NeRF-U with occluder
Used for nerfw branch.
Train command:
python train.py \
--dataset_name blender --img_wh 200 200 \
--root_dir /home/ubuntu/data/nerf_example_data/nerf_synthetic/lego \
--N_importance 64 --N_samples 64 --noise_std 0 \
--num_epochs 20 --batch_size 1024 \
--optimizer adam --lr 5e-4 --lr_scheduler cosine \
--exp_name lego_nerfu_occ --beta_min 0.1 --data_perturb occ --encode_t
Eval command:
python eval.py \
--root_dir /home/ubuntu/data/nerf_example_data/nerf_synthetic/lego \
--dataset_name blender --img_wh 200 200 --split test \
--N_importance 64 \
--ckpt_path ckpts/lego_nerfw_occ/epoch\=19.ckpt \
--encode_t --beta_min 0.1 \
--scene_name nerfu_occ
Eval output: Mean PSNR : 28.60
Note I use a very small image size (200x200) to speed up my experiments.
Profiler Report
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
-----------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 1.0901e+04 | 100 % |
-----------------------------------------------------------------------------------------------------------------------------
run_training_epoch | 544.74 |20 | 1.0895e+04 | 99.947 |
run_training_batch | 0.13381 |78140 | 1.0456e+04 | 95.921 |
optimizer_step_and_closure_0 | 0.11587 |78140 | 9053.8 | 83.057 |
training_step_and_backward | 0.11173 |78140 | 8730.4 | 80.09 |
model_backward | 0.088715 |78140 | 6932.2 | 63.595 |
model_forward | 0.02281 |78140 | 1782.4 | 16.351 |
evaluation_step_and_end | 1.7842 |161 | 287.25 | 2.6352 |
on_train_batch_end | 0.00042159 |78140 | 32.943 | 0.30221 |
get_train_batch | 0.00025085 |78140 | 19.602 | 0.17982 |
cache_result | 1.4803e-05 |391370 | 5.7934 | 0.053147 |
on_batch_start | 1.0333e-05 |78140 | 0.80743 | 0.0074072 |
on_after_backward | 9.5508e-06 |78140 | 0.7463 | 0.0068464 |
on_batch_end | 9.4638e-06 |78140 | 0.7395 | 0.006784 |
on_before_zero_grad | 8.3572e-06 |78140 | 0.65303 | 0.0059908 |
on_validation_end | 0.025442 |21 | 0.53429 | 0.0049014 |
training_step_end | 6.2163e-06 |78140 | 0.48574 | 0.0044561 |
on_train_batch_start | 5.99e-06 |78140 | 0.46806 | 0.0042939 |
on_validation_batch_end | 0.00042104 |161 | 0.067788 | 0.00062187 |
on_epoch_start | 0.00079988 |20 | 0.015998 | 0.00014676 |
on_validation_start | 0.00024023 |21 | 0.0050449 | 4.6281e-05 |
on_validation_batch_start | 1.391e-05 |161 | 0.0022395 | 2.0544e-05 |
validation_step_end | 8.4167e-06 |161 | 0.0013551 | 1.2431e-05 |
on_train_end | 0.0003507 |1 | 0.0003507 | 3.2172e-06 |
on_epoch_end | 1.611e-05 |20 | 0.0003222 | 2.9558e-06 |
on_train_epoch_start | 1.5704e-05 |20 | 0.00031408 | 2.8813e-06 |
on_validation_epoch_end | 1.4037e-05 |21 | 0.00029477 | 2.7041e-06 |
on_validation_epoch_start | 8.7303e-06 |21 | 0.00018334 | 1.6819e-06 |
on_train_start | 0.00016846 |1 | 0.00016846 | 1.5454e-06 |
on_train_epoch_end | 7.923e-06 |20 | 0.00015846 | 1.4537e-06 |
on_fit_start | 1.3867e-05 |1 | 1.3867e-05 | 1.2721e-07 |