Remove deprecated args and class for v1.4

monai/bundle/workflows.py

@@ -26,7 +26,7 @@
 from monai.bundle.properties import InferProperties, MetaProperties, TrainProperties
 from monai.bundle.utils import DEFAULT_EXP_MGMT_SETTINGS, EXPR_KEY, ID_REF_KEY, ID_SEP_KEY
 from monai.config import PathLike
-from monai.utils import BundleProperty, BundlePropertyConfig, deprecated_arg, deprecated_arg_default, ensure_tuple
+from monai.utils import BundleProperty, BundlePropertyConfig, deprecated_arg, ensure_tuple
 
 __all__ = ["BundleWorkflow", "ConfigWorkflow"]
 
@@ -43,7 +43,7 @@ class BundleWorkflow(ABC):
         workflow_type: specifies the workflow type: "train" or "training" for a training workflow,
             or "infer", "inference", "eval", "evaluation" for a inference workflow,
             other unsupported string will raise a ValueError.
-            default to `None` for common workflow.
+            default to `train` for train workflow.
         workflow: specifies the workflow type: "train" or "training" for a training workflow,
             or "infer", "inference", "eval", "evaluation" for a inference workflow,
             other unsupported string will raise a ValueError.
@@ -274,7 +274,6 @@ class ConfigWorkflow(BundleWorkflow):
         new_name="workflow_type",
         msg_suffix="please use `workflow_type` instead.",
     )
-    @deprecated_arg_default("workflow_type", None, "train", since="1.2", replaced="1.4")
     def __init__(
         self,
         config_file: str | Sequence[str],
@@ -284,7 +283,7 @@ def __init__(
         run_id: str = "run",
         final_id: str = "finalize",
         tracking: str | dict | None = None,
-        workflow_type: str | None = None,
+        workflow_type: str | None = "train",
         workflow: str | None = None,
         properties_path: PathLike | None = None,
         **override: Any,

monai/handlers/__init__.py

@@ -20,7 +20,7 @@
 from .earlystop_handler import EarlyStopHandler
 from .garbage_collector import GarbageCollector
 from .hausdorff_distance import HausdorffDistance
-from .ignite_metric import IgniteMetric, IgniteMetricHandler
+from .ignite_metric import IgniteMetricHandler
 from .logfile_handler import LogfileHandler
 from .lr_schedule_handler import LrScheduleHandler
 from .mean_dice import MeanDice

monai/handlers/ignite_metric.py

@@ -20,7 +20,7 @@
 
 from monai.config import IgniteInfo
 from monai.metrics import CumulativeIterationMetric, LossMetric
-from monai.utils import MetricReduction, deprecated, min_version, optional_import
+from monai.utils import MetricReduction, min_version, optional_import
 
 idist, _ = optional_import("ignite", IgniteInfo.OPT_IMPORT_VERSION, min_version, "distributed")
 
@@ -153,25 +153,3 @@ def attach(self, engine: Engine, name: str) -> None:  # type: ignore[override]
         self._name = name
         if self.save_details and not hasattr(engine.state, "metric_details"):
             engine.state.metric_details = {}  # type: ignore
-
-
-@deprecated(since="1.2", removed="1.4", msg_suffix="Use IgniteMetricHandler instead of IgniteMetric.")
-class IgniteMetric(IgniteMetricHandler):
-
-    def __init__(
-        self,
-        metric_fn: CumulativeIterationMetric | None = None,
-        loss_fn: _Loss | None = None,
-        output_transform: Callable = lambda x: x,
-        save_details: bool = True,
-        reduction: MetricReduction | str = MetricReduction.MEAN,
-        get_not_nans: bool = False,
-    ) -> None:
-        super().__init__(
-            metric_fn=metric_fn,
-            loss_fn=loss_fn,
-            output_transform=output_transform,
-            save_details=save_details,
-            reduction=reduction,
-            get_not_nans=get_not_nans,
-        )

monai/losses/dice.py

@@ -24,7 +24,7 @@
 from monai.losses.focal_loss import FocalLoss
 from monai.losses.spatial_mask import MaskedLoss
 from monai.networks import one_hot
-from monai.utils import DiceCEReduction, LossReduction, Weight, deprecated_arg, look_up_option, pytorch_after
+from monai.utils import DiceCEReduction, LossReduction, Weight, look_up_option, pytorch_after
 
 
 class DiceLoss(_Loss):
@@ -646,9 +646,6 @@ class DiceCELoss(_Loss):
 
     """
 
-    @deprecated_arg(
-        "ce_weight", since="1.2", removed="1.4", new_name="weight", msg_suffix="please use `weight` instead."
-    )
     def __init__(
         self,
         include_background: bool = True,
@@ -662,7 +659,6 @@ def __init__(
         smooth_nr: float = 1e-5,
         smooth_dr: float = 1e-5,
         batch: bool = False,
-        ce_weight: torch.Tensor | None = None,
         weight: torch.Tensor | None = None,
         lambda_dice: float = 1.0,
         lambda_ce: float = 1.0,
@@ -712,7 +708,6 @@ def __init__(
         """
         super().__init__()
         reduction = look_up_option(reduction, DiceCEReduction).value
-        weight = ce_weight if ce_weight is not None else weight
         dice_weight: torch.Tensor | None
         if weight is not None and not include_background:
             dice_weight = weight[1:]
@@ -825,9 +820,6 @@ class DiceFocalLoss(_Loss):
 
