Unable to fine-tune due to missing mask labels

Hi, I am currently fine-tuning the pre-trained model (epoch36.pth) but I am encountering an error whenever I load my custom dataset generated using LabelImg.

Traceback (most recent call last):
  File "tools/train.py", line 151, in <module>
    main()
  File "tools/train.py", line 147, in main
    meta=meta)
  File "/usr/local/lib/python3.6/dist-packages/mmdet-1.2.0+0f33c08-py3.6-linux-x86_64.egg/mmdet/apis/train.py", line 165, in train_detector
    runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
  File "/usr/local/lib/python3.6/dist-packages/mmcv/runner/runner.py", line 384, in run
    epoch_runner(data_loaders[i], **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/mmcv/runner/runner.py", line 279, in train
    for i, data_batch in enumerate(data_loader):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 345, in __next__
    data = self._next_data()
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 856, in _next_data
    return self._process_data(data)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py", line 881, in _process_data
    data.reraise()
  File "/usr/local/lib/python3.6/dist-packages/torch/_utils.py", line 394, in reraise
    raise self.exc_type(msg)
KeyError: Caught KeyError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop
    data = fetcher.fetch(index)
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
    data = [self.dataset[idx] for idx in possibly_batched_index]
  File "/usr/local/lib/python3.6/dist-packages/mmdet-1.2.0+0f33c08-py3.6-linux-x86_64.egg/mmdet/datasets/custom.py", line 132, in __getitem__
    data = self.prepare_train_img(idx)
  File "/usr/local/lib/python3.6/dist-packages/mmdet-1.2.0+0f33c08-py3.6-linux-x86_64.egg/mmdet/datasets/custom.py", line 145, in prepare_train_img
    return self.pipeline(results)
  File "/usr/local/lib/python3.6/dist-packages/mmdet-1.2.0+0f33c08-py3.6-linux-x86_64.egg/mmdet/datasets/pipelines/compose.py", line 24, in __call__
    data = t(data)
  File "/usr/local/lib/python3.6/dist-packages/mmdet-1.2.0+0f33c08-py3.6-linux-x86_64.egg/mmdet/datasets/pipelines/loading.py", line 147, in __call__
    results = self._load_masks(results)
  File "/usr/local/lib/python3.6/dist-packages/mmdet-1.2.0+0f33c08-py3.6-linux-x86_64.egg/mmdet/datasets/pipelines/loading.py", line 125, in _load_masks
    gt_masks = results['ann_info']['masks']
KeyError: 'masks'

I noticed specifically from the config file that the training pipeline requires masks to be enabled.

    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),

Is there something to be done when annotating using LabelImg that you guys did differently to indicate the existence of label masks? I saw the example provided and did the same but still getting an error about masks. I also set with_mask=False but I don’t honestly know how relevant would that be to the whole training process.

Example annotation from LabelImg:

<annotation>
	<folder>jpeg_images</folder>
	<filename>acc_2018_fs_008.jpg</filename>
	<path>/Users/rt/Desktop/99_annotated/jpeg_images/acc_2018_fs_008.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>4958</width>
		<height>7017</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
        <name>borderless</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>993</xmin>
            <ymin>1020</ymin>
            <xmax>4223</xmax>
            <ymax>5479</ymax>
        </bndbox>
    </object>
	<object>
		<name>cell</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>3559</xmin>
			<ymin>1047</ymin>
			<xmax>4021</xmax>
			<ymax>1107</ymax>
		</bndbox>
	</object>
</annotation>

Thank you and I appreciate this awesome work by the way.

[DevashishPrasad]

We did not mention the segmentation vertices coordinates in the annotation. We used labelimg tool to annotate the images. You can find sample annotations here https://drive.google.com/drive/folders/1ID1sTk1VKHCBeeDEC_BzHBGRE7JkC0qq?usp=sharing

We then converted all of these annotations to json files using a custom script. We will be releasing that script soon

[nkkanth]

Iam getting the same error. Dont see any conclusion on how this error is solved. Can someone post the fix here?