prototorch_models/prototorch/models/probabilistic.py
2021-06-14 20:31:39 +02:00

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