list index out of range about slimsam HOT 3 OPEN

Hello @BuKeMod ,
Consider verifying if you have altered the '--gradsize' parameter. The default setting for 'gradsize' is 1000. It's important to ensure that the 'gradsize' value is less than the 'trainsize'.
Thanks.

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I've encountered a new issue. Do you have any examples of training, such as using Colab or Kaggle? I'm a beginner and don't quite understand yet. Thank you.

CUDA visible devices: 1
CUDA Device Name: Tesla P100-PCIE-16GB
===========================Parameter Settings===========================
Pruning Ratio: 0.3
VIT num_heads: 12
norm_type: mean
imptype: Disturb
global: False
learning rate: 0.0001
a_weight: 0.5
round_to 12
TRAIN_SIZE 40 VAL_SIZE 12 GRAD_SIZE 0 Epochs 5
===========================Pruning Start===========================
/kaggle/working/SlimSAM/torch_pruning/dependency.py:639: UserWarning: Unwrapped parameters detected: ['pos_embed', 'neck.3.bias', 'neck.3.weight', 'neck.1.bias', 'neck.1.weight'].
Torch-Pruning will prune the last non-singleton dimension of a parameter. If you wish to customize this behavior, please provide an unwrapped_parameters argument.
warnings.warn(warning_str)
Traceback (most recent call last):
File "/kaggle/working/SlimSAM/prune_distill_step1.py", line 295, in
train_model()
File "/kaggle/working/SlimSAM/prune_distill_step1.py", line 136, in train_model
model.image_encoder = prune_sam_step1(model=model.image_encoder, example_inputs=example_inputs, model_name=model_name, round_to=round_to, ratio=ratio, imptype = imptype, norm_type=norm_type, global_way=global_way)
File "/kaggle/working/SlimSAM/prune_funcs.py", line 140, in prune_sam_step1
pruner.step()
File "/kaggle/working/SlimSAM/torch_pruning/pruner/algorithms/metapruner.py", line 162, in step
for group in pruning_fn():
File "/kaggle/working/SlimSAM/torch_pruning/pruner/algorithms/metapruner.py", line 212, in prune_local
imp = self.estimate_importance(group, ch_groups=ch_groups)
File "/kaggle/working/SlimSAM/torch_pruning/pruner/algorithms/metapruner.py", line 166, in estimate_importance
return self.importance(group, ch_groups=ch_groups)
File "/kaggle/working/SlimSAM/torch_pruning/pruner/importance.py", line 426, in call
dw = layer.weight.grad.data[idxs].flatten(1)
AttributeError: 'NoneType' object has no attribute 'data'

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Please ensure that the 'gradsize' parameter is set to a value greater than 0. The importance estimation algorithm relies on gradient information for each parameter, making it essential that 'gradsize' is not zero. Note that while 'gradsize' should be less than 'trainsize', a larger 'gradsize' can be more effective for pruning purposes.
Thanks！

from slimsam.