371 lines
12 KiB
Python
371 lines
12 KiB
Python
"""Models based on the GLVQ framework."""
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import torch
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import torchmetrics
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from prototorch.components import LabeledComponents
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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 (
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euclidean_distance,
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lomega_distance,
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omega_distance,
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squared_euclidean_distance,
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)
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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 import LambdaLayer
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from torch.nn.parameter import Parameter
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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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def __init__(self, hparams, **kwargs):
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super().__init__()
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# Hyperparameters
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self.save_hyperparameters(hparams)
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# Defaults
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self.hparams.setdefault("transfer_fn", "identity")
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self.hparams.setdefault("transfer_beta", 10.0)
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self.hparams.setdefault("lr", 0.01)
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distance_fn = kwargs.get("distance_fn", euclidean_distance)
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transfer_fn = get_activation(self.hparams.transfer_fn)
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# Layers
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self.proto_layer = LabeledComponents(
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distribution=self.hparams.distribution,
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initializer=self.prototype_initializer(**kwargs))
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self.distance_layer = LambdaLayer(distance_fn)
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self.transfer_layer = LambdaLayer(transfer_fn)
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self.loss = LambdaLayer(glvq_loss)
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# Prototype metrics
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self.initialize_prototype_win_ratios()
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self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
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def prototype_initializer(self, **kwargs):
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return kwargs.get("prototype_initializer", None)
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@property
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def prototype_labels(self):
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return self.proto_layer.component_labels.detach().cpu()
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@property
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def num_classes(self):
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return len(self.proto_layer.distribution)
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def _forward(self, x):
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protos, _ = self.proto_layer()
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distances = self.distance_layer(x, protos)
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return distances
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def forward(self, x):
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distances = self._forward(x)
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y_pred = self.predict_from_distances(distances)
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y_pred = torch.eye(self.num_classes, device=self.device)[y_pred.int()]
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return y_pred
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def predict_from_distances(self, distances):
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with torch.no_grad():
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plabels = self.proto_layer.component_labels
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y_pred = wtac(distances, plabels)
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return y_pred
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def predict(self, x):
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with torch.no_grad():
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distances = self._forward(x)
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y_pred = self.predict_from_distances(distances)
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return y_pred
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def log_acc(self, distances, targets, tag):
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preds = self.predict_from_distances(distances)
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accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int())
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# `.int()` because FloatTensors are assumed to be class probabilities
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self.log(tag,
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accuracy,
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on_step=False,
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on_epoch=True,
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prog_bar=True,
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logger=True)
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def initialize_prototype_win_ratios(self):
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self.prototype_win_ratios = torch.zeros(self.num_prototypes,
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device=self.device)
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def log_prototype_win_ratios(self, distances):
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batch_size = len(distances)
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prototype_wc = torch.zeros(self.num_prototypes,
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dtype=torch.long,
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device=self.device)
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wi, wc = torch.unique(distances.min(dim=-1).indices,
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sorted=True,
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return_counts=True)
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prototype_wc[wi] = wc
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prototype_wr = prototype_wc / batch_size
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self.prototype_win_ratios = torch.vstack([
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self.prototype_win_ratios,
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prototype_wr,
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])
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def shared_step(self, batch, batch_idx, optimizer_idx=None):
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x, y = batch
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out = self._forward(x)
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plabels = self.proto_layer.component_labels
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mu = self.loss(out, y, prototype_labels=plabels)
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batch_loss = self.transfer_layer(mu, beta=self.hparams.transfer_beta)
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loss = batch_loss.sum(dim=0)
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return out, loss
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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out, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
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self.log_prototype_win_ratios(out)
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self.log("train_loss", train_loss)
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self.log_acc(out, batch[-1], tag="train_acc")
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return train_loss
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def validation_step(self, batch, batch_idx):
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# `model.eval()` and `torch.no_grad()` handled by pl
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out, val_loss = self.shared_step(batch, batch_idx)
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self.log("val_loss", val_loss)
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self.log_acc(out, batch[-1], tag="val_acc")
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return val_loss
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def test_step(self, batch, batch_idx):
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# `model.eval()` and `torch.no_grad()` handled by pl
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out, test_loss = self.shared_step(batch, batch_idx)
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self.log_acc(out, batch[-1], tag="test_acc")
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return test_loss
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def test_epoch_end(self, outputs):
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test_loss = 0.0
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for batch_loss in outputs:
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test_loss += batch_loss.item()
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self.log("test_loss", test_loss)
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# TODO
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# def predict_step(self, batch, batch_idx, dataloader_idx=None):
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# pass
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def add_prototypes(self, initializer, distribution):
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self.proto_layer.add_components(initializer, distribution)
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self.trainer.accelerator_backend.setup_optimizers(self.trainer)
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def remove_prototypes(self, indices):
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self.proto_layer.remove_components(indices)
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self.trainer.accelerator_backend.setup_optimizers(self.trainer)
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def __repr__(self):
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super_repr = super().__repr__()
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return f"{super_repr}"
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class SiameseGLVQ(GLVQ):
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"""GLVQ in a Siamese setting.
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GLVQ model that applies an arbitrary transformation on the inputs and the
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prototypes before computing the distances between them. The weights in the
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transformation pipeline are only learned from the inputs.
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"""
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def __init__(self,
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hparams,
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backbone=torch.nn.Identity(),
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both_path_gradients=False,
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**kwargs):
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distance_fn = kwargs.pop("distance_fn", squared_euclidean_distance)
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super().__init__(hparams, distance_fn=distance_fn, **kwargs)
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self.backbone = backbone
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self.both_path_gradients = both_path_gradients
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def configure_optimizers(self):
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proto_opt = self.optimizer(self.proto_layer.parameters(),
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lr=self.hparams.proto_lr)
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if list(self.backbone.parameters()):
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# only add an optimizer is the backbone has trainable parameters
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# otherwise, the next line fails
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bb_opt = self.optimizer(self.backbone.parameters(),
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lr=self.hparams.bb_lr)
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return proto_opt, bb_opt
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else:
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return proto_opt
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def _forward(self, x):
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protos, _ = self.proto_layer()
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latent_x = self.backbone(x)
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self.backbone.requires_grad_(self.both_path_gradients)
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latent_protos = self.backbone(protos)
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self.backbone.requires_grad_(True)
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distances = self.distance_layer(latent_x, latent_protos)
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return distances
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def predict_latent(self, x, map_protos=True):
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"""Predict `x` assuming it is already embedded in the latent space.
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Only the prototypes are embedded in the latent space using the
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backbone.
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"""
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self.eval()
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with torch.no_grad():
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protos, plabels = self.proto_layer()
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if map_protos:
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protos = self.backbone(protos)
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d = self.distance_layer(x, protos)
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y_pred = wtac(d, plabels)
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return y_pred
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class GRLVQ(SiameseGLVQ):
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"""Generalized Relevance Learning Vector Quantization.
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TODO Make a RelevanceLayer. `bb_lr` is ignored otherwise.
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"""
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def __init__(self, hparams, **kwargs):
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distance_fn = kwargs.pop("distance_fn", omega_distance)
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super().__init__(hparams, distance_fn=distance_fn, **kwargs)
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relevances = torch.ones(self.hparams.input_dim, device=self.device)
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self.register_parameter("_relevances", Parameter(relevances))
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# Override the backbone.
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self.backbone = LambdaLayer(lambda x: x @ torch.diag(self.relevances),
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name="relevances")
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@property
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def relevance_profile(self):
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return self.relevances.detach().cpu()
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def _forward(self, x):
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protos, _ = self.proto_layer()
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distances = self.distance_layer(x, protos, torch.diag(self.relevances))
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return distances
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class SiameseGMLVQ(SiameseGLVQ):
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"""Generalized Matrix Learning Vector Quantization.
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Implemented as a Siamese network with a linear transformation backbone.
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"""
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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# Override the backbone.
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self.backbone = torch.nn.Linear(self.hparams.input_dim,
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self.hparams.latent_dim,
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bias=False)
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@property
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def omega_matrix(self):
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return self.backbone.weight.detach().cpu()
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@property
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def lambda_matrix(self):
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omega = self.backbone.weight # (latent_dim, input_dim)
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lam = omega.T @ omega
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return lam.detach().cpu()
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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.backbone(x)
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self.backbone.requires_grad_(self.both_path_gradients)
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latent_protos = self.backbone(protos)
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self.backbone.requires_grad_(True)
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distances = self.distance_layer(latent_x, latent_protos)
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return distances
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class LVQMLN(SiameseGLVQ):
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"""Learning Vector Quantization Multi-Layer Network.
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GLVQ model that applies an arbitrary transformation on the inputs, BUT NOT
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on the prototypes before computing the distances between them. This of
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course, means that the prototypes no longer live the input space, but
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rather in the embedding space.
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"""
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def _forward(self, x):
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latent_protos, _ = self.proto_layer()
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latent_x = self.backbone(x)
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distances = self.distance_layer(latent_x, latent_protos)
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return distances
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class GMLVQ(GLVQ):
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"""Generalized Matrix Learning Vector Quantization.
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Implemented as a regular GLVQ network that simply uses a different distance
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function.
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"""
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def __init__(self, hparams, **kwargs):
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distance_fn = kwargs.pop("distance_fn", omega_distance)
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super().__init__(hparams, distance_fn=distance_fn, **kwargs)
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omega = torch.randn(self.hparams.input_dim,
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self.hparams.latent_dim,
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device=self.device)
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self.register_parameter("_omega", Parameter(omega))
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def _forward(self, x):
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protos, _ = self.proto_layer()
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distances = self.distance_layer(x, protos, self._omega)
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return distances
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def extra_repr(self):
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return f"(omega): (shape: {tuple(self._omega.shape)})"
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class LGMLVQ(GMLVQ):
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"""Localized and Generalized Matrix Learning Vector Quantization."""
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def __init__(self, hparams, **kwargs):
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distance_fn = kwargs.pop("distance_fn", lomega_distance)
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super().__init__(hparams, distance_fn=distance_fn, **kwargs)
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# Re-register `_omega` to override the one from the super class.
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omega = torch.randn(
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self.num_prototypes,
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self.hparams.input_dim,
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self.hparams.latent_dim,
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device=self.device,
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)
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self.register_parameter("_omega", Parameter(omega))
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class GLVQ1(GLVQ):
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"""Generalized Learning Vector Quantization 1."""
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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self.loss = lvq1_loss
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self.optimizer = torch.optim.SGD
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class GLVQ21(GLVQ):
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"""Generalized Learning Vector Quantization 2.1."""
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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self.loss = lvq21_loss
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self.optimizer = torch.optim.SGD
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class ImageGLVQ(PrototypeImageModel, GLVQ):
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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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class ImageGMLVQ(PrototypeImageModel, GMLVQ):
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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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