Octree-GS
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IndexError: list index out of range
(octree-gs) I run the code:
bash single_train.sh
the output is:
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
/home//miniconda3/envs/octree-gs/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
warnings.warn(
/home//miniconda3/envs/octree-gs/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or None
for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing weights=VGG16_Weights.IMAGENET1K_V1
. You can also use weights=VGG16_Weights.DEFAULT
to get the most up-to-date weights.
warnings.warn(msg)
Loading model from: /home/**/miniconda3/envs/octree-gs/lib/python3.8/site-packages/lpips/weights/v0.1/vgg.pth
not found tf board
2024-04-05 13:12:23,960 - INFO: args: Namespace(add_color_dist=False, add_cov_dist=False, add_level=False, add_opacity_dist=False, appearance_dim=0, appearance_lr_delay_mult=0.01, appearance_lr_final=0.0005, appearance_lr_init=0.05, appearance_lr_max_steps=40000, base_layer=-1, checkpoint_iterations=[], coarse_factor=1.5, coarse_iter=5000, compute_cov3D_python=False, data_device='cuda', debug=False, debug_from=-1, densify_grad_threshold=0.0002, detect_anomaly=False, dist2level='round', dist_ratio=0.999, ds=1, eval=True, extend=1.1, extra_ratio=0.5, extra_up=0.01, feat_dim=32, feature_lr=0.0075, fork=2, gpu='-1', images='images', init_level=-1, ip='127.0.0.1', iterations=40000, lambda_dssim=0.2, levels=-1, min_opacity=0.005, mlp_color_lr_delay_mult=0.01, mlp_color_lr_final=5e-05, mlp_color_lr_init=0.008, mlp_color_lr_max_steps=40000, mlp_cov_lr_delay_mult=0.01, mlp_cov_lr_final=0.004, mlp_cov_lr_init=0.004, mlp_cov_lr_max_steps=40000, mlp_featurebank_lr_delay_mult=0.01, mlp_featurebank_lr_final=1e-05, mlp_featurebank_lr_init=0.01, mlp_featurebank_lr_max_steps=40000, mlp_opacity_lr_delay_mult=0.01, mlp_opacity_lr_final=2e-05, mlp_opacity_lr_init=0.002, mlp_opacity_lr_max_steps=40000, model_path='outputs/data/truck/baseline/2024-04-05_13:12:21', n_offsets=10, offset_lr_delay_mult=0.01, offset_lr_final=0.0001, offset_lr_init=0.01, offset_lr_max_steps=40000, opacity_lr=0.02, percent_dense=0.01, port=22315, position_lr_delay_mult=0.01, position_lr_final=0.0, position_lr_init=0.0, position_lr_max_steps=40000, progressive=True, quiet=False, random_background=False, ratio=1, resolution=-1, resolution_scales=[1.0], rotation_lr=0.002, save_iterations=[-1], scaling_lr=0.007, source_path='data/data/truck', start_checkpoint=None, start_stat=500, success_threshold=0.8, test_iterations=[-1], undistorted=False, update_anchor=True, update_from=1500, update_interval=100, update_ratio=0.2, update_until=20000, use_feat_bank=False, use_wandb=False, visible_threshold=0.9, warmup=False, white_background=False)
[10000, 20000, 30000, 40000]
[10000, 20000, 30000, 40000]
Backup Finished!
2024-04-05 13:12:24,129 - INFO: Optimizing outputs/data/truck/baseline/2024-04-05_13:12:21
Output folder: outputs/data/truck/baseline/2024-04-05_13:12:21 [05/04 13:12:24]
Tensorboard not available: not logging progress [05/04 13:12:24]
Traceback (most recent call last):
File "train.py", line 560, in
I also tried my custom data and the SfM data sets for Tanks&Temples and Deep Blending are hosted by 3D-Gaussian-Splatting. the error is the same.