StopIteration Error occurs during training while running the train.py file
I am trying to run a code from github. The file is called train.py. It is supposed to run a Neural Network for training on a dataset. However, I get the following error
(QGN) ubuntu@ip-172-31-13-114:~/QGN$ python train.py
Input arguments:
id               ade20k
arch_encoder     resnet50
arch_decoder     QGN_dense_resnet34
weights_encoder
weights_decoder
fc_dim           2048
list_train       ./data/train_ade20k.odgt
list_val         ./data/validation_ade20k.odgt
root_dataset     ./data/
num_gpus         0
batch_size_per_gpu 2
num_epoch        20
start_epoch      1
epoch_iters      5000
optim            SGD
lr_encoder       0.02
lr_decoder       0.02
lr_pow           0.9
beta1            0.9
weight_decay     0.0001
deep_sup_scale   1.0
prop_weight      2.0
enhance_weight   2.0
fix_bn           0
num_val          500
num_class        150
transform_dict   None
workers          40
imgSize          [300, 375, 450, 525, 600]
imgMaxSize       1000
cropSize         0
padding_constant 32
random_flip      True
seed             1337
ckpt             ./ckpt
disp_iter        20
visualize        False
result           ./result
gpu_id           0
Model ID: ade20k-resnet50-QGN_dense_resnet34-batchSize0-LR_encoder0.02-LR_decoder0.02-epoch20-lossScale1.0-classScale2.0
# samples: 20210
1 Epoch = 5000 iters
Starting Training!
Traceback (most recent call last):
  File &quot;train.py&quot;, line 355, in &lt;module&gt;
    main(args)
  File &quot;train.py&quot;, line 217, in main
    train(segmentation_module, iterator_train, optimizers, history, epoch, args)
  File &quot;train.py&quot;, line 33, in train
    batch_data = next(iterator)
  File &quot;/home/ubuntu/QGN/lib/utils/data/dataloader.py&quot;, line 274, in __next__
    raise StopIteration
StopIteration
Segmentation fault (core dumped)

The code from train.py (lines 211 to 231) is as follows
'''
Main loop
history = {'train': {'epoch': [], 'loss': [], 'acc': []}}

print('Starting Training!')

for epoch in range(args.start_epoch, args.num_epoch + 1):
    train(segmentation_module, iterator_train, optimizers, history, epoch, args)

    # checkpointing
    checkpoint(nets, history, args, epoch)

    # evaluation
    args.weights_encoder = os.path.join(args.ckpt, 'encoder_epoch_' + str(epoch) + '.pth')
    args.weights_decoder = os.path.join(args.ckpt, 'decoder_epoch_' + str(epoch) + '.pth')
    iou = eval_train(args)

    # adaptive class weighting
    adjust_crit_weights(segmentation_module, iou, args)


print('Training Done!')

'''
I am not sure if I have shared all the required information. I would appreciate if ant help could be provided to resolve this issue. Just to inform, I have tried using the try and except method as  shared on github on the link https://github.com/amdegroot/ssd.pytorch/issues/214. However the error still persists.
The code from line 30 in train.py is as follows
   # main loop
    tic = time.time()
    for i in range(args.epoch_iters):
        batch_data = next(iterator)
        data_time.update(time.time() - tic)

        segmentation_module.zero_grad()

I ammended the above code as follows
   # main loop
     loader_train = torchdata.DataLoader(
        dataset_train,
        batch_size=args.num_gpus,  # we have modified data_parallel
        shuffle=False,  # we do not use this param
        collate_fn=user_scattered_collate,num_workers=int(args.workers),
        drop_last=True,
        pin_memory=True)


    tic = time.time()
    for i in range(args.epoch_iters):
        try:
            batch_data = next(iterator)
        except StopIteration:
            iterator = iter(loader_train)
            batch_data = next(iterator)
        data_time.update(time.time() - tic)

        segmentation_module.zero_grad()

But still no joy. The error still remains.

TL;DR
Your args.epoch_iters is larger than the number of batches in loader_train. Python raises StopIteration error when you ask for more batches than there actually are.
When you iterate over some pythonic collection of elements (e.g., list, tuple, DataLoader...) python needs to know when it reaches the end of that collection. It is done by raising StopIteration exception. for loop in python explicitly listens to this exception and uses it to know when to stop. Alas, in your code you do not use a for loop over loader_train, but rather over range(args.epoch_iter) and you use next(iterator) to get the batches.
