diff options
Diffstat (limited to 'utils')
| -rw-r--r-- | utils/configuration.py | 8 | ||||
| -rw-r--r-- | utils/dataset.py | 30 | ||||
| -rw-r--r-- | utils/sampler.py | 4 |
3 files changed, 21 insertions, 21 deletions
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'] |