EigenGradCAM

README.md

@@ -11,6 +11,7 @@ Tested on Common CNN Networks and Vision Transformers!
 | AblationCAM  | Zero out activations and measure how the output drops (this repository includes a fast batched implementation) |
 | ScoreCAM  | Perbutate the image by the scaled activations and measure how the output drops |
 | EigenCAM  | Takes the first principle component of the 2D Activations (no class discrimination, but seems to give great results)|
+| EigenGradCAM  | Like EigenCAM but with class discrimination: First principle component of Activations*Grad --> Looks like GradCAM, but cleaner|
 
 
 ### What makes the network think the image label is 'pug, pug-dog' and 'tabby, tabby cat':

cam.py

@@ -9,7 +9,8 @@
                              GradCAMPlusPlus, \
                              AblationCAM, \
                              XGradCAM, \
-                             EigenCAM
+                             EigenCAM, \
+                             EigenGradCAM
 
 from pytorch_grad_cam import GuidedBackpropReLUModel
 from pytorch_grad_cam.utils.image import show_cam_on_image, \
@@ -49,7 +50,8 @@ def get_args():
          "gradcam++": GradCAMPlusPlus,
          "ablationcam": AblationCAM,
          "xgradcam": XGradCAM,
-         "eigencam": EigenCAM}
+         "eigencam": EigenCAM,
+         "eigengradcam": EigenGradCAM}
 
     if args.method not in list(methods.keys()):
         raise Exception(f"method should be one of {list(methods.keys())}")

pytorch_grad_cam/__init__.py

@@ -4,4 +4,5 @@
 from pytorch_grad_cam.grad_cam_plusplus import GradCAMPlusPlus
 from pytorch_grad_cam.score_cam import ScoreCAM
 from pytorch_grad_cam.eigen_cam import EigenCAM
-from pytorch_grad_cam.guided_backprop import GuidedBackpropReLUModel
+from pytorch_grad_cam.eigen_grad_cam import EigenGradCAM
+from pytorch_grad_cam.guided_backprop import GuidedBackpropReLUModel

pytorch_grad_cam/eigen_cam.py

@@ -15,8 +15,7 @@ def get_cam_image(self,
                       target_category,
                       activations,
                       grads):
-        reshaped_activations = activations.reshape(activations.shape[0], -1).transpose()
-
+        reshaped_activations = (activations).reshape(activations.shape[0], -1).transpose()
         # Centering before the SVD seems to be important here,
         # Otherwise the image returned is negative
         reshaped_activations = reshaped_activations - reshaped_activations.mean(axis=0)

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name='grad-cam',
-    version='1.2.3',
+    version='1.2.4',
     author='Jacob Gildenblat',
     author_email='[email protected]',
     description='Many Class Activation Map methods implemented in Pytorch. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM',