apply pre-commit hooks (linting)

Author: deruyter92

dlclive/modelzoo/__init__.py

@@ -6,4 +6,4 @@
     load_super_animal_config,
     download_super_animal_snapshot,
 )
-from dlclive.modelzoo.pytorch_model_zoo_export import export_modelzoo_model
+from dlclive.modelzoo.pytorch_model_zoo_export import export_modelzoo_model

dlclive/modelzoo/model_configs/fasterrcnn_resnet50_fpn_v2.yaml

@@ -48,4 +48,4 @@ train_settings:
   dataloader_workers: 0
   dataloader_pin_memory: false
   display_iters: 500
-  epochs: 250
+  epochs: 250

dlclive/modelzoo/model_configs/ssdlite.yaml

@@ -47,4 +47,4 @@ train_settings:
   dataloader_workers: 0
   dataloader_pin_memory: false
   display_iters: 500
-  epochs: 250
+  epochs: 250

dlclive/modelzoo/project_configs/superanimal_humanbody.yaml

@@ -87,4 +87,4 @@ corner2move2:
 move2corner:
 
 # Conversion tables to fine-tune SuperAnimal weights
-SuperAnimalConversionTables:
+SuperAnimalConversionTables:

dlclive/modelzoo/pytorch_model_zoo_export.py

@@ -4,7 +4,10 @@
 
 import torch
 
-from dlclive.modelzoo.utils import load_super_animal_config, download_super_animal_snapshot
+from dlclive.modelzoo.utils import (
+    load_super_animal_config,
+    download_super_animal_snapshot,
+)
 
 
 def export_modelzoo_model(
@@ -29,7 +32,9 @@ def export_modelzoo_model(
     """
     Path(export_path).parent.mkdir(parents=True, exist_ok=True)
     if Path(export_path).exists():
-        warnings.warn(f"Export path {export_path} already exists, skipping export", UserWarning)
+        warnings.warn(
+            f"Export path {export_path} already exists, skipping export", UserWarning
+        )
         return
 
     model_cfg = load_super_animal_config(
@@ -38,30 +43,40 @@ def export_modelzoo_model(
         detector_name=detector_name,
     )
 
-    def _load_model_weights(model_name: str, super_animal: str = super_animal) -> OrderedDict:
+    def _load_model_weights(
+        model_name: str, super_animal: str = super_animal
+    ) -> OrderedDict:
         """Download the model weights from huggingface and load them in torch state dict"""
-        checkpoint: Path = download_super_animal_snapshot(dataset=super_animal, model_name=model_name)
+        checkpoint: Path = download_super_animal_snapshot(
+            dataset=super_animal, model_name=model_name
+        )
         return torch.load(checkpoint, map_location="cpu", weights_only=True)["model"]
-    
+
     # Skip downloading the detector weights for humanbody models, as they are not on huggingface
-    skip_detector_download = (detector_name is None) or (super_animal == "superanimal_humanbody")
+    skip_detector_download = (detector_name is None) or (
+        super_animal == "superanimal_humanbody"
+    )
     export_dict = {
         "config": model_cfg,
         "pose": _load_model_weights(model_name),
-        "detector": None if skip_detector_download else _load_model_weights(detector_name),
+        "detector": None
+        if skip_detector_download
+        else _load_model_weights(detector_name),
     }
     torch.save(export_dict, export_path)
 
 
 if __name__ == "__main__":
-    """Example usage"""	
+    """Example usage"""
     from utils import _MODELZOO_PATH
-    
+
     model_name = "resnet_50"
     super_animal = "superanimal_quadruped"
 
     export_modelzoo_model(
-        export_path=_MODELZOO_PATH / 'exported_models' / f'exported_{super_animal}_{model_name}.pt',
+        export_path=_MODELZOO_PATH
+        / "exported_models"
+        / f"exported_{super_animal}_{model_name}.pt",
         super_animal=super_animal,
         model_name=model_name,
     )

dlclive/modelzoo/utils.py

@@ -9,11 +9,15 @@
 from pathlib import Path
 
 from dlclibrary.dlcmodelzoo.modelzoo_download import download_huggingface_model
-from dlclibrary.dlcmodelzoo.modelzoo_download import _load_model_names as huggingface_model_paths
+from dlclibrary.dlcmodelzoo.modelzoo_download import (
+    _load_model_names as huggingface_model_paths,
+)
 from ruamel.yaml import YAML
 
 from dlclive.modelzoo.resolve_config import update_config
-from dlclive.pose_estimation_pytorch.models.detectors.torchvision import SUPPORTED_TORCHVISION_DETECTORS
+from dlclive.pose_estimation_pytorch.models.detectors.torchvision import (
+    SUPPORTED_TORCHVISION_DETECTORS,
+)
 
 _MODELZOO_PATH = Path(__file__).parent
 
@@ -100,9 +104,13 @@ def add_metadata(
 def _get_torchvision_detector_config(detector_name: str) -> dict:
     """Get a torchvision detector configuration for the superanimal humanbody model"""
     if detector_name is None:
-        raise ValueError(f"Detector name is required for superanimal humanbody models. Must be one of {SUPPORTED_TORCHVISION_DETECTORS}.")
+        raise ValueError(
+            f"Detector name is required for superanimal humanbody models. Must be one of {SUPPORTED_TORCHVISION_DETECTORS}."
+        )
     if detector_name not in SUPPORTED_TORCHVISION_DETECTORS:
-        raise ValueError(f"Unsupported humanbody detector {detector_name}. Should be one of {SUPPORTED_TORCHVISION_DETECTORS}")
+        raise ValueError(
+            f"Unsupported humanbody detector {detector_name}. Should be one of {SUPPORTED_TORCHVISION_DETECTORS}"
+        )
     return {
         "type": "TorchvisionDetectorAdaptor",
         "model": detector_name,
@@ -145,12 +153,10 @@ def load_super_animal_config(
         model_config["method"] = "BU"
     else:
         model_config["method"] = "TD"
-        detector_cfg_path = get_super_animal_model_config_path(
-            model_name=detector_name
-        )
+        detector_cfg_path = get_super_animal_model_config_path(model_name=detector_name)
         detector_cfg = read_config_as_dict(detector_cfg_path)
         model_config["detector"] = detector_cfg
-    
+
     if super_animal == "superanimal_humanbody":
         # Raises ValueError if Detector name is not one of SUPPORTED_TORCHVISION_DETECTORS
         torchvision_detector_config = _get_torchvision_detector_config(detector_name)

dlclive/pose_estimation_pytorch/models/detectors/torchvision.py

@@ -9,12 +9,16 @@
 # Licensed under GNU Lesser General Public License v3.0
 #
 """Module to adapt torchvision detectors for DeepLabCut"""
+
 from __future__ import annotations
 
 import torch
 import torchvision.models.detection as detection
 
-from dlclive.pose_estimation_pytorch.models.detectors.base import DETECTORS, BaseDetector
+from dlclive.pose_estimation_pytorch.models.detectors.base import (
+    DETECTORS,
+    BaseDetector,
+)
 
 SUPPORTED_TORCHVISION_DETECTORS = ["fasterrcnn_mobilenet_v3_large_fpn"]