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Your work is excellent, but the pruning method you proposed results in an error when applied to dinov2: IndexError: index 768 is out of bounds for dimension 0 with size 768.
The error message is as follows:
Traceback (most recent call last):
File "/search/odin/prune/prune2.py", line 86, in <module>
for group in pruner.step(interactive=True): # Warning: groups must be handled sequentially. Do not keep them as a list.
File "/search/odin/anaconda3/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 35, in generator_context
response = gen.send(None)
File "/search/odin/anaconda3/lib/python3.9/site-packages/torch_pruning/pruner/algorithms/metapruner.py", line 420, in _prune
imp = self.estimate_importance(group) # raw importance score
File "/search/odin/anaconda3/lib/python3.9/site-packages/torch_pruning/pruner/algorithms/metapruner.py", line 282, in estimate_importance
return self.importance(group)
File "/search/odin/anaconda3/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/search/odin/anaconda3/lib/python3.9/site-packages/torch_pruning/pruner/importance.py", line 229, in __call__
local_imp = local_imp[idxs]
IndexError: index 768 is out of bounds for dimension 0 with size 768
Do you have any suggestions?
The text was updated successfully, but these errors were encountered:
Your work is excellent, but the pruning method you proposed results in an error when applied to dinov2:
IndexError: index 768 is out of bounds for dimension 0 with size 768
.The error message is as follows:
Do you have any suggestions?
The text was updated successfully, but these errors were encountered: