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OFA-Sys / ONE-PEACE

A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
Apache License 2.0
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I need help about training my own dataset #17

Closed huan085128 closed 1 year ago

huan085128 commented 1 year ago

Very interesting work, but I ran into problems while training on my own dataset (segmentation task). I want to use the onepeace_seg_cocostuff2ade20k.pth pretrained weights, trained with the command: train.py configs/ade20k/mask2former_onepeace_adapter_g_896_40k_ade20k_ss.py --load-from /home/ONE-PE ACE-main/onepeace_seg_cocostuff2ade20k.pth --work-dir /home/ONE-PEACE-main/output

I get this error, and I dont know how to solve this, and the model path is right:

2023-06-24 07:24:47,819 - mmseg - INFO - Set random seed to 303739764, deterministic: False /usr/local/lib/python3.10/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:2894.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] /home/ONE-PEACE-main/one_peace_vision/seg/mmseg_custom/models/losses/cross_entropy_loss.py:231: UserWarning: Default avg_non_ignore is False, if you would like to ignore the certain label and average loss over non-ignore labels, which is the same with PyTorch official cross_entropy, set avg_non_ignore=True. warnings.warn( 2023-06-24 07:25:10,809 - mmseg - INFO - load checkpoint from local path: /home/ONE-PEACE-main/onepeace_seg_cocostuff2ade20k.pth Traceback (most recent call last): File "/home/ONE-PEACE-main/one_peace_vision/seg/train.py", line 243, in main() File "/home/ONE-PEACE-main/one_peace_vision/seg/train.py", line 203, in main model.init_weights() File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/base_module.py", line 116, in init_weights m.init_weights() File "/home/ONE-PEACE-main/one_peace_vision/seg/mmseg_custom/models/backbones/onepeace.py", line 569, in init_weights model = checkpoint['model'] KeyError: 'model'

pytorch vision: 1.12.1 + cu116, and all other packages were installed according to the README.md.

simonJJJ commented 1 year ago

Hi @huan085128, thanks for pointing this out and I fix it. Pull this repo and try again~

huan085128 commented 1 year ago

Hi @huan085128, thanks for pointing this out and I fix it. Pull this repo and try again~

thank you very much, but I ran into another problem, like this: https://github.com/OFA-Sys/ONE-PEACE/issues/14

2023-06-24 11:20:08,158 - mmseg - INFO - load checkpoint from local path: /home/ONE-PEACE-main/onepeace_seg_cocostuff2ade20k.pth Traceback (most recent call last): File "/home/ONE-PEACE-main/one_peace_vision/seg/train.py", line 243, in main() File "/home/ONE-PEACE-main/one_peace_vision/seg/train.py", line 203, in main model.init_weights() File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/base_module.py", line 116, in init_weights m.init_weights() File "/home/ONE-PEACE-main/one_peace_vision/seg/mmseg_custom/models/backbones/onepeace.py", line 571, in init_weights state_dict = self.resize_abs_pos_embed(model) File "/home/ONE-PEACE-main/one_peace_vision/seg/mmseg_custom/models/backbones/onepeace.py", line 467, in resize_abs_pos_embed pos_embed_checkpoint = checkpoint['image_adapter.pos_embed'] KeyError: 'image_adapter.pos_embed'

Do I need to convert the model to.pkl?

simonJJJ commented 1 year ago

I think you use the one-peace.pt for pretrained path in the config file. Use the one-peace-vision.pkl can fix the problem. e.g.

_base_ = [
    '../_base_/models/mask2former_onepeace.py',
    '../_base_/datasets/ade20k.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_40k.py'
]
crop_size = (896, 896)
pretrained = '/path/to/one-peace-vision.pkl'
huan085128 commented 1 year ago

I think you use the one-peace.pt for pretrained path in the config file. Use the one-peace-vision.pkl can fix the problem. e.g.

_base_ = [
    '../_base_/models/mask2former_onepeace.py',
    '../_base_/datasets/ade20k.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_40k.py'
]
crop_size = (896, 896)
pretrained = '/path/to/one-peace-vision.pkl'

Thank you very much! I have solved this problem. But I ran into another problem, I'm sorry to trouble you again. I get this error:

2023-06-25 03:50:47,533 - mmseg - INFO - Loaded 0 images
fatal: not a git repository (or any parent up to mount point /)
Stopping at filesystem boundary (GIT_DISCOVERY_ACROSS_FILESYSTEM not set).
Traceback (most recent call last):
  File "/home/ONE-PEACE-main/one_peace_vision/seg/train.py", line 245, in <module>
    main()
  File "/home/ONE-PEACE-main/one_peace_vision/seg/train.py", line 234, in main
    train_segmentor(
  File "/usr/local/lib/python3.10/dist-packages/mmseg/apis/train.py", line 101, in train_segmentor
    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
  File "/usr/local/lib/python3.10/dist-packages/mmseg/apis/train.py", line 101, in <listcomp>
    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
  File "/usr/local/lib/python3.10/dist-packages/mmseg/datasets/builder.py", line 153, in build_dataloader
    data_loader = DataLoader(
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py", line 353, in __init__
    sampler = RandomSampler(dataset, generator=generator)  # type: ignore[arg-type]
  File "/usr/local/lib/python3.10/dist-packages/torch/utils/data/sampler.py", line 107, in __init__
    raise ValueError("num_samples should be a positive integer "
ValueError: num_samples should be a positive integer value, but got num_samples=0

This is my dataset format with images and annotations in .png format:

├── dataset
│   ├── toothSeg
│   │   ├── annotations
│   │   │   ├── training
│   │   │   ├── validation
│   │   ├── images
│   │   │   ├── training
│   │   │   ├── validation

This is my dataset configuration:

# dataset settings
dataset_type = 'ADE20KDataset'
data_root = '/home/ONE-PEACE-main/one_peace_vision/dataset/toothSeg'
img_norm_cfg = dict(
    mean=[122.771, 116.746, 104.094], std=[68.5, 66.632, 70.323], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2048, 512),
        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
data = dict(
    samples_per_gpu=4,
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='images/training',
        ann_dir='annotations/training',
        pipeline=train_pipeline),
    val=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=test_pipeline),
    test=dict(
        type=dataset_type,
        data_root=data_root,
        img_dir='images/validation',
        ann_dir='annotations/validation',
        pipeline=test_pipeline))
simonJJJ commented 1 year ago

You can refer mmseg doc to prepare the customized dataset.

huan085128 commented 1 year ago

You can refer mmseg doc to prepare the customized dataset.

I don't quite understand why images don't load in the same format as the Ade20k dataset

huan085128 commented 1 year ago

I found that the problem was that it could only load.jpg images, not.png images

huan085128 commented 1 year ago

You can refer mmseg doc to prepare the customized dataset.

I am so soryy to trouble you again. I first ran it on RTX3090 24G, but it told me that CUDA was out of memory. Then I ran it on A100 80G, and the following problems occurred. 

../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [6,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [8,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [79,0,0], thread: [34,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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Traceback (most recent call last):
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/train.py", line 244, in <module>
    main()
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/train.py", line 233, in main
    train_segmentor(
  File "/usr/local/lib/python3.10/dist-packages/mmseg/apis/train.py", line 194, in train_segmentor
    runner.run(data_loaders, cfg.workflow)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/iter_based_runner.py", line 134, in run
    iter_runner(iter_loaders[i], **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/iter_based_runner.py", line 61, in train
    outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/parallel/data_parallel.py", line 75, in train_step
    return self.module.train_step(*inputs[0], **kwargs[0])
  File "/usr/local/lib/python3.10/dist-packages/mmseg/models/segmentors/base.py", line 138, in train_step
    losses = self(**data_batch)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/fp16_utils.py", line 110, in new_func
    return old_func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmseg/models/segmentors/base.py", line 108, in forward
    return self.forward_train(img, img_metas, **kwargs)
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/segmentors/encoder_decoder_mask2former.py", line 144, in forward_train
    loss_decode = self._decode_head_forward_train(x, img_metas,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/segmentors/encoder_decoder_mask2former.py", line 88, in _decode_head_forward_train
    loss_decode = self.decode_head.forward_train(x, img_metas,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 553, in forward_train
    losses = self.loss(all_cls_scores, all_mask_preds, gt_labels, gt_masks,
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/fp16_utils.py", line 198, in new_func
    return old_func(*args, **kwargs)
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 386, in loss
    losses_cls, losses_mask, losses_dice = multi_apply(
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/core/utils/misc.py", line 21, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 296, in loss_single
    num_total_neg) = self.get_targets(cls_scores_list, mask_preds_list,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 191, in get_targets
    neg_inds_list) = multi_apply(self._get_target_single, cls_scores_list,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/core/utils/misc.py", line 21, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 247, in _get_target_single
    assign_result = self.assigner.assign(cls_score, mask_points_pred,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/utils/assigner.py", line 149, in assign
    cost = cost.detach().cpu()
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.
terminate called after throwing an instance of 'c10::CUDAError'
  what():  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.
Exception raised from create_event_internal at ../c10/cuda/CUDACachingAllocator.cpp:1387 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x3e (0x7fa3f57bb20e in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x26e61 (0x7fa41d27ee61 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #2: c10::cuda::CUDACachingAllocator::raw_delete(void*) + 0x257 (0x7fa41d283de7 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x466bf8 (0x7fa444af4bf8 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #4: c10::TensorImpl::release_resources() + 0x175 (0x7fa3f57a27a5 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #5: <unknown function> + 0x362685 (0x7fa4449f0685 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x67cf88 (0x7fa444d0af88 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #7: THPVariable_subclass_dealloc(_object*) + 0x2b5 (0x7fa444d0b335 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #8: python() [0x601d03]
frame #9: python() [0x5e7fcb]
frame #10: python() [0x5e7fcb]
frame #11: python() [0x53e3e8]
frame #12: python() [0x58e148]
frame #13: python() [0x58e262]
frame #14: python() [0x58e262]
frame #15: python() [0x58e262]
frame #16: python() [0x58e262]
frame #17: python() [0x58e262]
<omitting python frames>
frame #20: python() [0x6c112a]
frame #24: __libc_start_main + 0xf3 (0x7fa507adc083 in /lib/x86_64-linux-gnu/libc.so.6)

Aborted (core dumped)
simonJJJ commented 1 year ago

Can you pass CUDA_LAUNCH_BLOCKING=1 to run again and provide the traceback info?

huan085128 commented 1 year ago

Can you pass CUDA_LAUNCH_BLOCKING=1 to run again and provide the traceback info?

sure, I run the following command:

export CUDA_LAUNCH_BLOCKING=1
python train.py configs/ade20k/mask2former_onepeace_adapter_g_896_40k_ade20k_ss.py --load-from /ai/workshop/ONE-PEACE/one_peace_vision/onepeace_seg_cocostuff2ade20k.pth --work-dir /ai/workshop/ONE-PEACE/output

and I get this error:

2023-06-28 17:20:44,382 - mmseg - INFO - workflow: [('train', 1)], max: 40000 iters
2023-06-28 17:20:44,382 - mmseg - INFO - Checkpoints will be saved to /ai/workshop/ONE-PEACE/output by HardDiskBackend.
/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/utils/positional_encoding.py:81: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
  dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats)
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../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [24,0,0], thread: [64,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [34,0,0], thread: [101,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [34,0,0], thread: [102,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [34,0,0], thread: [103,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
../aten/src/ATen/native/cuda/IndexKernel.cu:91: operator(): block: [34,0,0], thread: [104,0,0] Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed.
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Traceback (most recent call last):
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/train.py", line 244, in <module>
    main()
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/train.py", line 233, in main
    train_segmentor(
  File "/usr/local/lib/python3.10/dist-packages/mmseg/apis/train.py", line 194, in train_segmentor
    runner.run(data_loaders, cfg.workflow)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/iter_based_runner.py", line 134, in run
    iter_runner(iter_loaders[i], **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/iter_based_runner.py", line 61, in train
    outputs = self.model.train_step(data_batch, self.optimizer, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/parallel/data_parallel.py", line 75, in train_step
    return self.module.train_step(*inputs[0], **kwargs[0])
  File "/usr/local/lib/python3.10/dist-packages/mmseg/models/segmentors/base.py", line 138, in train_step
    losses = self(**data_batch)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/fp16_utils.py", line 110, in new_func
    return old_func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/mmseg/models/segmentors/base.py", line 108, in forward
    return self.forward_train(img, img_metas, **kwargs)
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/segmentors/encoder_decoder_mask2former.py", line 144, in forward_train
    loss_decode = self._decode_head_forward_train(x, img_metas,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/segmentors/encoder_decoder_mask2former.py", line 88, in _decode_head_forward_train
    loss_decode = self.decode_head.forward_train(x, img_metas,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 553, in forward_train
    losses = self.loss(all_cls_scores, all_mask_preds, gt_labels, gt_masks,
  File "/usr/local/lib/python3.10/dist-packages/mmcv/runner/fp16_utils.py", line 198, in new_func
    return old_func(*args, **kwargs)
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 386, in loss
    losses_cls, losses_mask, losses_dice = multi_apply(
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/core/utils/misc.py", line 21, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 296, in loss_single
    num_total_neg) = self.get_targets(cls_scores_list, mask_preds_list,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 191, in get_targets
    neg_inds_list) = multi_apply(self._get_target_single, cls_scores_list,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/core/utils/misc.py", line 21, in multi_apply
    return tuple(map(list, zip(*map_results)))
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/decode_heads/mask2former_head.py", line 247, in _get_target_single
    assign_result = self.assigner.assign(cls_score, mask_points_pred,
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/utils/assigner.py", line 130, in assign
    cls_cost = self.cls_cost(cls_pred, gt_labels)
  File "/ai/workshop/ONE-PEACE/one_peace_vision/seg/mmseg_custom/models/losses/match_costs.py", line 133, in __call__
    cls_cost = -cls_score[:, gt_labels]
RuntimeError: CUDA error: device-side assert triggered
terminate called after throwing an instance of 'c10::CUDAError'
  what():  CUDA error: device-side assert triggered
Exception raised from create_event_internal at ../c10/cuda/CUDACachingAllocator.cpp:1387 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x3e (0x7fa92026820e in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x26e61 (0x7fa947d2be61 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #2: c10::cuda::CUDACachingAllocator::raw_delete(void*) + 0x257 (0x7fa947d30de7 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x466bf8 (0x7fa96f5a1bf8 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #4: c10::TensorImpl::release_resources() + 0x175 (0x7fa92024f7a5 in /usr/local/lib/python3.10/dist-packages/torch/lib/libc10.so)
frame #5: <unknown function> + 0x362685 (0x7fa96f49d685 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #6: <unknown function> + 0x67cf88 (0x7fa96f7b7f88 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #7: THPVariable_subclass_dealloc(_object*) + 0x2b5 (0x7fa96f7b8335 in /usr/local/lib/python3.10/dist-packages/torch/lib/libtorch_python.so)
frame #8: python() [0x601d03]
frame #9: python() [0x5e7fcb]
frame #10: python() [0x5e7fcb]
frame #11: python() [0x53e3e8]
frame #12: python() [0x58e148]
frame #13: python() [0x58e262]
frame #14: python() [0x58e262]
frame #15: python() [0x58e262]
frame #16: python() [0x58e262]
frame #17: python() [0x58e262]
<omitting python frames>
frame #20: python() [0x6c112a]
frame #24: __libc_start_main + 0xf3 (0x7faa32589083 in /lib/x86_64-linux-gnu/libc.so.6)

Aborted (core dumped)

I had this problem with my own dataset, but it worked on the ADE20K dataset. I think I should adjust the dataset configuration, but I don't know how to do it.

simonJJJ commented 1 year ago

There may be some reasons cause the problem:

  1. Check your dataset for outliers.
  2. Check the data augmentation to align the shape with the data and model.
simonJJJ commented 1 year ago

I think the reason is the crop_size is (512, 512). You should set the model.backbone.bucket_size=32

huan085128 commented 1 year ago

I think the reason is the crop_size is (512, 512). You should set the model.backbone.bucket_size=32

Thank you very much for your reply. But in _mask2former_onepeace_adapter_g_896_40k_ade20k_ss.py, cropsize = (896, 896), when I change the model.backbone.bucket_size=32, I get this error:

RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 1536 but got size 4704 for tensor number 1 in the list.

My dataset's size is (640, 320), I use the default set in _../base/datasets/ade20k.py_ and _mask2former_onepeace_adapter_g_896_40k_ade20kss.py. I dont know how to set these parameters.

And I have some questions that I would like to ask you.

  1. Why is the 'crop_size' and dataset settings different between '../base/datasets/ade20k.py' and 'mask2former_onepeace_adapter_g_896_40k_ade20k_ss.py'?

  2. What does 'img_scale' mean in the dict(type='Resize', img_scale=(3584, 896), ratio_range=(0.5, 2.0)) in the 'train_pipeline'? Why is it necessary to adjust it to four times the 'crop_size'?

  3. Why does the GPU memory requirement very high, with GPU memory usage exceeding 40GB when training the ADE20K dataset?

simonJJJ commented 1 year ago
  1. The crop_size in mask2former_onepeace_adapter_g_896_40k_ade20k_ss.py would override the crop_size in ade20k.py
  2. See here.
  3. Use small image size could reduce GPU memory requirement.