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run_batch.py
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import os
import threading, queue
import numpy as np
import time
if __name__ == '__main__':
################ per scene NeRF ################
commands = {
'-grid': '', \
# '-DVGO-like': 'model.basis_type=none model.coeff_reso=80', \
# '-noC': 'model.coeff_type=none', \
# '-SL':'model.basis_dims=[18] model.basis_resos=[70] model.freq_bands=[8.]', \
# '-CP': f'model.coeff_type=vec model.basis_type=cp model.freq_bands=[1.,1.,1.,1.,1.,1.] model.basis_resos=[512,512,512,512,512,512] model.basis_dims=[32,32,32,32,32,32]', \
# '-iNGP-like': 'model.basis_type=hash model.coeff_type=none', \
# '-hash': f'model.basis_type=hash model.coef_init=1.0', \
# '-sinc': f'model.basis_mapping=sinc', \
# '-tria': f'model.basis_mapping=triangle', \
# '-vm': f'model.coeff_type=vm model.basis_type=vm', \
# '-mlpB': 'model.basis_type=mlp', \
# '-mlpC': 'model.coeff_type=mlp', \
# '-occNet': f'model.basis_type=x model.coeff_type=none model.basis_mapping=x model.num_layers=8 model.hidden_dim=256 ', \
# '-nerf': f'model.basis_type=x model.coeff_type=none model.basis_mapping=trigonometric ' \
# f'model.num_layers=8 model.hidden_dim=256 ' \
# f'model.freq_bands=[1.,2.,4.,8.,16.,32.,64,128,256.,512.] model.basis_dims=[1,1,1,1,1,1,1,1,1,1] model.basis_resos=[1024,512,256,128,64,32,16,8,4,2]', \
# '-iNGP-like-sl': 'model.basis_type=hash model.coeff_type=none model.basis_dims=[16] model.freq_bands=[8.] model.basis_resos=[64] ', \
# '-hash-sl': f'model.basis_type=hash model.coef_init=1.0 model.basis_dims=[16] model.freq_bands=[8.] model.basis_resos=[64] ', \
# '-DCT':'model.basis_type=fix-grid', \
}
################ per scene NeRF ################
###### uncomment the following five lines if you want to train on all scenes #########
cmds = []
for name in commands.keys(): #
# for scene in ['ship', 'mic', 'chair', 'lego', 'drums', 'ficus', 'hotdog', 'materials']:#
# cmd = f'python train_per_scene.py configs/nerf.yaml defaults.expname={scene}{name} dataset.datadir=./data/nerf_synthetic/{scene} {commands[name]}'
# cmds.append(cmd)
for scene in ['Ignatius','Truck']:#
if scene != 'Ignatius':
cmd = f'python train_per_scene.py configs/nerf.yaml defaults.expname={scene}{name} dataset.datadir=./data/TanksAndTemple/{scene} {commands[name]} ' \
f' dataset.dataset_name=tankstemple '
cmds.append(cmd)
cmd = f'python train_per_scene.py configs/nerf.yaml defaults.expname={scene}{name} dataset.datadir=./data/TanksAndTemple/{scene} {commands[name]} ' \
f' dataset.dataset_name=tankstemple exportation.render_only=1 exportation.render_path=1 exportation.render_test=0 ' \
f' defaults.ckpt=/mnt/qb/home/geiger/zyu30/Projects/Anpei/Code/factor-fields/logs/{scene}-grid/{scene}-grid.th '
cmds.append(cmd)
################ generalization NeRF ################
commands = {
# '-grid': '', \
# '-DVGO-like': 'model.basis_type=none model.coeff_reso=48',
# '-SL':'model.basis_dims=[72] model.basis_resos=[48] model.freq_bands=[6.]', \
# '-CP': f'model.coeff_type=vec model.basis_type=cp model.freq_bands=[1.,1.,1.,1.,1.,1.] model.basis_resos=[512,512,512,512,512,512] model.basis_dims=[32,32,32,32,32,32]', \
# '-hash': f'model.basis_type=hash model.coef_init=1.0 ', \
# '-sinc': f'model.basis_mapping=sinc', \
# '-tria': f'model.basis_mapping=triangle', \
# '-vm': f'model.coeff_type=vm model.basis_type=vm', \
# '-mlpB': 'model.basis_type=mlp', \
# '-mlpC': 'model.coeff_type=mlp', \
# '-hash-sl': f'model.basis_type=hash model.coef_init=1.0 model.basis_dims=[16] model.freq_bands=[8.] model.basis_resos=[64] ', \
# '-DCT':'model.basis_type=fix-grid', \
}
