[Refactor] Refactor the nms3d op to support MLU (#2296)

* update iou3d

* fix parrot

* update to device

* replace count_nonzero with fill

* update build.yml

.github/workflows/build.yml

@@ -157,10 +157,8 @@ jobs:
     strategy:
       matrix:
         python-version: [3.7]
-        torch: [1.3.1, 1.5.1+cu101, 1.6.0+cu101, 1.7.0+cu101, 1.8.0+cu101]
+        torch: [1.5.1+cu101, 1.6.0+cu101, 1.7.0+cu101, 1.8.0+cu101]
         include:
-          - torch: 1.3.1
-            torchvision: 0.4.2
           - torch: 1.5.1+cu101
             torchvision: 0.6.1+cu101
           - torch: 1.6.0+cu101
@@ -362,10 +360,8 @@ jobs:
     runs-on: macos-latest
     strategy:
       matrix:
-        torch: [1.3.1, 1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0]
+        torch: [1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0]
         include:
-          - torch: 1.3.1
-            torchvision: 0.4.2
           - torch: 1.5.1
             torchvision: 0.6.1
           - torch: 1.6.0

mmcv/ops/csrc/common/cuda/nms_cuda_kernel.cuh

@@ -27,9 +27,9 @@ __device__ inline bool devIoU(float const *const a, float const *const b,
   return interS > threshold * (Sa + Sb - interS);
 }
 
-__global__ void nms_cuda(const int n_boxes, const float iou_threshold,
-                         const int offset, const float *dev_boxes,
-                         unsigned long long *dev_mask) {
+__global__ static void nms_cuda(const int n_boxes, const float iou_threshold,
+                                const int offset, const float *dev_boxes,
+                                unsigned long long *dev_mask) {
   int blocks = (n_boxes + threadsPerBlock - 1) / threadsPerBlock;
   CUDA_2D_KERNEL_BLOCK_LOOP(col_start, blocks, row_start, blocks) {
     const int tid = threadIdx.x;
@@ -73,9 +73,9 @@ __global__ void nms_cuda(const int n_boxes, const float iou_threshold,
   }
 }
 
-__global__ void gather_keep_from_mask(bool *keep,
-                                      const unsigned long long *dev_mask,
-                                      const int n_boxes) {
+__global__ static void gather_keep_from_mask(bool *keep,
+                                             const unsigned long long *dev_mask,
+                                             const int n_boxes) {
   const int col_blocks = (n_boxes + threadsPerBlock - 1) / threadsPerBlock;
   const int tid = threadIdx.x;
 

mmcv/ops/csrc/parrots/cudabind.cpp

@@ -570,14 +570,12 @@ void IoU3DBoxesOverlapBevForwardCUDAKernelLauncher(const int num_a,
                                                    const Tensor boxes_b,
                                                    Tensor ans_overlap);
 
-void IoU3DNMS3DForwardCUDAKernelLauncher(const Tensor boxes,
-                                         unsigned long long* mask,
-                                         int boxes_num,
+void IoU3DNMS3DForwardCUDAKernelLauncher(const Tensor boxes, Tensor& keep,
+                                         Tensor& keep_num,
                                          float nms_overlap_thresh);
 
-void IoU3DNMS3DNormalForwardCUDAKernelLauncher(const Tensor boxes,
-                                               unsigned long long* mask,
-                                               int boxes_num,
+void IoU3DNMS3DNormalForwardCUDAKernelLauncher(const Tensor boxes, Tensor& keep,
+                                               Tensor& keep_num,
                                                float nms_overlap_thresh);
 
 void iou3d_boxes_overlap_bev_forward_cuda(const int num_a, const Tensor boxes_a,
@@ -587,16 +585,16 @@ void iou3d_boxes_overlap_bev_forward_cuda(const int num_a, const Tensor boxes_a,
                                                 ans_overlap);
 };
 
