Merge branch 'clean_data-ptr' into 'master'

clean c files

See merge request open-mmlab/mmdet.3d!53

.pre-commit-config.yaml

@@ -10,11 +10,12 @@ repos:
   - repo: https://github.com/timothycrosley/isort
     rev: 4.3.21
     hooks:
-        - id: isort
+      - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf
     rev: v0.30.0
     hooks:
       - id: yapf
+
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v2.5.0
     hooks:

mmdet3d/ops/ball_query/src/ball_query.cpp

-#include <torch/serialize/tensor.h>
-#include <vector>
 #include <THC/THC.h>
 #include <cuda.h>
 #include <cuda_runtime_api.h>
 #include <torch/extension.h>
+#include <torch/serialize/tensor.h>
+
+#include <vector>
 
 extern THCState *state;
 
-#define CHECK_CUDA(x) AT_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ")
-#define CHECK_CONTIGUOUS(x) AT_CHECK(x.is_contiguous(), #x, " must be contiguous ")
-#define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x)
+#define CHECK_CUDA(x) \
+  TORCH_CHECK(x.type().is_cuda(), #x, " must be a CUDAtensor ")
+#define CHECK_CONTIGUOUS(x) \
+  TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous ")
+#define CHECK_INPUT(x) \
+  CHECK_CUDA(x);       \
+  CHECK_CONTIGUOUS(x)
 
 int ball_query_wrapper(int b, int n, int m, float radius, int nsample,
-	at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor);
+                       at::Tensor new_xyz_tensor, at::Tensor xyz_tensor,
+                       at::Tensor idx_tensor);
 
 void ball_query_kernel_launcher(int b, int n, int m, float radius, int nsample,
-	const float *xyz, const float *new_xyz, int *idx, cudaStream_t stream);
-
+                                const float *xyz, const float *new_xyz,
+                                int *idx, cudaStream_t stream);
 
 int ball_query_wrapper(int b, int n, int m, float radius, int nsample,
-    at::Tensor new_xyz_tensor, at::Tensor xyz_tensor, at::Tensor idx_tensor) {
-    CHECK_INPUT(new_xyz_tensor);
-    CHECK_INPUT(xyz_tensor);
-    const float *new_xyz = new_xyz_tensor.data<float>();
-    const float *xyz = xyz_tensor.data<float>();
-    int *idx = idx_tensor.data<int>();
-
-    cudaStream_t stream = THCState_getCurrentStream(state);
-    ball_query_kernel_launcher(b, n, m, radius, nsample, new_xyz, xyz, idx, stream);
-    return 1;
+                       at::Tensor new_xyz_tensor, at::Tensor xyz_tensor,
+                       at::Tensor idx_tensor) {
+  CHECK_INPUT(new_xyz_tensor);
+  CHECK_INPUT(xyz_tensor);
+  const float *new_xyz = new_xyz_tensor.data_ptr<float>();
+  const float *xyz = xyz_tensor.data_ptr<float>();
+  int *idx = idx_tensor.data_ptr<int>();
+
+  cudaStream_t stream = THCState_getCurrentStream(state);
+  ball_query_kernel_launcher(b, n, m, radius, nsample, new_xyz, xyz, idx,
+                             stream);
+  return 1;
 }
 
-
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("ball_query_wrapper", &ball_query_wrapper, "ball_query_wrapper");
+  m.def("ball_query_wrapper", &ball_query_wrapper, "ball_query_wrapper");
 }

mmdet3d/ops/ball_query/src/ball_query_cuda.cu

@@ -3,65 +3,70 @@
 #include <stdlib.h>
 
 #define THREADS_PER_BLOCK 256
-#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
 
+__global__ void ball_query_kernel(int b, int n, int m, float radius,
+                                  int nsample,
+                                  const float *__restrict__ new_xyz,
+                                  const float *__restrict__ xyz,
+                                  int *__restrict__ idx) {
+  // new_xyz: (B, M, 3)
+  // xyz: (B, N, 3)
+  // output:
+  //      idx: (B, M, nsample)
+  int bs_idx = blockIdx.y;
+  int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (bs_idx >= b || pt_idx >= m) return;
 
-__global__ void ball_query_kernel(int b, int n, int m, float radius, int nsample,
-    const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) {
-    // new_xyz: (B, M, 3)
-    // xyz: (B, N, 3)
-    // output:
-    //      idx: (B, M, nsample)
-    int bs_idx = blockIdx.y;
-    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
-    if (bs_idx >= b || pt_idx >= m) return;
+  new_xyz += bs_idx * m * 3 + pt_idx * 3;
+  xyz += bs_idx * n * 3;
+  idx += bs_idx * m * nsample + pt_idx * nsample;
 
-    new_xyz += bs_idx * m * 3 + pt_idx * 3;
-    xyz += bs_idx * n * 3;
-    idx += bs_idx * m * nsample + pt_idx * nsample;
+  float radius2 = radius * radius;
+  float new_x = new_xyz[0];
+  float new_y = new_xyz[1];
+  float new_z = new_xyz[2];
 
-    float radius2 = radius * radius;
-    float new_x = new_xyz[0];
-    float new_y = new_xyz[1];
-    float new_z = new_xyz[2];
-
-    int cnt = 0;
-    for (int k = 0; k < n; ++k) {
-        float x = xyz[k * 3 + 0];
-        float y = xyz[k * 3 + 1];
-        float z = xyz[k * 3 + 2];
-        float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z);
-        if (d2 < radius2){
-            if (cnt == 0){
-                for (int l = 0; l < nsample; ++l) {
-                    idx[l] = k;
-                }
-            }
-            idx[cnt] = k;
-            ++cnt;
-            if (cnt >= nsample) break;
+  int cnt = 0;
+  for (int k = 0; k < n; ++k) {
+    float x = xyz[k * 3 + 0];
+    float y = xyz[k * 3 + 1];
+    float z = xyz[k * 3 + 2];
+    float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) +
+               (new_z - z) * (new_z - z);
+    if (d2 < radius2) {
+      if (cnt == 0) {
+        for (int l = 0; l < nsample; ++l) {
+          idx[l] = k;
         }
+      }
+      idx[cnt] = k;
+      ++cnt;
+      if (cnt >= nsample) break;
     }
+  }
 }
 
+void ball_query_kernel_launcher(int b, int n, int m, float radius, int nsample,
+                                const float *new_xyz, const float *xyz,
+                                int *idx, cudaStream_t stream) {
+  // new_xyz: (B, M, 3)
+  // xyz: (B, N, 3)
+  // output:
+  //      idx: (B, M, nsample)
 
-void ball_query_kernel_launcher(int b, int n, int m, float radius, int nsample, \
-    const float *new_xyz, const float *xyz, int *idx, cudaStream_t stream) {
-    // new_xyz: (B, M, 3)
-    // xyz: (B, N, 3)
-    // output:
-    //      idx: (B, M, nsample)
-
-    cudaError_t err;
+  cudaError_t err;
 
-    dim3 blocks(DIVUP(m, THREADS_PER_BLOCK), b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
+  dim3 blocks(DIVUP(m, THREADS_PER_BLOCK),
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
 
-    ball_query_kernel<<<blocks, threads, 0, stream>>>(b, n, m, radius, nsample, new_xyz, xyz, idx);
-    // cudaDeviceSynchronize();  // for using printf in kernel function
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+  ball_query_kernel<<<blocks, threads, 0, stream>>>(b, n, m, radius, nsample,
+                                                    new_xyz, xyz, idx);
+  // cudaDeviceSynchronize();  // for using printf in kernel function
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }

mmdet3d/ops/furthest_point_sample/src/furthest_point_sample.cpp

-#include <torch/serialize/tensor.h>
 #include <ATen/cuda/CUDAContext.h>
-#include <vector>
 #include <THC/THC.h>
 #include <torch/extension.h>
+#include <torch/serialize/tensor.h>
+
+#include <vector>
 
 extern THCState *state;
 
 int furthest_point_sampling_wrapper(int b, int n, int m,
-    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor);
+                                    at::Tensor points_tensor,
+                                    at::Tensor temp_tensor,
+                                    at::Tensor idx_tensor);
 
 void furthest_point_sampling_kernel_launcher(int b, int n, int m,
-    const float *dataset, float *temp, int *idxs, cudaStream_t stream);
-
+                                             const float *dataset, float *temp,
+                                             int *idxs, cudaStream_t stream);
 
 int furthest_point_sampling_wrapper(int b, int n, int m,
-    at::Tensor points_tensor, at::Tensor temp_tensor, at::Tensor idx_tensor) {
-
-    const float *points = points_tensor.data<float>();
-    float *temp = temp_tensor.data<float>();
-    int *idx = idx_tensor.data<int>();
-
-    cudaStream_t stream = THCState_getCurrentStream(state);
-    furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx, stream);
-    return 1;
+                                    at::Tensor points_tensor,
+                                    at::Tensor temp_tensor,
+                                    at::Tensor idx_tensor) {
+  const float *points = points_tensor.data_ptr<float>();
+  float *temp = temp_tensor.data_ptr<float>();
+  int *idx = idx_tensor.data_ptr<int>();
+
+  cudaStream_t stream = THCState_getCurrentStream(state);
+  furthest_point_sampling_kernel_launcher(b, n, m, points, temp, idx, stream);
+  return 1;
 }
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper, "furthest_point_sampling_wrapper");
+  m.def("furthest_point_sampling_wrapper", &furthest_point_sampling_wrapper,
+        "furthest_point_sampling_wrapper");
 }

mmdet3d/ops/furthest_point_sample/src/furthest_point_sample_cuda.cu

@@ -3,179 +3,204 @@
 
 #define TOTAL_THREADS 1024
 #define THREADS_PER_BLOCK 256
-#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
 
 inline int opt_n_threads(int work_size) {
-    const int pow_2 = std::log(static_cast<double>(work_size)) / std::log(2.0);
+  const int pow_2 = std::log(static_cast<double>(work_size)) / std::log(2.0);
 
-    return max(min(1 << pow_2, TOTAL_THREADS), 1);
+  return max(min(1 << pow_2, TOTAL_THREADS), 1);
 }
 
-__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2){
-    const float v1 = dists[idx1], v2 = dists[idx2];
-    const int i1 = dists_i[idx1], i2 = dists_i[idx2];
-    dists[idx1] = max(v1, v2);
-    dists_i[idx1] = v2 > v1 ? i2 : i1;
+__device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i,
+                         int idx1, int idx2) {
+  const float v1 = dists[idx1], v2 = dists[idx2];
+  const int i1 = dists_i[idx1], i2 = dists_i[idx2];
+  dists[idx1] = max(v1, v2);
+  dists_i[idx1] = v2 > v1 ? i2 : i1;
 }
 
 template <unsigned int block_size>
-__global__ void furthest_point_sampling_kernel(int b, int n, int m,
-    const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) {
-    // dataset: (B, N, 3)
-    // tmp: (B, N)
-    // output:
-    //      idx: (B, M)
-
-    if (m <= 0) return;
-    __shared__ float dists[block_size];
-    __shared__ int dists_i[block_size];
-
-    int batch_index = blockIdx.x;
-    dataset += batch_index * n * 3;
-    temp += batch_index * n;
-    idxs += batch_index * m;
-
-    int tid = threadIdx.x;
-    const int stride = block_size;
-
-    int old = 0;
-    if (threadIdx.x == 0)
-    idxs[0] = old;
-
-    __syncthreads();
-    for (int j = 1; j < m; j++) {
+__global__ void furthest_point_sampling_kernel(
+    int b, int n, int m, const float *__restrict__ dataset,
+    float *__restrict__ temp, int *__restrict__ idxs) {
+  // dataset: (B, N, 3)
+  // tmp: (B, N)
+  // output:
+  //      idx: (B, M)
+
+  if (m <= 0) return;
+  __shared__ float dists[block_size];
+  __shared__ int dists_i[block_size];
+
+  int batch_index = blockIdx.x;
+  dataset += batch_index * n * 3;
+  temp += batch_index * n;
+  idxs += batch_index * m;
+
+  int tid = threadIdx.x;
+  const int stride = block_size;
+
+  int old = 0;
+  if (threadIdx.x == 0) idxs[0] = old;
+
+  __syncthreads();
+  for (int j = 1; j < m; j++) {
     int besti = 0;
     float best = -1;
     float x1 = dataset[old * 3 + 0];
     float y1 = dataset[old * 3 + 1];
     float z1 = dataset[old * 3 + 2];
     for (int k = tid; k < n; k += stride) {
-        float x2, y2, z2;
-        x2 = dataset[k * 3 + 0];
-        y2 = dataset[k * 3 + 1];
-        z2 = dataset[k * 3 + 2];
-        // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
-        // if (mag <= 1e-3)
-        // continue;
-
-        float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
-        float d2 = min(d, temp[k]);
-        temp[k] = d2;
-        besti = d2 > best ? k : besti;
-        best = d2 > best ? d2 : best;
+      float x2, y2, z2;
+      x2 = dataset[k * 3 + 0];
+      y2 = dataset[k * 3 + 1];
+      z2 = dataset[k * 3 + 2];
+      // float mag = (x2 * x2) + (y2 * y2) + (z2 * z2);
+      // if (mag <= 1e-3)
+      // continue;
+
+      float d =
+          (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1);
+      float d2 = min(d, temp[k]);
+      temp[k] = d2;
+      besti = d2 > best ? k : besti;
+      best = d2 > best ? d2 : best;
     }
     dists[tid] = best;
     dists_i[tid] = besti;
     __syncthreads();
 
     if (block_size >= 1024) {
-        if (tid < 512) {
-            __update(dists, dists_i, tid, tid + 512);
-        }
-        __syncthreads();
+      if (tid < 512) {
+        __update(dists, dists_i, tid, tid + 512);
+      }
+      __syncthreads();
     }
 
     if (block_size >= 512) {
-        if (tid < 256) {
-            __update(dists, dists_i, tid, tid + 256);
-        }
-        __syncthreads();
+      if (tid < 256) {
+        __update(dists, dists_i, tid, tid + 256);
+      }
+      __syncthreads();
     }
     if (block_size >= 256) {
-        if (tid < 128) {
-            __update(dists, dists_i, tid, tid + 128);
-        }
-        __syncthreads();
+      if (tid < 128) {
+        __update(dists, dists_i, tid, tid + 128);
+      }
+      __syncthreads();
     }
     if (block_size >= 128) {
-        if (tid < 64) {
-            __update(dists, dists_i, tid, tid + 64);
-        }
-        __syncthreads();
+      if (tid < 64) {
+        __update(dists, dists_i, tid, tid + 64);
+      }
+      __syncthreads();
     }
     if (block_size >= 64) {
-        if (tid < 32) {
-            __update(dists, dists_i, tid, tid + 32);
-        }
-        __syncthreads();
+      if (tid < 32) {
+        __update(dists, dists_i, tid, tid + 32);
+      }
+      __syncthreads();
     }
     if (block_size >= 32) {
-        if (tid < 16) {
-            __update(dists, dists_i, tid, tid + 16);
-        }
-        __syncthreads();
+      if (tid < 16) {
+        __update(dists, dists_i, tid, tid + 16);
+      }
+      __syncthreads();
     }
     if (block_size >= 16) {
-        if (tid < 8) {
-            __update(dists, dists_i, tid, tid + 8);
-        }
-        __syncthreads();
+      if (tid < 8) {
+        __update(dists, dists_i, tid, tid + 8);
+      }
+      __syncthreads();
     }
     if (block_size >= 8) {
-        if (tid < 4) {
-            __update(dists, dists_i, tid, tid + 4);
-        }
-        __syncthreads();
+      if (tid < 4) {
+        __update(dists, dists_i, tid, tid + 4);
+      }
+      __syncthreads();
     }
     if (block_size >= 4) {
-        if (tid < 2) {
-            __update(dists, dists_i, tid, tid + 2);
-        }
-        __syncthreads();
+      if (tid < 2) {
+        __update(dists, dists_i, tid, tid + 2);
+      }
+      __syncthreads();
     }
     if (block_size >= 2) {
-        if (tid < 1) {
-            __update(dists, dists_i, tid, tid + 1);
-        }
-        __syncthreads();
+      if (tid < 1) {
+        __update(dists, dists_i, tid, tid + 1);
+      }
+      __syncthreads();
     }
 
     old = dists_i[0];
-    if (tid == 0)
-        idxs[j] = old;
-    }
+    if (tid == 0) idxs[j] = old;
+  }
 }
 
