98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
"""Probabilistic GLVQ methods"""
|
|
|
|
import torch
|
|
|
|
from ..core.losses import nllr_loss, rslvq_loss
|
|
from ..core.pooling import stratified_min_pooling, stratified_sum_pooling
|
|
from ..nn.wrappers import LambdaLayer, LossLayer
|
|
from .extras import GaussianPrior, RankScaledGaussianPrior
|
|
from .glvq import GLVQ, SiameseGMLVQ
|
|
|
|
|
|
class CELVQ(GLVQ):
|
|
"""Cross-Entropy Learning Vector Quantization."""
|
|
def __init__(self, hparams, **kwargs):
|
|
super().__init__(hparams, **kwargs)
|
|
|
|
# Loss
|
|
self.loss = torch.nn.CrossEntropyLoss()
|
|
|
|
def shared_step(self, batch, batch_idx, optimizer_idx=None):
|
|
x, y = batch
|
|
out = self.compute_distances(x) # [None, num_protos]
|
|
plabels = self.proto_layer.labels
|
|
winning = stratified_min_pooling(out, plabels) # [None, num_classes]
|
|
probs = -1.0 * winning
|
|
batch_loss = self.loss(probs, y.long())
|
|
loss = batch_loss.sum(dim=0)
|
|
return out, loss
|
|
|
|
|
|
class ProbabilisticLVQ(GLVQ):
|
|
def __init__(self, hparams, rejection_confidence=0.0, **kwargs):
|
|
super().__init__(hparams, **kwargs)
|
|
|
|
self.conditional_distribution = None
|
|
self.rejection_confidence = rejection_confidence
|
|
|
|
def forward(self, x):
|
|
distances = self.compute_distances(x)
|
|
conditional = self.conditional_distribution(distances)
|
|
prior = (1. / self.num_prototypes) * torch.ones(self.num_prototypes,
|
|
device=self.device)
|
|
posterior = conditional * prior
|
|
plabels = self.proto_layer._labels
|
|
y_pred = stratified_sum_pooling(posterior, plabels)
|
|
return y_pred
|
|
|
|
def predict(self, x):
|
|
y_pred = self.forward(x)
|
|
confidence, prediction = torch.max(y_pred, dim=1)
|
|
prediction[confidence < self.rejection_confidence] = -1
|
|
return prediction
|
|
|
|
def training_step(self, batch, batch_idx, optimizer_idx=None):
|
|
x, y = batch
|
|
out = self.forward(x)
|
|
plabels = self.proto_layer.component_labels
|
|
batch_loss = self.loss(out, y, plabels)
|
|
loss = batch_loss.sum(dim=0)
|
|
return loss
|
|
|
|
|
|
class SLVQ(ProbabilisticLVQ):
|
|
"""Soft Learning Vector Quantization."""
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.loss = LossLayer(nllr_loss)
|
|
self.conditional_distribution = GaussianPrior(self.hparams.variance)
|
|
|
|
|
|
class RSLVQ(ProbabilisticLVQ):
|
|
"""Robust Soft Learning Vector Quantization."""
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.loss = LossLayer(rslvq_loss)
|
|
self.conditional_distribution = GaussianPrior(self.hparams.variance)
|
|
|
|
|
|
class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
|
|
"""Probabilistic Learning Vector Quantization.
|
|
|
|
TODO: Use Backbone LVQ instead
|
|
"""
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.conditional_distribution = RankScaledGaussianPrior(
|
|
self.hparams.lambd)
|
|
self.loss = torch.nn.KLDivLoss()
|
|
|
|
def training_step(self, batch, batch_idx, optimizer_idx=None):
|
|
x, y = batch
|
|
out = self.forward(x)
|
|
y_dist = torch.nn.functional.one_hot(
|
|
y.long(), num_classes=self.num_classes).float()
|
|
batch_loss = self.loss(out, y_dist)
|
|
loss = batch_loss.sum(dim=0)
|
|
return loss
|