encounter error, something about image loading.

[TxT1212]

here is my error message, I have no clue what's wrong.

python train.py -s /home/ezxr/data/mapper_sanxdxgcjcs_002617 -c /home/ezxr/data/mapper_sanxdxgcjcs_002617/gaussain_model/ -r density Will export a UV-textured mesh as an .obj file. Changing sh_levels to match the loaded model: 4 -----Parsed parameters----- Source path: /home/ezxr/data/mapper_sanxdxgcjcs_002617

Content: 4 Gaussian Splatting checkpoint path: /home/ezxr/data/mapper_sanxdxgcjcs_002617/gaussain_model/ Content: 7 SUGAR checkpoint path: ./output/coarse/mapper_sanxdxgcjcs_002617/sugarcoarse_3Dgs7000_densityestim02_sdfnorm02/ Iteration to load: 7000 Output directory: ./output/coarse/mapper_sanxdxgcjcs_002617 SDF estimation factor: 0.2 SDF better normal factor: 0.2 Eval split: True

Using device: 0	===========================================================================		PyTorch CUDA memory summary, device ID 0
CUDA OOMs: 0	cudaMalloc retries: 0
===========================================================================
Metric	Cur Usage	Peak Usage	Tot Alloc	Tot Freed
---------------------------------------------------------------------------
Allocated memory	0 B	0 B	0 B	0 B
from large pool	0 B	0 B	0 B	0 B
from small pool	0 B	0 B	0 B	0 B
---------------------------------------------------------------------------
Active memory	0 B	0 B	0 B	0 B
from large pool	0 B	0 B	0 B	0 B
from small pool	0 B	0 B	0 B	0 B
---------------------------------------------------------------------------
GPU reserved memory	0 B	0 B	0 B	0 B
from large pool	0 B	0 B	0 B	0 B
from small pool	0 B	0 B	0 B	0 B
---------------------------------------------------------------------------
Non-releasable memory	0 B	0 B	0 B	0 B
from large pool	0 B	0 B	0 B	0 B
from small pool	0 B	0 B	0 B	0 B
---------------------------------------------------------------------------
Allocations	0	0	0	0
from large pool	0	0	0	0
from small pool	0	0	0	0
---------------------------------------------------------------------------
Active allocs	0	0	0	0
from large pool	0	0	0	0
from small pool	0	0	0	0
---------------------------------------------------------------------------
GPU reserved segments	0	0	0	0
from large pool	0	0	0	0
from small pool	0	0	0	0
---------------------------------------------------------------------------
Non-releasable allocs	0	0	0	0
from large pool	0	0	0	0
from small pool	0	0	0	0
---------------------------------------------------------------------------
Oversize allocations	0	0	0	0
---------------------------------------------------------------------------
Oversize GPU segments	0	0	0	0
===========================================================================

