Merge branch 'dev' of github.com:si-cim/prototorch_models into dev

examples/cli/gmlvq.py

@@ -1,12 +1,11 @@
 """GLVQ example using the MNIST dataset."""
 
 from prototorch.models import ImageGLVQ
+from prototorch.models.data import train_on_mnist
 from pytorch_lightning.utilities.cli import LightningCLI
 
-from mnist import TrainOnMNIST
 
-
-class GLVQMNIST(TrainOnMNIST, ImageGLVQ):
+class GLVQMNIST(train_on_mnist(batch_size=64), ImageGLVQ):
     """Model Definition."""
 
 

examples/cli/mnist.py

@@ -1,55 +0,0 @@
-import prototorch as pt
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader, random_split
-from torchvision import transforms
-from torchvision.datasets import MNIST
-
-
-class MNISTDataModule(pl.LightningDataModule):
-    def __init__(self, batch_size=32):
-        super().__init__()
-        self.batch_size = batch_size
-
-    # When doing distributed training, Datamodules have two optional arguments for
-    # granular control over download/prepare/splitting data:
-
-    # OPTIONAL, called only on 1 GPU/machine
-    def prepare_data(self):
-        MNIST("~/datasets", train=True, download=True)
-        MNIST("~/datasets", train=False, download=True)
-
-    # OPTIONAL, 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)
-
-    # Return the dataloader for each split
-    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
-
-
-class TrainOnMNIST(pl.LightningModule):
-    datamodule = MNISTDataModule(batch_size=256)
-
-    def prototype_initializer(self, **kwargs):
-        return pt.components.Zeros((28, 28, 1))

examples/probabilistic.py

@@ -33,11 +33,12 @@ if __name__ == "__main__":
     )
 
     # Initialize the model
-    model = pt.models.probabilistic.RSLVQ(
+    model = pt.models.probabilistic.LikelihoodRatioLVQ(
+        #model = pt.models.probabilistic.RSLVQ(
         hparams,
         optimizer=torch.optim.Adam,
-        prototype_initializer=pt.components.SSI(train_ds, noise=2),
-        #prototype_initializer=pt.components.UniformInitializer(2),
+        #prototype_initializer=pt.components.SSI(train_ds, noise=2),
+        prototype_initializer=pt.components.UniformInitializer(2),
     )
 
     # Callbacks