tu-vit_base_patch16_224 not supported

[isaaccorley]

The following code using a plain ViT-B/16 as a backbone for a U-Net/DeepLabv3+/SegFormer/UPerNet returns the following trace:

model = smp.create_model("unet", "tu-vit_base_patch16_224", encoder_weights=None)

Traceback (most recent call last):
  File "<input>", line 1, in <module>
    model = smp.create_model("unet", "tu-vit_base_patch16_224", encoder_weights=None, in_chann
els=5, classes=2)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
^^^^^^^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/__init__.py", line 63, in create_model
    return model_class(
           ^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/base/hub_mixin.py", line 153, in wrapper
    return func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/decoders/unet/model.py", line 132, in __init__
    self.encoder = get_encoder(
                   ^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/encoders/__init__.py", line 87, in get_encoder
    encoder = TimmUniversalEncoder(
              ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/encoders/timm_universal.py", line 121, in __init__
    raise ValueError("Unsupported model downsampling pattern.")
ValueError: Unsupported model downsampling pattern.
>>> model = smp.create_model("unet", "tu-vit_base_patch16_224", encoder_weights=None)
Traceback (most recent call last):
  File "<input>", line 1, in <module>
    model = smp.create_model("unet", "tu-vit_base_patch16_224", encoder_weights=None)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/__init__.py", line 63, in create_model
    return model_class(
           ^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/base/hub_mixin.py", line 153, in wrapper
    return func(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/decoders/unet/model.py", line 132, in __init__
    self.encoder = get_encoder(
                   ^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/encoders/__init__.py", line 87, in get_encoder
    encoder = TimmUniversalEncoder(
              ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/isaaccorley/miniconda3/envs/torchgeo/lib/python3.11/site-packages/segmentation_
models_pytorch/encoders/timm_universal.py", line 121, in __init__
    raise ValueError("Unsupported model downsampling pattern.")
ValueError: Unsupported model downsampling pattern.

However this was made to work in TorchSeg https://github.com/isaaccorley/torchseg.

Any solution here?