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Ensure returns from noise are always Tensors

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This commit is contained in:
Dominik Moritz Roth 2023-05-21 20:28:52 +02:00
parent dcde2150ac
commit bf22f42cb3
1 changed files with 5 additions and 4 deletions
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9
priorConditionedAnnealing/noise.py
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@ -15,10 +15,10 @@ class Colored_Noise():
self.reset(random_state=random_state)
def __call__(self, shape, latent: th.Tensor = None) -> th.Tensor:
assert shape == self.knonw_shape
assert shape == self.known_shape
sample = self.samples[:, self.index]
self.index = (self.index+1) % self.num_samples
return sample
return th.Tensor(sample)
def reset(self, random_state=None):
self.samples = cn.powerlaw_psd_gaussian(
@ -100,8 +100,9 @@ class Perlin_Noise():
def __call__(self, shape):
self.index += 1
return [self.noise([self.index*self.scale, self.magic*(a+1)]) / self.normal_factor
for a in range(self.known_shape[-1])]
noise = [self.noise([self.index*self.scale, self.magic*(a+1)]) / self.normal_factor
for a in range(self.known_shape[-1])]
return th.Tensor(noise)
def reset(self):
self.index = 0