add pretrained weights loading for the segmentation encoder

torchgeo/trainers/segmentation.py

@@ -3,6 +3,7 @@
 
 """Segmentation tasks."""
 
+import os
 import warnings
 from typing import Any, cast
 
@@ -15,9 +16,11 @@
 from torch.optim.lr_scheduler import ReduceLROnPlateau
 from torchmetrics import MetricCollection
 from torchmetrics.classification import MulticlassAccuracy, MulticlassJaccardIndex
+from torchvision.models._api import WeightsEnum
 
 from ..datasets.utils import unbind_samples
-from ..models import FCN
+from ..models import FCN, get_weight
+from . import utils
 
 
 class SemanticSegmentationTask(LightningModule):  # type: ignore[misc]
@@ -31,17 +34,19 @@ class SemanticSegmentationTask(LightningModule):  # type: ignore[misc]
 
     def config_task(self) -> None:
         """Configures the task based on kwargs parameters passed to the constructor."""
+        weights = self.hyperparams["weights"]
+
         if self.hyperparams["model"] == "unet":
             self.model = smp.Unet(
                 encoder_name=self.hyperparams["backbone"],
-                encoder_weights=self.hyperparams["weights"],
+                encoder_weights="imagenet" if weights is True else None,
                 in_channels=self.hyperparams["in_channels"],
                 classes=self.hyperparams["num_classes"],
             )
         elif self.hyperparams["model"] == "deeplabv3+":
             self.model = smp.DeepLabV3Plus(
                 encoder_name=self.hyperparams["backbone"],
-                encoder_weights=self.hyperparams["weights"],
+                encoder_weights="imagenet" if weights is True else None,
                 in_channels=self.hyperparams["in_channels"],
                 classes=self.hyperparams["num_classes"],
             )
@@ -74,6 +79,16 @@ def config_task(self) -> None:
                 f"Currently, supports 'ce', 'jaccard' or 'focal' loss."
             )
 
+        if self.hyperparams["model"] != "fcn":
+            if weights and weights is not True:
+                if isinstance(weights, WeightsEnum):
+                    state_dict = weights.get_state_dict(progress=True)
+                elif os.path.exists(weights):
+                    _, state_dict = utils.extract_backbone(weights)
+                else:
+                    state_dict = get_weight(weights).get_state_dict(progress=True)
+                self.model.encoder = utils.load_state_dict(self.model, state_dict)
+
     def __init__(self, **kwargs: Any) -> None:
         """Initialize the LightningModule with a model and loss function.