fix: fix problems with y architecture and checkpoint
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fe729781fc
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@ -13,8 +13,8 @@ from torch.utils.data import DataLoader
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# ##############################################################################
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if __name__ == "__main__":
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def main():
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# ------------------------------------------------------------
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# DATA
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# ------------------------------------------------------------
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@ -51,7 +51,7 @@ if __name__ == "__main__":
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# Create Model
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model = GMLVQ(hyperparameters)
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print(model)
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print(model.hparams)
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# ------------------------------------------------------------
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# TRAINING
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@ -74,15 +74,27 @@ if __name__ == "__main__":
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vis = VisGMLVQ2D(data=train_ds)
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# Define trainer
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trainer = pl.Trainer(
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callbacks=[
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vis,
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stopping_criterion,
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es,
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PlotLambdaMatrixToTensorboard(),
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],
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max_epochs=1000,
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)
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trainer = pl.Trainer(callbacks=[
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vis,
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stopping_criterion,
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es,
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PlotLambdaMatrixToTensorboard(),
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], )
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# Train
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trainer.fit(model, train_loader)
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# Manual save
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trainer.save_checkpoint("./y_arch.ckpt")
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# Load saved model
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new_model = GMLVQ.load_from_checkpoint(
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checkpoint_path="./y_arch.ckpt",
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strict=True,
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)
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print(new_model.hparams)
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if __name__ == "__main__":
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main()
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@ -3,8 +3,11 @@ Proto Y Architecture
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Network architecture for Component based Learning.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import (
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Any,
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Callable,
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Dict,
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Set,
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@ -23,15 +26,20 @@ class BaseYArchitecture(pl.LightningModule):
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...
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# Fields
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registered_metrics: Dict[Type[Metric], Metric] = {}
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registered_metric_callbacks: Dict[Type[Metric], Set[Callable]] = {}
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registered_metrics: dict[type[Metric], Metric] = {}
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registered_metric_callbacks: dict[type[Metric], set[Callable]] = {}
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# Type Hints for Necessary Fields
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components_layer: torch.nn.Module
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def __init__(self, hparams) -> None:
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if type(hparams) is dict:
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hparams = self.HyperParameters(**hparams)
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super().__init__()
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self.save_hyperparameters(hparams.__dict__)
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# Common Steps
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self.init_components(hparams)
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self.init_latent(hparams)
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@ -165,7 +173,7 @@ class BaseYArchitecture(pl.LightningModule):
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def register_torchmetric(
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self,
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name: Callable,
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metric: Type[Metric],
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metric: type[Metric],
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**metric_kwargs,
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):
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if metric not in self.registered_metrics:
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@ -210,3 +218,9 @@ class BaseYArchitecture(pl.LightningModule):
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# Other Hooks
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def training_epoch_end(self, outs) -> None:
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self.update_metrics_epoch()
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def on_save_checkpoint(self, checkpoint: dict[str, Any]) -> None:
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checkpoint["hyper_parameters"] = {
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'hparams': checkpoint["hyper_parameters"]
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}
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return super().on_save_checkpoint(checkpoint)
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@ -24,17 +24,11 @@ class SingleLearningRateMixin(BaseYArchitecture):
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lr: float = 0.1
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optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
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# Steps
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# ----------------------------------------------------------------------------------------------------
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def __init__(self, hparams: HyperParameters) -> None:
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super().__init__(hparams)
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self.lr = hparams.lr
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self.optimizer = hparams.optimizer
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# Hooks
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# ----------------------------------------------------------------------------------------------------
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def configure_optimizers(self):
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return self.optimizer(self.parameters(), lr=self.lr) # type: ignore
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return self.hparams.optimizer(self.parameters(),
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lr=self.hparams.lr) # type: ignore
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class MultipleLearningRateMixin(BaseYArchitecture):
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@ -55,31 +49,24 @@ class MultipleLearningRateMixin(BaseYArchitecture):
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lr: dict = field(default_factory=lambda: dict())
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optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
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# Steps
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# ----------------------------------------------------------------------------------------------------
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def __init__(self, hparams: HyperParameters) -> None:
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super().__init__(hparams)
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self.lr = hparams.lr
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self.optimizer = hparams.optimizer
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# Hooks
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# ----------------------------------------------------------------------------------------------------
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def configure_optimizers(self):
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optimizers = []
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for name, lr in self.lr.items():
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for name, lr in self.hparams.lr.items():
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if not hasattr(self, name):
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raise ValueError(f"{name} is not a parameter of {self}")
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else:
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model_part = getattr(self, name)
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if isinstance(model_part, Parameter):
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optimizers.append(
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self.optimizer(
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self.hparams.optimizer(
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[model_part],
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lr=lr, # type: ignore
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))
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elif hasattr(model_part, "parameters"):
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optimizers.append(
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self.optimizer(
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self.hparams.optimizer(
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model_part.parameters(),
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lr=lr, # type: ignore
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))
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@ -1,5 +1,7 @@
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from .glvq import GLVQ
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from .gmlvq import GMLVQ
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__all__ = [
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"GLVQ",
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"GMLVQ",
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]
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