 void furthest_point_sampling_kernel_launcher(int b, int n, int m,
-    const float *dataset, float *temp, int *idxs, cudaStream_t stream) {
-    // dataset: (B, N, 3)
-    // tmp: (B, N)
-    // output:
-    //      idx: (B, M)
-
-    cudaError_t err;
-    unsigned int n_threads = opt_n_threads(n);
-
-    switch (n_threads) {
-        case 1024:
-        furthest_point_sampling_kernel<1024><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 512:
-        furthest_point_sampling_kernel<512><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 256:
-        furthest_point_sampling_kernel<256><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 128:
-        furthest_point_sampling_kernel<128><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 64:
-        furthest_point_sampling_kernel<64><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 32:
-        furthest_point_sampling_kernel<32><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 16:
-        furthest_point_sampling_kernel<16><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 8:
-        furthest_point_sampling_kernel<8><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 4:
-        furthest_point_sampling_kernel<4><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 2:
-        furthest_point_sampling_kernel<2><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        case 1:
-        furthest_point_sampling_kernel<1><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs); break;
-        default:
-        furthest_point_sampling_kernel<512><<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
-    }
-
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+                                             const float *dataset, float *temp,
+                                             int *idxs, cudaStream_t stream) {
+  // dataset: (B, N, 3)
+  // tmp: (B, N)
+  // output:
+  //      idx: (B, M)
+
+  cudaError_t err;
+  unsigned int n_threads = opt_n_threads(n);
+
+  switch (n_threads) {
+    case 1024:
+      furthest_point_sampling_kernel<1024>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 512:
+      furthest_point_sampling_kernel<512>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 256:
+      furthest_point_sampling_kernel<256>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 128:
+      furthest_point_sampling_kernel<128>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 64:
+      furthest_point_sampling_kernel<64>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 32:
+      furthest_point_sampling_kernel<32>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 16:
+      furthest_point_sampling_kernel<16>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 8:
+      furthest_point_sampling_kernel<8>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 4:
+      furthest_point_sampling_kernel<4>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 2:
+      furthest_point_sampling_kernel<2>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    case 1:
+      furthest_point_sampling_kernel<1>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+      break;
+    default:
+      furthest_point_sampling_kernel<512>
+          <<<b, n_threads, 0, stream>>>(b, n, m, dataset, temp, idxs);
+  }
+
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }

mmdet3d/ops/gather_points/src/gather_points.cpp

-#include <torch/serialize/tensor.h>
 #include <ATen/cuda/CUDAContext.h>
-#include <vector>
 #include <THC/THC.h>
 #include <torch/extension.h>
+#include <torch/serialize/tensor.h>
+
+#include <vector>
 
 extern THCState *state;
 
 int gather_points_wrapper(int b, int c, int n, int npoints,
-    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);
+                          at::Tensor points_tensor, at::Tensor idx_tensor,
+                          at::Tensor out_tensor);
 
 void gather_points_kernel_launcher(int b, int c, int n, int npoints,
-    const float *points, const int *idx, float *out, cudaStream_t stream);
-
+                                   const float *points, const int *idx,
+                                   float *out, cudaStream_t stream);
 
 int gather_points_grad_wrapper(int b, int c, int n, int npoints,
-    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);
+                               at::Tensor grad_out_tensor,
+                               at::Tensor idx_tensor,
+                               at::Tensor grad_points_tensor);
 
 void gather_points_grad_kernel_launcher(int b, int c, int n, int npoints,
-    const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream);
+                                        const float *grad_out, const int *idx,
+                                        float *grad_points,
+                                        cudaStream_t stream);
 
 int gather_points_wrapper(int b, int c, int n, int npoints,
-    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor){
-    const float *points = points_tensor.data<float>();
-    const int *idx = idx_tensor.data<int>();
-    float *out = out_tensor.data<float>();
-
-    cudaStream_t stream = THCState_getCurrentStream(state);
-    gather_points_kernel_launcher(b, c, n, npoints, points, idx, out, stream);
-    return 1;
+                          at::Tensor points_tensor, at::Tensor idx_tensor,
+                          at::Tensor out_tensor) {
+  const float *points = points_tensor.data_ptr<float>();
+  const int *idx = idx_tensor.data_ptr<int>();
+  float *out = out_tensor.data_ptr<float>();
+
+  cudaStream_t stream = THCState_getCurrentStream(state);
+  gather_points_kernel_launcher(b, c, n, npoints, points, idx, out, stream);
+  return 1;
 }
 
-
 int gather_points_grad_wrapper(int b, int c, int n, int npoints,
-    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) {
-
-    const float *grad_out = grad_out_tensor.data<float>();
-    const int *idx = idx_tensor.data<int>();
-    float *grad_points = grad_points_tensor.data<float>();
-
-    cudaStream_t stream = THCState_getCurrentStream(state);
-    gather_points_grad_kernel_launcher(b, c, n, npoints, grad_out, idx, grad_points, stream);
-    return 1;
+                               at::Tensor grad_out_tensor,
+                               at::Tensor idx_tensor,
+                               at::Tensor grad_points_tensor) {
+  const float *grad_out = grad_out_tensor.data_ptr<float>();
+  const int *idx = idx_tensor.data_ptr<int>();
+  float *grad_points = grad_points_tensor.data_ptr<float>();
+
+  cudaStream_t stream = THCState_getCurrentStream(state);
+  gather_points_grad_kernel_launcher(b, c, n, npoints, grad_out, idx,
+                                     grad_points, stream);
+  return 1;
 }
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("gather_points_wrapper", &gather_points_wrapper, "gather_points_wrapper");
-    m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper, "gather_points_grad_wrapper");
+  m.def("gather_points_wrapper", &gather_points_wrapper,
+        "gather_points_wrapper");
+  m.def("gather_points_grad_wrapper", &gather_points_grad_wrapper,
+        "gather_points_grad_wrapper");
 }

mmdet3d/ops/gather_points/src/gather_points_cuda.cu

@@ -3,82 +3,92 @@
 
 #define TOTAL_THREADS 1024
 #define THREADS_PER_BLOCK 256
-#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
-
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
 
 __global__ void gather_points_kernel(int b, int c, int n, int m,
-    const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) {
-    // points: (B, C, N)
-    // idx: (B, M)
-    // output:
-    //      out: (B, C, M)
-
-    int bs_idx = blockIdx.z;
-    int c_idx = blockIdx.y;
-    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
-    if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
-
-    out += bs_idx * c * m + c_idx * m + pt_idx;
-    idx += bs_idx * m + pt_idx;
-    points += bs_idx * c * n + c_idx * n;
-    out[0] = points[idx[0]];
+                                     const float *__restrict__ points,
+                                     const int *__restrict__ idx,
+                                     float *__restrict__ out) {
+  // points: (B, C, N)
+  // idx: (B, M)
+  // output:
+  //      out: (B, C, M)
+
+  int bs_idx = blockIdx.z;
+  int c_idx = blockIdx.y;
+  int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
+
+  out += bs_idx * c * m + c_idx * m + pt_idx;
+  idx += bs_idx * m + pt_idx;
+  points += bs_idx * c * n + c_idx * n;
+  out[0] = points[idx[0]];
 }
 
 void gather_points_kernel_launcher(int b, int c, int n, int npoints,
-    const float *points, const int *idx, float *out, cudaStream_t stream) {
-    // points: (B, C, N)
-    // idx: (B, npoints)
-    // output:
-    //      out: (B, C, npoints)
-
-    cudaError_t err;
-    dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
-
-    gather_points_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, points, idx, out);
-
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+                                   const float *points, const int *idx,
+                                   float *out, cudaStream_t stream) {
+  // points: (B, C, N)
+  // idx: (B, npoints)
+  // output:
+  //      out: (B, C, npoints)
+
+  cudaError_t err;
+  dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c,
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
+
+  gather_points_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, points,
+                                                       idx, out);
+
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }
 
-__global__ void gather_points_grad_kernel(int b, int c, int n, int m, const float *__restrict__ grad_out,
-    const int *__restrict__ idx, float *__restrict__ grad_points) {
-    // grad_out: (B, C, M)
-    // idx: (B, M)
-    // output:
-    //      grad_points: (B, C, N)
-
-    int bs_idx = blockIdx.z;
-    int c_idx = blockIdx.y;
-    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
-    if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
-
-    grad_out += bs_idx * c * m + c_idx * m + pt_idx;
-    idx += bs_idx * m + pt_idx;
-    grad_points += bs_idx * c * n + c_idx * n;
-
-    atomicAdd(grad_points + idx[0], grad_out[0]);
+__global__ void gather_points_grad_kernel(int b, int c, int n, int m,
+                                          const float *__restrict__ grad_out,
+                                          const int *__restrict__ idx,
+                                          float *__restrict__ grad_points) {
+  // grad_out: (B, C, M)
+  // idx: (B, M)
+  // output:
+  //      grad_points: (B, C, N)
+
+  int bs_idx = blockIdx.z;
+  int c_idx = blockIdx.y;
+  int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (bs_idx >= b || c_idx >= c || pt_idx >= m) return;
+
+  grad_out += bs_idx * c * m + c_idx * m + pt_idx;
+  idx += bs_idx * m + pt_idx;
+  grad_points += bs_idx * c * n + c_idx * n;
+
+  atomicAdd(grad_points + idx[0], grad_out[0]);
 }
 
 void gather_points_grad_kernel_launcher(int b, int c, int n, int npoints,
-    const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream) {
-    // grad_out: (B, C, npoints)
-    // idx: (B, npoints)
-    // output:
-    //      grad_points: (B, C, N)
-
-    cudaError_t err;
-    dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
-
-    gather_points_grad_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, grad_out, idx, grad_points);
-
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+                                        const float *grad_out, const int *idx,
+                                        float *grad_points,
+                                        cudaStream_t stream) {
+  // grad_out: (B, C, npoints)
+  // idx: (B, npoints)
+  // output:
+  //      grad_points: (B, C, N)
+
+  cudaError_t err;
+  dim3 blocks(DIVUP(npoints, THREADS_PER_BLOCK), c,
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
+
+  gather_points_grad_kernel<<<blocks, threads, 0, stream>>>(
+      b, c, n, npoints, grad_out, idx, grad_points);
+
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }

mmdet3d/ops/group_points/src/group_points.cpp

-#include <torch/serialize/tensor.h>
+#include <THC/THC.h>
 #include <cuda.h>
 #include <cuda_runtime_api.h>
-#include <vector>
-#include <THC/THC.h>
 #include <torch/extension.h>
+#include <torch/serialize/tensor.h>
+
+#include <vector>
 
 extern THCState *state;
 
 int group_points_wrapper(int b, int c, int n, int npoints, int nsample,
-    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor);
+                         at::Tensor points_tensor, at::Tensor idx_tensor,
+                         at::Tensor out_tensor);
 
 void group_points_kernel_launcher(int b, int c, int n, int npoints, int nsample,
-    const float *points, const int *idx, float *out, cudaStream_t stream);
+                                  const float *points, const int *idx,
+                                  float *out, cudaStream_t stream);
 
 int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample,
-    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor);
-
-void group_points_grad_kernel_launcher(int b, int c, int n, int npoints, int nsample,
-    const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream);
+                              at::Tensor grad_out_tensor, at::Tensor idx_tensor,
+                              at::Tensor grad_points_tensor);
 
+void group_points_grad_kernel_launcher(int b, int c, int n, int npoints,
+                                       int nsample, const float *grad_out,
+                                       const int *idx, float *grad_points,
+                                       cudaStream_t stream);
 
 int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample,
-    at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor grad_points_tensor) {
-
-    float *grad_points = grad_points_tensor.data<float>();
-    const int *idx = idx_tensor.data<int>();
-    const float *grad_out = grad_out_tensor.data<float>();
+                              at::Tensor grad_out_tensor, at::Tensor idx_tensor,
+                              at::Tensor grad_points_tensor) {
+  float *grad_points = grad_points_tensor.data_ptr<float>();
+  const int *idx = idx_tensor.data_ptr<int>();
+  const float *grad_out = grad_out_tensor.data_ptr<float>();
 
-    cudaStream_t stream = THCState_getCurrentStream(state);
+  cudaStream_t stream = THCState_getCurrentStream(state);
 
-    group_points_grad_kernel_launcher(b, c, n, npoints, nsample, grad_out, idx, grad_points, stream);
-    return 1;
+  group_points_grad_kernel_launcher(b, c, n, npoints, nsample, grad_out, idx,
+                                    grad_points, stream);
+  return 1;
 }
 
-
 int group_points_wrapper(int b, int c, int n, int npoints, int nsample,
-    at::Tensor points_tensor, at::Tensor idx_tensor, at::Tensor out_tensor) {
-
-    const float *points = points_tensor.data<float>();
-    const int *idx = idx_tensor.data<int>();
-    float *out = out_tensor.data<float>();
+                         at::Tensor points_tensor, at::Tensor idx_tensor,
+                         at::Tensor out_tensor) {
+  const float *points = points_tensor.data_ptr<float>();
+  const int *idx = idx_tensor.data_ptr<int>();
+  float *out = out_tensor.data_ptr<float>();
 
-    cudaStream_t stream = THCState_getCurrentStream(state);
+  cudaStream_t stream = THCState_getCurrentStream(state);
 
-    group_points_kernel_launcher(b, c, n, npoints, nsample, points, idx, out, stream);
-    return 1;
+  group_points_kernel_launcher(b, c, n, npoints, nsample, points, idx, out,
+                               stream);
+  return 1;
 }
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("forward", &group_points_wrapper, "group_points_wrapper");
-    m.def("backward", &group_points_grad_wrapper, "group_points_grad_wrapper");
+  m.def("forward", &group_points_wrapper, "group_points_wrapper");
+  m.def("backward", &group_points_grad_wrapper, "group_points_grad_wrapper");
 }

mmdet3d/ops/group_points/src/group_points_cuda.cu

@@ -2,84 +2,97 @@
 #include <stdlib.h>
 
 #define THREADS_PER_BLOCK 256
-#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
 
-__global__ void group_points_grad_kernel(int b, int c, int n, int npoints, int nsample,
-    const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) {
-    // grad_out: (B, C, npoints, nsample)
-    // idx: (B, npoints, nsample)
-    // output:
-    //      grad_points: (B, C, N)
-    int bs_idx = blockIdx.z;
-    int c_idx = blockIdx.y;
-    int index = blockIdx.x * blockDim.x + threadIdx.x;
-    int pt_idx = index / nsample;
-    if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
+__global__ void group_points_grad_kernel(int b, int c, int n, int npoints,
+                                         int nsample,
+                                         const float *__restrict__ grad_out,
+                                         const int *__restrict__ idx,
+                                         float *__restrict__ grad_points) {
+  // grad_out: (B, C, npoints, nsample)
+  // idx: (B, npoints, nsample)
+  // output:
+  //      grad_points: (B, C, N)
+  int bs_idx = blockIdx.z;
+  int c_idx = blockIdx.y;
+  int index = blockIdx.x * blockDim.x + threadIdx.x;
+  int pt_idx = index / nsample;
+  if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
 
-    int sample_idx = index % nsample;
-    grad_out += bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;
-    idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;
+  int sample_idx = index % nsample;
+  grad_out += bs_idx * c * npoints * nsample + c_idx * npoints * nsample +
+              pt_idx * nsample + sample_idx;
+  idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;
 
-    atomicAdd(grad_points + bs_idx * c * n + c_idx * n + idx[0] , grad_out[0]);
+  atomicAdd(grad_points + bs_idx * c * n + c_idx * n + idx[0], grad_out[0]);
 }
 
-void group_points_grad_kernel_launcher(int b, int c, int n, int npoints, int nsample,
-    const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream) {
-    // grad_out: (B, C, npoints, nsample)
-    // idx: (B, npoints, nsample)
-    // output:
-    //      grad_points: (B, C, N)
-    cudaError_t err;
-    dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
+void group_points_grad_kernel_launcher(int b, int c, int n, int npoints,
+                                       int nsample, const float *grad_out,
+                                       const int *idx, float *grad_points,
+                                       cudaStream_t stream) {
+  // grad_out: (B, C, npoints, nsample)
+  // idx: (B, npoints, nsample)
+  // output:
+  //      grad_points: (B, C, N)
+  cudaError_t err;
+  dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c,
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
 
-    group_points_grad_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, nsample, grad_out, idx, grad_points);
+  group_points_grad_kernel<<<blocks, threads, 0, stream>>>(
+      b, c, n, npoints, nsample, grad_out, idx, grad_points);
 
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }
 
+__global__ void group_points_kernel(int b, int c, int n, int npoints,
+                                    int nsample,
+                                    const float *__restrict__ points,
+                                    const int *__restrict__ idx,
+                                    float *__restrict__ out) {
+  // points: (B, C, N)
+  // idx: (B, npoints, nsample)
+  // output:
+  //      out: (B, C, npoints, nsample)
+  int bs_idx = blockIdx.z;
+  int c_idx = blockIdx.y;
+  int index = blockIdx.x * blockDim.x + threadIdx.x;
+  int pt_idx = index / nsample;
+  if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
 
-__global__ void group_points_kernel(int b, int c, int n, int npoints, int nsample,
-    const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) {
-    // points: (B, C, N)
-    // idx: (B, npoints, nsample)
-    // output:
-    //      out: (B, C, npoints, nsample)
-    int bs_idx = blockIdx.z;
-    int c_idx = blockIdx.y;
-    int index = blockIdx.x * blockDim.x + threadIdx.x;
-    int pt_idx = index / nsample;
-    if (bs_idx >= b || c_idx >= c || pt_idx >= npoints) return;
+  int sample_idx = index % nsample;
 
-    int sample_idx = index % nsample;
+  idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;
+  int in_idx = bs_idx * c * n + c_idx * n + idx[0];
+  int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample +
+                pt_idx * nsample + sample_idx;
 
-    idx += bs_idx * npoints * nsample + pt_idx * nsample + sample_idx;
-    int in_idx = bs_idx * c * n + c_idx * n + idx[0];
-    int out_idx = bs_idx * c * npoints * nsample + c_idx * npoints * nsample + pt_idx * nsample + sample_idx;
-
-    out[out_idx] = points[in_idx];
+  out[out_idx] = points[in_idx];
 }
 
-
 void group_points_kernel_launcher(int b, int c, int n, int npoints, int nsample,
-    const float *points, const int *idx, float *out, cudaStream_t stream) {
-    // points: (B, C, N)
-    // idx: (B, npoints, nsample)
-    // output:
-    //      out: (B, C, npoints, nsample)
-    cudaError_t err;
-    dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
+                                  const float *points, const int *idx,
+                                  float *out, cudaStream_t stream) {
+  // points: (B, C, N)
+  // idx: (B, npoints, nsample)
+  // output:
+  //      out: (B, C, npoints, nsample)
+  cudaError_t err;
+  dim3 blocks(DIVUP(npoints * nsample, THREADS_PER_BLOCK), c,
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
 
-    group_points_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, nsample, points, idx, out);
-    // cudaDeviceSynchronize();  // for using printf in kernel function
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+  group_points_kernel<<<blocks, threads, 0, stream>>>(b, c, n, npoints, nsample,
+                                                      points, idx, out);
+  // cudaDeviceSynchronize();  // for using printf in kernel function
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }

mmdet3d/ops/interpolate/src/interpolate.cpp

-#include <torch/serialize/tensor.h>
-#include <vector>
 #include <THC/THC.h>
+#include <cuda.h>
+#include <cuda_runtime_api.h>
 #include <math.h>
 #include <stdio.h>
 #include <stdlib.h>
-#include <cuda.h>
-#include <cuda_runtime_api.h>
 #include <torch/extension.h>
+#include <torch/serialize/tensor.h>
+
+#include <vector>
 
 extern THCState *state;
 
 void three_nn_wrapper(int b, int n, int m, at::Tensor unknown_tensor,
-  at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor);
+                      at::Tensor known_tensor, at::Tensor dist2_tensor,
+                      at::Tensor idx_tensor);
 
 void three_nn_kernel_launcher(int b, int n, int m, const float *unknown,
-	const float *known, float *dist2, int *idx, cudaStream_t stream);
+                              const float *known, float *dist2, int *idx,
+                              cudaStream_t stream);
 
-
-void three_interpolate_wrapper(int b, int c, int m, int n, at::Tensor points_tensor,
-    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor);
+void three_interpolate_wrapper(int b, int c, int m, int n,
+                               at::Tensor points_tensor, at::Tensor idx_tensor,
+                               at::Tensor weight_tensor, at::Tensor out_tensor);
 
 void three_interpolate_kernel_launcher(int b, int c, int m, int n,
-    const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream);
+                                       const float *points, const int *idx,
+                                       const float *weight, float *out,
+                                       cudaStream_t stream);
 
+void three_interpolate_grad_wrapper(int b, int c, int n, int m,
+                                    at::Tensor grad_out_tensor,
+                                    at::Tensor idx_tensor,
+                                    at::Tensor weight_tensor,
+                                    at::Tensor grad_points_tensor);
 
-void three_interpolate_grad_wrapper(int b, int c, int n, int m, at::Tensor grad_out_tensor,
-    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor grad_points_tensor);
-
-void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m, const float *grad_out,
-    const int *idx, const float *weight, float *grad_points, cudaStream_t stream);
-
+void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m,
+                                            const float *grad_out,
+                                            const int *idx, const float *weight,
+                                            float *grad_points,
+                                            cudaStream_t stream);
 
 void three_nn_wrapper(int b, int n, int m, at::Tensor unknown_tensor,
-    at::Tensor known_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor) {
-    const float *unknown = unknown_tensor.data<float>();
-    const float *known = known_tensor.data<float>();
-    float *dist2 = dist2_tensor.data<float>();
-    int *idx = idx_tensor.data<int>();
-
-    cudaStream_t stream = THCState_getCurrentStream(state);
-    three_nn_kernel_launcher(b, n, m, unknown, known, dist2, idx, stream);
+                      at::Tensor known_tensor, at::Tensor dist2_tensor,
+                      at::Tensor idx_tensor) {
+  const float *unknown = unknown_tensor.data_ptr<float>();
+  const float *known = known_tensor.data_ptr<float>();
+  float *dist2 = dist2_tensor.data_ptr<float>();
+  int *idx = idx_tensor.data_ptr<int>();
+
+  cudaStream_t stream = THCState_getCurrentStream(state);
+  three_nn_kernel_launcher(b, n, m, unknown, known, dist2, idx, stream);
 }
 
-
 void three_interpolate_wrapper(int b, int c, int m, int n,
-                         at::Tensor points_tensor,
-                         at::Tensor idx_tensor,
-                         at::Tensor weight_tensor,
-                         at::Tensor out_tensor) {
-
-    const float *points = points_tensor.data<float>();
-    const float *weight = weight_tensor.data<float>();
-    float *out = out_tensor.data<float>();
-    const int *idx = idx_tensor.data<int>();
-
-    cudaStream_t stream = THCState_getCurrentStream(state);
-    three_interpolate_kernel_launcher(b, c, m, n, points, idx, weight, out, stream);
+                               at::Tensor points_tensor, at::Tensor idx_tensor,
+                               at::Tensor weight_tensor,
+                               at::Tensor out_tensor) {
+  const float *points = points_tensor.data_ptr<float>();
+  const float *weight = weight_tensor.data_ptr<float>();
+  float *out = out_tensor.data_ptr<float>();
+  const int *idx = idx_tensor.data_ptr<int>();
+
+  cudaStream_t stream = THCState_getCurrentStream(state);
+  three_interpolate_kernel_launcher(b, c, m, n, points, idx, weight, out,
+                                    stream);
 }
 
 void three_interpolate_grad_wrapper(int b, int c, int n, int m,
-                            at::Tensor grad_out_tensor,
-                            at::Tensor idx_tensor,
-                            at::Tensor weight_tensor,
-                            at::Tensor grad_points_tensor) {
-
-    const float *grad_out = grad_out_tensor.data<float>();
-    const float *weight = weight_tensor.data<float>();
-    float *grad_points = grad_points_tensor.data<float>();
-    const int *idx = idx_tensor.data<int>();
-
-    cudaStream_t stream = THCState_getCurrentStream(state);
-    three_interpolate_grad_kernel_launcher(b, c, n, m, grad_out, idx, weight, grad_points, stream);
+                                    at::Tensor grad_out_tensor,
+                                    at::Tensor idx_tensor,
+                                    at::Tensor weight_tensor,
+                                    at::Tensor grad_points_tensor) {
+  const float *grad_out = grad_out_tensor.data_ptr<float>();
+  const float *weight = weight_tensor.data_ptr<float>();
+  float *grad_points = grad_points_tensor.data_ptr<float>();
+  const int *idx = idx_tensor.data_ptr<int>();
+
+  cudaStream_t stream = THCState_getCurrentStream(state);
+  three_interpolate_grad_kernel_launcher(b, c, n, m, grad_out, idx, weight,
+                                         grad_points, stream);
 }
 
-
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-    m.def("three_nn_wrapper", &three_nn_wrapper, "three_nn_wrapper");
-    m.def("three_interpolate_wrapper", &three_interpolate_wrapper, "three_interpolate_wrapper");
-    m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper, "three_interpolate_grad_wrapper");
+  m.def("three_nn_wrapper", &three_nn_wrapper, "three_nn_wrapper");
+  m.def("three_interpolate_wrapper", &three_interpolate_wrapper,
+        "three_interpolate_wrapper");
+  m.def("three_interpolate_grad_wrapper", &three_interpolate_grad_wrapper,
+        "three_interpolate_grad_wrapper");
 }

mmdet3d/ops/interpolate/src/three_interpolate_cuda.cu

@@ -3,91 +3,103 @@
 #include <stdlib.h>
 
 #define THREADS_PER_BLOCK 256
-#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
-
-
-__global__ void three_interpolate_kernel(int b, int c, int m, int n, const float *__restrict__ points,
-    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) {
-    // points: (B, C, M)
-    // idx: (B, N, 3)
-    // weight: (B, N, 3)
-    // output:
-    //      out: (B, C, N)
-
-    int bs_idx = blockIdx.z;
-    int c_idx = blockIdx.y;
-    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
-
-    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
-
-    weight += bs_idx * n * 3 + pt_idx * 3;
-    points += bs_idx * c * m + c_idx * m;
-    idx += bs_idx * n * 3 + pt_idx * 3;
-    out += bs_idx * c * n + c_idx * n;
-
-    out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] + weight[2] * points[idx[2]];
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
+
+__global__ void three_interpolate_kernel(int b, int c, int m, int n,
+                                         const float *__restrict__ points,
+                                         const int *__restrict__ idx,
+                                         const float *__restrict__ weight,
+                                         float *__restrict__ out) {
+  // points: (B, C, M)
+  // idx: (B, N, 3)
+  // weight: (B, N, 3)
+  // output:
+  //      out: (B, C, N)
+
+  int bs_idx = blockIdx.z;
+  int c_idx = blockIdx.y;
+  int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+
+  weight += bs_idx * n * 3 + pt_idx * 3;
+  points += bs_idx * c * m + c_idx * m;
+  idx += bs_idx * n * 3 + pt_idx * 3;
+  out += bs_idx * c * n + c_idx * n;
+
+  out[pt_idx] = weight[0] * points[idx[0]] + weight[1] * points[idx[1]] +
+                weight[2] * points[idx[2]];
 }
 
 void three_interpolate_kernel_launcher(int b, int c, int m, int n,
-    const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream) {
-    // points: (B, C, M)
-    // idx: (B, N, 3)
-    // weight: (B, N, 3)
-    // output:
-    //      out: (B, C, N)
-
-    cudaError_t err;
-    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
-    three_interpolate_kernel<<<blocks, threads, 0, stream>>>(b, c, m, n, points, idx, weight, out);
-
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+                                       const float *points, const int *idx,
+                                       const float *weight, float *out,
+                                       cudaStream_t stream) {
+  // points: (B, C, M)
+  // idx: (B, N, 3)
+  // weight: (B, N, 3)
+  // output:
+  //      out: (B, C, N)
+
+  cudaError_t err;
+  dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c,
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
+  three_interpolate_kernel<<<blocks, threads, 0, stream>>>(b, c, m, n, points,
+                                                           idx, weight, out);
+
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }
 
-
-__global__ void three_interpolate_grad_kernel(int b, int c, int n, int m, const float *__restrict__ grad_out,
-    const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) {
-    // grad_out: (B, C, N)
-    // weight: (B, N, 3)
-    // output:
-    //      grad_points: (B, C, M)
-
-    int bs_idx = blockIdx.z;
-    int c_idx = blockIdx.y;
-    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
-
-    if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
-
-    grad_out += bs_idx * c * n + c_idx * n + pt_idx;
-    weight += bs_idx * n * 3 + pt_idx * 3;
-    grad_points += bs_idx * c * m + c_idx * m;
-    idx += bs_idx * n * 3 + pt_idx * 3;
-
-
-    atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]);
-    atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]);
-    atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]);
+__global__ void three_interpolate_grad_kernel(
+    int b, int c, int n, int m, const float *__restrict__ grad_out,
+    const int *__restrict__ idx, const float *__restrict__ weight,
+    float *__restrict__ grad_points) {
+  // grad_out: (B, C, N)
+  // weight: (B, N, 3)
+  // output:
+  //      grad_points: (B, C, M)
+
+  int bs_idx = blockIdx.z;
+  int c_idx = blockIdx.y;
+  int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+  if (bs_idx >= b || c_idx >= c || pt_idx >= n) return;
+
+  grad_out += bs_idx * c * n + c_idx * n + pt_idx;
+  weight += bs_idx * n * 3 + pt_idx * 3;
+  grad_points += bs_idx * c * m + c_idx * m;
+  idx += bs_idx * n * 3 + pt_idx * 3;
+
+  atomicAdd(grad_points + idx[0], grad_out[0] * weight[0]);
+  atomicAdd(grad_points + idx[1], grad_out[0] * weight[1]);
+  atomicAdd(grad_points + idx[2], grad_out[0] * weight[2]);
 }
 
-void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m, const float *grad_out,
-    const int *idx, const float *weight, float *grad_points, cudaStream_t stream) {
-    // grad_out: (B, C, N)
-    // weight: (B, N, 3)
-    // output:
-    //      grad_points: (B, C, M)
-
-    cudaError_t err;
-    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c, b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
-    three_interpolate_grad_kernel<<<blocks, threads, 0, stream>>>(b, c, n, m, grad_out, idx, weight, grad_points);
-
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+void three_interpolate_grad_kernel_launcher(int b, int c, int n, int m,
+                                            const float *grad_out,
+                                            const int *idx, const float *weight,
+                                            float *grad_points,
+                                            cudaStream_t stream) {
+  // grad_out: (B, C, N)
+  // weight: (B, N, 3)
+  // output:
+  //      grad_points: (B, C, M)
+
+  cudaError_t err;
+  dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), c,
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
+  three_interpolate_grad_kernel<<<blocks, threads, 0, stream>>>(
+      b, c, n, m, grad_out, idx, weight, grad_points);
+
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }

mmdet3d/ops/interpolate/src/three_nn_cuda.cu

@@ -3,72 +3,84 @@
 #include <stdlib.h>
 
 #define THREADS_PER_BLOCK 256
-#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))
+#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0))
 
+__global__ void three_nn_kernel(int b, int n, int m,
+                                const float *__restrict__ unknown,
+                                const float *__restrict__ known,
+                                float *__restrict__ dist2,
+                                int *__restrict__ idx) {
+  // unknown: (B, N, 3)
+  // known: (B, M, 3)
+  // output:
+  //      dist2: (B, N, 3)
+  //      idx: (B, N, 3)
 
-__global__ void three_nn_kernel(int b, int n, int m, const float *__restrict__ unknown,
-    const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) {
-    // unknown: (B, N, 3)
-    // known: (B, M, 3)
-    // output:
-    //      dist2: (B, N, 3)
-    //      idx: (B, N, 3)
+  int bs_idx = blockIdx.y;
+  int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
+  if (bs_idx >= b || pt_idx >= n) return;
 
-    int bs_idx = blockIdx.y;
-    int pt_idx = blockIdx.x * blockDim.x + threadIdx.x;
-    if (bs_idx >= b || pt_idx >= n) return;
+  unknown += bs_idx * n * 3 + pt_idx * 3;
+  known += bs_idx * m * 3;
+  dist2 += bs_idx * n * 3 + pt_idx * 3;
+  idx += bs_idx * n * 3 + pt_idx * 3;
 
-    unknown += bs_idx * n * 3 + pt_idx * 3;
-    known += bs_idx * m * 3;
-    dist2 += bs_idx * n * 3 + pt_idx * 3;
-    idx += bs_idx * n * 3 + pt_idx * 3;
+  float ux = unknown[0];
+  float uy = unknown[1];
+  float uz = unknown[2];
 
