Update occ sens

monai/visualize/__init__.py

@@ -9,12 +9,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .visualizer import default_normalizer, default_upsampler  # isort:skip
-from .class_activation_maps import CAM, GradCAM, GradCAMpp, ModelWithHooks
+from .class_activation_maps import CAM, GradCAM, GradCAMpp, ModelWithHooks, default_normalizer
 from .img2tensorboard import (
     add_animated_gif,
     add_animated_gif_no_channels,
     make_animated_gif_summary,
     plot_2d_or_3d_image,
 )
 from .occlusion_sensitivity import OcclusionSensitivity
+from .visualizer import default_upsampler

monai/visualize/class_activation_maps.py

@@ -12,15 +12,30 @@
 import warnings
 from typing import Callable, Dict, Sequence, Union
 
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 from monai.networks.utils import eval_mode, train_mode
+from monai.transforms import ScaleIntensity
 from monai.utils import ensure_tuple
-from monai.visualize import default_normalizer, default_upsampler
+from monai.visualize.visualizer import default_upsampler
 
-__all__ = ["CAM", "GradCAM", "GradCAMpp", "ModelWithHooks"]
+__all__ = ["CAM", "GradCAM", "GradCAMpp", "ModelWithHooks", "default_normalizer"]
+
+
+def default_normalizer(x) -> np.ndarray:
+    """
+    A linear intensity scaling by mapping the (min, max) to (1, 0).
+
+    N.B.: This will flip magnitudes (i.e., smallest will become biggest and vice versa).
+    """
+    if isinstance(x, torch.Tensor):
+        x = x.detach().cpu().numpy()
+    scaler = ScaleIntensity(minv=1.0, maxv=0.0)
+    x = [scaler(x) for x in x]
+    return np.stack(x, axis=0)
 
 
 class ModelWithHooks:
@@ -221,7 +236,8 @@ def __init__(
                 N dimensional linear (bilinear, trilinear, etc.) depending on num spatial
                 dimensions of input.
             postprocessing: a callable that applies on the upsampled output image.
-                default is normalising between 0 and 1.
+                Default is normalizing between min=1 and max=0 (i.e., largest input will become 0 and
+                smallest input will become 1).
         """
         super().__init__(
             nn_module=nn_module,