[Typing][A-73,A-77] Add type annotations for some vision models (`Ale…

…xNet`, `LeNet`) (PaddlePaddle#65283)

python/paddle/vision/models/alexnet.py

@@ -12,11 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import math
+from typing import (
+    TYPE_CHECKING,
+    TypedDict,
+)
+
+from typing_extensions import NotRequired, Unpack
 
 import paddle
 import paddle.nn.functional as F
 from paddle import nn
+from paddle._typing import Size2
 from paddle.base.param_attr import ParamAttr
 from paddle.nn import Conv2D, Dropout, Linear, MaxPool2D, ReLU
 from paddle.nn.initializer import Uniform
@@ -31,19 +40,26 @@
 
 __all__ = []
 
+if TYPE_CHECKING:
+    from paddle import Tensor
+
+
+class _AlexNetOptions(TypedDict):
+    num_classes: NotRequired[int]
+
 
 class ConvPoolLayer(nn.Layer):
     def __init__(
         self,
-        input_channels,
-        output_channels,
-        filter_size,
-        stride,
-        padding,
-        stdv,
-        groups=1,
-        act=None,
-    ):
+        input_channels: int,
+        output_channels: int,
+        filter_size: Size2,
+        stride: Size2,
+        padding: Size2,
+        stdv: float,
+        groups: int = 1,
+        act: str | None = None,
+    ) -> None:
         super().__init__()
 
         self.relu = ReLU() if act == "relu" else None
@@ -60,7 +76,7 @@ def __init__(
         )
         self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
 
-    def forward(self, inputs):
+    def forward(self, inputs: Tensor) -> Tensor:
         x = self._conv(inputs)
         if self.relu is not None:
             x = self.relu(x)
@@ -93,7 +109,7 @@ class AlexNet(nn.Layer):
             [1, 1000]
     """
 
-    def __init__(self, num_classes=1000):
+    def __init__(self, num_classes: int = 1000) -> None:
         super().__init__()
         self.num_classes = num_classes
         stdv = 1.0 / math.sqrt(3 * 11 * 11)
@@ -147,7 +163,7 @@ def __init__(self, num_classes=1000):
                 bias_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
             )
 
-    def forward(self, inputs):
+    def forward(self, inputs: Tensor) -> Tensor:
         x = self._conv1(inputs)
         x = self._conv2(x)
         x = self._conv3(x)
@@ -169,7 +185,9 @@ def forward(self, inputs):
         return x
 
 
-def _alexnet(arch, pretrained, **kwargs):
+def _alexnet(
+    arch: str, pretrained: bool, **kwargs: Unpack[_AlexNetOptions]
+) -> AlexNet:
     model = AlexNet(**kwargs)
 
     if pretrained:
@@ -186,7 +204,9 @@ def _alexnet(arch, pretrained, **kwargs):
     return model
 
 
-def alexnet(pretrained=False, **kwargs):
+def alexnet(
+    pretrained: bool = False, **kwargs: Unpack[_AlexNetOptions]
+) -> AlexNet:
     """AlexNet model from
     `"ImageNet Classification with Deep Convolutional Neural Networks"
     <https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf>`_.

python/paddle/vision/models/lenet.py

@@ -12,9 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+)
+
 import paddle
 from paddle import nn
 
+if TYPE_CHECKING:
+    from paddle import Tensor
+
 __all__ = []
 
 
@@ -44,7 +53,7 @@ class LeNet(nn.Layer):
             [1, 10]
     """
 
-    def __init__(self, num_classes=10):
+    def __init__(self, num_classes: int = 10) -> None:
         super().__init__()
         self.num_classes = num_classes
         self.features = nn.Sequential(
@@ -63,7 +72,7 @@ def __init__(self, num_classes=10):
                 nn.Linear(84, num_classes),
             )
 
-    def forward(self, inputs):
+    def forward(self, inputs: Tensor) -> Tensor:
         x = self.features(inputs)
 
         if self.num_classes > 0: