julius/prototorch_models
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Improvement of model __repr__

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This commit is contained in:
Alexander Engelsberger 2021-05-31 11:19:06 +02:00
parent 0ac4ced85d
commit db064b5af1
1 changed files with 26 additions and 9 deletions
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35
prototorch/models/glvq.py
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@ -13,28 +13,44 @@ from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
from .abstract import AbstractPrototypeModel, PrototypeImageModel from .abstract import AbstractPrototypeModel, PrototypeImageModel
class FunctionLayer(torch.nn.Module):
def __init__(self, distance_fn):
super().__init__()
self.fn = distance_fn
self.name = distance_fn.__name__
def forward(self, *args, **kwargs):
return self.fn(*args, **kwargs)
def extra_repr(self):
return self.name
class GLVQ(AbstractPrototypeModel): class GLVQ(AbstractPrototypeModel):
"""Generalized Learning Vector Quantization.""" """Generalized Learning Vector Quantization."""
def __init__(self, hparams, **kwargs): def __init__(self, hparams, **kwargs):
super().__init__() super().__init__()
self.save_hyperparameters(hparams) # Hyperparameters
self.save_hyperparameters(hparams) # Default Values
self.distance_fn = kwargs.get("distance_fn", euclidean_distance)
self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
# Default Values
self.hparams.setdefault("transfer_fn", "identity") self.hparams.setdefault("transfer_fn", "identity")
self.hparams.setdefault("transfer_beta", 10.0) self.hparams.setdefault("transfer_beta", 10.0)
self.hparams.setdefault("lr", 0.01) self.hparams.setdefault("lr", 0.01)
distance_fn = kwargs.get("distance_fn", euclidean_distance)
tranfer_fn = get_activation(self.hparams.transfer_fn)
# Layers
self.proto_layer = LabeledComponents( self.proto_layer = LabeledComponents(
distribution=self.hparams.distribution, distribution=self.hparams.distribution,
initializer=self.prototype_initializer(**kwargs)) initializer=self.prototype_initializer(**kwargs))
self.transfer_fn = get_activation(self.hparams.transfer_fn) self.distance_layer = FunctionLayer(distance_fn)
self.transfer_layer = FunctionLayer(tranfer_fn)
self.loss = FunctionLayer(glvq_loss)
self.loss = glvq_loss self.optimizer = kwargs.get("optimizer", torch.optim.Adam)
def prototype_initializer(self, **kwargs): def prototype_initializer(self, **kwargs):
return kwargs.get("prototype_initializer", None) return kwargs.get("prototype_initializer", None)
@ -49,7 +65,7 @@ class GLVQ(AbstractPrototypeModel):
def _forward(self, x): def _forward(self, x):
protos, _ = self.proto_layer() protos, _ = self.proto_layer()
distances = self.distance_fn(x, protos) distances = self.distance_layer(x, protos)
return distances return distances
def forward(self, x): def forward(self, x):
@ -87,7 +103,7 @@ class GLVQ(AbstractPrototypeModel):
out = self._forward(x) out = self._forward(x)
plabels = self.proto_layer.component_labels plabels = self.proto_layer.component_labels
mu = self.loss(out, y, prototype_labels=plabels) mu = self.loss(out, y, prototype_labels=plabels)
batch_loss = self.transfer_fn(mu, beta=self.hparams.transfer_beta) batch_loss = self.transfer_layer(mu, beta=self.hparams.transfer_beta)
loss = batch_loss.sum(dim=0) loss = batch_loss.sum(dim=0)
return out, loss return out, loss
@ -121,6 +137,7 @@ class GLVQ(AbstractPrototypeModel):
def increase_prototypes(self, initializer, distribution): def increase_prototypes(self, initializer, distribution):
self.proto_layer.increase_components(initializer, distribution) self.proto_layer.increase_components(initializer, distribution)
#self.trainer.accelerated_backend.setup_optimizers(self)
def __repr__(self): def __repr__(self):
super_repr = super().__repr__() super_repr = super().__repr__()