81346785bd
Siamese architectures no longer accept a `backbone_module`. They have to be initialized with an pre-initialized backbone object instead. This is so that the visualization callbacks could use the very same object for visualization purposes. Also, there's no longer a dependent copy of the backbone. It is managed simply with `requires_grad` instead.
235 lines
7.4 KiB
Python
235 lines
7.4 KiB
Python
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 glvq_loss, lvq1_loss, lvq21_loss
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from .abstract import (AbstractPrototypeModel, PrototypeImageModel,
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SiamesePrototypeModel)
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class GLVQ(AbstractPrototypeModel):
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"""Generalized Learning Vector Quantization."""
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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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prototype_initializer = kwargs.get("prototype_initializer", None)
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# Default Values
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self.hparams.setdefault("transfer_function", "identity")
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self.hparams.setdefault("transfer_beta", 10.0)
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self.proto_layer = LabeledComponents(
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distribution=self.hparams.distribution,
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initializer=prototype_initializer)
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self.transfer_function = get_activation(self.hparams.transfer_function)
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self.train_acc = torchmetrics.Accuracy()
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self.loss = glvq_loss
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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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def forward(self, x):
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protos, _ = self.proto_layer()
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dis = self.distance_fn(x, protos)
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return dis
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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x, y = train_batch
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dis = self(x)
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plabels = self.proto_layer.component_labels
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mu = self.loss(dis, y, prototype_labels=plabels)
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batch_loss = self.transfer_function(mu,
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beta=self.hparams.transfer_beta)
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loss = batch_loss.sum(dim=0)
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# Compute training accuracy
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with torch.no_grad():
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preds = wtac(dis, plabels)
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self.train_acc(preds.int(), y.int())
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# `.int()` because FloatTensors are assumed to be class probabilities
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# Logging
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self.log("train_loss", loss)
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self.log("acc",
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self.train_acc,
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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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return loss
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def predict(self, x):
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# model.eval() # ?!
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with torch.no_grad():
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d = self(x)
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plabels = self.proto_layer.component_labels
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y_pred = wtac(d, plabels)
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return y_pred
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class SiameseGLVQ(SiamesePrototypeModel, 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 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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dis = self.distance_fn(latent_x, latent_protos)
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return dis
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class GRLVQ(SiamesePrototypeModel, GLVQ):
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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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self.distance_fn = kwargs.get("distance_fn", sed)
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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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dis = omega_distance(x, protos, torch.diag(self.relevances))
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return dis
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class GMLVQ(SiamesePrototypeModel, GLVQ):
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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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self.distance_fn = kwargs.get("distance_fn", sed)
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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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latent_protos = self.backbone(protos)
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dis = self.distance_fn(latent_x, latent_protos)
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return dis
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class LVQMLN(SiamesePrototypeModel, GLVQ):
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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 __init__(self, hparams, backbone=torch.nn.Identity(), **kwargs):
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super().__init__(hparams, **kwargs)
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self.backbone = backbone
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self.distance_fn = kwargs.get("distance_fn", sed)
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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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dis = self.distance_fn(latent_x, latent_protos)
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return dis
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class LVQ1(GLVQ):
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"""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 LVQ21(GLVQ):
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"""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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pass
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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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pass
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