Fix lint

Author: fmassa

setup.py

@@ -39,6 +39,7 @@ def find_version(*file_paths):
     'torch',
 ]
 
+
 def get_extensions():
     this_dir = os.path.dirname(os.path.abspath(__file__))
     extensions_dir = os.path.join(this_dir, 'torchvision', 'csrc')
@@ -51,12 +52,12 @@ def get_extensions():
     extension = CppExtension
 
     define_macros = []
-    
+
     if torch.cuda.is_available() and CUDA_HOME is not None:
         extension = CUDAExtension
         sources += source_cuda
         define_macros += [('WITH_CUDA', None)]
-    
+
     sources = [os.path.join(extensions_dir, s) for s in sources]
 
     include_dirs = [extensions_dir]

test/test_layers.py

@@ -18,7 +18,6 @@ def test_roi_align(self):
             [0, 0, 5, 5, 10],
             [0, 5, 5, 10, 10]], dtype=dtype)
 
-
         for device in ['cpu', 'cuda']:
             device = torch.device(device)
             x_n = x.to(device)
@@ -28,7 +27,6 @@ def test_roi_align(self):
 
         assert (outputs[0] - outputs[1]).abs().max() < 1e-6
 
-
     def test_roi_align_gradient(self):
         dtype = torch.float64
         device = torch.device('cuda')

torchvision/layers/__init__.py

@@ -1,7 +1,8 @@
-__all__ = [
-    'nms', 'roi_align', 'ROIAlign', 'roi_pool', 'ROIPool'
-]
-
 from .nms import nms
 from .roi_align import roi_align, ROIAlign
 from .roi_pool import roi_pool, ROIPool
+
+
+__all__ = [
+    'nms', 'roi_align', 'ROIAlign', 'roi_pool', 'ROIPool'
+]

torchvision/layers/nms.py

@@ -2,4 +2,5 @@
 from torchvision import _C
 
 nms = _C.nms
-
+nms.__doc__ = """
+This function performs Non-maximum suppresion"""

torchvision/layers/roi_align.py

@@ -6,7 +6,6 @@
 
 from torch.nn.modules.utils import _pair
 
-# from ._utils import _C
 from torchvision import _C
 
 
@@ -18,7 +17,8 @@ def forward(ctx, input, roi, output_size, spatial_scale, sampling_ratio):
         ctx.spatial_scale = spatial_scale
         ctx.sampling_ratio = sampling_ratio
         ctx.input_shape = input.size()
-        output = _C.roi_align_forward(input, roi, spatial_scale,
+        output = _C.roi_align_forward(
+                input, roi, spatial_scale,
                 output_size[0], output_size[1], sampling_ratio)
         return output
 
@@ -30,13 +30,15 @@ def backward(ctx, grad_output):
         spatial_scale = ctx.spatial_scale
         sampling_ratio = ctx.sampling_ratio
         bs, ch, h, w = ctx.input_shape
-        grad_input =  _C.roi_align_backward(grad_output, rois, spatial_scale,
+        grad_input = _C.roi_align_backward(
+                grad_output, rois, spatial_scale,
                 output_size[0], output_size[1], bs, ch, h, w, sampling_ratio)
         return grad_input, None, None, None, None
 
 
 roi_align = _ROIAlign.apply
 
+
 class ROIAlign(nn.Module):
     def __init__(self, output_size, spatial_scale, sampling_ratio):
         super(ROIAlign, self).__init__()
@@ -54,4 +56,3 @@ def __repr__(self):
         tmpstr += ', sampling_ratio=' + str(self.sampling_ratio)
         tmpstr += ')'
         return tmpstr
-

torchvision/layers/roi_pool.py

@@ -6,7 +6,6 @@
 
 from torch.nn.modules.utils import _pair
 
-# from ._utils import _C
 from torchvision import _C
 
 
@@ -16,7 +15,8 @@ def forward(ctx, input, roi, output_size, spatial_scale):
         ctx.output_size = _pair(output_size)
         ctx.spatial_scale = spatial_scale
         ctx.input_shape = input.size()
-        output, argmax = _C.roi_pool_forward(input, roi, spatial_scale,
+        output, argmax = _C.roi_pool_forward(
+                input, roi, spatial_scale,
                 output_size[0], output_size[1])
         ctx.save_for_backward(input, roi, argmax)
         return output
@@ -28,12 +28,14 @@ def backward(ctx, grad_output):
         output_size = ctx.output_size
         spatial_scale = ctx.spatial_scale
         bs, ch, h, w = ctx.input_shape
-        grad_input =  _C.roi_pool_backward(grad_output, input, rois, argmax, spatial_scale,
+        grad_input = _C.roi_pool_backward(
+                grad_output, input, rois, argmax, spatial_scale,
                 output_size[0], output_size[1], bs, ch, h, w)
         return grad_input, None, None, None
 
 roi_pool = _ROIPool.apply
 
+
 class ROIPool(nn.Module):
     def __init__(self, output_size, spatial_scale):
         super(ROIPool, self).__init__()
@@ -49,5 +51,3 @@ def __repr__(self):
         tmpstr += ', spatial_scale=' + str(self.spatial_scale)
         tmpstr += ')'
         return tmpstr
-
-