annotator/oneformer/detectron2/layers/csrc/box_iou_rotated/box_iou_rotated_cuda.cu

// Copyright (c) Facebook, Inc. and its affiliates.
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>
#include "box_iou_rotated_utils.h"

namespace detectron2 {

// 2D block with 32 * 16 = 512 threads per block
const int BLOCK_DIM_X = 32;
const int BLOCK_DIM_Y = 16;

template <typename T>
__global__ void box_iou_rotated_cuda_kernel(
    const int n_boxes1,
    const int n_boxes2,
    const T* dev_boxes1,
    const T* dev_boxes2,
    T* dev_ious) {
  const int row_start = blockIdx.x * blockDim.x;
  const int col_start = blockIdx.y * blockDim.y;

  const int row_size = min(n_boxes1 - row_start, blockDim.x);
  const int col_size = min(n_boxes2 - col_start, blockDim.y);

  __shared__ float block_boxes1[BLOCK_DIM_X * 5];
  __shared__ float block_boxes2[BLOCK_DIM_Y * 5];

  // It's safe to copy using threadIdx.x since BLOCK_DIM_X >= BLOCK_DIM_Y
  if (threadIdx.x < row_size && threadIdx.y == 0) {
    block_boxes1[threadIdx.x * 5 + 0] =
        dev_boxes1[(row_start + threadIdx.x) * 5 + 0];
    block_boxes1[threadIdx.x * 5 + 1] =
        dev_boxes1[(row_start + threadIdx.x) * 5 + 1];
    block_boxes1[threadIdx.x * 5 + 2] =
        dev_boxes1[(row_start + threadIdx.x) * 5 + 2];
    block_boxes1[threadIdx.x * 5 + 3] =
        dev_boxes1[(row_start + threadIdx.x) * 5 + 3];
    block_boxes1[threadIdx.x * 5 + 4] =
        dev_boxes1[(row_start + threadIdx.x) * 5 + 4];
  }

  if (threadIdx.x < col_size && threadIdx.y == 0) {
    block_boxes2[threadIdx.x * 5 + 0] =
        dev_boxes2[(col_start + threadIdx.x) * 5 + 0];
    block_boxes2[threadIdx.x * 5 + 1] =
        dev_boxes2[(col_start + threadIdx.x) * 5 + 1];
    block_boxes2[threadIdx.x * 5 + 2] =
        dev_boxes2[(col_start + threadIdx.x) * 5 + 2];
    block_boxes2[threadIdx.x * 5 + 3] =
        dev_boxes2[(col_start + threadIdx.x) * 5 + 3];
    block_boxes2[threadIdx.x * 5 + 4] =
        dev_boxes2[(col_start + threadIdx.x) * 5 + 4];
  }
  __syncthreads();

  if (threadIdx.x < row_size && threadIdx.y < col_size) {
    int offset = (row_start + threadIdx.x) * n_boxes2 + col_start + threadIdx.y;
    dev_ious[offset] = single_box_iou_rotated<T>(
        block_boxes1 + threadIdx.x * 5, block_boxes2 + threadIdx.y * 5);
  }
}

at::Tensor box_iou_rotated_cuda(
    // input must be contiguous
    const at::Tensor& boxes1,
    const at::Tensor& boxes2) {
  using scalar_t = float;
  AT_ASSERTM(
      boxes1.scalar_type() == at::kFloat, "boxes1 must be a float tensor");
  AT_ASSERTM(
      boxes2.scalar_type() == at::kFloat, "boxes2 must be a float tensor");
  AT_ASSERTM(boxes1.is_cuda(), "boxes1 must be a CUDA tensor");
  AT_ASSERTM(boxes2.is_cuda(), "boxes2 must be a CUDA tensor");
  at::cuda::CUDAGuard device_guard(boxes1.device());

  auto num_boxes1 = boxes1.size(0);
  auto num_boxes2 = boxes2.size(0);

  at::Tensor ious =
      at::empty({num_boxes1 * num_boxes2}, boxes1.options().dtype(at::kFloat));

  bool transpose = false;
  if (num_boxes1 > 0 && num_boxes2 > 0) {
    scalar_t *data1 = boxes1.data_ptr<scalar_t>(),
             *data2 = boxes2.data_ptr<scalar_t>();

    if (num_boxes2 > 65535 * BLOCK_DIM_Y) {
      AT_ASSERTM(
          num_boxes1 <= 65535 * BLOCK_DIM_Y,
          "Too many boxes for box_iou_rotated_cuda!");
      // x dim is allowed to be large, but y dim cannot,
      // so we transpose the two to avoid "invalid configuration argument"
      // error. We assume one of them is small. Otherwise the result is hard to
      // fit in memory anyway.
      std::swap(num_boxes1, num_boxes2);
      std::swap(data1, data2);
      transpose = true;
    }

    const int blocks_x =
        at::cuda::ATenCeilDiv(static_cast<int>(num_boxes1), BLOCK_DIM_X);
    const int blocks_y =
        at::cuda::ATenCeilDiv(static_cast<int>(num_boxes2), BLOCK_DIM_Y);

    dim3 blocks(blocks_x, blocks_y);
    dim3 threads(BLOCK_DIM_X, BLOCK_DIM_Y);
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();

    box_iou_rotated_cuda_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
        num_boxes1,
        num_boxes2,
        data1,
        data2,
        (scalar_t*)ious.data_ptr<scalar_t>());

    AT_CUDA_CHECK(cudaGetLastError());
  }

  // reshape from 1d array to 2d array
  auto shape = std::vector<int64_t>{num_boxes1, num_boxes2};
  if (transpose) {
    return ious.view(shape).t();
  } else {
    return ious.view(shape);
  }
}

} // namespace detectron2