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barbararoessle / dense_depth_priors_nerf

Dense Depth Priors for Neural Radiance Fields from Sparse Input Views
MIT License
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cannot load the depth completion model trained on ScanNet #6

Closed sMamooler closed 2 years ago

sMamooler commented 2 years ago

Hello,

I'm trying to optimize NeRF with dense depth priors using the depth completion network trained on ScanNet provided in the corresponding readme section and I get the following error: 


Traceback (most recent call last):
  File "run_nerf.py", line 1104, in <module>
    run_nerf()
  File "run_nerf.py", line 1070, in run_nerf
    train_nerf(images, depths, valid_depths, poses, intrinsics, i_split, args, scene_sample_params, lpips_alex, gt_depths, gt_valid_depths)
  File "run_nerf.py", line 789, in train_nerf
    scene_sample_params, args)
  File "run_nerf.py", line 728, in complete_and_check_depth
    invalidate_large_std_threshold=args.invalidate_large_std_threshold)
  File "run_nerf.py", line 678, in complete_depth
    ckpt = torch.load(model_path)
  File "/miniconda/envs/env/lib/python3.7/site-packages/torch/serialization.py", line 713, in load
    return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
  File "/miniconda/envs/env/lib/python3.7/site-packages/torch/serialization.py", line 920, in _legacy_load
    magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: A load persistent id instruction was encountered,
but no persistent_load function was specified.

I've tried the following pytorch versions: 1.11.0, 1.10.0, 1.9.1, and 1.9.0. Could you please confirm that this is the correct link for the depth completion network weights?

Thank you in advance for your help.

barbararoessle commented 2 years ago

Yes, the link is correct. In case it helps, here are more details on the setup used for testing this repo.