Open wuqingshan2010 opened 4 years ago
It will be included in http://images.cocodataset.org/annotations/annotations_trainval2017.zip from http://cocodataset.org/#download. Please also take a look at https://github.com/joe-siyuan-qiao/DetectoRS/tree/master/configs/htc to prepare the dataset.
hey man, have you solved the above problem? I also encountered a similar problem. So I want to ask you how to prepare seg_prefix=data_root + 'annotations/stuff_train2017_pixelmaps/'. Although I have referred https://github.com/joe-siyuan-qiao/DetectoRS/tree/master/configs/htc. I still cant figure it out.
Hi I met the same issue.
Traceback (most recent call last):
File "tools/train.py", line 142, in
I used default config file "DetectoRS_mstrain_400_1200_x101_32x4d_40e.py" and COCO 2017 dataset. I tried to Please let me know the reason asap. Thanks
I alse used default config file ‘’DetectoRS_mstrain_400_1200_r50_40e.py and COCO 2017 dataset,encountered the same problem as you.Has the problem been solved later? Can you let me know?
Traceback (most recent call last):
File "tools/train.py", line 143, in
Thank you very much.
hey man, have you solved the above problem? I also encountered a similar problem. So I want to ask you how to prepare seg_prefix=data_root + 'annotations/stuff_train2017_pixelmaps/'. Although I have referred https://github.com/joe-siyuan-qiao/DetectoRS/tree/master/configs/htc. I still cant figure it out.
How did you solve this problem later? Looking forward to your reply! @EnternalTwinkle
when i train DetectoRS with the config DetectoRS_mstrain_400_1200_r50_40e.py, i get an error: RuntimeError: 1only batches of spatial targets supported (non-empty 3D tensors) but got targets of size: : [1, 148, 152, 3]. the error is in "loss_semantic_seg = self.criterion(mask_pred, labels)" i have found the mask_pred size is [1, 163, 148, 152], but the label is [1, 148, 152, 3]. how to solve it?
i modified the config files: data = dict( imgs_per_gpu=1, workers_per_gpu=1, train=dict( type=dataset_type, ann_file=data_root + 'annotations/stuff_train2017.json', img_prefix=data_root + 'train2017/', seg_prefix=data_root + 'annotations/stuff_train2017_pixelmaps/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/stuff_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/stuff_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline))
i do not know how you get the instances_train2017.json.