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-rw-r--r--utils/dataset.py16
1 files changed, 8 insertions, 8 deletions
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