training error (unexpected stop)

[Zzh-tju]

[  2]   21850 || B: 2.368 | C: 3.131 | M: 3.495 | S: 0.635 | T: 9.628 || ETA: 3 days, 16:00:49 || timer: 0.839
[  2]   21860 || B: 2.365 | C: 3.135 | M: 3.502 | S: 0.615 | T: 9.618 || ETA: 3 days, 16:00:26 || timer: 0.837
[  2]   21870 || B: 2.371 | C: 3.115 | M: 3.519 | S: 0.627 | T: 9.632 || ETA: 3 days, 16:00:17 || timer: 0.832
[  2]   21880 || B: 2.402 | C: 3.120 | M: 3.543 | S: 0.614 | T: 9.679 || ETA: 3 days, 16:00:30 || timer: 0.870
[  2]   21890 || B: 2.362 | C: 3.127 | M: 3.511 | S: 0.613 | T: 9.613 || ETA: 3 days, 16:00:50 || timer: 0.831
[  2]   21900 || B: 2.370 | C: 3.139 | M: 3.518 | S: 0.604 | T: 9.632 || ETA: 3 days, 16:01:22 || timer: 0.850
[  2]   21910 || B: 2.332 | C: 3.116 | M: 3.494 | S: 0.609 | T: 9.551 || ETA: 3 days, 16:00:48 || timer: 0.841
[  2]   21920 || B: 2.380 | C: 3.149 | M: 3.538 | S: 0.595 | T: 9.663 || ETA: 3 days, 16:00:44 || timer: 0.839
[  2]   21930 || B: 2.377 | C: 3.167 | M: 3.552 | S: 0.627 | T: 9.723 || ETA: 3 days, 16:00:12 || timer: 0.824
[  2]   21940 || B: 2.384 | C: 3.191 | M: 3.565 | S: 0.649 | T: 9.789 || ETA: 3 days, 15:59:51 || timer: 0.828
[  2]   21950 || B: 2.381 | C: 3.204 | M: 3.544 | S: 0.644 | T: 9.773 || ETA: 3 days, 16:00:27 || timer: 0.835
[  2]   21960 || B: 2.398 | C: 3.214 | M: 3.557 | S: 0.653 | T: 9.822 || ETA: 3 days, 16:00:41 || timer: 0.835
[  2]   21970 || B: 2.384 | C: 3.215 | M: 3.540 | S: 0.619 | T: 9.758 || ETA: 3 days, 16:00:37 || timer: 0.834
[  2]   21980 || B: 2.377 | C: 3.225 | M: 3.543 | S: 0.632 | T: 9.778 || ETA: 3 days, 15:59:50 || timer: 0.829

Computing validation mAP (this may take a while)...

Traceback (most recent call last):
  File "train.py", line 504, in <module>
    train()
  File "train.py", line 371, in train
    compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None)
  File "train.py", line 492, in compute_validation_map
    val_info = eval_script.evaluate(yolact_net, dataset, train_mode=True)
  File "/mnt/sda/yolact/eval.py", line 938, in evaluate
    img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(image_idx)
  File "/mnt/sda/yolact/data/coco.py", line 142, in pull_item
    assert osp.exists(path), 'Image path does not exist: {}'.format(path)
AssertionError: Image path does not exist: /mnt/sda/yolact/data/coco/images/train2017/000000423104.jpg

The training will always stop in this iteration. I have cheched the image files. It contains 118287 images, and the image 000000423104.jpg does not exist.

But this datasets I have not changed, and it is normal when I trained Faster R-CNN.

So why does this error happen?

[Zzh-tju]

Ok, I know why now. You put val2017 into training set.