Inheritance diagram for dataug.Augmented_model:
Collaboration diagram for dataug.Augmented_model:
Public Member Functions | |
| def | __init__ (self, data_augmenter, model) |
| def | forward (self, x) |
| def | augment (self, mode=True) |
| def | train (self, mode=True) |
| def | eval (self) |
| def | items (self) |
| def | update (self, modules) |
| def | is_augmenting (self) |
| def | TF_names (self) |
| def | __getitem__ (self, key) |
| def | __str__ (self) |
Detailed Description
Wrapper for a Data Augmentation module and a model.
Attributes:
_mods (nn.ModuleDict): A dictionary containing the modules.
_data_augmentation (bool): Wether data augmentation should be used.
Constructor & Destructor Documentation
◆ __init__()
| def dataug.Augmented_model.__init__ | ( | self, | |
| data_augmenter, | |||
| model | |||
| ) |
Init Augmented Model.
By default, data augmentation will be performed.
Args:
data_augmenter (nn.Module): Data augmentation module.
model (nn.Module): Network.
Member Function Documentation
◆ __getitem__()
| def dataug.Augmented_model.__getitem__ | ( | self, | |
| key | |||
| ) |
Access to the modules.
Args:
key (string): Name of the module to access.
Returns:
nn.Module.
◆ __str__()
| def dataug.Augmented_model.__str__ | ( | self | ) |
Name of the module
Returns:
String containing the name of the module as well as the higher levels parameters.
◆ augment()
| def dataug.Augmented_model.augment | ( | self, | |
mode = True |
|||
| ) |
Set the augmentation mode.
Args:
mode (bool): Wether to perform data augmentation on the forward pass. (default: True)
◆ eval()
| def dataug.Augmented_model.eval | ( | self | ) |
Set the module to evaluation mode.
◆ forward()
| def dataug.Augmented_model.forward | ( | self, | |
| x | |||
| ) |
Main method of the Augmented model.
Perform the forward pass of both modules.
Args:
x (Tensor): Batch of data.
Returns:
Tensor : Output of the networks. Should be logits.
◆ is_augmenting()
| def dataug.Augmented_model.is_augmenting | ( | self | ) |
Return wether data augmentation is applied.
Returns:
bool : True if data augmentation is applied.
◆ items()
| def dataug.Augmented_model.items | ( | self | ) |
Return an iterable of the ModuleDict key/value pairs.
◆ TF_names()
| def dataug.Augmented_model.TF_names | ( | self | ) |
Get the transformations names used by the data augmentation module.
Returns:
list : names of the transformations of the data augmentation module.
◆ train()
| def dataug.Augmented_model.train | ( | self, | |
mode = True |
|||
| ) |
Set the module training mode.
Args:
mode (bool): Wether to learn the parameter of the module. (default: None)
◆ update()
| def dataug.Augmented_model.update | ( | self, | |
| modules | |||
| ) |
Update the module dictionnary.
The new dictionnary should keep the same structure.
The documentation for this class was generated from the following file:
- /home/antoineh/github/smart_augmentation/higher/smart_aug/dataug.py
