rename n_classes to num_classes (#2842)

* rename n_classes

Signed-off-by: Jirka <[email protected]>

* back compatibility

Signed-off-by: Jirka <[email protected]>

Author: Borda

CHANGELOG.md

@@ -5,6 +5,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
 ## [Unreleased]
+* renamed model's `n_classes` to `num_classes`
+
 ## [0.6.0] - 2021-07-08
 ### Added
 * 10 new transforms, a masked loss wrapper, and a `NetAdapter` for transfer learning

monai/losses/tversky.py

@@ -155,7 +155,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         if self.reduction == LossReduction.SUM.value:
             return torch.sum(score)  # sum over the batch and channel dims
         if self.reduction == LossReduction.NONE.value:
-            return score  # returns [N, n_classes] losses
+            return score  # returns [N, num_classes] losses
         if self.reduction == LossReduction.MEAN.value:
             return torch.mean(score)
         raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')

monai/metrics/meandice.py

@@ -114,7 +114,7 @@ def compute_meandice(
             the predicted output. Defaults to True.
 
     Returns:
-        Dice scores per batch and per class, (shape [batch_size, n_classes]).
+        Dice scores per batch and per class, (shape [batch_size, num_classes]).
 
     Raises:
         ValueError: when `y_pred` and `y` have different shapes.

monai/metrics/rocauc.py

@@ -131,9 +131,9 @@ def compute_roc_auc(
     y_pred_ndim = y_pred.ndimension()
     y_ndim = y.ndimension()
     if y_pred_ndim not in (1, 2):
-        raise ValueError("Predictions should be of shape (batch_size, n_classes) or (batch_size, ).")
+        raise ValueError("Predictions should be of shape (batch_size, num_classes) or (batch_size, ).")
     if y_ndim not in (1, 2):
-        raise ValueError("Targets should be of shape (batch_size, n_classes) or (batch_size, ).")
+        raise ValueError("Targets should be of shape (batch_size, num_classes) or (batch_size, ).")
     if y_pred_ndim == 2 and y_pred.shape[1] == 1:
         y_pred = y_pred.squeeze(dim=-1)
         y_pred_ndim = 1

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):
@@ -26,7 +27,7 @@ class NetAdapter(torch.nn.Module):
         model: a PyTorch model, support both 2D and 3D models. typically, it can be a pretrained model in Torchvision,
             like: ``resnet18``, ``resnet34m``, ``resnet50``, ``resnet101``, ``resnet152``, etc.
             more details: https://pytorch.org/vision/stable/models.html.
-        n_classes: number of classes for the last classification layer. Default to 1.
+        num_classes: number of classes for the last classification layer. Default to 1.
         dim: number of spatial dimensions, default to 2.
         in_channels: number of the input channels of last layer. if None, get it from `in_features` of last layer.
         use_conv: whether use convolutional layer to replace the last layer, default to False.
@@ -38,17 +39,22 @@ class NetAdapter(torch.nn.Module):
 
     """
 
+    @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
         model: torch.nn.Module,
-        n_classes: int = 1,
+        num_classes: int = 1,
         dim: int = 2,
         in_channels: Optional[int] = None,
         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
@@ -74,7 +80,7 @@ def __init__(
             # add 1x1 conv (it behaves like a FC layer)
             self.fc = Conv[Conv.CONV, dim](
                 in_channels=in_channels_,
-                out_channels=n_classes,
+                out_channels=num_classes,
                 kernel_size=1,
                 bias=bias,
             )
@@ -84,7 +90,7 @@ def __init__(
             # replace the out_features of FC layer
             self.fc = torch.nn.Linear(
                 in_features=in_channels_,
-                out_features=n_classes,
+                out_features=num_classes,
                 bias=bias,
             )
         self.use_conv = use_conv

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, Optional, Type, Union
 
 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]
@@ -162,9 +164,10 @@ class ResNet(nn.Module):
         no_max_pool: bool argument to determine if to use maxpool layer.
         shortcut_type: which downsample block to use.
         widen_factor: widen output for each layer.
-        n_classes: number of output (classifications)
+        num_classes: number of output (classifications)
     """
 
+    @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
         block: Type[Union[ResNetBlock, ResNetBottleneck]],
@@ -177,11 +180,15 @@ def __init__(
         no_max_pool: bool = False,
         shortcut_type: str = "B",
         widen_factor: float = 1.0,
-        n_classes: int = 400,
+        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]
@@ -215,7 +222,7 @@ def __init__(
         self.avgpool = avgp_type(block_avgpool[spatial_dims])
 
         if feed_forward:
-            self.fc = nn.Linear(block_inplanes[3] * block.expansion, n_classes)
+            self.fc = nn.Linear(block_inplanes[3] * block.expansion, num_classes)
 
         for m in self.modules():
             if isinstance(m, conv_type):
@@ -303,7 +310,7 @@ def _resnet(
     progress: bool,
     **kwargs: Any,
 ) -> ResNet:
-    model = ResNet(block, layers, block_inplanes, **kwargs)
+    model: ResNet = ResNet(block, layers, block_inplanes, **kwargs)
     if pretrained:
         # Author of paper zipped the state_dict on googledrive,
         # so would need to download, unzip and read (2.8gb file for a ~150mb state dict).

