whitespace cleanup

Author: christian-rauch

core/raft.py

@@ -31,7 +31,7 @@ def __init__(self, args):
             self.context_dim = cdim = 64
             args.corr_levels = 4
             args.corr_radius = 3
-        
+
         else:
             self.hidden_dim = hdim = 128
             self.context_dim = cdim = 128
@@ -46,12 +46,12 @@ def __init__(self, args):
 
         # feature network, context network, and update block
         if args.small:
-            self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)        
+            self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout)
             self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout)
             self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim)
 
         else:
-            self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)        
+            self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout)
             self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout)
             self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim)
 
@@ -97,8 +97,8 @@ def forward(self, image1, image2, iters=torch.tensor(12), flow_init=torch.tensor
 
         # run the feature network
         with autocast(enabled=self.args.mixed_precision):
-            fmap1, fmap2 = self.fnet([image1, image2])        
-        
+            fmap1, fmap2 = self.fnet([image1, image2])
+
         fmap1 = fmap1.float()
         fmap2 = fmap2.float()
         if self.args.alternate_corr:
@@ -135,10 +135,10 @@ def forward(self, image1, image2, iters=torch.tensor(12), flow_init=torch.tensor
                 flow_up = upflow8(coords1 - coords0)
             else:
                 flow_up = self.upsample_flow(coords1 - coords0, up_mask)
-            
+
             flow_predictions.append(flow_up)
 
         if test_mode:
             return coords1 - coords0, flow_up
-            
+
         return flow_predictions