Merge pull request #17 from nicolas-chaulet/formating

Formating

Author: nicolas-chaulet

.gitignore

@@ -36,4 +36,4 @@ build
 
 .vscode/
 dist/
-torch_points.egg-info/
+torch_points.egg-info/

.pre-commit-config.yaml

@@ -1,15 +1,23 @@
 exclude: "build|egg-info|dist"
 
 repos:
-    - repo: https://github.com/pre-commit/pre-commit-hooks
-      rev: v1.2.3
-      hooks:
-          - id: trailing-whitespace
-          - id: check-added-large-files
-          - id: end-of-file-fixer
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v1.2.3
+    hooks:
+      - id: trailing-whitespace
+      - id: check-added-large-files
+      - id: end-of-file-fixer
 
-    - repo: https://github.com/ambv/black
-      rev: stable
-      hooks:
-          - id: black
-            language_version: python3.6
+  - repo: https://github.com/ambv/black
+    rev: stable
+    hooks:
+      - id: black
+        language_version: python3.6
+  - repo: local
+    hooks:
+      - id: clang-format
+        name: Run clang-format
+        entry: clang-format --style google -i
+        types: [text]
+        files: '.*\.cpp$|.*\.h$|.*\.cu$|.*\.hpp$'
+        language: system

README.md

@@ -25,7 +25,7 @@ python -m unittest
 
 ## Projects using those kernels.
 
-[```Pytorch Point Cloud Benchmark```](https://github.com/nicolas-chaulet/deeppointcloud-benchmarks) by 
+[```Pytorch Point Cloud Benchmark```](https://github.com/nicolas-chaulet/deeppointcloud-benchmarks) by
 * [Thomas Chaton](https://github.com/tchaton)
 * Nicolas Chaulet
 * [Tristan Heywood](https://github.com/tristanheywood)

cpu/include/ball_query.h

@@ -1,15 +1,14 @@
 #pragma once
 #include <torch/extension.h>
 std::pair<at::Tensor, at::Tensor> ball_query(at::Tensor query,
-					     at::Tensor support,
-					     float radius, int max_num, int mode);
+                                             at::Tensor support, float radius,
+                                             int max_num, int mode);
 
-std::pair<at::Tensor, at::Tensor> batch_ball_query(at::Tensor query,
-						   at::Tensor support,
-						   at::Tensor query_batch,
-						   at::Tensor support_batch,
-						   float radius, int max_num, int mode);
+std::pair<at::Tensor, at::Tensor> batch_ball_query(
+    at::Tensor query, at::Tensor support, at::Tensor query_batch,
+    at::Tensor support_batch, float radius, int max_num, int mode);
 
 std::pair<at::Tensor, at::Tensor> dense_ball_query(at::Tensor query,
-						   at::Tensor support,
-						   float radius, int max_num, int mode);
+                                                   at::Tensor support,
+                                                   float radius, int max_num,
+                                                   int mode);

cpu/include/cloud.h

@@ -16,78 +16,77 @@
 //		Hugues THOMAS - 10/02/2017
 //
 
+#pragma once
 
-# pragma once
-
+#include <algorithm>
 #include <cmath>
-#include <vector>
-#include <unordered_map>
+#include <iomanip>
+#include <iostream>
 #include <map>
-#include <algorithm>
 #include <numeric>
-#include <iostream>
-#include <iomanip>
+#include <unordered_map>
+#include <vector>
 
