Stop passing component initializers as hparams
Pass the component initializer as an hparam slows down the script very much. The API has now been changed to pass it as a kwarg to the models instead. The example scripts have also been updated to reflect the new changes. Also, ImageGMLVQ and an example script `gmlvq_mnist.py` that uses it have also been added.
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@ -51,7 +51,7 @@ To assist in the development process, you may also find it useful to install
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## Available models
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- K-Nearest Neighbors (KNN)
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- k-Nearest Neighbors (KNN)
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- Learning Vector Quantization 1 (LVQ1)
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- Generalized Learning Vector Quantization (GLVQ)
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- Generalized Relevance Learning Vector Quantization (GRLVQ)
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@ -68,6 +68,7 @@ To assist in the development process, you may also find it useful to install
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## Planned models
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- Median-LVQ
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- Local-Matrix GMLVQ
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- Generalized Tangent Learning Vector Quantization (GTLVQ)
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- Robust Soft Learning Vector Quantization (RSLVQ)
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@ -21,12 +21,13 @@ if __name__ == "__main__":
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prototypes_per_class = 2
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hparams = dict(
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distribution=(nclasses, prototypes_per_class),
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prototype_initializer=pt.components.SMI(train_ds),
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lr=0.01,
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)
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# Initialize the model
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model = pt.models.GLVQ(hparams, optimizer=torch.optim.Adam)
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model = pt.models.GLVQ(hparams,
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optimizer=torch.optim.Adam,
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prototype_initializer=pt.components.SMI(train_ds))
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# Callbacks
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vis = pt.models.VisGLVQ2D(data=(x_train, y_train))
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@ -29,14 +29,15 @@ if __name__ == "__main__":
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prototypes_per_class = 20
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hparams = dict(
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distribution=(nclasses, prototypes_per_class),
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prototype_initializer=pt.components.SSI(train_ds, noise=1e-1),
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transfer_function="sigmoid_beta",
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transfer_beta=10.0,
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lr=0.01,
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)
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# Initialize the model
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model = pt.models.GLVQ(hparams)
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model = pt.models.GLVQ(hparams,
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prototype_initializer=pt.components.SSI(train_ds,
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noise=1e-1))
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# Callbacks
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vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True, block=True)
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@ -21,12 +21,12 @@ if __name__ == "__main__":
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distribution=(nclasses, prototypes_per_class),
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input_dim=x_train.shape[1],
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latent_dim=x_train.shape[1],
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prototype_initializer=pt.components.SMI(train_ds),
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lr=0.01,
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)
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# Initialize the model
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model = pt.models.GMLVQ(hparams)
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model = pt.models.GMLVQ(hparams,
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prototype_initializer=pt.components.SMI(train_ds))
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100)
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68
examples/gmlvq_mnist.py
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68
examples/gmlvq_mnist.py
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@ -0,0 +1,68 @@
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"""GMLVQ example using the MNIST dataset."""
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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from torchvision import transforms
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from torchvision.datasets import MNIST
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if __name__ == "__main__":
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# Dataset
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train_ds = MNIST(
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"~/datasets",
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train=True,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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]),
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)
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test_ds = MNIST(
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"~/datasets",
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train=False,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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]),
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)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds,
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num_workers=0,
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batch_size=256)
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test_loader = torch.utils.data.DataLoader(test_ds,
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num_workers=0,
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batch_size=256)
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# Hyperparameters
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nclasses = 10
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prototypes_per_class = 2
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hparams = dict(
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input_dim=28 * 28,
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latent_dim=28 * 28,
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distribution=(nclasses, prototypes_per_class),
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lr=0.01,
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)
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# Initialize the model
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model = pt.models.ImageGMLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototype_initializer=pt.components.SMI(train_ds),
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)
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# Callbacks
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vis = pt.models.VisImgComp(data=train_ds,
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nrow=5,
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show=False,
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tensorboard=True)
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=50,
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callbacks=[vis],
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# overfit_batches=1,
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# fast_dev_run=3,
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)
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# Training loop
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trainer.fit(model, train_loader)
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@ -23,12 +23,12 @@ if __name__ == "__main__":
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distribution=(nclasses, prototypes_per_class),
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input_dim=100,
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latent_dim=2,
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prototype_initializer=pt.components.SMI(train_ds),
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lr=0.001,
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)
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# Initialize the model
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model = pt.models.GMLVQ(hparams)
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model = pt.models.GMLVQ(hparams,
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prototype_initializer=pt.components.SMI(train_ds))
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# Callbacks
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vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)
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@ -37,7 +37,6 @@ if __name__ == "__main__":
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# Hyperparameters
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hparams = dict(
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distribution=[1, 2, 3],
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prototype_initializer=pt.components.SMI(train_ds),
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proto_lr=0.01,
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bb_lr=0.01,
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)
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@ -45,6 +44,7 @@ if __name__ == "__main__":
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# Initialize the model
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model = pt.models.SiameseGLVQ(
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hparams,
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prototype_initializer=pt.components.SMI(train_ds),
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backbone_module=Backbone,
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)
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@ -2,7 +2,7 @@ from importlib.metadata import PackageNotFoundError, version
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from .cbc import CBC
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from .glvq import (GLVQ, GMLVQ, GRLVQ, LVQ1, LVQ21, LVQMLN, ImageGLVQ,
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SiameseGLVQ)
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ImageGMLVQ, SiameseGLVQ)
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from .knn import KNN
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from .neural_gas import NeuralGas
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from .vis import *
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@ -8,6 +8,11 @@ class AbstractPrototypeModel(pl.LightningModule):
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def prototypes(self):
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return self.proto_layer.components.detach().cpu()
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@property
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def components(self):
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"""Only an alias for the prototypes."""
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return self.prototypes
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def configure_optimizers(self):
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optimizer = self.optimizer(self.parameters(), lr=self.hparams.lr)
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scheduler = ExponentialLR(optimizer,
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@ -19,3 +24,8 @@ class AbstractPrototypeModel(pl.LightningModule):
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"interval": "step",
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} # called after each training step
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return [optimizer], [sch]
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class PrototypeImageModel(pl.LightningModule):
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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self.proto_layer.components.data.clamp_(0.0, 1.0)
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@ -5,9 +5,18 @@ from prototorch.functions.activations import get_activation
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from prototorch.functions.competitions import wtac
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from prototorch.functions.distances import (euclidean_distance, omega_distance,
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squared_euclidean_distance)
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from prototorch.functions.helper import get_flat
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from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
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from prototorch.modules.mappings import OmegaMapping
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from .abstract import AbstractPrototypeModel
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from .abstract import AbstractPrototypeModel, PrototypeImageModel
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class GLVQ(AbstractPrototypeModel):
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"""Generalized Learning Vector Quantization."""
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from .abstract import AbstractPrototypeModel, PrototypeImageModel
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class GLVQ(AbstractPrototypeModel):
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@ -18,6 +27,7 @@ class GLVQ(AbstractPrototypeModel):
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self.save_hyperparameters(hparams)
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self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
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prototype_initializer = kwargs.get("prototype_initializer", None)
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# Default Values
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self.hparams.setdefault("distance", euclidean_distance)
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@ -26,7 +36,7 @@ class GLVQ(AbstractPrototypeModel):
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self.proto_layer = LabeledComponents(
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distribution=self.hparams.distribution,
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initializer=self.hparams.prototype_initializer)
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initializer=prototype_initializer)
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self.transfer_function = get_activation(self.hparams.transfer_function)
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self.train_acc = torchmetrics.Accuracy()
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@ -44,7 +54,6 @@ class GLVQ(AbstractPrototypeModel):
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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x, y = train_batch
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x = x.view(x.size(0), -1) # flatten
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dis = self(x)
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plabels = self.proto_layer.component_labels
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mu = self.loss(dis, y, prototype_labels=plabels)
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@ -95,15 +104,14 @@ class LVQ21(GLVQ):
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self.optimizer = torch.optim.SGD
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class ImageGLVQ(GLVQ):
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class ImageGLVQ(GLVQ, PrototypeImageModel):
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"""GLVQ for training on image data.
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GLVQ model that constrains the prototypes to the range [0, 1] by clamping
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after updates.
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"""
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def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
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self.proto_layer.components.data.clamp_(0.0, 1.0)
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pass
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class SiameseGLVQ(GLVQ):
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@ -235,6 +243,7 @@ class GMLVQ(GLVQ):
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def forward(self, x):
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protos, _ = self.proto_layer()
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x, protos = get_flat(x, protos)
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latent_x = self.omega_layer(x)
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latent_protos = self.omega_layer(protos)
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dis = squared_euclidean_distance(latent_x, latent_protos)
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@ -256,6 +265,16 @@ class GMLVQ(GLVQ):
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return y_pred.numpy()
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class ImageGMLVQ(GMLVQ, PrototypeImageModel):
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"""GMLVQ for training on image data.
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GMLVQ model that constrains the prototypes to the range [0, 1] by clamping
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after updates.
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"""
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pass
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class LVQMLN(GLVQ):
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"""Learning Vector Quantization Multi-Layer Network.
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@ -3,6 +3,7 @@ import os
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torchvision
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from matplotlib import pyplot as plt
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from matplotlib.offsetbox import AnchoredText
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from prototorch.utils.celluloid import Camera
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@ -270,6 +271,7 @@ class Vis2DAbstract(pl.Callback):
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border=1,
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resolution=50,
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show_protos=True,
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show=True,
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tensorboard=False,
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show_last_only=False,
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pause_time=0.1,
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@ -290,6 +292,7 @@ class Vis2DAbstract(pl.Callback):
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self.border = border
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self.resolution = resolution
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self.show_protos = show_protos
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self.show = show
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self.tensorboard = tensorboard
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self.show_last_only = show_last_only
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self.pause_time = pause_time
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@ -352,10 +355,11 @@ class Vis2DAbstract(pl.Callback):
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def log_and_display(self, trainer, pl_module):
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if self.tensorboard:
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self.add_to_tensorboard(trainer, pl_module)
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if not self.block:
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plt.pause(self.pause_time)
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else:
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plt.show(block=True)
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if self.show:
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if not self.block:
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plt.pause(self.pause_time)
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else:
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plt.show(block=True)
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def on_train_end(self, trainer, pl_module):
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plt.show()
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@ -458,3 +462,50 @@ class VisNG2D(Vis2DAbstract):
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)
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self.log_and_display(trainer, pl_module)
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class VisImgComp(Vis2DAbstract):
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def __init__(self,
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*args,
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random_data=0,
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dataformats="CHW",
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nrow=2,
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**kwargs):
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super().__init__(*args, **kwargs)
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self.random_data = random_data
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self.dataformats = dataformats
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self.nrow = nrow
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def on_epoch_end(self, trainer, pl_module):
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if not self.precheck(trainer):
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return True
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if self.show:
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components = pl_module.components
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grid = torchvision.utils.make_grid(components, nrow=self.nrow)
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plt.imshow(grid.permute((1, 2, 0)).cpu(), cmap=self.cmap)
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self.log_and_display(trainer, pl_module)
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def add_to_tensorboard(self, trainer, pl_module):
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tb = pl_module.logger.experiment
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components = pl_module.components
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grid = torchvision.utils.make_grid(components, nrow=self.nrow)
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tb.add_image(
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tag="Components",
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img_tensor=grid,
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global_step=trainer.current_epoch,
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dataformats=self.dataformats,
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)
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if self.random_data:
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ind = np.random.choice(len(self.x_train),
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size=self.random_data,
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replace=False)
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data_img = self.x_train[ind]
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grid = torchvision.utils.make_grid(data_img, nrow=self.nrow)
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tb.add_image(tag="Data",
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img_tensor=grid,
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global_step=trainer.current_epoch,
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dataformats=self.dataformats)
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