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Solved problem tuning the distribution parameters for CIFAR

This commit is contained in:
Eduardo Cueto-Mendoza 2024-09-24 10:35:00 +01:00
parent 17fa3b3e77
commit 0c38b8745d
1 changed files with 3 additions and 3 deletions
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6
making_noise.py
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@ -29,6 +29,7 @@ class AddRaleighNoise(object):
self.mean = a + np.sqrt((np.pi * b) / 4)
def __call__(self, tensor):
print('(mean={0}, std={1})'.format(self.mean, self.std))
return tensor + torch.randn(tensor.size()) * self.std + self.mean
def __repr__(self):
@ -85,7 +86,6 @@ class AddUniformNoise(object):
if self.mean == 0.0:
return tensor * self.mean
else:
print('(mean={0}, std={1})'.format(self.mean, self.std))
return tensor + (torch.randn(tensor.size()) * self.std + self.mean)
def __repr__(self):
@ -150,10 +150,10 @@ def get_cifar_loaders(batch_size=128, test_batch_size=1000, perc=1.0):
transforms.RandomCrop(32, padding=0),
# transforms.Normalize((0.5,), (0.5,)),
# AddGaussianNoise(0., 0.25),
# AddRaleighNoise(1, 2), # Not worinkg for CIFAR
AddRaleighNoise(1, 200), # CIFAR requires big b value
# AddErlangNoise(0.0001, 0.0001),
# AddExponentialNoise(2),
AddUniformNoise(2, 1), # Not working for CIFAR
# AddUniformNoise(100, 1), # CIFAR requires big a value
# AddInpulseNoise(0.5),
])