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import bisect |
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import torch |
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import torch.nn.functional as F |
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import lpips |
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perceptual_loss = lpips.LPIPS() |
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def distance(img_a, img_b): |
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return perceptual_loss(img_a, img_b).item() |
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class AlphaScheduler: |
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def __init__(self): |
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... |
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def from_imgs(self, imgs): |
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self.__num_values = len(imgs) |
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self.__values = [0] |
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for i in range(self.__num_values - 1): |
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dis = distance(imgs[i], imgs[i + 1]) |
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self.__values.append(dis) |
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self.__values[i + 1] += self.__values[i] |
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for i in range(self.__num_values): |
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self.__values[i] /= self.__values[-1] |
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def save(self, filename): |
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torch.save(torch.tensor(self.__values), filename) |
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def load(self, filename): |
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self.__values = torch.load(filename).tolist() |
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self.__num_values = len(self.__values) |
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def get_x(self, y): |
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assert y >= 0 and y <= 1 |
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id = bisect.bisect_left(self.__values, y) |
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id -= 1 |
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if id < 0: |
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id = 0 |
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yl = self.__values[id] |
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yr = self.__values[id + 1] |
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xl = id * (1 / (self.__num_values - 1)) |
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xr = (id + 1) * (1 / (self.__num_values - 1)) |
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x = (y - yl) / (yr - yl) * (xr - xl) + xl |
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return x |
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def get_list(self, len=None): |
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if len is None: |
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len = self.__num_values |
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ys = torch.linspace(0, 1, len) |
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res = [self.get_x(y) for y in ys] |
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return res |
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