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baseline_null

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baseline_null
File size: 3,326 Bytes 
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#include <torch/serialize/tensor.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#include <vector>
// #include <THC/THC.h>
#include "group_points_gpu.h"

// extern THCState *state;

#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDAEvent.h>
// cudaStream_t stream = at::cuda::getCurrentCUDAStream();

#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)



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;
}







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;
}