Update argument type hints where the default is set to None to use Op…

…tional (#1495)

Signed-off-by: Otavio Napoli <[email protected]>

lightly/api/download.py

@@ -72,7 +72,7 @@ def load_image(url, req, request_kwargs):
 
     def download_all_video_frames(
         url: str,
-        timestamp: Union[int, None] = None,
+        timestamp: Optional[int] = None,
         as_pil_image: int = True,
         thread_type: av.codec.context.ThreadType = av.codec.context.ThreadType.AUTO,
         video_channel: int = 0,

lightly/api/swagger_api_client.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 from lightly.api.swagger_rest_client import LightlySwaggerRESTClientObject
 from lightly.openapi_generated.swagger_client.api_client import ApiClient, Configuration
@@ -49,9 +49,9 @@ def __init__(
         self,
         configuration: Configuration,
         timeout: Union[None, int, Tuple[int, int]] = DEFAULT_API_TIMEOUT,
-        header_name: Union[str, None] = None,
-        header_value: Union[str, None] = None,
-        cookie: Union[str, None] = None,
+        header_name: Optional[str] = None,
+        header_value: Optional[str] = None,
+        cookie: Optional[str] = None,
     ):
         super().__init__(
             configuration=configuration,

lightly/api/swagger_rest_client.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Tuple, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 from lightly.openapi_generated.swagger_client.api_client import Configuration
 from lightly.openapi_generated.swagger_client.rest import RESTClientObject
@@ -29,7 +29,7 @@ def __init__(
         configuration: Configuration,
         timeout: Union[None, int, Tuple[int, int]],
         pools_size: int = 4,
-        maxsize: Union[None, int] = None,
+        maxsize: Optional[int] = None,
     ):
         # Save args as attributes to make the class picklable.
         self.configuration = configuration

lightly/data/collate.py

@@ -159,7 +159,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: dict = imagenet_normalize,
     ):
         if isinstance(input_size, tuple):
@@ -289,7 +289,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: dict = imagenet_normalize,
     ):
         super(SimCLRCollateFunction, self).__init__(
@@ -376,7 +376,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: dict = imagenet_normalize,
     ):
         super(MoCoCollateFunction, self).__init__(
@@ -523,7 +523,7 @@ def __init__(
         hf_prob: float = 0.5,
         vf_prob: float = 0.0,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         cj_prob: float = 0.8,
         cj_strength: float = 0.8,
         random_gray_scale: float = 0.2,
@@ -638,7 +638,7 @@ def __init__(
         hf_prob=0.5,
         vf_prob=0,
         rr_prob=0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         cj_prob=0.8,
         cj_bright=0.4,
         cj_contrast=0.4,
@@ -1148,7 +1148,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: dict = imagenet_normalize,
     ):
         if isinstance(input_size, tuple):

lightly/data/dataset.py

@@ -5,7 +5,7 @@
 import os
 import shutil
 import tempfile
-from typing import Any, Callable, Dict, List, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import torchvision.datasets as datasets
 from PIL import Image
@@ -254,8 +254,8 @@ def get_filenames(self) -> List[str]:
     def dump(
         self,
         output_dir: str,
-        filenames: Union[List[str], None] = None,
-        format: Union[str, None] = None,
+        filenames: Optional[List[str]] = None,
+        format: Optional[str] = None,
     ):
         """Saves images in the dataset to the output directory.
 

lightly/loss/msn_loss.py

@@ -1,6 +1,6 @@
 import math
 import warnings
-from typing import Union
+from typing import Optional
 
 import torch
 import torch.distributed as dist
@@ -152,7 +152,7 @@ def __init__(
         temperature: float = 0.1,
         sinkhorn_iterations: int = 3,
         regularization_weight: float = 1.0,
-        me_max_weight: Union[float, None] = None,
+        me_max_weight: Optional[float] = None,
         gather_distributed: bool = False,
     ):
         super().__init__()

lightly/models/modules/memory_bank.py

@@ -4,7 +4,7 @@
 # All Rights Reserved
 
 import warnings
-from typing import Sequence, Tuple, Union
+from typing import Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor
@@ -44,7 +44,7 @@ class MemoryBankModule(Module):
         >>>     def __init__(self, memory_bank_size: Tuple[int, int] = (2 ** 16, 128)):
         >>>         super().__init__(memory_bank_size)
         >>>
-        >>>     def forward(self, output: Tensor, labels: Union[Tensor, None] = None):
+        >>>     def forward(self, output: Tensor, labels: Optional[Tensor] = None):
         >>>         output, negatives = super().forward(output)
         >>>
         >>>         if negatives is not None:
@@ -134,7 +134,7 @@ def _dequeue_and_enqueue(self, batch: Tensor) -> None:
     def forward(
         self,
         output: Tensor,
-        labels: Union[Tensor, None] = None,
+        labels: Optional[Tensor] = None,
         update: bool = False,
     ) -> Tuple[Tensor, Union[Tensor, None]]:
         """Query memory bank for additional negative samples

lightly/transforms/byol_transform.py

@@ -30,7 +30,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         color_jitter = T.ColorJitter(
@@ -90,7 +90,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         color_jitter = T.ColorJitter(
@@ -168,8 +168,8 @@ class BYOLTransform(MultiViewTransform):
 
