load data to transfer learning

[Ricardo-Fellini4]

==========Options============ means: [0.485, 0.456, 0.406] stds: [0.229, 0.224, 0.225] gpu: 2 exp_name: Totaltext num_workers: 0 batch_size: 6 max_epoch: 250 start_epoch: 0 lr: 0.001 cuda: True output_dir: output input_size: 640 max_annotation: 2000 adj_num: 4 num_points: 20 use_hard: True load_memory: False scale: 1 grad_clip: 25 dis_threshold: 0.35 cls_threshold: 0.875 approx_factor: 0.004 know: False knownet: MixNet_FSNet_M know_resume: /kaggle/working/Mixnet/model/Totaltext_mid/MixNet_FSNet_M_622.pth resume: None mgpu: False save_dir: ./model/ vis_dir: ./vis/ log_dir: ./logs/ loss: CrossEntropyLoss pretrain: False verbose: True viz: False lr_adjust: fix stepvalues: [] weight_decay: 0.0 gamma: 0.1 momentum: 0.9 optim: Adam save_freq: 5 display_freq: 10 viz_freq: 50 log_freq: 10000 val_freq: 1000 net: FSNet_M mid: True embed: False onlybackbone: False rescale: 255.0 test_size: [640, 960] checkepoch: 1070 img_root: None device: cuda =============End============= MixNet backbone parameter size: 29339968 load pretrain weight from /kaggle/working/Mixnet/pretrained/triHRnet_Synth_weight.pth. Start training MixNet. Epoch: 0 : LR = [0.001] Traceback (most recent call last): File "/kaggle/working/Mixnet/train_mixNet.py", line 418, in main() File "/kaggle/working/Mixnet/train_mixNet.py", line 399, in main train(model, train_loader, criterion, scheduler, optimizer, epoch) File "/kaggle/working/Mixnet/train_mixNet.py", line 99, in train input_dict = _parse_data(inputs) File "/kaggle/working/Mixnet/train_mixNet.py", line 74, in _parse_data input_dict['edge_field'] = inputs[10] IndexError: list index out of range

i have cut the MixNet_FSNet_M_622.pth to transfer learning and get an error when loading the data, although i have same format when testing