Added typing annotations to models/video (#4229)

* style: Added typing to models/video

* style: Fixed typing

* style: Fixed typing

* style: Fixed typing

* refactor: Removed default value for stem

* docs: Fixed docstring of VideoResNet

* style: Refactored typing

* docs: Fixed docstring

* style: Fixed typing

* docs: Specified docstring

* typing: Fixed tying

* docs: Fixed docstring

* Undoing change.

torchvision/models/video/resnet.py

+from torch import Tensor
 import torch.nn as nn
+from typing import Tuple, Optional, Callable, List, Type, Any, Union
 
 from ..._internally_replaced_utils import load_state_dict_from_url
 
@@ -13,12 +15,14 @@ model_urls = {
 
 
 class Conv3DSimple(nn.Conv3d):
-    def __init__(self,
-                 in_planes,
-                 out_planes,
-                 midplanes=None,
-                 stride=1,
-                 padding=1):
+    def __init__(
+        self,
+        in_planes: int,
+        out_planes: int,
+        midplanes: Optional[int] = None,
+        stride: int = 1,
+        padding: int = 1
+    ) -> None:
 
         super(Conv3DSimple, self).__init__(
             in_channels=in_planes,
@@ -29,18 +33,20 @@ class Conv3DSimple(nn.Conv3d):
             bias=False)
 
     @staticmethod
-    def get_downsample_stride(stride):
+    def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
         return stride, stride, stride
 
 
 class Conv2Plus1D(nn.Sequential):
 
-    def __init__(self,
-                 in_planes,
-                 out_planes,
-                 midplanes,
-                 stride=1,
-                 padding=1):
+    def __init__(
+        self,
+        in_planes: int,
+        out_planes: int,
+        midplanes: int,
+        stride: int = 1,
+        padding: int = 1
+    ) -> None:
         super(Conv2Plus1D, self).__init__(
             nn.Conv3d(in_planes, midplanes, kernel_size=(1, 3, 3),
                       stride=(1, stride, stride), padding=(0, padding, padding),
@@ -52,18 +58,20 @@ class Conv2Plus1D(nn.Sequential):
                       bias=False))
 
     @staticmethod
-    def get_downsample_stride(stride):
+    def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
         return stride, stride, stride
 
 
 class Conv3DNoTemporal(nn.Conv3d):
 
-    def __init__(self,
-                 in_planes,
-                 out_planes,
-                 midplanes=None,
-                 stride=1,
-                 padding=1):
+    def __init__(
+        self,
+        in_planes: int,
+        out_planes: int,
+        midplanes: Optional[int] = None,
+        stride: int = 1,
+        padding: int = 1
+    ) -> None:
 
         super(Conv3DNoTemporal, self).__init__(
             in_channels=in_planes,
@@ -74,7 +82,7 @@ class Conv3DNoTemporal(nn.Conv3d):
             bias=False)
 
     @staticmethod
-    def get_downsample_stride(stride):
+    def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
         return 1, stride, stride
 
 
@@ -82,7 +90,14 @@ class BasicBlock(nn.Module):
 
     expansion = 1
 
-    def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
+    def __init__(
+        self,
+        inplanes: int,
+        planes: int,
+        conv_builder: Callable[..., nn.Module],
+        stride: int = 1,
+        downsample: Optional[nn.Module] = None,
+    ) -> None:
         midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
 
         super(BasicBlock, self).__init__()
@@ -99,7 +114,7 @@ class BasicBlock(nn.Module):
         self.downsample = downsample
         self.stride = stride
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
         residual = x
 
         out = self.conv1(x)
@@ -116,7 +131,14 @@ class BasicBlock(nn.Module):
 class Bottleneck(nn.Module):
     expansion = 4
 
-    def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
+    def __init__(
+        self,
+        inplanes: int,
+        planes: int,
+        conv_builder: Callable[..., nn.Module],
+        stride: int = 1,
+        downsample: Optional[nn.Module] = None,
+    ) -> None:
 
         super(Bottleneck, self).__init__()
         midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
@@ -143,7 +165,7 @@ class Bottleneck(nn.Module):
         self.downsample = downsample
         self.stride = stride
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
         residual = x
 
         out = self.conv1(x)
@@ -162,7 +184,7 @@ class Bottleneck(nn.Module):
 class BasicStem(nn.Sequential):
     """The default conv-batchnorm-relu stem
     """
-    def __init__(self):
+    def __init__(self) -> None:
         super(BasicStem, self).__init__(
             nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2),
                       padding=(1, 3, 3), bias=False),
@@ -173,7 +195,7 @@ class BasicStem(nn.Sequential):
 class R2Plus1dStem(nn.Sequential):
     """R(2+1)D stem is different than the default one as it uses separated 3D convolution
     """
-    def __init__(self):
+    def __init__(self) -> None:
         super(R2Plus1dStem, self).__init__(
             nn.Conv3d(3, 45, kernel_size=(1, 7, 7),
                       stride=(1, 2, 2), padding=(0, 3, 3),
@@ -189,16 +211,23 @@ class R2Plus1dStem(nn.Sequential):
 
 class VideoResNet(nn.Module):
 
