high = 0;
         T w1, w2, w3, w4;
         bilinear_interpolate(height, width, y, x, &x_low, &y_low, &x_high, &y_high, &w1, &w2, &w3, &w4);
-        T g1 = top_diff_this_bin * w1 / count_points_in_grid_cell;
-        T g2 = top_diff_this_bin * w2 / count_points_in_grid_cell;
-        T g3 = top_diff_this_bin * w3 / count_points_in_grid_cell;
-        T g4 = top_diff_this_bin * w4 / count_points_in_grid_cell;
+        if (x_low != -1 || x_high != -1 || y_low != -1 || y_high != -1) {
+          T g1 = top_diff_this_bin * w1 / count_points_in_grid_cell;
+          T g2 = top_diff_this_bin * w2 / count_points_in_grid_cell;
+          T g3 = top_diff_this_bin * w3 / count_points_in_grid_cell;
+          T g4 = top_diff_this_bin * w4 / count_points_in_grid_cell;
 
-        T *dx_1 = dx + offset + y_low * width + x_low;
-        T *dx_2 = dx + offset + y_low * width + x_high;
-        T *dx_3 = dx + offset + y_high * width + x_low;
-        T *dx_4 = dx + offset + y_high * width + x_high;
-        if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
-          MsAtomicAdd(dx_1, g1);
-          MsAtomicAdd(dx_2, g2);
-          MsAtomicAdd(dx_3, g3);
-          MsAtomicAdd(dx_4, g4);
+          T *dx_1 = dx + offset + y_low * width + x_low;
+          T *dx_2 = dx + offset + y_low * width + x_high;
+          T *dx_3 = dx + offset + y_high * width + x_low;
+          T *dx_4 = dx + offset + y_high * width + x_high;
+          if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) {
+            MsAtomicAdd(dx_1, g1);
+            MsAtomicAdd(dx_2, g2);
+            MsAtomicAdd(dx_3, g3);
+            MsAtomicAdd(dx_4, g4);
+          }
         }
       }
     }
diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/roi_align_gpu_kernel.h b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/roi_align_gpu_kernel.h
index 78bc1d0b61f..555779dcfe1 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/roi_align_gpu_kernel.h
+++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/nn/roi_align_gpu_kernel.h
@@ -71,12 +71,12 @@ class ROIAlignGpuFwdKernel : public GpuKernel {
     }
 
     // Get channels, height & width
-    int batch_N = x_shape[0];
+    batch_N_ = x_shape[0];
     channels_ = x_shape[1];
     height_ = x_shape[2];
     width_ = x_shape[3];
-    x_shape_ = {batch_N, channels_, height_, width_};
-    x_size_ = batch_N * channels_ * height_ * width_ * sizeof(T);
+    x_shape_ = {batch_N_, channels_, height_, width_};
+    x_size_ = batch_N_ * channels_ * height_ * width_ * sizeof(T);
 
     // Get rois rows and cols
     roi_rows_ = rois_shape[0];
@@ -119,6 +119,7 @@ class ROIAlignGpuFwdKernel : public GpuKernel {
 
   int roi_rows_;
   int roi_cols_;
+  int batch_N_;
   int channels_;
   int height_;
   int width_;
diff --git a/tests/st/ops/gpu/test_roi_align_op.py b/tests/st/ops/gpu/test_roi_align_op.py
index 5a7f4d49528..106a92c8dfa 100644
--- a/tests/st/ops/gpu/test_roi_align_op.py
+++ b/tests/st/ops/gpu/test_roi_align_op.py
@@ -80,6 +80,6 @@ def test_roi_align():
     roi_align = P.ROIAlign(pooled_height, pooled_width, spatial_scale, sample_num)
     output = roi_align(x, rois)
     print(output)
-    expect = [[[[4.625, 0.],
+    expect = [[[[8.2222, 0.],
                 [0., 0.]]]]
     np.testing.assert_almost_equal(output.asnumpy(), expect, decimal=4)