Missing positional argument 'tile_bounds' about nerfstudio HOT 4 CLOSED

Try: pip install --upgrade gsplat

from nerfstudio.

Try: pip install --upgrade gsplat

I've tried it but it's still the same.

from nerfstudio.

Try: pip install --upgrade gsplat

I've checked again and I saw that it's actually a new error after using your command, here's the new log:

(nerfstudio) C:\Users\alexa>ns-train splatfacto --data "C:\Users\alexa\nerfstudio\output\COLMAP\Blocuri_Calea_Sagului"
[15:04:39] Using --data alias for --data.pipeline.datamanager.data train.py:230
──────────────────────────────────────────────────────── Config ────────────────────────────────────────────────────────
TrainerConfig(
_target=<class 'nerfstudio.engine.trainer.Trainer'>,
output_dir=WindowsPath('outputs'),
method_name='splatfacto',
experiment_name=None,
project_name='nerfstudio-project',
timestamp='2024-04-07_150439',
machine=MachineConfig(seed=42, num_devices=1, num_machines=1, machine_rank=0, dist_url='auto', device_type='cuda'),
logging=LoggingConfig(
relative_log_dir=WindowsPath('.'),
steps_per_log=10,
max_buffer_size=20,
local_writer=LocalWriterConfig(
_target=<class 'nerfstudio.utils.writer.LocalWriter'>,
enable=True,
stats_to_track=(
<EventName.ITER_TRAIN_TIME: 'Train Iter (time)'>,
<EventName.TRAIN_RAYS_PER_SEC: 'Train Rays / Sec'>,
<EventName.CURR_TEST_PSNR: 'Test PSNR'>,
<EventName.VIS_RAYS_PER_SEC: 'Vis Rays / Sec'>,
<EventName.TEST_RAYS_PER_SEC: 'Test Rays / Sec'>,
<EventName.ETA: 'ETA (time)'>
),
max_log_size=10
),
profiler='basic'
),
viewer=ViewerConfig(
relative_log_filename='viewer_log_filename.txt',
websocket_port=None,
websocket_port_default=7007,
websocket_host='0.0.0.0',
num_rays_per_chunk=32768,
max_num_display_images=512,
quit_on_train_completion=False,
image_format='jpeg',
jpeg_quality=75,
make_share_url=False,
camera_frustum_scale=0.1,
default_composite_depth=True
),
pipeline=VanillaPipelineConfig(
_target=<class 'nerfstudio.pipelines.base_pipeline.VanillaPipeline'>,
datamanager=FullImageDatamanagerConfig(
_target=<class 'nerfstudio.data.datamanagers.full_images_datamanager.FullImageDatamanager'>,
data=WindowsPath('C:/Users/alexa/nerfstudio/output/COLMAP/Blocuri_Calea_Sagului'),
masks_on_gpu=False,
images_on_gpu=False,
dataparser=NerfstudioDataParserConfig(
_target=<class 'nerfstudio.data.dataparsers.nerfstudio_dataparser.Nerfstudio'>,
data=WindowsPath('.'),
scale_factor=1.0,
downscale_factor=None,
scene_scale=1.0,
orientation_method='up',
center_method='poses',
auto_scale_poses=True,
eval_mode='fraction',
train_split_fraction=0.9,
eval_interval=8,
depth_unit_scale_factor=0.001,
mask_color=None,
load_3D_points=True
),
camera_res_scale_factor=1.0,
eval_num_images_to_sample_from=-1,
eval_num_times_to_repeat_images=-1,
eval_image_indices=(0,),
cache_images='cpu',
cache_images_type='uint8',
max_thread_workers=None
),
model=SplatfactoModelConfig(
_target=<class 'nerfstudio.models.splatfacto.SplatfactoModel'>,
enable_collider=True,
collider_params={'near_plane': 2.0, 'far_plane': 6.0},
loss_coefficients={'rgb_loss_coarse': 1.0, 'rgb_loss_fine': 1.0},
eval_num_rays_per_chunk=4096,
prompt=None,
warmup_length=500,
refine_every=100,
resolution_schedule=3000,
background_color='random',
num_downscales=2,
cull_alpha_thresh=0.1,
cull_scale_thresh=0.5,
continue_cull_post_densification=True,
reset_alpha_every=30,
densify_grad_thresh=0.0002,
densify_size_thresh=0.01,
n_split_samples=2,
sh_degree_interval=1000,
cull_screen_size=0.15,
split_screen_size=0.05,
stop_screen_size_at=4000,
random_init=False,
num_random=50000,
random_scale=10.0,
ssim_lambda=0.2,
stop_split_at=15000,
sh_degree=3,
use_scale_regularization=False,
max_gauss_ratio=10.0,
output_depth_during_training=False,
rasterize_mode='classic'
)
),
optimizers={
'means': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.00016,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': ExponentialDecaySchedulerConfig(
_target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>,
lr_pre_warmup=1e-08,
lr_final=1.6e-06,
warmup_steps=0,
max_steps=30000,
ramp='cosine'
)
},
'features_dc': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.0025,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'features_rest': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.000125,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'opacities': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.05,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'scales': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.005,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'quats': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.001,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'camera_opt': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.001,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': ExponentialDecaySchedulerConfig(
_target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>,
lr_pre_warmup=1e-08,
lr_final=5e-05,
warmup_steps=0,
max_steps=30000,
ramp='cosine'
)
}
},
vis='viewer',
data=WindowsPath('C:/Users/alexa/nerfstudio/output/COLMAP/Blocuri_Calea_Sagului'),
prompt=None,
relative_model_dir=WindowsPath('nerfstudio_models'),
load_scheduler=True,
steps_per_save=2000,
steps_per_eval_batch=0,
steps_per_eval_image=100,
steps_per_eval_all_images=1000,
max_num_iterations=30000,
mixed_precision=False,
use_grad_scaler=False,
save_only_latest_checkpoint=True,
load_dir=None,
load_step=None,
load_config=None,
load_checkpoint=None,
log_gradients=False,
gradient_accumulation_steps={}
)
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Saving config to: experiment_config.py:136
outputs\Blocuri_Calea_Sagului\splatfacto\2024-04-07_150439\config.yml
Saving checkpoints to: trainer.py:136
outputs\Blocuri_Calea_Sagului\splatfacto\2024-04-07_150439\nerfstudio_models
Auto image downscale factor of 4 nerfstudio_dataparser.py:484
╭─────────────── viser ───────────────╮
│ ╷ │
│ HTTP │ http://0.0.0.0:7007 │
│ Websocket │ ws://0.0.0.0:7007 │
│ ╵ │
╰─────────────────────────────────────╯
[NOTE] Not running eval iterations since only viewer is enabled.
Use --vis {wandb, tensorboard, viewer+wandb, viewer+tensorboard} to run with eval.
No Nerfstudio checkpoint to load, so training from scratch.
Disabled comet/tensorboard/wandb event writers
[15:04:49] Caching / undistorting train images full_images_datamanager.py:179
C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py:383: UserWarning: Error checking compiler version
for cl: [WinError 2] The system cannot find the file specified
warnings.warn(f'Error checking compiler version for {compiler}: {error}')
( ● ) gsplat: Setting up CUDA (This may take a few minutes the first time)INFO: Could not find files for the given pattern(s).
Printing profiling stats, from longest to shortest duration in seconds
VanillaPipeline.get_train_loss_dict: 3.1626
Trainer.train_iteration: 3.1626
Traceback (most recent call last):
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\gsplat\cuda_backend.py", line 56, in
from gsplat import csrc as C
ImportError: cannot import name 'csrc' from 'gsplat' (C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\gsplat_init.py)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\Users\alexa.conda\envs\nerfstudio\lib\runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\alexa.conda\envs\nerfstudio\lib\runpy.py", line 87, in run_code
exec(code, run_globals)
File "C:\Users\alexa.conda\envs\nerfstudio\Scripts\ns-train.exe_main.py", line 7, in
File "C:\Users\alexa\nerfstudio\nerfstudio\scripts\train.py", line 262, in entrypoint
main(
File "C:\Users\alexa\nerfstudio\nerfstudio\scripts\train.py", line 247, in main
launch(
File "C:\Users\alexa\nerfstudio\nerfstudio\scripts\train.py", line 189, in launch
main_func(local_rank=0, world_size=world_size, config=config)
File "C:\Users\alexa\nerfstudio\nerfstudio\scripts\train.py", line 100, in train_loop
trainer.train()
File "C:\Users\alexa\nerfstudio\nerfstudio\engine\trainer.py", line 250, in train
loss, loss_dict, metrics_dict = self.train_iteration(step)
File "C:\Users\alexa\nerfstudio\nerfstudio\utils\profiler.py", line 112, in inner
out = func(*args, **kwargs)
File "C:\Users\alexa\nerfstudio\nerfstudio\engine\trainer.py", line 471, in train_iteration
_, loss_dict, metrics_dict = self.pipeline.get_train_loss_dict(step=step)
File "C:\Users\alexa\nerfstudio\nerfstudio\utils\profiler.py", line 112, in inner
out = func(*args, **kwargs)
File "C:\Users\alexa\nerfstudio\nerfstudio\pipelines\base_pipeline.py", line 300, in get_train_loss_dict
model_outputs = self._model(ray_bundle) # train distributed data parallel model if world_size > 1
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\nn\modules\module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\nn\modules\module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "C:\Users\alexa\nerfstudio\nerfstudio\models\base_model.py", line 143, in forward
return self.get_outputs(ray_bundle)
File "C:\Users\alexa\nerfstudio\nerfstudio\models\splatfacto.py", line 711, in get_outputs
self.xys, depths, self.radii, conics, comp, num_tiles_hit, cov3d = project_gaussians( # type: ignore
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\gsplat\project_gaussians.py", line 61, in project_gaussians
return _ProjectGaussians.apply(
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\autograd\function.py", line 539, in apply
return super().apply(*args, **kwargs) # type: ignore[misc]
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\gsplat\project_gaussians.py", line 110, in forward
) = C.project_gaussians_forward(
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\gsplat\cuda_init.py", line 7, in call_cuda
from ._backend import _C
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\gsplat\cuda_backend.py", line 88, in
_C = load(
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 1308, in load
return _jit_compile(
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 1710, in _jit_compile
_write_ninja_file_and_build_library(
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 1810, in _write_ninja_file_and_build_library
_write_ninja_file_to_build_library(
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 2242, in _write_ninja_file_to_build_library
_write_ninja_file(
File "C:\Users\alexa.conda\envs\nerfstudio\lib\site-packages\torch\utils\cpp_extension.py", line 2382, in _write_ninja_file
cl_paths = subprocess.check_output(['where',
File "C:\Users\alexa.conda\envs\nerfstudio\lib\subprocess.py", line 415, in check_output
return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,
File "C:\Users\alexa.conda\envs\nerfstudio\lib\subprocess.py", line 516, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['where', 'cl']' returned non-zero exit status 1.

from nerfstudio.

I've found the solution, you just have to add these paths to the PATH system environment variables and now the training starts with no problem:

1. C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.39.33519\bin\Hostx64\x64
2. C:\Program Files\Microsoft Visual Studio\2022\Community\Common7\IDE

from nerfstudio.