test(githooks): Add githooks for automatic commit checks

prototorch/models/data.py

@@ -1,10 +1,19 @@
-import prototorch as pt
+"""Prototorch Data Modules
+
+This allows to store the used dataset inside a Lightning Module.
+Mainly used for PytorchLightningCLI configurations.
+"""
+from typing import Any, Optional, Type
+
 import pytorch_lightning as pl
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader, Dataset, random_split
 from torchvision import transforms
 from torchvision.datasets import MNIST
 
+import prototorch as pt
 
+
+# MNIST
 class MNISTDataModule(pl.LightningDataModule):
     def __init__(self, batch_size=32):
         super().__init__()
@@ -49,15 +58,67 @@ class MNISTDataModule(pl.LightningDataModule):
         return mnist_test
 
 
-def train_on_mnist(batch_size=256) -> type:
-    class DataClass(pl.LightningModule):
-        datamodule = MNISTDataModule(batch_size=batch_size)
+# def train_on_mnist(batch_size=256) -> type:
+#     class DataClass(pl.LightningModule):
+#         datamodule = MNISTDataModule(batch_size=batch_size)
 
-        def __init__(self, *args, **kwargs):
-            prototype_initializer = kwargs.pop(
-                "prototype_initializer", pt.components.Zeros((28, 28, 1)))
-            super().__init__(*args,
-                             prototype_initializer=prototype_initializer,
-                             **kwargs)
+#         def __init__(self, *args, **kwargs):
+#             prototype_initializer = kwargs.pop(
+#                 "prototype_initializer", pt.components.Zeros((28, 28, 1)))
+#             super().__init__(*args,
+#                              prototype_initializer=prototype_initializer,
+#                              **kwargs)
 
-    return DataClass
+#     dc: Type[DataClass] = DataClass
+#     return dc
+
+
+# ABSTRACT
+class GeneralDataModule(pl.LightningDataModule):
+    def __init__(self, dataset: Dataset, batch_size: int = 32) -> None:
+        super().__init__()
+        self.train_dataset = dataset
+        self.batch_size = batch_size
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(self.train_dataset, batch_size=self.batch_size)
+
+
+# def train_on_dataset(dataset: Dataset, batch_size: int = 256):
+#     class DataClass(pl.LightningModule):
+#         datamodule = GeneralDataModule(dataset, batch_size)
+#         datashape = dataset[0][0].shape
+#         example_input_array = torch.zeros_like(dataset[0][0]).unsqueeze(0)
+
+#         def __init__(self, *args: Any, **kwargs: Any) -> None:
+#             prototype_initializer = kwargs.pop(
+#                 "prototype_initializer",
+#                 pt.components.Zeros(self.datashape),
+#             )
+#             super().__init__(*args,
+#                              prototype_initializer=prototype_initializer,
+#                              **kwargs)
+
+#     return DataClass
+
+# if __name__ == "__main__":
+#     from prototorch.models import GLVQ
+
+#     demo_dataset = pt.datasets.Iris()
+
+#     TrainingClass: Type = train_on_dataset(demo_dataset)
+
+#     class DemoGLVQ(TrainingClass, GLVQ):
+#         """Model Definition."""
+
+#     # Hyperparameters
+#     hparams = dict(
+#         distribution={
+#             "num_classes": 3,
+#             "prototypes_per_class": 4
+#         },
+#         lr=0.01,
+#     )
+
+#     initialized = DemoGLVQ(hparams)
+#     print(initialized)

prototorch/models/glvq.py

@@ -3,11 +3,8 @@
 import torch
 from prototorch.functions.activations import get_activation
 from prototorch.functions.competitions import wtac
-from prototorch.functions.distances import (
-    lomega_distance,
-    omega_distance,
-    squared_euclidean_distance,
-)
+from prototorch.functions.distances import (lomega_distance, omega_distance,
+                                            squared_euclidean_distance)
 from prototorch.functions.helper import get_flat
 from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
 from prototorch.modules import LambdaLayer, LossLayer