Add IntermediateLayerGetter on segmentation. (#5298)

torchvision/models/segmentation/deeplabv3.py

@@ -6,7 +6,7 @@ from torch.nn import functional as F
 
 from .. import mobilenetv3
 from .. import resnet
-from ..feature_extraction import create_feature_extractor
+from .._utils import IntermediateLayerGetter
 from ._utils import _SimpleSegmentationModel, _load_weights
 from .fcn import FCNHead
 
@@ -121,7 +121,7 @@ def _deeplabv3_resnet(
     return_layers = {"layer4": "out"}
     if aux:
         return_layers["layer3"] = "aux"
-    backbone = create_feature_extractor(backbone, return_layers)
+    backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
 
     aux_classifier = FCNHead(1024, num_classes) if aux else None
     classifier = DeepLabHead(2048, num_classes)
@@ -144,7 +144,7 @@ def _deeplabv3_mobilenetv3(
     return_layers = {str(out_pos): "out"}
     if aux:
         return_layers[str(aux_pos)] = "aux"
-    backbone = create_feature_extractor(backbone, return_layers)
+    backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
 
     aux_classifier = FCNHead(aux_inplanes, num_classes) if aux else None
     classifier = DeepLabHead(out_inplanes, num_classes)

torchvision/models/segmentation/fcn.py

@@ -3,7 +3,7 @@ from typing import Optional
 from torch import nn
 
 from .. import resnet
-from ..feature_extraction import create_feature_extractor
+from .._utils import IntermediateLayerGetter
 from ._utils import _SimpleSegmentationModel, _load_weights
 
 
@@ -57,7 +57,7 @@ def _fcn_resnet(
     return_layers = {"layer4": "out"}
     if aux:
         return_layers["layer3"] = "aux"
-    backbone = create_feature_extractor(backbone, return_layers)
+    backbone = IntermediateLayerGetter(backbone, return_layers=return_layers)
 
     aux_classifier = FCNHead(1024, num_classes) if aux else None
     classifier = FCNHead(2048, num_classes)

torchvision/models/segmentation/lraspp.py

@@ -6,7 +6,7 @@ from torch.nn import functional as F
 
 from ...utils import _log_api_usage_once
 from .. import mobilenetv3
-from ..feature_extraction import create_feature_extractor
+from .._utils import IntermediateLayerGetter
 from ._utils import _load_weights
 
 
@@ -90,7 +90,7 @@ def _lraspp_mobilenetv3(backbone: mobilenetv3.MobileNetV3, num_classes: int) ->
     high_pos = stage_indices[-1]  # use C5 which has output_stride = 16
     low_channels = backbone[low_pos].out_channels
     high_channels = backbone[high_pos].out_channels
-    backbone = create_feature_extractor(backbone, {str(low_pos): "low", str(high_pos): "high"})
+    backbone = IntermediateLayerGetter(backbone, return_layers={str(low_pos): "low", str(high_pos): "high"})
 
     return LRASPP(backbone, low_channels, high_channels, num_classes)