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Commit 962fc027 authored by Alexander Liao's avatar Alexander Liao
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attempting to fix windows build memory error

parent d4fe021d
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Showing with 48 additions and 46 deletions
csrc/cpu/radius_cpu.cpp
View file @ 962fc027
#include "radius_cpu.h"
#include <algorithm>
#include "utils.h"
#include <cstdint>
torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support,
float radius, int max_num){
double radius, int64_t max_num){
CHECK_CPU(query);
CHECK_CPU(support);
torch::Tensor out;
std::vector<long> neighbors_indices;
std::vector<size_t>* neighbors_indices = new std::vector<size_t>();
auto options = torch::TensorOptions().dtype(torch::kLong).device(torch::kCPU);
int max_count = 0;
......@@ -28,9 +30,9 @@ torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support,
});
long* neighbors_indices_ptr = neighbors_indices.data();
size_t* neighbors_indices_ptr = neighbors_indices->data();
const long long tsize = static_cast<long long>(neighbors_indices.size()/2);
const long long tsize = static_cast<long long>(neighbors_indices->size()/2);
out = torch::from_blob(neighbors_indices_ptr, {tsize, 2}, options=options);
out = out.t();
......@@ -60,7 +62,7 @@ torch::Tensor batch_radius_cpu(torch::Tensor query,
torch::Tensor support,
torch::Tensor query_batch,
torch::Tensor support_batch,
float radius, int max_num) {
double radius, int64_t max_num) {
torch::Tensor out;
auto data_qb = query_batch.data_ptr<int64_t>();
......@@ -71,7 +73,7 @@ torch::Tensor batch_radius_cpu(torch::Tensor query,
std::vector<long> support_batch_stl = std::vector<long>(data_sb, data_sb+support_batch.size(0));
std::vector<long> size_support_batch_stl;
get_size_batch(support_batch_stl, size_support_batch_stl);
std::vector<long> neighbors_indices;
std::vector<size_t>* neighbors_indices = new std::vector<size_t>();
auto options = torch::TensorOptions().dtype(torch::kLong).device(torch::kCPU);
int max_count = 0;
......@@ -95,10 +97,10 @@ torch::Tensor batch_radius_cpu(torch::Tensor query,
);
});
long* neighbors_indices_ptr = neighbors_indices.data();
size_t* neighbors_indices_ptr = neighbors_indices->data();
const long long tsize = static_cast<long long>(neighbors_indices.size()/2);
const long long tsize = static_cast<long long>(neighbors_indices->size()/2);
out = torch::from_blob(neighbors_indices_ptr, {tsize, 2}, options=options);
out = out.t();
......
csrc/cpu/radius_cpu.h
View file @ 962fc027
......@@ -7,10 +7,10 @@
#include "compat.h"
torch::Tensor radius_cpu(torch::Tensor query, torch::Tensor support,
float radius, int max_num);
double radius, int64_t max_num);
torch::Tensor batch_radius_cpu(torch::Tensor query,
torch::Tensor support,
torch::Tensor query_batch,
torch::Tensor support_batch,
float radius, int max_num);
\ No newline at end of file
double radius, int64_t max_num);
\ No newline at end of file
csrc/cpu/utils/cloud.h
View file @ 962fc027
......@@ -20,17 +20,17 @@
template<typename scalar_t>
struct PointCloud
{
std::vector<std::vector<scalar_t>> pts;
std::vector<std::vector<scalar_t>*> pts;
void set(std::vector<scalar_t> new_pts, int dim){
std::vector<std::vector<scalar_t>> temp(new_pts.size()/dim);
std::vector<std::vector<scalar_t>*> temp(new_pts.size()/dim);
for(size_t i=0; i < new_pts.size(); i++){
if(i%dim == 0){
std::vector<scalar_t> point(dim);
std::vector<scalar_t>* point = new std::vector<scalar_t>(dim);
for (size_t j = 0; j < (size_t)dim; j++) {
point[j]=new_pts[i+j];
(*point)[j]=new_pts[i+j];
}
temp[i/dim] = point;
}
......@@ -38,12 +38,12 @@ struct PointCloud
pts = temp;
}
void set_batch(std::vector<scalar_t> new_pts, int begin, int size, int dim){
std::vector<std::vector<scalar_t>> temp(size);
for(int i=0; i < size; i++){
std::vector<scalar_t> point(dim);
void set_batch(std::vector<scalar_t> new_pts, size_t begin, long size, int dim){
std::vector<std::vector<scalar_t>*> temp(size);
for(size_t i=0; i < (size_t)size; i++){
std::vector<scalar_t>* point = new std::vector<scalar_t>(dim);
for (size_t j = 0; j < (size_t)dim; j++) {
point[j] = new_pts[dim*(begin+i)+j];
(*point)[j] = new_pts[dim*(begin+i)+j];
}
temp[i] = point;
......@@ -58,7 +58,7 @@ struct PointCloud
// Returns the dim'th component of the idx'th point in the class:
inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const
{
return pts[idx][dim];
return (*pts[idx])[dim];
}
// Optional bounding-box computation: return false to default to a standard bbox computation loop.
