mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-04-06 22:14:07 +00:00
46 lines
2.6 KiB
Python
46 lines
2.6 KiB
Python
# python train.py --source_path ../../Dataset/3DGS_Dataset/linggongtang --model_path output/linggongtang --data_device 'cpu' --eval --resolution 1
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# scene: {'kejiguan': 'cuda', 'wanfota': 'cuda', 'zhiwu': 'cuda', 'linggongtang': 'cuda', 'xiangjiadang': 'cuda', 'town-train-cpy': 'cuda', 'town2-train-cpy': 'cuda', 'sipingguzhai': 'cpu'}
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# device = cuda: 科技馆、万佛塔、植物
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# = cpu: 凌公塘、湘家荡、寺平古宅
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import os
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# for idx, scene in enumerate({'town-train': 'cuda', 'town2-train': 'cuda', 'building1-train': 'cuda'}.items()):
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# print('---------------------------------------------------------------------------------')
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# one_cmd = f'python train.py --source_path /data2/lpl/data/carla-dataset/{scene[0]} --model_path output/{scene[0]} --data_device "{scene[1]}" --resolution 1 --checkpoint_iterations 30000'
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# print(one_cmd)
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# os.system(one_cmd)
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#
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# # python render.py -m <path to trained model>
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# for idx, scene in enumerate(['town-train-cpy', 'town2-train-cpy', 'building1-train']):
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# print('---------------------------------------------------------------------------------')
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# one_cmd = f'python render.py -m output/{scene}'
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# print(one_cmd)
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# os.system(one_cmd)
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#
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# # python metrics.py -m <path to trained model>
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# for idx, scene in enumerate(['town-train-cpy', 'town2-train-cpy', 'building1-train']):
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# print('---------------------------------------------------------------------------------')
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# one_cmd = f'python metrics.py -m output/{scene}'
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# print(one_cmd)
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# os.system(one_cmd)
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for idx, scene in enumerate({'building2-train': 'cpu', 'building3-train': 'cuda'}.items()):
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print('---------------------------------------------------------------------------------')
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one_cmd = f'python train.py --source_path /data2/lpl/data/carla-dataset/{scene[0]} --model_path output/{scene[0]} --data_device "{scene[1]}" --resolution 1 --checkpoint_iterations 30000 --port 6009'
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print(one_cmd)
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os.system(one_cmd)
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# python render.py -m <path to trained model>
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for idx, scene in enumerate(['building2-train', 'building3-train']):
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print('---------------------------------------------------------------------------------')
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one_cmd = f'python render.py -m output/{scene}'
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print(one_cmd)
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os.system(one_cmd)
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# python metrics.py -m <path to trained model>
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for idx, scene in enumerate(['building2-train', 'building3-train']):
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print('---------------------------------------------------------------------------------')
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one_cmd = f'python metrics.py -m output/{scene}'
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print(one_cmd)
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os.system(one_cmd) |