101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
import numpy as np
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import torch
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from torch.utils.data import Dataset
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from pathlib import Path
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import json
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from PIL import Image
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from torchvision import transforms
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from einops import rearrange
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from ldm.util import instantiate_from_config
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from datasets import load_dataset
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class FolderData(Dataset):
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def __init__(self, root_dir, caption_file, image_transforms, ext="jpg") -> None:
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self.root_dir = Path(root_dir)
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with open(caption_file, "rt") as f:
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captions = json.load(f)
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self.captions = captions
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self.paths = list(self.root_dir.rglob(f"*.{ext}"))
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image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
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image_transforms.extend([transforms.ToTensor(),
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transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
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image_transforms = transforms.Compose(image_transforms)
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self.tform = image_transforms
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# assert all(['full/' + str(x.name) in self.captions for x in self.paths])
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def __len__(self):
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return len(self.captions.keys())
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def __getitem__(self, index):
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chosen = list(self.captions.keys())[index]
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im = Image.open(self.root_dir/chosen)
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im = self.process_im(im)
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caption = self.captions[chosen]
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if caption is None:
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caption = "old book illustration"
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return {"jpg": im, "txt": caption}
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def process_im(self, im):
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im = im.convert("RGB")
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return self.tform(im)
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def hf_dataset(
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name,
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image_transforms=[],
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image_column="image",
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text_column="text",
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split='train',
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image_key='image',
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caption_key='txt',
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):
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"""Make huggingface dataset with appropriate list of transforms applied
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"""
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ds = load_dataset(name, split=split)
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image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
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image_transforms.extend([transforms.ToTensor(),
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transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
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tform = transforms.Compose(image_transforms)
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assert image_column in ds.column_names, f"Didn't find column {image_column} in {ds.column_names}"
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assert text_column in ds.column_names, f"Didn't find column {text_column} in {ds.column_names}"
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def pre_process(examples):
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processed = {}
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processed[image_key] = [tform(im) for im in examples[image_column]]
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processed[caption_key] = examples[text_column]
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return processed
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ds.set_transform(pre_process)
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return ds
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class TextOnly(Dataset):
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def __init__(self, captions, output_size, image_key="image", caption_key="txt", n_gpus=1):
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"""Returns only captions with dummy images"""
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self.output_size = output_size
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self.image_key = image_key
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self.caption_key = caption_key
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if isinstance(captions, Path):
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self.captions = self._load_caption_file(captions)
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else:
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self.captions = captions
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if n_gpus > 1:
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# hack to make sure that all the captions appear on each gpu
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repeated = [n_gpus*[x] for x in self.captions]
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self.captions = []
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[self.captions.extend(x) for x in repeated]
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def __len__(self):
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return len(self.captions)
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def __getitem__(self, index):
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dummy_im = torch.zeros(3, self.output_size, self.output_size)
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dummy_im = rearrange(dummy_im * 2. - 1., 'c h w -> h w c')
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return {self.image_key: dummy_im, self.caption_key: self.captions[index]}
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def _load_caption_file(self, filename):
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with open(filename, 'rt') as f:
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captions = f.readlines()
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return [x.strip('\n') for x in captions] |