OSError: [Errno 22] Invalid argument Epoch 0: : 0it [02:18, ?it/s]

[ForestEco]

I got [Errno 22] code, and I tried using this command to write the filepath, I tried using AI to to double-check my writing, it appears to be fine. What might resolve this?

It appears to be dumping the obj just before, File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj)

This is the command input in anaconda:

cd C:\Users\jsafr\PycharmProjects\pythonProject\Wonder3D
conda activate myenv

cd ./instant-nsr-pl
python launch.py --config configs/neuralangelo-ortho-wmask.yaml --gpu 0 --train dataset.root_dir=..\outputs\cropsize-240.0-cfg1.0/ dataset.scene=scene

This is the output:

(myenv) C:\Users\jsafr\PycharmProjects\pythonProject\Wonder3D\instant-nsr-pl>python launch.py --config configs/neuralangelo-ortho-wmask.yaml --gpu 0 --train dataset.root_dir=../outputs/cropsize-240.0-cfg1.0/ dataset.scene=scene
Global seed set to 42
Using finite difference to compute gradients with eps=progressive
Using 16bit None Automatic Mixed Precision (AMP)
GPU available: True (cuda), 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..
You are using a CUDA device ('NVIDIA GeForce RTX 3060 Laptop GPU') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
../outputs/cropsize-240.0-cfg1.0/scene
(1024, 1024, 3)
the loaded normals are defined in the system of front view
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
C:\Users\jsafr\PycharmProjects\pythonProject\Wonder3D\instant-nsr-pl\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 | cos   | CosineSimilarity | 0
1 | model | NeuSModel        | 7.7 M
-------------------------------------------
7.7 M     Trainable params
0         Non-trainable params
7.7 M     Total params
15.371    Total estimated model params size (MB)
Epoch 0: : 0it [00:00, ?it/s]Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\spawn.py", line 116, in spawn_main
    exitcode = _main(fd, parent_sentinel)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\spawn.py", line 126, in _main
    self = reduction.pickle.load(from_parent)
_pickle.UnpicklingError: pickle data was truncated
Traceback (most recent call last):
  File "C:\Users\jsafr\PycharmProjects\pythonProject\Wonder3D\instant-nsr-pl\launch.py", line 125, in <module>
    main()
  File "C:\Users\jsafr\PycharmProjects\pythonProject\Wonder3D\instant-nsr-pl\launch.py", line 114, in main
    trainer.fit(system, datamodule=dm)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 608, in fit
    call._call_and_handle_interrupt(
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\call.py", line 38, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 650, in _fit_impl
    self._run(model, ckpt_path=self.ckpt_path)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1112, in _run
    results = self._run_stage()
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1191, in _run_stage
    self._run_train()
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1214, in _run_train
    self.fit_loop.run()
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\loops\loop.py", line 199, in run
    self.advance(*args, **kwargs)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\loops\fit_loop.py", line 267, in advance
    self._outputs = self.epoch_loop.run(self._data_fetcher)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\loops\loop.py", line 194, in run
    self.on_run_start(*args, **kwargs)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\loops\epoch\training_epoch_loop.py", line 160, in on_run_start
    _ = iter(data_fetcher)  # creates the iterator inside the fetcher
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\utilities\fetching.py", line 179, in __iter__
    self._apply_patch()
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\utilities\fetching.py", line 120, in _apply_patch
    apply_to_collections(self.loaders, self.loader_iters, (Iterator, DataLoader), _apply_patch_fn)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\utilities\fetching.py", line 156, in loader_iters
    return self.dataloader_iter.loader_iters
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\supporters.py", line 558, in loader_iters
    self._loader_iters = self.create_loader_iters(self.loaders)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\pytorch_lightning\trainer\supporters.py", line 598, in create_loader_iters
    return apply_to_collection(loaders, Iterable, iter, wrong_dtype=(Sequence, Mapping))
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\lightning_utilities\core\apply_func.py", line 52, in apply_to_collection
    return _apply_to_collection_slow(
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\lightning_utilities\core\apply_func.py", line 96, in _apply_to_collection_slow
    return function(data, *args, **kwargs)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\torch\utils\data\dataloader.py", line 435, in __iter__
    return self._get_iterator()
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\torch\utils\data\dataloader.py", line 381, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\site-packages\torch\utils\data\dataloader.py", line 1034, in __init__
    w.start()
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\process.py", line 121, in start
    self._popen = self._Popen(self)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\context.py", line 224, in _Popen
    return _default_context.get_context().Process._Popen(process_obj)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\context.py", line 336, in _Popen
    return Popen(process_obj)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\popen_spawn_win32.py", line 93, in __init__
    reduction.dump(process_obj, to_child)
  File "C:\Users\jsafr\anaconda3\envs\myenv\lib\multiprocessing\reduction.py", line 60, in dump
    ForkingPickler(file, protocol).dump(obj)
OSError: [Errno 22] Invalid argument
Epoch 0: : 0it [02:18, ?it/s]