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feat: ImageGTLVQ and SiameseGTLVQ with examples

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This commit is contained in:
Christoph
2021-11-15 08:50:33 +00:00
committed by Jensun Ravichandran
parent d3bb430104
commit a9edf06507
5 changed files with 218 additions and 79 deletions
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104
examples/gtlvq_mnist.py Normal file
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@@ -0,0 +1,104 @@
"""GMLVQ example using the MNIST dataset."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
from torchvision import transforms
from torchvision.datasets import MNIST
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = MNIST(
"~/datasets",
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
]),
)
test_ds = MNIST(
"~/datasets",
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
]),
)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds,
num_workers=0,
batch_size=256)
test_loader = torch.utils.data.DataLoader(test_ds,
num_workers=0,
batch_size=256)
# Hyperparameters
num_classes = 10
prototypes_per_class = 1
hparams = dict(
input_dim=28 * 28,
latent_dim=28,
distribution=(num_classes, prototypes_per_class),
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the model
model = pt.models.ImageGTLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
# Use one batch of data for subspace initiator.
# omega_initializer=pt.initializers.PCALinearTransformInitializer(next(iter(train_loader))[0].reshape(256,28*28))
)
# Callbacks
vis = pt.models.VisImgComp(
data=train_ds,
num_columns=10,
show=False,
tensorboard=True,
random_data=100,
add_embedding=True,
embedding_data=200,
flatten_data=False,
)
pruning = pt.models.PruneLoserPrototypes(
threshold=0.01,
idle_epochs=1,
prune_quota_per_epoch=10,
frequency=1,
verbose=True,
)
es = pl.callbacks.EarlyStopping(
monitor="train_loss",
min_delta=0.001,
patience=15,
mode="min",
check_on_train_epoch_end=True,
)
# Setup trainer
# using GPUs here is strongly recommended!
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
pruning,
# es,
],
terminate_on_nan=True,
weights_summary=None,
accelerator="ddp",
)
# Training loop
trainer.fit(model, train_loader)

73
examples/gtlvq_moons.py
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@@ -24,79 +24,12 @@ if __name__ == "__main__":
shuffle=True)
# Hyperparameters
hparams = dict(distribution=[1, 3], input_dim=2, latent_dim=2)
# Latent_dim should be lower than input dim.
hparams = dict(distribution=[1, 3], input_dim=2, latent_dim=1)
# Initialize the model
model = pt.models.GTLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
omega_initializer=-pt.initializers.PCALinearTransformInitializer(
train_ds))
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
print(model)
# Callbacks
vis = pt.models.VisGLVQ2D(data=train_ds)
es = pl.callbacks.EarlyStopping(
monitor="train_acc",
min_delta=0.001,
patience=20,
mode="max",
verbose=False,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
es,
],
weights_summary="full",
accelerator="ddp",
)
# Training loop
trainer.fit(model, train_loader)
"""Localized-GMLVQ example using the Moons dataset."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
pl.utilities.seed.seed_everything(seed=2)
# Dataset
train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds,
batch_size=256,
shuffle=True)
# Hyperparameters
hparams = dict(distribution=[1, 3], input_dim=2, latent_dim=2)
# Initialize the model
model = pt.models.GTLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
omega_initializer=-pt.initializers.PCALinearTransformInitializer(
train_ds))
hparams, prototypes_initializer=pt.initializers.SMCI(train_ds))
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)

72
examples/siamese_gtlvq_iris.py Normal file
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@@ -0,0 +1,72 @@
"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.latent_size = latent_size
self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
self.activation = torch.nn.Sigmoid()
def forward(self, x):
x = self.activation(self.dense1(x))
out = self.activation(self.dense2(x))
return out
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Iris()
# Reproducibility
pl.utilities.seed.seed_everything(seed=2)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=150)
# Hyperparameters
hparams = dict(distribution=[1, 2, 3],
proto_lr=0.01,
bb_lr=0.01,
input_dim=2,
latent_dim=1)
# Initialize the backbone
backbone = Backbone(latent_size=hparams["input_dim"])
# Initialize the model
model = pt.models.SiameseGTLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
backbone=backbone,
both_path_gradients=False,
)
# Model summary
print(model)
# Callbacks
vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[vis],
)
# Training loop
trainer.fit(model, train_loader)

2
prototorch/models/__init__.py
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@@ -13,8 +13,10 @@ from .glvq import (
LVQMLN,
ImageGLVQ,
ImageGMLVQ,
ImageGTLVQ,
SiameseGLVQ,
SiameseGMLVQ,
SiameseGTLVQ,
)
from .knn import KNN
from .lvq import LVQ1, LVQ21, MedianLVQ

46
prototorch/models/glvq.py
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@@ -284,22 +284,28 @@ class LGMLVQ(GMLVQ):
class GTLVQ(LGMLVQ):
"""Localized and Generalized Matrix Learning Vector Quantization."""
"""Localized and Generalized Tangent Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
distance_fn = kwargs.pop("distance_fn", ltangent_distance)
super().__init__(hparams, distance_fn=distance_fn, **kwargs)
omega_initializer = kwargs.get("omega_initializer")
omega = omega_initializer.generate(self.hparams.input_dim,
self.hparams.latent_dim)
if omega_initializer is not None:
subspace = omega_initializer.generate(self.hparams.input_dim,
self.hparams.latent_dim)
omega = torch.repeat_interleave(subspace.unsqueeze(0),
self.num_prototypes,
dim=0)
else:
omega = torch.rand(
self.num_prototypes,
self.hparams.input_dim,
self.hparams.latent_dim,
device=self.device,
)
# Re-register `_omega` to override the one from the super class.
omega = torch.rand(
self.num_prototypes,
self.hparams.input_dim,
self.hparams.latent_dim,
device=self.device,
)
self.register_parameter("_omega", Parameter(omega))
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
@@ -307,6 +313,14 @@ class GTLVQ(LGMLVQ):
self._omega.copy_(orthogonalization(self._omega))
class SiameseGTLVQ(SiameseGLVQ, GTLVQ):
"""Generalized Tangent Learning Vector Quantization.
Implemented as a Siamese network with a linear transformation backbone.
"""
class GLVQ1(GLVQ):
"""Generalized Learning Vector Quantization 1."""
def __init__(self, hparams, **kwargs):
@@ -339,3 +353,17 @@ class ImageGMLVQ(ImagePrototypesMixin, GMLVQ):
after updates.
"""
class ImageGTLVQ(ImagePrototypesMixin, GTLVQ):
"""GTLVQ for training on image data.
GTLVQ model that constrains the prototypes to the range [0, 1] by clamping
after updates.
"""
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
"""Constrain the components to the range [0, 1] by clamping after updates."""
self.proto_layer.components.data.clamp_(0.0, 1.0)
with torch.no_grad():
self._omega.copy_(orthogonalization(self._omega))