detectron2: Problem with register_coco_instances while registering a COCO dataset

Hi, I am following this getting started Colab notebook. I am trying to train a custom model using the TACO dataset which comes as a COCO-formatted dataset.

I prepared this Colab notebook for doing the experiments with the dataset. After I registered the dataset using register_coco_instances I am not able to start the training process and the error I get looks like so:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/content/detectron2_repo/detectron2/data/catalog.py in get(name)
     51         try:
---> 52             f = DatasetCatalog._REGISTERED[name]
     53         except KeyError:

KeyError: 'd'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
6 frames
/content/detectron2_repo/detectron2/data/catalog.py in get(name)
     54             raise KeyError(
     55                 "Dataset '{}' is not registered! Available datasets are: {}".format(
---> 56                     name, ", ".join(DatasetCatalog._REGISTERED.keys())
     57                 )
     58             )

KeyError: "Dataset 'd' is not registered! Available datasets are: coco_2014_train, coco_2014_val, coco_2014_minival, coco_2014_minival_100, coco_2014_valminusminival, coco_2017_train, coco_2017_val, coco_2017_val_100, keypoints_coco_2014_train, keypoints_coco_2014_val, keypoints_coco_2014_minival, keypoints_coco_2014_valminusminival, keypoints_coco_2014_minival_100, keypoints_coco_2017_train, keypoints_coco_2017_val, keypoints_coco_2017_val_100, coco_2017_train_panoptic_separated, coco_2017_train_panoptic_stuffonly, coco_2017_val_panoptic_separated, coco_2017_val_panoptic_stuffonly, coco_2017_val_100_panoptic_separated, coco_2017_val_100_panoptic_stuffonly, lvis_v0.5_train, lvis_v0.5_val, lvis_v0.5_val_rand_100, lvis_v0.5_test, cityscapes_fine_instance_seg_train, cityscapes_fine_sem_seg_train, cityscapes_fine_instance_seg_val, cityscapes_fine_sem_seg_val, cityscapes_fine_instance_seg_test, cityscapes_fine_sem_seg_test, voc_2007_trainval, voc_2007_train, voc_2007_val, voc_2007_test, voc_2012_trainval, voc_2012_train, voc_2012_val, my_dataset, taco_dataset"

The above-mentioned notebook can be used to reproduce the issue.

About this issue

Original URL
State: closed
Created 5 years ago
Comments: 17 (5 by maintainers)

Most upvoted comments

@zsc1220 it seems the dataset is not registered when you use multiple GPUs. Where do you register the dataset? In train_net you might need to register it in the main() function.

+16

ppwwyyxx on Nov 15, 2019

cfg.DATASETS.TRAIN should be a tuple of strings however yours is a string.

+10

ppwwyyxx on Nov 6, 2019

I face the same error and according to your tutorial it should be a string (path) Here are lines from your code:

cfg = get_cfg()
cfg.merge_from_file("./detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.DATASETS.TRAIN = ("balloon/train",)
cfg.DATASETS.TEST = ()   # no metrics implemented for this dataset
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"  # initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025
cfg.SOLVER.MAX_ITER = 300    # 300 iterations seems good enough, but you can certainly train longer
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128   # faster, and good enough for this toy dataset
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1  # only has one class (ballon)

os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
trainer = DefaultTrainer(cfg) 
trainer.resume_or_load(resume=False)
trainer.train()

AvivSham on Nov 7, 2019