normalizing frames with custom mean_std instead of defaulting to resnet

[Dee61298]

• Replaced norm_resnet by norm_meanstd : 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 :

  
  import torch
  import aloscene

x=torch.rand(3,600,600) x=aloscene.Frame(x,mean_std=((0.333,0.333,0.333),(0.333,0.333,0.333)))

x=x.norm_meanstd() print("normalization de x ",x.normalization) print("Mean_std de x",x.mean_std)

x=x.norm_meanstd(name="resnet") print("normalization de x ",x.normalization) print("Mean_std de x",x.mean_std)

x=x.norm_meanstd(mean_std=((0.440,0.220,0.880),(0.333,0.333,0.333)), name="my_norm") print("normalization de x ",x.normalization) print("Mean_std de x",x.mean_std)

* ***Output*** : 
```python
normalization de x  None
Mean_std de x ((0.333, 0.333, 0.333), (0.333, 0.333, 0.333))
normalization de x  resnet
Mean_std de x ((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
normalization de x  my_norm
Mean_std de x ((0.44, 0.22, 0.88), (0.333, 0.333, 0.333))

---

This pull request includes

• [ ] Bug fix (non-breaking change which fixes an issue)
• [X] New feature (non-breaking change which adds functionality)
• [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
• [ ] This change requires a documentation update

[Data-Iab]

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

[Dee61298]

Duplicated in #317 with the correct commits. Closing this one.