NeRF-W with color perturbation and occluder
Used for nerfw branch.
Train command:
python train.py \
--root_dir /home/ubuntu/data/nerf_example_data/nerf_synthetic/lego \
--dataset_name blender --img_wh 200 200 --data_perturb color occ \
--N_importance 64 --N_samples 64 --noise_std 0 --encode_a --encode_t --beta_min 0.1 \
--num_epochs 20 --batch_size 1024 \
--optimizer adam --lr 5e-4 --lr_scheduler cosine \
--exp_name lego_nerfw_all
Eval command:
python eval.py \
--root_dir /home/ubuntu/data/nerf_example_data/nerf_synthetic/lego \
--dataset_name blender --split test --img_wh 200 200 \
--N_importance 64 --encode_a --encode_t --beta_min 0.1 \
--ckpt_path ckpts/lego_nerfw_all/epoch\=19.ckpt \
--scene_name nerfw_all
Eval output: Mean PSNR : 24.86
Profiler Report
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
-----------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 1.1659e+04 | 100 % |
-----------------------------------------------------------------------------------------------------------------------------
run_training_epoch | 582.57 |20 | 1.1651e+04 | 99.931 |
run_training_batch | 0.14307 |78140 | 1.1179e+04 | 95.882 |
optimizer_step_and_closure_0 | 0.12437 |78140 | 9718.4 | 83.352 |
training_step_and_backward | 0.12006 |78140 | 9381.8 | 80.465 |
model_backward | 0.095661 |78140 | 7475.0 | 64.111 |
model_forward | 0.024116 |78140 | 1884.5 | 16.162 |
evaluation_step_and_end | 1.8998 |161 | 305.86 | 2.6233 |
on_train_batch_end | 0.00053565 |78140 | 41.856 | 0.35898 |
get_train_batch | 0.00026832 |78140 | 20.966 | 0.17982 |
cache_result | 1.6708e-05 |391370 | 6.5391 | 0.056084 |
on_after_backward | 1.3945e-05 |78140 | 1.0897 | 0.0093458 |
on_batch_start | 1.1257e-05 |78140 | 0.87959 | 0.007544 |
on_batch_end | 1.0574e-05 |78140 | 0.82626 | 0.0070866 |
on_before_zero_grad | 9.9755e-06 |78140 | 0.77948 | 0.0066854 |
training_step_end | 7.3524e-06 |78140 | 0.57452 | 0.0049275 |
on_train_batch_start | 7.0481e-06 |78140 | 0.55074 | 0.0047235 |
on_validation_end | 0.025579 |21 | 0.53715 | 0.004607 |
on_validation_batch_end | 0.00039767 |161 | 0.064025 | 0.00054912 |
on_epoch_start | 0.00074399 |20 | 0.01488 | 0.00012762 |
on_validation_start | 0.00024646 |21 | 0.0051757 | 4.439e-05 |
on_train_end | 0.0033677 |1 | 0.0033677 | 2.8884e-05 |
on_validation_batch_start | 1.301e-05 |161 | 0.0020947 | 1.7965e-05 |
validation_step_end | 9.2702e-06 |161 | 0.0014925 | 1.2801e-05 |
on_epoch_end | 1.6658e-05 |20 | 0.00033316 | 2.8575e-06 |
on_validation_epoch_end | 1.4696e-05 |21 | 0.00030862 | 2.6469e-06 |
on_train_start | 0.00020975 |1 | 0.00020975 | 1.799e-06 |
on_validation_epoch_start | 9.7831e-06 |21 | 0.00020545 | 1.7621e-06 |
on_train_epoch_start | 9.096e-06 |20 | 0.00018192 | 1.5603e-06 |
on_train_epoch_end | 8.8208e-06 |20 | 0.00017642 | 1.5131e-06 |
on_fit_start | 1.3749e-05 |1 | 1.3749e-05 | 1.1792e-07 |