Merge branch 'master' of github.com:rusty1s/pytorch_sparse

CMakeLists.txt

@@ -9,6 +9,7 @@ if(WITH_CUDA)
   enable_language(CUDA)
   add_definitions(-D__CUDA_NO_HALF_OPERATORS__)
   add_definitions(-DWITH_CUDA)
+  set(CMAKE_CUDA_FLAGS "${CMAKE_CUDA_FLAGS} -arch=sm_35 --expt-relaxed-constexpr")
 endif()
 
 find_package(Python3 COMPONENTS Development)

test/test_tensor.py

+from itertools import product
+
+import pytest
+import torch
+from torch_sparse import SparseTensor
+
+from .utils import grad_dtypes, devices
+
+
+@pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
+def test_getitem(dtype, device):
+    mat = torch.randn(50, 40, dtype=dtype, device=device)
+    mat = SparseTensor.from_dense(mat)
+
+    idx1 = torch.randint(0, 50, (10, ), dtype=torch.long, device=device)
+    idx2 = torch.randint(0, 40, (10, ), dtype=torch.long, device=device)
+
+    assert mat[:10, :10].sizes() == [10, 10]
+    assert mat[..., :10].sizes() == [50, 10]
+    assert mat[idx1, idx2].sizes() == [10, 10]
+    assert mat[idx1.tolist()].sizes() == [10, 40]

torch_sparse/tensor.py

@@ -442,6 +442,8 @@ def __getitem__(self: SparseTensor, index: Any) -> SparseTensor:
     out = self
     while len(index) > 0:
         item = index.pop(0)
+        if isinstance(item, (list, tuple)):
+            item = torch.tensor(item, dtype=torch.long, device=self.device())
         if isinstance(item, int):
             out = out.select(dim, item)
             dim += 1