Linter, deprecated type()

Summary: Run linter after recent changes. Fix long comment in knn.h which clang-format has reflowed badly. Add crude test that code doesn't call deprecated `.type()` or `.data()`.

Reviewed By: nikhilaravi

Differential Revision: D20692935

fbshipit-source-id: 28ce0308adae79a870cb41a810b7cf8744f41ab8

pytorch3d/csrc/dispatch.cuh

 // Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 //
-// This file provides utilities for dispatching to specialized versions of functions.
-// This is especially useful for CUDA kernels, since specializing them to particular
-// input sizes can often allow the compiler to unroll loops and place arrays into
-// registers, which can give huge performance speedups.
+// This file provides utilities for dispatching to specialized versions of
+// functions. This is especially useful for CUDA kernels, since specializing
+// them to particular input sizes can often allow the compiler to unroll loops
+// and place arrays into registers, which can give huge performance speedups.
 //
 // As an example, suppose we have the following function which is specialized
 // based on a compile-time int64_t value:
@@ -92,14 +92,13 @@ namespace {
 // In order to dispatch, we will take an additional template argument curN,
 // and increment it via template recursion until it is equal to the run-time
 // argument N.
-template<
-  template<typename, int64_t> class Kernel,
-  typename T,
-  int64_t minN,
-  int64_t maxN,
-  int64_t curN,
-  typename... Args
->
+template <
+    template <typename, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    int64_t curN,
+    typename... Args>
 struct DispatchKernelHelper1D {
   static void run(const int64_t N, Args... args) {
     if (curN == N) {
@@ -108,22 +107,21 @@ struct DispatchKernelHelper1D {
       Kernel<T, curN>::run(args...);
     } else if (curN < N) {
       // Increment curN via template recursion
-      DispatchKernelHelper1D<Kernel, T, minN, maxN, curN + 1, Args...>::run(N, args...);
+      DispatchKernelHelper1D<Kernel, T, minN, maxN, curN + 1, Args...>::run(
+          N, args...);
     }
     // We shouldn't get here -- throw an error?
   }
 };
 
-
 // 1D dispatch: Specialization when curN == maxN
 // We need this base case to avoid infinite template recursion.
-template<
-  template<typename, int64_t> class Kernel,
-  typename T,
-  int64_t minN,
-  int64_t maxN,
-  typename... Args
->
+template <
+    template <typename, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    typename... Args>
 struct DispatchKernelHelper1D<Kernel, T, minN, maxN, maxN, Args...> {
   static void run(const int64_t N, Args... args) {
     if (N == maxN) {
@@ -133,19 +131,21 @@ struct DispatchKernelHelper1D<Kernel, T, minN, maxN, maxN, Args...> {
   }
 };
 
-
 // 2D dispatch, general case.
 // This is similar to the 1D case: we take additional template args curN and
 // curM, and increment them via template recursion until they are equal to
 // the run-time values of N and M, at which point we dispatch to the run
 // method of the kernel.
-template<
-  template<typename, int64_t, int64_t> class Kernel,
-  typename T,
-  int64_t minN, int64_t maxN, int64_t curN,
-  int64_t minM, int64_t maxM, int64_t curM,
-  typename... Args
->
+template <
+    template <typename, int64_t, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    int64_t curN,
+    int64_t minM,
+    int64_t maxM,
+    int64_t curM,
+    typename... Args>
 struct DispatchKernelHelper2D {
   static void run(const int64_t N, const int64_t M, Args... args) {
     if (curN == N && curM == M) {
@@ -154,67 +154,141 @@ struct DispatchKernelHelper2D {
       // Increment both curN and curM. This isn't strictly necessary; we could
       // just increment one or the other at each step. But this helps to cut
       // on the number of recursive calls we make.
-      DispatchKernelHelper2D<Kernel, T, minN, maxN, curN + 1, minM, maxM, curM + 1, Args...>::run(N, M, args...);
+      DispatchKernelHelper2D<
+          Kernel,
+          T,
+          minN,
+          maxN,
+          curN + 1,
+          minM,
+          maxM,
+          curM + 1,
+          Args...>::run(N, M, args...);
     } else if (curN < N) {
       // Increment curN only
-      DispatchKernelHelper2D<Kernel, T, minN, maxN, curN + 1, minM, maxM, curM, Args...>::run(N, M, args...);
+      DispatchKernelHelper2D<
+          Kernel,
+          T,
+          minN,
+          maxN,
+          curN + 1,
+          minM,
+          maxM,
+          curM,
+          Args...>::run(N, M, args...);
     } else if (curM < M) {
       // Increment curM only
-      DispatchKernelHelper2D<Kernel, T, minN, maxN, curN, minM, maxM, curM + 1, Args...>::run(N, M, args...);
+      DispatchKernelHelper2D<
+          Kernel,
+          T,
+          minN,
+          maxN,
+          curN,
+          minM,
+          maxM,
+          curM + 1,
+          Args...>::run(N, M, args...);
     }
   }
 };
 
