add returns in sfnet head

configs/sfnet/sfnet_r18-d8_512x1024_80k_cityscapes.py → configs/sfnet/sfnet_r18-d32_512x1024_80k_cityscapes.py

@@ -1,4 +1,4 @@
-_base_ = './sfnet_r50-d8_512x1024_80k_cityscapes.py'
+_base_ = './sfnet_r50-d32_512x1024_80k_cityscapes.py'
 model = dict(
     pretrained='open-mmlab://resnet18_v1c',
     backbone=dict(depth=18),

mmseg/models/decode_heads/sfnet_head.py

@@ -164,10 +164,15 @@ def flow_warp(self, input, flow, size):
 
         Args:
             input (Tensor): High Resolution Feature Map.
-            flow (Tensor): Semantic Flow Field that will give dynamic
-            indication about how to align these two feature maps effectively.
+            flow (Tensor): Semantic Flow Field that will give
+                dynamic indication about how to align these
+                two feature maps effectively.
             size (Tuple): Shape of height and width of output.
 
+        Returns:
+            output (Tensor): High Resolution Feature Map after
+                warped offset and bilinear interpolation.
+
         For example, in cityscapes 1024x2048 dataset with ResNet18 config,
         feature map from backbone is:
         [[1, 64, 256, 512],