     """
 
-    @deprecated_arg(
-        "focal_weight", since="1.2", removed="1.4", new_name="weight", msg_suffix="please use `weight` instead."
-    )
     def __init__(
         self,
         include_background: bool = True,
@@ -842,7 +834,6 @@ def __init__(
         smooth_dr: float = 1e-5,
         batch: bool = False,
         gamma: float = 2.0,
-        focal_weight: Sequence[float] | float | int | torch.Tensor | None = None,
         weight: Sequence[float] | float | int | torch.Tensor | None = None,
         lambda_dice: float = 1.0,
         lambda_focal: float = 1.0,
@@ -885,7 +876,6 @@ def __init__(
                 [0, 1]. Defaults to None.
         """
         super().__init__()
-        weight = focal_weight if focal_weight is not None else weight
         self.dice = DiceLoss(
             include_background=include_background,
             to_onehot_y=False,
@@ -994,9 +984,6 @@ class GeneralizedDiceFocalLoss(_Loss):
         ValueError: if either `lambda_gdl` or `lambda_focal` is less than 0.
     """
 
-    @deprecated_arg(
-        "focal_weight", since="1.2", removed="1.4", new_name="weight", msg_suffix="please use `weight` instead."
-    )
     def __init__(
         self,
         include_background: bool = True,
@@ -1010,7 +997,6 @@ def __init__(
         smooth_dr: float = 1e-5,
         batch: bool = False,
         gamma: float = 2.0,
-        focal_weight: Sequence[float] | float | int | torch.Tensor | None = None,
         weight: Sequence[float] | float | int | torch.Tensor | None = None,
         lambda_gdl: float = 1.0,
         lambda_focal: float = 1.0,
@@ -1028,7 +1014,6 @@ def __init__(
             smooth_dr=smooth_dr,
             batch=batch,
         )
-        weight = focal_weight if focal_weight is not None else weight
         self.focal = FocalLoss(
             include_background=include_background,
             to_onehot_y=to_onehot_y,

monai/networks/blocks/patchembedding.py

@@ -21,7 +21,7 @@
 
 from monai.networks.blocks.pos_embed_utils import build_sincos_position_embedding
 from monai.networks.layers import Conv, trunc_normal_
-from monai.utils import deprecated_arg, ensure_tuple_rep, optional_import
+from monai.utils import ensure_tuple_rep, optional_import
 from monai.utils.module import look_up_option
 
 Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
@@ -42,17 +42,13 @@ class PatchEmbeddingBlock(nn.Module):
 
     """
 
-    @deprecated_arg(
-        name="pos_embed", since="1.2", removed="1.4", new_name="proj_type", msg_suffix="please use `proj_type` instead."
-    )
     def __init__(
         self,
         in_channels: int,
         img_size: Sequence[int] | int,
         patch_size: Sequence[int] | int,
         hidden_size: int,
         num_heads: int,
-        pos_embed: str = "conv",
         proj_type: str = "conv",
         pos_embed_type: str = "learnable",
         dropout_rate: float = 0.0,
@@ -69,8 +65,6 @@ def __init__(
             pos_embed_type: position embedding layer type.
             dropout_rate: fraction of the input units to drop.
             spatial_dims: number of spatial dimensions.
-        .. deprecated:: 1.4
-            ``pos_embed`` is deprecated in favor of ``proj_type``.
         """
 
         super().__init__()

monai/networks/nets/unetr.py

@@ -18,7 +18,7 @@
 from monai.networks.blocks.dynunet_block import UnetOutBlock
 from monai.networks.blocks.unetr_block import UnetrBasicBlock, UnetrPrUpBlock, UnetrUpBlock
 from monai.networks.nets.vit import ViT
-from monai.utils import deprecated_arg, ensure_tuple_rep
+from monai.utils import ensure_tuple_rep
 
 
 class UNETR(nn.Module):
@@ -27,9 +27,6 @@ class UNETR(nn.Module):
     UNETR: Transformers for 3D Medical Image Segmentation <https://arxiv.org/abs/2103.10504>"
     """
 
-    @deprecated_arg(
-        name="pos_embed", since="1.2", removed="1.4", new_name="proj_type", msg_suffix="please use `proj_type` instead."
-    )
     def __init__(
         self,
         in_channels: int,
@@ -39,7 +36,6 @@ def __init__(
         hidden_size: int = 768,
         mlp_dim: int = 3072,
         num_heads: int = 12,
-        pos_embed: str = "conv",
         proj_type: str = "conv",
         norm_name: tuple | str = "instance",
         conv_block: bool = True,
@@ -67,9 +63,6 @@ def __init__(
             qkv_bias: apply the bias term for the qkv linear layer in self attention block. Defaults to False.
             save_attn: to make accessible the attention in self attention block. Defaults to False.
 
