[fix] Add and correct docstring

open-mmlab · Sep 19, 2022 · 2ee92b9 · 2ee92b9
1 parent fbf1e11
commit 2ee92b9
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Showing 3 changed files with 8 additions and 3 deletions.
diff --git a/mmselfsup/datasets/pipelines/transforms.py b/mmselfsup/datasets/pipelines/transforms.py
@@ -496,9 +496,10 @@ class MaskfeatMaskGenerator(object):
         min_num_patches (int): Minimum number of patches that require masking.
             Defaults to 15.
         max_num_patches (int, optional): Maximum number of patches that
-            require masking.
+            require masking. Defaults to None.
         min_aspect (int): Minimum aspect of patches. Defaults to 0.3.
         max_aspect (float, optional): Maximum aspect of patches.
+            Defaults to None.
     """
 
     def __init__(

diff --git a/mmselfsup/models/algorithms/maskfeat.py b/mmselfsup/models/algorithms/maskfeat.py
@@ -18,6 +18,10 @@ class MaskFeat(BaseModel):
         backbone (dict): Config dict for encoder. Defaults to None.
         head (dict): Config dict for loss functions. Defaults to None.
         hog_para (dict): Config dict for hog layer. Defaults to None.
+            dict['nbins', int]: Number of bin. Defaults to 9.
+            dict['pool', float]: Number of cell. Defaults to 8.
+            dict['gaussian_window', int]: Size of gaussian kernel.
+                Defaults to 16.
         init_cfg (dict): Config dict for weight initialization.
             Defaults to None.
     """

diff --git a/mmselfsup/models/heads/maskfeat_head.py b/mmselfsup/models/heads/maskfeat_head.py
@@ -16,8 +16,6 @@ class MaskFeatPretrainHead(BaseModule):
         embed_dim (int): The dim of the feature before the classifier head.
             Defaults to 768.
         hog_dim (int): The dim of the hog feature. Defaults to 108.
-        reduction (str): Specifies reduction to apply to the output.
-            Defaults to "mean" (default) or "none".
     """
 
     def __init__(self, embed_dim: int = 768, hog_dim: int = 108) -> None:
@@ -73,6 +71,8 @@ class MaskFeatFinetuneHead(BaseModule):
     Args:
         embed_dim (int): The dim of the feature before the classifier head.
         num_classes (int): The total classes. Defaults to 1000.
+        label_smooth_val (float): The degree of label smoothing.
+            Defaults to 0.1.
     """
 
     def __init__(self,