-
 // 2D dispatch, specialization for curN == maxN
-template<
-  template<typename, int64_t, int64_t> class Kernel,
-  typename T,
-  int64_t minN, int64_t maxN,
-  int64_t minM, int64_t maxM, int64_t curM,
-  typename... Args
->
-struct DispatchKernelHelper2D<Kernel, T, minN, maxN, maxN, minM, maxM, curM, Args...> {
+template <
+    template <typename, int64_t, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    int64_t minM,
+    int64_t maxM,
+    int64_t curM,
+    typename... Args>
+struct DispatchKernelHelper2D<
+    Kernel,
+    T,
+    minN,
+    maxN,
+    maxN,
+    minM,
+    maxM,
+    curM,
+    Args...> {
   static void run(const int64_t N, const int64_t M, Args... args) {
     if (maxN == N && curM == M) {
       Kernel<T, maxN, curM>::run(args...);
     } else if (curM < maxM) {
-      DispatchKernelHelper2D<Kernel, T, minN, maxN, maxN, minM, maxM, curM + 1, Args...>::run(N, M, args...);
+      DispatchKernelHelper2D<
+          Kernel,
+          T,
+          minN,
+          maxN,
+          maxN,
+          minM,
+          maxM,
+          curM + 1,
+          Args...>::run(N, M, args...);
     }
     // We should not get here -- throw an error?
   }
 };
 
-
 // 2D dispatch, specialization for curM == maxM
-template<
-  template<typename, int64_t, int64_t> class Kernel,
-  typename T,
-  int64_t minN, int64_t maxN, int64_t curN,
-  int64_t minM, int64_t maxM,
-  typename... Args
->
-struct DispatchKernelHelper2D<Kernel, T, minN, maxN, curN, minM, maxM, maxM, Args...> {
+template <
+    template <typename, int64_t, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    int64_t curN,
+    int64_t minM,
+    int64_t maxM,
+    typename... Args>
+struct DispatchKernelHelper2D<
+    Kernel,
+    T,
+    minN,
+    maxN,
+    curN,
+    minM,
+    maxM,
+    maxM,
+    Args...> {
   static void run(const int64_t N, const int64_t M, Args... args) {
     if (curN == N && maxM == M) {
       Kernel<T, curN, maxM>::run(args...);
     } else if (curN < maxN) {
-      DispatchKernelHelper2D<Kernel, T, minN, maxN, curN + 1, minM, maxM, maxM, Args...>::run(N, M, args...);
+      DispatchKernelHelper2D<
+          Kernel,
+          T,
+          minN,
+          maxN,
+          curN + 1,
+          minM,
+          maxM,
+          maxM,
+          Args...>::run(N, M, args...);
     }
     // We should not get here -- throw an error?
   }
 };
 
-
 // 2D dispatch, specialization for curN == maxN, curM == maxM
-template<
-  template<typename, int64_t, int64_t> class Kernel,
-  typename T,
-  int64_t minN, int64_t maxN,
-  int64_t minM, int64_t maxM,
-  typename... Args
->
-struct DispatchKernelHelper2D<Kernel, T, minN, maxN, maxN, minM, maxM, maxM, Args...> {
+template <
+    template <typename, int64_t, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    int64_t minM,
+    int64_t maxM,
+    typename... Args>
+struct DispatchKernelHelper2D<
+    Kernel,
+    T,
+    minN,
+    maxN,
+    maxN,
+    minM,
+    maxM,
+    maxM,
+    Args...> {
   static void run(const int64_t N, const int64_t M, Args... args) {
     if (maxN == N && maxM == M) {
       Kernel<T, maxN, maxM>::run(args...);
@@ -225,37 +299,45 @@ struct DispatchKernelHelper2D<Kernel, T, minN, maxN, maxN, minM, maxM, maxM, Arg
 
