build: bump version 1.0.0a4 → 1.0.0a5

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 1.0.0a3
+current_version = 1.0.0a5
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)((?P<release>[a-zA-Z0-9_.-]+))?

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "1.0.0-a3"
+release = "1.0.0-a5"
 
 # -- General configuration ---------------------------------------------------
 

examples/y_architecture_example.py

@@ -13,8 +13,8 @@ from torch.utils.data import DataLoader
 
 # ##############################################################################
 
-if __name__ == "__main__":
 
+def main():
     # ------------------------------------------------------------
     # DATA
     # ------------------------------------------------------------
@@ -51,7 +51,7 @@ if __name__ == "__main__":
     # Create Model
     model = GMLVQ(hyperparameters)
 
-    print(model)
+    print(model.hparams)
 
     # ------------------------------------------------------------
     # TRAINING
@@ -74,15 +74,27 @@ if __name__ == "__main__":
     vis = VisGMLVQ2D(data=train_ds)
 
     # Define trainer
-    trainer = pl.Trainer(
-        callbacks=[
+    trainer = pl.Trainer(callbacks=[
         vis,
         stopping_criterion,
         es,
         PlotLambdaMatrixToTensorboard(),
-        ],
-        max_epochs=1000,
-    )
+    ], )
 
     # Train
     trainer.fit(model, train_loader)
+
+    # Manual save
+    trainer.save_checkpoint("./y_arch.ckpt")
+
+    # Load saved model
+    new_model = GMLVQ.load_from_checkpoint(
+        checkpoint_path="./y_arch.ckpt",
+        strict=True,
+    )
+
+    print(new_model.hparams)
+
+
+if __name__ == "__main__":
+    main()

prototorch/models/__init__.py

@@ -36,4 +36,4 @@ from .unsupervised import (
 )
 from .vis import *
 
-__version__ = "1.0.0-a3"
+__version__ = "1.0.0-a5"

prototorch/y/architectures/base.py

@@ -3,13 +3,10 @@ Proto Y Architecture
 
 Network architecture for Component based Learning.
 """
-from dataclasses import dataclass
-from typing import (
-    Callable,
-    Dict,
-    Set,
-    Type,
-)
+from __future__ import annotations
+
+from dataclasses import asdict, dataclass
+from typing import Any, Callable
 
 import pytorch_lightning as pl
 import torch
@@ -23,13 +20,23 @@ class BaseYArchitecture(pl.LightningModule):
         ...
 
     # Fields
-    registered_metrics: Dict[Type[Metric], Metric] = {}
-    registered_metric_callbacks: Dict[Type[Metric], Set[Callable]] = {}
+    registered_metrics: dict[type[Metric], Metric] = {}
+    registered_metric_callbacks: dict[type[Metric], set[Callable]] = {}
 
     # Type Hints for Necessary Fields
     components_layer: torch.nn.Module
 
     def __init__(self, hparams) -> None:
+        if type(hparams) is dict:
+            self.save_hyperparameters(hparams)
+            # TODO: => Move into Component Child
+            del hparams["initialized_proto_shape"]
+            hparams = self.HyperParameters(**hparams)
+        else:
+            hparam_dict = asdict(hparams)
+            hparam_dict["component_initializer"] = None
+            self.save_hyperparameters(hparam_dict, )
+
         super().__init__()
 
         # Common Steps
@@ -165,7 +172,7 @@ class BaseYArchitecture(pl.LightningModule):
     def register_torchmetric(
         self,
         name: Callable,
-        metric: Type[Metric],
+        metric: type[Metric],
         **metric_kwargs,
     ):
         if metric not in self.registered_metrics:
@@ -210,3 +217,9 @@ class BaseYArchitecture(pl.LightningModule):
     # Other Hooks
     def training_epoch_end(self, outs) -> None:
         self.update_metrics_epoch()
+
+    def on_save_checkpoint(self, checkpoint: dict[str, Any]) -> None:
+        checkpoint["hyper_parameters"] = {
+            'hparams': checkpoint["hyper_parameters"]
+        }
+        return super().on_save_checkpoint(checkpoint)

prototorch/y/architectures/components.py

@@ -4,6 +4,7 @@ from prototorch.core.components import LabeledComponents
 from prototorch.core.initializers import (
     AbstractComponentsInitializer,
     LabelsInitializer,
+    ZerosCompInitializer,
 )
 from prototorch.y import BaseYArchitecture
 
