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half-potato / nmf

Our method takes as input a collection of images (100 in our experiments) with known cameras, and outputs the volumetric density and normals, materials (BRDFs), and far-field illumination (environment map) of the scene.
https://half-potato.gitlab.io/posts/nmf/
MIT License
53 stars 3 forks source link

llff dataset - gardenshperes #13

Closed prakashknaikade closed 4 months ago

prakashknaikade commented 1 year ago

I am trying to run refnerf - gardenshperes real dataset (llff type), https://dorverbin.github.io/refnerf/, using following config: scenedir: gardenspheres dataset_name: llff downsample_train: 4 downsample_test: 4 ndc_ray: false near_far: [1, 6]

stack_norms: false

aabb_scale: 2

But it is not working,

Tue 21 Nov 2023 04:40:12 PM CET
Warp 0.10.1 initialized:
   CUDA Toolkit: 11.5, Driver: 12.0
   Devices:
     "cpu"    | CPU
     "cuda:0" | NVIDIA A40 (sm_86)
   Kernel cache: /home/pnaikade/.cache/warp/0.10.1
[2023-11-21 16:41:31,520][HYDRA] Launching 1 jobs locally
[2023-11-21 16:41:31,520][HYDRA]    #0 : expname=gardenspheres_test model=microfacet_tensorf2 dataset=toycar vis_every=5000 datadir=/HPS/ColorNeRF/work/ref_nerf_dataset
ic| expname: 'toycar_gardenspheres_test'
ic| self.N_voxel_list: [4283103, 7622116, 12358440, 18736316, 27000000]
ic| self.use_predicted_normals: False
    self.align_pred_norms: True
    self.orient_world_normals: True
2023-11-21 16:41:44.815 | INFO     | __main__:reconstruction:322 - initial ortho_reg_weight
2023-11-21 16:41:44.816 | INFO     | __main__:reconstruction:325 - initial L1_reg_weight
2023-11-21 16:41:44.816 | INFO     | __main__:reconstruction:328 - initial TV_weight density: 0.0 appearance: 0.0
2023-11-21 16:41:45.113 | INFO     | __main__:reconstruction:338 - TensorNeRF(
  (rf): TensorVMSplit(
    (density_rf): TensoRF(
      (app_plane): ParameterList(
          (0): Parameter containing: [torch.float32 of size 1x16x128x128 (cuda:0)]
          (1): Parameter containing: [torch.float32 of size 1x16x128x128 (cuda:0)]
          (2): Parameter containing: [torch.float32 of size 1x16x128x128 (cuda:0)]
      )
      (app_line): ParameterList(
          (0): Parameter containing: [torch.float32 of size 1x16x128x1 (cuda:0)]
          (1): Parameter containing: [torch.float32 of size 1x16x128x1 (cuda:0)]
          (2): Parameter containing: [torch.float32 of size 1x16x128x1 (cuda:0)]
      )
    )
    (app_rf): TensoRF(
      (app_plane): ParameterList(
          (0): Parameter containing: [torch.float32 of size 1x24x128x128 (cuda:0)]
          (1): Parameter containing: [torch.float32 of size 1x24x128x128 (cuda:0)]
          (2): Parameter containing: [torch.float32 of size 1x24x128x128 (cuda:0)]
      )
      (app_line): ParameterList(
          (0): Parameter containing: [torch.float32 of size 1x24x128x1 (cuda:0)]
          (1): Parameter containing: [torch.float32 of size 1x24x128x1 (cuda:0)]
          (2): Parameter containing: [torch.float32 of size 1x24x128x1 (cuda:0)]
      )
    )
    (basis_mat): Linear(in_features=72, out_features=24, bias=False)
    (dbasis_mat): Linear(in_features=48, out_features=1, bias=False)
  )
  (sampler): AlphaGridSampler()
  (model): Microfacet(
    (diffuse_module): RandHydraMLPDiffuse(
      (diffuse_mlp): Sequential(
        (0): Linear(in_features=24, out_features=3, bias=True)
      )
      (tint_mlp): Sequential(
        (0): Linear(in_features=24, out_features=3, bias=True)
      )
      (f0_mlp): Sequential(
        (0): Linear(in_features=24, out_features=3, bias=True)
      )
      (roughness_mlp): Sequential(
        (0): Linear(in_features=24, out_features=2, bias=True)
      )
    )
    (brdf): MLPBRDF(
      (h_encoder): ListISH()
      (d_encoder): ListISH()
      (mlp): Sequential(
        (0): Linear(in_features=66, out_features=64, bias=True)
        (1): ReLU(inplace=True)
        (2): Linear(in_features=64, out_features=64, bias=True)
        (3): ReLU(inplace=True)
        (4): Linear(in_features=64, out_features=4, bias=True)
      )
    )
    (brdf_sampler): GGXSampler()
  )
  (bg_module): IntegralEquirect()
  (tonemap): SRGBTonemap()
)
ic| white_bg: False
ic| self.nSamples: 625, self.stepsize: tensor(0.0157, device='cuda:0')
ic| self.nSamples: 625, self.stepsize: tensor(0.0157, device='cuda:0')
ic| self.diffuse_bias: 2.326634076573745
    mean_brightness: tensor(0.5488, device='cuda:0')
    v: 0.9110595349675754
ic| bg_brightness: tensor(0.5488, device='cuda:0')
    target_val: 0.9110595349675754
    self.bias: 2.2158484777592573
grid size tensor([128, 128, 128])
aabb tensor([-3.0000, -3.3400, -2.0000,  3.0000,  3.3400,  2.0000], device='cuda:0')
sampling step size:  tensor(0.0157)
sampling number:  625

