ReceptiveField calculation misses the last neurons of a layer
maxdreyer opened this issue · comments
Hi all,
when I viewed the top-k most relevant samples of a concept with receptive field "on", I got as a result less than k samples.
I spotted a bug in the method analyze_layer of the ReceptiveField class:
def analyze_layer(self, concept: Concept, layer_name: str, c_indices, canonizer=None, batch_size=16, verbose=True):
composite = AllFlatComposite(canonizer)
conditions = [{layer_name: [index]} for index in c_indices]
batch = 0
for attr in self.attribution.generate(
self.single_sample, conditions, composite, [], concept.mask_rf,
layer_name, 1, batch_size, None, verbose):
heat = self.norm_rf(attr.heatmap, layer_name)
try:
rf_array[batch * len(heat): (batch+1) * len(heat)] = heat
except UnboundLocalError:
rf_array = torch.zeros((len(c_indices), *heat.shape[1:]), dtype=torch.uint8)
rf_array[batch * len(heat): (batch+1) * len(heat)] = heat
batch += 1
return rf_array
The error occurs in the line
rf_array[batch * len(heat): (batch+1) * len(heat)] = heat
For the last batch, len(heat) might be smaller than the batch size. Then, the indexing (supposedly beginning at the position of the last neuron index) is not correct anymore.
Possible solution: counting len(heat) for every batch with an index and using the index instead of batch*len(heat).
Best,
Max
