Fixup, fix type hint

Author: qubvel

segmentation_models_pytorch/decoders/dpt/decoder.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from segmentation_models_pytorch.base.modules import Activation
-from typing import Optional, Sequence
+from typing import Optional, Sequence, Union, Callable
 
 
 class ProjectionBlock(nn.Module):
@@ -241,7 +241,7 @@ def __init__(
         self,
         in_channels: int,
         out_channels: int,
-        activation: Optional[str] = None,
+        activation: Optional[Union[str, Callable]] = None,
         kernel_size: int = 3,
         upsampling: float = 2.0,
     ):

segmentation_models_pytorch/decoders/dpt/model.py

@@ -25,7 +25,7 @@ class DPT(SegmentationModel):
 
     Note:
         Since this model uses a Vision Transformer backbone, it typically requires a fixed input image size.
-        To handle variable input sizes, you can set `dynamic_img_size=True` in the model initialization 
+        To handle variable input sizes, you can set `dynamic_img_size=True` in the model initialization
         (if supported by the specific `timm` encoder). You can check if an encoder requires fixed size
         using `model.encoder.is_fixed_input_size`, and get the required input dimensions from
         `model.encoder.input_size`, however it's no guarantee that information is available.