fix fps implementation

csrc/cpu/fps_cpu.cpp

@@ -35,7 +35,7 @@ torch::Tensor fps_cpu(torch::Tensor src, torch::Tensor ptr, double ratio,
 
     int64_t start_idx = 0;
     if (random_start) {
-      start_idx = rand() % src.size(0);
+      start_idx = rand() % y.size(0);
     }
 
     out_data[out_start] = src_start + start_idx;

csrc/cuda/fps_cuda.cu

@@ -4,7 +4,7 @@
 
 #include "utils.cuh"
 
-#define THREADS 1024
+#define THREADS 256
 
 template <typename scalar_t>
 __global__ void fps_kernel(const scalar_t *src, const int64_t *ptr,
@@ -31,15 +31,15 @@ __global__ void fps_kernel(const scalar_t *src, const int64_t *ptr,
     int64_t best_idx = 0;
 
     for (int64_t n = start_idx + thread_idx; n < end_idx; n += THREADS) {
-      scalar_t tmp;
-      scalar_t dd = (scalar_t)0.;
+      scalar_t tmp, dd = (scalar_t)0.;
       for (int64_t d = 0; d < dim; d++) {
         tmp = src[dim * old + d] - src[dim * n + d];
         dd += tmp * tmp;
       }
-      dist[n] = min(dist[n], dd);
-      if (dist[n] > best) {
-        best = dist[n];
+      dd = min(dist[n], dd);
+      dist[n] = dd;
+      if (dd > best) {
+        best = dd;
         best_idx = n;
       }
     }

setup.py

@@ -63,7 +63,7 @@ def get_extensions():
 
 setup(
     name='torch_cluster',
-    version='1.5.2',
+    version='1.5.3',
     author='Matthias Fey',
     author_email='matthias.fey@tu-dortmund.de',
     url='https://github.com/rusty1s/pytorch_cluster',

test/test_fps.py

@@ -26,3 +26,15 @@ def test_fps(dtype, device):
 
     out = fps(x, ratio=0.5, random_start=False)
     assert out.sort()[0].tolist() == [0, 5, 6, 7]
+
+
+@pytest.mark.parametrize('device', devices)
+def test_random_fps(device):
+    N = 1024
+    for _ in range(5):
+        pos = torch.randn((2 * N, 3), device=device)
+        batch_1 = torch.zeros(N, dtype=torch.long, device=device)
+        batch_2 = torch.ones(N, dtype=torch.long, device=device)
+        batch = torch.cat([batch_1, batch_2])
+        idx = fps(pos, batch, ratio=0.5)
+        assert idx.min() >= 0 and idx.max() < 2 * N

torch_cluster/__init__.py

@@ -3,7 +3,7 @@
 
 import torch
 
-__version__ = '1.5.2'
+__version__ = '1.5.3'
 expected_torch_version = (1, 4)
 
 try: