Layer CAM

README.md

@@ -23,6 +23,7 @@
 | 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|
+| LayerCAM  | Spatially weight the activations by positive gradients. Works better especially in lower layers |
 
 
 ### What makes the network think the image label is 'pug, pug-dog' and 'tabby, tabby cat':
@@ -142,7 +143,7 @@ To use with CUDA:
 
 You can choose between:
 
-`GradCAM` , `ScoreCAM`, `GradCAMPlusPlus`, `AblationCAM`, `XGradCAM` and `EigenCAM`.
+`GradCAM` , `ScoreCAM`, `GradCAMPlusPlus`, `AblationCAM`, `XGradCAM` , `LayerCAM` and `EigenCAM`.
 
 Some methods like ScoreCAM and AblationCAM require a large number of forward passes,
 and have a batched implementation.
@@ -269,3 +270,7 @@ Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, Biao Li`
 https://arxiv.org/abs/2008.00299 <br>
 `Eigen-CAM: Class Activation Map using Principal Components
 Mohammed Bany Muhammad, Mohammed Yeasin`
+
+https://arxiv.org/abs/2008.00299 <br>
+`LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
+Peng-Tao Jiang; Chang-Bin Zhang; Qibin Hou; Ming-Ming Cheng; Yunchao Wei`

cam.py

@@ -10,7 +10,8 @@
                              AblationCAM, \
                              XGradCAM, \
                              EigenCAM, \
-                             EigenGradCAM
+                             EigenGradCAM, \
+                             LayerCAM    
 
 from pytorch_grad_cam import GuidedBackpropReLUModel
 from pytorch_grad_cam.utils.image import show_cam_on_image, \
@@ -30,10 +31,12 @@ def get_args():
                         help='Reduce noise by taking the first principle componenet'
                         'of cam_weights*activations')
     parser.add_argument('--method', type=str, default='gradcam',
-                        choices=['gradcam', 'gradcam++', 'scorecam', 'xgradcam',
-                                 'ablationcam', 'eigencam', 'eigengradcam'],
+                        choices=['gradcam', 'gradcam++', 
+                                 'scorecam', 'xgradcam',
+                                 'ablationcam', 'eigencam', 
+                                 'eigengradcam', 'layercam'],
                         help='Can be gradcam/gradcam++/scorecam/xgradcam'
-                             '/ablationcam/eigencam/eigengradcam')
+                             '/ablationcam/eigencam/eigengradcam/layercam')
 
     args = parser.parse_args()
     args.use_cuda = args.use_cuda and torch.cuda.is_available()
@@ -61,7 +64,8 @@ def get_args():
          "ablationcam": AblationCAM,
          "xgradcam": XGradCAM,
          "eigencam": EigenCAM,
-         "eigengradcam": EigenGradCAM}
+         "eigengradcam": EigenGradCAM,
+         "layercam": LayerCAM}
 
     model = models.resnet50(pretrained=True)
 
@@ -113,4 +117,4 @@ def get_args():
 
     cv2.imwrite(f'{args.method}_cam.jpg', cam_image)
     cv2.imwrite(f'{args.method}_gb.jpg', gb)
-    cv2.imwrite(f'{args.method}_cam_gb.jpg', cam_gb)
+    cv2.imwrite(f'{args.method}_cam_gb.jpg', cam_gb)

pytorch_grad_cam/__init__.py

@@ -3,6 +3,7 @@
 from pytorch_grad_cam.xgrad_cam import XGradCAM
 from pytorch_grad_cam.grad_cam_plusplus import GradCAMPlusPlus
 from pytorch_grad_cam.score_cam import ScoreCAM
+from pytorch_grad_cam.layer_cam import LayerCAM
 from pytorch_grad_cam.eigen_cam import EigenCAM
 from pytorch_grad_cam.eigen_grad_cam import EigenGradCAM
 from pytorch_grad_cam.guided_backprop import GuidedBackpropReLUModel

pytorch_grad_cam/layer_cam.py

@@ -0,0 +1,24 @@
+import cv2
+import numpy as np
+import torch
+from pytorch_grad_cam.base_cam import BaseCAM
+from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection
+
+# https://ieeexplore.ieee.org/document/9462463
+class LayerCAM(BaseCAM):
+    def __init__(self, model, target_layer, use_cuda=False, reshape_transform=None):
+        super(LayerCAM, self).__init__(model, target_layer, use_cuda, reshape_transform)
+
+    def get_cam_image(self,
+                      input_tensor,
+                      target_category,
+                      activations,
+                      grads,
+                      eigen_smooth):
+        spatial_weighted_activations = np.maximum(grads, 0) * activations 
+
+        if eigen_smooth:
+            cam = get_2d_projection(spatial_weighted_activations)
+        else:
+            cam = spatial_weighted_activations.sum(axis=1)
+        return cam

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name='grad-cam',
-    version='1.2.8',
+    version='1.3.0',
     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',