-void iou3d_nms3d_forward_cuda(const Tensor boxes, unsigned long long* mask,
-                              int boxes_num, float nms_overlap_thresh) {
-  IoU3DNMS3DForwardCUDAKernelLauncher(boxes, mask, boxes_num,
+void iou3d_nms3d_forward_cuda(const Tensor boxes, Tensor& keep,
+                              Tensor& keep_num, float nms_overlap_thresh) {
+  IoU3DNMS3DForwardCUDAKernelLauncher(boxes, keep, keep_num,
                                       nms_overlap_thresh);
 };
 
-void iou3d_nms3d_normal_forward_cuda(const Tensor boxes,
-                                     unsigned long long* mask, int boxes_num,
+void iou3d_nms3d_normal_forward_cuda(const Tensor boxes, Tensor& keep,
+                                     Tensor& keep_num,
                                      float nms_overlap_thresh) {
-  IoU3DNMS3DNormalForwardCUDAKernelLauncher(boxes, mask, boxes_num,
+  IoU3DNMS3DNormalForwardCUDAKernelLauncher(boxes, keep, keep_num,
                                             nms_overlap_thresh);
 };
 
@@ -604,11 +602,11 @@ void iou3d_boxes_overlap_bev_forward_impl(const int num_a, const Tensor boxes_a,
                                           const int num_b, const Tensor boxes_b,
                                           Tensor ans_overlap);
 
-void iou3d_nms3d_forward_impl(const Tensor boxes, unsigned long long* mask,
-                              int boxes_num, float nms_overlap_thresh);
+void iou3d_nms3d_forward_impl(const Tensor boxes, Tensor& keep,
+                              Tensor& keep_num, float nms_overlap_thresh);
 
-void iou3d_nms3d_normal_forward_impl(const Tensor boxes,
-                                     unsigned long long* mask, int boxes_num,
+void iou3d_nms3d_normal_forward_impl(const Tensor boxes, Tensor& keep,
+                                     Tensor& keep_num,
                                      float nms_overlap_thresh);
 
 REGISTER_DEVICE_IMPL(iou3d_boxes_overlap_bev_forward_impl, CUDA,

mmcv/ops/csrc/parrots/iou3d.cpp

@@ -19,16 +19,16 @@ void iou3d_boxes_overlap_bev_forward_impl(const int num_a, const Tensor boxes_a,
                        num_b, boxes_b, ans_overlap);
 }
 
-void iou3d_nms3d_forward_impl(const Tensor boxes, unsigned long long *mask,
-                              int boxes_num, float nms_overlap_thresh) {
-  DISPATCH_DEVICE_IMPL(iou3d_nms3d_forward_impl, boxes, mask, boxes_num,
+void iou3d_nms3d_forward_impl(const Tensor boxes, Tensor &keep,
+                              Tensor &keep_num, float nms_overlap_thresh) {
+  DISPATCH_DEVICE_IMPL(iou3d_nms3d_forward_impl, boxes, keep, keep_num,
                        nms_overlap_thresh);
 }
 
-void iou3d_nms3d_normal_forward_impl(const Tensor boxes,
-                                     unsigned long long *mask, int boxes_num,
+void iou3d_nms3d_normal_forward_impl(const Tensor boxes, Tensor &keep,
+                                     Tensor &keep_num,
                                      float nms_overlap_thresh) {
-  DISPATCH_DEVICE_IMPL(iou3d_nms3d_normal_forward_impl, boxes, mask, boxes_num,
+  DISPATCH_DEVICE_IMPL(iou3d_nms3d_normal_forward_impl, boxes, keep, keep_num,
                        nms_overlap_thresh);
 }
 
@@ -51,41 +51,7 @@ void iou3d_nms3d_forward(Tensor boxes, Tensor keep, Tensor keep_num,
   CHECK_CONTIGUOUS(boxes);
   CHECK_CONTIGUOUS(keep);
 
