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