Open BuKeMod opened 9 months ago
czg1225
commented
9 months ago 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.
BuKeMod
commented
9 months ago 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
czg1225
commented
9 months ago 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！
I'm using the dataset from roboflow. Can you help me?
!CUDA_VISIBLE_DEVICES=0 python prune_distill_step1.py --traindata_path "/kaggle/working/Crop-Fields-LOD-13-14-15-4/train/_annotations.coco.json" --valdata_path "/kaggle/working/Crop-Fields-LOD-13-14-15-4/valid/_annotations.coco.json" --trainsize 480 --valsize 126 --prune_ratio 0.3 --epochs 20
===========================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 480 VAL_SIZE 126 GRAD_SIZE 1000 Epochs 20 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 118, in train_model
batch = next(grad_iter)
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 634, in next
data = self._next_data()
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1346, in _next_data
return self._process_data(data)
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1372, in _process_data
data.reraise()
File "/opt/conda/lib/python3.10/site-packages/torch/_utils.py", line 644, in reraise
raise exception
IndexError: Caught IndexError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop
data = fetcher.fetch(index)
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 51, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/kaggle/working/SlimSAM/load_sam_json.py", line 39, in getitem
image = cv2.imread(self._image_paths[index])
IndexError: list index out of range
https://universe.roboflow.com/cropfields/crop-fields-lod-13-14-15/dataset/4