julius/prototorch_models
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fix: labels where on cpu in forward pass

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Alexander Engelsberger 2021-08-05 09:14:32 +02:00
parent f8ad1d83eb
commit 0af8cf36f8
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GPG Key ID: BE3F5909FF0D83E3
4 changed files with 7 additions and 10 deletions
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4
prototorch/models/abstract.py
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@ -136,14 +136,14 @@ class SupervisedPrototypeModel(PrototypeModel):
def forward(self, x):
distances = self.compute_distances(x)
plabels = self.proto_layer.labels
_, plabels = self.proto_layer()
winning = stratified_min_pooling(distances, plabels)
y_pred = torch.nn.functional.softmin(winning)
return y_pred
def predict_from_distances(self, distances):
with torch.no_grad():
plabels = self.proto_layer.labels
_, plabels = self.proto_layer()
y_pred = self.competition_layer(distances, plabels)
return y_pred

2
prototorch/models/glvq.py
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@ -55,7 +55,7 @@ class GLVQ(SupervisedPrototypeModel):
def shared_step(self, batch, batch_idx, optimizer_idx=None):
x, y = batch
out = self.compute_distances(x)
plabels = self.proto_layer.labels
_, plabels = self.proto_layer()
loss = self.loss(out, y, plabels)
return out, loss

7
prototorch/models/lvq.py
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@ -10,9 +10,7 @@ from .glvq import GLVQ
class LVQ1(NonGradientMixin, GLVQ):
"""Learning Vector Quantization 1."""
def training_step(self, train_batch, batch_idx, optimizer_idx=None):
protos = self.proto_layer.components
plabels = self.proto_layer.labels
protos, plables = self.proto_layer()
x, y = train_batch
dis = self.compute_distances(x)
# TODO Vectorized implementation
@ -41,8 +39,7 @@ class LVQ1(NonGradientMixin, GLVQ):
class LVQ21(NonGradientMixin, GLVQ):
"""Learning Vector Quantization 2.1."""
def training_step(self, train_batch, batch_idx, optimizer_idx=None):
protos = self.proto_layer.components
plabels = self.proto_layer.labels
protos, plabels = self.proto_layer()
x, y = train_batch
dis = self.compute_distances(x)

4
prototorch/models/probabilistic.py
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@ -20,7 +20,7 @@ class CELVQ(GLVQ):
def shared_step(self, batch, batch_idx, optimizer_idx=None):
x, y = batch
out = self.compute_distances(x) # [None, num_protos]
plabels = self.proto_layer.labels
_, plabels = self.proto_layer()
winning = stratified_min_pooling(out, plabels) # [None, num_classes]
probs = -1.0 * winning
batch_loss = self.loss(probs, y.long())
@ -54,7 +54,7 @@ class ProbabilisticLVQ(GLVQ):
def training_step(self, batch, batch_idx, optimizer_idx=None):
x, y = batch
out = self.forward(x)
plabels = self.proto_layer.labels
_, plabels = self.proto_layer()
batch_loss = self.loss(out, y, plabels)
loss = batch_loss.sum()
return loss