Remove `test/__init__.py` (#298)

* set version

* update

* update

* update

* fix test

* fix test

test/test_add.py

@@ -2,9 +2,9 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse import SparseTensor, add
 
-from .utils import dtypes, devices, tensor
+from torch_sparse import SparseTensor, add
+from torch_sparse.testing import devices, dtypes, tensor
 
 
 @pytest.mark.parametrize('dtype,device', product(dtypes, devices))

test/test_cat.py

 import pytest
 import torch
-from torch_sparse.tensor import SparseTensor
-from torch_sparse.cat import cat
 
-from .utils import devices, tensor
+from torch_sparse.cat import cat
+from torch_sparse.tensor import SparseTensor
+from torch_sparse.testing import devices, tensor
 
 
 @pytest.mark.parametrize('device', devices)

test/test_diag.py

@@ -2,9 +2,9 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse.tensor import SparseTensor
 
-from .utils import dtypes, devices, tensor
+from torch_sparse.tensor import SparseTensor
+from torch_sparse.testing import devices, dtypes, tensor
 
 
 @pytest.mark.parametrize('dtype,device', product(dtypes, devices))

test/test_eye.py

 from itertools import product
 
 import pytest
-from torch_sparse.tensor import SparseTensor
 
-from .utils import dtypes, devices
+from torch_sparse.tensor import SparseTensor
+from torch_sparse.testing import devices, dtypes
 
 
 @pytest.mark.parametrize('dtype,device', product(dtypes, devices))

test/test_matmul.py

@@ -6,8 +6,7 @@ import torch_scatter
 
 from torch_sparse.matmul import matmul
 from torch_sparse.tensor import SparseTensor
-
-from .utils import devices, grad_dtypes, reductions
+from torch_sparse.testing import devices, grad_dtypes, reductions
 
 
 @pytest.mark.parametrize('dtype,device,reduce',

test/test_metis.py

@@ -2,9 +2,9 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse.tensor import SparseTensor
 
-from .utils import devices
+from torch_sparse.tensor import SparseTensor
+from torch_sparse.testing import devices
 
 try:
     rowptr = torch.tensor([0, 1])

test/test_permute.py

 import pytest
 import torch
-from torch_sparse.tensor import SparseTensor
 
-from .utils import devices, tensor
+from torch_sparse.tensor import SparseTensor
+from torch_sparse.testing import devices, tensor
 
 
 @pytest.mark.parametrize('device', devices)

test/test_spmm.py

@@ -2,9 +2,9 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse import spmm
 
-from .utils import dtypes, devices, tensor
+from torch_sparse import spmm
+from torch_sparse.testing import devices, dtypes, tensor
 
 
 @pytest.mark.parametrize('dtype,device', product(dtypes, devices))

test/test_spspmm.py

@@ -4,8 +4,7 @@ import pytest
 import torch
 
 from torch_sparse import SparseTensor, spspmm
-
-from .utils import devices, grad_dtypes, tensor
+from torch_sparse.testing import devices, grad_dtypes, tensor
 
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))

test/test_storage.py

@@ -2,9 +2,9 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse.storage import SparseStorage
 
-from .utils import dtypes, devices, tensor
+from torch_sparse.storage import SparseStorage
+from torch_sparse.testing import devices, dtypes, tensor
 
 
 @pytest.mark.parametrize('device', devices)

test/test_tensor.py

@@ -2,9 +2,9 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse import SparseTensor
 
-from .utils import grad_dtypes, devices
+from torch_sparse import SparseTensor
+from torch_sparse.testing import devices, grad_dtypes
 
 
 @pytest.mark.parametrize('dtype,device', product(grad_dtypes, devices))
@@ -15,8 +15,8 @@ def test_getitem(dtype, device):
     mat = torch.randn(m, n, dtype=dtype, device=device)
     mat = SparseTensor.from_dense(mat)
 
-    idx1 = torch.randint(0, m, (k,), dtype=torch.long, device=device)
-    idx2 = torch.randint(0, n, (k,), dtype=torch.long, device=device)
+    idx1 = torch.randint(0, m, (k, ), dtype=torch.long, device=device)
+    idx2 = torch.randint(0, n, (k, ), dtype=torch.long, device=device)
     bool1 = torch.zeros(m, dtype=torch.bool, device=device)
     bool2 = torch.zeros(n, dtype=torch.bool, device=device)
     bool1.scatter_(0, idx1, 1)

test/test_transpose.py

@@ -2,9 +2,9 @@ from itertools import product
 
 import pytest
 import torch
-from torch_sparse import transpose
 
-from .utils import dtypes, devices, tensor
+from torch_sparse import transpose
+from torch_sparse.testing import devices, dtypes, tensor
 
 
 @pytest.mark.parametrize('dtype,device', product(dtypes, devices))

test/utils.py → torch_sparse/testing.py

+from typing import Any
+
 import torch
 import torch_scatter
 from packaging import version
@@ -13,8 +15,8 @@ if version.parse(torch_scatter.__version__) > version.parse("2.0.9"):
 
 devices = [torch.device('cpu')]
 if torch.cuda.is_available():
-    devices += [torch.device(f'cuda:{torch.cuda.current_device()}')]
+    devices += [torch.device('cuda:0')]
 
 
-def tensor(x, dtype, device):
+def tensor(x: Any, dtype: torch.dtype, device: torch.device):
     return None if x is None else torch.tensor(x, dtype=dtype, device=device)