 } // namespace
 
-
 // This is the function we expect users to call to dispatch to 1D functions
-template<
-  template<typename, int64_t> class Kernel,
-  typename T,
-  int64_t minN,
-  int64_t maxN,
-  typename... Args
->
+template <
+    template <typename, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    typename... Args>
 void DispatchKernel1D(const int64_t N, Args... args) {
   if (minN <= N && N <= maxN) {
     // Kick off the template recursion by calling the Helper with curN = minN
-    DispatchKernelHelper1D<Kernel, T, minN, maxN, minN, Args...>::run(N, args...);
+    DispatchKernelHelper1D<Kernel, T, minN, maxN, minN, Args...>::run(
+        N, args...);
   }
   // Maybe throw an error if we tried to dispatch outside the allowed range?
 }
 
-
 // This is the function we expect users to call to dispatch to 2D functions
-template<
-  template<typename, int64_t, int64_t> class Kernel,
-  typename T,
-  int64_t minN, int64_t maxN,
-  int64_t minM, int64_t maxM,
-  typename... Args
->
+template <
+    template <typename, int64_t, int64_t> class Kernel,
+    typename T,
+    int64_t minN,
+    int64_t maxN,
+    int64_t minM,
+    int64_t maxM,
+    typename... Args>
 void DispatchKernel2D(const int64_t N, const int64_t M, Args... args) {
   if (minN <= N && N <= maxN && minM <= M && M <= maxM) {
     // Kick off the template recursion by calling the Helper with curN = minN
     // and curM = minM
-    DispatchKernelHelper2D<Kernel, T, minN, maxN, minN, minM, maxM, minM, Args...>::run(N, M, args...);
+    DispatchKernelHelper2D<
+        Kernel,
+        T,
+        minN,
+        maxN,
+        minN,
+        minM,
+        maxM,
+        minM,
+        Args...>::run(N, M, args...);
   }
   // Maybe throw an error if we tried to dispatch outside the specified range?
 }

pytorch3d/csrc/index_utils.cuh

@@ -39,82 +39,180 @@
 // approach for this might lead to extra function calls at runtime if the
 // compiler fails to optimize them away, which could be very slow on device.
 // However I didn't actually benchmark or test this.
-template<typename T, int N>
+template <typename T, int N>
 struct RegisterIndexUtils {
   __device__ __forceinline__ static T get(const T arr[N], int idx) {
-    if (idx < 0 || idx >= N) return T();
+    if (idx < 0 || idx >= N)
+      return T();
     switch (idx) {
-      case 0: return arr[0];
-      case 1: return arr[1];
-      case 2: return arr[2];
-      case 3: return arr[3];
-      case 4: return arr[4];
-      case 5: return arr[5];
-      case 6: return arr[6];
-      case 7: return arr[7];
-      case 8: return arr[8];
-      case 9: return arr[9];
-      case 10: return arr[10];
-      case 11: return arr[11];
-      case 12: return arr[12];
-      case 13: return arr[13];
-      case 14: return arr[14];
-      case 15: return arr[15];
-      case 16: return arr[16];
-      case 17: return arr[17];
-      case 18: return arr[18];
-      case 19: return arr[19];
-      case 20: return arr[20];
-      case 21: return arr[21];
-      case 22: return arr[22];
-      case 23: return arr[23];
-      case 24: return arr[24];
-      case 25: return arr[25];
-      case 26: return arr[26];
-      case 27: return arr[27];
-      case 28: return arr[28];
-      case 29: return arr[29];
-      case 30: return arr[30];
-      case 31: return arr[31];
+      case 0:
+        return arr[0];
+      case 1:
+        return arr[1];
+      case 2:
+        return arr[2];
+      case 3:
+        return arr[3];
+      case 4:
+        return arr[4];
+      case 5:
+        return arr[5];
+      case 6:
+        return arr[6];
+      case 7:
+        return arr[7];
+      case 8:
+        return arr[8];
+      case 9:
+        return arr[9];
+      case 10:
+        return arr[10];
+      case 11:
+        return arr[11];
+      case 12:
+        return arr[12];
+      case 13:
+        return arr[13];
+      case 14:
+        return arr[14];
+      case 15:
+        return arr[15];
+      case 16:
+        return arr[16];
+      case 17:
+        return arr[17];
+      case 18:
+        return arr[18];
+      case 19:
+        return arr[19];
+      case 20:
+        return arr[20];
+      case 21:
+        return arr[21];
+      case 22:
+        return arr[22];
+      case 23:
+        return arr[23];
+      case 24:
+        return arr[24];
+      case 25:
+        return arr[25];
+      case 26:
+        return arr[26];
+      case 27:
+        return arr[27];
+      case 28:
+        return arr[28];
+      case 29:
+        return arr[29];
+      case 30:
+        return arr[30];
+      case 31:
+        return arr[31];
     };
     return T();
   }
 
   __device__ __forceinline__ static void set(T arr[N], int idx, T val) {
-    if (idx < 0 || idx >= N) return;
+    if (idx < 0 || idx >= N)
+      return;
     switch (idx) {
-      case 0: arr[0] = val; break;
-      case 1: arr[1] = val; break;
-      case 2: arr[2] = val; break;
-      case 3: arr[3] = val; break;
-      case 4: arr[4] = val; break;
-      case 5: arr[5] = val; break;
-      case 6: arr[6] = val; break;
-      case 7: arr[7] = val; break;
-      case 8: arr[8] = val; break;
-      case 9: arr[9] = val; break;
-      case 10: arr[10] = val; break;
-      case 11: arr[11] = val; break;
-      case 12: arr[12] = val; break;
-      case 13: arr[13] = val; break;
-      case 14: arr[14] = val; break;
-      case 15: arr[15] = val; break;
-      case 16: arr[16] = val; break;
-      case 17: arr[17] = val; break;
-      case 18: arr[18] = val; break;
-      case 19: arr[19] = val; break;
-      case 20: arr[20] = val; break;
-      case 21: arr[21] = val; break;
-      case 22: arr[22] = val; break;
-      case 23: arr[23] = val; break;
-      case 24: arr[24] = val; break;
-      case 25: arr[25] = val; break;
-      case 26: arr[26] = val; break;
-      case 27: arr[27] = val; break;
-      case 28: arr[28] = val; break;
-      case 29: arr[29] = val; break;
-      case 30: arr[30] = val; break;
-      case 31: arr[31] = val; break;
+      case 0:
+        arr[0] = val;
+        break;
+      case 1:
+        arr[1] = val;
+        break;
+      case 2:
+        arr[2] = val;
+        break;
+      case 3:
+        arr[3] = val;
+        break;
+      case 4:
+        arr[4] = val;
+        break;
+      case 5:
+        arr[5] = val;
+        break;
+      case 6:
+        arr[6] = val;
+        break;
+      case 7:
+        arr[7] = val;
+        break;
+      case 8:
+        arr[8] = val;
+        break;
+      case 9:
+        arr[9] = val;
+        break;
+      case 10:
+        arr[10] = val;
+        break;
+      case 11:
+        arr[11] = val;
+        break;
+      case 12:
+        arr[12] = val;
+        break;
+      case 13:
+        arr[13] = val;
+        break;
+      case 14:
+        arr[14] = val;
+        break;
+      case 15:
+        arr[15] = val;
+        break;
+      case 16:
+        arr[16] = val;
+        break;
+      case 17:
+        arr[17] = val;
+        break;
+      case 18:
+        arr[18] = val;
+        break;
+      case 19:
+        arr[19] = val;
+        break;
+      case 20:
+        arr[20] = val;
+        break;
+      case 21:
+        arr[21] = val;
+        break;
+      case 22:
+        arr[22] = val;
+        break;
+      case 23:
+        arr[23] = val;
+        break;
+      case 24:
+        arr[24] = val;
+        break;
+      case 25:
+        arr[25] = val;
+        break;
+      case 26:
+        arr[26] = val;
+        break;
+      case 27:
+        arr[27] = val;
+        break;
+      case 28:
+        arr[28] = val;
+        break;
+      case 29:
+        arr[29] = val;
+        break;
+      case 30:
+        arr[30] = val;
+        break;
+      case 31:
+        arr[31] = val;
+        break;
     }
   }
 };

pytorch3d/csrc/knn/knn.cu

@@ -289,7 +289,7 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
   const size_t threads = 256;
   const size_t blocks = 256;
   if (version == 0) {
-    AT_DISPATCH_FLOATING_TYPES(p1.type(), "knn_kernel_cuda", ([&] {
+    AT_DISPATCH_FLOATING_TYPES(p1.scalar_type(), "knn_kernel_cuda", ([&] {
                                  KNearestNeighborKernelV0<scalar_t>
                                      <<<blocks, threads>>>(
                                          p1.data_ptr<scalar_t>(),
@@ -303,7 +303,7 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
                                          K);
                                }));
   } else if (version == 1) {
-    AT_DISPATCH_FLOATING_TYPES(p1.type(), "knn_kernel_cuda", ([&] {
+    AT_DISPATCH_FLOATING_TYPES(p1.scalar_type(), "knn_kernel_cuda", ([&] {
                                  DispatchKernel1D<
                                      KNearestNeighborV1Functor,
                                      scalar_t,
@@ -322,7 +322,7 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
                                      K);
                                }));
   } else if (version == 2) {
-    AT_DISPATCH_FLOATING_TYPES(p1.type(), "knn_kernel_cuda", ([&] {
+    AT_DISPATCH_FLOATING_TYPES(p1.scalar_type(), "knn_kernel_cuda", ([&] {
                                  DispatchKernel2D<
                                      KNearestNeighborKernelV2Functor,
                                      scalar_t,
@@ -343,7 +343,7 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
                                      P2);
                                }));
   } else if (version == 3) {
-    AT_DISPATCH_FLOATING_TYPES(p1.type(), "knn_kernel_cuda", ([&] {
+    AT_DISPATCH_FLOATING_TYPES(p1.scalar_type(), "knn_kernel_cuda", ([&] {
                                  DispatchKernel2D<
                                      KNearestNeighborKernelV3Functor,
                                      scalar_t,

pytorch3d/csrc/knn/knn.h

@@ -13,11 +13,11 @@
 //        containing P1 points of dimension D.
 //    p2: FloatTensor of shape (N, P2, D) giving a batch of pointclouds each
 //        containing P2 points of dimension D.
-//     K: int giving the number of nearest points to return.
-//     sorted: bool telling whether to sort the K returned points by their
-//     distance version: Integer telling which implementation to use.
-//              TODO(jcjohns): Document this more, or maybe remove it before
-//              landing.
+//    K: int giving the number of nearest points to return.
+//    sorted: bool telling whether to sort the K returned points by their
+//        distance.
+//    version: Integer telling which implementation to use.
+//        TODO(jcjohns): Document this more, or maybe remove it before landing.
 //
 // Returns:
 //    p1_neighbor_idx: LongTensor of shape (N, P1, K), where
@@ -41,7 +41,7 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdx(
     const at::Tensor& p2,
     int K,
     int version) {
-  if (p1.type().is_cuda() || p2.type().is_cuda()) {
+  if (p1.is_cuda() || p2.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CONTIGUOUS_CUDA(p1);
     CHECK_CONTIGUOUS_CUDA(p2);

pytorch3d/csrc/knn/knn_cpu.cpp

@@ -4,49 +4,48 @@
 #include <queue>
 #include <tuple>
 
-
 std::tuple<at::Tensor, at::Tensor>
 KNearestNeighborIdxCpu(const at::Tensor& p1, const at::Tensor& p2, int K) {
-    const int N = p1.size(0);
-    const int P1 = p1.size(1);
-    const int D = p1.size(2);
-    const int P2 = p2.size(1);
+  const int N = p1.size(0);
+  const int P1 = p1.size(1);
+  const int D = p1.size(2);
+  const int P2 = p2.size(1);
 
-    auto long_opts = p1.options().dtype(torch::kInt64);
-    torch::Tensor idxs = torch::full({N, P1, K}, -1, long_opts);
-    torch::Tensor dists = torch::full({N, P1, K}, 0, p1.options());
+  auto long_opts = p1.options().dtype(torch::kInt64);
+  torch::Tensor idxs = torch::full({N, P1, K}, -1, long_opts);
+  torch::Tensor dists = torch::full({N, P1, K}, 0, p1.options());
 
-    auto p1_a = p1.accessor<float, 3>();
-    auto p2_a = p2.accessor<float, 3>();
-    auto idxs_a = idxs.accessor<int64_t, 3>();
-    auto dists_a = dists.accessor<float, 3>();
+  auto p1_a = p1.accessor<float, 3>();
+  auto p2_a = p2.accessor<float, 3>();
+  auto idxs_a = idxs.accessor<int64_t, 3>();
+  auto dists_a = dists.accessor<float, 3>();
 