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, deprecated_arg, optional_import
 
 models, _ = optional_import("torchvision.models")
 
@@ -29,7 +29,7 @@ class TorchVisionFCModel(NetAdapter):
             ``resnet18`` (default), ``resnet34m``, ``resnet50``, ``resnet101``, ``resnet152``,
             ``resnext50_32x4d``, ``resnext101_32x8d``, ``wide_resnet50_2``, ``wide_resnet101_2``.
             model details: https://pytorch.org/vision/stable/models.html.
-        n_classes: number of classes for the last classification layer. Default to 1.
+        num_classes: number of classes for the last classification layer. Default to 1.
         dim: number of spatial dimensions, default to 2.
         in_channels: number of the input channels of last layer. if None, get it from `in_features` of last layer.
         use_conv: whether use convolutional layer to replace the last layer, default to False.
@@ -41,25 +41,30 @@ class TorchVisionFCModel(NetAdapter):
         pretrained: whether to use the imagenet pretrained weights. Default to False.
     """
 
+    @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
         model_name: str = "resnet18",
-        n_classes: int = 1,
+        num_classes: int = 1,
         dim: int = 2,
         in_channels: Optional[int] = None,
         use_conv: bool = False,
         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"):
             raise ValueError(f"Model ['{model_name}'] does not have a Linear layer at the end.")
 
         super().__init__(
             model=model,
-            n_classes=n_classes,
+            num_classes=num_classes,
             dim=dim,
             in_channels=in_channels,
             use_conv=use_conv,
@@ -77,7 +82,7 @@ class TorchVisionFullyConvModel(TorchVisionFCModel):
         model_name: name of any torchvision with adaptive avg pooling and fully connected layer at the end.
             ``resnet18`` (default), ``resnet34m``, ``resnet50``, ``resnet101``, ``resnet152``,
             ``resnext50_32x4d``, ``resnext101_32x8d``, ``wide_resnet50_2``, ``wide_resnet101_2``.
-        n_classes: number of classes for the last classification layer. Default to 1.
+        num_classes: number of classes for the last classification layer. Default to 1.
         pool_size: the kernel size for `AvgPool2d` to replace `AdaptiveAvgPool2d`. Default to (7, 7).
         pool_stride: the stride for `AvgPool2d` to replace `AdaptiveAvgPool2d`. Default to 1.
         pretrained: whether to use the imagenet pretrained weights. Default to False.
@@ -87,17 +92,22 @@ class TorchVisionFullyConvModel(TorchVisionFCModel):
 
     """
 
+    @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
         model_name: str = "resnet18",
-        n_classes: int = 1,
+        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,
-            n_classes=n_classes,
+            num_classes=num_classes,
             use_conv=True,
             pool=("avg", {"kernel_size": pool_size, "stride": pool_stride}),
             pretrained=pretrained,

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 deprecated_arg, ensure_tuple, look_up_option
 
 __all__ = [
     "Activations",
@@ -120,7 +120,7 @@ class AsDiscrete(Transform):
             Defaults to ``False``.
         to_onehot: whether to convert input data into the one-hot format.
             Defaults to ``False``.
-        n_classes: the number of classes to convert to One-Hot format.
+        num_classes: the number of classes to convert to One-Hot format.
             Defaults to ``None``.
         threshold_values: whether threshold the float value to int number 0 or 1.
             Defaults to ``False``.
@@ -131,31 +131,38 @@ class AsDiscrete(Transform):
 
     """
 
+    @deprecated_arg("n_classes", since="0.6")
     def __init__(
         self,
         argmax: bool = False,
         to_onehot: bool = False,
-        n_classes: Optional[int] = None,
+        num_classes: Optional[int] = None,
         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.n_classes = n_classes
+        self.num_classes = num_classes
         self.threshold_values = threshold_values
         self.logit_thresh = logit_thresh
         self.rounding = rounding
 
+    @deprecated_arg("n_classes", since="0.6")
     def __call__(
         self,
         img: torch.Tensor,
         argmax: Optional[bool] = None,
         to_onehot: Optional[bool] = None,
-        n_classes: Optional[int] = None,
+        num_classes: Optional[int] = None,
         threshold_values: Optional[bool] = None,
         logit_thresh: Optional[float] = None,
         rounding: Optional[str] = None,
+        n_classes: Optional[int] = None,
     ) -> torch.Tensor:
         """
         Args:
@@ -165,8 +172,8 @@ def __call__(
                 Defaults to ``self.argmax``.
             to_onehot: whether to convert input data into the one-hot format.
                 Defaults to ``self.to_onehot``.
-            n_classes: the number of classes to convert to One-Hot format.
-                Defaults to ``self.n_classes``.
+            num_classes: the number of classes to convert to One-Hot format.
+                Defaults to ``self.num_classes``.
             threshold_values: whether threshold the float value to int number 0 or 1.
                 Defaults to ``self.threshold_values``.
             logit_thresh: the threshold value for thresholding operation..
@@ -175,13 +182,16 @@ 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)
 
         if to_onehot or self.to_onehot:
-            _nclasses = self.n_classes if n_classes is None else n_classes
+            _nclasses = self.num_classes if num_classes is None else num_classes
             if not isinstance(_nclasses, int):
-                raise AssertionError("One of self.n_classes or n_classes must be an integer")
+                raise AssertionError("One of self.num_classes or num_classes must be an integer")
             img = one_hot(img, num_classes=_nclasses, dim=0)
 
         if threshold_values or self.threshold_values: