UNet Algorithm Config
UNet-based algorithm Pydantic model.
UNetBasedAlgorithm
Bases: BaseModel
General UNet-based algorithm configuration.
This Pydantic model validates the parameters governing the components of the training algorithm: which algorithm, loss function, model architecture, optimizer, and learning rate scheduler to use.
Currently, we only support N2V, CARE, N2N, and PN2V algorithms. In order to train
these algorithms, use the corresponding configuration child classes (e.g.
N2VAlgorithm) to ensure coherent parameters (e.g. specific losses).
Attributes:
-
algorithm({n2v, care, n2n, pn2v}) –Algorithm to use.
-
loss({n2v, mae, mse}) –Loss function to use.
-
model(UNetConfig) –Model architecture to use.
-
optimizer((OptimizerConfig, optional)) –Optimizer to use.
-
lr_scheduler((LrSchedulerConfig, optional)) –Learning rate scheduler to use.
Raises:
-
ValueError–Algorithm parameter type validation errors.
-
ValueError–If the algorithm, loss and model are not compatible.
algorithm
instance-attribute
Algorithm name, as defined in SupportedAlgorithm.
loss
instance-attribute
Loss function to use, as defined in SupportedLoss.
lr_scheduler = LrSchedulerConfig()
class-attribute
instance-attribute
Learning rate scheduler to use, defined in SupportedLrScheduler.
model
instance-attribute
UNet model configuration.
optimizer = OptimizerConfig()
class-attribute
instance-attribute
Optimizer to use, defined in SupportedOptimizer.
__str__()
get_num_input_channels()
uses_batch_norm()
Return whether the model uses batch normalization.
Returns:
-
bool–Whether the model uses batch normalization.