add_npu_border_align

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

@@ -0,0 +1,34 @@
+#include "pytorch_npu_helper.hpp"
+
+using namespace NPU_NAME_SPACE;
+using namespace std;
+
+void border_align_forward_impl(const Tensor &input, const Tensor &boxes, Tensor output,
+                               Tensor argmax_idx, const int pool_size);
+
+void border_align_forward_npu(const Tensor &input, const Tensor &boxes, Tensor output,
+                              Tensor argmax_idx, const int pool_size){
+    TORCH_CHECK(input.size(0) == boxes.size(0), "The batch sizes of feature map and rois must be the same.");
+    TORCH_CHECK(input.size(1) % 4 == 0, "The number of channels must be divisible by 4.");
+    TORCH_CHECK(pool_size >= 2, "The pool size should be larger than 2.");
+    int32_t batch_size = input.size(0);
+    int32_t channels = input.size(1);
+    int32_t height = input.size(2);
+    int32_t width = input.size(3);
+    at::Tensor feature_map = input.permute({0, 2, 3, 1}).contiguous();
+    at::Tensor rois_map = boxes.contiguous();
+    at::Tensor temp_tensor = at::zeros({batch_size, height * width, pool_size + 1, channels}, input.options()); 
+    EXEC_NPU_CMD(aclnnBorderAlign, feature_map, rois_map, pool_size, temp_tensor);
+
+    auto max_result = temp_tensor.max(-2);
+    at::Tensor output_ = std::get<0>(max_result).to(at::kFloat);
+    output_ = output_.reshape({batch_size, height * width, 4, channels / 4}).permute({0, 3, 1, 2}).contiguous();
+    output.copy_(output_);
+
+    at::Tensor argmax_idx_ = std::get<1>(max_result).to(at::kInt);
+    argmax_idx_ = argmax_idx_.reshape({batch_size, height * width, 4, channels / 4}).permute({0, 3, 1, 2}).contiguous();
+    argmax_idx.copy_(argmax_idx_);
+}
+
+
+REGISTER_NPU_IMPL(border_align_forward_impl, border_align_forward_npu);