Added annotation typing to squeezenet (#2865)

* style: Added annotation typing for squeezenet

* feat: Added typing for kwargs

torchvision/models/squeezenet.py

@@ -2,6 +2,7 @@ import torch
 import torch.nn as nn
 import torch.nn.init as init
 from .utils import load_state_dict_from_url
+from typing import Any
 
 __all__ = ['SqueezeNet', 'squeezenet1_0', 'squeezenet1_1']
 
@@ -13,8 +14,13 @@ model_urls = {
 
 class Fire(nn.Module):
 
-    def __init__(self, inplanes, squeeze_planes,
-                 expand1x1_planes, expand3x3_planes):
+    def __init__(
+        self,
+        inplanes: int,
+        squeeze_planes: int,
+        expand1x1_planes: int,
+        expand3x3_planes: int
+    ):
         super(Fire, self).__init__()
         self.inplanes = inplanes
         self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
@@ -26,7 +32,7 @@ class Fire(nn.Module):
                                    kernel_size=3, padding=1)
         self.expand3x3_activation = nn.ReLU(inplace=True)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.squeeze_activation(self.squeeze(x))
         return torch.cat([
             self.expand1x1_activation(self.expand1x1(x)),
@@ -36,7 +42,11 @@ class Fire(nn.Module):
 
 class SqueezeNet(nn.Module):
 
-    def __init__(self, version='1_0', num_classes=1000):
+    def __init__(
+        self,
+        version: str = '1_0',
+        num_classes: int = 1000
+    ):
         super(SqueezeNet, self).__init__()
         self.num_classes = num_classes
         if version == '1_0':
@@ -96,13 +106,13 @@ class SqueezeNet(nn.Module):
                 if m.bias is not None:
                     init.constant_(m.bias, 0)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.features(x)
         x = self.classifier(x)
         return torch.flatten(x, 1)
 
 
-def _squeezenet(version, pretrained, progress, **kwargs):
+def _squeezenet(version: str, pretrained: bool, progress: bool, **kwargs: Any) -> SqueezeNet:
     model = SqueezeNet(version, **kwargs)
     if pretrained:
         arch = 'squeezenet' + version
@@ -112,7 +122,7 @@ def _squeezenet(version, pretrained, progress, **kwargs):
     return model
 
 
-def squeezenet1_0(pretrained=False, progress=True, **kwargs):
+def squeezenet1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
     r"""SqueezeNet model architecture from the `"SqueezeNet: AlexNet-level
     accuracy with 50x fewer parameters and <0.5MB model size"
     <https://arxiv.org/abs/1602.07360>`_ paper.
@@ -124,7 +134,7 @@ def squeezenet1_0(pretrained=False, progress=True, **kwargs):
     return _squeezenet('1_0', pretrained, progress, **kwargs)
 
 
-def squeezenet1_1(pretrained=False, progress=True, **kwargs):
+def squeezenet1_1(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> SqueezeNet:
     r"""SqueezeNet 1.1 model from the `official SqueezeNet repo
     <https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_.
     SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters