baseline_null / pc_util /src /interpolate.cpp

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#include <torch/serialize/tensor.h>
#include <vector>
// #include <THC/THC.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <cuda_runtime_api.h>
#include "interpolate_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)


void three_nn_wrapper_fast(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>();

    three_nn_kernel_launcher_fast(b, n, m, unknown, known, dist2, idx);
}


void three_interpolate_wrapper_fast(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>();


    three_interpolate_kernel_launcher_fast(b, c, m, n, points, idx, weight, out);
}

void three_interpolate_grad_wrapper_fast(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>();

    three_interpolate_grad_kernel_launcher_fast(b, c, n, m, grad_out, idx, weight, grad_points);
}


void three_nn_wrapper_stack(at::Tensor unknown_tensor,
    at::Tensor unknown_batch_cnt_tensor, at::Tensor known_tensor,
    at::Tensor known_batch_cnt_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor){
    // unknown: (N1 + N2 ..., 3)
    // unknown_batch_cnt: (batch_size), [N1, N2, ...]
    // known: (M1 + M2 ..., 3)
    // known_batch_cnt: (batch_size), [M1, M2, ...]
    // Return:
    // dist: (N1 + N2 ..., 3)  l2 distance to the three nearest neighbors
    // idx: (N1 + N2 ..., 3)  index of the three nearest neighbors
    CHECK_INPUT(unknown_tensor);
    CHECK_INPUT(unknown_batch_cnt_tensor);
    CHECK_INPUT(known_tensor);
    CHECK_INPUT(known_batch_cnt_tensor);
    CHECK_INPUT(dist2_tensor);
    CHECK_INPUT(idx_tensor);

    int batch_size = unknown_batch_cnt_tensor.size(0);
    int N = unknown_tensor.size(0);
    int M = known_tensor.size(0);
    const float *unknown = unknown_tensor.data<float>();
    const int *unknown_batch_cnt = unknown_batch_cnt_tensor.data<int>();
    const float *known = known_tensor.data<float>();
    const int *known_batch_cnt = known_batch_cnt_tensor.data<int>();
    float *dist2 = dist2_tensor.data<float>();
    int *idx = idx_tensor.data<int>();

    three_nn_kernel_launcher_stack(batch_size, N, M, unknown, unknown_batch_cnt, known, known_batch_cnt, dist2, idx);
}


void three_interpolate_wrapper_stack(at::Tensor features_tensor,
    at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor) {
    // features_tensor: (M1 + M2 ..., C)
    // idx_tensor: [N1 + N2 ..., 3]
    // weight_tensor: [N1 + N2 ..., 3]
    // Return:
    // out_tensor: (N1 + N2 ..., C)
    CHECK_INPUT(features_tensor);
    CHECK_INPUT(idx_tensor);
    CHECK_INPUT(weight_tensor);
    CHECK_INPUT(out_tensor);

    int N = out_tensor.size(0);
    int channels = features_tensor.size(1);
    const float *features = features_tensor.data<float>();
    const float *weight = weight_tensor.data<float>();
    const int *idx = idx_tensor.data<int>();
    float *out = out_tensor.data<float>();

    three_interpolate_kernel_launcher_stack(N, channels, features, idx, weight, out);
}


void three_interpolate_grad_wrapper_stack(at::Tensor grad_out_tensor, at::Tensor idx_tensor,
    at::Tensor weight_tensor, at::Tensor grad_features_tensor) {
    // grad_out_tensor: (N1 + N2 ..., C)
    // idx_tensor: [N1 + N2 ..., 3]
    // weight_tensor: [N1 + N2 ..., 3]
    // Return:
    // grad_features_tensor: (M1 + M2 ..., C)
    CHECK_INPUT(grad_out_tensor);
    CHECK_INPUT(idx_tensor);
    CHECK_INPUT(weight_tensor);
    CHECK_INPUT(grad_features_tensor);

    int N = grad_out_tensor.size(0);
    int channels = grad_out_tensor.size(1);
    const float *grad_out = grad_out_tensor.data<float>();
    const float *weight = weight_tensor.data<float>();
    const int *idx = idx_tensor.data<int>();
    float *grad_features = grad_features_tensor.data<float>();

    // printf("N=%d, channels=%d\n", N, channels);
    three_interpolate_grad_kernel_launcher_stack(N, channels, grad_out, idx, weight, grad_features);
}

