57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import numpy as np
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from torch.utils.data import Dataset
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from pathlib import Path
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from ldm.data.data_loader import DataLoaderMultiAspect as dlma
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import math
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import ldm.data.dl_singleton as dls
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from ldm.data.image_train_item import ImageTrainItem
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class EveryDreamBatch(Dataset):
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def __init__(self,
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data_root,
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repeats=10,
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flip_p=0.0,
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debug_level=0,
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batch_size=1,
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set='train'
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):
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self.data_root = data_root
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self.batch_size = batch_size
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self.debug_level = debug_level
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if not dls.shared_dataloader:
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print(" * Creating new dataloader singleton")
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dls.shared_dataloader = dlma(data_root=data_root, debug_level=debug_level, batch_size=self.batch_size, flip_p=flip_p)
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self.image_train_items = dls.shared_dataloader.get_all_images()
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self.num_images = len(self.image_train_items)
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self._length = math.trunc(self.num_images * repeats)
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print()
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print(f" ** Trainer Set: {set}, steps: {self._length / batch_size:.0f}, num_images: {self.num_images}, batch_size: {self.batch_size}, length w/repeats: {self._length}")
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print()
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def __len__(self):
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return self._length
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def __getitem__(self, i):
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idx = i % self.num_images
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image_train_item = self.image_train_items[idx]
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example = self.__get_image_for_trainer(image_train_item, self.debug_level)
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return example
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@staticmethod
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def __get_image_for_trainer(image_train_item: ImageTrainItem, debug_level=0):
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example = {}
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save = debug_level > 1
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image_train_tmp = image_train_item.hydrate(crop=False, save=save)
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example["image"] = image_train_tmp.image
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example["caption"] = image_train_tmp.caption
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return example
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