Used for nerfw branch.
Train command (trained on 8 time downscaled images, just for proof of implementation):
python prepare_phototourism.py --root_dir /home/ubuntu/data/IMC-PT/brandenburg_gate/ --img_downscale 8
python train.py \
--root_dir /home/ubuntu/data/IMC-PT/brandenburg_gate/ --dataset_name phototourism \
--img_downscale 8 --use_cache \
--N_importance 64 --N_samples 64 --encode_a --encode_t --beta_min 0.03 --N_vocab 1500 --N_emb_xyz 15 \
--num_epochs 20 --batch_size 1024 \
--optimizer adam --lr 5e-4 --lr_scheduler cosine \
--exp_name brandenburg_scale8_nerfw
Profiler Report
Action | Mean duration (s) |Num calls | Total time (s) | Percentage % |
-----------------------------------------------------------------------------------------------------------------------------
Total | - |_ | 2.5398e+04 | 100 % |
-----------------------------------------------------------------------------------------------------------------------------
run_training_epoch | 1269.8 |20 | 2.5396e+04 | 99.991 |
run_training_batch | 0.14633 |170760 | 2.4988e+04 | 98.384 |
optimizer_step_and_closure_0 | 0.12823 |170760 | 2.1896e+04 | 86.212 |
training_step_and_backward | 0.1241 |170760 | 2.1192e+04 | 83.438 |
model_backward | 0.099837 |170760 | 1.7048e+04 | 67.124 |
model_forward | 0.024055 |170760 | 4107.6 | 16.173 |
on_train_batch_end | 0.00052083 |170760 | 88.938 | 0.35018 |
get_train_batch | 0.00023393 |170760 | 39.946 | 0.15728 |
evaluation_step_and_end | 0.52576 |21 | 11.041 | 0.043472 |
cache_result | 1.2894e-05 |854050 | 11.012 | 0.043357 |
on_after_backward | 1.0743e-05 |170760 | 1.8345 | 0.007223 |
on_batch_start | 1.0535e-05 |170760 | 1.799 | 0.0070832 |
on_batch_end | 9.6894e-06 |170760 | 1.6546 | 0.0065145 |
on_before_zero_grad | 8.5198e-06 |170760 | 1.4548 | 0.0057282 |
training_step_end | 6.6891e-06 |170760 | 1.1422 | 0.0044974 |
on_train_batch_start | 5.9285e-06 |170760 | 1.0124 | 0.003986 |
on_validation_end | 0.027978 |21 | 0.58754 | 0.0023133 |
on_validation_batch_end | 0.00055518 |21 | 0.011659 | 4.5904e-05 |
on_epoch_start | 0.00054319 |20 | 0.010864 | 4.2774e-05 |
on_validation_start | 0.00024484 |21 | 0.0051417 | 2.0244e-05 |
on_validation_batch_start | 5.3095e-05 |21 | 0.001115 | 4.3901e-06 |
validation_step_end | 2.1799e-05 |21 | 0.00045779 | 1.8024e-06 |
on_train_epoch_start | 1.7319e-05 |20 | 0.00034637 | 1.3638e-06 |
on_epoch_end | 1.5776e-05 |20 | 0.00031551 | 1.2423e-06 |
on_train_end | 0.0002874 |1 | 0.0002874 | 1.1316e-06 |
on_validation_epoch_end | 1.1708e-05 |21 | 0.00024586 | 9.6803e-07 |
on_validation_epoch_start | 8.0324e-06 |21 | 0.00016868 | 6.6415e-07 |
on_train_start | 0.00015864 |1 | 0.00015864 | 6.2463e-07 |
on_train_epoch_end | 7.2367e-06 |20 | 0.00014473 | 5.6986e-07 |
on_fit_start | 1.4059e-05 |1 | 1.4059e-05 | 5.5355e-08 |
Eval command (used for scale2_epoch29 model):
python eval.py \
--root_dir /home/ubuntu/data/IMC-PT/brandenburg_gate/ \
--dataset_name phototourism --scene_name brandenburg_test \
--split test --N_samples 256 --N_importance 256 \
--N_vocab 1500 --encode_a --encode_t \
--ckpt_path ckpts/brandenburg/scale2/epoch\=29.ckpt \
--chunk 16384 --img_wh 320 240
You can change the test camera path in eval.py.