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Running on Zero
Running on Zero
| # Author: Bingxin Ke | |
| # Last modified: 2024-02-26 | |
| import os | |
| import tarfile | |
| from io import BytesIO | |
| import numpy as np | |
| import torch | |
| from .base_depth_dataset import BaseDepthDataset, DepthFileNameMode, DatasetMode | |
| class DIODEDataset(BaseDepthDataset): | |
| def __init__( | |
| self, | |
| **kwargs, | |
| ) -> None: | |
| super().__init__( | |
| # DIODE data parameter | |
| min_depth=0.6, | |
| max_depth=350, | |
| has_filled_depth=False, | |
| name_mode=DepthFileNameMode.id, | |
| **kwargs, | |
| ) | |
| # self.filenames = self.filenames[kwargs['start']:] | |
| def _read_npy_file(self, rel_path): | |
| if self.is_tar: | |
| if self.tar_obj is None: | |
| self.tar_obj = tarfile.open(self.dataset_dir) | |
| fileobj = self.tar_obj.extractfile("./" + rel_path) | |
| npy_path_or_content = BytesIO(fileobj.read()) | |
| else: | |
| npy_path_or_content = os.path.join(self.dataset_dir, rel_path) | |
| data = np.load(npy_path_or_content).squeeze()[np.newaxis, :, :] | |
| return data | |
| def _read_depth_file(self, rel_path): | |
| depth = self._read_npy_file(rel_path) | |
| return depth | |
| def _get_data_path(self, index): | |
| return self.filenames[index] | |
| def _get_data_item(self, index): | |
| # Special: depth mask is read from data | |
| rgb_rel_path, depth_rel_path, mask_rel_path = self._get_data_path( | |
| index=index) | |
| rasters = {} | |
| # RGB data | |
| rasters.update(self._load_rgb_data(rgb_rel_path=rgb_rel_path)) | |
| # Depth data | |
| if DatasetMode.RGB_ONLY != self.mode: | |
| # load data | |
| depth_data = self._load_depth_data( | |
| depth_rel_path=depth_rel_path, filled_rel_path=None | |
| ) | |
| rasters.update(depth_data) | |
| # valid mask | |
| mask = self._read_npy_file(mask_rel_path).astype(bool) | |
| mask = torch.from_numpy(mask).bool() | |
| rasters["valid_mask_raw"] = mask.clone() | |
| rasters["valid_mask_filled"] = mask.clone() | |
| other = {"index": index, "rgb_relative_path": rgb_rel_path} | |
| return rasters, other | |
| if __name__ == '__main__': | |
| from omegaconf import OmegaConf | |
| from torch.utils.data import DataLoader | |
| config_path = 'configs/data_diode_all.yaml' | |
| config = OmegaConf.load(config_path) | |
| diode_dataset = DIODEDataset(mode=DatasetMode.EVAL, **config) | |
| dataloader = DataLoader(diode_dataset, batch_size=1, shuffle=False) | |
| for data in dataloader: | |
| print(data.keys()) | |
| for k, v in data.items(): | |
| if isinstance(v, torch.Tensor): | |
| print( | |
| f"{k}: {v.shape}, range: {v.min()}, {v.max()}, dtype: {v.dtype} ") | |
| else: | |
| print(k, v) | |