-    for (int n = 0; n < N; ++n) {
-        for (int i1 = 0; i1 < P1; ++i1) {
-            // Use a priority queue to store (distance, index) tuples.
-            std::priority_queue<std::tuple<float, int>> q;
-            for (int i2 = 0; i2 < P2; ++i2) {
-                float dist = 0;
-                for (int d = 0; d < D; ++d) {
-                    float diff = p1_a[n][i1][d] - p2_a[n][i2][d];
-                    dist += diff * diff;
-                }
-                int size = static_cast<int>(q.size());
-                if (size < K || dist < std::get<0>(q.top())) {
-                    q.emplace(dist, i2);
-                    if (size >= K) {
-                        q.pop();
-                    }
-                }
-            }
-            while (!q.empty()) {
-                auto t = q.top();
-                q.pop();
-                const int k = q.size();
-                dists_a[n][i1][k] = std::get<0>(t);
-                idxs_a[n][i1][k] = std::get<1>(t);
-            }
+  for (int n = 0; n < N; ++n) {
+    for (int i1 = 0; i1 < P1; ++i1) {
+      // Use a priority queue to store (distance, index) tuples.
+      std::priority_queue<std::tuple<float, int>> q;
+      for (int i2 = 0; i2 < P2; ++i2) {
+        float dist = 0;
+        for (int d = 0; d < D; ++d) {
+          float diff = p1_a[n][i1][d] - p2_a[n][i2][d];
+          dist += diff * diff;
+        }
+        int size = static_cast<int>(q.size());
+        if (size < K || dist < std::get<0>(q.top())) {
+          q.emplace(dist, i2);
+          if (size >= K) {
+            q.pop();
+          }
         }
+      }
+      while (!q.empty()) {
+        auto t = q.top();
+        q.pop();
+        const int k = q.size();
+        dists_a[n][i1][k] = std::get<0>(t);
+        idxs_a[n][i1][k] = std::get<1>(t);
+      }
     }
-    return std::make_tuple(idxs, dists);
+  }
+  return std::make_tuple(idxs, dists);
 }

pytorch3d/csrc/mink.cuh

@@ -5,7 +5,6 @@
 
 #include "index_utils.cuh"
 
-
 // A data structure to keep track of the smallest K keys seen so far as well
 // as their associated values, intended to be used in device code.
 // This data structure doesn't allocate any memory; keys and values are stored
@@ -32,18 +31,17 @@
 //   float key_k = keys[k];
 //   int value_k = values[k];
 // }
-template<typename key_t, typename value_t>
+template <typename key_t, typename value_t>
 class MinK {
  public:
-
   // Constructor.
   //
   // Arguments:
   //   keys: Array in which to store keys
   //   values: Array in which to store values
   //   K: How many values to keep track of
-  __device__ MinK(key_t *keys, value_t *vals, int K) :
-                  keys(keys), vals(vals), K(K), _size(0) { }
+  __device__ MinK(key_t* keys, value_t* vals, int K)
+      : keys(keys), vals(vals), K(K), _size(0) {}
 
   // Try to add a new key and associated value to the data structure. If the key
   // is one of the smallest K seen so far then it will be kept; otherwise it
@@ -55,7 +53,7 @@ class MinK {
   // Arguments:
   //   key: The key to add
   //   val: The value associated to the key
-  __device__ __forceinline__ void add(const key_t &key, const value_t &val) {
+  __device__ __forceinline__ void add(const key_t& key, const value_t& val) {
     if (_size < K) {
       keys[_size] = key;
       vals[_size] = val;
@@ -71,8 +69,8 @@ class MinK {
       for (int k = 0; k < K; ++k) {
         key_t cur_key = keys[k];
         if (cur_key > max_key) {
-            max_key = cur_key;
-            max_idx = k;
+          max_key = cur_key;
+          max_idx = k;
         }
       }
     }
@@ -102,15 +100,14 @@ class MinK {
   }
 
  private:
-  key_t *keys;
-  value_t *vals;
+  key_t* keys;
+  value_t* vals;
   int K;
   int _size;
   key_t max_key;
   int max_idx;
 };
 
-
 // This is a version of MinK that only touches the arrays using static indexing
 // via RegisterIndexUtils. If the keys and values are stored in thread-local
 // arrays, then this may allow the compiler to place them in registers for
@@ -120,13 +117,13 @@ class MinK {
 // We found that sorting via RegisterIndexUtils gave very poor performance,
 // and suspect it may have prevented the compiler from placing the arrays
 // into registers.
-template<typename key_t, typename value_t, int K>
+template <typename key_t, typename value_t, int K>
 class RegisterMinK {
  public:
-  __device__ RegisterMinK(key_t *keys, value_t *vals) :
-                          keys(keys), vals(vals), _size(0) {}
+  __device__ RegisterMinK(key_t* keys, value_t* vals)
+      : keys(keys), vals(vals), _size(0) {}
 
-  __device__ __forceinline__ void add(const key_t &key, const value_t &val) {
+  __device__ __forceinline__ void add(const key_t& key, const value_t& val) {
     if (_size < K) {
       RegisterIndexUtils<key_t, K>::set(keys, _size, key);
       RegisterIndexUtils<value_t, K>::set(vals, _size, val);
@@ -154,8 +151,8 @@ class RegisterMinK {
   }
 
  private:
-  key_t *keys;
-  value_t *vals;
+  key_t* keys;
+  value_t* vals;
   int _size;
   key_t max_key;
   int max_idx;

pytorch3d/ops/knn.py

 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 import torch
+
 from pytorch3d import _C
 
 

tests/bm_knn.py

 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from itertools import product
-
 import torch
 from fvcore.common.benchmark import benchmark
 
@@ -30,21 +29,13 @@ def benchmark_knn_cuda_versions() -> None:
             continue
         if version == 3 and K > 4:
             continue
-        knn_kwargs.append({'N': N, 'D': D, 'P': P, 'K': K, 'v': version})
+        knn_kwargs.append({"N": N, "D": D, "P": P, "K": K, "v": version})
     for N, P, D in product(Ns, Ps, Ds):
-        nn_kwargs.append({'N': N, 'D': D, 'P': P})
-    benchmark(
-        knn_cuda_with_init,
-        'KNN_CUDA_VERSIONS',
-        knn_kwargs,
-        warmup_iters=1,
-    )
+        nn_kwargs.append({"N": N, "D": D, "P": P})
     benchmark(
-        nn_cuda_with_init,
-        'NN_CUDA',
-        nn_kwargs,
-        warmup_iters=1,
+        knn_cuda_with_init, "KNN_CUDA_VERSIONS", knn_kwargs, warmup_iters=1
     )
+    benchmark(nn_cuda_with_init, "NN_CUDA", nn_kwargs, warmup_iters=1)
 
 
 def benchmark_knn_cuda_vs_naive() -> None:
@@ -55,21 +46,16 @@ def benchmark_knn_cuda_vs_naive() -> None:
     Ks = [1, 2, 4, 8, 16]
     knn_kwargs, naive_kwargs = [], []
     for N, P, D, K in product(Ns, Ps, Ds, Ks):
-        knn_kwargs.append({'N': N, 'D': D, 'P': P, 'K': K})
+        knn_kwargs.append({"N": N, "D": D, "P": P, "K": K})
         if P <= 4096:
-            naive_kwargs.append({'N': N, 'D': D, 'P': P, 'K': K})
+            naive_kwargs.append({"N": N, "D": D, "P": P, "K": K})
     benchmark(
         knn_python_cuda_with_init,
-        'KNN_CUDA_PYTHON',
+        "KNN_CUDA_PYTHON",
         naive_kwargs,
         warmup_iters=1,
     )
-    benchmark(
-        knn_cuda_with_init,
-        'KNN_CUDA',
-        knn_kwargs,
-        warmup_iters=1,
-    )
+    benchmark(knn_cuda_with_init, "KNN_CUDA", knn_kwargs, warmup_iters=1)
 
 
 def benchmark_knn_cpu() -> None:
@@ -79,31 +65,18 @@ def benchmark_knn_cpu() -> None:
     Ks = [1, 2, 4]
     knn_kwargs, nn_kwargs = [], []
     for N, P, D, K in product(Ns, Ps, Ds, Ks):
-        knn_kwargs.append({'N': N, 'D': D, 'P': P, 'K': K})
+        knn_kwargs.append({"N": N, "D": D, "P": P, "K": K})
     for N, P, D in product(Ns, Ps, Ds):
-        nn_kwargs.append({'N': N, 'D': D, 'P': P})
+        nn_kwargs.append({"N": N, "D": D, "P": P})
     benchmark(
-        knn_python_cpu_with_init,
-        'KNN_CPU_PYTHON',
-        knn_kwargs,
-        warmup_iters=1,
-    )
-    benchmark(
-        knn_cpu_with_init,
-        'KNN_CPU_CPP',
-        knn_kwargs,
-        warmup_iters=1,
-    )
-    benchmark(
-        nn_cpu_with_init,
-        'NN_CPU_CPP',
-        nn_kwargs,
-        warmup_iters=1,
+        knn_python_cpu_with_init, "KNN_CPU_PYTHON", knn_kwargs, warmup_iters=1
     )
+    benchmark(knn_cpu_with_init, "KNN_CPU_CPP", knn_kwargs, warmup_iters=1)
+    benchmark(nn_cpu_with_init, "NN_CPU_CPP", nn_kwargs, warmup_iters=1)
 
 
 def knn_cuda_with_init(N, D, P, K, v=-1):
-    device = torch.device('cuda:0')
+    device = torch.device("cuda:0")
     x = torch.randn(N, P, D, device=device)
     y = torch.randn(N, P, D, device=device)
     torch.cuda.synchronize()
@@ -116,7 +89,7 @@ def knn_cuda_with_init(N, D, P, K, v=-1):
 
 
 def knn_cpu_with_init(N, D, P, K):
-    device = torch.device('cpu')
+    device = torch.device("cpu")
     x = torch.randn(N, P, D, device=device)
     y = torch.randn(N, P, D, device=device)
 
@@ -127,7 +100,7 @@ def knn_cpu_with_init(N, D, P, K):
 
 
 def knn_python_cuda_with_init(N, D, P, K):
-    device = torch.device('cuda')
+    device = torch.device("cuda")
     x = torch.randn(N, P, D, device=device)
     y = torch.randn(N, P, D, device=device)
     torch.cuda.synchronize()
@@ -140,7 +113,7 @@ def knn_python_cuda_with_init(N, D, P, K):
 
 
 def knn_python_cpu_with_init(N, D, P, K):
-    device = torch.device('cpu')
+    device = torch.device("cpu")
     x = torch.randn(N, P, D, device=device)
     y = torch.randn(N, P, D, device=device)
 
@@ -151,7 +124,7 @@ def knn_python_cpu_with_init(N, D, P, K):
 
 
 def nn_cuda_with_init(N, D, P):
-    device = torch.device('cuda')
+    device = torch.device("cuda")
     x = torch.randn(N, P, D, device=device)
     y = torch.randn(N, P, D, device=device)
     torch.cuda.synchronize()
@@ -164,7 +137,7 @@ def nn_cuda_with_init(N, D, P):
 
 
 def nn_cpu_with_init(N, D, P):
-    device = torch.device('cpu')
+    device = torch.device("cpu")
     x = torch.randn(N, P, D, device=device)
     y = torch.randn(N, P, D, device=device)
 

tests/test_build.py

@@ -22,6 +22,27 @@ class TestBuild(unittest.TestCase):
         for k, v in counter.items():
             self.assertEqual(v, 1, f"Too many files with stem {k}.")
 
+    def test_deprecated_usage(self):
+        # Check certain expressions do not occur in the csrc code
+        test_dir = Path(__file__).resolve().parent
+        source_dir = test_dir.parent / "pytorch3d" / "csrc"
+
+        files = sorted(source_dir.glob("**/*.*"))
+        self.assertGreater(len(files), 4)
+
+        patterns = [".type()", ".data()"]
+
+        for file in files:
+            with open(file) as f:
+                text = f.read()
+                for pattern in patterns:
+                    found = pattern in text
+                    msg = (
+                        f"{pattern} found in {file.name}"
+                        + ", this has been deprecated."
+                    )
+                    self.assertFalse(found, msg)
+
     def test_copyright(self):
         test_dir = Path(__file__).resolve().parent
         root_dir = test_dir.parent

tests/test_knn.py

@@ -28,7 +28,7 @@ class TestKNN(unittest.TestCase):
 
     def test_knn_vs_python_cpu(self):
         """ Test CPU output vs PyTorch implementation """
-        device = torch.device('cpu')
+        device = torch.device("cpu")
         Ns = [1, 4]
         Ds = [2, 3]
         P1s = [1, 10, 101]
@@ -45,7 +45,7 @@ class TestKNN(unittest.TestCase):
 
     def test_knn_vs_python_cuda(self):
         """ Test CUDA output vs PyTorch implementation """
-        device = torch.device('cuda')
+        device = torch.device("cuda")
         Ns = [1, 4]
         Ds = [2, 3, 8]
         P1s = [1, 8, 64, 128, 1001]