command fails

[antithing]

Hi, and thank you for making this code available.

I am trying to run on the mip360 garden dataset, and when I run:

python launch.py --config configs/neus-colmap.yaml --gpu 0 --train dataset.root_dir=D://NERF//BakedSDF//torch-bakedsdf-main//torch-bakedsdf-main//load//unbounded360//garden//

I see this error:

D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main>python launch.py --config configs/neus-colmap.yaml --gpu 0 --train
Global seed set to 42
Using 16bit native Automatic Mixed Precision (AMP)
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
`Trainer(limit_train_batches=1.0)` was configured so 100% of the batches per epoch will be used..
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
fatal: not a git repository (or any of the parent directories): .git
D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\utils\callbacks.py:76: UserWarning: Code snapshot is not saved. Please make sure you have git installed and are in a git repository.
  rank_zero_warn("Code snapshot is not saved. Please make sure you have git installed and are in a git repository.")

  | Name  | Type      | Params
------------------------------------
0 | model | NeuSModel | 28.0 M
------------------------------------
28.0 M    Trainable params
0         Non-trainable params
28.0 M    Total params
55.913    Total estimated model params size (MB)
Traceback (most recent call last):
  File "D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\launch.py", line 130, in <module>
    main()
  File "D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\launch.py", line 119, in main
    trainer.fit(system, datamodule=dm)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 770, in fit
    self._call_and_handle_interrupt(
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 723, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 811, in _fit_impl
    results = self._run(model, ckpt_path=self.ckpt_path)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1236, in _run
    results = self._run_stage()
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1323, in _run_stage
    return self._run_train()
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1353, in _run_train
    self.fit_loop.run()
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\fit_loop.py", line 266, in advance
    self._outputs = self.epoch_loop.run(self._data_fetcher)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\epoch\training_epoch_loop.py", line 208, in advance
    batch_output = self.batch_loop.run(batch, batch_idx)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\batch\training_batch_loop.py", line 88, in advance
    outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\base.py", line 204, in run
    self.advance(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 203, in advance
    result = self._run_optimization(
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 256, in _run_optimization
    self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 369, in _optimizer_step
    self.trainer._call_lightning_module_hook(
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1595, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\core\lightning.py", line 1646, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\core\optimizer.py", line 168, in step
    step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\strategies\strategy.py", line 193, in optimizer_step
    return self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\plugins\precision\native_amp.py", line 85, in optimizer_step
    closure_result = closure()
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 148, in __call__
    self._result = self.closure(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 134, in closure
    step_output = self._step_fn()
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 427, in _training_step
    training_step_output = self.trainer._call_strategy_hook("training_step", *step_kwargs.values())
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1765, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\strategies\dp.py", line 125, in training_step
    return self.model(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\parallel\data_parallel.py", line 169, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\overrides\data_parallel.py", line 64, in forward
    output = super().forward(*inputs, **kwargs)
  File "C:\Users\B\AppData\Local\Programs\Python\Python39\lib\site-packages\pytorch_lightning\overrides\base.py", line 82, in forward
    output = self.module.training_step(*inputs, **kwargs)
  File "D:\NERF\BakedSDF\torch-bakedsdf-main\torch-bakedsdf-main\systems\neus.py", line 95, in training_step
    train_num_rays = int(self.train_num_rays * (self.train_num_samples / out['num_samples_full'].sum().item()))
ZeroDivisionError: division by zero
Epoch 0: : 0it [02:25, ?it/s]
[W C:\actions-runner\_work\pytorch\pytorch\builder\windows\pytorch\torch\csrc\CudaIPCTypes.cpp:15] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]

What might be causing this?

Thank you!

[winterjonas]

Following up on this, did you manage to solve this error? Running into the same issue

[antithing]

@winterjonas I never solved it. Please post here if you do!

[gnoilednad]

A similar problem has been resolved in another repository; it appears to be a compatibility issue between Windows and pytorh_lighting. Can you solve the problem in a similar way to this one? https://github.com/xxlong0/Wonder3D/issues/22

・remove all ".to(self.rank)" and "device=self.dataset.all_images.device" ・ add ".to(self.device)" to the data that need to send to gpu.