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

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     	|

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