mirror of https://github.com/hpcaitech/ColossalAI
93 lines
3.2 KiB
Python
93 lines
3.2 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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class LSUNBase(Dataset):
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def __init__(self,
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txt_file,
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data_root,
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size=None,
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interpolation="bicubic",
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flip_p=0.5
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):
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self.data_paths = txt_file
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self.data_root = data_root
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with open(self.data_paths, "r") as f:
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self.image_paths = f.read().splitlines()
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self._length = len(self.image_paths)
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self.labels = {
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"relative_file_path_": [l for l in self.image_paths],
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"file_path_": [os.path.join(self.data_root, l)
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for l in self.image_paths],
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}
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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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def __len__(self):
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return self._length
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def __getitem__(self, i):
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example = dict((k, self.labels[k][i]) for k in self.labels)
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image = Image.open(example["file_path_"])
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if not image.mode == "RGB":
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image = image.convert("RGB")
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# default to score-sde preprocessing
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img = np.array(image).astype(np.uint8)
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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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class LSUNChurchesTrain(LSUNBase):
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def __init__(self, **kwargs):
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super().__init__(txt_file="data/lsun/church_outdoor_train.txt", data_root="data/lsun/churches", **kwargs)
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class LSUNChurchesValidation(LSUNBase):
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def __init__(self, flip_p=0., **kwargs):
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super().__init__(txt_file="data/lsun/church_outdoor_val.txt", data_root="data/lsun/churches",
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flip_p=flip_p, **kwargs)
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class LSUNBedroomsTrain(LSUNBase):
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def __init__(self, **kwargs):
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super().__init__(txt_file="data/lsun/bedrooms_train.txt", data_root="data/lsun/bedrooms", **kwargs)
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class LSUNBedroomsValidation(LSUNBase):
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def __init__(self, flip_p=0.0, **kwargs):
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super().__init__(txt_file="data/lsun/bedrooms_val.txt", data_root="data/lsun/bedrooms",
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flip_p=flip_p, **kwargs)
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class LSUNCatsTrain(LSUNBase):
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def __init__(self, **kwargs):
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super().__init__(txt_file="data/lsun/cat_train.txt", data_root="data/lsun/cats", **kwargs)
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class LSUNCatsValidation(LSUNBase):
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def __init__(self, flip_p=0., **kwargs):
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super().__init__(txt_file="data/lsun/cat_val.txt", data_root="data/lsun/cats",
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flip_p=flip_p, **kwargs)
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