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Fix unused-variable issues, mostly relating to AMD/HIP

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Reviewed By: meyering

Differential Revision: D70845538

fbshipit-source-id: 8e52b5e1f1d96b86404fc3b8cbc6fb952e2cb1a6
This commit is contained in:
Richard Barnes 2025-03-08 13:03:17 -08:00 committed by Facebook GitHub Bot
parent 7e09505538
commit 21205730d9
6 changed files with 0 additions and 13 deletions
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2
pytorch3d/csrc/compositing/alpha_composite.cu
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@ -28,7 +28,6 @@ __global__ void alphaCompositeCudaForwardKernel(
const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
// clang-format on
const int64_t batch_size = result.size(0);
const int64_t C = features.size(0);
const int64_t H = points_idx.size(2);
const int64_t W = points_idx.size(3);
@ -79,7 +78,6 @@ __global__ void alphaCompositeCudaBackwardKernel(
const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
// clang-format on
const int64_t batch_size = points_idx.size(0);
const int64_t C = features.size(0);
const int64_t H = points_idx.size(2);
const int64_t W = points_idx.size(3);

2
pytorch3d/csrc/compositing/norm_weighted_sum.cu
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@ -28,7 +28,6 @@ __global__ void weightedSumNormCudaForwardKernel(
const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
// clang-format on
const int64_t batch_size = result.size(0);
const int64_t C = features.size(0);
const int64_t H = points_idx.size(2);
const int64_t W = points_idx.size(3);
@ -92,7 +91,6 @@ __global__ void weightedSumNormCudaBackwardKernel(
const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
// clang-format on
const int64_t batch_size = points_idx.size(0);
const int64_t C = features.size(0);
const int64_t H = points_idx.size(2);
const int64_t W = points_idx.size(3);

2
pytorch3d/csrc/compositing/weighted_sum.cu
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@ -26,7 +26,6 @@ __global__ void weightedSumCudaForwardKernel(
const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
// clang-format on
const int64_t batch_size = result.size(0);
const int64_t C = features.size(0);
const int64_t H = points_idx.size(2);
const int64_t W = points_idx.size(3);
@ -74,7 +73,6 @@ __global__ void weightedSumCudaBackwardKernel(
const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
// clang-format on
const int64_t batch_size = points_idx.size(0);
const int64_t C = features.size(0);
const int64_t H = points_idx.size(2);
const int64_t W = points_idx.size(3);

3
pytorch3d/csrc/iou_box3d/iou_utils.cuh
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@ -461,10 +461,8 @@ __device__ inline std::tuple<float3, float3> ArgMaxVerts(
__device__ inline bool IsCoplanarTriTri(
const FaceVerts& tri1,
const FaceVerts& tri2) {
const float3 tri1_ctr = FaceCenter({tri1.v0, tri1.v1, tri1.v2});
const float3 tri1_n = FaceNormal({tri1.v0, tri1.v1, tri1.v2});
const float3 tri2_ctr = FaceCenter({tri2.v0, tri2.v1, tri2.v2});
const float3 tri2_n = FaceNormal({tri2.v0, tri2.v1, tri2.v2});
// Check if parallel
@ -500,7 +498,6 @@ __device__ inline bool IsCoplanarTriPlane(
const FaceVerts& tri,
const FaceVerts& plane,
const float3& normal) {
const float3 tri_ctr = FaceCenter({tri.v0, tri.v1, tri.v2});
const float3 nt = FaceNormal({tri.v0, tri.v1, tri.v2});
// check if parallel

2
pytorch3d/csrc/sample_farthest_points/sample_farthest_points.cu
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@ -35,8 +35,6 @@ __global__ void FarthestPointSamplingKernel(
__shared__ int64_t selected_store;
// Get constants
const int64_t N = points.size(0);
const int64_t P = points.size(1);
const int64_t D = points.size(2);
// Get batch index and thread index

2
pytorch3d/csrc/utils/geometry_utils.cuh
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@ -376,8 +376,6 @@ PointLineDistanceBackward(
float tt = t_top / t_bot;
tt = __saturatef(tt);
const float2 p_proj = (1.0f - tt) * v0 + tt * v1;
const float2 d = p - p_proj;
const float dist = sqrt(dot(d, d));
const float2 grad_p = -1.0f * grad_dist * 2.0f * (p_proj - p);
const float2 grad_v0 = grad_dist * (1.0f - tt) * 2.0f * (p_proj - p);