-    float ux = unknown[0];
-    float uy = unknown[1];
-    float uz = unknown[2];
-
-    double best1 = 1e40, best2 = 1e40, best3 = 1e40;
-    int besti1 = 0, besti2 = 0, besti3 = 0;
-    for (int k = 0; k < m; ++k) {
-        float x = known[k * 3 + 0];
-        float y = known[k * 3 + 1];
-        float z = known[k * 3 + 2];
-        float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
-        if (d < best1) {
-            best3 = best2; besti3 = besti2;
-            best2 = best1; besti2 = besti1;
-            best1 = d; besti1 = k;
-        }
-        else if (d < best2) {
-            best3 = best2; besti3 = besti2;
-            best2 = d; besti2 = k;
-        }
-        else if (d < best3) {
-            best3 = d; besti3 = k;
-        }
+  double best1 = 1e40, best2 = 1e40, best3 = 1e40;
+  int besti1 = 0, besti2 = 0, besti3 = 0;
+  for (int k = 0; k < m; ++k) {
+    float x = known[k * 3 + 0];
+    float y = known[k * 3 + 1];
+    float z = known[k * 3 + 2];
+    float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z);
+    if (d < best1) {
+      best3 = best2;
+      besti3 = besti2;
+      best2 = best1;
+      besti2 = besti1;
+      best1 = d;
+      besti1 = k;
+    } else if (d < best2) {
+      best3 = best2;
+      besti3 = besti2;
+      best2 = d;
+      besti2 = k;
+    } else if (d < best3) {
+      best3 = d;
+      besti3 = k;
     }
-    dist2[0] = best1; dist2[1] = best2; dist2[2] = best3;
-    idx[0] = besti1; idx[1] = besti2; idx[2] = besti3;
+  }
+  dist2[0] = best1;
+  dist2[1] = best2;
+  dist2[2] = best3;
+  idx[0] = besti1;
+  idx[1] = besti2;
+  idx[2] = besti3;
 }
 
-
 void three_nn_kernel_launcher(int b, int n, int m, const float *unknown,
-    const float *known, float *dist2, int *idx, cudaStream_t stream) {
-    // unknown: (B, N, 3)
-    // known: (B, M, 3)
-    // output:
-    //      dist2: (B, N, 3)
-    //      idx: (B, N, 3)
+                              const float *known, float *dist2, int *idx,
+                              cudaStream_t stream) {
+  // unknown: (B, N, 3)
+  // known: (B, M, 3)
+  // output:
+  //      dist2: (B, N, 3)
+  //      idx: (B, N, 3)
 
-    cudaError_t err;
-    dim3 blocks(DIVUP(n, THREADS_PER_BLOCK), b);  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK);
+  cudaError_t err;
+  dim3 blocks(DIVUP(n, THREADS_PER_BLOCK),
+              b);  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK);
 
-    three_nn_kernel<<<blocks, threads, 0, stream>>>(b, n, m, unknown, known, dist2, idx);
+  three_nn_kernel<<<blocks, threads, 0, stream>>>(b, n, m, unknown, known,
+                                                  dist2, idx);
 
-    err = cudaGetLastError();
-    if (cudaSuccess != err) {
-        fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
-        exit(-1);
-    }
+  err = cudaGetLastError();
+  if (cudaSuccess != err) {
+    fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err));
+    exit(-1);
+  }
 }

mmdet3d/ops/iou3d/src/iou3d_kernel.cu

@@ -6,376 +6,425 @@
 const int THREADS_PER_BLOCK_NMS = sizeof(unsigned long long) * 8;
 const float EPS = 1e-8;
 struct Point {
-    float x, y;
-    __device__ Point() {}
-    __device__ Point(double _x, double _y){
-        x = _x, y = _y;
-    }
-
-    __device__ void set(float _x, float _y){
-        x = _x; y = _y;
-    }
-
-    __device__ Point operator +(const Point &b)const{
-        return Point(x + b.x, y + b.y);
-    }
-
-    __device__ Point operator -(const Point &b)const{
-        return Point(x - b.x, y - b.y);
-    }
+  float x, y;
+  __device__ Point() {}
+  __device__ Point(double _x, double _y) { x = _x, y = _y; }
+
+  __device__ void set(float _x, float _y) {
+    x = _x;
+    y = _y;
+  }
+
+  __device__ Point operator+(const Point &b) const {
+    return Point(x + b.x, y + b.y);
+  }
+
+  __device__ Point operator-(const Point &b) const {
+    return Point(x - b.x, y - b.y);
+  }
 };
 
-__device__ inline float cross(const Point &a, const Point &b){
-    return a.x * b.y - a.y * b.x;
+__device__ inline float cross(const Point &a, const Point &b) {
+  return a.x * b.y - a.y * b.x;
 }
 
-__device__ inline float cross(const Point &p1, const Point &p2, const Point &p0){
-    return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
+__device__ inline float cross(const Point &p1, const Point &p2,
+                              const Point &p0) {
+  return (p1.x - p0.x) * (p2.y - p0.y) - (p2.x - p0.x) * (p1.y - p0.y);
 }
 
-__device__ int check_rect_cross(const Point &p1, const Point &p2, const Point &q1, const Point &q2){
-    int ret = min(p1.x,p2.x) <= max(q1.x,q2.x)  &&
-              min(q1.x,q2.x) <= max(p1.x,p2.x) &&
-              min(p1.y,p2.y) <= max(q1.y,q2.y) &&
-              min(q1.y,q2.y) <= max(p1.y,p2.y);
-    return ret;
+__device__ int check_rect_cross(const Point &p1, const Point &p2,
+                                const Point &q1, const Point &q2) {
+  int ret = min(p1.x, p2.x) <= max(q1.x, q2.x) &&
+            min(q1.x, q2.x) <= max(p1.x, p2.x) &&
+            min(p1.y, p2.y) <= max(q1.y, q2.y) &&
+            min(q1.y, q2.y) <= max(p1.y, p2.y);
+  return ret;
 }
 
-__device__ inline int check_in_box2d(const float *box, const Point &p){
-    //params: box (5) [x1, y1, x2, y2, angle]
-    const float MARGIN = 1e-5;
-
-    float center_x = (box[0] + box[2]) / 2;
-    float center_y = (box[1] + box[3]) / 2;
-    float angle_cos = cos(-box[4]), angle_sin = sin(-box[4]);  // rotate the point in the opposite direction of box
-    float rot_x = (p.x - center_x) * angle_cos + (p.y - center_y) * angle_sin + center_x;
-    float rot_y = -(p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos + center_y;
+__device__ inline int check_in_box2d(const float *box, const Point &p) {
+  // params: box (5) [x1, y1, x2, y2, angle]
+  const float MARGIN = 1e-5;
+
+  float center_x = (box[0] + box[2]) / 2;
+  float center_y = (box[1] + box[3]) / 2;
+  float angle_cos = cos(-box[4]),
+        angle_sin =
+            sin(-box[4]);  // rotate the point in the opposite direction of box
+  float rot_x =
+      (p.x - center_x) * angle_cos + (p.y - center_y) * angle_sin + center_x;
+  float rot_y =
+      -(p.x - center_x) * angle_sin + (p.y - center_y) * angle_cos + center_y;
 #ifdef DEBUG
-    printf("box: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", box[0], box[1], box[2], box[3], box[4]);
-    printf("center: (%.3f, %.3f), cossin(%.3f, %.3f), src(%.3f, %.3f), rot(%.3f, %.3f)\n", center_x, center_y,
-            angle_cos, angle_sin, p.x, p.y, rot_x, rot_y);
+  printf("box: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", box[0], box[1], box[2],
+         box[3], box[4]);
+  printf(
+      "center: (%.3f, %.3f), cossin(%.3f, %.3f), src(%.3f, %.3f), rot(%.3f, "
+      "%.3f)\n",
+      center_x, center_y, angle_cos, angle_sin, p.x, p.y, rot_x, rot_y);
 #endif
-    return (rot_x > box[0] - MARGIN && rot_x < box[2] + MARGIN && rot_y > box[1] - MARGIN && rot_y < box[3] + MARGIN);
+  return (rot_x > box[0] - MARGIN && rot_x < box[2] + MARGIN &&
+          rot_y > box[1] - MARGIN && rot_y < box[3] + MARGIN);
 }
 
-__device__ inline int intersection(const Point &p1, const Point &p0, const Point &q1, const Point &q0, Point &ans){
-    // fast exclusion
-    if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
+__device__ inline int intersection(const Point &p1, const Point &p0,
+                                   const Point &q1, const Point &q0,
+                                   Point &ans) {
+  // fast exclusion
+  if (check_rect_cross(p0, p1, q0, q1) == 0) return 0;
 
-    // check cross standing
-    float s1 = cross(q0, p1, p0);
-    float s2 = cross(p1, q1, p0);
-    float s3 = cross(p0, q1, q0);
-    float s4 = cross(q1, p1, q0);
+  // check cross standing
+  float s1 = cross(q0, p1, p0);
+  float s2 = cross(p1, q1, p0);
+  float s3 = cross(p0, q1, q0);
+  float s4 = cross(q1, p1, q0);
 
-    if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
+  if (!(s1 * s2 > 0 && s3 * s4 > 0)) return 0;
 
-    // calculate intersection of two lines
-    float s5 = cross(q1, p1, p0);
-    if(fabs(s5 - s1) > EPS){
-        ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
-        ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
+  // calculate intersection of two lines
+  float s5 = cross(q1, p1, p0);
+  if (fabs(s5 - s1) > EPS) {
+    ans.x = (s5 * q0.x - s1 * q1.x) / (s5 - s1);
+    ans.y = (s5 * q0.y - s1 * q1.y) / (s5 - s1);
 
-    }
-    else{
-        float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
-        float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
-        float D = a0 * b1 - a1 * b0;
+  } else {
+    float a0 = p0.y - p1.y, b0 = p1.x - p0.x, c0 = p0.x * p1.y - p1.x * p0.y;
+    float a1 = q0.y - q1.y, b1 = q1.x - q0.x, c1 = q0.x * q1.y - q1.x * q0.y;
+    float D = a0 * b1 - a1 * b0;
 
-        ans.x = (b0 * c1 - b1 * c0) / D;
-        ans.y = (a1 * c0 - a0 * c1) / D;
-    }
+    ans.x = (b0 * c1 - b1 * c0) / D;
+    ans.y = (a1 * c0 - a0 * c1) / D;
+  }
 
-    return 1;
+  return 1;
 }
 
-__device__ inline void rotate_around_center(const Point &center, const float angle_cos, const float angle_sin, Point &p){
-    float new_x = (p.x - center.x) * angle_cos + (p.y - center.y) * angle_sin + center.x;
-    float new_y = -(p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
-    p.set(new_x, new_y);
+__device__ inline void rotate_around_center(const Point &center,
+                                            const float angle_cos,
+                                            const float angle_sin, Point &p) {
+  float new_x =
+      (p.x - center.x) * angle_cos + (p.y - center.y) * angle_sin + center.x;
+  float new_y =
+      -(p.x - center.x) * angle_sin + (p.y - center.y) * angle_cos + center.y;
+  p.set(new_x, new_y);
 }
 
-__device__ inline int point_cmp(const Point &a, const Point &b, const Point &center){
-    return atan2(a.y - center.y, a.x - center.x) > atan2(b.y - center.y, b.x - center.x);
+__device__ inline int point_cmp(const Point &a, const Point &b,
+                                const Point &center) {
+  return atan2(a.y - center.y, a.x - center.x) >
+         atan2(b.y - center.y, b.x - center.x);
 }
 
-__device__ inline float box_overlap(const float *box_a, const float *box_b){
-    // params: box_a (5) [x1, y1, x2, y2, angle]
-    // params: box_b (5) [x1, y1, x2, y2, angle]
+__device__ inline float box_overlap(const float *box_a, const float *box_b) {
+  // params: box_a (5) [x1, y1, x2, y2, angle]
+  // params: box_b (5) [x1, y1, x2, y2, angle]
 
-    float a_x1 = box_a[0], a_y1 = box_a[1], a_x2 = box_a[2], a_y2 = box_a[3], a_angle = box_a[4];
-    float b_x1 = box_b[0], b_y1 = box_b[1], b_x2 = box_b[2], b_y2 = box_b[3], b_angle = box_b[4];
+  float a_x1 = box_a[0], a_y1 = box_a[1], a_x2 = box_a[2], a_y2 = box_a[3],
+        a_angle = box_a[4];
+  float b_x1 = box_b[0], b_y1 = box_b[1], b_x2 = box_b[2], b_y2 = box_b[3],
+        b_angle = box_b[4];
 
-    Point center_a((a_x1 + a_x2) / 2, (a_y1 + a_y2) / 2);
-    Point center_b((b_x1 + b_x2) / 2, (b_y1 + b_y2) / 2);
+  Point center_a((a_x1 + a_x2) / 2, (a_y1 + a_y2) / 2);
+  Point center_b((b_x1 + b_x2) / 2, (b_y1 + b_y2) / 2);
 #ifdef DEBUG
-    printf("a: (%.3f, %.3f, %.3f, %.3f, %.3f), b: (%.3f, %.3f, %.3f, %.3f, %.3f)\n", a_x1, a_y1, a_x2, a_y2, a_angle,
-           b_x1, b_y1, b_x2, b_y2, b_angle);
-    printf("center a: (%.3f, %.3f), b: (%.3f, %.3f)\n", center_a.x, center_a.y, center_b.x, center_b.y);
+  printf(
+      "a: (%.3f, %.3f, %.3f, %.3f, %.3f), b: (%.3f, %.3f, %.3f, %.3f, %.3f)\n",
+      a_x1, a_y1, a_x2, a_y2, a_angle, b_x1, b_y1, b_x2, b_y2, b_angle);
+  printf("center a: (%.3f, %.3f), b: (%.3f, %.3f)\n", center_a.x, center_a.y,
+         center_b.x, center_b.y);
 #endif
 
-    Point box_a_corners[5];
-    box_a_corners[0].set(a_x1, a_y1);
-    box_a_corners[1].set(a_x2, a_y1);
-    box_a_corners[2].set(a_x2, a_y2);
-    box_a_corners[3].set(a_x1, a_y2);
+  Point box_a_corners[5];
+  box_a_corners[0].set(a_x1, a_y1);
+  box_a_corners[1].set(a_x2, a_y1);
+  box_a_corners[2].set(a_x2, a_y2);
+  box_a_corners[3].set(a_x1, a_y2);
 
-    Point box_b_corners[5];
-    box_b_corners[0].set(b_x1, b_y1);
-    box_b_corners[1].set(b_x2, b_y1);
-    box_b_corners[2].set(b_x2, b_y2);
-    box_b_corners[3].set(b_x1, b_y2);
+  Point box_b_corners[5];
+  box_b_corners[0].set(b_x1, b_y1);
+  box_b_corners[1].set(b_x2, b_y1);
+  box_b_corners[2].set(b_x2, b_y2);
+  box_b_corners[3].set(b_x1, b_y2);
 
-    // get oriented corners
-    float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
-    float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
+  // get oriented corners
+  float a_angle_cos = cos(a_angle), a_angle_sin = sin(a_angle);
+  float b_angle_cos = cos(b_angle), b_angle_sin = sin(b_angle);
 
