nerfstudio-project
Training doesnt start with alpha channel images for nerfacto
I have this transform.json and an example image. I had to terminate since the training never starts.
(nerfstudio) adas@VectorSigma:~$ ns-train nerfacto --pipeline.model.background_color "random" --pipeline.model.disable-scene-contraction True --data Desktop/thesis/demo/ [12:51:55] Using --data alias for --data.pipeline.datamanager.data train.py:230 ──────────────────────────────────────────────────────── Config ──────────────────────────────────────────────────────── TrainerConfig( _target=<class 'nerfstudio.engine.trainer.Trainer'>, output_dir=PosixPath('outputs'), method_name='nerfacto', experiment_name=None, project_name='nerfstudio-project', timestamp='2025-09-03_125155', machine=MachineConfig(seed=42, num_devices=1, num_machines=1, machine_rank=0, dist_url='auto', device_type='cuda'), 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 ), 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=ParallelDataManagerConfig( _target=<class 'nerfstudio.data.datamanagers.parallel_datamanager.ParallelDataManager'>, data=PosixPath('Desktop/thesis/demo'), masks_on_gpu=False, images_on_gpu=False, dataparser=NerfstudioDataParserConfig( _target=<class 'nerfstudio.data.dataparsers.nerfstudio_dataparser.Nerfstudio'>, data=PosixPath('.'), 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=False ), 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,), collate_fn=<function nerfstudio_collate at 0x7efdff13faf0>, camera_res_scale_factor=1.0, patch_size=1, camera_optimizer=None, pixel_sampler=PixelSamplerConfig( _target=<class 'nerfstudio.data.pixel_samplers.PixelSampler'>, num_rays_per_batch=4096, keep_full_image=False, is_equirectangular=False, ignore_mask=False, fisheye_crop_radius=None, rejection_sample_mask=True, max_num_iterations=100 ), num_processes=1, queue_size=2, max_thread_workers=None ), 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, prompt=None, near_plane=0.05, far_plane=1000.0, background_color='random', hidden_dim=64, hidden_dim_color=64, hidden_dim_transient=64, num_levels=16, base_res=16, max_res=2048, log2_hashmap_size=19, features_per_level=2, 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, 'use_linear': False}, {'hidden_dim': 16, 'log2_hashmap_size': 17, 'num_levels': 5, 'max_res': 256, 'use_linear': False} ], proposal_initial_sampler='piecewise', 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_appearance_embedding=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, disable_scene_contraction=True, use_gradient_scaling=False, implementation='tcnn', appearance_embed_dim=32, average_init_density=0.01, camera_optimizer=CameraOptimizerConfig( _target=<class 'nerfstudio.cameras.camera_optimizers.CameraOptimizer'>, mode='SO3xR3', trans_l2_penalty=0.01, rot_l2_penalty=0.001, optimizer=None, scheduler=None ) ) ), optimizers={ 'proposal_networks': { 'optimizer': AdamOptimizerConfig( _target=<class 'torch.optim.adam.Adam'>, lr=0.01, eps=1e-15, max_norm=None, weight_decay=0 ), 'scheduler': ExponentialDecaySchedulerConfig( _target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>, lr_pre_warmup=1e-08, lr_final=0.0001, warmup_steps=0, max_steps=200000, ramp='cosine' ) }, 'fields': { 'optimizer': AdamOptimizerConfig( _target=<class 'torch.optim.adam.Adam'>, lr=0.01, eps=1e-15, max_norm=None, weight_decay=0 ), 'scheduler': ExponentialDecaySchedulerConfig( _target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>, lr_pre_warmup=1e-08, lr_final=0.0001, warmup_steps=0, max_steps=200000, ramp='cosine' ) }, '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=0.0001, warmup_steps=0, max_steps=5000, ramp='cosine' ) } }, vis='viewer', data=PosixPath('Desktop/thesis/demo'), prompt=None, relative_model_dir=PosixPath('nerfstudio_models'), load_scheduler=True, 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, 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={}, start_paused=False ) ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── Saving config to: outputs/demo/nerfacto/2025-09-03_125155/config.yml experiment_config.py:136 Saving checkpoints to: outputs/demo/nerfacto/2025-09-03_125155/nerfstudio_models trainer.py:142 Auto image downscale factor of 1 nerfstudio_dataparser.py:484 Started threads Setting up evaluation dataset... Caching all 20 images. Loading data batch ━━━━━━━━━━━━━━╸━━━━━━━━━━━━━━━━━━━━━━━━━ 37% 0:00:05╭─────────────── viser ───────────────╮ │ ╷ │ │ HTTP │ http://0.0.0.0:7007 │ │ Websocket │ ws://0.0.0.0:7007 │ │ ╵ │ ╰─────────────────────────────────────╯ Loading data batch ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸━━━━ 89% 0:00:01[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 Loading data batch ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:06 (viser) Connection opened (0, 1 total), 996 persistent messages (viser) Connection closed (0, 0 total) ^CProcess ForkProcess-7: Process ForkProcess-2: Process ForkProcess-6: Process ForkProcess-5: Process ForkProcess-8: Process ForkProcess-3: Process ForkProcess-4: Process ForkProcess-1: Traceback (most recent call last): Traceback (most recent call last): Traceback (most recent call last): File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 96, in get with self._rlock: File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/synchronize.py", line 95, in enter return self._semlock.enter() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 96, in get with self._rlock: File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/synchronize.py", line 95, in enter return self._semlock.enter() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 96, in get with self._rlock: KeyboardInterrupt File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/synchronize.py", line 95, in enter return self._semlock.enter() KeyboardInterrupt KeyboardInterrupt Traceback (most recent call last): Traceback (most recent call last): File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) Traceback (most recent call last): File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 96, in get with self._rlock: File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/synchronize.py", line 95, in enter return self._semlock.enter() KeyboardInterrupt Traceback (most recent call last): File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 96, in get with self._rlock: File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/synchronize.py", line 95, in enter return self._semlock.enter() KeyboardInterrupt File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 96, in get with self._rlock: File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 97, in get res = self._recv_bytes() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/connection.py", line 216, in recv_bytes buf = self._recv_bytes(maxlength) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) KeyboardInterrupt Traceback (most recent call last): File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/synchronize.py", line 95, in enter return self._semlock.enter() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) KeyboardInterrupt File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/concurrent/futures/process.py", line 233, in _process_worker call_item = call_queue.get(block=True) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/queues.py", line 96, in get with self._rlock: File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/multiprocessing/synchronize.py", line 95, in enter return self._semlock.enter() KeyboardInterrupt Traceback (most recent call last): File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/scripts/train.py", line 189, in launch main_func(local_rank=0, world_size=world_size, config=config) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/scripts/train.py", line 100, in train_loop trainer.train() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/engine/trainer.py", line 266, in train loss, loss_dict, metrics_dict = self.train_iteration(step) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/utils/profiler.py", line 111, in inner out = func(*args, **kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/engine/trainer.py", line 502, in train_iteration _, loss_dict, metrics_dict = self.pipeline.get_train_loss_dict(step=step) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/utils/profiler.py", line 111, in inner out = func(*args, **kwargs) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/pipelines/base_pipeline.py", line 299, in get_train_loss_dict ray_bundle, batch = self.datamanager.next_train(step) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/nerfstudio/data/datamanagers/parallel_datamanager.py" , line 291, in next_train bundle, batch = self.data_queue.get() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/multiprocess/queues.py", line 100, in get res = self._recv_bytes() File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/multiprocess/connection.py", line 219, in recv_bytes buf = self._recv_bytes(maxlength) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/multiprocess/connection.py", line 417, in _recv_bytes buf = self._recv(4) File "/home/adas/miniconda3/envs/nerfstudio/lib/python3.8/site-packages/multiprocess/connection.py", line 382, in _recv chunk = read(handle, remaining) KeyboardInterrupt
Printing profiling stats, from longest to shortest duration in seconds
Trainer.train_iteration: 90.6036
VanillaPipeline.get_train_loss_dict: 90.5937
terminate called without an active exception
Aborted (core dumped)
I unfortunately haven't worked on/ used neural rendering for a while now, and am out of date with the current Nerfstudio code. I won't be able to help look into the current state of the code. I know it used to work in March. You could try checking out a commit from them and validating whether something broke since then, or whether your setup is broken.
I unfortunately haven't worked on/ used neural rendering for a while now, and am out of date with the current Nerfstudio code. I won't be able to help look into the current state of the code. I know it used to work in March. You could try checking out a commit from them and validating whether something broke since then, or whether your setup is broken.
1 followup question how did u get your alpha channel images do you remember? for me i had already png 3 channel images and a mask and i tried creating the alpha channel image with just the foreground. btw your dataset still works
You need to add a 4th channel that makes the background transparent.
Great to hear that the dataset still works! I would try to compare both datasets and try to identify any differences. Most image apps should show the 4th alpha channel.
I tried to check it online and it does show the alpha channel for my data but its weird that the training doesnt start
hey @nepfaff im still at a loss idk why its not starting. All online images apps show me it a 4 channel imag