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https://github.com/nerfstudio-project/nerfstudio.git
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54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
import os
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import typing
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from abc import ABC, ABCMeta, abstractmethod
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from pathlib import Path
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import json
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import numpy as np
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import torch
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class FeatureDataloader(ABC):
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def __init__(
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self,
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cfg: dict,
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device: torch.device,
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image_list: torch.Tensor, # (N, 3, H, W)
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cache_path: Path,
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):
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self.cfg = cfg
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self.device = device
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self.cache_path = cache_path
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self.data = None # only expect data to be cached, nothing else
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self.try_load(image_list) # don't save image_list, avoid duplicates
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@abstractmethod
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def __call__(self, img_points):
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# img_points: (B, 3) # (img_ind, x, y)
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pass
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@abstractmethod
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def create(self, image_list: torch.Tensor):
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pass
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def load(self):
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cache_info_path = self.cache_path.with_suffix(".info")
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if not cache_info_path.exists():
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raise FileNotFoundError
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with open(cache_info_path, "r") as f:
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cfg = json.loads(f.read())
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if cfg != self.cfg:
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raise ValueError("Config mismatch")
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self.data = torch.from_numpy(np.load(self.cache_path))
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def save(self):
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cache_info_path = self.cache_path.with_suffix(".info")
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with open(cache_info_path, "w") as f:
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f.write(json.dumps(self.cfg))
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np.save(self.cache_path, self.data)
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def try_load(self, img_list: torch.Tensor):
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try:
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self.load()
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except (FileNotFoundError, ValueError):
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self.create(img_list)
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self.save() |