Type hint for python version lower than 3.9

9 files changed, 59 insertions, 53 deletions

diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index 7c1f7ef..1e7c323 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -1,3 +1,5 @@
+from typing import Tuple
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -12,7 +14,7 @@ class Encoder(nn.Module):
             self,
             in_channels: int = 3,
             feature_channels: int = 64,
-            output_dims: tuple[int, int, int] = (128, 128, 64)
+            output_dims: Tuple[int, int, int] = (128, 128, 64)
     ):
         super().__init__()
         self.feature_channels = feature_channels
@@ -67,7 +69,7 @@ class Decoder(nn.Module):
 
     def __init__(
             self,
-            input_dims: tuple[int, int, int] = (128, 128, 64),
+            input_dims: Tuple[int, int, int] = (128, 128, 64),
             feature_channels: int = 64,
             out_channels: int = 3,
     ):
@@ -116,7 +118,7 @@ class AutoEncoder(nn.Module):
             num_class: int = 74,
             channels: int = 3,
             feature_channels: int = 64,
-            embedding_dims: tuple[int, int, int] = (128, 128, 64)
+            embedding_dims: Tuple[int, int, int] = (128, 128, 64)
     ):
         super().__init__()
         self.encoder = Encoder(channels, feature_channels, embedding_dims)
diff --git a/models/hpm.py b/models/hpm.py
index 66503e3..7505ed7 100644
--- a/models/hpm.py
+++ b/models/hpm.py
@@ -1,3 +1,5 @@
+from typing import Tuple
+
 import torch
 import torch.nn as nn
 
@@ -9,7 +11,7 @@ class HorizontalPyramidMatching(nn.Module):
             self,
             in_channels: int,
             out_channels: int = 128,
-            scales: tuple[int, ...] = (1, 2, 4),
+            scales: Tuple[int, ...] = (1, 2, 4),
             use_avg_pool: bool = True,
             use_max_pool: bool = True,
             **kwargs
diff --git a/models/layers.py b/models/layers.py
index a9f04b3..7f2ccec 100644
--- a/models/layers.py
+++ b/models/layers.py
@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, Tuple
 
 import torch
 import torch.nn as nn
@@ -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,
@@ -104,7 +104,7 @@ 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
     ):
@@ -124,8 +124,8 @@ class FocalConv2dBlock(nn.Module):
             self,
             in_channels: int,
             out_channels: int,
-            kernel_sizes: tuple[int, int],
-            paddings: tuple[int, int],
+            kernel_sizes: Tuple[int, int],
+            paddings: Tuple[int, int],
             halving: int,
             use_pool: bool = True,
             **kwargs
@@ -151,7 +151,7 @@ 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().__init__()
@@ -167,7 +167,7 @@ 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 = True,
             use_max_pool: bool = True,
             **kwargs
diff --git a/models/model.py b/models/model.py
index 1dc0f23..4deced0 100644
--- a/models/model.py
+++ b/models/model.py
@@ -1,5 +1,5 @@
 import os
-from typing import Union, Optional
+from typing import Union, Optional, Tuple, List
 
 import numpy as np
 import torch
@@ -195,9 +195,9 @@ class Model:
 
     def _batch_splitter(
             self,
-            batch: list[dict[str, Union[np.int64, str, torch.Tensor]]]
-    ) -> tuple[dict[str, Union[list[str], torch.Tensor]],
-               dict[str, Union[list[str], torch.Tensor]]]:
+            batch: List[dict[str, Union[np.int64, str, torch.Tensor]]]
+    ) -> Tuple[dict[str, Union[List[str], torch.Tensor]],
+               dict[str, Union[List[str], torch.Tensor]]]:
         """
         Disentanglement need two random conditions, this function will
         split pr * k * 2 samples to 2 dicts each containing pr * k
@@ -212,7 +212,7 @@ class Model:
 
     def _make_signature(self,
                         config: dict,
-                        popped_keys: Optional[list] = None) -> str:
+                        popped_keys: Optional[List] = None) -> str:
         _config = config.copy()
         if popped_keys:
             for key in popped_keys:
@@ -220,12 +220,12 @@ class Model:
 
         return self._gen_sig(list(_config.values()))
 
-    def _gen_sig(self, values: Union[tuple, list, str, int, float]) -> str:
+    def _gen_sig(self, values: Union[Tuple, List, str, int, float]) -> str:
         strings = []
         for v in values:
             if isinstance(v, str):
                 strings.append(v)
-            elif isinstance(v, (tuple, list)):
+            elif isinstance(v, (Tuple, List)):
                 strings.append(self._gen_sig(v))
             else:
                 strings.append(str(v))
diff --git a/models/part_net.py b/models/part_net.py
index ac7c434..6d8d4e1 100644
--- a/models/part_net.py
+++ b/models/part_net.py
@@ -1,4 +1,5 @@
 import copy
+from typing import Tuple
 
 import torch
 import torch.nn as nn
@@ -12,9 +13,9 @@ class FrameLevelPartFeatureExtractor(nn.Module):
             self,
             in_channels: int = 3,
             feature_channels: int = 32,
-            kernel_sizes: tuple[tuple, ...] = ((5, 3), (3, 3), (3, 3)),
-            paddings: tuple[tuple, ...] = ((2, 1), (1, 1), (1, 1)),
-            halving: tuple[int, ...] = (0, 2, 3)
+            kernel_sizes: Tuple[Tuple, ...] = ((5, 3), (3, 3), (3, 3)),
+            paddings: Tuple[Tuple, ...] = ((2, 1), (1, 1), (1, 1)),
+            halving: Tuple[int, ...] = (0, 2, 3)
     ):
         super().__init__()
         num_blocks = len(kernel_sizes)
@@ -112,9 +113,9 @@ class PartNet(nn.Module):
             self,
             in_channels: int = 3,
             feature_channels: int = 32,
-            kernel_sizes: tuple[tuple, ...] = ((5, 3), (3, 3), (3, 3)),
-            paddings: tuple[tuple, ...] = ((2, 1), (1, 1), (1, 1)),
-            halving: tuple[int, ...] = (0, 2, 3),
+            kernel_sizes: Tuple[Tuple, ...] = ((5, 3), (3, 3), (3, 3)),
+            paddings: Tuple[Tuple, ...] = ((2, 1), (1, 1), (1, 1)),
+            halving: Tuple[int, ...] = (0, 2, 3),
             squeeze_ratio: int = 4,
             num_part: int = 16
     ):
diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py
index 3037da0..39cbed6 100644
--- a/models/rgb_part_net.py
+++ b/models/rgb_part_net.py
@@ -1,4 +1,5 @@
 import random
+from typing import Tuple, List
 
 import torch
 import torch.nn as nn
@@ -16,14 +17,14 @@ class RGBPartNet(nn.Module):
             num_class: int = 74,
             ae_in_channels: int = 3,
             ae_feature_channels: int = 64,
-            f_a_c_p_dims: tuple[int, int, int] = (128, 128, 64),
-            hpm_scales: tuple[int, ...] = (1, 2, 4),
+            f_a_c_p_dims: Tuple[int, int, int] = (128, 128, 64),
+            hpm_scales: Tuple[int, ...] = (1, 2, 4),
             hpm_use_avg_pool: bool = True,
             hpm_use_max_pool: bool = True,
             fpfe_feature_channels: int = 32,
-            fpfe_kernel_sizes: tuple[tuple, ...] = ((5, 3), (3, 3), (3, 3)),
-            fpfe_paddings: tuple[tuple, ...] = ((2, 1), (1, 1), (1, 1)),
-            fpfe_halving: tuple[int, ...] = (0, 2, 3),
+            fpfe_kernel_sizes: Tuple[Tuple, ...] = ((5, 3), (3, 3), (3, 3)),
+            fpfe_paddings: Tuple[Tuple, ...] = ((2, 1), (1, 1), (1, 1)),
+            fpfe_halving: Tuple[int, ...] = (0, 2, 3),
             tfa_squeeze_ratio: int = 4,
             tfa_num_parts: int = 16,
             embedding_dims: int = 256,
@@ -142,8 +143,8 @@ class RGBPartNet(nn.Module):
             return (x_c_c1, x_p_c1), None
 
     @staticmethod
-    def _pose_sim_loss(f_p_c1: list[torch.Tensor],
-                       f_p_c2: list[torch.Tensor]) -> torch.Tensor:
+    def _pose_sim_loss(f_p_c1: List[torch.Tensor],
+                       f_p_c2: List[torch.Tensor]) -> torch.Tensor:
         f_p_c1_mean = torch.stack(f_p_c1).mean(dim=0)
         f_p_c2_mean = torch.stack(f_p_c2).mean(dim=0)
         return F.mse_loss(f_p_c1_mean, f_p_c2_mean)
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']