-        .. deprecated:: 1.4
-            ``pos_embed`` is deprecated in favor of ``proj_type``.
-
         Examples::
 
             # for single channel input 4-channel output with image size of (96,96,96), feature size of 32 and batch norm

monai/networks/nets/vit.py

@@ -18,7 +18,6 @@
 
 from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
 from monai.networks.blocks.transformerblock import TransformerBlock
-from monai.utils import deprecated_arg
 
 __all__ = ["ViT"]
 
@@ -31,9 +30,6 @@ class ViT(nn.Module):
     ViT supports Torchscript but only works for Pytorch after 1.8.
     """
 
-    @deprecated_arg(
-        name="pos_embed", since="1.2", removed="1.4", new_name="proj_type", msg_suffix="please use `proj_type` instead."
-    )
     def __init__(
         self,
         in_channels: int,
@@ -43,7 +39,6 @@ def __init__(
         mlp_dim: int = 3072,
         num_layers: int = 12,
         num_heads: int = 12,
-        pos_embed: str = "conv",
         proj_type: str = "conv",
         pos_embed_type: str = "learnable",
         classification: bool = False,
@@ -75,9 +70,6 @@ def __init__(
             qkv_bias (bool, optional): apply bias to the qkv linear layer in self attention block. Defaults to False.
             save_attn (bool, optional): to make accessible the attention in self attention block. Defaults to False.
 
-        .. deprecated:: 1.4
-            ``pos_embed`` is deprecated in favor of ``proj_type``.
-
         Examples::
 
             # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone

monai/networks/nets/vitautoenc.py

@@ -20,7 +20,7 @@
 from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
 from monai.networks.blocks.transformerblock import TransformerBlock
 from monai.networks.layers import Conv
-from monai.utils import deprecated_arg, ensure_tuple_rep, is_sqrt
+from monai.utils import ensure_tuple_rep, is_sqrt
 
 __all__ = ["ViTAutoEnc"]
 
@@ -33,9 +33,6 @@ class ViTAutoEnc(nn.Module):
     Modified to also give same dimension outputs as the input size of the image
     """
 
-    @deprecated_arg(
-        name="pos_embed", since="1.2", removed="1.4", new_name="proj_type", msg_suffix="please use `proj_type` instead."
-    )
     def __init__(
         self,
         in_channels: int,
@@ -47,7 +44,6 @@ def __init__(
         mlp_dim: int = 3072,
         num_layers: int = 12,
         num_heads: int = 12,
-        pos_embed: str = "conv",
         proj_type: str = "conv",
         dropout_rate: float = 0.0,
         spatial_dims: int = 3,
@@ -71,9 +67,6 @@ def __init__(
             qkv_bias: apply bias to the qkv linear layer in self attention block. Defaults to False.
             save_attn: to make accessible the attention in self attention block. Defaults to False. Defaults to False.
 
-        .. deprecated:: 1.4
-            ``pos_embed`` is deprecated in favor of ``proj_type``.
-
         Examples::
 
             # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone

monai/utils/enums.py

@@ -16,7 +16,6 @@
 from typing import TYPE_CHECKING
 
 from monai.config import IgniteInfo
-from monai.utils import deprecated
 from monai.utils.module import min_version, optional_import
 
 __all__ = [
@@ -56,7 +55,6 @@
     "DataStatsKeys",
     "ImageStatsKeys",
     "LabelStatsKeys",
-    "AlgoEnsembleKeys",
     "HoVerNetMode",
     "HoVerNetBranch",
     "LazyAttr",
@@ -615,17 +613,6 @@ class LabelStatsKeys(StrEnum):
     LABEL_NCOMP = "ncomponents"
 
 
-@deprecated(since="1.2", removed="1.4", msg_suffix="please use `AlgoKeys` instead.")
-class AlgoEnsembleKeys(StrEnum):
-    """
-    Default keys for Mixed Ensemble
-    """
-
-    ID = "identifier"
-    ALGO = "infer_algo"
-    SCORE = "best_metric"
-
-
 class HoVerNetMode(StrEnum):
     """
     Modes for HoVerNet model:

monai/utils/misc.py

@@ -887,7 +887,7 @@ def run_cmd(cmd_list: list[str], **kwargs: Any) -> subprocess.CompletedProcess:
     if kwargs.pop("run_cmd_verbose", False):
         import monai
 
-        monai.apps.utils.get_logger("run_cmd").info(f"{cmd_list}")
+        monai.apps.utils.get_logger("run_cmd").info(f"{cmd_list}")  # type: ignore[attr-defined]
     try:
         return subprocess.run(cmd_list, **kwargs)
     except subprocess.CalledProcessError as e: