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drprojects / superpoint_transformer

Official PyTorch implementation of Superpoint Transformer introduced in [ICCV'23] "Efficient 3D Semantic Segmentation with Superpoint Transformer" and SuperCluster introduced in [3DV'24 Oral] "Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering"
MIT License
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AttributeError: 'list' object has no attribute 'sizes' #141

Closed gitKincses closed 3 months ago

gitKincses commented 3 months ago

Hi! I am training the model on a DALES dataset, using the command "python src/train.py experiment=semantic/dales". After the dataset preprocessing is done, the program prints this. Could you tell me how can I fix this error?

Non-trainable params: 0                                                                                                                                                               
Total params: 212 K                                                                                                                                                                   
Total estimated model params size (MB): 0                                                                                                                                             

[2024-07-19 14:10:29,446][src.utils.utils][ERROR] - 
Traceback (most recent call last):
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/utils/utils.py", line 45, in wrap
    metric_dict, object_dict = task_func(cfg=cfg)
  File "src/train.py", line 115, in train
    trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
    call._call_and_handle_interrupt(
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 44, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
    results = self._run_stage()
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1028, in _run_stage
    self._run_sanity_check()
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1057, in _run_sanity_check
    val_loop.run()
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py", line 182, in _decorator
    return loop_run(self, *args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 135, in run
    self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 370, in _evaluation_step
    batch = call._call_strategy_hook(trainer, "batch_to_device", batch, dataloader_idx=dataloader_idx)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 277, in batch_to_device
    return model._apply_batch_transfer_handler(batch, device=device, dataloader_idx=dataloader_idx)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/core/module.py", line 359, in _apply_batch_transfer_handler
    batch = self._call_batch_hook("on_after_batch_transfer", batch, dataloader_idx)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/core/module.py", line 347, in _call_batch_hook
    return trainer_method(trainer, hook_name, *args)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 181, in _call_lightning_datamodule_hook
    return fn(*args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/datamodules/base.py", line 333, in on_after_batch_transfer
    return on_device_transform(nag)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/torch_geometric/transforms/compose.py", line 24, in __call__
    data = transform(data)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/transforms/transforms.py", line 23, in __call__
    return self._process(x)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/transforms/graph.py", line 1359, in _process
    nag[i_level].node_size = nag.get_sub_size(i_level, low=self.low)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/data/nag.py", line 54, in get_sub_size
    sub_sizes = self[low + 1].sub.sizes
AttributeError: 'list' object has no attribute 'sizes'
[2024-07-19 14:10:29,460][src.utils.utils][INFO] - Closing loggers...
[2024-07-19 14:10:29,463][src.utils.utils][INFO] - Closing wandb!
wandb:                                                                                
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /home/hqu/ljc/SPT/superpoint_transformer/logs/train/runs/2024-07-19_14-09-46/wandb/offline-run-20240719_140955-3dwpno44
wandb: Find logs at: ./logs/train/runs/2024-07-19_14-09-46/wandb/offline-run-20240719_140955-3dwpno44/logs
wandb: WARNING The new W&B backend becomes opt-out in version 0.18.0; try it out with `wandb.require("core")`! See https://wandb.me/wandb-core for more information.
Error executing job with overrides: ['experiment=semantic/dales']
Traceback (most recent call last):
  File "src/train.py", line 140, in main
    metric_dict, _ = train(cfg)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/utils/utils.py", line 48, in wrap
    raise ex
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/utils/utils.py", line 45, in wrap
    metric_dict, object_dict = task_func(cfg=cfg)
  File "src/train.py", line 115, in train
    trainer.fit(model=model, datamodule=datamodule, ckpt_path=cfg.get("ckpt_path"))
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 543, in fit
    call._call_and_handle_interrupt(
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 44, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 579, in _fit_impl
    self._run(model, ckpt_path=ckpt_path)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _run
    results = self._run_stage()
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1028, in _run_stage
    self._run_sanity_check()
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1057, in _run_sanity_check
    val_loop.run()
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/loops/utilities.py", line 182, in _decorator
    return loop_run(self, *args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 135, in run
    self._evaluation_step(batch, batch_idx, dataloader_idx, dataloader_iter)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/loops/evaluation_loop.py", line 370, in _evaluation_step
    batch = call._call_strategy_hook(trainer, "batch_to_device", batch, dataloader_idx=dataloader_idx)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 311, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 277, in batch_to_device
    return model._apply_batch_transfer_handler(batch, device=device, dataloader_idx=dataloader_idx)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/core/module.py", line 359, in _apply_batch_transfer_handler
    batch = self._call_batch_hook("on_after_batch_transfer", batch, dataloader_idx)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/core/module.py", line 347, in _call_batch_hook
    return trainer_method(trainer, hook_name, *args)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/pytorch_lightning/trainer/call.py", line 181, in _call_lightning_datamodule_hook
    return fn(*args, **kwargs)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/datamodules/base.py", line 333, in on_after_batch_transfer
    return on_device_transform(nag)
  File "/home/hqu/anaconda3/envs/spt/lib/python3.8/site-packages/torch_geometric/transforms/compose.py", line 24, in __call__
    data = transform(data)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/transforms/transforms.py", line 23, in __call__
    return self._process(x)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/transforms/graph.py", line 1359, in _process
    nag[i_level].node_size = nag.get_sub_size(i_level, low=self.low)
  File "/home/hqu/ljc/SPT/superpoint_transformer/src/data/nag.py", line 54, in get_sub_size
    sub_sizes = self[low + 1].sub.sizes
AttributeError: 'list' object has no attribute 'sizes'

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.

Thanks!

gitKincses commented 3 months ago

I found a same issue( #139 ), and it has been solved.

drprojects commented 3 months ago

Hi @gitKincses indeed this seems related to #139. I will look into this issue today and update the codebase accordingly.

PS: If you ❤️ or even simply use this project, don't forget to give the repository a ⭐, it means a lot to us !