TypeError: forward_train() missing 2 required positional arguments: 'gt_bboxes' and 'gt_labels'

When I use config to train my own data, workflow is set to [(‘train’, 1)] and the training runs normally. When workflow is set to [(‘train’, 1),(‘val’, 1)], val reports the following error： File "/media/nnir712/F264A15264A119FD/zzh/detect/mmcv/mmcv/runner/runner.py", line 265, in train [0/1828] self.model, data_batch, train_mode=True, **kwargs) File "/media/nnir712/F264A15264A119FD/zzh/detect/mmdetection/mmdet/apis/train.py", line 38, in batch_processor losses = model(**data) File "/home/nnir712/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 547, in __call__ result = self.forward(*input, **kwargs) File "/home/nnir712/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 151, in forward return self.module(*inputs[0], **kwargs[0]) File "/home/nnir712/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/nn/modules/module.py", line 547, in __call__ result = self.forward(*input, **kwargs) File "/media/nnir712/F264A15264A119FD/zzh/detect/mmdetection/mmdet/core/fp16/decorators.py", line 49, in new_func return old_func(*args, **kwargs) File "/media/nnir712/F264A15264A119FD/zzh/detect/mmdetection/mmdet/models/detectors/base.py", line 86, in forward return self.forward_train(img, img_meta, **kwargs) TypeError: forward_train() missing 2 required positional arguments: 'gt_bboxes' and 'gt_labels'

[hznuhisejeffrey]

perhaps reduce learn rate will solve your problem

[yhcao6]

Thanks for reporting the bug.

There is no gt in test_pipeline which leads this error. Here is a temporary solution:

1. Add a val_pipiline in the config:

val_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.0),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]

1. Modify data.val.pipeline:

pipeline=val_pipeline,

https://github.com/open-mmlab/mmdetection/blob/82c533bee0de1a84f5959e257815eb7df4e69162/configs/mask_rcnn_r50_fpn_1x.py#L156