  0%|          | 0/30000 [00:00<?, ?it/s]ic| ori_decay: 1
ic| normal_decay: 1
ic| gt_bg_path: None
/HPS/ColorNeRF/work/opt/anaconda3/envs/nmf/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.
  return _methods._mean(a, axis=axis, dtype=dtype,
/HPS/ColorNeRF/work/opt/anaconda3/envs/nmf/lib/python3.10/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide
  ret = ret.dtype.type(ret / rcount)

1.0e+00:   6%|▋         | 1907/30000 [02:27<35:07, 13.33it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1909/30000 [02:27<35:10, 13.31it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1909/30000 [02:27<35:10, 13.31it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1909/30000 [02:27<35:10, 13.31it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1911/30000 [02:27<35:09, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1911/30000 [02:28<35:09, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1911/30000 [02:28<35:09, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1913/30000 [02:28<35:08, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1913/30000 [02:28<35:08, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1913/30000 [02:28<35:08, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1915/30000 [02:28<35:08, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1915/30000 [02:28<35:08, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1915/30000 [02:28<35:08, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1917/30000 [02:28<35:16, 13.27it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1917/30000 [02:28<35:16, 13.27it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1917/30000 [02:28<35:16, 13.27it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1919/30000 [02:28<35:18, 13.25it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1919/30000 [02:28<35:18, 13.25it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1919/30000 [02:28<35:18, 13.25it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1921/30000 [02:28<35:15, 13.27it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1921/30000 [02:28<35:15, 13.27it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1921/30000 [02:28<35:15, 13.27it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1923/30000 [02:28<35:12, 13.29it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1923/30000 [02:28<35:12, 13.29it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1923/30000 [02:29<35:12, 13.29it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1925/30000 [02:29<35:11, 13.30it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1931/30000 [02:29<35:10, 13.30it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1931/30000 [02:29<35:10, 13.30it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1931/30000 [02:29<35:10, 13.30it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1933/30000 [02:29<35:06, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1933/30000 [02:29<35:06, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1939/30000 [02:30<35:16, 13.26it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1941/30000 [02:30<36:16, 12.89it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1941/30000 [02:30<36:16, 12.89it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1941/30000 [02:30<36:16, 12.89it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1943/30000 [02:30<37:41, 12.41it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1943/30000 [02:30<37:41, 12.41it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1943/30000 [02:30<37:41, 12.41it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1945/30000 [02:30<38:57, 12.00it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1945/30000 [02:30<38:57, 12.00it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1945/30000 [02:30<38:57, 12.00it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1947/30000 [02:30<38:21, 12.19it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1947/30000 [02:30<38:21, 12.19it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1947/30000 [02:30<38:21, 12.19it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1949/30000 [02:30<37:30, 12.47it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1949/30000 [02:30<37:30, 12.47it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   6%|▋         | 1949/30000 [02:31<37:30, 12.47it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1951/30000 [02:31<36:53, 12.67it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1951/30000 [02:31<36:53, 12.67it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1951/30000 [02:31<36:53, 12.67it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1953/30000 [02:31<36:31, 12.80it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1953/30000 [02:31<36:31, 12.80it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1953/30000 [02:31<36:31, 12.80it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1955/30000 [02:31<36:10, 12.92it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1955/30000 [02:31<36:10, 12.92it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1955/30000 [02:31<36:10, 12.92it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1957/30000 [02:31<35:48, 13.05it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1957/30000 [02:31<35:48, 13.05it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1957/30000 [02:31<35:48, 13.05it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1995/30000 [02:34<35:02, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1997/30000 [02:34<35:03, 13.31it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1997/30000 [02:34<35:03, 13.31it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1997/30000 [02:34<35:03, 13.31it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1999/30000 [02:34<35:02, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1999/30000 [02:34<35:02, 13.32it/s]
psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 1999/30000 [02:34<35:02, 13.32it/s]/HPS/ColorNeRF/work/opt/anaconda3/envs/nmf/lib/python3.10/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at ../aten/src/ATen/native/TensorShape.cpp:3526.)
  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]

psnr = nan test_psnr = 0.00 loss = 0.00000 envmap = 0.00000 diffuse = 0.00000 brdf = 0.00000 nrays = [100, 1000] mipbias = 1.0e+00:   7%|▋         | 2000/30000 [02:35<36:17, 12.86it/s]
Error executing job with overrides: ['expname=gardenspheres_test', 'model=microfacet_tensorf2', 'dataset=toycar', 'vis_every=5000', 'datadir=/HPS/ColorNeRF/work/ref_nerf_dataset']
Traceback (most recent call last):
  File "/HPS/ColorNeRF/work/nmf/train.py", line 915, in train
    reconstruction(cfg)
  File "/HPS/ColorNeRF/work/nmf/train.py", line 805, in reconstruction
    if tensorf.check_schedule(iteration, 1):
  File "/HPS/ColorNeRF/work/nmf/modules/tensor_nerf.py", line 180, in check_schedule
    require_reassignment |= self.sampler.check_schedule(iter, batch_mul, self.rf)
  File "/HPS/ColorNeRF/work/nmf/samplers/alphagrid.py", line 93, in check_schedule
    self.update(rf)
  File "/HPS/ColorNeRF/work/opt/anaconda3/envs/nmf/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/HPS/ColorNeRF/work/nmf/samplers/alphagrid.py", line 105, in update
    new_aabb = self.updateAlphaMask(rf, rf.grid_size)
  File "/HPS/ColorNeRF/work/opt/anaconda3/envs/nmf/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/HPS/ColorNeRF/work/nmf/samplers/alphagrid.py", line 267, in updateAlphaMask
    xyz_min = valid_xyz.amin(0)
IndexError: amin(): Expected reduction dim 0 to have non-zero size.

Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Tue 21 Nov 2023 04:44:44 PM CET
half-potato commented 1 year ago

I would tune the starting density to ensure that the rays hit something within the scene.