......
csrc/cpu/utils/neighbors.cpp
View file @ 962fc027
......@@ -4,8 +4,8 @@
#include "neighbors.h"
template<typename scalar_t>
int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
vector<long>& neighbors_indices, float radius, int dim, int max_num){
size_t nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
vector<size_t>*& neighbors_indices, double radius, int dim, int64_t max_num){
const scalar_t search_radius = static_cast<scalar_t>(radius*radius);
......@@ -37,13 +37,13 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
// search_params.sorted = true;
std::vector< std::vector<std::pair<size_t, scalar_t> > > list_matches(pcd_query.pts.size());
float eps = 0.000001;
double eps = 0.000001;
// indices
size_t i0 = 0;
for (auto& p0 : pcd_query.pts){
for (auto& p : pcd_query.pts){
auto p0 = *p;
// Find neighbors
scalar_t* query_pt = new scalar_t[dim];
std::copy(p0.begin(), p0.end(), query_pt);
......@@ -51,7 +51,7 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
list_matches[i0].reserve(max_count);
std::vector<std::pair<size_t, scalar_t> > ret_matches;
const size_t nMatches = index->radiusSearch(query_pt, search_radius+eps, ret_matches, search_params);
const size_t nMatches = index->radiusSearch(query_pt, (scalar_t)(search_radius+eps), ret_matches, search_params);
list_matches[i0] = ret_matches;
if(max_count < nMatches) max_count = nMatches;
......@@ -71,14 +71,14 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
size += max_count;
}
neighbors_indices.resize(size*2);
neighbors_indices->resize(size*2);
size_t i1 = 0; // index of the query points
size_t u = 0; // curent index of the neighbors_indices
for (auto& inds : list_matches){
for (size_t j = 0; j < max_count; j++){
if(j < inds.size()){
neighbors_indices[u] = inds[j].first;
neighbors_indices[u + 1] = i1;
(*neighbors_indices)[u] = inds[j].first;
(*neighbors_indices)[u + 1] = i1;
u += 2;
}
}
......@@ -93,12 +93,12 @@ int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
}
template<typename scalar_t>
int batch_nanoflann_neighbors (vector<scalar_t>& queries,
size_t batch_nanoflann_neighbors (vector<scalar_t>& queries,
vector<scalar_t>& supports,
vector<long>& q_batches,
vector<long>& s_batches,
vector<long>& neighbors_indices,
float radius, int dim, int max_num){
vector<size_t>*& neighbors_indices,
double radius, int dim, int64_t max_num){
// Initiate variables
......@@ -117,7 +117,7 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
size_t sum_qb = 0;
size_t sum_sb = 0;
float eps = 0.000001;
double eps = 0.000001;
// Nanoflann related variables
// ***************************
......@@ -145,7 +145,8 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
nanoflann::SearchParams search_params;
search_params.sorted = true;
for (auto& p0 : query_pcd.pts){
for (auto& p : query_pcd.pts){
auto p0 = *p;
// Check if we changed batch
scalar_t* query_pt = new scalar_t[dim];
......@@ -193,7 +194,7 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
else
size += max_count;
}
neighbors_indices.resize(size * 2);
neighbors_indices->resize(size * 2);
i0 = 0;
sum_sb = 0;
sum_qb = 0;
......@@ -207,8 +208,8 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
}
for (size_t j = 0; j < max_count; j++){
if (j < inds_dists.size()){
neighbors_indices[u] = inds_dists[j].first + sum_sb;
neighbors_indices[u + 1] = i0;
(*neighbors_indices)[u] = inds_dists[j].first + sum_sb;
(*neighbors_indices)[u + 1] = i0;
u += 2;
}
}
......
csrc/cpu/utils/neighbors.h
View file @ 962fc027
......@@ -11,7 +11,7 @@ using namespace std;
template<typename scalar_t>
int nanoflann_neighbors(vector<scalar_t>& queries, vector<scalar_t>& supports,
vector<long>& neighbors_indices, float radius, int dim, int max_num);
vector<long>& neighbors_indices, double radius, int dim, int64_t max_num);
template<typename scalar_t>
int batch_nanoflann_neighbors (vector<scalar_t>& queries,
......@@ -19,4 +19,4 @@ int batch_nanoflann_neighbors (vector<scalar_t>& queries,
vector<long>& q_batches,
vector<long>& s_batches,
vector<long>& neighbors_indices,
float radius, int dim, int max_num);
\ No newline at end of file
double radius, int dim, int64_t max_num);
\ No newline at end of file
test/test_radius.py
View file @ 962fc027
......@@ -3,7 +3,6 @@ from itertools import product
import pytest
import torch
from torch_cluster import radius, radius_graph
from .utils import grad_dtypes, devices, tensor
......@@ -115,8 +114,8 @@ def test_radius_graph_pointnet_small(dtype, device):
row, col = radius_graph(x, r=0.2, flow='source_to_target', batch=batch)
edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
col.cpu().numpy())])
edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
list(col.cpu().numpy()))])
truth_row = [10, 11, 7, 9, 9, 1, 9, 1, 6, 7, 0, 11, 0, 10, 15, 12, 20, 16,
34, 31, 44, 43, 42, 41]
......@@ -404,8 +403,8 @@ def test_radius_graph_pointnet_medium(dtype, device):
row, col = radius_graph(x, r=0.2, flow='source_to_target', batch=batch)
edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
col.cpu().numpy())])
edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
list(col.cpu().numpy()))])
truth_row = [6, 27, 17, 31, 3, 23, 62, 2, 14, 23, 36, 38, 62, 15, 0, 11,
27, 29, 50, 49, 54, 56, 12, 61, 16, 21, 24, 39, 6, 27, 29,
......@@ -573,8 +572,8 @@ def test_radius_graph_ndim(dtype, device):
row, col = radius_graph(x, r=4.4, flow='source_to_target', batch=batch)
edges = set([(i, j) for (i, j) in zip(row.cpu().numpy(),
col.cpu().numpy())])
edges = set([(i, j) for (i, j) in zip(list(row.cpu().numpy()),
list(col.cpu().numpy()))])
truth_row = [2, 3, 2, 3, 0, 1, 3, 4, 0, 1, 2, 4, 2, 3, 6, 7, 9, 10, 5, 7,
8, 9, 10, 5, 6, 10, 6, 5, 6, 10, 5, 6, 7, 9, 13, 11, 16, 17,
......
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