Xudong Zeng

the same problem。did you fixed it ？

However, it seems that directly modifying the code to use l1-norm as the criterion is not compatible because using the l1-norm of filters cannot prune the input to the first...

``` for name, module in model.named_modules(): if name in layers: layer = layers[name] out = get_score_layer(name,module, wg=wg, criterion=criterion) score = out['score'] layer.score = score layer.prescore = out['act_scale_pre'] if raw_pr[name] >...

I'm sorry, I just started working on pruning-related tasks, so there may be issues with my expression. What you mean is that the L1-norm calculated using the absolute average of...

``` all_scores = np.append(all_scores, score) if hasattr(module, 'act_scale_pre'): all_scores = np.append(all_scores, out["act_scale_pre"]) ``` The `score` can be set as` L1-norm` or `act_scale`. I understand that when it is set as...

If the `criterion = 'l1-norm'` in the `get_score_layer(name,module, wg='filter', criterion='l1-norm') `function. Then at the the second-to-last line in `get_score_layer` function, `out['score'] = out[criterion]` would be `out['score'] = out['l1-norm']`. The value...