RuntimeError: min(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.

请问您有遇到过类似的问题吗？ input： /mnt/data/Lightning-NeRF$ ns-train lightning_nerf --mixed-precision True --pipeline.model.point-cloud-path /mnt/data/Lightning-NeRF/argo/nerf_data/2aea7bd1-432a-43c5-9445-651102487f65/pcd.ply --pipeline.model.frontal-axis x --pipeline.model.init-density-value 10.0 --pipeline.model.density-grid-base-res 256 --pipeline.model.density-log2-hashmap-size 24 --pipeline.model.bg-density-grid-res 32 --pipeline.model.bg-density-log2-hashmap-size 18 --pipeline.model.near-plane 0.01 --pipeline.model.far-plane 10.0 --pipeline.model.vi-mlp-num-layers 3 --pipeline.model.vi-mlp-hidden-size 64 --pipeline.model.vd-mlp-num-layers 2 --pipeline.model.vd-mlp-hidden-size 32 --pipeline.model.color-grid-base-res 128 --pipeline.model.color-grid-max-res 2048 --pipeline.model.color-grid-fpl 2 --pipeline.model.color-grid-num-levels 8 --pipeline.model.bg-color-grid-base-res 32 --pipeline.model.bg-color-grid-max-res 128 --pipeline.model.bg-color-log2-hashmap-size 16 --pipeline.model.alpha-thre 0.02 --pipeline.model.occ-grid-base-res 256 --pipeline.model.occ-grid-num-levels 4 --pipeline.model.occ-num-samples-per-ray 750 --pipeline.model.occ-grid-update-warmup-step 2 --pipeline.model.pdf-num-samples-per-ray 8 --pipeline.model.pdf-samples-warmup-step 1000 --pipeline.model.pdf-samples-fixed-step 3000 --pipeline.model.pdf-samples-fixed-ratio 0.5 --pipeline.datamanager.train-num-images-to-sample-from 128 --pipeline.datamanager.train-num-times-to-repeat-images 256 --pipeline.model.appearance-embedding-dim 0 argo-data --data /mnt/data/Lightning-NeRF/argo/nerf_data/2aea7bd1-432a-43c5-9445-651102487f65 --orientation-method none

output /home/bo1-dai/anaconda3/envs/lightning-nerf/lib/python3.8/site-packages/tyro/_fields.py:330: UserWarning: The field optimizer is annotated with type <class 'nerfstudio.engine.optimizers.AdamOptimizerConfig'>, but the default value RAdamOptimizerConfig: _target: <class 'torch.optim.radam.RAdam'> lr: 0.0006 eps: 1e-08 max_norm: None weight_decay: 0.001 has type <class 'nerfstudio.engine.optimizers.RAdamOptimizerConfig'>. We'll try to handle this gracefully, but it may cause unexpected behavior. warnings.warn( ──────────────────────────────────────────────────────── Config ──────────────────────────────────────────────────────── TrainerConfig( _target=<class 'nerfstudio.engine.trainer.Trainer'>, output_dir=PosixPath('outputs'), method_name='lightning_nerf', experiment_name=None, timestamp='2024-07-12_135036', 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 ), 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=90 ), pipeline=VanillaPipelineConfig( _target=<class 'nerfstudio.pipelines.base_pipeline.VanillaPipeline'>, datamanager=VanillaDataManagerConfig( _target=<class 'nerfstudio.data.datamanagers.base_datamanager.VanillaDataManager'>, data=None, camera_optimizer=CameraOptimizerConfig( _target=<class 'nerfstudio.cameras.camera_optimizers.CameraOptimizer'>, mode='off', position_noise_std=0.0, orientation_noise_std=0.0, optimizer=AdamOptimizerConfig( _target=<class 'torch.optim.adam.Adam'>, lr=0.0006, eps=1e-15, max_norm=None, weight_decay=0 ), scheduler=ExponentialDecaySchedulerConfig( _target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>, lr_pre_warmup=1e-08, lr_final=None, warmup_steps=0, max_steps=10000, ramp='cosine' ), param_group='camera_opt' ), dataparser=ArgoDataParserConfig( _target=<class 'nerfstudio.data.dataparsers.argo_dataparser.Argo'>, data=PosixPath('/mnt/data/Lightning-NeRF/argo/nerf_data/2aea7bd1-432a-43c5-9445-651102487f65'), scale_factor=1.0, scene_scale=1.0, orientation_method='none', center_method='poses', auto_scale_poses=True, load_depth=False, depth_unit_scale_factor=1.0 ), train_num_rays_per_batch=65536, train_num_images_to_sample_from=128, train_num_times_to_repeat_images=256, eval_num_rays_per_batch=2048, eval_num_images_to_sample_from=-1, eval_num_times_to_repeat_images=-1, eval_image_indices=(0,), camera_res_scale_factor=1.0, patch_size=1 ), model=LightningNeRFModelConfig( _target=<class 'lightning_nerf.model.LightningNeRFModel'>, enable_collider=True, collider_params={'near_plane': 2.0, 'far_plane': 6.0}, loss_coefficients={'rgb_loss': 1.0, 'res_rgb_loss': 0.01}, eval_num_rays_per_chunk=131072, near_plane=0.01, far_plane=10.0, vi_mlp_num_layers=3, vi_mlp_hidden_size=64, vd_mlp_num_layers=2, vd_mlp_hidden_size=32, appearance_embedding_dim=0, use_average_appearance_embedding=True, background_color='random', alpha_thre=0.02, cone_angle=0.004, point_cloud_path=PosixPath('/mnt/data/Lightning-NeRF/argo/nerf_data/2aea7bd1-432a-43c5-9445-651102487f65/pcd .ply'), frontal_axis='x', init_density_value=10.0, density_grid_base_res=256, density_log2_hashmap_size=24, color_grid_base_res=128, color_grid_max_res=2048, color_grid_fpl=2, color_log2_hashmap_size=19, color_grid_num_levels=8, bg_density_grid_res=32, bg_density_log2_hashmap_size=18, bg_color_grid_base_res=32, bg_color_grid_max_res=128, bg_color_log2_hashmap_size=16, occ_grid_base_res=256, occ_grid_num_levels=4, occ_grid_update_warmup_step=2, occ_num_samples_per_ray=750, pdf_num_samples_per_ray=8, pdf_samples_warmup_step=1000, pdf_samples_fixed_step=3000, pdf_samples_fixed_ratio=0.5, rgb_padding=None ) ), optimizers={ 'den_encoder': { 'optimizer': RAdamOptimizerConfig( _target=<class 'torch.optim.radam.RAdam'>, lr=1.0, eps=1e-08, max_norm=None, weight_decay=0 ), 'scheduler': ExponentialDecaySchedulerConfig( _target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>, lr_pre_warmup=1e-08, lr_final=0.01, warmup_steps=10, max_steps=10000, ramp='linear' ) }, 'col_encoder': { 'optimizer': RAdamOptimizerConfig( _target=<class 'torch.optim.radam.RAdam'>, lr=1.0, eps=1e-08, max_norm=None, weight_decay=0 ), 'scheduler': ExponentialDecaySchedulerConfig( _target=<class 'nerfstudio.engine.schedulers.ExponentialDecayScheduler'>, lr_pre_warmup=1e-08, lr_final=0.01, warmup_steps=10, max_steps=10000, ramp='linear' ) }, 'network': { '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=30000, ramp='cosine' ) } }, vis='viewer', data=None, relative_model_dir=PosixPath('nerfstudio_models'), steps_per_save=1000, steps_per_eval_batch=500, steps_per_eval_image=30000, steps_per_eval_all_images=30000, max_num_iterations=30001, mixed_precision=True, use_grad_scaler=False, save_only_latest_checkpoint=True, load_dir=None, load_step=None, load_config=None, log_gradients=False ) ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── [13:50:36] Saving config to: outputs/unnamed/lightning_nerf/2024-07-12_135036/config.yml experiment_config.py:129 [13:50:36] Saving checkpoints to: outputs/unnamed/lightning_nerf/2024-07-12_135036/nerfstudio_models trainer.py:139 [13:50:36] Argoverse 2 dataset loaded with #images 289, #masks 289, #depth 0. argo_dataparser.py:188 [13:50:37] Argoverse 2 dataset loaded with #images 31, #masks 31, #depth 0. argo_dataparser.py:188 Setting up training dataset... Caching 128 out of 289 images, resampling every 256 iters. Setting up evaluation dataset... Caching all 31 images. [13:50:57] Scene box: model.py:368 [-1.0, -0.30000001192092896, -0.10000000149011612]
[1.5, 0.30000001192092896, 0.4000000059604645].
Density grid size (m): tensor([0.6793, 0.1630, 0.1359]). model.py:372 Max-Res color grid size (m): tensor([0.0849, 0.0204, 0.0170]). model.py:375 [13:50:57] vi mlp input: 16, vd mlp input: 40 field.py:156 Render step size: 0.003492213567097982. model.py:314 [13:50:59] Sampler: LightningNeRFSampler( model.py:329 (occupancy_grid): OccGridEstimator()
).
Collider: 0.01, 10.0. model.py:406 [13:51:00] Load vertices: torch.Size([517136, 3]) model.py:192 tensor(134.6774) tensor(138.0226) model.py:193 tensor(73.5035) tensor(76.4893) model.py:194 [13:51:01] tensor(-0.3844) tensor(-0.1551) model.py:195 [13:51:03] #Point cloud in FG bbox: 0, ratio: 0.0. model.py:431 Augmenting bg points with x as frontal axis. model.py:225 [13:51:06] augment bg points: torch.Size([262144, 3]) model.py:246 [13:51:07] occ grid: model.py:247 tensor([[-1.0000, -0.3000, -0.1000, 1.5000, 0.3000, 0.4000],
[-2.2500, -0.6000, -0.3500, 2.7500, 0.6000, 0.6500],
[-4.7500, -1.2000, -0.8500, 5.2500, 1.2000, 1.1500],
[-9.7500, -2.4000, -1.8500, 10.2500, 2.4000, 2.1500]])
[13:51:13] density encoder: density_encoding.params, torch.Size([16777216]) model.py:463 density encoder: bg_density_encoding.params, torch.Size([262144]) model.py:463 color encoder: color_encoding.params, torch.Size([8388608]) model.py:466 color encoder: bg_color_encoding.params, torch.Size([1048576]) model.py:466 network: vi_mlp.params, torch.Size([6144]) model.py:469 network: direction_encoding.params, torch.Size([0]) model.py:469 network: vd_mlp.params, torch.Size([2048]) model.py:469 ╭─────────────────────────────────────────── Viewer ───────────────────────────────────────────╮ │ ╷ │ │ HTTP │ https://viewer.nerf.studio/versions/23-05-01-0/?websocket_url=ws://localhost:7007 │ │ ╵ │ ╰──────────────────────────────────────────────────────────────────────────────────────────────╯ [NOTE] Not running eval iterations since only viewer is enabled. Use --vis {wandb, tensorboard, viewer+wandb, viewer+tensorboard} to run with eval. No checkpoints to load, training from scratch FG vertices: torch.Size([0, 3]) model.py:257 Traceback (most recent call last): File "/mnt/data/anaconda3/envs/lightning-nerf/bin/ns-train", line 8, in sys.exit(entrypoint()) File "/home/bo1-dai/anaconda3/envs/lightning-nerf/lib/python3.8/site-packages/scripts/train.py", line 247, in entrypoint main( File "/home/bo1-dai/anaconda3/envs/lightning-nerf/lib/python3.8/site-packages/scripts/train.py", line 233, in main launch( File "/home/bo1-dai/anaconda3/envs/lightning-nerf/lib/python3.8/site-packages/scripts/train.py", line 172, in launch main_func(local_rank=0, world_size=world_size, config=config) File "/home/bo1-dai/anaconda3/envs/lightning-nerf/lib/python3.8/site-packages/scripts/train.py", line 86, in train_loop trainer.setup() File "/home/bo1-dai/anaconda3/envs/lightning-nerf/lib/python3.8/site-packages/nerfstudio/engine/trainer.py", line 177, in setup self.callbacks = self.pipeline.get_training_callbacks( File "/home/bo1-dai/anaconda3/envs/lightning-nerf/lib/python3.8/site-packages/nerfstudio/pipelines/base_pipeline.py", line 397, in get_training_callbacks model_callbacks = self.model.get_training_callbacks(training_callback_attributes) File "/mnt/data/Lightning-NeRF/lightning_nerf/model.py", line 508, in get_training_callbacks self._pretrain_density_grid() File "/mnt/data/Lightning-NeRF/lightning_nerf/model.py", line 258, in _pretrain_density_grid CONSOLE.log(vertices_fg[:,0].min(), vertices_fg[:,0].max()) RuntimeError: min(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument.

VISION-SJTU / Lightning-NeRF

RuntimeError: min(): Expected reduction dim to be specified for input.numel() == 0. Specify the reduction dim with the 'dim' argument. #10