After fine-tune a 3-class dataset, how to load its fine-tuned weighted to update pre-trained ast model?

[jmren168]

Hi @YuanGongND,

In egs/audioset/inference.py, it tells I can load a pre-trained model as follows,

    # 2. load the best model and the weights
    checkpoint_path = args.model_path
    ast_mdl = ASTModel(label_dim=527, input_tdim=input_tdim, imagenet_pretrain=False, audioset_pretrain=False)
    print(f'[*INFO] load checkpoint: {checkpoint_path}')
    checkpoint = torch.load(checkpoint_path, map_location='cuda')
    audio_model = torch.nn.DataParallel(ast_mdl, device_ids=[0])
    audio_model.load_state_dict(checkpoint)

However, when I fine-tuend the pre-trained model on a 3-class datasets, then reload it again. Error message is,

size mismatch for module.v.pos_embed: copying a param with shape torch.Size([1, 602, 768]) from checkpoint, the shape in current model is torch.Size([1, 362, 768]).

My python script

    input_tdim=312
    class_num=3
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model_path = '/home/...'/

    sd = torch.load(model_path, map_location=device)
    model = ASTModel(label_dim=class_num, fstride=10, tstride=10, input_fdim=128, input_tdim=input_tdim, audioset_pretrain=True, model_size='base384',verbose=False)
    model = torch.nn.DataParallel(model)
    model.load_state_dict(sd)

Do I miss sth here? Any suggestions are appreciated.

[YuanGongND]

it seems it is due to the inconsistent input_tdim in training and inference, could you share the training script (in particular, what is the input_tdim?). Thanks!

[jmren168]

Thanks for the reply. Here's the training script:

set=full
imagenetpretrain=True
if [ $set == balanced ]
then
  bal=none
  lr=5e-5
  epoch=25
  #tr_data=/data/sls/scratch/yuangong/aed-pc/src/enhance_label/datafiles_local/balanced_train_data_type1_2_mean.json
  tr_data=./data/datafiles/train_data.json
  lrscheduler_start=10
  lrscheduler_step=5
  lrscheduler_decay=0.5
  wa_start=6
  wa_end=25
else
  bal=bal
  lr=1e-5
  epoch=15 #5
  tr_data=./data/datafiles/train_data.json
  lrscheduler_start=4 #2
  lrscheduler_step=1 #1
  lrscheduler_decay=0.25 #0.5
  wa_start=1
  wa_end=15 #5
fi
#te_data=/data/sls/scratch/yuangong/audioset/datafiles/eval_data.json
te_data=./data/datafiles/valid_data.json
freqm=48
timem=62 # 192
mixup=0
# corresponding to overlap of 6 for 16*16 patches
fstride=10
tstride=10
batch_size=4 # 12

dataset_mean=-4.2677393
dataset_std=4.5689974
audio_length=512 #1024
noise=False

[YuanGongND]

If you set audio_length=512 in training, then in inference, shouldn't the input_tdim=312 be 512?

[jmren168]

It works, and thanks again.

BTW, when I loaded fine-tuned weights to update audioset pretrained model, do I set audioset_pretrain=True or audioset_pretrain=False?

model = ASTModel(label_dim=class_num, fstride=10, tstride=10, input_fdim=128, input_tdim=input_tdim, **audioset_pretrain=True**, model_size='base384',verbose=False)

[YuanGongND]

I guess it doesn't matter.

You can check by

model.load_state_dict(sd, strict=True), so it ensures the new weight fully covers all parameters (so which initial model does not matter).

-Yuan

[jmren168]

Just setting strict=True forces new weights are loaded. Thanks for the reply.

[YuanGongND]

thanks for letting me know.

• Just to clarify, strict=True itself does not change the loading behavior, but it will throw an error if the model parameter and the checkpoint are mismatched. If you don't see an error, that means all parameters are loaded from the checkpoint.

-Yuan