-    for (int k = 0; k < 4; k++){
+  for (int k = 0; k < 4; k++) {
 #ifdef DEBUG
-        printf("before corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
+    printf("before corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k,
+           box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x,
+           box_b_corners[k].y);
 #endif
-        rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
-        rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
+    rotate_around_center(center_a, a_angle_cos, a_angle_sin, box_a_corners[k]);
+    rotate_around_center(center_b, b_angle_cos, b_angle_sin, box_b_corners[k]);
 #ifdef DEBUG
-        printf("corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x, box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
+    printf("corner %d: a(%.3f, %.3f), b(%.3f, %.3f) \n", k, box_a_corners[k].x,
+           box_a_corners[k].y, box_b_corners[k].x, box_b_corners[k].y);
 #endif
+  }
+
+  box_a_corners[4] = box_a_corners[0];
+  box_b_corners[4] = box_b_corners[0];
+
+  // get intersection of lines
+  Point cross_points[16];
+  Point poly_center;
+  int cnt = 0, flag = 0;
+
+  poly_center.set(0, 0);
+  for (int i = 0; i < 4; i++) {
+    for (int j = 0; j < 4; j++) {
+      flag = intersection(box_a_corners[i + 1], box_a_corners[i],
+                          box_b_corners[j + 1], box_b_corners[j],
+                          cross_points[cnt]);
+      if (flag) {
+        poly_center = poly_center + cross_points[cnt];
+        cnt++;
+      }
     }
-
-    box_a_corners[4] = box_a_corners[0];
-    box_b_corners[4] = box_b_corners[0];
-
-    // get intersection of lines
-    Point cross_points[16];
-    Point poly_center;
-    int cnt = 0, flag = 0;
-
-    poly_center.set(0, 0);
-    for (int i = 0; i < 4; i++){
-        for (int j = 0; j < 4; j++){
-            flag = intersection(box_a_corners[i + 1], box_a_corners[i], box_b_corners[j + 1], box_b_corners[j], cross_points[cnt]);
-            if (flag){
-                poly_center = poly_center + cross_points[cnt];
-                cnt++;
-            }
-        }
+  }
+
+  // check corners
+  for (int k = 0; k < 4; k++) {
+    if (check_in_box2d(box_a, box_b_corners[k])) {
+      poly_center = poly_center + box_b_corners[k];
+      cross_points[cnt] = box_b_corners[k];
+      cnt++;
     }
-
-    // check corners
-    for (int k = 0; k < 4; k++){
-        if (check_in_box2d(box_a, box_b_corners[k])){
-            poly_center = poly_center + box_b_corners[k];
-            cross_points[cnt] = box_b_corners[k];
-            cnt++;
-        }
-        if (check_in_box2d(box_b, box_a_corners[k])){
-            poly_center = poly_center + box_a_corners[k];
-            cross_points[cnt] = box_a_corners[k];
-            cnt++;
-        }
+    if (check_in_box2d(box_b, box_a_corners[k])) {
+      poly_center = poly_center + box_a_corners[k];
+      cross_points[cnt] = box_a_corners[k];
+      cnt++;
     }
-
-    poly_center.x /= cnt;
-    poly_center.y /= cnt;
-
-    // sort the points of polygon
-    Point temp;
-    for (int j = 0; j < cnt - 1; j++){
-        for (int i = 0; i < cnt - j - 1; i++){
-            if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)){
-                temp = cross_points[i];
-                cross_points[i] = cross_points[i + 1];
-                cross_points[i + 1] = temp;
-            }
-        }
+  }
+
+  poly_center.x /= cnt;
+  poly_center.y /= cnt;
+
+  // sort the points of polygon
+  Point temp;
+  for (int j = 0; j < cnt - 1; j++) {
+    for (int i = 0; i < cnt - j - 1; i++) {
+      if (point_cmp(cross_points[i], cross_points[i + 1], poly_center)) {
+        temp = cross_points[i];
+        cross_points[i] = cross_points[i + 1];
+        cross_points[i + 1] = temp;
+      }
     }
+  }
 
 #ifdef DEBUG
-    printf("cnt=%d\n", cnt);
-    for (int i = 0; i < cnt; i++){
-        printf("All cross point %d: (%.3f, %.3f)\n", i, cross_points[i].x, cross_points[i].y);
-    }
+  printf("cnt=%d\n", cnt);
+  for (int i = 0; i < cnt; i++) {
+    printf("All cross point %d: (%.3f, %.3f)\n", i, cross_points[i].x,
+           cross_points[i].y);
+  }
 #endif
 
-    // get the overlap areas
-    float area = 0;
-    for (int k = 0; k < cnt - 1; k++){
-        area += cross(cross_points[k] - cross_points[0], cross_points[k + 1] - cross_points[0]);
-    }
+  // get the overlap areas
+  float area = 0;
+  for (int k = 0; k < cnt - 1; k++) {
+    area += cross(cross_points[k] - cross_points[0],
+                  cross_points[k + 1] - cross_points[0]);
+  }
 
-    return fabs(area) / 2.0;
+  return fabs(area) / 2.0;
 }
 
-__device__ inline float iou_bev(const float *box_a, const float *box_b){
-    // params: box_a (5) [x1, y1, x2, y2, angle]
-    // params: box_b (5) [x1, y1, x2, y2, angle]
-    float sa = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1]);
-    float sb = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1]);
-    float s_overlap = box_overlap(box_a, box_b);
-    return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
+__device__ inline float iou_bev(const float *box_a, const float *box_b) {
+  // params: box_a (5) [x1, y1, x2, y2, angle]
+  // params: box_b (5) [x1, y1, x2, y2, angle]
+  float sa = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1]);
+  float sb = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1]);
+  float s_overlap = box_overlap(box_a, box_b);
+  return s_overlap / fmaxf(sa + sb - s_overlap, EPS);
 }
 
-__global__ void boxes_overlap_kernel(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
-    const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
-    const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
-
-    if (a_idx >= num_a || b_idx >= num_b){
-        return;
-    }
-    const float * cur_box_a = boxes_a + a_idx * 5;
-    const float * cur_box_b = boxes_b + b_idx * 5;
-    float s_overlap = box_overlap(cur_box_a, cur_box_b);
-    ans_overlap[a_idx * num_b + b_idx] = s_overlap;
+__global__ void boxes_overlap_kernel(const int num_a, const float *boxes_a,
+                                     const int num_b, const float *boxes_b,
+                                     float *ans_overlap) {
+  const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+  const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+
+  if (a_idx >= num_a || b_idx >= num_b) {
+    return;
+  }
+  const float *cur_box_a = boxes_a + a_idx * 5;
+  const float *cur_box_b = boxes_b + b_idx * 5;
+  float s_overlap = box_overlap(cur_box_a, cur_box_b);
+  ans_overlap[a_idx * num_b + b_idx] = s_overlap;
 }
 
-__global__ void boxes_iou_bev_kernel(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou){
-    const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
-    const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
+__global__ void boxes_iou_bev_kernel(const int num_a, const float *boxes_a,
+                                     const int num_b, const float *boxes_b,
+                                     float *ans_iou) {
+  const int a_idx = blockIdx.y * THREADS_PER_BLOCK + threadIdx.y;
+  const int b_idx = blockIdx.x * THREADS_PER_BLOCK + threadIdx.x;
 
-    if (a_idx >= num_a || b_idx >= num_b){
-        return;
-    }
+  if (a_idx >= num_a || b_idx >= num_b) {
+    return;
+  }
 
-    const float * cur_box_a = boxes_a + a_idx * 5;
-    const float * cur_box_b = boxes_b + b_idx * 5;
-    float cur_iou_bev = iou_bev(cur_box_a, cur_box_b);
-    ans_iou[a_idx * num_b + b_idx] = cur_iou_bev;
+  const float *cur_box_a = boxes_a + a_idx * 5;
+  const float *cur_box_b = boxes_b + b_idx * 5;
+  float cur_iou_bev = iou_bev(cur_box_a, cur_box_b);
+  ans_iou[a_idx * num_b + b_idx] = cur_iou_bev;
 }
 
 __global__ void nms_kernel(const int boxes_num, const float nms_overlap_thresh,
-                           const float *boxes, unsigned long long *mask){
-    //params: boxes (N, 5) [x1, y1, x2, y2, ry]
-    //params: mask (N, N/THREADS_PER_BLOCK_NMS)
-
-    const int row_start = blockIdx.y;
-    const int col_start = blockIdx.x;
-
-    // if (row_start > col_start) return;
-
-    const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
-    const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
-
-    __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
-
-    if (threadIdx.x < col_size) {
-        block_boxes[threadIdx.x * 5 + 0] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
-        block_boxes[threadIdx.x * 5 + 1] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
-        block_boxes[threadIdx.x * 5 + 2] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
-        block_boxes[threadIdx.x * 5 + 3] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
-        block_boxes[threadIdx.x * 5 + 4] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
+                           const float *boxes, unsigned long long *mask) {
+  // params: boxes (N, 5) [x1, y1, x2, y2, ry]
+  // params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  // if (row_start > col_start) return;
+
+  const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+  const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+
+  __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
+
+  if (threadIdx.x < col_size) {
+    block_boxes[threadIdx.x * 5 + 0] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
+    block_boxes[threadIdx.x * 5 + 1] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
+    block_boxes[threadIdx.x * 5 + 2] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
+    block_boxes[threadIdx.x * 5 + 3] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
+    block_boxes[threadIdx.x * 5 + 4] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size) {
+    const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+    const float *cur_box = boxes + cur_box_idx * 5;
+
+    int i = 0;
+    unsigned long long t = 0;
+    int start = 0;
+    if (row_start == col_start) {
+      start = threadIdx.x + 1;
     }
-    __syncthreads();
-
-    if (threadIdx.x < row_size) {
-        const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
-        const float *cur_box = boxes + cur_box_idx * 5;
-
-        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 (iou_bev(cur_box, block_boxes + i * 5) > nms_overlap_thresh){
-                t |= 1ULL << i;
-            }
-        }
-        const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
-        mask[cur_box_idx * col_blocks + col_start] = t;
+    for (i = start; i < col_size; i++) {
+      if (iou_bev(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
+        t |= 1ULL << i;
+      }
     }
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+    mask[cur_box_idx * col_blocks + col_start] = t;
+  }
 }
 
-
-__device__ inline float iou_normal(float const * const a, float const * const b) {
-    float left = fmaxf(a[0], b[0]), right = fminf(a[2], b[2]);
-    float top = fmaxf(a[1], b[1]), bottom = fminf(a[3], b[3]);
-    float width = fmaxf(right - left, 0.f), height = fmaxf(bottom - top, 0.f);
-    float interS = width * height;
-    float Sa = (a[2] - a[0]) * (a[3] - a[1]);
-    float Sb = (b[2] - b[0]) * (b[3] - b[1]);
-    return interS / fmaxf(Sa + Sb - interS, EPS);
+__device__ inline float iou_normal(float const *const a, float const *const b) {
+  float left = fmaxf(a[0], b[0]), right = fminf(a[2], b[2]);
+  float top = fmaxf(a[1], b[1]), bottom = fminf(a[3], b[3]);
+  float width = fmaxf(right - left, 0.f), height = fmaxf(bottom - top, 0.f);
+  float interS = width * height;
+  float Sa = (a[2] - a[0]) * (a[3] - a[1]);
+  float Sb = (b[2] - b[0]) * (b[3] - b[1]);
+  return interS / fmaxf(Sa + Sb - interS, EPS);
 }
 
-
-__global__ void nms_normal_kernel(const int boxes_num, const float nms_overlap_thresh,
-                           const float *boxes, unsigned long long *mask){
-    //params: boxes (N, 5) [x1, y1, x2, y2, ry]
-    //params: mask (N, N/THREADS_PER_BLOCK_NMS)
-
-    const int row_start = blockIdx.y;
-    const int col_start = blockIdx.x;
-
-    // if (row_start > col_start) return;
-
-    const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
-    const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS, THREADS_PER_BLOCK_NMS);
-
-    __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
-
-    if (threadIdx.x < col_size) {
-        block_boxes[threadIdx.x * 5 + 0] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
-        block_boxes[threadIdx.x * 5 + 1] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
-        block_boxes[threadIdx.x * 5 + 2] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
-        block_boxes[threadIdx.x * 5 + 3] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
-        block_boxes[threadIdx.x * 5 + 4] = boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
+__global__ void nms_normal_kernel(const int boxes_num,
+                                  const float nms_overlap_thresh,
+                                  const float *boxes,
+                                  unsigned long long *mask) {
+  // params: boxes (N, 5) [x1, y1, x2, y2, ry]
+  // params: mask (N, N/THREADS_PER_BLOCK_NMS)
+
+  const int row_start = blockIdx.y;
+  const int col_start = blockIdx.x;
+
+  // if (row_start > col_start) return;
+
+  const int row_size = fminf(boxes_num - row_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+  const int col_size = fminf(boxes_num - col_start * THREADS_PER_BLOCK_NMS,
+                             THREADS_PER_BLOCK_NMS);
+
+  __shared__ float block_boxes[THREADS_PER_BLOCK_NMS * 5];
+
+  if (threadIdx.x < col_size) {
+    block_boxes[threadIdx.x * 5 + 0] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 0];
+    block_boxes[threadIdx.x * 5 + 1] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 1];
+    block_boxes[threadIdx.x * 5 + 2] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 2];
+    block_boxes[threadIdx.x * 5 + 3] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 3];
+    block_boxes[threadIdx.x * 5 + 4] =
+        boxes[(THREADS_PER_BLOCK_NMS * col_start + threadIdx.x) * 5 + 4];
+  }
+  __syncthreads();
+
+  if (threadIdx.x < row_size) {
+    const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
+    const float *cur_box = boxes + cur_box_idx * 5;
+
+    int i = 0;
+    unsigned long long t = 0;
+    int start = 0;
+    if (row_start == col_start) {
+      start = threadIdx.x + 1;
     }
-    __syncthreads();
-
-    if (threadIdx.x < row_size) {
-        const int cur_box_idx = THREADS_PER_BLOCK_NMS * row_start + threadIdx.x;
-        const float *cur_box = boxes + cur_box_idx * 5;
-
-        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 (iou_normal(cur_box, block_boxes + i * 5) > nms_overlap_thresh){
-                t |= 1ULL << i;
-            }
-        }
-        const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
-        mask[cur_box_idx * col_blocks + col_start] = t;
+    for (i = start; i < col_size; i++) {
+      if (iou_normal(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {
+        t |= 1ULL << i;
+      }
     }
+    const int col_blocks = DIVUP(boxes_num, THREADS_PER_BLOCK_NMS);
+    mask[cur_box_idx * col_blocks + col_start] = t;
+  }
 }
 
+void boxesoverlapLauncher(const int num_a, const float *boxes_a,
+                          const int num_b, const float *boxes_b,
+                          float *ans_overlap) {
+  dim3 blocks(
+      DIVUP(num_b, THREADS_PER_BLOCK),
+      DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
 
-
-
-
-void boxesoverlapLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_overlap){
-
-    dim3 blocks(DIVUP(num_b, THREADS_PER_BLOCK), DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
-
-    boxes_overlap_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b, ans_overlap);
+  boxes_overlap_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b,
+                                            ans_overlap);
 #ifdef DEBUG
-    cudaDeviceSynchronize();  // for using printf in kernel function
+  cudaDeviceSynchronize();  // for using printf in kernel function
 #endif
 }
 
-void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b, const float *boxes_b, float *ans_iou){
-
-    dim3 blocks(DIVUP(num_b, THREADS_PER_BLOCK), DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
-    dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
+void boxesioubevLauncher(const int num_a, const float *boxes_a, const int num_b,
+                         const float *boxes_b, float *ans_iou) {
+  dim3 blocks(
+      DIVUP(num_b, THREADS_PER_BLOCK),
+      DIVUP(num_a, THREADS_PER_BLOCK));  // blockIdx.x(col), blockIdx.y(row)
+  dim3 threads(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
 
-    boxes_iou_bev_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b, ans_iou);
+  boxes_iou_bev_kernel<<<blocks, threads>>>(num_a, boxes_a, num_b, boxes_b,
+                                            ans_iou);
 }
 
-
-void nmsLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh){
-    dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
-                DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
-    dim3 threads(THREADS_PER_BLOCK_NMS);
-    nms_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
+void nmsLauncher(const float *boxes, unsigned long long *mask, int boxes_num,
+                 float nms_overlap_thresh) {
+  dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+              DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+  dim3 threads(THREADS_PER_BLOCK_NMS);
+  nms_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
 }
 
-
-void nmsNormalLauncher(const float *boxes, unsigned long long * mask, int boxes_num, float nms_overlap_thresh){
-    dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
-                DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
-    dim3 threads(THREADS_PER_BLOCK_NMS);
-    nms_normal_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes, mask);
+void nmsNormalLauncher(const float *boxes, unsigned long long *mask,
+                       int boxes_num, float nms_overlap_thresh) {
+  dim3 blocks(DIVUP(boxes_num, THREADS_PER_BLOCK_NMS),
+              DIVUP(boxes_num, THREADS_PER_BLOCK_NMS));
+  dim3 threads(THREADS_PER_BLOCK_NMS);
+  nms_normal_kernel<<<blocks, threads>>>(boxes_num, nms_overlap_thresh, boxes,
+                                         mask);
 }

mmdet3d/ops/roiaware_pool3d/src/points_in_boxes_cuda.cu

@@ -78,9 +78,9 @@ __global__ void points_in_boxes_kernel(int batch_size, int boxes_num,
 }
 
