Rename parameter

Signed-off-by: Francesc Marti Escofet <[email protected]>

terratorch/tasks/base_task.py

@@ -55,15 +55,15 @@ def configure_optimizers(
             optimizer = "Adam"
 
         parameters: Iterable
-        if self.hparams.get("reduce_lr", None) is not None and len(self.hparams["reduce_lr"]) > 0:
+        if self.hparams.get("lr_overrides", None) is not None and len(self.hparams["lr_overrides"]) > 0:
             parameters = []
-            for param_name, reduce_factor in self.hparams["reduce_lr"]:
+            for param_name, custom_lr in self.hparams["lr_overrides"]:
                 p = [p for n, p in self.model.named_parameters() if param_name in n]
-                parameters.append({"params": p, "lr": self.hparams["lr"] / reduce_factor})
+                parameters.append({"params": p, "lr": custom_lr})
             rest_p = [
                 p
                 for n, p in self.model.named_parameters()
-                if all(param_name not in n for param_name, _ in self.hparams["reduce_lr"])
+                if all(param_name not in n for param_name, _ in self.hparams["lr_overrides"])
             ]
             parameters.append({"params": rest_p})
         else:

terratorch/tasks/classification_tasks.py

@@ -16,7 +16,8 @@
 from terratorch.tasks.optimizer_factory import optimizer_factory
 from terratorch.tasks.base_task import TerraTorchTask
 
-logger = logging.getLogger('terratorch')
+logger = logging.getLogger("terratorch")
+
 
 def to_class_prediction(y: ModelOutput) -> Tensor:
     y_hat = y.output
@@ -62,7 +63,7 @@ def __init__(
         freeze_backbone: bool = False,  # noqa: FBT001, FBT002
         freeze_decoder: bool = False,  # noqa: FBT002, FBT001
         class_names: list[str] | None = None,
-        reduce_lr: list[tuple[str, float]] | None = None,
+        lr_overrides: list[tuple[str, float]] | None = None,
     ) -> None:
         """Constructor
 
@@ -98,8 +99,9 @@ def __init__(
             freeze_decoder (bool, optional): Whether to freeze the decoder and segmentation head. Defaults to False.
             class_names (list[str] | None, optional): List of class names passed to metrics for better naming.
                 Defaults to numeric ordering.
-            reduce_lr (list[tuple[str, float]] | None, optional): List of tuples with a substring of the parameter names
-                to reduce the learning rate and the factor to reduce it by. Defaults to None.
+            lr_overrides (list[tuple[str, float]] | None, optional): List of tuples with a substring of the parameter
+                names (it will check the substring is contained in the parameter name) to override the learning rate and
+                the new lr. Defaults to None.
         """
         self.aux_loss = aux_loss
         self.aux_heads = aux_heads

terratorch/tasks/regression_tasks.py

@@ -24,7 +24,8 @@
 
 BATCH_IDX_FOR_VALIDATION_PLOTTING = 10
 
-logger = logging.getLogger('terratorch')
+logger = logging.getLogger("terratorch")
+
 
 class RootLossWrapper(nn.Module):
     def __init__(self, loss_function: nn.Module, reduction: None | str = "mean") -> None:
@@ -152,7 +153,7 @@ def __init__(
         freeze_decoder: bool = False,  # noqa: FBT001, FBT002
         plot_on_val: bool | int = 10,
         tiled_inference_parameters: TiledInferenceParameters | None = None,
-        reduce_lr: list[tuple[str, float]] | None = None,
+        lr_overrides: list[tuple[str, float]] | None = None,
     ) -> None:
         """Constructor
 
@@ -187,8 +188,9 @@ def __init__(
                 If true, log every epoch. Defaults to 10. If int, will plot every plot_on_val epochs.
             tiled_inference_parameters (TiledInferenceParameters | None, optional): Inference parameters
                 used to determine if inference is done on the whole image or through tiling.
-            reduce_lr (list[tuple[str, float]] | None, optional): List of tuples with a substring of the parameter names
-                to reduce the learning rate and the factor to reduce it by. Defaults to None.
+            lr_overrides (list[tuple[str, float]] | None, optional): List of tuples with a substring of the parameter
+                names (it will check the substring is contained in the parameter name) to override the learning rate and
+                the new lr. Defaults to None.
         """
         self.tiled_inference_parameters = tiled_inference_parameters
         self.aux_loss = aux_loss

terratorch/tasks/segmentation_tasks.py

@@ -64,7 +64,7 @@ def __init__(
         class_names: list[str] | None = None,
         tiled_inference_parameters: TiledInferenceParameters = None,
         test_dataloaders_names: list[str] | None = None,
-        reduce_lr: list[tuple[str, float]] | None = None,
+        lr_overrides: list[tuple[str, float]] | None = None,
     ) -> None:
         """Constructor
 
@@ -107,8 +107,9 @@ def __init__(
             test_dataloaders_names (list[str] | None, optional): Names used to differentiate metrics when
                 multiple dataloaders are returned by test_dataloader in the datamodule. Defaults to None,
                 which assumes only one test dataloader is used.
-            reduce_lr (list[tuple[str, float]] | None, optional): List of tuples with a substring of the parameter names
-                to reduce the learning rate and the factor to reduce it by. Defaults to None.
+            lr_overrides (list[tuple[str, float]] | None, optional): List of tuples with a substring of the parameter
+                names (it will check the substring is contained in the parameter name) to override the learning rate and
+                the new lr. Defaults to None.
         """
         self.tiled_inference_parameters = tiled_inference_parameters
         self.aux_loss = aux_loss