Adopt type hinting generics in standard collections (PEP 585)

6 files changed, 36 insertions, 38 deletions

diff --git a/models/hpm.py b/models/hpm.py
index f387154..1773f56 100644
--- a/models/hpm.py
+++ b/models/hpm.py
@@ -1,5 +1,3 @@
-from typing import Tuple
-
 import torch
 import torch.nn as nn
 from torchvision.models import resnet50
@@ -10,7 +8,7 @@ from models.layers import HorizontalPyramidPooling
 class HorizontalPyramidMatching(nn.Module):
     def __init__(
             self,
-            scales: Tuple[int] = (1, 2, 4, 8),
+            scales: tuple[int, ...] = (1, 2, 4, 8),
             out_channels: int = 256,
             use_avg_pool: bool = False,
             **kwargs
diff --git a/models/layers.py b/models/layers.py
index 9b17205..cba6e47 100644
--- a/models/layers.py
+++ b/models/layers.py
@@ -1,8 +1,8 @@
-from typing import Union, Tuple
+from typing import Union
 
 import torch
-import torch.nn.functional as F
 import torch.nn as nn
+import torch.nn.functional as F
 
 
 class BasicConv2d(nn.Module):
@@ -10,7 +10,7 @@ class BasicConv2d(nn.Module):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_size: Union[int, Tuple[int, int]],
+            kernel_size: Union[int, tuple[int, int]],
             **kwargs
     ):
         super().__init__()
@@ -29,7 +29,7 @@ class VGGConv2d(BasicConv2d):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_size: Union[int, Tuple[int, int]] = 3,
+            kernel_size: Union[int, tuple[int, int]] = 3,
             padding: int = 1,
             **kwargs
     ):
@@ -47,7 +47,7 @@ class BasicConvTranspose2d(nn.Module):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_size: Union[int, Tuple[int, int]],
+            kernel_size: Union[int, tuple[int, int]],
             **kwargs
     ):
         super().__init__()
@@ -66,7 +66,7 @@ class DCGANConvTranspose2d(BasicConvTranspose2d):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_size: Union[int, Tuple[int, int]] = 4,
+            kernel_size: Union[int, tuple[int, int]] = 4,
             stride: int = 2,
             padding: int = 1,
             is_last_layer: bool = False,
@@ -88,7 +88,7 @@ class FocalConv2d(BasicConv2d):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_size: Union[int, Tuple[int, int]],
+            kernel_size: Union[int, tuple[int, int]],
             halving: int,
             **kwargs
     ):
@@ -108,7 +108,7 @@ class BasicConv1d(nn.Module):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_size: Union[int, Tuple[int]],
+            kernel_size: Union[int, tuple[int]],
             **kwargs
     ):
         super(BasicConv1d, self).__init__()
@@ -124,7 +124,7 @@ class HorizontalPyramidPooling(BasicConv2d):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_size: Union[int, Tuple[int, int]] = 1,
+            kernel_size: Union[int, tuple[int, int]] = 1,
             use_avg_pool: bool = False,
             **kwargs
     ):
diff --git a/models/model.py b/models/model.py
index 369d6c2..cb0e756 100644
--- a/models/model.py
+++ b/models/model.py
@@ -1,4 +1,4 @@
-from typing import List, Dict, Union, Tuple
+from typing import Union
 
 import torch
 from torch.utils.data.dataloader import default_collate
@@ -7,15 +7,15 @@ from torch.utils.data.dataloader import default_collate
 class Model:
     def __init__(
             self,
-            batch_size: Tuple[int, int]
+            batch_size: tuple[int, int]
     ):
         (self.pr, self.k) = batch_size
 
     def _batch_splitter(
             self,
-            batch: List[Dict[str, Union[str, torch.Tensor]]]
-    ) -> List[Tuple[Dict[str, List[Union[str, torch.Tensor]]],
-                    Dict[str, List[Union[str, torch.Tensor]]]]]:
+            batch: list[dict[str, Union[str, torch.Tensor]]]
+    ) -> list[tuple[dict[str, list[Union[str, torch.Tensor]]],
+                    dict[str, list[Union[str, torch.Tensor]]]]]:
         """
         Disentanglement cannot be processed on different subjects at the
         same time, we need to load `pr` subjects one by one. The batch
diff --git a/utils/configuration.py b/utils/configuration.py
index 32b9bec..84bd064 100644
--- a/utils/configuration.py
+++ b/utils/configuration.py
@@ -1,4 +1,4 @@
-from typing import TypedDict, Tuple
+from typing import TypedDict
 
 import torch
 
@@ -16,12 +16,12 @@ class DatasetConfiguration(TypedDict):
     num_sampled_frames: int
     discard_threshold: int
     num_input_channels: int
-    frame_size: Tuple[int, int]
+    frame_size: tuple[int, int]
     cache_on: bool
 
 
 class DataloaderConfiguration(TypedDict):
-    batch_size: Tuple[int, int]
+    batch_size: tuple[int, int]
     num_workers: int
     pin_memory: bool
 