-  int boxes_num = boxes.size(0);
-  int64_t *keep_data = keep.data_ptr<int64_t>();
-  int64_t *keep_num_data = keep_num.data_ptr<int64_t>();
-
-  const int col_blocks =
-      (boxes_num + THREADS_PER_BLOCK_NMS - 1) / THREADS_PER_BLOCK_NMS;
-
-  Tensor mask =
-      at::empty({boxes_num, col_blocks}, boxes.options().dtype(at::kLong));
-  unsigned long long *mask_data =
-      (unsigned long long *)mask.data_ptr<int64_t>();
-  iou3d_nms3d_forward_impl(boxes, mask_data, boxes_num, nms_overlap_thresh);
-
-  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_cpu(col_blocks);
-  memset(&remv_cpu[0], 0, sizeof(unsigned long long) * col_blocks);
-
-  int num_to_keep = 0;
-
-  for (int i = 0; i < boxes_num; i++) {
-    int nblock = i / THREADS_PER_BLOCK_NMS;
-    int inblock = i % THREADS_PER_BLOCK_NMS;
-
-    if (!(remv_cpu[nblock] & (1ULL << inblock))) {
-      keep_data[num_to_keep++] = i;
-      unsigned long long *p = &mask_host[0] + i * col_blocks;
-      for (int j = nblock; j < col_blocks; j++) {
-        remv_cpu[j] |= p[j];
-      }
-    }
-    *keep_num_data = num_to_keep;
-  }
+  iou3d_nms3d_forward_impl(boxes, keep, keep_num, nms_overlap_thresh);
 }
 
 void iou3d_nms3d_normal_forward(Tensor boxes, Tensor keep, Tensor keep_num,
@@ -96,40 +62,5 @@ void iou3d_nms3d_normal_forward(Tensor boxes, Tensor keep, Tensor keep_num,
   CHECK_CONTIGUOUS(boxes);
   CHECK_CONTIGUOUS(keep);
 
-  int boxes_num = boxes.size(0);
-  int64_t *keep_data = keep.data_ptr<int64_t>();
-  int64_t *keep_num_data = keep_num.data_ptr<int64_t>();
-
-  const int col_blocks =
-      (boxes_num + THREADS_PER_BLOCK_NMS - 1) / THREADS_PER_BLOCK_NMS;
-
-  Tensor mask =
-      at::empty({boxes_num, col_blocks}, boxes.options().dtype(at::kLong));
-  unsigned long long *mask_data =
-      (unsigned long long *)mask.data_ptr<int64_t>();
-  iou3d_nms3d_normal_forward_impl(boxes, mask_data, boxes_num,
-                                  nms_overlap_thresh);
-
-  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_cpu(col_blocks);
-  memset(&remv_cpu[0], 0, sizeof(unsigned long long) * col_blocks);
-  int num_to_keep = 0;
-
-  for (int i = 0; i < boxes_num; i++) {
-    int nblock = i / THREADS_PER_BLOCK_NMS;
-    int inblock = i % THREADS_PER_BLOCK_NMS;
-
-    if (!(remv_cpu[nblock] & (1ULL << inblock))) {
-      keep_data[num_to_keep++] = i;
-      unsigned long long *p = &mask_host[0] + i * col_blocks;
-      for (int j = nblock; j < col_blocks; j++) {
-        remv_cpu[j] |= p[j];
-      }
-    }
-  }
-
-  *keep_num_data = num_to_keep;
+  iou3d_nms3d_normal_forward_impl(boxes, keep, keep_num, nms_overlap_thresh);
 }

mmcv/ops/csrc/pytorch/cuda/cudabind.cpp

@@ -570,14 +570,12 @@ void IoU3DBoxesOverlapBevForwardCUDAKernelLauncher(const int num_a,
                                                    const Tensor boxes_b,
                                                    Tensor ans_overlap);
 
-void IoU3DNMS3DForwardCUDAKernelLauncher(const Tensor boxes,
-                                         unsigned long long* mask,
-                                         int boxes_num,
+void IoU3DNMS3DForwardCUDAKernelLauncher(const Tensor boxes, Tensor& keep,
+                                         Tensor& keep_num,
                                          float nms_overlap_thresh);
 
-void IoU3DNMS3DNormalForwardCUDAKernelLauncher(const Tensor boxes,
-                                               unsigned long long* mask,
-                                               int boxes_num,
+void IoU3DNMS3DNormalForwardCUDAKernelLauncher(const Tensor boxes, Tensor& keep,
+                                               Tensor& keep_num,
                                                float nms_overlap_thresh);
 
 void iou3d_boxes_overlap_bev_forward_cuda(const int num_a, const Tensor boxes_a,
@@ -587,16 +585,16 @@ void iou3d_boxes_overlap_bev_forward_cuda(const int num_a, const Tensor boxes_a,
                                                 ans_overlap);
 };
 
-void iou3d_nms3d_forward_cuda(const Tensor boxes, unsigned long long* mask,
-                              int boxes_num, float nms_overlap_thresh) {
-  IoU3DNMS3DForwardCUDAKernelLauncher(boxes, mask, boxes_num,
+void iou3d_nms3d_forward_cuda(const Tensor boxes, Tensor& keep,
+                              Tensor& keep_num, float nms_overlap_thresh) {
+  IoU3DNMS3DForwardCUDAKernelLauncher(boxes, keep, keep_num,
                                       nms_overlap_thresh);
 };
 
-void iou3d_nms3d_normal_forward_cuda(const Tensor boxes,
-                                     unsigned long long* mask, int boxes_num,
+void iou3d_nms3d_normal_forward_cuda(const Tensor boxes, Tensor& keep,
+                                     Tensor& keep_num,
                                      float nms_overlap_thresh) {
-  IoU3DNMS3DNormalForwardCUDAKernelLauncher(boxes, mask, boxes_num,
+  IoU3DNMS3DNormalForwardCUDAKernelLauncher(boxes, keep, keep_num,
                                             nms_overlap_thresh);
 };
 
@@ -604,11 +602,11 @@ void iou3d_boxes_overlap_bev_forward_impl(const int num_a, const Tensor boxes_a,
                                           const int num_b, const Tensor boxes_b,
                                           Tensor ans_overlap);
 
-void iou3d_nms3d_forward_impl(const Tensor boxes, unsigned long long* mask,
-                              int boxes_num, float nms_overlap_thresh);
+void iou3d_nms3d_forward_impl(const Tensor boxes, Tensor& keep,
+                              Tensor& keep_num, float nms_overlap_thresh);
 
-void iou3d_nms3d_normal_forward_impl(const Tensor boxes,
-                                     unsigned long long* mask, int boxes_num,
+void iou3d_nms3d_normal_forward_impl(const Tensor boxes, Tensor& keep,
+                                     Tensor& keep_num,
                                      float nms_overlap_thresh);
 
 REGISTER_DEVICE_IMPL(iou3d_boxes_overlap_bev_forward_impl, CUDA,

mmcv/ops/csrc/pytorch/cuda/iou3d_cuda.cu

@@ -10,6 +10,7 @@ All Rights Reserved 2019-2020.
 #include <stdio.h>
 
 #include "iou3d_cuda_kernel.cuh"
+#include "nms_cuda_kernel.cuh"
 #include "pytorch_cuda_helper.hpp"
 
 void IoU3DBoxesOverlapBevForwardCUDAKernelLauncher(const int num_a,
@@ -32,36 +33,72 @@ void IoU3DBoxesOverlapBevForwardCUDAKernelLauncher(const int num_a,
   AT_CUDA_CHECK(cudaGetLastError());
 }
 
