Added annotation typing to densenet (#2860)

* style: Added annotation typing for densenet

* fix: Fixed import

* refactor: Removed un-necessary import

* fix: Fixed constructor typing

* chore: Updated mypy.ini

* fix: Fixed tuple typing

* style: Ignored some mypy errors

* style: Fixed typing

* fix: Added missing constructor typing

mypy.ini

@@ -16,10 +16,6 @@ ignore_errors = True
 
 ignore_errors = True
 
-[mypy-torchvision.models.densenet.*]
-
-ignore_errors = True
-
 [mypy-torchvision.models.quantization.*]
 
 ignore_errors = True

torchvision/models/densenet.py

@@ -6,7 +6,7 @@ import torch.utils.checkpoint as cp
 from collections import OrderedDict
 from .utils import load_state_dict_from_url
 from torch import Tensor
-from torch.jit.annotations import List
+from typing import Any, List, Tuple
 
 
 __all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
@@ -20,56 +20,64 @@ model_urls = {
 
 
 class _DenseLayer(nn.Module):
-    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False):
+    def __init__(
+        self,
+        num_input_features: int,
+        growth_rate: int,
+        bn_size: int,
+        drop_rate: float,
+        memory_efficient: bool = False
+    ) -> None:
         super(_DenseLayer, self).__init__()
-        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
-        self.add_module('relu1', nn.ReLU(inplace=True)),
+        self.norm1: nn.BatchNorm2d
+        self.add_module('norm1', nn.BatchNorm2d(num_input_features))
+        self.relu1: nn.ReLU
+        self.add_module('relu1', nn.ReLU(inplace=True))
+        self.conv1: nn.Conv2d
         self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                                            growth_rate, kernel_size=1, stride=1,
-                                           bias=False)),
-        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
-        self.add_module('relu2', nn.ReLU(inplace=True)),
+                                           bias=False))
+        self.norm2: nn.BatchNorm2d
+        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate))
+        self.relu2: nn.ReLU
+        self.add_module('relu2', nn.ReLU(inplace=True))
+        self.conv2: nn.Conv2d
         self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                                            kernel_size=3, stride=1, padding=1,
-                                           bias=False)),
+                                           bias=False))
         self.drop_rate = float(drop_rate)
         self.memory_efficient = memory_efficient
 
-    def bn_function(self, inputs):
-        # type: (List[Tensor]) -> Tensor
+    def bn_function(self, inputs: List[Tensor]) -> Tensor:
         concated_features = torch.cat(inputs, 1)
         bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
         return bottleneck_output
 
     # todo: rewrite when torchscript supports any
-    def any_requires_grad(self, input):
-        # type: (List[Tensor]) -> bool
+    def any_requires_grad(self, input: List[Tensor]) -> bool:
         for tensor in input:
             if tensor.requires_grad:
                 return True
         return False
 
     @torch.jit.unused  # noqa: T484
-    def call_checkpoint_bottleneck(self, input):
-        # type: (List[Tensor]) -> Tensor
+    def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor:
         def closure(*inputs):
             return self.bn_function(inputs)
 
         return cp.checkpoint(closure, *input)
 
     @torch.jit._overload_method  # noqa: F811
-    def forward(self, input):
-        # type: (List[Tensor]) -> (Tensor)
+    def forward(self, input: List[Tensor]) -> Tensor:
         pass
 
-    @torch.jit._overload_method  # noqa: F811
-    def forward(self, input):
-        # type: (Tensor) -> (Tensor)
+    @torch.jit._overload_method  # type: ignore[no-redef] # noqa: F811
+    def forward(self, input: Tensor) -> Tensor:
         pass
 
     # torchscript does not yet support *args, so we overload method
     # allowing it to take either a List[Tensor] or single Tensor
-    def forward(self, input):  # noqa: F811
+    def forward(self, input: Tensor) -> Tensor:  # type: ignore[no-redef] # noqa: F811
         if isinstance(input, Tensor):
             prev_features = [input]
         else:
@@ -93,7 +101,15 @@ class _DenseLayer(nn.Module):
 class _DenseBlock(nn.ModuleDict):
     _version = 2
 
