no f strings

setup.py

@@ -11,7 +11,7 @@ from torch.utils.cpp_extension import CppExtension, CUDAExtension, CUDA_HOME
 WITH_CUDA = torch.cuda.is_available() and CUDA_HOME is not None
 if os.getenv('FORCE_CUDA', '0') == '1':
     WITH_CUDA = True
-if os.getenv('FORCE_NON_CUDA', '0') == '1':
+if os.getenv('FORCE_CPU', '0') == '1':
     WITH_CUDA = False
 
 BUILD_DOCS = os.getenv('BUILD_DOCS', '0') == '1'
@@ -23,39 +23,37 @@ def get_extensions():
     extra_compile_args = {'cxx': [], 'nvcc': []}
     extra_link_args = []
 
-    # Windows users: Edit both of these to contain your VS include path, i.e.:
-    # extra_compile_args['cxx'] += ['-I{VISUAL_STUDIO_DIR}\\include']
-    # extra_compile_args['nvcc'] += ['-I{VISUAL_STUDIO_DIR}\\include']
-
     if WITH_CUDA:
         Extension = CUDAExtension
         define_macros += [('WITH_CUDA', None)]
         nvcc_flags = os.getenv('NVCC_FLAGS', '')
         nvcc_flags = [] if nvcc_flags == '' else nvcc_flags.split(' ')
         nvcc_flags += ['-arch=sm_35', '--expt-relaxed-constexpr']
-        extra_compile_args['cxx'] += ['-O0']
         extra_compile_args['nvcc'] += nvcc_flags
-        if sys.platform == 'win32':
 
+        if sys.platform == 'win32':
             extra_link_args = ['cusparse.lib']
         else:
             extra_link_args = ['-lcusparse', '-l', 'cusparse']
 
-    if sys.platform == 'win32':
-        extra_compile_args['cxx'] += ['/MP']
-
     extensions_dir = osp.join(osp.dirname(osp.abspath(__file__)), 'csrc')
     main_files = glob.glob(osp.join(extensions_dir, '*.cpp'))
     extensions = []
     for main in main_files:
         name = main.split(os.sep)[-1][:-4]
 
-        sources = [main, osp.join(extensions_dir, 'cpu', f'{name}_cpu.cpp')]
-        if WITH_CUDA:
-            sources += [osp.join(extensions_dir, 'cuda', f'{name}_cuda.cu')]
+        sources = [main]
+
+        path = osp.join(extensions_dir, 'cpu', name + '_cpu.cpp')
+        if osp.exists(path):
+            sources += [path]
+
+        path = osp.join(extensions_dir, 'cuda', name + '_cuda.cu')
+        if WITH_CUDA and osp.exists(path):
+            sources += [path]
 
         extension = Extension(
-            f'torch_sparse._{name}',
+            'torch_sparse._' + name,
             sources,
             include_dirs=[extensions_dir],
             define_macros=define_macros,
@@ -73,7 +71,7 @@ tests_require = ['pytest', 'pytest-cov']
 
 setup(
     name='torch_sparse',
-    version='0.5.0',
+    version='0.5.1',
     author='Matthias Fey',
     author_email='matthias.fey@tu-dortmund.de',
     url='https://github.com/rusty1s/pytorch_sparse',
@@ -81,6 +79,7 @@ setup(
                  'Matrix Operations'),
     keywords=['pytorch', 'sparse', 'sparse-matrices', 'autograd'],
     license='MIT',
+    python_requires='>=3.5',
     install_requires=install_requires,
     setup_requires=setup_requires,
     tests_require=tests_require,

torch_sparse/add.py

@@ -14,9 +14,9 @@ def add(src: SparseTensor, other: torch.Tensor) -> SparseTensor:
     elif other.size(0) == 1 and other.size(1) == src.size(1):  # Col-wise...
         other = other.squeeze(0)[col]
     else:
-        raise ValueError(f'Size mismatch: Expected size ({src.size(0)}, 1,'
-                         f' ...) or (1, {src.size(1)}, ...), but got size '
-                         f'{other.size()}.')
+        raise ValueError(
+            'Size mismatch: Expected size ({}, 1, ...) or (1, {}, ...), but '
+            'got size {}.'.format(src.size(0), src.size(1), other.size()))
 