-void IoU3DNMS3DForwardCUDAKernelLauncher(const Tensor boxes,
-                                         unsigned long long *mask,
-                                         int boxes_num,
+void IoU3DNMS3DForwardCUDAKernelLauncher(const Tensor boxes, Tensor& keep,
+                                         Tensor& keep_num,
                                          float nms_overlap_thresh) {
+  using namespace at::indexing;
   at::cuda::CUDAGuard device_guard(boxes.device());
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
+  int boxes_num = boxes.size(0);
+
+  const int col_blocks =
+      (boxes_num + THREADS_PER_BLOCK_NMS - 1) / THREADS_PER_BLOCK_NMS;
+  Tensor mask =
+      at::empty({boxes_num, col_blocks}, boxes.options().dtype(at::kLong));
+
   dim3 blocks(GET_BLOCKS(boxes_num, THREADS_PER_BLOCK_NMS),
               GET_BLOCKS(boxes_num, THREADS_PER_BLOCK_NMS));
   dim3 threads(THREADS_PER_BLOCK_NMS);
 
   iou3d_nms3d_forward_cuda_kernel<<<blocks, threads, 0, stream>>>(
-      boxes_num, nms_overlap_thresh, boxes.data_ptr<float>(), mask);
+      boxes_num, nms_overlap_thresh, boxes.data_ptr<float>(),
+      (unsigned long long*)mask.data_ptr<int64_t>());
+
+  at::Tensor keep_t = at::zeros(
+      {boxes_num}, boxes.options().dtype(at::kBool).device(at::kCUDA));
+  gather_keep_from_mask<<<1, min(col_blocks, THREADS_PER_BLOCK),
+                          col_blocks * sizeof(unsigned long long), stream>>>(
+      keep_t.data_ptr<bool>(), (unsigned long long*)mask.data_ptr<int64_t>(),
+      boxes_num);
 
+  auto keep_data = keep_t.nonzero().index({Slice(), 0});
+  keep_num.fill_(at::Scalar(keep_data.size(0)));
+  keep.index_put_({Slice(0, keep_data.size(0))}, keep_data);
   AT_CUDA_CHECK(cudaGetLastError());
 }
 
-void IoU3DNMS3DNormalForwardCUDAKernelLauncher(const Tensor boxes,
-                                               unsigned long long *mask,
-                                               int boxes_num,
+void IoU3DNMS3DNormalForwardCUDAKernelLauncher(const Tensor boxes, Tensor& keep,
+                                               Tensor& keep_num,
                                                float nms_overlap_thresh) {
+  using namespace at::indexing;
   at::cuda::CUDAGuard device_guard(boxes.device());
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
+  int boxes_num = boxes.size(0);
+
+  const int col_blocks =
+      (boxes_num + THREADS_PER_BLOCK_NMS - 1) / THREADS_PER_BLOCK_NMS;
+  Tensor mask =
+      at::empty({boxes_num, col_blocks}, boxes.options().dtype(at::kLong));
+
   dim3 blocks(GET_BLOCKS(boxes_num, THREADS_PER_BLOCK_NMS),
               GET_BLOCKS(boxes_num, THREADS_PER_BLOCK_NMS));
   dim3 threads(THREADS_PER_BLOCK_NMS);
 
   iou3d_nms3d_normal_forward_cuda_kernel<<<blocks, threads, 0, stream>>>(
-      boxes_num, nms_overlap_thresh, boxes.data_ptr<float>(), mask);
+      boxes_num, nms_overlap_thresh, boxes.data_ptr<float>(),
+      (unsigned long long*)mask.data_ptr<int64_t>());
+
+  at::Tensor keep_t = at::zeros(
+      {boxes_num}, boxes.options().dtype(at::kBool).device(at::kCUDA));
+  gather_keep_from_mask<<<1, min(col_blocks, THREADS_PER_BLOCK),
+                          col_blocks * sizeof(unsigned long long), stream>>>(
+      keep_t.data_ptr<bool>(), (unsigned long long*)mask.data_ptr<int64_t>(),
+      boxes_num);
 
+  auto keep_data = keep_t.nonzero().index({Slice(), 0});
+  keep_num.fill_(at::Scalar(keep_data.size(0)));
+  keep.index_put_({Slice(0, keep_data.size(0))}, keep_data);
   AT_CUDA_CHECK(cudaGetLastError());
 }

mmcv/ops/csrc/pytorch/iou3d.cpp

@@ -19,16 +19,16 @@ void iou3d_boxes_overlap_bev_forward_impl(const int num_a, const Tensor boxes_a,
                        num_b, boxes_b, ans_overlap);
 }
 
-void iou3d_nms3d_forward_impl(const Tensor boxes, unsigned long long *mask,
-                              int boxes_num, float nms_overlap_thresh) {
-  DISPATCH_DEVICE_IMPL(iou3d_nms3d_forward_impl, boxes, mask, boxes_num,
+void iou3d_nms3d_forward_impl(const Tensor boxes, Tensor &keep,
+                              Tensor &keep_num, float nms_overlap_thresh) {
+  DISPATCH_DEVICE_IMPL(iou3d_nms3d_forward_impl, boxes, keep, keep_num,
                        nms_overlap_thresh);
 }
 
-void iou3d_nms3d_normal_forward_impl(const Tensor boxes,
-                                     unsigned long long *mask, int boxes_num,
+void iou3d_nms3d_normal_forward_impl(const Tensor boxes, Tensor &keep,
+                                     Tensor &keep_num,
                                      float nms_overlap_thresh) {
-  DISPATCH_DEVICE_IMPL(iou3d_nms3d_normal_forward_impl, boxes, mask, boxes_num,
+  DISPATCH_DEVICE_IMPL(iou3d_nms3d_normal_forward_impl, boxes, keep, keep_num,
                        nms_overlap_thresh);
 }
 
@@ -51,41 +51,7 @@ void iou3d_nms3d_forward(Tensor boxes, Tensor keep, Tensor keep_num,
   CHECK_CONTIGUOUS(boxes);
   CHECK_CONTIGUOUS(keep);
 
-  int boxes_num = boxes.size(0);
-  int64_t *keep_data = keep.data_ptr<int64_t>();
-  int64_t *keep_num_data = keep_num.data_ptr<int64_t>();
-
-  const int col_blocks =
-      (boxes_num + THREADS_PER_BLOCK_NMS - 1) / THREADS_PER_BLOCK_NMS;
-
-  Tensor mask =
-      at::empty({boxes_num, col_blocks}, boxes.options().dtype(at::kLong));
-  unsigned long long *mask_data =
-      (unsigned long long *)mask.data_ptr<int64_t>();
-  iou3d_nms3d_forward_impl(boxes, mask_data, boxes_num, nms_overlap_thresh);
-
-  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_cpu(col_blocks);
-  memset(&remv_cpu[0], 0, sizeof(unsigned long long) * col_blocks);
-
-  int num_to_keep = 0;
-
-  for (int i = 0; i < boxes_num; i++) {
-    int nblock = i / THREADS_PER_BLOCK_NMS;
-    int inblock = i % THREADS_PER_BLOCK_NMS;
-
-    if (!(remv_cpu[nblock] & (1ULL << inblock))) {
-      keep_data[num_to_keep++] = i;
-      unsigned long long *p = &mask_host[0] + i * col_blocks;
-      for (int j = nblock; j < col_blocks; j++) {
-        remv_cpu[j] |= p[j];
-      }
-    }
-    *keep_num_data = num_to_keep;
-  }
+  iou3d_nms3d_forward_impl(boxes, keep, keep_num, nms_overlap_thresh);
 }
 
 void iou3d_nms3d_normal_forward(Tensor boxes, Tensor keep, Tensor keep_num,
@@ -96,40 +62,5 @@ void iou3d_nms3d_normal_forward(Tensor boxes, Tensor keep, Tensor keep_num,
   CHECK_CONTIGUOUS(boxes);
   CHECK_CONTIGUOUS(keep);
 