 #include <time.h>
 
-
-template<typename scalar_t>
-struct PointCloud
-{
-	struct PointXYZ {
-		scalar_t x,y,z;
-	};
-
-	std::vector<PointXYZ> pts;
-
-	void set(std::vector<scalar_t> new_pts){
-
-		// pts = std::vector<PointXYZ>((PointXYZ*)new_pts, (PointXYZ*)new_pts+new_pts.size()/3);
-		std::vector<PointXYZ> temp(new_pts.size()/3);
-		for(unsigned int i=0; i < new_pts.size(); i++){
-			if(i%3 == 0){
-
-				PointXYZ point;
-				point.x = new_pts[i];
-				point.y = new_pts[i+1];
-				point.z = new_pts[i+2];
-				temp[i/3] = point;
-			}
-		}
-		pts = temp;
-	}
-	void set_batch(std::vector<scalar_t> new_pts, int begin, int size){
-
-		std::vector<PointXYZ> temp(size);
-		for(int i=0; i < size; i++){
-			PointXYZ point;
-			point.x = new_pts[3*(begin+i)];
-			point.y = new_pts[3*(begin+i) + 1];
-			point.z = new_pts[3*(begin+i) + 2];
-			temp[i] = point;
-
-		}
-		pts = temp;
-	}
-
-	// Must return the number of data points
-	inline size_t kdtree_get_point_count() const { return pts.size(); }
-
-	// Returns the dim'th component of the idx'th point in the class:
-	// Since this is inlined and the "dim" argument is typically an immediate value, the
-	//  "if/else's" are actually solved at compile time.
-	inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const
-	{
-		if (dim == 0) return pts[idx].x;
-		else if (dim == 1) return pts[idx].y;
-		else return pts[idx].z;
-	}
-
-	// Optional bounding-box computation: return false to default to a standard bbox computation loop.
-	//   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
-	//   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
-	template <class BBOX>
-	bool kdtree_get_bbox(BBOX& /* bb */) const { return false; }
-
-
+template <typename scalar_t>
+struct PointCloud {
+  struct PointXYZ {
+    scalar_t x, y, z;
+  };
+
+  std::vector<PointXYZ> pts;
+
+  void set(std::vector<scalar_t> new_pts) {
+    // pts = std::vector<PointXYZ>((PointXYZ*)new_pts,
+    // (PointXYZ*)new_pts+new_pts.size()/3);
+    std::vector<PointXYZ> temp(new_pts.size() / 3);
+    for (unsigned int i = 0; i < new_pts.size(); i++) {
+      if (i % 3 == 0) {
+        PointXYZ point;
+        point.x = new_pts[i];
+        point.y = new_pts[i + 1];
+        point.z = new_pts[i + 2];
+        temp[i / 3] = point;
+      }
+    }
+    pts = temp;
+  }
+  void set_batch(std::vector<scalar_t> new_pts, int begin, int size) {
+    std::vector<PointXYZ> temp(size);
+    for (int i = 0; i < size; i++) {
+      PointXYZ point;
+      point.x = new_pts[3 * (begin + i)];
+      point.y = new_pts[3 * (begin + i) + 1];
+      point.z = new_pts[3 * (begin + i) + 2];
+      temp[i] = point;
+    }
+    pts = temp;
+  }
+
+  // Must return the number of data points
+  inline size_t kdtree_get_point_count() const { return pts.size(); }
+
+  // Returns the dim'th component of the idx'th point in the class:
+  // Since this is inlined and the "dim" argument is typically an immediate
+  // value, the
+  //  "if/else's" are actually solved at compile time.
+  inline scalar_t kdtree_get_pt(const size_t idx, const size_t dim) const {
+    if (dim == 0)
+      return pts[idx].x;
+    else if (dim == 1)
+      return pts[idx].y;
+    else
+      return pts[idx].z;
+  }
+
+  // Optional bounding-box computation: return false to default to a standard
+  // bbox computation loop.
+  //   Return true if the BBOX was already computed by the class and returned in
+  //   "bb" so it can be avoided to redo it again. Look at bb.size() to find out
+  //   the expected dimensionality (e.g. 2 or 3 for point clouds)
+  template <class BBOX>
+  bool kdtree_get_bbox(BBOX& /* bb */) const {
+    return false;
+  }
 };

cpu/include/group_points.h

@@ -1,5 +1,5 @@
 #pragma once
-#include <torch/extension.h> 
+#include <torch/extension.h>
 
 at::Tensor group_points(at::Tensor points, at::Tensor idx);
 at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n);