@@ -29,7 +29,7 @@ class DataloaderConfiguration(TypedDict):
 class HyperparameterConfiguration(TypedDict):
     hidden_dim: int
     lr: int
-    betas: Tuple[float, float]
+    betas: tuple[float, float]
     hard_or_all: str
     margin: float
 
diff --git a/utils/dataset.py b/utils/dataset.py
index ecdd2d9..9f9229a 100644
--- a/utils/dataset.py
+++ b/utils/dataset.py
@@ -1,18 +1,18 @@
 import os
 import random
 import re
-from typing import Optional, Dict, NewType, Union, List, Set, Tuple
+from typing import Optional, NewType, Union
 
 import numpy as np
 import torch
+import torchvision.transforms as transforms
 from PIL import Image
 from torch.utils import data
-import torchvision.transforms as transforms
 from tqdm import tqdm
 
-ClipLabels = NewType('ClipLabels', Set[str])
-ClipConditions = NewType('ClipConditions', Set[str])
-ClipViews = NewType('ClipViews', Set[str])
+ClipLabels = NewType('ClipLabels', set[str])
+ClipConditions = NewType('ClipConditions', set[str])
+ClipViews = NewType('ClipViews', set[str])
 
 
 class CASIAB(data.Dataset):
@@ -25,11 +25,11 @@ class CASIAB(data.Dataset):
             train_size: int = 74,
             num_sampled_frames: int = 30,
             discard_threshold: int = 15,
-            selector: Optional[Dict[
+            selector: Optional[dict[
                 str, Union[ClipLabels, ClipConditions, ClipLabels]
             ]] = None,
             num_input_channels: int = 3,
-            frame_size: Tuple[int, int] = (64, 32),
+            frame_size: tuple[int, int] = (64, 32),
             cache_on: bool = False
     ):
         """
@@ -77,15 +77,15 @@ class CASIAB(data.Dataset):
         self.conditions: np.ndarray[np.str_]
         self.views: np.ndarray[np.str_]
         # Video clip directory names
-        self._clip_names: List[str] = []
+        self._clip_names: list[str] = []
         # Labels, conditions and views in dataset,
         #   set of three attributes above
-        self.metadata = Dict[str, Set[str]]
+        self.metadata = dict[str, set[str]]
 
         # Dictionaries for indexing frames and frame names by clip name
         # and chip path when cache is on
-        self._cached_clips_frame_names: Optional[Dict[str, List[str]]] = None
-        self._cached_clips: Optional[Dict[str, torch.Tensor]] = None
+        self._cached_clips_frame_names: Optional[dict[str, list[str]]] = None
+        self._cached_clips: Optional[dict[str, torch.Tensor]] = None
 
         clip_names = sorted(os.listdir(self.root_dir))
 
@@ -172,7 +172,7 @@ class CASIAB(data.Dataset):
     def __len__(self) -> int:
         return len(self.labels)
 
-    def __getitem__(self, index: int) -> Dict[str, Union[str, torch.Tensor]]:
+    def __getitem__(self, index: int) -> dict[str, Union[str, torch.Tensor]]:
         label = self.labels[index]
         condition = self.conditions[index]
         view = self.views[index]
@@ -217,8 +217,8 @@ class CASIAB(data.Dataset):
     def _load_cached_video(
             self,
             clip: torch.Tensor,
-            frame_names: List[str],
-            sampled_frame_names: List[str]
+            frame_names: list[str],
+            sampled_frame_names: list[str]
     ) -> torch.Tensor:
         # Mask the original clip when it is long enough
         if len(frame_names) >= self.num_sampled_frames:
@@ -248,7 +248,7 @@ class CASIAB(data.Dataset):
         return clip
 
     def _sample_frames(self, clip_path: str,
-                       is_caching: bool = False) -> List[str]:
+                       is_caching: bool = False) -> list[str]:
         if self.cache_on:
             if is_caching:
                 # Sort frame in advance for loading convenience
diff --git a/utils/sampler.py b/utils/sampler.py
index 8dec846..0a177d1 100644
--- a/utils/sampler.py
+++ b/utils/sampler.py
@@ -1,5 +1,5 @@
 import random
-from typing import Iterator, Tuple
+from collections.abc import Iterator
 
 import numpy as np
 from torch.utils import data
@@ -11,7 +11,7 @@ class TripletSampler(data.Sampler):
     def __init__(
             self,
             data_source: CASIAB,
-            batch_size: Tuple[int, int]
+            batch_size: tuple[int, int]
     ):
         super().__init__(data_source)
         self.metadata_labels = data_source.metadata['labels']