-  int boxes_num = boxes.size(0);
-  int64_t *keep_data = keep.data_ptr<int64_t>();
-  int64_t *keep_num_data = keep_num.data_ptr<int64_t>();
-
-  const int col_blocks =
-      (boxes_num + THREADS_PER_BLOCK_NMS - 1) / THREADS_PER_BLOCK_NMS;
-
-  Tensor mask =
-      at::empty({boxes_num, col_blocks}, boxes.options().dtype(at::kLong));
-  unsigned long long *mask_data =
-      (unsigned long long *)mask.data_ptr<int64_t>();
-  iou3d_nms3d_normal_forward_impl(boxes, mask_data, boxes_num,
-                                  nms_overlap_thresh);
-
-  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_cpu(col_blocks);
-  memset(&remv_cpu[0], 0, sizeof(unsigned long long) * col_blocks);
-  int num_to_keep = 0;
-
-  for (int i = 0; i < boxes_num; i++) {
-    int nblock = i / THREADS_PER_BLOCK_NMS;
-    int inblock = i % THREADS_PER_BLOCK_NMS;
-
-    if (!(remv_cpu[nblock] & (1ULL << inblock))) {
-      keep_data[num_to_keep++] = i;
-      unsigned long long *p = &mask_host[0] + i * col_blocks;
-      for (int j = nblock; j < col_blocks; j++) {
-        remv_cpu[j] |= p[j];
-      }
-    }
-  }
-
-  *keep_num_data = num_to_keep;
+  iou3d_nms3d_normal_forward_impl(boxes, keep, keep_num, nms_overlap_thresh);
 }

mmcv/ops/iou3d.py

@@ -82,11 +82,11 @@ def nms3d(boxes: Tensor, scores: Tensor, iou_threshold: float) -> Tensor:
     order = scores.sort(0, descending=True)[1]
     boxes = boxes[order].contiguous()
 
-    keep = torch.zeros(boxes.size(0), dtype=torch.long)
-    num_out = torch.zeros(size=(), dtype=torch.long)
+    keep = boxes.new_zeros(boxes.size(0), dtype=torch.long)
+    num_out = boxes.new_zeros(size=(), dtype=torch.long)
     ext_module.iou3d_nms3d_forward(
         boxes, keep, num_out, nms_overlap_thresh=iou_threshold)
-    keep = order[keep[:num_out].cuda(boxes.device)].contiguous()
+    keep = order[keep[:num_out].to(boxes.device)].contiguous()
     return keep
 
 
@@ -109,11 +109,11 @@ def nms3d_normal(boxes: Tensor, scores: Tensor,
     order = scores.sort(0, descending=True)[1]
     boxes = boxes[order].contiguous()
 
-    keep = torch.zeros(boxes.size(0), dtype=torch.long)
-    num_out = torch.zeros(size=(), dtype=torch.long)
+    keep = boxes.new_zeros(boxes.size(0), dtype=torch.long)
+    num_out = boxes.new_zeros(size=(), dtype=torch.long)
     ext_module.iou3d_nms3d_normal_forward(
         boxes, keep, num_out, nms_overlap_thresh=iou_threshold)
-    return order[keep[:num_out].cuda(boxes.device)].contiguous()
+    return order[keep[:num_out].to(boxes.device)].contiguous()
 
 
 def _xyxyr2xywhr(boxes: Tensor) -> Tensor:

tests/test_ops/test_iou3d.py

@@ -4,11 +4,16 @@ import pytest
 import torch
 
 from mmcv.ops import boxes_iou3d, boxes_overlap_bev, nms3d, nms3d_normal
+from mmcv.utils import IS_CUDA_AVAILABLE
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_boxes_overlap_bev():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
+])
+def test_boxes_overlap_bev(device):
     np_boxes1 = np.asarray([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 0.0],
                             [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0],
                             [3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 0.0]],
@@ -22,8 +27,8 @@ def test_boxes_overlap_bev():
           (3 + 2 * 2**0.5)], [1.0, 1.0, 1.0], [0.0, 0.0, 0.0]],
         dtype=np.float32)
 
