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2 changes: 1 addition & 1 deletion torchsparse/src/hashmap/hashmap_cpu.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ HashTableCPU::lookup_vals(const int64_t * const keys, int64_t * const results, c



int
void
HashTableCPU::insert_vals(const int64_t * const keys, const int64_t * const vals, const int n) {
for(int i = 0; i < 10; i++){
printf("%d, %d, %d, %d\n", i, i<n, n, i<10);
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2 changes: 1 addition & 1 deletion torchsparse/src/hashmap/hashmap_cpu_header.hpp
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ class HashTableCPU {
//hashmap.set_empty_key(0);
}
~HashTableCPU(){}
int insert_vals(const int64_t * const keys, const int64_t * const vals, const int n);
void insert_vals(const int64_t * const keys, const int64_t * const vals, const int n);
void lookup_vals(const int64_t * const keys, int64_t * const results, const int n);

};
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2 changes: 1 addition & 1 deletion torchsparse/src/interpolation/devox_cpu.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ at::Tensor cpu_devoxelize_forward(
const at::Tensor indices,
const at::Tensor weight)
{
int b = feat.size(0);
//int b = feat.size(0);
//printf("%d\n", b);
int c = feat.size(1);
int N = indices.size(0);
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2 changes: 1 addition & 1 deletion torchsparse/src/interpolation/devox_deterministic.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ at::Tensor deterministic_devoxelize_forward(
const at::Tensor indices,
const at::Tensor weight)
{
int b = feat.size(0);
//int b = feat.size(0);
//printf("%d\n", b);
int c = feat.size(1);
int N = indices.size(0);
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2 changes: 1 addition & 1 deletion torchsparse/src/others/insertion_cpu.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ at::Tensor cpu_insertion_backward(
//return group_point_forward_gpu(points, indices);

int c = top_grad.size(1);
int N1 = counts.size(0);
//int N1 = counts.size(0);
at::Tensor bottom_grad = torch::zeros({N, c}, at::device(idx.device()).dtype(at::ScalarType::Float));
for (int i = 0; i < N; i++)
{
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