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Running on Zero
Running on Zero
| import math | |
| import os | |
| import json | |
| import re | |
| import cv2 | |
| from dataclasses import dataclass, field | |
| import pytorch_lightning as pl | |
| import torch | |
| import torch.nn.functional as F | |
| from torch.utils.data import DataLoader | |
| from diffusionGS import register | |
| from diffusionGS.utils.typing import * | |
| from diffusionGS.utils.config import parse_structured | |
| from .base import BaseDataModuleConfig, BaseDataset | |
| class ObjaverseDataModuleConfig(BaseDataModuleConfig): | |
| pass | |
| class ObjaverseDataset(BaseDataset): | |
| pass | |
| class ObjaverseDataModule(pl.LightningDataModule): | |
| cfg: ObjaverseDataModuleConfig | |
| def __init__(self, cfg: Optional[Union[dict, DictConfig]] = None) -> None: | |
| super().__init__() | |
| self.cfg = parse_structured(ObjaverseDataModuleConfig, cfg) | |
| def setup(self, stage=None) -> None: | |
| if stage in [None, "fit"]: | |
| self.train_dataset = ObjaverseDataset(self.cfg, "train") | |
| if stage in [None, "fit", "validate"]: | |
| self.val_dataset = ObjaverseDataset(self.cfg, "val") | |
| if stage in [None, "test", "predict"]: | |
| self.test_dataset = ObjaverseDataset(self.cfg, "test") | |
| def prepare_data(self): | |
| pass | |
| def general_loader(self, dataset, batch_size, collate_fn=None, num_workers=0) -> DataLoader: | |
| return DataLoader( | |
| dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=num_workers | |
| ) | |
| def train_dataloader(self) -> DataLoader: | |
| return self.general_loader( | |
| self.train_dataset, | |
| batch_size=self.cfg.batch_size, | |
| collate_fn=self.train_dataset.collate, | |
| num_workers=self.cfg.num_workers | |
| ) | |
| def val_dataloader(self) -> DataLoader: | |
| return self.general_loader(self.val_dataset, batch_size=1) | |
| def test_dataloader(self) -> DataLoader: | |
| return self.general_loader(self.test_dataset, batch_size=1) | |
| def predict_dataloader(self) -> DataLoader: | |
| return self.general_loader(self.test_dataset, batch_size=1) |