-    boxes1 = torch.from_numpy(np_boxes1).cuda()
-    boxes2 = torch.from_numpy(np_boxes2).cuda()
+    boxes1 = torch.from_numpy(np_boxes1).to(device)
+    boxes2 = torch.from_numpy(np_boxes2).to(device)
 
     # test for 3 boxes
     overlaps = boxes_overlap_bev(boxes1, boxes2)
@@ -37,9 +42,13 @@ def test_boxes_overlap_bev():
         overlaps.cpu().numpy(), np_expect_overlaps.repeat(555, 1), atol=1e-4)
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_boxes_iou3d():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
+])
+def test_boxes_iou3d(device):
     np_boxes1 = np.asarray([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 0.0],
                             [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0],
                             [3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 0.0]],
@@ -53,16 +62,20 @@ def test_boxes_iou3d():
          [0.0, 0.0, 0.0]],
         dtype=np.float32)
 
-    boxes1 = torch.from_numpy(np_boxes1).cuda()
-    boxes2 = torch.from_numpy(np_boxes2).cuda()
+    boxes1 = torch.from_numpy(np_boxes1).to(device)
+    boxes2 = torch.from_numpy(np_boxes2).to(device)
 
     ious = boxes_iou3d(boxes1, boxes2)
     assert np.allclose(ious.cpu().numpy(), np_expect_ious, atol=1e-4)
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_nms3d():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
+])
+def test_nms3d(device):
     # test for 5 boxes
     np_boxes = np.asarray([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 0.0],
                            [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0],
@@ -74,7 +87,7 @@ def test_nms3d():
     np_inds = np.array([1, 0, 3])
     boxes = torch.from_numpy(np_boxes)
     scores = torch.from_numpy(np_scores)
-    inds = nms3d(boxes.cuda(), scores.cuda(), iou_threshold=0.3)
+    inds = nms3d(boxes.to(device), scores.to(device), iou_threshold=0.3)
 
     assert np.allclose(inds.cpu().numpy(), np_inds)
 
@@ -84,14 +97,18 @@ def test_nms3d():
     np_scores = np.random.rand(555).astype(np.float32)
     boxes = torch.from_numpy(np_boxes)
     scores = torch.from_numpy(np_scores)
-    inds = nms3d(boxes.cuda(), scores.cuda(), iou_threshold=0.3)
+    inds = nms3d(boxes.to(device), scores.to(device), iou_threshold=0.3)
 
     assert len(inds.cpu().numpy()) == 176
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_nms3d_normal():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
+])
+def test_nms3d_normal(device):
     # test for 5 boxes
     np_boxes = np.asarray([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 0.0],
                            [2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0],
@@ -103,7 +120,7 @@ def test_nms3d_normal():
     np_inds = np.array([1, 0, 3])
     boxes = torch.from_numpy(np_boxes)
     scores = torch.from_numpy(np_scores)
-    inds = nms3d_normal(boxes.cuda(), scores.cuda(), iou_threshold=0.3)
+    inds = nms3d_normal(boxes.to(device), scores.to(device), iou_threshold=0.3)
 
     assert np.allclose(inds.cpu().numpy(), np_inds)
 
@@ -113,6 +130,6 @@ def test_nms3d_normal():
     np_scores = np.random.rand(555).astype(np.float32)
     boxes = torch.from_numpy(np_boxes)
     scores = torch.from_numpy(np_scores)
-    inds = nms3d_normal(boxes.cuda(), scores.cuda(), iou_threshold=0.3)
+    inds = nms3d_normal(boxes.to(device), scores.to(device), iou_threshold=0.3)
 
     assert len(inds.cpu().numpy()) == 148