     def __init__(
         self,
-        view_1_transform: Union[BYOLView1Transform, None] = None,
-        view_2_transform: Union[BYOLView2Transform, None] = None,
+        view_1_transform: Optional[BYOLView1Transform] = None,
+        view_2_transform: Optional[BYOLView2Transform] = None,
     ):
         # We need to initialize the transforms here
         view_1_transform = view_1_transform or BYOLView1Transform()

lightly/transforms/dino_transform.py

@@ -104,7 +104,7 @@ def __init__(
         hf_prob: float = 0.5,
         vf_prob: float = 0,
         rr_prob: float = 0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         cj_prob: float = 0.8,
         cj_strength: float = 0.5,
         cj_bright: float = 0.8,
@@ -200,7 +200,7 @@ def __init__(
         hf_prob: float = 0.5,
         vf_prob: float = 0,
         rr_prob: float = 0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         cj_prob: float = 0.8,
         cj_strength: float = 0.5,
         cj_bright: float = 0.8,

lightly/transforms/fast_siam_transform.py

@@ -88,7 +88,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         transforms = [

lightly/transforms/gaussian_blur.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2020. Lightly AG and its affiliates.
 # All Rights Reserved
 
-from typing import Tuple, Union
+from typing import Optional, Tuple
 from warnings import warn
 
 import numpy as np
@@ -34,9 +34,9 @@ class GaussianBlur:
 
     def __init__(
         self,
-        kernel_size: Union[float, None] = None,
+        kernel_size: Optional[float] = None,
         prob: float = 0.5,
-        scale: Union[float, None] = None,
+        scale: Optional[float] = None,
         sigmas: Tuple[float, float] = (0.2, 2),
     ):
         if scale != None or kernel_size != None:

lightly/transforms/mmcr_transform.py

@@ -61,7 +61,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         if k < 1:

lightly/transforms/moco_transform.py

@@ -82,7 +82,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         super().__init__(
@@ -191,7 +191,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         super().__init__(

lightly/transforms/simclr_transform.py

@@ -101,7 +101,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         view_transform = SimCLRViewTransform(
@@ -144,7 +144,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         color_jitter = T.ColorJitter(

lightly/transforms/simsiam_transform.py

@@ -90,7 +90,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         view_transform = SimSiamViewTransform(
@@ -133,7 +133,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         color_jitter = T.ColorJitter(

lightly/transforms/swav_transform.py

@@ -85,7 +85,7 @@ def __init__(
         hf_prob: float = 0.5,
         vf_prob: float = 0.0,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         cj_prob: float = 0.8,
         cj_strength: float = 1.0,
         cj_bright: float = 0.8,
@@ -131,7 +131,7 @@ def __init__(
         hf_prob: float = 0.5,
         vf_prob: float = 0.0,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         cj_prob: float = 0.8,
         cj_strength: float = 1.0,
         cj_bright: float = 0.8,

lightly/transforms/vicreg_transform.py

@@ -96,7 +96,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         view_transform = VICRegViewTransform(
@@ -141,7 +141,7 @@ def __init__(
         vf_prob: float = 0.0,
         hf_prob: float = 0.5,
         rr_prob: float = 0.0,
-        rr_degrees: Union[None, float, Tuple[float, float]] = None,
+        rr_degrees: Optional[Union[float, Tuple[float, float]]] = None,
         normalize: Union[None, Dict[str, List[float]]] = IMAGENET_NORMALIZE,
     ):
         color_jitter = T.ColorJitter(

lightly/transforms/wmse_transform.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Tuple, Union
+from typing import Dict, List, Optional, Tuple
 
 import torchvision.transforms as T
 
@@ -78,7 +78,7 @@ def __init__(
         random_gray_scale: float = 0.1,
         hf_prob: float = 0.5,
         gaussian_blur: float = 0.5,
-        kernel_size: Union[int, None] = None,
+        kernel_size: Optional[int] = None,
         sigmas: Tuple[float, float] = (0.1, 2.0),
         normalize: Dict[str, List[float]] = IMAGENET_NORMALIZE,
     ):

lightly/utils/benchmarking/knn_classifier.py

@@ -1,4 +1,4 @@
-from typing import Tuple, Union
+from typing import Optional, Tuple
 
 import torch
 import torch.nn.functional as F
@@ -97,8 +97,8 @@ def __init__(
 
         self._train_features = []
         self._train_targets = []
-        self._train_features_tensor: Union[Tensor, None] = None
-        self._train_targets_tensor: Union[Tensor, None] = None
+        self._train_features_tensor: Optional[Tensor] = None
+        self._train_targets_tensor: Optional[Tensor] = None
 
     @torch.no_grad()
     def training_step(self, batch, batch_idx) -> None:

lightly/utils/hipify.py

@@ -1,6 +1,6 @@
 import copy
 import warnings
-from typing import Type, Union
+from typing import Optional, Type, Union
 
 
 class bcolors:
@@ -19,7 +19,7 @@ def _custom_formatwarning(
     category: Type[Warning],
     filename: str,
     lineno: int,
-    line: Union[str, None] = None,
+    line: Optional[str] = None,
 ) -> str:
     # ignore everything except the message
     return f"{bcolors.WARNING}{message}{bcolors.WARNING}\n"