COCO fine-tuning without panoptic stuffthingmaps

[titusss]

Hello thank you for the great work,

I want to fine-tune the pre-trained CBnet on a COCO-like custom dataset. However, the htc_cbv2_swin_base_adamw_20e_coco.py config expects COCO-stuff pixelmaps. (See configs/BigDetection/cbnetv2/htc_cbv2_swin_base_adamw_20e_coco.py)

In line 92 it tries to load stuffthingmaps into the data dict seg_prefix=data_root + 'stuffthingmaps/train2017/',

The whole code block, starting at line 89:

samples_per_gpu=1
data = dict(samples_per_gpu=samples_per_gpu,
            train=dict(
                seg_prefix=data_root + 'stuffthingmaps/train2017/',
                pipeline=train_pipeline),
            val=dict(pipeline=test_pipeline),
            test=dict(pipeline=test_pipeline))
optimizer = dict(_delete_=True, type='AdamW', lr=0.0001*(samples_per_gpu/2), betas=(0.9, 0.999), weight_decay=0.05,
                 paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
                                                 'relative_position_bias_table': dict(decay_mult=0.),
                                                 'norm': dict(decay_mult=0.)}))

What if I do not want to train panoptic stuffthingmaps? I only want to train on train and val COCO sets. If I set seg_prefix=None, it will fail as it expects a string or path. How do I disable loading these panoptic thing maps, all together?

I am not sure if it's even possible to train native COCO (not COCO-stuff) without fixing that issue.