Example: Add option to ignore residual

- add option to ignore residual/ skip-connection for the computation of
  attribution scores in feed_forward.py

share/example/feed_forward.py

@@ -12,7 +12,9 @@
 
 from zennit.attribution import Gradient, SmoothGrad, IntegratedGradients, Occlusion
 from zennit.composites import COMPOSITES
+from zennit.core import Hook
 from zennit.image import imsave, CMAPS
+from zennit.layer import Sum
 from zennit.torchvision import VGGCanonizer, ResNetCanonizer
 
 
@@ -34,6 +36,17 @@
 }
 
 
+class SumSingle(Hook):
+    def __init__(self, dim=1):
+        super().__init__()
+        self.dim = dim
+
+    def backward(self, module, grad_input, grad_output):
+        elems = [torch.zeros_like(grad_output[0])] * (grad_input[0].shape[-1])
+        elems[self.dim] = grad_output[0]
+        return (torch.stack(elems, dim=-1),)
+
+
 class BatchNormalize:
     def __init__(self, mean, std, device=None):
         self.mean = torch.tensor(mean, device=device)[None, :, None, None]
@@ -77,6 +90,7 @@ def find_classes(self, directory):
 @click.option('--cpu/--gpu', default=True)
 @click.option('--shuffle/--no-shuffle', default=False)
 @click.option('--with-bias/--no-bias', default=True)
+@click.option('--with-residual/--no-residual', default=True)
 @click.option('--relevance-norm', type=click.Choice(['symmetric', 'absolute', 'unaligned']), default='symmetric')
 @click.option('--cmap', type=click.Choice(list(CMAPS)), default='coldnhot')
 @click.option('--level', type=float, default=1.0)
@@ -95,6 +109,7 @@ def main(
     cpu,
     shuffle,
     with_bias,
+    with_residual,
     cmap,
     level,
     relevance_norm,
@@ -164,6 +179,9 @@ def attr_output_fn(output, target):
             # the highest and lowest pixel values for the ZBox rule
             composite_kwargs['low'] = norm_fn(torch.zeros(*shape, device=device))
             composite_kwargs['high'] = norm_fn(torch.ones(*shape, device=device))
+        if not with_residual and 'resnet' in model_name:
+            # skip the residual connection through the Sum added by the ResNetCanonizer
+            composite_kwargs['layer_map'] = [(Sum, SumSingle(1))]
 
         # provide the name 'bias' in zero_params if no bias should be used to compute the relevance
         if not with_bias and composite_name in [