[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -1,22 +1,22 @@
 repos:
   - repo: https://github.com/PyCQA/flake8
-    rev: 6.1.0
+    rev: 7.0.0
     hooks:
       - id: flake8
         args: ["--config", ".flake8"]
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.5.0
+    rev: v4.6.0
     hooks:
       - id: check-toml
       - id: check-yaml
       - id: end-of-file-fixer
       - id: mixed-line-ending
   - repo: https://github.com/psf/black
-    rev: 23.11.0
+    rev: 24.4.2
     hooks:
       - id: black
   - repo: https://github.com/PyCQA/isort
-    rev: 5.13.1
+    rev: 5.13.2
     hooks:
       - id: isort
         args: ["--profile", "black"]

dataset_loaders/dataset_loaders.py

@@ -887,9 +887,11 @@ def __init__(
 
         # Allow up to one level of nesting
         self.feats = [
-            [load_features(df, feats_inner) for feats_inner in feats]
-            if isinstance(feats, list)
-            else load_features(df, feats)
+            (
+                [load_features(df, feats_inner) for feats_inner in feats]
+                if isinstance(feats, list)
+                else load_features(df, feats)
+            )
             for feats in input_features
         ]
 

evaluation/retrieval_evaluation.py

@@ -179,14 +179,16 @@ def retrieval_evaluation(
             frames = frames[:, ::frame_stride]
             splits = torch.split(frames, nframes, 1)
             splits_pad = [
-                x
-                if x.shape[1] == nframes
-                else torch.index_select(
-                    x,
-                    dim=1,
-                    index=torch.floor(
-                        torch.linspace(0, x.shape[1] - 1, nframes, device=device)
-                    ).to(torch.int64),
+                (
+                    x
+                    if x.shape[1] == nframes
+                    else torch.index_select(
+                        x,
+                        dim=1,
+                        index=torch.floor(
+                            torch.linspace(0, x.shape[1] - 1, nframes, device=device)
+                        ).to(torch.int64),
+                    )
                 )
                 for x in splits
             ]