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67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
"""
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Process the Mipnerf360 dataset.
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"""
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from nerfstudio.utils.scripts import run_command
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# # split = ["bicycle", "flowers", "garden", "stump", "treehill", "room", "counter", "kitchen", "bonsai"]
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# splits = ["bicycle", "garden", "stump", "room", "counter", "kitchen", "bonsai"]
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# # for processing data
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# for split in splits:
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# print(f"Processing {split}...")
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# working_dir = "/home/ethanweber/nerfactory/data/_prep/images/mipnerf360"
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# cmd = f"ns-process-data images --data {working_dir}/{split}/images --output-dir {working_dir}/processed/{split}/"
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# output = run_command(cmd, verbose=True)
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# for training
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# 1/8 of input images used in the paper = 0.125 -> 1 - this = 0.875
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# ns-train nerfacto --vis wandb --data data/nerfstudio/garden nerfstudio-data --downscale-factor 1 --train-split-percentage 0.875
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# X turn off pose optimization
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# turn off appearance optimization
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# increase hash map size. max_res 2048
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# ns-train nerfacto --vis wandb --data data/_prep/images/mipnerf360/processed/garden --pipeline.datamanager.camera-optimizer.mode off --trainer.steps-per-eval-all-images 5000 nerfstudio-data --downscale-factor 4
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# mipnerf 360 experiments
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splits = ["bicycle", "garden", "stump", "room", "counter", "kitchen", "bonsai"] # 7 splits
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gpus = [0, 1, 3, 4, 5, 6, 7]
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commands = []
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for split, gpu in zip(splits, gpus):
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wandb_name = f"mipnerf360-{split}"
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command = " ".join(
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(
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f"CUDA_VISIBLE_DEVICES={gpu}",
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"ns-train nerfacto",
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"--vis wandb",
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f"--wandb-name {wandb_name}",
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f"--data data/_prep/images/mipnerf360/processed/{split}",
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"--pipeline.datamanager.camera-optimizer.mode off",
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"--trainer.steps-per-eval-all-images 5000",
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"--trainer.max-num-iterations 250000",
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"--pipeline.model.use_appearance_embedding False",
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"nerfstudio-data",
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"--downscale-factor 4",
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)
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)
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print(command)
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commands.append(command)
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# import subprocess
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# import sys
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# def f(cmd):
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# out = subprocess.run(cmd, capture_output=False, shell=True, check=False)
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# if out.returncode != 0:
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# print(f"Error running command: {cmd}")
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# sys.exit(1)
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# if out.stdout is not None:
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# return out.stdout.decode("utf-8")
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# from multiprocessing import Pool
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# with Pool() as p:
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# print(p.map(f, commands))
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