367 lines
12 KiB
Python
367 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 (euclidean_distance, omega_distance,
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sed)
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from prototorch.functions.helper import get_flat
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from prototorch.functions.losses import (_get_dp_dm, glvq_loss, lvq1_loss,
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lvq21_loss)
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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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self.save_hyperparameters(hparams)
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self.distance_fn = kwargs.get("distance_fn", euclidean_distance)
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self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
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# Default Values
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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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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.transfer_fn = get_activation(self.hparams.transfer_fn)
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self.acc_metric = torchmetrics.Accuracy()
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self.loss = glvq_loss
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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_fn(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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self.acc_metric(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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self.acc_metric,
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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 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_fn(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("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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# def predict_step(self, batch, batch_idx, dataloader_idx=None):
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# pass
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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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super().__init__(hparams, **kwargs)
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self.backbone = backbone
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self.both_path_gradients = both_path_gradients
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self.distance_fn = kwargs.get("distance_fn", sed)
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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_fn(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_fn(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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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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self.relevances = torch.nn.parameter.Parameter(
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torch.ones(self.hparams.input_dim))
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# Overwrite backbone
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self.backbone = self._backbone
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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 _backbone(self, x):
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"""Namespace hook for the visualization callbacks to work."""
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return x @ torch.diag(self.relevances)
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def _forward(self, x):
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protos, _ = self.proto_layer()
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distances = omega_distance(x, protos, torch.diag(self.relevances))
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return distances
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class GMLVQ(SiameseGLVQ):
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"""Generalized Matrix Learning Vector Quantization."""
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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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 show_lambda(self):
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import matplotlib.pyplot as plt
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title = "Lambda matrix"
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plt.figure(title)
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plt.title(title)
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plt.imshow(self.lambda_matrix, cmap="gray")
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plt.axis("off")
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plt.colorbar()
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plt.show(block=True)
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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_fn(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_fn(latent_x, latent_protos)
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return distances
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class NonGradientGLVQ(GLVQ):
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"""Abstract Model for Models that do not use gradients in their update phase."""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.automatic_optimization = False
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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raise NotImplementedError
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class LVQ1(NonGradientGLVQ):
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"""Learning Vector Quantization 1."""
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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protos = self.proto_layer.components
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plabels = self.proto_layer.component_labels
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x, y = train_batch
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dis = self._forward(x)
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# TODO Vectorized implementation
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for xi, yi in zip(x, y):
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d = self(xi.view(1, -1))
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preds = wtac(d, plabels)
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w = d.argmin(1)
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if yi == preds:
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shift = xi - protos[w]
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else:
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shift = protos[w] - xi
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updated_protos = protos + 0.0
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updated_protos[w] = protos[w] + (self.hparams.lr * shift)
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self.proto_layer.load_state_dict({"_components": updated_protos},
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strict=False)
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# Logging
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self.log_acc(dis, y, tag="train_acc")
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return None
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class LVQ21(NonGradientGLVQ):
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"""Learning Vector Quantization 2.1."""
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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protos = self.proto_layer.components
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plabels = self.proto_layer.component_labels
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x, y = train_batch
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dis = self._forward(x)
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# TODO Vectorized implementation
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for xi, yi in zip(x, y):
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xi = xi.view(1, -1)
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yi = yi.view(1, )
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d = self(xi)
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(_, wp), (_, wn) = _get_dp_dm(d, yi, plabels, with_indices=True)
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shiftp = xi - protos[wp]
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shiftn = protos[wn] - xi
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updated_protos = protos + 0.0
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updated_protos[wp] = protos[wp] + (self.hparams.lr * shiftp)
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updated_protos[wn] = protos[wn] + (self.hparams.lr * shiftn)
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self.proto_layer.load_state_dict({"_components": updated_protos},
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strict=False)
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# Logging
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self.log_acc(dis, y, tag="train_acc")
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return None
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class MedianLVQ(NonGradientGLVQ):
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"""Median LVQ"""
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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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