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src/transformers/models/beit/configuration_beit.py

@@ -84,13 +84,13 @@ class BeitConfig(PretrainedConfig):
             Pooling scales used in Pooling Pyramid Module applied on the last feature map.
         use_auxiliary_head (:obj:`bool`, `optional`, defaults to :obj:`True`):
             Whether to use an auxiliary head during training.
-        loss_weight (:obj:`float`, `optional`, defaults to 0.4):
+        auxiliary_loss_weight (:obj:`float`, `optional`, defaults to 0.4):
             Weight of the cross-entropy loss of the auxiliary head.
-        channels (:obj:`int`, `optional`, defaults to 256):
+        auxiliary_channels (:obj:`int`, `optional`, defaults to 256):
             Number of channels to use in the auxiliary head.
-        num_convs (:obj:`int`, `optional`, defaults to 1):
+        auxiliary_num_convs (:obj:`int`, `optional`, defaults to 1):
             Number of convolutional layers to use in the auxiliary head.
-        concat_input (:obj:`bool`, `optional`, defaults to :obj:`False`):
+        auxiliary_concat_input (:obj:`bool`, `optional`, defaults to :obj:`False`):
             Whether to concatenate the output of the auxiliary head with the input before the classification layer.
 
     Example::
@@ -134,10 +134,10 @@ def __init__(
         out_indices=[3, 5, 7, 11],
         pool_scales=[1, 2, 3, 6],
         use_auxiliary_head=True,
-        loss_weight=0.4,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
+        auxiliary_loss_weight=0.4,
+        auxiliary_channels=256,
+        auxiliary_num_convs=1,
+        auxiliary_concat_input=False,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -168,7 +168,7 @@ def __init__(
         self.pool_scales = pool_scales
         # auxiliary head attributes (semantic segmentation)
         self.use_auxiliary_head = use_auxiliary_head
-        self.loss_weight = loss_weight
-        self.channels = channels
-        self.num_convs = num_convs
-        self.concat_input = concat_input
+        self.auxiliary_loss_weight = auxiliary_loss_weight
+        self.auxiliary_channels = auxiliary_channels
+        self.auxiliary_num_convs = auxiliary_num_convs
+        self.auxiliary_concat_input = auxiliary_concat_input

src/transformers/models/beit/modeling_beit.py

@@ -1025,9 +1025,9 @@ class BeitFCNHead(nn.Module):
     def __init__(self, config, in_index=2, kernel_size=3, dilation=1):
         super().__init__()
         self.in_channels = config.hidden_size
-        self.channels = config.channels
-        self.num_convs = config.num_convs
-        self.concat_input = config.concat_input
+        self.channels = config.auxiliary_channels
+        self.num_convs = config.auxiliary_num_convs
+        self.concat_input = config.auxiliary_concat_input
         self.in_index = in_index
 
         conv_padding = (kernel_size // 2) * dilation
@@ -1109,7 +1109,7 @@ def compute_loss(self, logits, auxiliary_logits, labels):
         loss_fct = CrossEntropyLoss(ignore_index=255)
         main_loss = loss_fct(upsampled_logits, labels)
         auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels)
-        loss = main_loss + self.config.loss_weight * auxiliary_loss
+        loss = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
 
         return loss
 

tests/test_modeling_beit.py

@@ -170,8 +170,6 @@ class BeitModelTest(ModelTesterMixin, unittest.TestCase):
     attention_mask and seq_length.
     """
 
-    maxDiff = None
-
     all_model_classes = (
         (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
         if is_torch_available()