 __global__ void points_in_boxes_batch_kernel(int batch_size, int boxes_num,
-                                       int pts_num, const float *boxes,
-                                       const float *pts,
-                                       int *box_idx_of_points) {
+                                             int pts_num, const float *boxes,
+                                             const float *pts,
+                                             int *box_idx_of_points) {
   // params boxes: (B, N, 7) [x, y, z, w, l, h, rz] in LiDAR coordinate, z is
   // the bottom center, each box DO NOT overlaps params pts: (B, npoints, 3) [x,
   // y, z] in LiDAR coordinate params boxes_idx_of_points: (B, npoints), default
@@ -131,17 +131,17 @@ void points_in_boxes_launcher(int batch_size, int boxes_num, int pts_num,
 }
 
 void points_in_boxes_batch_launcher(int batch_size, int boxes_num, int pts_num,
-                              const float *boxes, const float *pts,
-                              int *box_idx_of_points) {
+                                    const float *boxes, const float *pts,
+                                    int *box_idx_of_points) {
   // params boxes: (B, N, 7) [x, y, z, w, l, h, rz] in LiDAR coordinate, z is
   // the bottom center, each box params pts: (B, npoints, 3) [x, y, z] in
-  //LiDAR coordinate params boxes_idx_of_points: (B, npoints), default -1
+  // LiDAR coordinate params boxes_idx_of_points: (B, npoints), default -1
   cudaError_t err;
 
   dim3 blocks(DIVUP(pts_num, THREADS_PER_BLOCK), batch_size);
   dim3 threads(THREADS_PER_BLOCK);
-  points_in_boxes_batch_kernel<<<blocks, threads>>>(batch_size, boxes_num, pts_num,
-                                              boxes, pts, box_idx_of_points);
+  points_in_boxes_batch_kernel<<<blocks, threads>>>(
+      batch_size, boxes_num, pts_num, boxes, pts, box_idx_of_points);
 
   err = cudaGetLastError();
   if (cudaSuccess != err) {
@@ -180,7 +180,7 @@ int points_in_boxes_gpu(at::Tensor boxes_tensor, at::Tensor pts_tensor,
 }
 
 int points_in_boxes_batch(at::Tensor boxes_tensor, at::Tensor pts_tensor,
-                        at::Tensor box_idx_of_points_tensor) {
+                          at::Tensor box_idx_of_points_tensor) {
   // params boxes: (B, N, 7) [x, y, z, w, l, h, rz] in LiDAR coordinate, z is
   // the bottom center. params pts: (B, npoints, 3) [x, y, z] in LiDAR
   // coordinate params boxes_idx_of_points: (B, npoints), default -1

mmdet3d/ops/spconv/include/paramsgrid.h

@@ -18,13 +18,19 @@
 #include <vector>
 
 namespace detail {
-template <class T> int getTotalSize(std::vector<T> arg) { return arg.size(); }
+template <class T>
+int getTotalSize(std::vector<T> arg) {
+  return arg.size();
+}
 
 template <class T, class... TArgs>
 int getTotalSize(std::vector<T> arg, std::vector<TArgs>... args) {
   return arg.size() * getTotalSize(args...);
 }
-template <typename T> int getSize(std::vector<T> arg) { return arg.size(); }
+template <typename T>
+int getSize(std::vector<T> arg) {
+  return arg.size();
+}
 
 template <int Idx, class TT, class T>
 void assigner(TT &src, std::vector<int> counter, std::vector<T> &arg) {
@@ -37,7 +43,7 @@ void assigner(TT &src, std::vector<int> counter, std::vector<T> &arg,
   std::get<Idx>(src) = arg[counter[Idx]];
   assigner<Idx + 1>(src, counter, args...);
 }
-} // namespace detail
+}  // namespace detail
 template <class... TArgs>
 std::vector<std::tuple<TArgs...>> paramsGrid(std::vector<TArgs>... args) {
   int length = detail::getTotalSize(args...);

mmdet3d/ops/spconv/include/prettyprint.h

@@ -22,424 +22,472 @@
 #include <utility>
 #include <valarray>
 
-namespace pretty_print
-{
-    namespace detail
-    {
-        // SFINAE type trait to detect whether T::const_iterator exists.
-
-        struct sfinae_base
-        {
-            using yes = char;
-            using no  = yes[2];
-        };
-
-        template <typename T>
-        struct has_const_iterator : private sfinae_base
-        {
-        private:
-            template <typename C> static yes & test(typename C::const_iterator*);
-            template <typename C> static no  & test(...);
-        public:
-            static const bool value = sizeof(test<T>(nullptr)) == sizeof(yes);
-            using type =  T;
-        };
-
-        template <typename T>
-        struct has_begin_end : private sfinae_base
-        {
-        private:
-            template <typename C>
-            static yes & f(typename std::enable_if<
-                std::is_same<decltype(static_cast<typename C::const_iterator(C::*)() const>(&C::begin)),
-                             typename C::const_iterator(C::*)() const>::value>::type *);
-
-            template <typename C> static no & f(...);
-
-            template <typename C>
-            static yes & g(typename std::enable_if<
-                std::is_same<decltype(static_cast<typename C::const_iterator(C::*)() const>(&C::end)),
-                             typename C::const_iterator(C::*)() const>::value, void>::type*);
-
-            template <typename C> static no & g(...);
-
-        public:
-            static bool const beg_value = sizeof(f<T>(nullptr)) == sizeof(yes);
-            static bool const end_value = sizeof(g<T>(nullptr)) == sizeof(yes);
-        };
-
-    }  // namespace detail
-
-
-    // Holds the delimiter values for a specific character type
-
-    template <typename TChar>
-    struct delimiters_values
-    {
-        using char_type = TChar;
-        const char_type * prefix;
-        const char_type * delimiter;
-        const char_type * postfix;
-    };
-
-
-    // Defines the delimiter values for a specific container and character type
-
-    template <typename T, typename TChar>
-    struct delimiters
-    {
-        using type = delimiters_values<TChar>;
-        static const type values;
-    };
-
-
-    // Functor to print containers. You can use this directly if you want
-    // to specificy a non-default delimiters type. The printing logic can
-    // be customized by specializing the nested template.
-
-    template <typename T,
-              typename TChar = char,
-              typename TCharTraits = ::std::char_traits<TChar>,
-              typename TDelimiters = delimiters<T, TChar>>
-    struct print_container_helper
-    {
-        using delimiters_type = TDelimiters;
-        using ostream_type = std::basic_ostream<TChar, TCharTraits>;
-
-        template <typename U>
-        struct printer
-        {
-            static void print_body(const U & c, ostream_type & stream)
-            {
-                using std::begin;
-                using std::end;
-
-                auto it = begin(c);
-                const auto the_end = end(c);
-
-                if (it != the_end)
-                {
-                    for ( ; ; )
-                    {
-                        stream << *it;
-
-                    if (++it == the_end) break;
-
-                    if (delimiters_type::values.delimiter != NULL)
-                        stream << delimiters_type::values.delimiter;
-                    }
-                }
-            }
-        };
-
-        print_container_helper(const T & container)
-        : container_(container)
-        { }
-
-        inline void operator()(ostream_type & stream) const
-        {
-            if (delimiters_type::values.prefix != NULL)
-                stream << delimiters_type::values.prefix;
-
-            printer<T>::print_body(container_, stream);
-
-            if (delimiters_type::values.postfix != NULL)
-                stream << delimiters_type::values.postfix;
+namespace pretty_print {
+namespace detail {
+// SFINAE type trait to detect whether T::const_iterator exists.
+
+struct sfinae_base {
+  using yes = char;
+  using no = yes[2];
+};
+
+template <typename T>
+struct has_const_iterator : private sfinae_base {
+ private:
+  template <typename C>
+  static yes &test(typename C::const_iterator *);
+  template <typename C>
+  static no &test(...);
+
+ public:
+  static const bool value = sizeof(test<T>(nullptr)) == sizeof(yes);
+  using type = T;
+};
+
+template <typename T>
+struct has_begin_end : private sfinae_base {
+ private:
+  template <typename C>
+  static yes &
+  f(typename std::enable_if<
+      std::is_same<decltype(static_cast<typename C::const_iterator (C::*)()
+                                            const>(&C::begin)),
+                   typename C::const_iterator (C::*)() const>::value>::type *);
+
+  template <typename C>
+  static no &f(...);
+
+  template <typename C>
+  static yes &g(typename std::enable_if<
+                std::is_same<decltype(static_cast<typename C::const_iterator (
+                                          C::*)() const>(&C::end)),
+                             typename C::const_iterator (C::*)() const>::value,
+                void>::type *);
+
+  template <typename C>
+  static no &g(...);
+
+ public:
+  static bool const beg_value = sizeof(f<T>(nullptr)) == sizeof(yes);
+  static bool const end_value = sizeof(g<T>(nullptr)) == sizeof(yes);
+};
+
+}  // namespace detail
+
+// Holds the delimiter values for a specific character type
+
+template <typename TChar>
+struct delimiters_values {
+  using char_type = TChar;
+  const char_type *prefix;
+  const char_type *delimiter;
+  const char_type *postfix;
+};
+
+// Defines the delimiter values for a specific container and character type
+
+template <typename T, typename TChar>
+struct delimiters {
+  using type = delimiters_values<TChar>;
+  static const type values;
+};
+
+// Functor to print containers. You can use this directly if you want
+// to specificy a non-default delimiters type. The printing logic can
+// be customized by specializing the nested template.
+
+template <typename T, typename TChar = char,
+          typename TCharTraits = ::std::char_traits<TChar>,
+          typename TDelimiters = delimiters<T, TChar>>
+struct print_container_helper {
+  using delimiters_type = TDelimiters;
+  using ostream_type = std::basic_ostream<TChar, TCharTraits>;
+
+  template <typename U>
+  struct printer {
+    static void print_body(const U &c, ostream_type &stream) {
+      using std::begin;
+      using std::end;
+
+      auto it = begin(c);
+      const auto the_end = end(c);
+
+      if (it != the_end) {
+        for (;;) {
+          stream << *it;
+
+          if (++it == the_end) break;
+
+          if (delimiters_type::values.delimiter != NULL)
+            stream << delimiters_type::values.delimiter;
         }
-
-    private:
-        const T & container_;
-    };
-
-    // Specialization for pairs
-
-    template <typename T, typename TChar, typename TCharTraits, typename TDelimiters>
-    template <typename T1, typename T2>
-    struct print_container_helper<T, TChar, TCharTraits, TDelimiters>::printer<std::pair<T1, T2>>
-    {
-        using ostream_type = typename print_container_helper<T, TChar, TCharTraits, TDelimiters>::ostream_type;
-
-        static void print_body(const std::pair<T1, T2> & c, ostream_type & stream)
-        {
-            stream << c.first;
-            if (print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter != NULL)
-                stream << print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter;
-            stream << c.second;
-        }
-    };
-
-    // Specialization for tuples
-
-    template <typename T, typename TChar, typename TCharTraits, typename TDelimiters>
-    template <typename ...Args>
-    struct print_container_helper<T, TChar, TCharTraits, TDelimiters>::printer<std::tuple<Args...>>
-    {
-        using ostream_type = typename print_container_helper<T, TChar, TCharTraits, TDelimiters>::ostream_type;
-        using element_type = std::tuple<Args...>;
-
-        template <std::size_t I> struct Int { };
-
-        static void print_body(const element_type & c, ostream_type & stream)
-        {
-            tuple_print(c, stream, Int<0>());
-        }
-
-        static void tuple_print(const element_type &, ostream_type &, Int<sizeof...(Args)>)
-        {
-        }
-
-        static void tuple_print(const element_type & c, ostream_type & stream,
-                                typename std::conditional<sizeof...(Args) != 0, Int<0>, std::nullptr_t>::type)
-        {
-            stream << std::get<0>(c);
-            tuple_print(c, stream, Int<1>());
-        }
-
-        template <std::size_t N>
-        static void tuple_print(const element_type & c, ostream_type & stream, Int<N>)
-        {
-            if (print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter != NULL)
-                stream << print_container_helper<T, TChar, TCharTraits, TDelimiters>::delimiters_type::values.delimiter;
-
-            stream << std::get<N>(c);
-
-            tuple_print(c, stream, Int<N + 1>());
-        }
-    };
-
-    // Prints a print_container_helper to the specified stream.
-
-    template<typename T, typename TChar, typename TCharTraits, typename TDelimiters>
-    inline std::basic_ostream<TChar, TCharTraits> & operator<<(
-        std::basic_ostream<TChar, TCharTraits> & stream,
-        const print_container_helper<T, TChar, TCharTraits, TDelimiters> & helper)
-    {
-        helper(stream);
-        return stream;
-    }
-
-
-    // Basic is_container template; specialize to derive from std::true_type for all desired container types
-
-    template <typename T>
-    struct is_container : public std::integral_constant<bool,
-                                                        detail::has_const_iterator<T>::value &&
-                                                        detail::has_begin_end<T>::beg_value  &&
-                                                        detail::has_begin_end<T>::end_value> { };
-
-    template <typename T, std::size_t N>
-    struct is_container<T[N]> : std::true_type { };
-
-    template <std::size_t N>
-    struct is_container<char[N]> : std::false_type { };
-
-    template <typename T>
-    struct is_container<std::valarray<T>> : std::true_type { };
-
-    template <typename T1, typename T2>
-    struct is_container<std::pair<T1, T2>> : std::true_type { };
-
-    template <typename ...Args>
-    struct is_container<std::tuple<Args...>> : std::true_type { };
-
-
-    // Default delimiters
-
-    template <typename T> struct delimiters<T, char> { static const delimiters_values<char> values; };
-    template <typename T> const delimiters_values<char> delimiters<T, char>::values = { "[", ", ", "]" };
-    template <typename T> struct delimiters<T, wchar_t> { static const delimiters_values<wchar_t> values; };
-    template <typename T> const delimiters_values<wchar_t> delimiters<T, wchar_t>::values = { L"[", L", ", L"]" };
-
-
-    // Delimiters for (multi)set and unordered_(multi)set
-
-    template <typename T, typename TComp, typename TAllocator>
-    struct delimiters< ::std::set<T, TComp, TAllocator>, char> { static const delimiters_values<char> values; };
-
-    template <typename T, typename TComp, typename TAllocator>
-    const delimiters_values<char> delimiters< ::std::set<T, TComp, TAllocator>, char>::values = { "{", ", ", "}" };
-
-    template <typename T, typename TComp, typename TAllocator>
-    struct delimiters< ::std::set<T, TComp, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
-
-    template <typename T, typename TComp, typename TAllocator>
-    const delimiters_values<wchar_t> delimiters< ::std::set<T, TComp, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
-
-    template <typename T, typename TComp, typename TAllocator>
-    struct delimiters< ::std::multiset<T, TComp, TAllocator>, char> { static const delimiters_values<char> values; };
-
-    template <typename T, typename TComp, typename TAllocator>
-    const delimiters_values<char> delimiters< ::std::multiset<T, TComp, TAllocator>, char>::values = { "{", ", ", "}" };
-
-    template <typename T, typename TComp, typename TAllocator>
-    struct delimiters< ::std::multiset<T, TComp, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
-
-    template <typename T, typename TComp, typename TAllocator>
-    const delimiters_values<wchar_t> delimiters< ::std::multiset<T, TComp, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    struct delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, char> { static const delimiters_values<char> values; };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    const delimiters_values<char> delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, char>::values = { "{", ", ", "}" };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    struct delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    const delimiters_values<wchar_t> delimiters< ::std::unordered_set<T, THash, TEqual, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    struct delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, char> { static const delimiters_values<char> values; };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    const delimiters_values<char> delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, char>::values = { "{", ", ", "}" };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    struct delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, wchar_t> { static const delimiters_values<wchar_t> values; };
-
-    template <typename T, typename THash, typename TEqual, typename TAllocator>
-    const delimiters_values<wchar_t> delimiters< ::std::unordered_multiset<T, THash, TEqual, TAllocator>, wchar_t>::values = { L"{", L", ", L"}" };
-
-
-    // Delimiters for pair and tuple
-
-    template <typename T1, typename T2> struct delimiters<std::pair<T1, T2>, char> { static const delimiters_values<char> values; };
-    template <typename T1, typename T2> const delimiters_values<char> delimiters<std::pair<T1, T2>, char>::values = { "(", ", ", ")" };
-    template <typename T1, typename T2> struct delimiters< ::std::pair<T1, T2>, wchar_t> { static const delimiters_values<wchar_t> values; };
-    template <typename T1, typename T2> const delimiters_values<wchar_t> delimiters< ::std::pair<T1, T2>, wchar_t>::values = { L"(", L", ", L")" };
-
-    template <typename ...Args> struct delimiters<std::tuple<Args...>, char> { static const delimiters_values<char> values; };
-    template <typename ...Args> const delimiters_values<char> delimiters<std::tuple<Args...>, char>::values = { "(", ", ", ")" };
-    template <typename ...Args> struct delimiters< ::std::tuple<Args...>, wchar_t> { static const delimiters_values<wchar_t> values; };
-    template <typename ...Args> const delimiters_values<wchar_t> delimiters< ::std::tuple<Args...>, wchar_t>::values = { L"(", L", ", L")" };
-
-
-    // Type-erasing helper class for easy use of custom delimiters.
-    // Requires TCharTraits = std::char_traits<TChar> and TChar = char or wchar_t, and MyDelims needs to be defined for TChar.
-    // Usage: "cout << pretty_print::custom_delims<MyDelims>(x)".
-
-    struct custom_delims_base
-    {
-        virtual ~custom_delims_base() { }
-        virtual std::ostream & stream(::std::ostream &) = 0;
-        virtual std::wostream & stream(::std::wostream &) = 0;
-    };
-
-    template <typename T, typename Delims>
-    struct custom_delims_wrapper : custom_delims_base
-    {
-        custom_delims_wrapper(const T & t_) : t(t_) { }
-
-        std::ostream & stream(std::ostream & s)
-        {
-            return s << print_container_helper<T, char, std::char_traits<char>, Delims>(t);
-        }
-
-        std::wostream & stream(std::wostream & s)
-        {
-            return s << print_container_helper<T, wchar_t, std::char_traits<wchar_t>, Delims>(t);
-        }
-
-    private:
-        const T & t;
-    };
-
-    template <typename Delims>
-    struct custom_delims
-    {
-        template <typename Container>
-        custom_delims(const Container & c) : base(new custom_delims_wrapper<Container, Delims>(c)) { }
-
-        std::unique_ptr<custom_delims_base> base;
-    };
-
-    template <typename TChar, typename TCharTraits, typename Delims>
-    inline std::basic_ostream<TChar, TCharTraits> & operator<<(std::basic_ostream<TChar, TCharTraits> & s, const custom_delims<Delims> & p)
-    {
-        return p.base->stream(s);
+      }
     }
+  };
+
+  print_container_helper(const T &container) : container_(container) {}
+
+  inline void operator()(ostream_type &stream) const {
+    if (delimiters_type::values.prefix != NULL)
+      stream << delimiters_type::values.prefix;
+
+    printer<T>::print_body(container_, stream);
+
+    if (delimiters_type::values.postfix != NULL)
+      stream << delimiters_type::values.postfix;
+  }
+
+ private:
+  const T &container_;
+};
+
+// Specialization for pairs
+
+template <typename T, typename TChar, typename TCharTraits,
+          typename TDelimiters>
+template <typename T1, typename T2>
+struct print_container_helper<T, TChar, TCharTraits,
+                              TDelimiters>::printer<std::pair<T1, T2>> {
+  using ostream_type =
+      typename print_container_helper<T, TChar, TCharTraits,
+                                      TDelimiters>::ostream_type;
+
+  static void print_body(const std::pair<T1, T2> &c, ostream_type &stream) {
+    stream << c.first;
+    if (print_container_helper<T, TChar, TCharTraits,
+                               TDelimiters>::delimiters_type::values
+            .delimiter != NULL)
+      stream << print_container_helper<T, TChar, TCharTraits,
+                                       TDelimiters>::delimiters_type::values
+                    .delimiter;
+    stream << c.second;
+  }
+};
+
+// Specialization for tuples
+
+template <typename T, typename TChar, typename TCharTraits,
+          typename TDelimiters>
+template <typename... Args>
+struct print_container_helper<T, TChar, TCharTraits,
+                              TDelimiters>::printer<std::tuple<Args...>> {
+  using ostream_type =
+      typename print_container_helper<T, TChar, TCharTraits,
+                                      TDelimiters>::ostream_type;
+  using element_type = std::tuple<Args...>;
+
+  template <std::size_t I>
+  struct Int {};
+
+  static void print_body(const element_type &c, ostream_type &stream) {
+    tuple_print(c, stream, Int<0>());
+  }
+
+  static void tuple_print(const element_type &, ostream_type &,
+                          Int<sizeof...(Args)>) {}
+
+  static void tuple_print(
+      const element_type &c, ostream_type &stream,
+      typename std::conditional<sizeof...(Args) != 0, Int<0>,
+                                std::nullptr_t>::type) {
+    stream << std::get<0>(c);
+    tuple_print(c, stream, Int<1>());
+  }
+
+  template <std::size_t N>
+  static void tuple_print(const element_type &c, ostream_type &stream, Int<N>) {
+    if (print_container_helper<T, TChar, TCharTraits,
+                               TDelimiters>::delimiters_type::values
+            .delimiter != NULL)
+      stream << print_container_helper<T, TChar, TCharTraits,
+                                       TDelimiters>::delimiters_type::values
+                    .delimiter;
+
+    stream << std::get<N>(c);
+
+    tuple_print(c, stream, Int<N + 1>());
+  }
+};
+
+// Prints a print_container_helper to the specified stream.
+
+template <typename T, typename TChar, typename TCharTraits,
+          typename TDelimiters>
+inline std::basic_ostream<TChar, TCharTraits> &operator<<(
+    std::basic_ostream<TChar, TCharTraits> &stream,
+    const print_container_helper<T, TChar, TCharTraits, TDelimiters> &helper) {
+  helper(stream);
+  return stream;
+}
 
+// Basic is_container template; specialize to derive from std::true_type for all
+// desired container types
+
+template <typename T>
+struct is_container
+    : public std::integral_constant<bool,
+                                    detail::has_const_iterator<T>::value &&
+                                        detail::has_begin_end<T>::beg_value &&
+                                        detail::has_begin_end<T>::end_value> {};
+
+template <typename T, std::size_t N>
+struct is_container<T[N]> : std::true_type {};
+
+template <std::size_t N>
+struct is_container<char[N]> : std::false_type {};
+
+template <typename T>
+struct is_container<std::valarray<T>> : std::true_type {};
+
+template <typename T1, typename T2>
+struct is_container<std::pair<T1, T2>> : std::true_type {};
+
+template <typename... Args>
+struct is_container<std::tuple<Args...>> : std::true_type {};
+
+// Default delimiters
+
+template <typename T>
+struct delimiters<T, char> {
+  static const delimiters_values<char> values;
+};
+template <typename T>
+const delimiters_values<char> delimiters<T, char>::values = {"[", ", ", "]"};
+template <typename T>
+struct delimiters<T, wchar_t> {
+  static const delimiters_values<wchar_t> values;
+};
+template <typename T>
+const delimiters_values<wchar_t> delimiters<T, wchar_t>::values = {L"[", L", ",
+                                                                   L"]"};
+
+// Delimiters for (multi)set and unordered_(multi)set
+
+template <typename T, typename TComp, typename TAllocator>
+struct delimiters<::std::set<T, TComp, TAllocator>, char> {
+  static const delimiters_values<char> values;
+};
+
+template <typename T, typename TComp, typename TAllocator>
+const delimiters_values<char>
+    delimiters<::std::set<T, TComp, TAllocator>, char>::values = {"{", ", ",
+                                                                  "}"};
+
+template <typename T, typename TComp, typename TAllocator>
+struct delimiters<::std::set<T, TComp, TAllocator>, wchar_t> {
+  static const delimiters_values<wchar_t> values;
+};
+
+template <typename T, typename TComp, typename TAllocator>
+const delimiters_values<wchar_t>
+    delimiters<::std::set<T, TComp, TAllocator>, wchar_t>::values = {
+        L"{", L", ", L"}"};
+
+template <typename T, typename TComp, typename TAllocator>
+struct delimiters<::std::multiset<T, TComp, TAllocator>, char> {
+  static const delimiters_values<char> values;
+};
+
+template <typename T, typename TComp, typename TAllocator>
+const delimiters_values<char>
+    delimiters<::std::multiset<T, TComp, TAllocator>, char>::values = {
+        "{", ", ", "}"};
+
+template <typename T, typename TComp, typename TAllocator>
+struct delimiters<::std::multiset<T, TComp, TAllocator>, wchar_t> {
+  static const delimiters_values<wchar_t> values;
+};
+
+template <typename T, typename TComp, typename TAllocator>
+const delimiters_values<wchar_t>
+    delimiters<::std::multiset<T, TComp, TAllocator>, wchar_t>::values = {
+        L"{", L", ", L"}"};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+struct delimiters<::std::unordered_set<T, THash, TEqual, TAllocator>, char> {
+  static const delimiters_values<char> values;
+};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+const delimiters_values<char> delimiters<
+    ::std::unordered_set<T, THash, TEqual, TAllocator>, char>::values = {
+    "{", ", ", "}"};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+struct delimiters<::std::unordered_set<T, THash, TEqual, TAllocator>, wchar_t> {
+  static const delimiters_values<wchar_t> values;
+};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+const delimiters_values<wchar_t> delimiters<
+    ::std::unordered_set<T, THash, TEqual, TAllocator>, wchar_t>::values = {
+    L"{", L", ", L"}"};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+struct delimiters<::std::unordered_multiset<T, THash, TEqual, TAllocator>,
+                  char> {
+  static const delimiters_values<char> values;
+};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+const delimiters_values<char> delimiters<
+    ::std::unordered_multiset<T, THash, TEqual, TAllocator>, char>::values = {
+    "{", ", ", "}"};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+struct delimiters<::std::unordered_multiset<T, THash, TEqual, TAllocator>,
+                  wchar_t> {
+  static const delimiters_values<wchar_t> values;
+};
+
+template <typename T, typename THash, typename TEqual, typename TAllocator>
+const delimiters_values<wchar_t>
+    delimiters<::std::unordered_multiset<T, THash, TEqual, TAllocator>,
+               wchar_t>::values = {L"{", L", ", L"}"};
+
+// Delimiters for pair and tuple
+
+template <typename T1, typename T2>
+struct delimiters<std::pair<T1, T2>, char> {
+  static const delimiters_values<char> values;
+};
+template <typename T1, typename T2>
+const delimiters_values<char> delimiters<std::pair<T1, T2>, char>::values = {
+    "(", ", ", ")"};
+template <typename T1, typename T2>
+struct delimiters<::std::pair<T1, T2>, wchar_t> {
+  static const delimiters_values<wchar_t> values;
+};
+template <typename T1, typename T2>
+const delimiters_values<wchar_t>
+    delimiters<::std::pair<T1, T2>, wchar_t>::values = {L"(", L", ", L")"};
+
+template <typename... Args>
+struct delimiters<std::tuple<Args...>, char> {
+  static const delimiters_values<char> values;
+};
+template <typename... Args>
+const delimiters_values<char> delimiters<std::tuple<Args...>, char>::values = {
+    "(", ", ", ")"};
+template <typename... Args>
+struct delimiters<::std::tuple<Args...>, wchar_t> {
+  static const delimiters_values<wchar_t> values;
+};
+template <typename... Args>
+const delimiters_values<wchar_t>
+    delimiters<::std::tuple<Args...>, wchar_t>::values = {L"(", L", ", L")"};
+
+// Type-erasing helper class for easy use of custom delimiters.
+// Requires TCharTraits = std::char_traits<TChar> and TChar = char or wchar_t,
+// and MyDelims needs to be defined for TChar. Usage: "cout <<
+// pretty_print::custom_delims<MyDelims>(x)".
+
+struct custom_delims_base {
+  virtual ~custom_delims_base() {}
+  virtual std::ostream &stream(::std::ostream &) = 0;
+  virtual std::wostream &stream(::std::wostream &) = 0;
+};
+
+template <typename T, typename Delims>
+struct custom_delims_wrapper : custom_delims_base {
+  custom_delims_wrapper(const T &t_) : t(t_) {}
+
+  std::ostream &stream(std::ostream &s) {
+    return s << print_container_helper<T, char, std::char_traits<char>, Delims>(
+               t);
+  }
+
+  std::wostream &stream(std::wostream &s) {
+    return s << print_container_helper<T, wchar_t, std::char_traits<wchar_t>,
+                                       Delims>(t);
+  }
+
+ private:
+  const T &t;
+};
+
+template <typename Delims>
+struct custom_delims {
+  template <typename Container>
+  custom_delims(const Container &c)
+      : base(new custom_delims_wrapper<Container, Delims>(c)) {}
+
+  std::unique_ptr<custom_delims_base> base;
+};
+
+template <typename TChar, typename TCharTraits, typename Delims>
+inline std::basic_ostream<TChar, TCharTraits> &operator<<(
+    std::basic_ostream<TChar, TCharTraits> &s, const custom_delims<Delims> &p) {
+  return p.base->stream(s);
+}
 
-    // A wrapper for a C-style array given as pointer-plus-size.
-    // Usage: std::cout << pretty_print_array(arr, n) << std::endl;
-
-    template<typename T>
-    struct array_wrapper_n
-    {
-        typedef const T * const_iterator;
-        typedef T value_type;
+// A wrapper for a C-style array given as pointer-plus-size.
+// Usage: std::cout << pretty_print_array(arr, n) << std::endl;
 
-        array_wrapper_n(const T * const a, size_t n) : _array(a), _n(n) { }
-        inline const_iterator begin() const { return _array; }
-        inline const_iterator end() const { return _array + _n; }
+template <typename T>
+struct array_wrapper_n {
+  typedef const T *const_iterator;
+  typedef T value_type;
 
-    private:
-        const T * const _array;
-        size_t _n;
-    };
+  array_wrapper_n(const T *const a, size_t n) : _array(a), _n(n) {}
+  inline const_iterator begin() const { return _array; }
+  inline const_iterator end() const { return _array + _n; }
 
+ private:
+  const T *const _array;
+  size_t _n;
+};
 
-    // A wrapper for hash-table based containers that offer local iterators to each bucket.
-    // Usage: std::cout << bucket_print(m, 4) << std::endl;  (Prints bucket 5 of container m.)
+// A wrapper for hash-table based containers that offer local iterators to each
+// bucket. Usage: std::cout << bucket_print(m, 4) << std::endl;  (Prints bucket
+// 5 of container m.)
 
-    template <typename T>
-    struct bucket_print_wrapper
-    {
-        typedef typename T::const_local_iterator const_iterator;
-        typedef typename T::size_type size_type;
+template <typename T>
+struct bucket_print_wrapper {
+  typedef typename T::const_local_iterator const_iterator;
+  typedef typename T::size_type size_type;
 
-        const_iterator begin() const
-        {
-            return m_map.cbegin(n);
-        }
-
-        const_iterator end() const
-        {
-            return m_map.cend(n);
-        }
+  const_iterator begin() const { return m_map.cbegin(n); }
 
-        bucket_print_wrapper(const T & m, size_type bucket) : m_map(m), n(bucket) { }
+  const_iterator end() const { return m_map.cend(n); }
 
-    private:
-        const T & m_map;
-        const size_type n;
-    };
+  bucket_print_wrapper(const T &m, size_type bucket) : m_map(m), n(bucket) {}
 
-}   // namespace pretty_print
+ private:
+  const T &m_map;
+  const size_type n;
+};
 
+}  // namespace pretty_print
 
 // Global accessor functions for the convenience wrappers
 
-template<typename T>
-inline pretty_print::array_wrapper_n<T> pretty_print_array(const T * const a, size_t n)
-{
-    return pretty_print::array_wrapper_n<T>(a, n);
+template <typename T>
+inline pretty_print::array_wrapper_n<T> pretty_print_array(const T *const a,
+                                                           size_t n) {
+  return pretty_print::array_wrapper_n<T>(a, n);
 }
 
-template <typename T> pretty_print::bucket_print_wrapper<T>
-bucket_print(const T & m, typename T::size_type n)
-{
-    return pretty_print::bucket_print_wrapper<T>(m, n);
+template <typename T>
+pretty_print::bucket_print_wrapper<T> bucket_print(const T &m,
+                                                   typename T::size_type n) {
+  return pretty_print::bucket_print_wrapper<T>(m, n);
 }
 
-
 // Main magic entry point: An overload snuck into namespace std.
 // Can we do better?
 
