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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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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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