Type hint for python version lower than 3.9

3 files changed, 19 insertions, 19 deletions

diff --git a/utils/configuration.py b/utils/configuration.py
index f3ae0b3..aa04b32 100644
--- a/utils/configuration.py
+++ b/utils/configuration.py
@@ -1,4 +1,4 @@
-from typing import TypedDict, Optional, Union
+from typing import TypedDict, Optional, Union, Tuple
 
 from utils.dataset import ClipClasses, ClipConditions, ClipViews
 
@@ -17,32 +17,32 @@ class DatasetConfiguration(TypedDict):
     discard_threshold: int
     selector: Optional[dict[str, Union[ClipClasses, ClipConditions, ClipViews]]]
     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
 
 
 class HyperparameterConfiguration(TypedDict):
     ae_feature_channels: int
-    f_a_c_p_dims: tuple[int, int, int]
-    hpm_scales: tuple[int, ...]
+    f_a_c_p_dims: Tuple[int, int, int]
+    hpm_scales: Tuple[int, ...]
     hpm_use_avg_pool: bool
     hpm_use_max_pool: bool
     fpfe_feature_channels: int
-    fpfe_kernel_sizes: tuple[tuple, ...]
-    fpfe_paddings: tuple[tuple, ...]
-    fpfe_halving: tuple[int, ...]
+    fpfe_kernel_sizes: Tuple[Tuple, ...]
+    fpfe_paddings: Tuple[Tuple, ...]
+    fpfe_halving: Tuple[int, ...]
     tfa_squeeze_ratio: int
     tfa_num_parts: int
     embedding_dims: int
     triplet_margin: float
     lr: int
-    betas: tuple[float, float]
+    betas: Tuple[float, float]
 
 
 class ModelConfiguration(TypedDict):
diff --git a/utils/dataset.py b/utils/dataset.py
index ded9fd5..0a33693 100644
--- a/utils/dataset.py
+++ b/utils/dataset.py
@@ -1,7 +1,7 @@
 import os
 import random
 import re
-from typing import Optional, NewType, Union
+from typing import Optional, NewType, Union, List, Tuple
 
 import numpy as np
 import torch
@@ -30,7 +30,7 @@ class CASIAB(data.Dataset):
                 str, Union[ClipClasses, ClipConditions, ClipViews]
             ]] = None,
             num_input_channels: int = 3,
-            frame_size: tuple[int, int] = (64, 32),
+            frame_size: Tuple[int, int] = (64, 32),
             cache_on: bool = False
     ):
         """
@@ -75,15 +75,15 @@ class CASIAB(data.Dataset):
         self.views: np.ndarray[np.str_]
         # Labels, classes, conditions and views in dataset,
         #   set of three attributes above
-        self.metadata = dict[str, list[np.int64, str]]
+        self.metadata = dict[str, List[np.int64, 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_frame_names: Optional[dict[str, List[str]]] = None
         self._cached_clips: Optional[dict[str, torch.Tensor]] = None
 
         # Video clip directory names
-        self._clip_names: list[str] = []
+        self._clip_names: List[str] = []
         clip_names = sorted(os.listdir(self._root_dir))
 
         if self._is_train:
@@ -215,8 +215,8 @@ 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:
@@ -246,7 +246,7 @@ 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 cdf1984..734acf9 100644
--- a/utils/sampler.py
+++ b/utils/sampler.py
@@ -1,6 +1,6 @@
 import random
 from collections.abc import Iterator
-from typing import Union
+from typing import Union, Tuple
 
 import numpy as np
 from torch.utils import data
@@ -12,7 +12,7 @@ class TripletSampler(data.Sampler):
     def __init__(
             self,
             data_source: Union[CASIAB],
-            batch_size: tuple[int, int]
+            batch_size: Tuple[int, int]
     ):
         super().__init__(data_source)
         self.metadata_labels = data_source.metadata['labels']