| #include <torch/serialize/tensor.h> |
| #include <cuda.h> |
| #include <cuda_runtime_api.h> |
| #include <vector> |
| |
| #include "group_points_gpu.h" |
|
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| |
|
|
| #include <ATen/cuda/CUDAContext.h> |
| #include <ATen/cuda/CUDAEvent.h> |
| |
|
|
| #define CHECK_CUDA(x) do { \ |
| if (!x.type().is_cuda()) { \ |
| fprintf(stderr, "%s must be CUDA tensor at %s:%d\n", #x, __FILE__, __LINE__); \ |
| exit(-1); \ |
| } \ |
| } while (0) |
| #define CHECK_CONTIGUOUS(x) do { \ |
| if (!x.is_contiguous()) { \ |
| fprintf(stderr, "%s must be contiguous tensor at %s:%d\n", #x, __FILE__, __LINE__); \ |
| exit(-1); \ |
| } \ |
| } while (0) |
| #define CHECK_INPUT(x) CHECK_CUDA(x);CHECK_CONTIGUOUS(x) |
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|
| int group_points_grad_wrapper_fast(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>(); |
|
|
| group_points_grad_kernel_launcher_fast(b, c, n, npoints, nsample, grad_out, idx, grad_points); |
| return 1; |
| } |
|
|
|
|
| int group_points_wrapper_fast(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>(); |
|
|
| group_points_kernel_launcher_fast(b, c, n, npoints, nsample, points, idx, out); |
| return 1; |
| } |
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| int group_points_grad_wrapper_stack(int B, int M, int C, int N, int nsample, |
| at::Tensor grad_out_tensor, at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor, |
| at::Tensor features_batch_cnt_tensor, at::Tensor grad_features_tensor) { |
|
|
| CHECK_INPUT(grad_out_tensor); |
| CHECK_INPUT(idx_tensor); |
| CHECK_INPUT(idx_batch_cnt_tensor); |
| CHECK_INPUT(features_batch_cnt_tensor); |
| CHECK_INPUT(grad_features_tensor); |
|
|
| const float *grad_out = grad_out_tensor.data<float>(); |
| const int *idx = idx_tensor.data<int>(); |
| const int *idx_batch_cnt = idx_batch_cnt_tensor.data<int>(); |
| const int *features_batch_cnt = features_batch_cnt_tensor.data<int>(); |
| float *grad_features = grad_features_tensor.data<float>(); |
|
|
| group_points_grad_kernel_launcher_stack(B, M, C, N, nsample, grad_out, idx, idx_batch_cnt, features_batch_cnt, grad_features); |
| return 1; |
| } |
|
|
|
|
| int group_points_wrapper_stack(int B, int M, int C, int nsample, |
| at::Tensor features_tensor, at::Tensor features_batch_cnt_tensor, |
| at::Tensor idx_tensor, at::Tensor idx_batch_cnt_tensor, at::Tensor out_tensor) { |
|
|
| CHECK_INPUT(features_tensor); |
| CHECK_INPUT(features_batch_cnt_tensor); |
| CHECK_INPUT(idx_tensor); |
| CHECK_INPUT(idx_batch_cnt_tensor); |
| CHECK_INPUT(out_tensor); |
|
|
| const float *features = features_tensor.data<float>(); |
| const int *idx = idx_tensor.data<int>(); |
| const int *features_batch_cnt = features_batch_cnt_tensor.data<int>(); |
| const int *idx_batch_cnt = idx_batch_cnt_tensor.data<int>(); |
| float *out = out_tensor.data<float>(); |
|
|
| group_points_kernel_launcher_stack(B, M, C, nsample, features, features_batch_cnt, idx, idx_batch_cnt, out); |
| return 1; |
| } |
