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