	#include <torch/serialize/tensor.h>
	#include <vector>
	// #include <THC/THC.h>
	#include <math.h>
	#include <stdio.h>
	#include <stdlib.h>
	#include <cuda.h>
	#include <cuda_runtime_api.h>
	#include "interpolate_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)


	void three_nn_wrapper_fast(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>();

	three_nn_kernel_launcher_fast(b, n, m, unknown, known, dist2, idx);
	}


	void three_interpolate_wrapper_fast(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>();


	three_interpolate_kernel_launcher_fast(b, c, m, n, points, idx, weight, out);
	}

	void three_interpolate_grad_wrapper_fast(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>();

	three_interpolate_grad_kernel_launcher_fast(b, c, n, m, grad_out, idx, weight, grad_points);
	}


	void three_nn_wrapper_stack(at::Tensor unknown_tensor,
	at::Tensor unknown_batch_cnt_tensor, at::Tensor known_tensor,
	at::Tensor known_batch_cnt_tensor, at::Tensor dist2_tensor, at::Tensor idx_tensor){
	// unknown: (N1 + N2 ..., 3)
	// unknown_batch_cnt: (batch_size), [N1, N2, ...]
	// known: (M1 + M2 ..., 3)
	// known_batch_cnt: (batch_size), [M1, M2, ...]
	// Return:
	// dist: (N1 + N2 ..., 3) l2 distance to the three nearest neighbors
	// idx: (N1 + N2 ..., 3) index of the three nearest neighbors
	CHECK_INPUT(unknown_tensor);
	CHECK_INPUT(unknown_batch_cnt_tensor);
	CHECK_INPUT(known_tensor);
	CHECK_INPUT(known_batch_cnt_tensor);
	CHECK_INPUT(dist2_tensor);
	CHECK_INPUT(idx_tensor);

	int batch_size = unknown_batch_cnt_tensor.size(0);
	int N = unknown_tensor.size(0);
	int M = known_tensor.size(0);
	const float *unknown = unknown_tensor.data<float>();
	const int *unknown_batch_cnt = unknown_batch_cnt_tensor.data<int>();
	const float *known = known_tensor.data<float>();
	const int *known_batch_cnt = known_batch_cnt_tensor.data<int>();
	float *dist2 = dist2_tensor.data<float>();
	int *idx = idx_tensor.data<int>();

	three_nn_kernel_launcher_stack(batch_size, N, M, unknown, unknown_batch_cnt, known, known_batch_cnt, dist2, idx);
	}


	void three_interpolate_wrapper_stack(at::Tensor features_tensor,
	at::Tensor idx_tensor, at::Tensor weight_tensor, at::Tensor out_tensor) {
	// features_tensor: (M1 + M2 ..., C)
	// idx_tensor: [N1 + N2 ..., 3]
	// weight_tensor: [N1 + N2 ..., 3]
	// Return:
	// out_tensor: (N1 + N2 ..., C)
	CHECK_INPUT(features_tensor);
	CHECK_INPUT(idx_tensor);
	CHECK_INPUT(weight_tensor);
	CHECK_INPUT(out_tensor);

	int N = out_tensor.size(0);
	int channels = features_tensor.size(1);
	const float *features = features_tensor.data<float>();
	const float *weight = weight_tensor.data<float>();
	const int *idx = idx_tensor.data<int>();
	float *out = out_tensor.data<float>();

	three_interpolate_kernel_launcher_stack(N, channels, features, idx, weight, out);
	}


	void three_interpolate_grad_wrapper_stack(at::Tensor grad_out_tensor, at::Tensor idx_tensor,
	at::Tensor weight_tensor, at::Tensor grad_features_tensor) {
	// grad_out_tensor: (N1 + N2 ..., C)
	// idx_tensor: [N1 + N2 ..., 3]
	// weight_tensor: [N1 + N2 ..., 3]
	// Return:
	// grad_features_tensor: (M1 + M2 ..., C)
	CHECK_INPUT(grad_out_tensor);
	CHECK_INPUT(idx_tensor);
	CHECK_INPUT(weight_tensor);
	CHECK_INPUT(grad_features_tensor);

	int N = grad_out_tensor.size(0);
	int channels = grad_out_tensor.size(1);
	const float *grad_out = grad_out_tensor.data<float>();
	const float *weight = weight_tensor.data<float>();
	const int *idx = idx_tensor.data<int>();
	float *grad_features = grad_features_tensor.data<float>();

	// printf("N=%d, channels=%d\n", N, channels);
	three_interpolate_grad_kernel_launcher_stack(N, channels, grad_out, idx, weight, grad_features);
	}