Misc lint fixes (#1020)

references/detection/coco_utils.py

@@ -195,7 +195,7 @@ def convert_to_coco_api(ds):
 
 
 def get_coco_api_from_dataset(dataset):
-    for i in range(10):
+    for _ in range(10):
         if isinstance(dataset, torchvision.datasets.CocoDetection):
             break
         if isinstance(dataset, torch.utils.data.Subset):

references/detection/group_by_aspect_ratio.py

@@ -101,7 +101,7 @@ def _compute_aspect_ratios_slow(dataset, indices=None):
         collate_fn=lambda x: x[0])
     aspect_ratios = []
     with tqdm(total=len(dataset)) as pbar:
-        for i, (img, _) in enumerate(data_loader):
+        for _i, (img, _) in enumerate(data_loader):
             pbar.update(1)
             height, width = img.shape[-2:]
             aspect_ratio = float(height) / float(width)

references/detection/train.py

@@ -9,8 +9,6 @@ import torchvision
 import torchvision.models.detection
 import torchvision.models.detection.mask_rcnn
 
-from torchvision import transforms
-
 from coco_utils import get_coco, get_coco_kp
 
 from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups

test/test_ops.py

@@ -14,7 +14,9 @@ class RoIPoolTester(unittest.TestCase):
         cls.dtype = torch.float64
 
     def slow_roi_pooling(self, x, rois, pool_h, pool_w, spatial_scale=1,
-                         device=torch.device('cpu'), dtype=torch.float64):
+                         device=None, dtype=torch.float64):
+        if device is None:
+            device = torch.device("cpu")
         c = x.size(1)
         y = torch.zeros(rois.size(0), c, pool_h, pool_w, dtype=dtype, device=device)
 

test/test_transforms.py

@@ -141,7 +141,7 @@ class Tester(unittest.TestCase):
         img = to_pil_image(img)
         size = 100
         epsilon = 0.05
-        for i in range(10):
+        for _ in range(10):
             scale_min = round(random.random(), 2)
             scale_range = (scale_min, scale_min + round(random.random(), 2))
             aspect_min = max(round(random.random(), 2), epsilon)
@@ -153,7 +153,7 @@ class Tester(unittest.TestCase):
                     aspect_ratio_obtained == 1.0)
 
     def test_randomperspective(self):
-        for i in range(10):
+        for _ in range(10):
             height = random.randint(24, 32) * 2
             width = random.randint(24, 32) * 2
             img = torch.ones(3, height, width)