prototorch_models/prototorch/models/glvq.py
Alexander Engelsberger 4bbe73e3a9 Add GRLVQ with examples.
2021-05-06 18:42:06 +02:00

247 lines
8.3 KiB
Python

import torch
import torchmetrics
from prototorch.components import LabeledComponents
from prototorch.functions.activations import get_activation
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import (euclidean_distance, omega_distance,
squared_euclidean_distance)
from prototorch.functions.losses import glvq_loss
from .abstract import AbstractPrototypeModel
class GLVQ(AbstractPrototypeModel):
"""Generalized Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
super().__init__()
self.save_hyperparameters(hparams)
# Default Values
self.hparams.setdefault("distance", euclidean_distance)
self.hparams.setdefault("optimizer", torch.optim.Adam)
self.hparams.setdefault("transfer_function", "identity")
self.hparams.setdefault("transfer_beta", 10.0)
self.proto_layer = LabeledComponents(
labels=(self.hparams.nclasses, self.hparams.prototypes_per_class),
initializer=self.hparams.prototype_initializer)
self.transfer_function = get_activation(self.hparams.transfer_function)
self.train_acc = torchmetrics.Accuracy()
@property
def prototype_labels(self):
return self.proto_layer.component_labels.detach().cpu()
def forward(self, x):
protos, _ = self.proto_layer()
dis = self.hparams.distance(x, protos)
return dis
def training_step(self, train_batch, batch_idx, optimizer_idx=None):
x, y = train_batch
x = x.view(x.size(0), -1) # flatten
dis = self(x)
plabels = self.proto_layer.component_labels
mu = glvq_loss(dis, y, prototype_labels=plabels)
batch_loss = self.transfer_function(mu,
beta=self.hparams.transfer_beta)
loss = batch_loss.sum(dim=0)
# Compute training accuracy
with torch.no_grad():
preds = wtac(dis, plabels)
self.train_acc(preds.int(), y.int())
# `.int()` because FloatTensors are assumed to be class probabilities
# Logging
self.log("train_loss", loss)
self.log("acc",
self.train_acc,
on_step=False,
on_epoch=True,
prog_bar=True,
logger=True)
return loss
def predict(self, x):
# model.eval() # ?!
with torch.no_grad():
d = self(x)
plabels = self.proto_layer.component_labels
y_pred = wtac(d, plabels)
return y_pred.numpy()
class ImageGLVQ(GLVQ):
"""GLVQ for training on image data.
GLVQ 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):
self.proto_layer.components.data.clamp_(0.0, 1.0)
class SiameseGLVQ(GLVQ):
"""GLVQ in a Siamese setting.
GLVQ model that applies an arbitrary transformation on the inputs and the
prototypes before computing the distances between them. The weights in the
transformation pipeline are only learned from the inputs.
"""
def __init__(self,
hparams,
backbone_module=torch.nn.Identity,
backbone_params={},
sync=True,
**kwargs):
super().__init__(hparams, **kwargs)
self.backbone = backbone_module(**backbone_params)
self.backbone_dependent = backbone_module(
**backbone_params).requires_grad_(False)
self.sync = sync
def sync_backbones(self):
master_state = self.backbone.state_dict()
self.backbone_dependent.load_state_dict(master_state, strict=True)
def configure_optimizers(self):
optim = self.hparams.optimizer
proto_opt = optim(self.proto_layer.parameters(),
lr=self.hparams.proto_lr)
if list(self.backbone.parameters()):
# only add an optimizer is the backbone has trainable parameters
# otherwise, the next line fails
bb_opt = optim(self.backbone.parameters(), lr=self.hparams.bb_lr)
return proto_opt, bb_opt
else:
return proto_opt
def forward(self, x):
if self.sync:
self.sync_backbones()
protos, _ = self.proto_layer()
latent_x = self.backbone(x)
latent_protos = self.backbone_dependent(protos)
dis = euclidean_distance(latent_x, latent_protos)
return dis
def predict_latent(self, x):
"""Predict `x` assuming it is already embedded in the latent space.
Only the prototypes are embedded in the latent space using the
backbone.
"""
# model.eval() # ?!
with torch.no_grad():
protos, plabels = self.proto_layer()
latent_protos = self.backbone_dependent(protos)
d = euclidean_distance(x, latent_protos)
y_pred = wtac(d, plabels)
return y_pred.numpy()
class GRLVQ(GLVQ):
"""Generalized Relevance Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.relevances = torch.nn.parameter.Parameter(
torch.ones(self.hparams.input_dim))
def forward(self, x):
protos, _ = self.proto_layer()
dis = omega_distance(x, protos, torch.diag(self.relevances))
return dis
def backbone(self, x):
return x @ torch.diag(self.relevances)
@property
def relevance_profile(self):
return self.relevances.detach().cpu()
def predict_latent(self, x):
"""Predict `x` assuming it is already embedded in the latent space.
Only the prototypes are embedded in the latent space using the
backbone.
"""
# model.eval() # ?!
with torch.no_grad():
protos, plabels = self.proto_layer()
latent_protos = protos @ torch.diag(self.relevances)
d = squared_euclidean_distance(x, latent_protos)
y_pred = wtac(d, plabels)
return y_pred.numpy()
class GMLVQ(GLVQ):
"""Generalized Matrix Learning Vector Quantization."""
def __init__(self, hparams, **kwargs):
super().__init__(hparams, **kwargs)
self.omega_layer = torch.nn.Linear(self.hparams.input_dim,
self.hparams.latent_dim,
bias=False)
def forward(self, x):
protos, _ = self.proto_layer()
latent_x = self.omega_layer(x)
latent_protos = self.omega_layer(protos)
dis = squared_euclidean_distance(latent_x, latent_protos)
return dis
def predict_latent(self, x):
"""Predict `x` assuming it is already embedded in the latent space.
Only the prototypes are embedded in the latent space using the
backbone.
"""
# model.eval() # ?!
with torch.no_grad():
protos, plabels = self.proto_layer()
latent_protos = self.omega_layer(protos)
d = squared_euclidean_distance(x, latent_protos)
y_pred = wtac(d, plabels)
return y_pred.numpy()
class LVQMLN(GLVQ):
"""Learning Vector Quantization Multi-Layer Network.
GLVQ model that applies an arbitrary transformation on the inputs, BUT NOT
on the prototypes before computing the distances between them. This of
course, means that the prototypes no longer live the input space, but
rather in the embedding space.
"""
def __init__(self,
hparams,
backbone_module=torch.nn.Identity,
backbone_params={},
**kwargs):
super().__init__(hparams, **kwargs)
self.backbone = backbone_module(**backbone_params)
def forward(self, x):
latent_protos, _ = self.proto_layer()
latent_x = self.backbone(x)
dis = euclidean_distance(latent_x, latent_protos)
return dis
def predict_latent(self, x):
"""Predict `x` assuming it is already embedded in the latent space."""
with torch.no_grad():
latent_protos, plabels = self.proto_layer()
d = euclidean_distance(x, latent_protos)
y_pred = wtac(d, plabels)
return y_pred.numpy()