-    def __init__(self, block, conv_makers, layers,
-                 stem, num_classes=400,
-                 zero_init_residual=False):
+    def __init__(
+        self,
+        block: Type[Union[BasicBlock, Bottleneck]],
+        conv_makers: List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]],
+        layers: List[int],
+        stem: Callable[..., nn.Module],
+        num_classes: int = 400,
+        zero_init_residual: bool = False,
+    ) -> None:
         """Generic resnet video generator.
 
         Args:
-            block (nn.Module): resnet building block
-            conv_makers (list(functions)): generator function for each layer
+            block (Type[Union[BasicBlock, Bottleneck]]): resnet building block
+            conv_makers (List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]]): generator
+                function for each layer
             layers (List[int]): number of blocks per layer
-            stem (nn.Module, optional): Resnet stem, if None, defaults to conv-bn-relu. Defaults to None.
+            stem (Callable[..., nn.Module]): module specifying the ResNet stem.
             num_classes (int, optional): Dimension of the final FC layer. Defaults to 400.
             zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False.
         """
@@ -221,9 +250,9 @@ class VideoResNet(nn.Module):
         if zero_init_residual:
             for m in self.modules():
                 if isinstance(m, Bottleneck):
-                    nn.init.constant_(m.bn3.weight, 0)
+                    nn.init.constant_(m.bn3.weight, 0)  # type: ignore[union-attr, arg-type]
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
         x = self.stem(x)
 
         x = self.layer1(x)
@@ -238,7 +267,14 @@ class VideoResNet(nn.Module):
 
         return x
 
-    def _make_layer(self, block, conv_builder, planes, blocks, stride=1):
+    def _make_layer(
+        self,
+        block: Type[Union[BasicBlock, Bottleneck]],
+        conv_builder: Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]],
+        planes: int,
+        blocks: int,
+        stride: int = 1
+    ) -> nn.Sequential:
         downsample = None
 
         if stride != 1 or self.inplanes != planes * block.expansion:
@@ -257,7 +293,7 @@ class VideoResNet(nn.Module):
 
         return nn.Sequential(*layers)
 
-    def _initialize_weights(self):
+    def _initialize_weights(self) -> None:
         for m in self.modules():
             if isinstance(m, nn.Conv3d):
                 nn.init.kaiming_normal_(m.weight, mode='fan_out',
@@ -272,7 +308,7 @@ class VideoResNet(nn.Module):
                 nn.init.constant_(m.bias, 0)
 
 
-def _video_resnet(arch, pretrained=False, progress=True, **kwargs):
+def _video_resnet(arch: str, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
     model = VideoResNet(**kwargs)
 
     if pretrained:
@@ -282,7 +318,7 @@ def _video_resnet(arch, pretrained=False, progress=True, **kwargs):
     return model
 
 
-def r3d_18(pretrained=False, progress=True, **kwargs):
+def r3d_18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
     """Construct 18 layer Resnet3D model as in
     https://arxiv.org/abs/1711.11248
 
@@ -302,7 +338,7 @@ def r3d_18(pretrained=False, progress=True, **kwargs):
                          stem=BasicStem, **kwargs)
 
 
-def mc3_18(pretrained=False, progress=True, **kwargs):
+def mc3_18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
     """Constructor for 18 layer Mixed Convolution network as in
     https://arxiv.org/abs/1711.11248
 
@@ -316,12 +352,12 @@ def mc3_18(pretrained=False, progress=True, **kwargs):
     return _video_resnet('mc3_18',
                          pretrained, progress,
                          block=BasicBlock,
-                         conv_makers=[Conv3DSimple] + [Conv3DNoTemporal] * 3,
+                         conv_makers=[Conv3DSimple] + [Conv3DNoTemporal] * 3,  # type: ignore[list-item]
                          layers=[2, 2, 2, 2],
                          stem=BasicStem, **kwargs)
 
 
-def r2plus1d_18(pretrained=False, progress=True, **kwargs):
+def r2plus1d_18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
     """Constructor for the 18 layer deep R(2+1)D network as in
     https://arxiv.org/abs/1711.11248