[Fix] fix ut and hog_layer.

open-mmlab · Sep 15, 2022 · fbf1e11 · fbf1e11
1 parent f996da1
commit fbf1e11
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Showing 3 changed files with 6 additions and 9 deletions.
diff --git a/mmselfsup/datasets/pipelines/transforms.py b/mmselfsup/datasets/pipelines/transforms.py
@@ -486,10 +486,10 @@ def __repr__(self):
 
 @PIPELINES.register_module()
 class MaskfeatMaskGenerator(object):
-    """Generate random block mask for each image. This module is used in
-    Maskfeat to generate masks. This code is borrowed from.
+    """Generate random block mask for each image.
 
-    <https://github.com/facebookresearch/SlowFast/blob/main/slowfast/datasets/transform.py>
+    This module is borrowed from
+    https://github.com/facebookresearch/SlowFast/blob/main/slowfast/datasets/transform.py
     Args:
         mask_window_size (int): Size of input image. Defaults to 14.
         mask_ratio (float): The mask ratio of image. Defaults to 0.4.

diff --git a/mmselfsup/models/algorithms/maskfeat.py b/mmselfsup/models/algorithms/maskfeat.py
@@ -35,9 +35,6 @@ def __init__(self,
         assert hog_para is not None
         self.hog_layer = HOGLayerC(**hog_para)
 
-    def init_weights(self) -> None:
-        super().init_weights()
-
     def extract_feat(self, input: List[torch.Tensor]) -> torch.Tensor:
         """Function to extract features from backbone.
 

diff --git a/mmselfsup/models/utils/hog_layer.py b/mmselfsup/models/utils/hog_layer.py
@@ -10,7 +10,7 @@ class HOGLayerC(nn.Module):
     """Generate hog feature for each batch images. This module is used in
     Maskfeat to generate hog feature. This code is borrowed from.
 
-    <https://github.com/facebookresearch/SlowFast/blob/main/slowfast/models/masked.py>
+    <https://github.com/facebookresearch/SlowFast/blob/main/slowfast/models/operators.py>
     Args:
         nbins (int): Number of bin. Defaults to 9.
         pool (float): Number of cell. Defaults to 8.
@@ -21,7 +21,7 @@ def __init__(self,
                  nbins: int = 9,
                  pool: int = 8,
                  gaussian_window: int = 16) -> None:
-        super(HOGLayerC, self).__init__()
+        super().__init__()
         self.nbins = nbins
         self.pool = pool
         self.pi = math.pi
@@ -47,7 +47,7 @@ def _gaussian_fn(kernlen: int, std: int) -> torch.Tensor:
             return w
 
         gkern1d = _gaussian_fn(kernlen, std)
-        gkern2d = torch.outer(gkern1d, gkern1d)
+        gkern2d = gkern1d[:, None] * gkern1d[None, :]
         return gkern2d / gkern2d.sum()
 
     def _reshape(self, hog_feat: torch.Tensor) -> torch.Tensor: