version fix

mmcv/ops/csrc/pytorch/npu/bbox_overlaps_npu.cpp

@@ -1,3 +1,4 @@
+#include "common_util.h"
 #include "pytorch_npu_helper.hpp"
 
 using namespace NPU_NAME_SPACE;
@@ -24,7 +25,7 @@ void bbox_overlaps_npu(const Tensor bboxes1, const Tensor bboxes2, Tensor ious,
     bboxesFP32 = bboxesFP32.to(at::kFloat);
     gtboxesFP32 = gtboxesFP32.to(at::kFloat);
   }
-  c10::SmallVector<int64_t, SIZE> iousSize = {gtboxesFP32.size(0),
+  c10::SmallVector<int64_t, 8> iousSize = {gtboxesFP32.size(0),
                                               bboxesFP32.size(0)};
   if (aligned) {
     iousSize = {gtboxesFP32.size(0), 1};

mmcv/ops/csrc/pytorch/npu/common_util.h

@@ -2,11 +2,4 @@
 #define MMCV_OPS_CSRC_COMMON__UTIL_HPP_
 const int SIZE = 8;
 
-c10::SmallVector<int64_t, SIZE> array_to_vector(c10::IntArrayRef shape) {
-  c10::SmallVector<int64_t, SIZE> shape_small_vec;
-  for (uint64_t i = 0; i < shape.size(); i++) {
-    shape_small_vec.emplace_back(shape[i]);
-  }
-}
-
 #endif  // MMCV_OPS_CSRC_COMMON__UTIL_HPP_

mmcv/ops/csrc/pytorch/npu/fused_bias_leakyrelu_npu.cpp

@@ -1,3 +1,4 @@
+#include "common_util.h"
 #include "pytorch_npu_helper.hpp"
 
 using namespace NPU_NAME_SPACE;
@@ -15,7 +16,7 @@ Tensor fused_bias_leakyrelu_npu(const Tensor &input, const Tensor &bias,
   if (grad == 0) {
     auto input_size = input.sizes();
     int input_length = input_size.size();
-    c10::SmallVector<int64_t, SIZE> input_size_tmp;
+    c10::SmallVector<int64_t, 8> input_size_tmp;
     for (uint64_t i = 0; i < input_size.size(); i++) {
       input_size_tmp.emplace_back(input_size[i]);
     }

mmcv/ops/csrc/pytorch/npu/nms_npu.cpp

@@ -1,3 +1,4 @@
+#include "common_util.h"
 #include "pytorch_npu_helper.hpp"
 
 using namespace NPU_NAME_SPACE;
@@ -14,7 +15,7 @@ Tensor nms_npu(Tensor boxes, Tensor scores, float iou_threshold, int offset) {
   at::Tensor max_outputsize_y =
       at::empty({}, boxes.options().dtype(at::kInt)).fill_(boxes.size(0));
 
-  c10::SmallVector<int64_t, SIZE> outputsize = {boxes.size(0)};
+  c10::SmallVector<int64_t, 8> outputsize = {boxes.size(0)};
   at::Tensor output =
       at::empty(outputsize, boxes.options().dtype(at::kInt)).fill_(-1);
   OpCommand cmd;

mmcv/ops/csrc/pytorch/npu/nms_rotated_npu.cpp

@@ -1,3 +1,4 @@
+#include "common_util.h"
 #include "pytorch_npu_helper.hpp"
 
 using namespace NPU_NAME_SPACE;
@@ -11,7 +12,7 @@ Tensor nms_rotated_npu(const Tensor dets, const Tensor scores,
     detsCast = detsCast.to(at::kFloat);
     scoresCast = scoresCast.to(at::kFloat);
   }
-  c10::SmallVector<int64_t, SIZE> selectedIndexSize = {dets.size(0)};
+  c10::SmallVector<int64_t, 8> selectedIndexSize = {dets.size(0)};
 
   at::Tensor selectedBox = at::empty_like(dets);
   at::Tensor selectedIndex =

mmcv/ops/csrc/pytorch/npu/roi_align_npu.cpp

@@ -1,3 +1,4 @@
+#include "common_util.h"
 #include "pytorch_npu_helper.hpp"
 
 using namespace NPU_NAME_SPACE;
@@ -42,7 +43,7 @@ void roi_align_backward_npu(Tensor grad_output, Tensor rois, Tensor argmax_y,
     roi_end_mode = 0;
   }
   auto shape = grad_input.sizes();
-  c10::SmallVector<int64_t, SIZE> xdiff_shape;
+  c10::SmallVector<int64_t, 8> xdiff_shape;
   for (uint64_t i = 0; i < shape.size(); i++) {
     xdiff_shape.emplace_back(shape[i]);
   }

mmcv/ops/csrc/pytorch/npu/roi_align_rotated_npu.cpp

@@ -1,3 +1,4 @@
+#include "common_util.h"
 #include "pytorch_npu_helper.hpp"
 
 using namespace NPU_NAME_SPACE;
@@ -32,7 +33,7 @@ void roi_align_rotated_backward_npu(Tensor top_grad, Tensor rois,
   int64_t aligned_height_64 = aligned_height;
   int64_t aligned_width_64 = aligned_width;
   int64_t sampling_ratio_64 = sampling_ratio;
-  c10::SmallVector<int64_t, SIZE> y_grad_shape;
+  c10::SmallVector<int64_t, 8> y_grad_shape;
   auto shape = bottom_grad.sizes();
   for (uint64_t i = 0; i < shape.size(); i++) {
     y_grad_shape.emplace_back(shape[i]);

mmcv/ops/csrc/pytorch/npu/roipoint_pool3d_forward.cpp

@@ -1,3 +1,4 @@
+#include "common_util.h"
 #include "pytorch_npu_helper.hpp"
 
 using namespace NPU_NAME_SPACE;
@@ -12,7 +13,7 @@ void roipoint_pool3d_forward_impl_npu(int batch_size, int pts_num,
                                       Tensor pooled_empty_flag) {
   auto points_trans = xyz.transpose(1, 2).contiguous();
   auto point_features_trans = pts_feature.transpose(1, 2).contiguous();
-  c10::SmallVector<int64_t, SIZE> features_trans_size = {
+  c10::SmallVector<int64_t, 8> features_trans_size = {
       xyz.size(0), boxes3d.size(1), xyz.size(2) + pts_feature.size(2),
       sampled_pts_num};
   at::Tensor pooled_features_trans =