220 lines
6.9 KiB
Python
220 lines
6.9 KiB
Python
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import os
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import numpy as np
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import PIL
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from PIL import Image
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from torch.utils.data import Dataset
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from torchvision import transforms
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import random
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training_templates_smallest = [
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'photo of a sks {}',
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]
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reg_templates_smallest = [
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'photo of a {}',
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]
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imagenet_templates_small = [
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'a photo of a {}',
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'a rendering of a {}',
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'a cropped photo of the {}',
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'the photo of a {}',
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'a photo of a clean {}',
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'a photo of a dirty {}',
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'a dark photo of the {}',
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'a photo of my {}',
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'a photo of the cool {}',
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'a close-up photo of a {}',
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'a bright photo of the {}',
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'a cropped photo of a {}',
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'a photo of the {}',
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'a good photo of the {}',
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'a photo of one {}',
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'a close-up photo of the {}',
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'a rendition of the {}',
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'a photo of the clean {}',
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'a rendition of a {}',
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'a photo of a nice {}',
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'a good photo of a {}',
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'a photo of the nice {}',
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'a photo of the small {}',
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'a photo of the weird {}',
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'a photo of the large {}',
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'a photo of a cool {}',
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'a photo of a small {}',
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'an illustration of a {}',
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'a rendering of a {}',
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'a cropped photo of the {}',
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'the photo of a {}',
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'an illustration of a clean {}',
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'an illustration of a dirty {}',
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'a dark photo of the {}',
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'an illustration of my {}',
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'an illustration of the cool {}',
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'a close-up photo of a {}',
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'a bright photo of the {}',
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'a cropped photo of a {}',
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'an illustration of the {}',
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'a good photo of the {}',
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'an illustration of one {}',
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'a close-up photo of the {}',
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'a rendition of the {}',
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'an illustration of the clean {}',
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'a rendition of a {}',
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'an illustration of a nice {}',
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'a good photo of a {}',
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'an illustration of the nice {}',
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'an illustration of the small {}',
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'an illustration of the weird {}',
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'an illustration of the large {}',
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'an illustration of a cool {}',
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'an illustration of a small {}',
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'a depiction of a {}',
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'a rendering of a {}',
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'a cropped photo of the {}',
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'the photo of a {}',
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'a depiction of a clean {}',
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'a depiction of a dirty {}',
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'a dark photo of the {}',
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'a depiction of my {}',
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'a depiction of the cool {}',
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'a close-up photo of a {}',
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'a bright photo of the {}',
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'a cropped photo of a {}',
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'a depiction of the {}',
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'a good photo of the {}',
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'a depiction of one {}',
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'a close-up photo of the {}',
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'a rendition of the {}',
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'a depiction of the clean {}',
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'a rendition of a {}',
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'a depiction of a nice {}',
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'a good photo of a {}',
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'a depiction of the nice {}',
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'a depiction of the small {}',
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'a depiction of the weird {}',
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'a depiction of the large {}',
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'a depiction of a cool {}',
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'a depiction of a small {}',
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]
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imagenet_dual_templates_small = [
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'a photo of a {} with {}',
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'a rendering of a {} with {}',
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'a cropped photo of the {} with {}',
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'the photo of a {} with {}',
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'a photo of a clean {} with {}',
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'a photo of a dirty {} with {}',
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'a dark photo of the {} with {}',
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'a photo of my {} with {}',
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'a photo of the cool {} with {}',
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'a close-up photo of a {} with {}',
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'a bright photo of the {} with {}',
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'a cropped photo of a {} with {}',
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'a photo of the {} with {}',
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'a good photo of the {} with {}',
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'a photo of one {} with {}',
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'a close-up photo of the {} with {}',
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'a rendition of the {} with {}',
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'a photo of the clean {} with {}',
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'a rendition of a {} with {}',
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'a photo of a nice {} with {}',
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'a good photo of a {} with {}',
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'a photo of the nice {} with {}',
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'a photo of the small {} with {}',
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'a photo of the weird {} with {}',
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'a photo of the large {} with {}',
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'a photo of a cool {} with {}',
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'a photo of a small {} with {}',
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]
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per_img_token_list = [
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'א', 'ב', 'ג', 'ד', 'ה', 'ו', 'ז', 'ח', 'ט', 'י', 'כ', 'ל', 'מ', 'נ', 'ס', 'ע', 'פ', 'צ', 'ק', 'ר', 'ש', 'ת',
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]
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class PersonalizedBase(Dataset):
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def __init__(self,
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data_root,
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size=None,
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repeats=100,
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interpolation="bicubic",
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flip_p=0.5,
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set="train",
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placeholder_token="dog",
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per_image_tokens=False,
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center_crop=False,
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mixing_prob=0.25,
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coarse_class_text=None,
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reg = False
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):
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self.data_root = data_root
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self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
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# self._length = len(self.image_paths)
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self.num_images = len(self.image_paths)
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self._length = self.num_images
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self.placeholder_token = placeholder_token
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self.per_image_tokens = per_image_tokens
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self.center_crop = center_crop
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self.mixing_prob = mixing_prob
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self.coarse_class_text = coarse_class_text
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if per_image_tokens:
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assert self.num_images < len(per_img_token_list), f"Can't use per-image tokens when the training set contains more than {len(per_img_token_list)} tokens. To enable larger sets, add more tokens to 'per_img_token_list'."
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if set == "train":
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self._length = self.num_images * repeats
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self.size = size
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self.interpolation = {"linear": PIL.Image.LINEAR,
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"bilinear": PIL.Image.BILINEAR,
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"bicubic": PIL.Image.BICUBIC,
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"lanczos": PIL.Image.LANCZOS,
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}[interpolation]
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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self.reg = reg
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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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example = {}
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image = Image.open(self.image_paths[i % self.num_images])
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if not image.mode == "RGB":
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image = image.convert("RGB")
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placeholder_string = self.placeholder_token
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if self.coarse_class_text:
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placeholder_string = f"{self.coarse_class_text} {placeholder_string}"
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if not self.reg:
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text = random.choice(training_templates_smallest).format(placeholder_string)
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else:
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text = random.choice(reg_templates_smallest).format(placeholder_string)
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example["caption"] = text
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# default to score-sde preprocessing
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img = np.array(image).astype(np.uint8)
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if self.center_crop:
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crop = min(img.shape[0], img.shape[1])
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h, w, = img.shape[0], img.shape[1]
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img = img[(h - crop) // 2:(h + crop) // 2,
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(w - crop) // 2:(w + crop) // 2]
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image = Image.fromarray(img)
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if self.size is not None:
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image = image.resize((self.size, self.size), resample=self.interpolation)
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image = self.flip(image)
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image = np.array(image).astype(np.uint8)
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example["image"] = (image / 127.5 - 1.0).astype(np.float32)
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return example
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