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Feature: Improve Iterative Pruning: Verify Pruning Status Before Training #368

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Feature: Improve Iterative Pruning: Verify Pruning Status Before Training #368

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770cbc3
feature pruned info from step
janthmueller Apr 11, 2024
a386c6c
check if model got pruned after step
janthmueller Apr 11, 2024
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10 changes: 5 additions & 5 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -201,11 +201,11 @@ if isinstance(imp, tp.importance.GroupTaylorImportance):
loss = model(example_inputs).sum() # A dummy loss, please replace this line with your loss function and data!
loss.backward() # before pruner.step()

pruner.step()
macs, nparams = tp.utils.count_ops_and_params(model, example_inputs)
# finetune the pruned model here
# finetune(model)
# ...
if pruner.step():
macs, nparams = tp.utils.count_ops_and_params(model, example_inputs)
# finetune the pruned model here
# finetune(model)
# ...
```
#### Global Pruning

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5 changes: 5 additions & 0 deletions torch_pruning/pruner/algorithms/metapruner.py
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Expand Up @@ -227,10 +227,15 @@ def step(self, interactive=False)-> typing.Union[typing.Generator, None]:
if interactive: # yield groups for interactive pruning
return pruning_method()
else:
pruned = False
for group in pruning_method():
group.prune()
pruned = True
# print("gg")
# exit(0)
return pruned



def manual_prune(self, layer, pruning_fn, pruning_ratios_or_idxs):
if isinstance(pruning_ratios_or_idxs, float):
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