An error occurs when running on the source data

[nuistji]

Excellent work! I met an error when training the pre-trained model, it shows like that:

Backbone # param.: 23561205
Learnable # param.: 2580968
Total # param.: 26142173

available GPUs: 1

Total (trn) images are : 13680
Total (val) images are : 0
[Epoch: 00] [Batch: 0001/0684] L: 0.68586 Avg L: 0.68586 mIoU: 0.04 | FB-IoU: 37.67
[Epoch: 00] [Batch: 0051/0684] L: 0.40346 Avg L: 0.52482 mIoU: 0.00 | FB-IoU: 38.23
[Epoch: 00] [Batch: 0101/0684] L: 0.37764 Avg L: 0.46712 mIoU: 12.09 | FB-IoU: 45.07
[Epoch: 00] [Batch: 0151/0684] L: 0.46177 Avg L: 0.44117 mIoU: 21.00 | FB-IoU: 50.16
[Epoch: 00] [Batch: 0201/0684] L: 0.46565 Avg L: 0.42391 mIoU: 25.59 | FB-IoU: 53.12
[Epoch: 00] [Batch: 0251/0684] L: 0.39822 Avg L: 0.41749 mIoU: 28.23 | FB-IoU: 54.64
[Epoch: 00] [Batch: 0301/0684] L: 0.30663 Avg L: 0.41056 mIoU: 30.69 | FB-IoU: 55.98
[Epoch: 00] [Batch: 0351/0684] L: 0.31297 Avg L: 0.39998 mIoU: 32.92 | FB-IoU: 57.27
[Epoch: 00] [Batch: 0401/0684] L: 0.40371 Avg L: 0.39329 mIoU: 34.65 | FB-IoU: 58.27
[Epoch: 00] [Batch: 0451/0684] L: 0.30678 Avg L: 0.38822 mIoU: 35.85 | FB-IoU: 59.02
[Epoch: 00] [Batch: 0501/0684] L: 0.33376 Avg L: 0.38439 mIoU: 36.85 | FB-IoU: 59.63
[Epoch: 00] [Batch: 0551/0684] L: 0.49764 Avg L: 0.38038 mIoU: 37.92 | FB-IoU: 60.25
[Epoch: 00] [Batch: 0601/0684] L: 0.37989 Avg L: 0.37663 mIoU: 38.79 | FB-IoU: 60.69
[Epoch: 00] [Batch: 0651/0684] L: 0.34453 Avg L: 0.37386 mIoU: 39.91 | FB-IoU: 61.22

*** Training [@epoch 00] Avg L: 0.37309 mIoU: 40.20 FB-IoU: 61.40 ***

Traceback (most recent call last):
File "train.py", line 97, in
val_loss, val_miou, val_fb_iou = train(epoch, model, dataloader_val, optimizer, training=False)
File "train.py", line 27, in train
for idx, batch in enumerate(dataloader):
File "/home/jifanfan/anaconda3/envs/torch/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 435, in next
data = self._next_data()
File "/home/jifanfan/anaconda3/envs/torch/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/home/jifanfan/anaconda3/envs/torch/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/jifanfan/anaconda3/envs/torch/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/home/jifanfan/PATNet/data/pascal.py", line 32, in getitem
idx %= len(self.img_metadata) # for testing, as n_images < 1000
ZeroDivisionError: integer division or modulo by zero

I think the reason for this is that the validation data cannot be loaded. I think the following code leads to this error (For the self.fold of vallidation data is 4).

img_metadata = []
if self.split == 'trn': # For training, read image-metadata of "the other" folds
for fold_id in range(self.nfolds):
if fold_id == self.fold: # Skip validation fold
continue
img_metadata += read_metadata(self.split, fold_id)

    elif self.split == 'val':  # For validation, read image-metadata of "current" fold
        if self.fold != 4:
            img_metadata = read_metadata(self.split, self.fold)
    else:
        raise Exception('Undefined split %s: ' % self.split)

I use the pytorch 1.7+cuda11.0
Thank you for yout time.

[slei109]

Yes, you're right. It's because your validation split is null. The default training dataset is the whole PASCAL dataset. Thus your validation dataset should be different from the training dataset. I updated the train.py file and revised the default setting for your better understanding. Right now, the validation set is the val split of FSS-1000 dataset. And you can revise it for your experiment setting :)