make linter happy

face_alignment/detection/blazeface/net_blazeface.py

@@ -45,14 +45,14 @@ class BlazeBlock(nn.Module):
 class BlazeFace(nn.Module):
     """The BlazeFace face detection model from MediaPipe.
 
-    The version from MediaPipe is simpler than the one in the paper; 
+    The version from MediaPipe is simpler than the one in the paper;
     it does not use the "double" BlazeBlocks.
 
     Because we won't be training this model, it doesn't need to have
-    batchnorm layers. These have already been "folded" into the conv 
+    batchnorm layers. These have already been "folded" into the conv
     weights by TFLite.
 
-    The conversion to PyTorch is fairly straightforward, but there are 
+    The conversion to PyTorch is fairly straightforward, but there are
     some small differences between TFLite and PyTorch in how they handle
     padding on conv layers with stride 2.
 
@@ -179,7 +179,7 @@ class BlazeFace(nn.Module):
 
         Arguments:
             img: a NumPy array of shape (H, W, 3) or a PyTorch tensor of
-                 shape (3, H, W). The image's height and width should be 
+                 shape (3, H, W). The image's height and width should be
                  128 pixels.
 
         Returns:
@@ -198,7 +198,7 @@ class BlazeFace(nn.Module):
                shape (b, 3, H, W). The height and width should be 128 pixels.
 
         Returns:
-            A list containing a tensor of face detections for each image in 
+            A list containing a tensor of face detections for each image in
             the batch. If no faces are found for an image, returns a tensor
             of shape (0, 17).
 
@@ -237,7 +237,7 @@ class BlazeFace(nn.Module):
 
     def _tensors_to_detections(self, raw_box_tensor, raw_score_tensor, anchors):
         """The output of the neural network is a tensor of shape (b, 896, 16)
-        containing the bounding box regressor predictions, as well as a tensor 
+        containing the bounding box regressor predictions, as well as a tensor
         of shape (b, 896, 1) with the classification confidences.
 
         This function converts these two "raw" tensors into proper detections.
@@ -404,10 +404,10 @@ def jaccard(box_a, box_b):
         jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
     """
     inter = intersect(box_a, box_b)
-    area_a = ((box_a[:, 2] - box_a[:, 0]) *
-              (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
-    area_b = ((box_b[:, 2] - box_b[:, 0]) *
-              (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
+    area_a = ((box_a[:, 2] - box_a[:, 0])
+              * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
+    area_b = ((box_b[:, 2] - box_b[:, 0])
+              * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
     union = area_a + area_b - inter
     return inter / union  # [A,B]
 

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.0.1
+current_version = 1.1.0
 commit = True
 tag = True
 

tox.ini

@@ -1,3 +1,3 @@
 [flake8]
 max-line-length = 120
-ignore = E305,E402,E721,F401,F403,F405,F821,F841,F999
+ignore = E305,E402,E721,F401,F403,F405,F821,F841,F999,W503