Fix format

yolort/models/yolo_lite.py

@@ -1,9 +1,12 @@
+from typing import Dict, List, Callable, Optional
+
 from torch import nn
 from torchvision.models import mobilenet
 from torchvision.models._utils import IntermediateLayerGetter
 from torchvision.models.detection.backbone_utils import _validate_trainable_layers
 from torchvision.ops import misc as misc_nn_ops
-from torchvision.ops.feature_pyramid_network import FeaturePyramidNetwork, LastLevelMaxPool
+from torchvision.ops.feature_pyramid_network import ExtraFPNBlock, FeaturePyramidNetwork, LastLevelMaxPool
+from yolort.utils import load_state_dict_from_url
 
 from .anchor_utils import AnchorGenerator
 from .box_head import YOLOHead
@@ -33,7 +36,14 @@ 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().__init__()
 
         if extra_blocks is None:
@@ -55,12 +65,12 @@ def forward(self, x):
 
 
 def mobilenet_backbone(
-    backbone_name,
-    pretrained,
-    norm_layer=misc_nn_ops.FrozenBatchNorm2d,
-    trainable_layers=2,
-    returned_layers=None,
-):
+    backbone_name: str,
+    pretrained: bool,
+    norm_layer: Callable[..., nn.Module] = misc_nn_ops.FrozenBatchNorm2d,
+    trainable_layers: int = 2,
+    returned_layers: Optional[List[int]] = 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.
@@ -96,12 +106,12 @@ def mobilenet_backbone(
 
 
 def _yolov5_mobilenet_v3_small_fpn(
-    weights_name,
-    pretrained=False,
-    progress=True,
-    num_classes=80,
-    pretrained_backbone=True,
-    trainable_backbone_layers=None,
+    weights_name: str,
+    pretrained: bool = False,
+    progress: bool = True,
+    num_classes: int = 80,
+    pretrained_backbone: bool = True,
+    trainable_backbone_layers: Optional[int] = None,
     **kwargs,
 ):
     trainable_backbone_layers = _validate_trainable_layers(
@@ -123,24 +133,32 @@ def _yolov5_mobilenet_v3_small_fpn(
         [436, 615, 739, 380, 925, 792],
     ]
     anchor_generator = AnchorGenerator(strides, anchor_grids)
-
     head = YOLOHead(
         backbone.out_channels,
         anchor_generator.num_anchors,
         anchor_generator.strides,
         num_classes,
     )
     model = YOLO(backbone, num_classes, anchor_generator=anchor_generator, head=head, **kwargs)
-
+    if pretrained:
+        if model_urls.get(weights_name, None) is None:
+            raise ValueError(f"No checkpoint is available for model {weights_name}")
+        state_dict = load_state_dict_from_url(model_urls["retinanet_resnet50_fpn_coco"], progress=progress)
+        model.load_state_dict(state_dict)
     return model
 
 
+model_urls = {
+    "yolov5_mobilenet_v3_small_fpn_coco": None,
+}
+
+
 def yolov5_mobilenet_v3_small_fpn(
-    pretrained=False,
-    progress=True,
-    num_classes=80,
-    pretrained_backbone=True,
-    trainable_backbone_layers=None,
+    pretrained: bool = False,
+    progress: bool = True,
+    num_classes: int = 80,
+    pretrained_backbone: bool = True,
+    trainable_backbone_layers: Optional[int] = None,
     **kwargs,
 ):
     """