# for name in commands.keys(): #
# config = commands[name]
# config = f'python train_across_scene2.py configs/nerf_set.yaml defaults.expname=google-obj{name} {config} ' \
# f'training.volume_resoFinal=128 dataset.datadir=./data/google_scanned_objects/'
# cmds.append(config)
# # =========> fine tuning <================
# views = 5
# for name in commands.keys(): #
# for scene in [183,199,298,467,957,244,963,527]:#
# cmd = f'python train_across_scene.py configs/nerf_ft.yaml defaults.expname=google_objs_{name}_{scene}_{views}_views ' \
# f'dataset.datadir=/home/anpei/Dataset/google_scanned_objects/ {commands[name]} ' \
# f'dataset.train_views={views} ' \
# f'dataset.train_scene_list=[{scene}] ' \
# f'dataset.test_scene_list=[{scene}] ' \
# f'defaults.ckpt=/home/anpei/Code/NeuBasis/log/google-obj{name}//google-obj{name}.th '
# cmds.append(cmd)
# for scene in ['Ignatius','Barn','Truck','Family','Caterpillar']:#'Ignatius','Barn','Truck','Family','Caterpillar'
# cmds.append(f'python train_basis.py configs/tnt.yaml defaults.expname=tnt_{scene} ' \
# f'dataset.datadir=./data/TanksAndTemple/{scene}'
# )
# cmds = []
# for scene in ['room']:#,'hall','kitchen','living_room','room2','sofa','meeting_room','room','salon2'
# cmds.append(f'python train_basis.py configs/colmap_new.yaml defaults.expname=indoor_{scene} ' \
# f'dataset.datadir=./data/indoor/real/{scene}'
# # f'defaults.ckpt=/home/anpei/code/NeuBasis2/log/basis_ship/basis_ship.th exporation.render_only=True'
# )
# cmds = []
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeRF model.basis_type=x model.coeff_type=none model.basis_mapping=trigonometric ' \
# f'model.num_layers=8 model.hidden_dim=256 ' \
# f'model.freq_bands=[1.,2.,4.,8.,16.,32.,64,128,256.,512.] model.basis_dims=[1,1,1,1,1,1,1,1,1,1] model.basis_resos=[1024,512,256,128,64,32,16,8,4,2]')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-grid')
# cmds.append(f'python 2D_regression.py defaults.expname=NeuBasis-mlpB model.basis_type=mlp')
# cmds.append(f'python 2D_regression.py defaults.expname=NeuBasis-mlpC model.coeff_type=mlp')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=DVGO-like model.basis_type=none')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-noC model.coeff_type=none')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-sinc model.basis_mapping=sinc')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-tria model.basis_mapping=triangle')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-SL model.basis_dims=[144] model.basis_resos=[14] model.freq_bands=[73.14]')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-DCT model.basis_type=fix-grid')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-CP model.coeff_type=vec model.basis_type=cp \
# model.freq_bands=[1.,1.,1.,1.,1.,1.] model.basis_resos=[1024,1024,1024,1024,1024,1024] model.basis_dims=[64,64,64,32,32,32]')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=iNGP-like model.basis_type=hash model.coeff_type=none')
# cmds.append(f'python 2D_regression.py configs/image.yaml defaults.expname=NeuBasis-hash model.basis_type=hash model.coef_init=0.1 basis_dims=[16,16,16,16,16,16]')
#setting available gpus
gpu_idx = [0]
gpus_que = queue.Queue(len(gpu_idx))
for i in gpu_idx:
gpus_que.put(i)
# os.makedirs(f"log/{expFolder}", exist_ok=True)
def run_program(gpu, cmd):
cmd = f'{cmd} '
print(cmd)
os.system(cmd)
gpus_que.put(gpu)
ths = []
for i in range(len(cmds)):
gpu = gpus_que.get()
t = threading.Thread(target=run_program, args=(gpu, cmds[i]), daemon=True)
t.start()
ths.append(t)
for th in ths:
th.join()
# import os
# import numpy as np
# root = f'/mnt/qb/home/geiger/zyu30/Projects/Anpei/Code/factor-fields/logs/'
# # root = '/cluster/home/anchen/root/Code/NeuBasis/log/'
# scores = []
# # for scene in ['ship', 'mic', 'chair', 'lego', 'drums', 'ficus', 'hotdog', 'materials']:
# for scene in ['Caterpillar','Family','Ignatius','Truck']:
# scores.append(np.loadtxt(f'{root}/{scene}-grid/imgs_test_all/mean.txt'))
# # os.system(f'cp {root}/{scene}-grid/imgs_test_all/video.mp4 /mnt/qb/home/geiger/zyu30/Projects/Anpei/Code/factor-fields/logs/video/{scene}.mp4')
# os.system(f'cp {root}/{scene}-grid/{scene}-grid/imgs_path_all/video.mp4 /mnt/qb/home/geiger/zyu30/Projects/Anpei/Code/factor-fields/logs/video/{scene}.mp4')
# # print(np.mean(np.stack(scores),axis=0))