Loading config /home/ezxr/data/mapper_sanxdxgcjcs_002617/gaussain_model/... Performing train/eval split... Loading camera 21907/1695894918480.jpeg Loading camera 21907/1695894919015.jpeg Loading camera 21907/1695894919681.jpeg Loading camera 21907/1695894920349.jpeg Loading camera 21907/1695894921016.jpeg Loading camera 21907/1695894922349.jpeg Loading camera 21907/1695894923682.jpeg Loading camera 21907/1695894924349.jpeg Loading camera 21907/1695894925016.jpeg Loading camera 21907/1695894925682.jpeg Loading camera 21907/1695894926349.jpeg Loading camera 21907/1695894927016.jpeg Loading camera 21907/1695894927684.jpeg Loading camera 21907/1695894928350.jpeg Loading camera 21907/1695894929016.jpeg Loading camera 21907/1695894929684.jpeg Loading camera 21907/1695894930349.jpeg Loading camera 21907/1695894931016.jpeg Loading camera 21907/1695894931684.jpeg Loading camera 21907/1695894932350.jpeg Loading camera 21907/1695894933016.jpeg Loading camera 21907/1695894933684.jpeg Loading camera 21907/1695894934350.jpeg Loading camera 21907/1695894935017.jpeg Loading camera 21907/1695894935684.jpeg Loading camera 21907/1695894937016.jpeg Loading camera 21907/1695894937684.jpeg Loading camera 21907/1695894938350.jpeg Loading camera 21907/1695894939016.jpeg Loading camera 21907/1695894939684.jpeg Loading camera 21907/1695894940350.jpeg Loading camera 21907/1695894941017.jpeg Loading camera 21907/1695894941687.jpeg Loading camera 21907/1695894942351.jpeg Loading camera 21907/1695894943017.jpeg Loading camera 21907/1695894943684.jpeg Loading camera 21907/1695894944351.jpeg Loading camera 21907/1695894945018.jpeg Loading camera 21907/1695894945686.jpeg Loading camera 21907/1695894946351.jpeg Loading camera 21907/1695894947018.jpeg Loading camera 21907/1695894947686.jpeg Loading camera 21907/1695894948351.jpeg Loading camera 21907/1695894949018.jpeg Loading camera 21907/1695894949686.jpeg Loading camera 21907/1695894950350.jpeg Loading camera 21907/1695894951018.jpeg Loading camera 21907/1695894951686.jpeg Loading camera 21907/1695894952351.jpeg Loading camera 21907/1695894953019.jpeg Loading camera 21907/1695894953686.jpeg Loading camera 21907/1695894954351.jpeg Loading camera 21907/1695894955018.jpeg Loading camera 21907/1695894955685.jpeg Loading camera 21907/1695894956352.jpeg Loading camera 21907/1695894957019.jpeg Loading camera 21907/1695894957687.jpeg Loading camera 21907/1695894958352.jpeg Loading camera 21907/1695894959019.jpeg Loading camera 21907/1695894959687.jpeg Loading camera 21907/1695894960352.jpeg Loading camera 21907/1695894961019.jpeg Loading camera 21907/1695894961687.jpeg Loading camera 21907/1695894962352.jpeg Loading camera 21907/1695894963019.jpeg Loading camera 21907/1695894963714.jpeg Loading camera 21907/1695894964352.jpeg Loading camera 21907/1695894965019.jpeg Loading camera 21907/1695894965687.jpeg Loading camera 21907/1695894966352.jpeg Loading camera 21907/1695894967019.jpeg Loading camera 21907/1695894967687.jpeg Loading camera 21907/1695894968352.jpeg Loading camera 21907/1695894969019.jpeg Loading camera 21907/1695894969687.jpeg Loading camera 21907/1695894970353.jpeg Loading camera 21907/1695894971020.jpeg Loading camera 21907/1695894971687.jpeg Loading camera 21907/1695894972353.jpeg Loading camera 21907/1695894973020.jpeg Loading camera 21907/1695894973687.jpeg Loading camera 21907/1695894974352.jpeg Loading camera 21907/1695894975020.jpeg Loading camera 21907/1695894975687.jpeg Loading camera 21907/1695894976353.jpeg Loading camera 21907/1695894977022.jpeg Loading camera 21907/1695894977687.jpeg Loading camera 21907/1695894978353.jpeg Loading camera 21907/1695894979020.jpeg Loading camera 21907/1695894979687.jpeg Loading camera 21907/1695894980354.jpeg Loading camera 21907/1695894981020.jpeg Loading camera 21907/1695894981690.jpeg Loading camera 21907/1695894982353.jpeg Loading camera 21907/1695894983020.jpeg Loading camera 21907/1695894983688.jpeg Loading camera 21907/1695894984354.jpeg Loading camera 21907/1695894985020.jpeg Loading camera 21907/1695894985689.jpeg Loading camera 21907/1695894986357.jpeg Loading camera 21907/1695894987020.jpeg Loading camera 21907/1695894987689.jpeg Loading camera 21907/1695894988353.jpeg Loading camera 21907/1695894989020.jpeg Loading camera 21907/1695894989687.jpeg Loading camera 21907/1695894990353.jpeg Loading camera 21907/1695894991021.jpeg 93 training images detected. The model has been trained for 7000 steps.

Camera resolution scaled to 360 x 640 Initializing model from trained 3DGS... Point cloud generated. Number of points: 252966 Use min to initialize scales. Initialized radiuses for 3D Gauss Rasterizer Initializing 3D gaussians from 3D gaussians...

SuGaR model has been initialized. SuGaR() Number of parameters: 14924994 Checkpoints will be saved in ./output/coarse/mapper_sanxdxgcjcs_002617/sugarcoarse_3Dgs7000_densityestim02_sdfnorm02/

Model parameters: _points torch.Size([252966, 3]) True all_densities torch.Size([252966, 1]) True _scales torch.Size([252966, 3]) True _quaternions torch.Size([252966, 4]) True _sh_coordinates_dc torch.Size([252966, 1, 3]) True _sh_coordinates_rest torch.Size([252966, 15, 3]) True Using camera spatial extent as spatial_lr_scale: 7.771456003189088 Optimizer initialized. Optimization parameters: OptimizationParams( iterations=15000, position_lr_init=0.00016, position_lr_final=1.6e-06, position_lr_delay_mult=0.01, position_lr_max_steps=30000, feature_lr=0.0025, opacity_lr=0.05, scaling_lr=0.005, rotation_lr=0.001, ) Optimizable parameters: points 0.001243432960510254 sh_coordinates_dc 0.0025 sh_coordinates_rest 0.000125 all_densities 0.05 scales 0.005 quaternions 0.001 Densifier initialized. Using loss function: l1+dssim Starting regularization...

---

Iteration: 7000 loss: 0.030430 [ 7000/15000] computed in 0.0077037890752156574 minutes. ------Stats----- ---Min, Max, Mean, Std Points: -36.427547454833984 25.382139205932617 0.1579069197177887 4.527556419372559 Scaling factors: 1.8439502014189202e-07 0.9512976408004761 0.032854754477739334 0.05896857753396034 Quaternions: -0.9968045353889465 0.9999953508377075 0.22015613317489624 0.4489225745201111 Sh coordinates dc: -2.2877933979034424 4.295931339263916 -0.06274425983428955 1.2825195789337158 Sh coordinates rest: -0.7043451070785522 0.6325781345367432 0.0003956133150495589 0.049302585422992706 Opacities: 0.0008357342449016869 1.0 0.23739202320575714 0.2816372513771057

---INFO--- Starting entropy regularization.

---INFO--- Resetting neighbors... Traceback (most recent call last): File "/home/mm/ARWorkspace/SuGaR/train.py", line 120, in coarse_sugar_path = coarse_training_with_density_regularization(coarse_args) File "/home/mm/ARWorkspace/SuGaR/sugar_trainers/coarse_density.py", line 526, in coarse_training_with_density_regularization gt_rgb = gt_image.view(-1, sugar.image_height, sugar.image_width, 3) RuntimeError: shape '[-1, 360, 640, 3]' is invalid for input of size 689280

[yuedajiong]

your input image has 689280 float numbers, -1 means any integer, 689280 / 360 / 640 / 3 != integer.

so please check the image integrity firstly, maybe has incomplete image(s).

of course, maybe other cause.

[Tao-11-chen]

Or maybe you have images with different resolutions in your dataset which is not supported yet.

[Dargonxzy]

How to solve it?I also faced similar problem on dtu dataset

[yuedajiong]

1. debug and fix. (if you can)
2. keep same environment as official，just in 20 minutes: CUDA, Python,Pytorch,... (recommended: life is limited, do not waste time on non-core technology.)

[TxT1212]

How to solve it?I also faced similar problem on dtu dataset

My dataset has slightly different image size because I use colmap to undistort images. Some of images are 640x360 and others are 640x359

[yuedajiong]

resize pls. (PIL.Image, CV2, or pytorch torchvision.transorms.Resize)