-    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False):
+    def __init__(
+        self,
+        num_layers: int,
+        num_input_features: int,
+        bn_size: int,
+        growth_rate: int,
+        drop_rate: float,
+        memory_efficient: bool = False
+    ) -> None:
         super(_DenseBlock, self).__init__()
         for i in range(num_layers):
             layer = _DenseLayer(
@@ -105,7 +121,7 @@ class _DenseBlock(nn.ModuleDict):
             )
             self.add_module('denselayer%d' % (i + 1), layer)
 
-    def forward(self, init_features):
+    def forward(self, init_features: Tensor) -> Tensor:  # type: ignore[override]
         features = [init_features]
         for name, layer in self.items():
             new_features = layer(features)
@@ -114,7 +130,7 @@ class _DenseBlock(nn.ModuleDict):
 
 
 class _Transition(nn.Sequential):
-    def __init__(self, num_input_features, num_output_features):
+    def __init__(self, num_input_features: int, num_output_features: int) -> None:
         super(_Transition, self).__init__()
         self.add_module('norm', nn.BatchNorm2d(num_input_features))
         self.add_module('relu', nn.ReLU(inplace=True))
@@ -139,8 +155,16 @@ class DenseNet(nn.Module):
           but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
     """
 
-    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
-                 num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False):
+    def __init__(
+        self,
+        growth_rate: int = 32,
+        block_config: Tuple[int, int, int, int] = (6, 12, 24, 16),
+        num_init_features: int = 64,
+        bn_size: int = 4,
+        drop_rate: float = 0,
+        num_classes: int = 1000,
+        memory_efficient: bool = False
+    ) -> None:
 
         super(DenseNet, self).__init__()
 
@@ -188,7 +212,7 @@ class DenseNet(nn.Module):
             elif isinstance(m, nn.Linear):
                 nn.init.constant_(m.bias, 0)
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Tensor:
         features = self.features(x)
         out = F.relu(features, inplace=True)
         out = F.adaptive_avg_pool2d(out, (1, 1))
@@ -197,7 +221,7 @@ class DenseNet(nn.Module):
         return out
 
 
-def _load_state_dict(model, model_url, progress):
+def _load_state_dict(model: nn.Module, model_url: str, progress: bool) -> None:
     # '.'s are no longer allowed in module names, but previous _DenseLayer
     # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
     # They are also in the checkpoints in model_urls. This pattern is used
@@ -215,15 +239,22 @@ def _load_state_dict(model, model_url, progress):
     model.load_state_dict(state_dict)
 
 
-def _densenet(arch, growth_rate, block_config, num_init_features, pretrained, progress,
-              **kwargs):
+def _densenet(
+    arch: str,
+    growth_rate: int,
+    block_config: Tuple[int, int, int, int],
+    num_init_features: int,
+    pretrained: bool,
+    progress: bool,
+    **kwargs: Any
+) -> DenseNet:
     model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
     if pretrained:
         _load_state_dict(model, model_urls[arch], progress)
     return model
 
 
-def densenet121(pretrained=False, progress=True, **kwargs):
+def densenet121(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-121 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
 
@@ -237,7 +268,7 @@ def densenet121(pretrained=False, progress=True, **kwargs):
                      **kwargs)
 
 
-def densenet161(pretrained=False, progress=True, **kwargs):
+def densenet161(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-161 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
 
@@ -251,7 +282,7 @@ def densenet161(pretrained=False, progress=True, **kwargs):
                      **kwargs)
 
 
-def densenet169(pretrained=False, progress=True, **kwargs):
+def densenet169(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-169 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
 
@@ -265,7 +296,7 @@ def densenet169(pretrained=False, progress=True, **kwargs):
                      **kwargs)
 
 
-def densenet201(pretrained=False, progress=True, **kwargs):
+def densenet201(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> DenseNet:
     r"""Densenet-201 model from
     `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_