nms.cu

/*
NMS implementation in CUDA from pytorch framework
(https://github.com/pytorch/vision/tree/master/torchvision/csrc/cuda on Nov 13 2019)

Adapted for additional 3D capability by G. Ramien, DKFZ Heidelberg

Parts of this code are from torchvision and thus licensed under

BSD 3-Clause License

Copyright (c) Soumith Chintala 2016, 
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

*/

#include <torch/extension.h>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>

#include "cuda_helpers.h"

#include <iostream>
#include <vector>

int const threadsPerBlock = sizeof(unsigned long long) * 8;

template <typename T>
__device__ inline float devIoU(T const* const a, T const* const b) {
  // a, b hold box coords as (y1, x1, y2, x2) with y1 < y2 etc.
  T bottom = max(a[0], b[0]), top = min(a[2], b[2]);
  T left = max(a[1], b[1]), right = min(a[3], b[3]);
  T width = max(right - left, (T)0), height = max(top - bottom, (T)0);
  T interS = width * height;

  T Sa = (a[2] - a[0]) * (a[3] - a[1]);
  T Sb = (b[2] - b[0]) * (b[3] - b[1]);

  return interS / (Sa + Sb - interS);
}

template <typename T>
__device__ inline float devIoU_3d(T const* const a, T const* const b) {
  // a, b hold box coords as (y1, x1, y2, x2, z1, z2) with y1 < y2 etc.
  // get coordinates of intersection, calc intersection
  T bottom = max(a[0], b[0]), top = min(a[2], b[2]);
  T left = max(a[1], b[1]), right = min(a[3], b[3]);
  T front = max(a[4], b[4]), back = min(a[5], b[5]);
  T width = max(right - left, (T)0), height = max(top - bottom, (T)0);
  T depth = max(back - front, (T)0);
  T interS = width * height * depth;
  // calc separate boxes volumes
  T Sa = (a[2] - a[0]) * (a[3] - a[1]) * (a[5] - a[4]);
  T Sb = (b[2] - b[0]) * (b[3] - b[1]) * (b[5] - b[4]);

  return interS / (Sa + Sb - interS);
}


template <typename T>
__global__ void nms_kernel(const int n_boxes, const float iou_threshold, const T* dev_boxes,
    unsigned long long* dev_mask) {
  const int row_start = blockIdx.y;
  const int col_start = blockIdx.x;

  // if (row_start > col_start) return;
  const int row_size =
      min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
  const int col_size =
      min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);

  __shared__ T block_boxes[threadsPerBlock * 4];
  if (threadIdx.x < col_size) {
    block_boxes[threadIdx.x * 4 + 0] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 4 + 0];
    block_boxes[threadIdx.x * 4 + 1] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 4 + 1];
    block_boxes[threadIdx.x * 4 + 2] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 4 + 2];
    block_boxes[threadIdx.x * 4 + 3] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 4 + 3];
  }
  __syncthreads();

  if (threadIdx.x < row_size) {
    const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
    const T* cur_box = dev_boxes + cur_box_idx * 4;
    int i = 0;
    unsigned long long t = 0;
    int start = 0;
    if (row_start == col_start) {
      start = threadIdx.x + 1;
    }
    for (i = start; i < col_size; i++) {
      if (devIoU<T>(cur_box, block_boxes + i * 4) > iou_threshold) {
        t |= 1ULL << i;
      }
    }
    const int col_blocks = at::cuda::ATenCeilDiv(n_boxes, threadsPerBlock);
    dev_mask[cur_box_idx * col_blocks + col_start] = t;
  }
}


template <typename T>
__global__ void nms_kernel_3d(const int n_boxes, const float iou_threshold, const T* dev_boxes,
    unsigned long long* dev_mask) {
  const int row_start = blockIdx.y;
  const int col_start = blockIdx.x;

  // if (row_start > col_start) return;
  const int row_size =
      min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);
  const int col_size =
      min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);

  __shared__ T block_boxes[threadsPerBlock * 6];
  if (threadIdx.x < col_size) {
    block_boxes[threadIdx.x * 6 + 0] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 0];
    block_boxes[threadIdx.x * 6 + 1] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 1];
    block_boxes[threadIdx.x * 6 + 2] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 2];
    block_boxes[threadIdx.x * 6 + 3] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 3];
    block_boxes[threadIdx.x * 6 + 4] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 4];
    block_boxes[threadIdx.x * 6 + 5] =
        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 6 + 5];
  }
  __syncthreads();

  if (threadIdx.x < row_size) {
    const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;
    const T* cur_box = dev_boxes + cur_box_idx * 6;
    int i = 0;
    unsigned long long t = 0;
    int start = 0;
    if (row_start == col_start) {
      start = threadIdx.x + 1;
    }
    for (i = start; i < col_size; i++) {
      if (devIoU_3d<T>(cur_box, block_boxes + i * 6) > iou_threshold) {
        t |= 1ULL << i;
      }
    }
    const int col_blocks = at::cuda::ATenCeilDiv(n_boxes, threadsPerBlock);
    dev_mask[cur_box_idx * col_blocks + col_start] = t;
  }
}


at::Tensor nms_cuda(const at::Tensor& dets, const at::Tensor& scores, float iou_threshold) {
  /* dets expected as (n_dets, dim) where dim=4 in 2D, dim=6 in 3D */
  AT_ASSERTM(dets.type().is_cuda(), "dets must be a CUDA tensor");
  AT_ASSERTM(scores.type().is_cuda(), "scores must be a CUDA tensor");
  at::cuda::CUDAGuard device_guard(dets.device());

  bool is_3d = dets.size(1) == 6;
  auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
  auto dets_sorted = dets.index_select(0, order_t);

  int dets_num = dets.size(0);

  const int col_blocks = at::cuda::ATenCeilDiv(dets_num, threadsPerBlock);

  at::Tensor mask =
      at::empty({dets_num * col_blocks}, dets.options().dtype(at::kLong));

  dim3 blocks(col_blocks, col_blocks);
  dim3 threads(threadsPerBlock);
  cudaStream_t stream = at::cuda::getCurrentCUDAStream();


  if (is_3d) {
  //std::cout << "performing NMS on 3D boxes in CUDA" << std::endl;
  AT_DISPATCH_FLOATING_TYPES_AND_HALF(
      dets_sorted.type(), "nms_kernel_cuda", [&] {
        nms_kernel_3d<scalar_t><<<blocks, threads, 0, stream>>>(
            dets_num,
            iou_threshold,
            dets_sorted.data_ptr<scalar_t>(),
            (unsigned long long*)mask.data_ptr<int64_t>());
      });
   }
   else {
   AT_DISPATCH_FLOATING_TYPES_AND_HALF(
      dets_sorted.type(), "nms_kernel_cuda", [&] {
        nms_kernel<scalar_t><<<blocks, threads, 0, stream>>>(
            dets_num,
            iou_threshold,
            dets_sorted.data_ptr<scalar_t>(),
            (unsigned long long*)mask.data_ptr<int64_t>());
      });

   }

  at::Tensor mask_cpu = mask.to(at::kCPU);
  unsigned long long* mask_host = (unsigned long long*)mask_cpu.data_ptr<int64_t>();

  std::vector<unsigned long long> remv(col_blocks);
  memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);

  at::Tensor keep =
      at::empty({dets_num}, dets.options().dtype(at::kLong).device(at::kCPU));
  int64_t* keep_out = keep.data_ptr<int64_t>();

  int num_to_keep = 0;
  for (int i = 0; i < dets_num; i++) {
    int nblock = i / threadsPerBlock;
    int inblock = i % threadsPerBlock;

    if (!(remv[nblock] & (1ULL << inblock))) {
      keep_out[num_to_keep++] = i;
      unsigned long long* p = mask_host + i * col_blocks;
      for (int j = nblock; j < col_blocks; j++) {
        remv[j] |= p[j];
      }
    }
  }

  AT_CUDA_CHECK(cudaGetLastError());
  return order_t.index(
      {keep.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep)
           .to(order_t.device(), keep.scalar_type())});
}