Add typing annotations to detection/backbone_utils

mypy.ini

@@ -21,10 +21,6 @@ ignore_errors=True
 
 ignore_errors = True
 
-[mypy-torchvision.models.detection.backbone_utils]
-
-ignore_errors = True
-
 [mypy-torchvision.models.detection.transform]
 
 ignore_errors = True

torchvision/models/detection/backbone_utils.py

@@ -1,7 +1,7 @@
 import warnings
-from typing import List, Optional
+from typing import Callable, Dict, Optional, List
 
-from torch import nn
+from torch import nn, Tensor
 from torchvision.ops import misc as misc_nn_ops
 from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool, ExtraFPNBlock
 
@@ -29,7 +29,14 @@ class BackboneWithFPN(nn.Module):
         out_channels (int): the number of channels in the FPN
     """
 
-    def __init__(self, backbone, return_layers, in_channels_list, out_channels, extra_blocks=None):
+    def __init__(
+        self,
+        backbone: nn.Module,
+        return_layers: Dict[str, str],
+        in_channels_list: List[int],
+        out_channels: int,
+        extra_blocks: Optional[ExtraFPNBlock] = None,
+    ) -> None:
         super(BackboneWithFPN, self).__init__()
 
         if extra_blocks is None:
@@ -43,20 +50,20 @@ def __init__(self, backbone, return_layers, in_channels_list, out_channels, extr
         )
         self.out_channels = out_channels
 
-    def forward(self, x):
+    def forward(self, x: Tensor) -> Dict[str, Tensor]:
         x = self.body(x)
         x = self.fpn(x)
         return x
 
 
 def resnet_fpn_backbone(
-    backbone_name,
-    pretrained,
-    norm_layer=misc_nn_ops.FrozenBatchNorm2d,
-    trainable_layers=3,
-    returned_layers=None,
-    extra_blocks=None,
-):
+    backbone_name: str,
+    pretrained: bool,
+    norm_layer: Callable[..., nn.Module] = misc_nn_ops.FrozenBatchNorm2d,
+    trainable_layers: int = 3,
+    returned_layers: Optional[List[int]] = None,
+    extra_blocks: Optional[ExtraFPNBlock] = None,
+) -> BackboneWithFPN:
     """
     Constructs a specified ResNet backbone with FPN on top. Freezes the specified number of layers in the backbone.
 
@@ -80,7 +87,7 @@ def resnet_fpn_backbone(
         backbone_name (string): resnet architecture. Possible values are 'ResNet', 'resnet18', 'resnet34', 'resnet50',
              'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2'
         pretrained (bool): If True, returns a model with backbone pre-trained on Imagenet
-        norm_layer (torchvision.ops): it is recommended to use the default value. For details visit:
+        norm_layer (callable): it is recommended to use the default value. For details visit:
             (https://github.com/facebookresearch/maskrcnn-benchmark/issues/267)
         trainable_layers (int): number of trainable (not frozen) resnet layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable.
@@ -101,7 +108,8 @@ def _resnet_backbone_config(
     trainable_layers: int,
     returned_layers: Optional[List[int]],
     extra_blocks: Optional[ExtraFPNBlock],
-):
+) -> BackboneWithFPN:
+
     # select layers that wont be frozen
     assert 0 <= trainable_layers <= 5
     layers_to_train = ["layer4", "layer3", "layer2", "layer1", "conv1"][:trainable_layers]
@@ -125,8 +133,13 @@ def _resnet_backbone_config(
     return BackboneWithFPN(backbone, return_layers, in_channels_list, out_channels, extra_blocks=extra_blocks)
 
 
-def _validate_trainable_layers(pretrained, trainable_backbone_layers, max_value, default_value):
-    # dont freeze any layers if pretrained model or backbone is not used
+def _validate_trainable_layers(
+    pretrained: bool,
+    trainable_backbone_layers: Optional[int],
+    max_value: int,
+    default_value: int,
+) -> int:
+    # don't freeze any layers if pretrained model or backbone is not used
     if not pretrained:
         if trainable_backbone_layers is not None:
             warnings.warn(
@@ -144,14 +157,15 @@ def _validate_trainable_layers(pretrained, trainable_backbone_layers, max_value,
 
 
 def mobilenet_backbone(
-    backbone_name,
-    pretrained,
-    fpn,
-    norm_layer=misc_nn_ops.FrozenBatchNorm2d,
-    trainable_layers=2,
-    returned_layers=None,
-    extra_blocks=None,
-):
+    backbone_name: str,
+    pretrained: bool,
+    fpn: bool,
+    norm_layer: Callable[..., nn.Module] = misc_nn_ops.FrozenBatchNorm2d,
+    trainable_layers: int = 2,
+    returned_layers: Optional[List[int]] = None,
+    extra_blocks: Optional[ExtraFPNBlock] = None,
+) -> nn.Module:
+
     backbone = mobilenet.__dict__[backbone_name](pretrained=pretrained, norm_layer=norm_layer).features
 
     # Gather the indices of blocks which are strided. These are the locations of C1, ..., Cn-1 blocks.
@@ -185,5 +199,5 @@ def mobilenet_backbone(
             # depthwise linear combination of channels to reduce their size
             nn.Conv2d(backbone[-1].out_channels, out_channels, 1),
         )
-        m.out_channels = out_channels
+        m.out_channels = out_channels  # type: ignore[assignment]
         return m

torchvision/prototype/models/detection/backbone_utils.py

@@ -1,14 +1,20 @@
-from ....models.detection.backbone_utils import misc_nn_ops, _resnet_backbone_config
+from typing import Callable, Optional, List
+
+from torch import nn
+
+from ....models.detection.backbone_utils import misc_nn_ops, _resnet_backbone_config, BackboneWithFPN, ExtraFPNBlock
 from .. import resnet
+from .._api import Weights
 
 
 def resnet_fpn_backbone(
-    backbone_name,
-    weights,
-    norm_layer=misc_nn_ops.FrozenBatchNorm2d,
-    trainable_layers=3,
-    returned_layers=None,
-    extra_blocks=None,
-):
+    backbone_name: str,
+    weights: Optional[Weights],
+    norm_layer: Callable[..., nn.Module] = misc_nn_ops.FrozenBatchNorm2d,
+    trainable_layers: int = 3,
+    returned_layers: Optional[List[int]] = None,
+    extra_blocks: Optional[ExtraFPNBlock] = None,
+) -> BackboneWithFPN:
+
     backbone = resnet.__dict__[backbone_name](weights=weights, norm_layer=norm_layer)
     return _resnet_backbone_config(backbone, trainable_layers, returned_layers, extra_blocks)