chore: Remove PytorchLightning CLI related code

Could be moved in a seperate plugin.

examples/cli/README.md

@@ -1,8 +0,0 @@
-# Examples using Lightning CLI
-
-Examples in this folder use the experimental [Lightning CLI](https://pytorch-lightning.readthedocs.io/en/latest/common/lightning_cli.html).
-
-To use the example run
-```
-python gmlvq.py --config gmlvq.yaml
-```

examples/cli/gmlvq.py

@@ -1,19 +0,0 @@
-"""GMLVQ example using the MNIST dataset."""
-
-import prototorch as pt
-import torch
-from prototorch.models import ImageGMLVQ
-from prototorch.models.abstract import PrototypeModel
-from prototorch.models.data import MNISTDataModule
-from pytorch_lightning.utilities.cli import LightningCLI
-
-
-class ExperimentClass(ImageGMLVQ):
-    def __init__(self, hparams, **kwargs):
-        super().__init__(hparams,
-                         optimizer=torch.optim.Adam,
-                         prototype_initializer=pt.components.zeros(28 * 28),
-                         **kwargs)
-
-
-cli = LightningCLI(ImageGMLVQ, MNISTDataModule)

examples/cli/gmlvq.yaml

@@ -1,11 +0,0 @@
-model:
-  hparams:
-    input_dim: 784
-    latent_dim: 784
-    distribution:
-      num_classes: 10
-      prototypes_per_class: 2
-    proto_lr: 0.01
-    bb_lr: 0.01
-data:
-  batch_size: 32

prototorch/models/data.py

@@ -1,123 +0,0 @@
-"""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 prototorch as pt
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader, Dataset, random_split
-from torchvision import transforms
-from torchvision.datasets import MNIST
-
-
-# MNIST
-class MNISTDataModule(pl.LightningDataModule):
-    def __init__(self, batch_size=32):
-        super().__init__()
-        self.batch_size = batch_size
-
-    # Download mnist dataset as side-effect, only called on the first cpu
-    def prepare_data(self):
-        MNIST("~/datasets", train=True, download=True)
-        MNIST("~/datasets", train=False, download=True)
-
-    # called for every GPU/machine (assigning state is OK)
-    def setup(self, stage=None):
-        # Transforms
-        transform = transforms.Compose([
-            transforms.ToTensor(),
-        ])
-        # Split dataset
-        if stage in (None, "fit"):
-            mnist_train = MNIST("~/datasets", train=True, transform=transform)
-            self.mnist_train, self.mnist_val = random_split(
-                mnist_train,
-                [55000, 5000],
-            )
-        if stage == (None, "test"):
-            self.mnist_test = MNIST(
-                "~/datasets",
-                train=False,
-                transform=transform,
-            )
-
-    # Dataloaders
-    def train_dataloader(self):
-        mnist_train = DataLoader(self.mnist_train, batch_size=self.batch_size)
-        return mnist_train
-
-    def val_dataloader(self):
-        mnist_val = DataLoader(self.mnist_val, batch_size=self.batch_size)
-        return mnist_val
-
-    def test_dataloader(self):
-        mnist_test = DataLoader(self.mnist_test, batch_size=self.batch_size)
-        return mnist_test
-
-
-# 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)
-
-#     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)