errors on training custom instance segmentation dataset

[kklots]

I have prepared a custom instance segmentation dataset, which contains 5 classes (not counting background). It worked fine on original MMDetection framework training (such as: detectoRS, mask-RCNN, HTC), but when I modified file lsnet_segm_r50_fpn_1x_coco.py to train on this dataset, the system report errors:

File "/data2/lixuan/workspace/LSNet/code/mmdet/models/dense_heads/lsnet_head.py", line 1299, in loss gt_polygons, gt_bboxes = self.process_polygons(gt_masks, cls_scores) File "/data2/lixuan/workspace/LSNet/code/mmdet/models/dense_heads/lsnet_head.py", line 1742, in process_polygons gt_polygons_stack = torch.stack(gt_polygons) RuntimeError: stack expects a non-empty TensorList

I checked file lsnet_head.py, and found that the gt_masks is empty:

def forward_train(self,
                  x,
                  img_metas,
                  gt_bboxes,
                  gt_extremes = None,
                  gt_keypoints = None,
                  gt_masks = None,
                  gt_labels = None,
                  gt_bboxes_ignore=None,
                  proposal_cfg = None,
                  **kwargs):
    outs = self(x)
    print(gt_masks)
    input()

results: [PolygonMasks(num_masks=0, height=800, width=1088)]

what causes this error and how can I solve it.

[Duankaiwen]

@kklots add coco_lsvr.py to code/configs/base/datasets, modify coco.py at code/mmdet/datasets and loading.py and transforms.py at code/mmdet/datasets/pipelines

[dongdongyee]

Maybe you forgot to modify the CLASSES in coco.py at code/mmdet/datasets. You must comment the 80 classes and add your own classes.