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scratch
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# generate chunks
CUDA_VISIBLE_DEVICES=1 python -m torch.distributed.launch --use_env --master_port=12345 --nproc_per_node=1 -m switch_nerf.train --config=switch_nerf/configs/switch_nerf/building.yaml --use_moe --exp_name=building/absolute/experiment/path --dataset_path=/data/zcq/switchnerf/building-pixsfm --chunk_paths=building/absolute/chunk/path/building_chunk_factor_1_bg --generate_chunk
# train
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch \
--use_env --master_port=12345 --nproc_per_node=8 -m \
switch_nerf.train \
--config=switch_nerf/configs/switch_nerf/moe.yaml \
--use_moe \
--exp_name=building/absolute/experiment/path \
--dataset_path=/data/zcq/switchnerf/building-pixsfm \
--chunk_paths=building/absolute/chunk/path/building_chunk_factor_1_bg \
--use_balance_loss \
--i_print=1000 \
--batch_size=8192 \
--moe_expert_type=expertmlp \
--moe_train_batch \
--moe_test_batch \
--model_chunk_size=131072 \
--moe_capacity_factor=1.0 \
--batch_prioritized_routing \
--moe_l_aux_wt=0.0005 \
--amp_use_bfloat16 \
--use_moe_external_gate \
--use_gate_input_norm \
--use_sigma_noise \
--sigma_noise_std=1.0
# train for moe
CUDA_VISIBLE_DEVICES=1,2,3,5,6,7 python -m torch.distributed.launch \
--use_env --master_port=12345 --nproc_per_node=6 -m \
switch_nerf.train \
--config=switch_nerf/configs/switch_nerf/moe.yaml \
--use_moe \
--exp_name=moe/ \
--use_balance_loss \
--i_print=1000 \
--batch_size=1200000 \
--moe_expert_type=expertmlp \
--moe_train_batch \
--moe_test_batch \
--moe_capacity_factor=1.0 \
--batch_prioritized_routing \
--moe_l_aux_wt=0.0005 \
--amp_use_bfloat16 \
--use_moe_external_gate \
--use_gate_input_norm \
--use_sigma_noise \
--sigma_noise_std=1.0
# test for moe
# 还是跑train的代码，moe_expert_type=seqexperts
CUDA_VISIBLE_DEVICES=1,2,3,5,6,7 python -m torch.distributed.launch \
--use_env --master_port=12345 --nproc_per_node=6 -m \
switch_nerf.eval_image \
--config=switch_nerf/configs/switch_nerf/moe.yaml \
--use_moe --exp_name=moe \
--dataset_path=/data/zcq/switchnerf/building-pixsfm \
--i_print=1000 \
--moe_expert_type=seqexperts \
--model_chunk_size=131072 \
--ckpt_path=Switch-NeRF-ckpt/building.pt \
--expertmlp2seqexperts \
--use_moe_external_gate \
--use_gate_input_norm
#checkpoint
CUDA_VISIBLE_DEVICES=4,5,6,7 python -m torch.distributed.launch \
--use_env --master_port=12345 --nproc_per_node=4 -m \
switch_nerf.eval_image \
--config=switch_nerf/configs/switch_nerf/building.yaml \
--use_moe --exp_name=building/absolute/experiment/path \
--dataset_path=/data/zcq/switchnerf/building-pixsfm \
--i_print=1000 \
--moe_expert_type=seqexperts \
--model_chunk_size=131072 \
--ckpt_path=Switch-NeRF-ckpt/building.pt \
--expertmlp2seqexperts \
--use_moe_external_gate \
--use_gate_input_norm
#visualization,生成点云
CUDA_VISIBLE_DEVICES=4,5,6,7 python -m torch.distributed.launch \
--use_env --master_port=12345 --nproc_per_node=4 -m \
switch_nerf.eval_points \
--config=switch_nerf/configs/switch_nerf/building.yaml \
--use_moe --exp_name=building/absolute/experiment/path \
--dataset_path=/data/zcq/switchnerf/building-pixsfm \
--i_print=1000 \
--moe_expert_type=seqexperts \
--model_chunk_size=131072 \
--ckpt_path=Switch-NeRF-ckpt/building.pt \
--expertmlp2seqexperts \
--use_moe_external_gate \
--use_gate_input_norm \
--moe_return_gates \
--return_pts \
--return_pts_rgb \
--return_pts_alpha \
--render_test_points_sample_skip=4 \
--val_scale_factor=8 \
--render_test_points_image_num=20
# Merge point clouds from differents
python -m switch_nerf.scripts.merge_points \
--data_path=/data/Switch-NeRF/building/absolute/experiment/path/0/eval_points \
--merge_all \
--image_num=20 \
--model_type=switch \
-r=0.2