-namespace std
-{
-    // Prints a container to the stream using default delimiters
+namespace std {
+// Prints a container to the stream using default delimiters
 
-    template<typename T, typename TChar, typename TCharTraits>
-    inline typename enable_if< ::pretty_print::is_container<T>::value,
-                              basic_ostream<TChar, TCharTraits> &>::type
-    operator<<(basic_ostream<TChar, TCharTraits> & stream, const T & container)
-    {
-        return stream << ::pretty_print::print_container_helper<T, TChar, TCharTraits>(container);
-    }
+template <typename T, typename TChar, typename TCharTraits>
+inline typename enable_if<::pretty_print::is_container<T>::value,
+                          basic_ostream<TChar, TCharTraits> &>::type
+operator<<(basic_ostream<TChar, TCharTraits> &stream, const T &container) {
+  return stream
+         << ::pretty_print::print_container_helper<T, TChar, TCharTraits>(
+                container);
 }
+}  // namespace std
 
-
-
-#endif // H_PRETTY_PRINT
+#endif  // H_PRETTY_PRINT

mmdet3d/ops/spconv/include/spconv/box_iou.h

@@ -12,15 +12,15 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-
 #ifndef BOX_IOU_H
 #define BOX_IOU_H
 
 #include <pybind11/pybind11.h>
 // must include pybind11/eigen.h if using eigen matrix as arguments.
+#include <pybind11/numpy.h>
+
 #include <algorithm>
 #include <boost/geometry.hpp>
-#include <pybind11/numpy.h>
 
 namespace spconv {
 // #include "voxelnet/core/cc/pybind11_helper.h"
@@ -40,9 +40,10 @@ inline py::array_t<DType> zeros(std::vector<long int> shape) {
 }
 
 template <typename DType>
-py::array_t<DType>
-rbbox_iou(py::array_t<DType> box_corners, py::array_t<DType> qbox_corners,
-          py::array_t<DType> standup_iou, DType standup_thresh) {
+py::array_t<DType> rbbox_iou(py::array_t<DType> box_corners,
+                             py::array_t<DType> qbox_corners,
+                             py::array_t<DType> standup_iou,
+                             DType standup_thresh) {
   namespace bg = boost::geometry;
   typedef bg::model::point<DType, 2, bg::cs::cartesian> point_t;
   typedef bg::model::polygon<point_t> polygon_t;
@@ -61,8 +62,7 @@ rbbox_iou(py::array_t<DType> box_corners, py::array_t<DType> qbox_corners,
   }
   for (int k = 0; k < K; ++k) {
     for (int n = 0; n < N; ++n) {
-      if (standup_iou_r(n, k) <= standup_thresh)
-        continue;
+      if (standup_iou_r(n, k) <= standup_thresh) continue;
       bg::append(poly, point_t(box_corners_r(n, 0, 0), box_corners_r(n, 0, 1)));
       bg::append(poly, point_t(box_corners_r(n, 1, 0), box_corners_r(n, 1, 1)));
       bg::append(poly, point_t(box_corners_r(n, 2, 0), box_corners_r(n, 2, 1)));
@@ -99,9 +99,10 @@ rbbox_iou(py::array_t<DType> box_corners, py::array_t<DType> qbox_corners,
 }
 
 template <typename DType>
-py::array_t<DType>
-rbbox_intersection(py::array_t<DType> box_corners, py::array_t<DType> qbox_corners,
-          py::array_t<DType> standup_iou, DType standup_thresh) {
+py::array_t<DType> rbbox_intersection(py::array_t<DType> box_corners,
+                                      py::array_t<DType> qbox_corners,
+                                      py::array_t<DType> standup_iou,
+                                      DType standup_thresh) {
   namespace bg = boost::geometry;
   typedef bg::model::point<DType, 2, bg::cs::cartesian> point_t;
   typedef bg::model::polygon<point_t> polygon_t;
@@ -120,8 +121,7 @@ rbbox_intersection(py::array_t<DType> box_corners, py::array_t<DType> qbox_corne
   }
   for (int k = 0; k < K; ++k) {
     for (int n = 0; n < N; ++n) {
-      if (standup_iou_r(n, k) <= standup_thresh)
-        continue;
+      if (standup_iou_r(n, k) <= standup_thresh) continue;
       bg::append(poly, point_t(box_corners_r(n, 0, 0), box_corners_r(n, 0, 1)));
       bg::append(poly, point_t(box_corners_r(n, 1, 0), box_corners_r(n, 1, 1)));
       bg::append(poly, point_t(box_corners_r(n, 2, 0), box_corners_r(n, 2, 1)));
@@ -152,6 +152,5 @@ rbbox_intersection(py::array_t<DType> box_corners, py::array_t<DType> qbox_corne
   return overlaps;
 }
 
-
-} // namespace spconv
+}  // namespace spconv
 #endif

mmdet3d/ops/spconv/include/spconv/geometry.h

@@ -15,9 +15,10 @@
 #ifndef SPCONV_GEOMETRY_H_
 #define SPCONV_GEOMETRY_H_
 
+#include <tensorview/tensorview.h>
+
 #include <iostream>
 #include <limits>
-#include <tensorview/tensorview.h>
 
 namespace spconv {
 template <typename Index, unsigned NDim>
@@ -70,8 +71,7 @@ TV_HOST_DEVICE Index getValidOutPos(const Index *input_pos,
     }
 
     out[pointCounter * (NDim + 1) + NDim] = offset;
-    if (valid)
-      ++pointCounter;
+    if (valid) ++pointCounter;
     counter[NDim - 1] += 1;
 #pragma unroll
     for (int c = NDim - 1; c >= 0; --c) {
@@ -128,8 +128,7 @@ TV_HOST_DEVICE Index getValidOutPosTranspose(
       m *= kernelSize[j];
     }
     out[pointCounter * (NDim + 1) + NDim] = offset;
-    if (valid)
-      ++pointCounter;
+    if (valid) ++pointCounter;
     counter[NDim - 1] += 1;
 #pragma unroll
     for (int c = NDim - 1; c >= 0; --c) {
@@ -167,7 +166,7 @@ Index getIndicePairsConv(tv::TensorView<const Index> indicesIn,
   }
   Index numValidPoints = 0;
   std::vector<Index> validPoints_(kernelVolume * (NDim + 1));
-  Index* validPoints = validPoints_.data();
+  Index *validPoints = validPoints_.data();
   Index *pointPtr = nullptr;
   for (int j = 0; j < numActIn; ++j) {
     batchIdx = indicesIn(j, 0);
@@ -218,7 +217,7 @@ Index getIndicePairsDeConv(tv::TensorView<const Index> indicesIn,
   }
   Index numValidPoints = 0;
   std::vector<Index> validPoints_(kernelVolume * (NDim + 1));
-  Index* validPoints = validPoints_.data();
+  Index *validPoints = validPoints_.data();
   Index *pointPtr = nullptr;
   for (int j = 0; j < numActIn; ++j) {
     batchIdx = indicesIn(j, 0);
@@ -252,7 +251,8 @@ Index getIndicePairsSubM(tv::TensorView<const Index> indicesIn,
                          tv::TensorView<Index> indiceNum,
                          const Index *const kernelSize,
                          const Index *const stride, const Index *const padding,
-                         const Index *dilation, const Index *const outSpatialShape) {
+                         const Index *dilation,
+                         const Index *const outSpatialShape) {
   Index numAct = 0;
   auto numActIn = indicesIn.dim(0);
   Index batchIdx = 0;
@@ -269,7 +269,7 @@ Index getIndicePairsSubM(tv::TensorView<const Index> indicesIn,
   Index numValidPoints = 0;
   // Index validPoints[kernelVolume * (NDim + 1)];
   std::vector<Index> validPoints_(kernelVolume * (NDim + 1));
-  Index* validPoints = validPoints_.data();
+  Index *validPoints = validPoints_.data();
   Index *pointPtr = nullptr;
   Index index = 0;
   for (int j = 0; j < numActIn; ++j) {
@@ -296,6 +296,6 @@ Index getIndicePairsSubM(tv::TensorView<const Index> indicesIn,
   return numActIn;
 }
 
-} // namespace spconv
+}  // namespace spconv
 
 #endif

mmdet3d/ops/spconv/include/spconv/indice.cu.h

@@ -14,9 +14,9 @@
 
 #ifndef INDICE_CU_H_
 #define INDICE_CU_H_
-#include <tensorview/tensorview.h>
-#include <tensorview/helper_kernel.cu.h>
 #include <spconv/geometry.h>
+#include <tensorview/helper_kernel.cu.h>
+#include <tensorview/tensorview.h>
 
 namespace spconv {
 template <typename Index, typename IndexGrid, unsigned NDim,
@@ -115,7 +115,6 @@ __global__ void assignGridAndIndiceOutKernel(
     int numAct, tv::TensorView<Index> indicePairs,
     tv::TensorView<Index> indicePairUnique,
     const tv::SimpleVector<Index, NDim> outSpatialShape, int batchSize) {
-
   Index index;
   auto indicesOutPtr = indicesOut.data();
   for (int ix : tv::KernelLoopX<int>(numAct)) {
@@ -128,13 +127,11 @@ __global__ void assignGridAndIndiceOutKernel(
 }
 
 template <typename Index, typename IndexGrid, unsigned NDim>
-__global__ void
-assignIndicePairsKernel(tv::TensorView<Index> indicesOut,
-                        tv::TensorView<IndexGrid> gridsOut, int numActIn,
-                        tv::TensorView<Index> indicePairs,
-                        tv::TensorView<Index> indicePairUnique,
-                        const tv::SimpleVector<Index, NDim> outSpatialShape) {
-
+__global__ void assignIndicePairsKernel(
+    tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
+    int numActIn, tv::TensorView<Index> indicePairs,
+    tv::TensorView<Index> indicePairUnique,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
   Index index;
   int kernelVolume = indicePairs.dim(0);
   for (int ix : tv::KernelLoopX<int>(numActIn)) {
@@ -148,10 +145,9 @@ assignIndicePairsKernel(tv::TensorView<Index> indicesOut,
 }
 
 template <typename Index, typename IndexGrid, unsigned NDim>
-__global__ void
-prepareSubMGridKernel(tv::TensorView<const Index> indicesIn,
-                  tv::TensorView<IndexGrid> gridsOut,
-                  const tv::SimpleVector<Index, NDim> outSpatialShape) {
+__global__ void prepareSubMGridKernel(
+    tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
+    const tv::SimpleVector<Index, NDim> outSpatialShape) {
   auto numActIn = indicesIn.dim(0);
   Index spatialVolume = 1;
 #pragma unroll
@@ -216,10 +212,9 @@ __global__ void resetGridKernel(const Index *indicePairUnique,
 }
 
 template <typename Index, typename IndexGrid, unsigned NDim>
-__global__ void
-resetGridSubMKernel(const Index *indices, tv::TensorView<IndexGrid> gridsOut,
-                    const tv::SimpleVector<Index, NDim> outSpatialShape,
-                    int numAct) {
+__global__ void resetGridSubMKernel(
+    const Index *indices, tv::TensorView<IndexGrid> gridsOut,
+    const tv::SimpleVector<Index, NDim> outSpatialShape, int numAct) {
   int outSpatialShapeReg[NDim];
   for (int i = 0; i < NDim; ++i) {
     outSpatialShapeReg[i] = outSpatialShape[i];
@@ -238,6 +233,6 @@ resetGridSubMKernel(const Index *indices, tv::TensorView<IndexGrid> gridsOut,
   }
 }
 
-} // namespace spconv
+}  // namespace spconv
 
 #endif

mmdet3d/ops/spconv/include/spconv/indice.h

@@ -16,64 +16,65 @@
 #define SPARSE_CONV_INDICE_FUNCTOR_H_
 #include <tensorview/tensorview.h>
 
-namespace spconv
-{
-namespace functor
-{
+namespace spconv {
+namespace functor {
 template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
-struct CreateConvIndicePairFunctorP1
-{
-    Index operator()(
-        const Device& d, tv::TensorView<const Index> indicesIn,
-        tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
-        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
-        tv::TensorView<Index> indicePairUnique,
-        const tv::SimpleVector<Index, NDim> kernelSize,
-        const tv::SimpleVector<Index, NDim> stride,
-        const tv::SimpleVector<Index, NDim> padding,
-        const tv::SimpleVector<Index, NDim> dilation,
-        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose);
+struct CreateConvIndicePairFunctorP1 {
+  Index operator()(const Device& d, tv::TensorView<const Index> indicesIn,
+                   tv::TensorView<Index> indicesOut,
+                   tv::TensorView<IndexGrid> gridsOut,
+                   tv::TensorView<Index> indicePairs,
+                   tv::TensorView<Index> indiceNum,
+                   tv::TensorView<Index> indicePairUnique,
+                   const tv::SimpleVector<Index, NDim> kernelSize,
+                   const tv::SimpleVector<Index, NDim> stride,
+                   const tv::SimpleVector<Index, NDim> padding,
+                   const tv::SimpleVector<Index, NDim> dilation,
+                   const tv::SimpleVector<Index, NDim> outSpatialShape,
+                   bool transpose);
 };
 
 template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
-struct CreateConvIndicePairFunctorP2
-{
-    Index operator()(
-        const Device& d, tv::TensorView<const Index> indicesIn,
-        tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
-        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
-        tv::TensorView<Index> indicePairUnique,
-        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose,
-        bool resetGrid=false);
+struct CreateConvIndicePairFunctorP2 {
+  Index operator()(const Device& d, tv::TensorView<const Index> indicesIn,
+                   tv::TensorView<Index> indicesOut,
+                   tv::TensorView<IndexGrid> gridsOut,
+                   tv::TensorView<Index> indicePairs,
+                   tv::TensorView<Index> indiceNum,
+                   tv::TensorView<Index> indicePairUnique,
+                   const tv::SimpleVector<Index, NDim> outSpatialShape,
+                   bool transpose, bool resetGrid = false);
 };
 
 template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
-struct CreateConvIndicePairFunctor
-{
-    Index operator()(
-        const Device& d, tv::TensorView<const Index> indicesIn,
-        tv::TensorView<Index> indicesOut, tv::TensorView<IndexGrid> gridsOut,
-        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
-        const tv::SimpleVector<Index, NDim> kernelSize,
-        const tv::SimpleVector<Index, NDim> stride,
-        const tv::SimpleVector<Index, NDim> padding,
-        const tv::SimpleVector<Index, NDim> dilation,
-        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose, bool resetGrid=false);
+struct CreateConvIndicePairFunctor {
+  Index operator()(const Device& d, tv::TensorView<const Index> indicesIn,
+                   tv::TensorView<Index> indicesOut,
+                   tv::TensorView<IndexGrid> gridsOut,
+                   tv::TensorView<Index> indicePairs,
+                   tv::TensorView<Index> indiceNum,
+                   const tv::SimpleVector<Index, NDim> kernelSize,
+                   const tv::SimpleVector<Index, NDim> stride,
+                   const tv::SimpleVector<Index, NDim> padding,
+                   const tv::SimpleVector<Index, NDim> dilation,
+                   const tv::SimpleVector<Index, NDim> outSpatialShape,
+                   bool transpose, bool resetGrid = false);
 };
 
 template <typename Device, typename Index, typename IndexGrid, unsigned NDim>
-struct CreateSubMIndicePairFunctor
-{
-    Index operator()(
-        const Device& d, tv::TensorView<const Index> indicesIn, tv::TensorView<IndexGrid> gridsOut,
-        tv::TensorView<Index> indicePairs, tv::TensorView<Index> indiceNum,
-        const tv::SimpleVector<Index, NDim> kernelSize,
-        const tv::SimpleVector<Index, NDim> stride,
-        const tv::SimpleVector<Index, NDim> padding,
-        const tv::SimpleVector<Index, NDim> dilation,
-        const tv::SimpleVector<Index, NDim> outSpatialShape, bool transpose, bool resetGrid=false);
+struct CreateSubMIndicePairFunctor {
+  Index operator()(const Device& d, tv::TensorView<const Index> indicesIn,
+                   tv::TensorView<IndexGrid> gridsOut,
+                   tv::TensorView<Index> indicePairs,
+                   tv::TensorView<Index> indiceNum,
+                   const tv::SimpleVector<Index, NDim> kernelSize,
+                   const tv::SimpleVector<Index, NDim> stride,
+                   const tv::SimpleVector<Index, NDim> padding,
+                   const tv::SimpleVector<Index, NDim> dilation,
+                   const tv::SimpleVector<Index, NDim> outSpatialShape,
+                   bool transpose, bool resetGrid = false);
 };
-} // namespace functor
-} // namespace spconv
+}  // namespace functor
+}  // namespace spconv
 
 #endif