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An error occurred during the replication of the PASCAL VOC dataset. #4

Closed qjl988 closed 8 months ago

qjl988 commented 8 months ago

When I was replicating the code on the PASCAL VOC dataset, I encountered the following error after changing num_classes to 21. I really don't know how to solve it and hope to get some help.

C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\aten\src\ATen\native\cuda\IndexKernel.cu:92: block: [10,0,0], thread: [32,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && 
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Traceback (most recent call last):
  File "mmsegmentation\tools\train.py", line 104, in <module>
    main()
  File "mmsegmentation\tools\train.py", line 100, in main
    runner.train()
  File "C:\code\Anaconda\envs\mmseg2\lib\site-packages\mmengine\runner\runner.py", line 1777, in train
    model = self.train_loop.run()  # type: ignore
  File "C:\code\Anaconda\envs\mmseg2\lib\site-packages\mmengine\runner\loops.py", line 278, in run
    self.run_iter(data_batch)
  File "C:\code\Anaconda\envs\mmseg2\lib\site-packages\mmengine\runner\loops.py", line 301, in run_iter
    outputs = self.runner.model.train_step(
  File "C:\code\Anaconda\envs\mmseg2\lib\site-packages\mmengine\model\base_model\base_model.py", line 114, in train_step
    losses = self._run_forward(data, mode='loss')  # type: ignore
  File "C:\code\Anaconda\envs\mmseg2\lib\site-packages\mmengine\model\base_model\base_model.py", line 346, in _run_forward
    results = self(**data, mode=mode)
  File "C:\code\Anaconda\envs\mmseg2\lib\site-packages\torch\nn\modules\module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "c:\code\jupyterspace\medical_seg\mmsegmentation\mmseg\models\segmentors\base.py", line 94, in forward
    return self.loss(inputs, data_samples)
  File "c:\code\jupyterspace\medical_seg\mmsegmentation\mmseg\models\segmentors\encoder_decoder.py", line 178, in loss
    loss_decode = self._decode_head_forward_train(x, data_samples)
  File "c:\code\jupyterspace\medical_seg\mmsegmentation\mmseg\models\segmentors\encoder_decoder.py", line 139, in _decode_head_forward_train
    loss_decode = self.decode_head.loss(inputs, data_samples,
  File "c:\code\jupyterspace\medical_seg\mmsegmentation\mmseg\models\decode_heads\decode_head.py", line 262, in loss
    losses = self.loss_by_feat(seg_logits, batch_data_samples)
  File "c:\code\jupyterspace\medical_seg\mmsegmentation\mmseg\models\decode_heads\decode_head.py", line 336, in loss_by_feat
    loss['acc_seg'] = accuracy(
  File "c:\code\jupyterspace\medical_seg\mmsegmentation\mmseg\models\losses\accuracy.py", line 49, in accuracy
    correct = correct[:, target != ignore_index]
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
haoshao-nku commented 8 months ago

Please reference/medical_seg/mmsegmentation/configs/pspnet/pspnet_r50 - d8_4xb4-40 k_voc12aug - 512 x512. Set in py. As far as I know, you first need to convert loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), use_sigmoid is set to False.

qjl988 commented 8 months ago

Please reference/medical_seg/mmsegmentation/configs/pspnet/pspnet_r50 - d8_4xb4-40 k_voc12aug - 512 x512. Set in py. As far as I know, you first need to convert loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), use_sigmoid is set to False.

Thank you very much. After setting use_sigmoid to False, it have solved.