@@ -30,11 +31,21 @@ class SupervisedArchitecture(BaseYArchitecture):
     # Steps
     # ----------------------------------------------------------------------------------------------------
     def init_components(self, hparams: HyperParameters):
+        if hparams.component_initializer is not None:
             self.components_layer = LabeledComponents(
                 distribution=hparams.distribution,
                 components_initializer=hparams.component_initializer,
                 labels_initializer=LabelsInitializer(),
             )
+            proto_shape = self.components_layer.components.shape[1:]
+            self.hparams["initialized_proto_shape"] = proto_shape
+        else:
+            # when restoring a checkpointed model
+            self.components_layer = LabeledComponents(
+                distribution=hparams.distribution,
+                components_initializer=ZerosCompInitializer(
+                    self.hparams["initialized_proto_shape"]),
+            )
 
     # Properties
     # ----------------------------------------------------------------------------------------------------

prototorch/y/architectures/optimization.py

@@ -24,17 +24,11 @@ class SingleLearningRateMixin(BaseYArchitecture):
         lr: float = 0.1
         optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
 
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def __init__(self, hparams: HyperParameters) -> None:
-        super().__init__(hparams)
-        self.lr = hparams.lr
-        self.optimizer = hparams.optimizer
-
     # Hooks
     # ----------------------------------------------------------------------------------------------------
     def configure_optimizers(self):
-        return self.optimizer(self.parameters(), lr=self.lr)  # type: ignore
+        return self.hparams.optimizer(self.parameters(),
+                                      lr=self.hparams.lr)  # type: ignore
 
 
 class MultipleLearningRateMixin(BaseYArchitecture):
@@ -55,31 +49,24 @@ class MultipleLearningRateMixin(BaseYArchitecture):
         lr: dict = field(default_factory=lambda: dict())
         optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
 
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def __init__(self, hparams: HyperParameters) -> None:
-        super().__init__(hparams)
-        self.lr = hparams.lr
-        self.optimizer = hparams.optimizer
-
     # Hooks
     # ----------------------------------------------------------------------------------------------------
     def configure_optimizers(self):
         optimizers = []
-        for name, lr in self.lr.items():
+        for name, lr in self.hparams.lr.items():
             if not hasattr(self, name):
                 raise ValueError(f"{name} is not a parameter of {self}")
             else:
                 model_part = getattr(self, name)
                 if isinstance(model_part, Parameter):
                     optimizers.append(
-                        self.optimizer(
+                        self.hparams.optimizer(
                             [model_part],
                             lr=lr,  # type: ignore
                         ))
                 elif hasattr(model_part, "parameters"):
                     optimizers.append(
-                        self.optimizer(
+                        self.hparams.optimizer(
                             model_part.parameters(),
                             lr=lr,  # type: ignore
                         ))

prototorch/y/library/__init__.py

@@ -1,5 +1,7 @@
 from .glvq import GLVQ
+from .gmlvq import GMLVQ
 
 __all__ = [
     "GLVQ",
+    "GMLVQ",
 ]

setup.py

@@ -55,7 +55,7 @@ ALL = CLI + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name=safe_name("prototorch_" + PLUGIN_NAME),
-    version="1.0.0-a3",
+    version="1.0.0-a5",
     description="Pre-packaged prototype-based "
     "machine learning models using ProtoTorch and PyTorch-Lightning.",
     long_description=long_description,