normalizing frames with custom mean_std instead of defaulting to resnet #316
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Data-Iab
requested changes
Jan 13, 2023
| @@ -507,10 +510,27 @@ def mean_std_norm(self, mean, std, name) -> Frame: | |||
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| return tensor | |||
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| def norm_meanstd(self, mean_std=None, name=None) -> Frame: | |||
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Having two methodes with nearly the same name is not a good practice. try to fix mean_std_norm instead
| return self.norm_resnet() | ||
| elif mean_std is not None and len(mean_std) == 2: | ||
| return self.mean_std_norm(mean=mean_std[0], std=mean_std[1], name=name) | ||
| elif self.mean_std is not None: |
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there's no difference between this condition and the one above.
| elif mean_std is not None and len(mean_std) == 2: | ||
| return self.mean_std_norm(mean=mean_std[0], std=mean_std[1], name=name) | ||
| elif self.mean_std is not None: | ||
| return self.mean_std_norm(mean=self.mean_std[0], std=self.mean_std[1], name=name) |
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The attribute mean_std is used to store the current normalization parameters. You can delete this
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Thing you can add : normlization from minmax -> meanstd import torch
import aloscene
x = torch.rand(3,10,10)
x = aloscene.Frame(x,mean_std=((0.333,0.333,0.333),(0.333,0.333,0.333)))
x = x.norm_minmax_sym()
x = x.mean_std_norm(mean=(0.333,0.333,0.333), std=(0.333,0.333,0.333), name="custom") # Exception raised |
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Duplicated in #317 with the correct commits. Closing this one. |
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You can now normalize frame with custom mean_std instead of defaulting to resnet.
Either you pass the mean_std at instanciation or you pass it as a parameter to the norm_meanstd function :
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