comments

Author: cai2-huaiguang

pytorch_grad_cam/base_cam.py

@@ -111,7 +111,7 @@ def forward(
                 loss.backward(retain_graph=True)
             else:
                 # keep the computational graph, create_graph = True is needed for hvp
-                torch.autograd.grad(loss, input_tensor,  retain_graph = True, create_graph = True)
+                torch.autograd.grad(loss, input_tensor, retain_graph = True, create_graph = True)
                 # When using the following loss.backward() method, a warning is raised: "UserWarning: Using backward() with create_graph=True will create a reference cycle"
                 # loss.backward(retain_graph=True, create_graph=True)
             if 'hpu' in str(self.device):

pytorch_grad_cam/shapley_cam.py

@@ -5,7 +5,7 @@
 import numpy as np
 
 """
-Weighting the activation maps using Gradient and Hessian-Vector Product.
+Weights the activation maps using the gradient and Hessian-Vector product.
 This method (https://arxiv.org/abs/2501.06261) reinterpret CAM methods (include GradCAM, HiResCAM and the original CAM) from a Shapley value perspective.
 """
 class ShapleyCAM(BaseCAM):
@@ -51,12 +51,10 @@ def get_cam_weights(self,
         if len(activations.shape) == 4:
             weight = np.mean(weight, axis=(2, 3))
             return weight
-        
         # 3D image
         elif len(activations.shape) == 5:
             weight = np.mean(weight, axis=(2, 3, 4))
             return weight
-        
         else:
             raise ValueError("Invalid grads shape."
                              "Shape of grads should be 4 (2D image) or 5 (3D image).")