     if value is not None:
         value = other.to(value.dtype).add_(value)
@@ -34,9 +34,9 @@ def add_(src: SparseTensor, other: torch.Tensor) -> SparseTensor:
     elif other.size(0) == 1 and other.size(1) == src.size(1):  # Col-wise...
         other = other.squeeze(0)[col]
     else:
-        raise ValueError(f'Size mismatch: Expected size ({src.size(0)}, 1,'
-                         f' ...) or (1, {src.size(1)}, ...), but got size '
-                         f'{other.size()}.')
+        raise ValueError(
+            'Size mismatch: Expected size ({}, 1, ...) or (1, {}, ...), but '
+            'got size {}.'.format(src.size(0), src.size(1), other.size()))
 
     if value is not None:
         value = value.add_(other.to(value.dtype))

torch_sparse/cat.py

@@ -138,8 +138,8 @@ def cat(tensors: List[SparseTensor], dim: int) -> SparseTensor:
         return tensors[0].set_value(value, layout='coo')
     else:
         raise IndexError(
-            (f'Dimension out of range: Expected to be in range of '
-             f'[{-tensors[0].dim()}, {tensors[0].dim() - 1}, but got {dim}]'))
+            'Dimension out of range: Expected to be in range of [{}, {}], but '
+            'got {}.'.format(-tensors[0].dim(), tensors[0].dim() - 1, dim))
 
 
 @torch.jit.script

torch_sparse/mul.py

@@ -14,9 +14,9 @@ def mul(src: SparseTensor, other: torch.Tensor) -> SparseTensor:
     elif other.size(0) == 1 and other.size(1) == src.size(1):  # Col-wise...
         other = other.squeeze(0)[col]
     else:
-        raise ValueError(f'Size mismatch: Expected size ({src.size(0)}, 1,'
-                         f' ...) or (1, {src.size(1)}, ...), but got size '
-                         f'{other.size()}.')
+        raise ValueError(
+            'Size mismatch: Expected size ({}, 1, ...) or (1, {}, ...), but '
+            'got size {}.'.format(src.size(0), src.size(1), other.size()))
 
     if value is not None:
         value = other.to(value.dtype).mul_(value)
@@ -34,9 +34,9 @@ def mul_(src: SparseTensor, other: torch.Tensor) -> SparseTensor:
     elif other.size(0) == 1 and other.size(1) == src.size(1):  # Col-wise...
         other = other.squeeze(0)[col]
     else:
-        raise ValueError(f'Size mismatch: Expected size ({src.size(0)}, 1,'
-                         f' ...) or (1, {src.size(1)}, ...), but got size '
-                         f'{other.size()}.')
+        raise ValueError(
+            'Size mismatch: Expected size ({}, 1, ...) or (1, {}, ...), but '
+            'got size {}.'.format(src.size(0), src.size(1), other.size()))
 
     if value is not None:
         value = value.mul_(other.to(value.dtype))

torch_sparse/tensor.py

@@ -487,21 +487,22 @@ def __repr__(self: SparseTensor) -> str:
     i = ' ' * 6
     row, col, value = self.coo()
     infos = []
-    infos += [f'row={indent(row.__repr__(), i)[len(i):]}']
-    infos += [f'col={indent(col.__repr__(), i)[len(i):]}']
+    infos += ['row={}'.format(indent(row.__repr__(), i)[len(i):])]
+    infos += ['col={}'.format(indent(col.__repr__(), i)[len(i):])]
 
     if value is not None:
-        infos += [f'val={indent(value.__repr__(), i)[len(i):]}']
+        infos += ['val={}'.format(indent(value.__repr__(), i)[len(i):])]
 
     infos += [
-        f'size={tuple(self.sizes())}, '
-        f'nnz={self.nnz()}, '
-        f'density={100 * self.density():.02f}%'
+        'size={}, '.format(tuple(self.sizes())) +
+        'nnz={}, '.format(self.nnz()) +
+        'density={:.02f}%'.format(100 * self.density())
     ]
+
     infos = ',\n'.join(infos)
 
     i = ' ' * (len(self.__class__.__name__) + 1)
-    return f'{self.__class__.__name__}({indent(infos, i)[len(i):]})'
+    return '{}({})'.format(self.__class__.__name__, indent(infos, i)[len(i):])
 
 
 SparseTensor.share_memory_ = share_memory_