Open TosakaUCW opened 1 month ago
@kcheng1021 I am stuck at this exact point too. Can you please help us?
I think I found a fix. Somehow, train.py wasn't able to create cost folder inside the repository for some reason.
I think I found a fix. Somehow, train.py wasn't able to create cost folder inside the repository for some reason.
Can you share how to fix it? Thanks a lot
Could you please share how to fix it? Thanks u so much!
I think I found a fix. Somehow, train.py wasn't able to create cost folder inside the repository for some reason.
You can add somewhere in train.py file (I put it inside training function), so that the 'cost' folder is created inside the repository folder. I hope it works for you. Also, check the folder permission or run you code in "sudo" mode
output_dir = "cost/" os.makedirs(output_dir,exist_ok=True)
Sorry for the late reply. cost folder is created for debug. You could just remove the codes related to it.
When I run the train script:
(gaussianpro) jovyan@9694f458d1ae:~/work/tosaka/GaussianPro$ python train.py -s ~/work/tosaka/GaussianPro/datasets/segment-102751/ -m ~/work/tosaka/GaussianPro/result/waymo_1 --eval --flatten_loss --position_lr_init 0.000016 --scaling_lr 0.001 --percent_dense 0.0005 --port 1021 --dataset waymo --sky_seg --normal_loss --depth_loss --propagation_interval 30 --depth_error_min_threshold 0.8 --depth_error_max_threshold 1.0 --propagated_iteration_begin 1000 --propagated_iteration_after 12000 --patch_size 20 --lambda_l1_normal 0.001 --lambda_cos_normal 0.001
I have got the error:
Optimizing /home/jovyan/work/tosaka/GaussianPro/result/waymo_1 Output folder: /home/jovyan/work/tosaka/GaussianPro/result/waymo_1 [12/07 17:35:02] Tensorboard not available: not logging progress [12/07 17:35:02] Reading camera 198/198 [12/07 17:35:03] Loading Training Cameras [12/07 17:35:03] [ INFO ] Encountered quite large input images (>1.6K pixels width), rescaling to 1.6K. If this is not desired, please explicitly specify '--resolution/-r' as 1 [12/07 17:35:03] Loading Test Cameras [12/07 17:35:34] Number of points at initialisation : 72378 [12/07 17:35:38] Training progress: 0%| | 0/30000 [00:00<?, ?it/s] [ITER 1] Evaluating test: L1 0.2074004739522934 PSNR 11.672923622131348 [12/07 17:35:38] [ITER 1] Evaluating train: L1 0.23484502732753754 PSNR 10.758105468750001 [12/07 17:35:38] [ITER 1] Saving Gaussians [12/07 17:35:38] Training progress: 3%|▍ | 1010/30000 [00:49<21:47, 22.18it/s, Loss=0.3907213]Traceback (most recent call last): File "train.py", line 387, in <module> training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from) File "train.py", line 132, in training torchvision.utils.save_image(viewpoint_cam.original_image, "cost/"+viewpoint_cam.image_name+"_"+str(iteration)+"gt.png") File "/opt/conda/envs/gaussianpro/lib/python3.7/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/opt/conda/envs/gaussianpro/lib/python3.7/site-packages/torchvision/utils.py", line 160, in save_image im.save(fp, format=format) File "/opt/conda/envs/gaussianpro/lib/python3.7/site-packages/PIL/Image.py", line 2428, in save fp = builtins.open(filename, "w+b") FileNotFoundError: [Errno 2] No such file or directory: 'cost/0070_cam0_1020gt.png' Training progress: 3%|▍ | 1010/30000 [00:50<24:03, 20.08it/s, Loss=0.3907213]
Has the problem been solved?
When I run the train script:
(gaussianpro) jovyan@9694f458d1ae:~/work/tosaka/GaussianPro$ python train.py -s ~/work/tosaka/GaussianPro/datasets/segment-102751/ -m ~/work/tosaka/GaussianPro/result/waymo_1 --eval --flatten_loss --position_lr_init 0.000016 --scaling_lr 0.001 --percent_dense 0.0005 --port 1021 --dataset waymo --sky_seg --normal_loss --depth_loss --propagation_interval 30 --depth_error_min_threshold 0.8 --depth_error_max_threshold 1.0 --propagated_iteration_begin 1000 --propagated_iteration_after 12000 --patch_size 20 --lambda_l1_normal 0.001 --lambda_cos_normal 0.001
I have got the error: