back compatibility

Signed-off-by: Jirka <jirka.borovec@seznam.cz>

monai/networks/nets/netadapter.py

@@ -14,6 +14,7 @@
 import torch
 
 from monai.networks.layers import Conv, get_pool_layer
+from monai.utils import deprecated_arg
 
 
 class NetAdapter(torch.nn.Module):
@@ -38,6 +39,7 @@ class NetAdapter(torch.nn.Module):
 
     """
 
+    @deprecated_arg("n_classes")
     def __init__(
         self,
         model: torch.nn.Module,
@@ -47,8 +49,12 @@ def __init__(
         use_conv: bool = False,
         pool: Optional[Tuple[str, Dict[str, Any]]] = ("avg", {"kernel_size": 7, "stride": 1}),
         bias: bool = True,
+        n_classes: Optional[int] = None,
     ):
         super().__init__()
+        # in case the new num_classes is default but you still call deprecated n_classes
+        if n_classes is not None and num_classes == 1:
+            num_classes = n_classes
         layers = list(model.children())
         orig_fc = layers[-1]
         in_channels_: int

monai/networks/nets/resnet.py

@@ -10,7 +10,7 @@
 # limitations under the License.
 
 from functools import partial
-from typing import Any, Callable, List, Type, Union
+from typing import Any, Callable, List, Type, Union, Optional
 
 import torch
 import torch.nn as nn
@@ -20,6 +20,8 @@
 
 __all__ = ["ResNet", "resnet10", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnet200"]
 
+from monai.utils import deprecated_arg
+
 
 def get_inplanes():
     return [64, 128, 256, 512]
@@ -165,6 +167,7 @@ class ResNet(nn.Module):
         num_classes: number of output (classifications)
     """
 
+    @deprecated_arg("n_classes")
     def __init__(
         self,
         block: Type[Union[ResNetBlock, ResNetBottleneck]],
@@ -179,9 +182,13 @@ def __init__(
         widen_factor: float = 1.0,
         num_classes: int = 400,
         feed_forward: bool = True,
+        n_classes: Optional[int] = None,
     ) -> None:
 
         super(ResNet, self).__init__()
+        # in case the new num_classes is default but you still call deprecated n_classes
+        if n_classes is not None and num_classes == 400:
+            num_classes = n_classes
 
         conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]
         norm_type: Type[Union[nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]

monai/networks/nets/torchvision_fc.py

@@ -12,7 +12,7 @@
 from typing import Any, Dict, Optional, Tuple, Union
 
 from monai.networks.nets import NetAdapter
-from monai.utils import deprecated, optional_import
+from monai.utils import deprecated, optional_import, deprecated_arg
 
 models, _ = optional_import("torchvision.models")
 
@@ -41,6 +41,7 @@ class TorchVisionFCModel(NetAdapter):
         pretrained: whether to use the imagenet pretrained weights. Default to False.
     """
 
+    @deprecated_arg("n_classes")
     def __init__(
         self,
         model_name: str = "resnet18",
@@ -51,7 +52,11 @@ def __init__(
         pool: Optional[Tuple[str, Dict[str, Any]]] = ("avg", {"kernel_size": 7, "stride": 1}),
         bias: bool = True,
         pretrained: bool = False,
+        n_classes: Optional[int] = None,
     ):
+        # in case the new num_classes is default but you still call deprecated n_classes
+        if n_classes is not None and num_classes == 1:
+            num_classes = n_classes
         model = getattr(models, model_name)(pretrained=pretrained)
         # check if the model is compatible, should have a FC layer at the end
         if not str(list(model.children())[-1]).startswith("Linear"):
@@ -83,14 +88,19 @@ class TorchVisionFullyConvModel(TorchVisionFCModel):
         pretrained: whether to use the imagenet pretrained weights. Default to False.
     """
 
+    @deprecated_arg("n_classes")
     def __init__(
         self,
         model_name: str = "resnet18",
         num_classes: int = 1,
         pool_size: Union[int, Tuple[int, int]] = (7, 7),
         pool_stride: Union[int, Tuple[int, int]] = 1,
         pretrained: bool = False,
+        n_classes: Optional[int] = None,
     ):
+        # in case the new num_classes is default but you still call deprecated n_classes
+        if n_classes is not None and num_classes == 1:
+            num_classes = n_classes
         super().__init__(
             model_name=model_name,
             num_classes=num_classes,

monai/transforms/post/array.py

@@ -25,7 +25,7 @@
 from monai.networks.layers import GaussianFilter
 from monai.transforms.transform import Transform
 from monai.transforms.utils import fill_holes, get_largest_connected_component_mask
-from monai.utils import ensure_tuple, look_up_option
+from monai.utils import ensure_tuple, look_up_option, deprecated_arg
 
 __all__ = [
     "Activations",
@@ -131,6 +131,7 @@ class AsDiscrete(Transform):
 
     """
 
+    @deprecated_arg("n_classes")
     def __init__(
         self,
         argmax: bool = False,
@@ -139,14 +140,19 @@ def __init__(
         threshold_values: bool = False,
         logit_thresh: float = 0.5,
         rounding: Optional[str] = None,
+        n_classes: Optional[int] = None,
     ) -> None:
+        # in case the new num_classes is default but you still call deprecated n_classes
+        if n_classes is not None and num_classes is None:
+            num_classes = n_classes
         self.argmax = argmax
         self.to_onehot = to_onehot
         self.num_classes = num_classes
         self.threshold_values = threshold_values
         self.logit_thresh = logit_thresh
         self.rounding = rounding
 
+    @deprecated_arg("n_classes")
     def __call__(
         self,
         img: torch.Tensor,
@@ -156,6 +162,7 @@ def __call__(
         threshold_values: Optional[bool] = None,
         logit_thresh: Optional[float] = None,
         rounding: Optional[str] = None,
+        n_classes: Optional[int] = None,
     ) -> torch.Tensor:
         """
         Args:
@@ -175,6 +182,9 @@ def __call__(
                 available options: ["torchrounding"].
 
         """
+        # in case the new num_classes is default but you still call deprecated n_classes
+        if n_classes is not None and num_classes is None:
+            num_classes = n_classes
         if argmax or self.argmax:
             img = torch.argmax(img, dim=0, keepdim=True)
 

monai/transforms/post/dictionary.py

@@ -39,7 +39,7 @@
 from monai.transforms.transform import MapTransform
 from monai.transforms.utility.array import ToTensor
 from monai.transforms.utils import allow_missing_keys_mode, convert_inverse_interp_mode
-from monai.utils import ensure_tuple, ensure_tuple_rep
+from monai.utils import ensure_tuple, ensure_tuple_rep, deprecated_arg
 from monai.utils.enums import InverseKeys
 
 __all__ = [
@@ -126,6 +126,7 @@ class AsDiscreted(MapTransform):
     Dictionary-based wrapper of :py:class:`monai.transforms.AsDiscrete`.
     """
 
+    @deprecated_arg("n_classes")
     def __init__(
         self,
         keys: KeysCollection,
@@ -136,6 +137,7 @@ def __init__(
         logit_thresh: Union[Sequence[float], float] = 0.5,
         rounding: Union[Sequence[Optional[str]], Optional[str]] = None,
         allow_missing_keys: bool = False,
+        n_classes: Optional[int] = None,
     ) -> None:
         """
         Args:
@@ -157,6 +159,9 @@ def __init__(
             allow_missing_keys: don't raise exception if key is missing.
 
         """
+        # in case the new num_classes is default but you still call deprecated n_classes
+        if n_classes is not None and num_classes is None:
+            num_classes = n_classes
         super().__init__(keys, allow_missing_keys)
         self.argmax = ensure_tuple_rep(argmax, len(self.keys))
         self.to_onehot = ensure_tuple_rep(to_onehot, len(self.keys))