nerfstudio
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nerfstudio-project
When testing with the "# Train model ns-train nerfacto --data data/nerfstudio/poster"
(nerfstudio) undergrad@Enigma:~/nerfstudio$ ns-train nerfacto --data data/nerfstudio/poster
[01:24:43] Using --data alias for --data.pipeline.datamanager.dataparser.data train.py:222
──────────────────────────────────────────────────────── Config ────────────────────────────────────────────────────────
TrainerConfig(
_target=<class 'nerfstudio.engine.trainer.Trainer'>,
output_dir=PosixPath('outputs'),
method_name='nerfacto',
experiment_name=None,
timestamp='2023-02-10_012443',
machine=MachineConfig(seed=42, num_gpus=1, num_machines=1, machine_rank=0, dist_url='auto'),
logging=LoggingConfig(
relative_log_dir=PosixPath('.'),
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
),
enable_profiler=True
),
viewer=ViewerConfig(
relative_log_filename='viewer_log_filename.txt',
start_train=True,
zmq_port=None,
launch_bridge_server=True,
websocket_port=7007,
ip_address='127.0.0.1',
num_rays_per_chunk=32768,
max_num_display_images=512,
quit_on_train_completion=False,
skip_openrelay=False,
codec='VP8',
local=False
),
pipeline=VanillaPipelineConfig(
_target=<class 'nerfstudio.pipelines.base_pipeline.VanillaPipeline'>,
datamanager=VanillaDataManagerConfig(
_target=<class 'nerfstudio.data.datamanagers.base_datamanager.VanillaDataManager'>,
dataparser=NerfstudioDataParserConfig(
_target=<class 'nerfstudio.data.dataparsers.nerfstudio_dataparser.Nerfstudio'>,
data=PosixPath('data/nerfstudio/poster'),
scale_factor=1.0,
downscale_factor=None,
scene_scale=1.0,
orientation_method='up',
center_poses=True,
auto_scale_poses=True,
train_split_percentage=0.9,
depth_unit_scale_factor=0.001
),
train_num_rays_per_batch=4096,
train_num_images_to_sample_from=-1,
train_num_times_to_repeat_images=-1,
eval_num_rays_per_batch=4096,
eval_num_images_to_sample_from=-1,
eval_num_times_to_repeat_images=-1,
eval_image_indices=(0,),
camera_optimizer=CameraOptimizerConfig(
_target=<class 'nerfstudio.cameras.camera_optimizers.CameraOptimizer'>,
mode='SO3xR3',
position_noise_std=0.0,
orientation_noise_std=0.0,
optimizer=AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.0006,
eps=1e-08,
max_norm=None,
weight_decay=0.01
),
scheduler=SchedulerConfig(
_target=<class 'nerfstudio.engine.schedulers.ExponentialDecaySchedule'>,
lr_final=5e-06,
max_steps=10000
),
param_group='camera_opt'
),
camera_res_scale_factor=1.0
),
model=NerfactoModelConfig(
_target=<class 'nerfstudio.models.nerfacto.NerfactoModel'>,
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=32768,
near_plane=0.05,
far_plane=1000.0,
background_color='last_sample',
num_levels=16,
max_res=2048,
log2_hashmap_size=19,
num_proposal_samples_per_ray=(256, 96),
num_nerf_samples_per_ray=48,
proposal_update_every=5,
proposal_warmup=5000,
num_proposal_iterations=2,
use_same_proposal_network=False,
proposal_net_args_list=[
{'hidden_dim': 16, 'log2_hashmap_size': 17, 'num_levels': 5, 'max_res': 128},
{'hidden_dim': 16, 'log2_hashmap_size': 17, 'num_levels': 5, 'max_res': 256}
],
interlevel_loss_mult=1.0,
distortion_loss_mult=0.002,
orientation_loss_mult=0.0001,
pred_normal_loss_mult=0.001,
use_proposal_weight_anneal=True,
use_average_appearance_embedding=True,
proposal_weights_anneal_slope=10.0,
proposal_weights_anneal_max_num_iters=1000,
use_single_jitter=True,
predict_normals=False
)
),
optimizers={
'proposal_networks': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.01,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
},
'fields': {
'optimizer': AdamOptimizerConfig(
_target=<class 'torch.optim.adam.Adam'>,
lr=0.01,
eps=1e-15,
max_norm=None,
weight_decay=0
),
'scheduler': None
}
},
vis='viewer',
data=PosixPath('data/nerfstudio/poster'),
relative_model_dir=PosixPath('nerfstudio_models'),
steps_per_save=2000,
steps_per_eval_batch=500,
steps_per_eval_image=500,
steps_per_eval_all_images=25000,
max_num_iterations=30000,
mixed_precision=True,
save_only_latest_checkpoint=True,
load_dir=None,
load_step=None,
load_config=None,
log_gradients=False
)
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
[01:24:43] Saving config to: experiment_config.py:124
outputs/data-nerfstudio-poster/nerfacto/2023-02-10_012443/config.yml
[01:24:43] Saving checkpoints to: trainer.py:123
outputs/data-nerfstudio-poster/nerfacto/2023-02-10_012443/nerfstudio_models
Using ZMQ port: 34705
======================================================================================================================== [Public] Open the viewer at https://viewer.nerf.studio/versions/23-02-3-0/?websocket_url=ws://localhost:7007
Sending ping to the viewer Bridge Server...
Successfully connected.
Sending ping to the viewer Bridge Server...
Successfully connected.
[NOTE] Not running eval iterations since only viewer is enabled. Use --vis wandb or --vis tensorboard to run with eval
instead.
Disabled tensorboard/wandb event writers
[01:24:43] Auto image downscale factor of 2 nerfstudio_dataparser.py:314
Skipping 0 files in dataset split train. nerfstudio_dataparser.py:165
Skipping 0 files in dataset split val. nerfstudio_dataparser.py:165
Setting up training dataset...
Caching all 204 images.
Setting up evaluation dataset...
Caching all 22 images.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
Warning: FullyFusedMLP is not supported for the selected architecture 61. Falling back to CutlassMLP. For maximum performance, raise the target GPU architecture to 75+.
No checkpoints to load, training from scratch
Printing profiling stats, from longest to shortest duration in seconds
Traceback (most recent call last):
File "/home/undergrad/anaconda3/envs/nerfstudio/bin/ns-train", line 8, in cublasCreate(handle)
Does this means that there's something wrong with the package dependencies ? Thanks for your help!
Looks like a pytorch cuda error. Maybe try reinstalling pytorch and checking your cuda version. https://github.com/pytorch/pytorch/issues/20860
