UniqAI/gaussian-splatting
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add best last results..

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This commit is contained in:
Ammar Qammaz 2023-11-03 09:07:24 +02:00
parent 1ceaf718bb
commit af89ad77ce
2 changed files with 8 additions and 1 deletions
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2
docker/run.sh
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@ -21,7 +21,7 @@ fi
python3.10 convert.py -s $1-data/ --camera SIMPLE_RADIAL --no_gpu #GPU produces worse results (?) python3.10 convert.py -s $1-data/ --camera SIMPLE_RADIAL --no_gpu #GPU produces worse results (?)
python3.10 train.py -s $1-data/ -r 1 --model_path=$1-data/output/ --position_lr_init 0.000016 --position_lr_final 0.000001 --scaling_lr 0.001 --iterations 100000 #Test more training budget python3.10 train.py -s $1-data/ -r 1 --model_path=$1-data/output/ --position_lr_init 0.000016 --scaling_lr 0.001 --iterations 35000 #Test more training budget
python3.10 3dgsconverter/3dgsconverter.py -i $1-data/output/point_cloud/iteration_30000/point_cloud.ply -o $1-data/output/point_cloud/iteration_30000/output_cc.ply -f cc --rgb --density_filter --remove_flyers python3.10 3dgsconverter/3dgsconverter.py -i $1-data/output/point_cloud/iteration_30000/point_cloud.ply -o $1-data/output/point_cloud/iteration_30000/output_cc.ply -f cc --rgb --density_filter --remove_flyers
python3.10 3dgsconverter/3dgsconverter.py -i $1-data/output/output/point_cloud/iteration_30000/output_cc.ply -o $1-data/output/output/point_cloud/iteration_30000/point_cloud_clean.ply -f 3dgs python3.10 3dgsconverter/3dgsconverter.py -i $1-data/output/output/point_cloud/iteration_30000/output_cc.ply -o $1-data/output/output/point_cloud/iteration_30000/point_cloud_clean.ply -f 3dgs

7
train.py
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@ -29,6 +29,9 @@ except ImportError:
TENSORBOARD_FOUND = False TENSORBOARD_FOUND = False
def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from): def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
bestLossEncountered = 1000000
pickBetweenFinalNLosses = 10
first_iter = 0 first_iter = 0
tb_writer = prepare_output_and_logger(dataset) tb_writer = prepare_output_and_logger(dataset)
gaussians = GaussianModel(dataset.sh_degree) gaussians = GaussianModel(dataset.sh_degree)
@ -103,6 +106,10 @@ def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoi
if iteration == opt.iterations: if iteration == opt.iterations:
progress_bar.close() progress_bar.close()
if (bestLossEncountered>ema_loss_for_log) and (pickBetweenFinalNLosses+iteration > opt.iterations):
print("\n[ITER {}] Also remembering this iteration..".format(iteration))
saving_iterations.append(iteration)
# Log and save # Log and save
training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background)) training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background))
if (iteration in saving_iterations): if (iteration in saving_iterations):