[BUGFIX] KNN works again
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@ -88,13 +88,11 @@ class UnsupervisedPrototypeModel(PrototypeModel):
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super().__init__(hparams, **kwargs)
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# Layers
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prototype_initializer = kwargs.get("prototype_initializer", None)
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initialized_prototypes = kwargs.get("initialized_prototypes", None)
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if prototype_initializer is not None or initialized_prototypes is not None:
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prototypes_initializer = kwargs.get("prototypes_initializer", None)
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if prototypes_initializer is not None:
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self.proto_layer = Components(
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self.hparams.num_prototypes,
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initializer=prototype_initializer,
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initialized_components=initialized_prototypes,
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initializer=prototypes_initializer,
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)
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def compute_distances(self, x):
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@ -112,19 +110,17 @@ class SupervisedPrototypeModel(PrototypeModel):
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super().__init__(hparams, **kwargs)
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# Layers
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prototype_initializer = kwargs.get("prototype_initializer", None)
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initialized_prototypes = kwargs.get("initialized_prototypes", None)
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if prototype_initializer is not None or initialized_prototypes is not None:
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prototypes_initializer = kwargs.get("prototypes_initializer", None)
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if prototypes_initializer is not None:
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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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initialized_components=initialized_prototypes,
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components_initializer=prototypes_initializer,
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)
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self.competition_layer = WTAC()
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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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return self.proto_layer.labels.detach().cpu()
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@property
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def num_classes(self):
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@ -137,15 +133,14 @@ class SupervisedPrototypeModel(PrototypeModel):
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def forward(self, x):
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distances = self.compute_distances(x)
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y_pred = self.predict_from_distances(distances)
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# TODO
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y_pred = torch.eye(self.num_classes, device=self.device)[
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y_pred.long()] # depends on labels {0,...,num_classes}
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plabels = self.proto_layer.labels
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winning = stratified_min_pooling(distances, plabels)
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y_pred = torch.nn.functional.softmin(winning)
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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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plabels = self.proto_layer.labels
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y_pred = self.competition_layer(distances, plabels)
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return y_pred
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@ -20,9 +20,13 @@ class KNN(SupervisedPrototypeModel):
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data = kwargs.get("data", None)
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if data is None:
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raise ValueError("KNN requires data, but was not provided!")
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data, targets = parse_data_arg(data)
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# Layers
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self.proto_layer = LabeledComponents(initialized_components=data)
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self.proto_layer = LabeledComponents(
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distribution=[],
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components_initializer=LiteralCompInitializer(data),
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labels_initializer=LiteralLabelsInitializer(targets))
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self.competition_layer = KNNC(k=self.hparams.k)
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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