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