🤗 HuggingFace for pretrained model weights hosting

requirements/requirements.txt

@@ -9,6 +9,7 @@ filelock>=3.9.0
 flask>=2.2.2
 flask-cors>=4.0.0
 glymur>=0.12.7, < 0.14 # 0.14 is not compatible with python3.9
+huggingface_hub # do we need to pin it to 0.33.3?
 imagecodecs>=2022.9.26
 joblib>=1.1.1
 jupyterlab>=3.5.2
@@ -37,4 +38,3 @@ tqdm>=4.64.1
 umap-learn>=0.5.3
 wsidicom>=0.18.0
 zarr>=2.13.3, <3.0.0
-huggingface_hub # do we need to pin it to 0.33.3?

tiatoolbox/models/architecture/__init__.py

@@ -7,12 +7,11 @@
 from typing import TYPE_CHECKING
 
 import torch
+from huggingface_hub import hf_hub_download
 
 from tiatoolbox import rcParam
 from tiatoolbox.models.dataset.classification import predefined_preproc_func
 
-from huggingface_hub import hf_hub_download
-
 if TYPE_CHECKING:  # pragma: no cover
     from tiatoolbox.models.models_abc import IOConfigABC
 
@@ -60,7 +59,7 @@ def fetch_pretrained_weights(
 
     return hf_hub_download(
         repo_id="TIACentre/TIAToolbox_pretrained_weights",
-        filename=file_name, 
+        filename=file_name,
         cache_dir=cache_dir,
     )
 

tiatoolbox/models/architecture/vanilla.py

@@ -87,7 +87,6 @@ def _get_architecture(
         return model.features
     if "alexnet" in arch_name:
         return model.features
-
 
     return model.features