Add type hints in alexnet.py (#1983)

* Update alexnet.py

* Update alexnet.py

* Update mmcv/cnn/alexnet.py
Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

* Update alexnet.py

* Update mmcv/cnn/alexnet.py
Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

* Update mmcv/cnn/alexnet.py
Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

* Update alexnet.py

* fix importing format

* Update alexnet.py
Co-authored-by: Zaida Zhou <58739961+zhouzaida@users.noreply.github.com>

mmcv/cnn/alexnet.py

 # Copyright (c) OpenMMLab. All rights reserved.
 import logging
+from typing import Optional
 
+import torch
 import torch.nn as nn
 
 
@@ -11,7 +13,7 @@ class AlexNet(nn.Module):
         num_classes (int): number of classes for classification.
     """
 
-    def __init__(self, num_classes=-1):
+    def __init__(self, num_classes: int = -1):
         super().__init__()
         self.num_classes = num_classes
         self.features = nn.Sequential(
@@ -40,7 +42,7 @@ class AlexNet(nn.Module):
                 nn.Linear(4096, num_classes),
             )
 
-    def init_weights(self, pretrained=None):
+    def init_weights(self, pretrained: Optional[str] = None) -> None:
         if isinstance(pretrained, str):
             logger = logging.getLogger()
             from ..runner import load_checkpoint
@@ -51,7 +53,7 @@ class AlexNet(nn.Module):
         else:
             raise TypeError('pretrained must be a str or None')
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         x = self.features(x)
         if self.num_classes > 0: