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chore: remove optimizer_idx from all steps

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Alexander Engelsberger 2023-10-25 15:03:13 +02:00
parent 60990f42d2
commit 71167a8f77
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6 changed files with 14 additions and 19 deletions
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3
src/prototorch/models/abstract.py
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@ -2,7 +2,6 @@
import logging import logging
import prototorch
import pytorch_lightning as pl import pytorch_lightning as pl
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
@ -228,7 +227,7 @@ class NonGradientMixin(ProtoTorchMixin):
super().__init__(*args, **kwargs) super().__init__(*args, **kwargs)
self.automatic_optimization = False self.automatic_optimization = False
def training_step(self, train_batch, batch_idx, optimizer_idx=None): def training_step(self, train_batch, batch_idx):
raise NotImplementedError raise NotImplementedError

6
src/prototorch/models/cbc.py
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@ -44,7 +44,7 @@ class CBC(SiameseGLVQ):
probs = self.competition_layer(detections, reasonings) probs = self.competition_layer(detections, reasonings)
return probs return probs
def shared_step(self, batch, batch_idx, optimizer_idx=None): def shared_step(self, batch, batch_idx):
x, y = batch x, y = batch
y_pred = self(x) y_pred = self(x)
num_classes = self.num_classes num_classes = self.num_classes
@ -52,8 +52,8 @@ class CBC(SiameseGLVQ):
loss = self.loss(y_pred, y_true).mean() loss = self.loss(y_pred, y_true).mean()
return y_pred, loss return y_pred, loss
def training_step(self, batch, batch_idx, optimizer_idx=None): def training_step(self, batch, batch_idx):
y_pred, train_loss = self.shared_step(batch, batch_idx, optimizer_idx) y_pred, train_loss = self.shared_step(batch, batch_idx)
preds = torch.argmax(y_pred, dim=1) preds = torch.argmax(y_pred, dim=1)
accuracy = torchmetrics.functional.accuracy( accuracy = torchmetrics.functional.accuracy(
preds.int(), preds.int(),

10
src/prototorch/models/glvq.py
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@ -66,15 +66,15 @@ class GLVQ(SupervisedPrototypeModel):
prototype_wr, prototype_wr,
]) ])
def shared_step(self, batch, batch_idx, optimizer_idx=None): def shared_step(self, batch, batch_idx):
x, y = batch x, y = batch
out = self.compute_distances(x) out = self.compute_distances(x)
_, plabels = self.proto_layer() _, plabels = self.proto_layer()
loss = self.loss(out, y, plabels) loss = self.loss(out, y, plabels)
return out, loss return out, loss
def training_step(self, batch, batch_idx, optimizer_idx=None): def training_step(self, batch, batch_idx):
out, train_loss = self.shared_step(batch, batch_idx, optimizer_idx) out, train_loss = self.shared_step(batch, batch_idx)
self.log_prototype_win_ratios(out) self.log_prototype_win_ratios(out)
self.log("train_loss", train_loss) self.log("train_loss", train_loss)
self.log_acc(out, batch[-1], tag="train_acc") self.log_acc(out, batch[-1], tag="train_acc")
@ -99,10 +99,6 @@ class GLVQ(SupervisedPrototypeModel):
test_loss += batch_loss.item() test_loss += batch_loss.item()
self.log("test_loss", test_loss) self.log("test_loss", test_loss)
# TODO
# def predict_step(self, batch, batch_idx, dataloader_idx=None):
# pass
class SiameseGLVQ(GLVQ): class SiameseGLVQ(GLVQ):
"""GLVQ in a Siamese setting. """GLVQ in a Siamese setting.

2
src/prototorch/models/knn.py
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@ -34,7 +34,7 @@ class KNN(SupervisedPrototypeModel):
labels_initializer=LiteralLabelsInitializer(targets)) labels_initializer=LiteralLabelsInitializer(targets))
self.competition_layer = KNNC(k=self.hparams.k) self.competition_layer = KNNC(k=self.hparams.k)
def training_step(self, train_batch, batch_idx, optimizer_idx=None): def training_step(self, train_batch, batch_idx):
return 1 # skip training step return 1 # skip training step
def on_train_batch_start(self, train_batch, batch_idx): def on_train_batch_start(self, train_batch, batch_idx):

6
src/prototorch/models/lvq.py
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@ -13,7 +13,7 @@ from .glvq import GLVQ
class LVQ1(NonGradientMixin, GLVQ): class LVQ1(NonGradientMixin, GLVQ):
"""Learning Vector Quantization 1.""" """Learning Vector Quantization 1."""
def training_step(self, train_batch, batch_idx, optimizer_idx=None): def training_step(self, train_batch, batch_idx):
protos, plables = self.proto_layer() protos, plables = self.proto_layer()
x, y = train_batch x, y = train_batch
dis = self.compute_distances(x) dis = self.compute_distances(x)
@ -43,7 +43,7 @@ class LVQ1(NonGradientMixin, GLVQ):
class LVQ21(NonGradientMixin, GLVQ): class LVQ21(NonGradientMixin, GLVQ):
"""Learning Vector Quantization 2.1.""" """Learning Vector Quantization 2.1."""
def training_step(self, train_batch, batch_idx, optimizer_idx=None): def training_step(self, train_batch, batch_idx):
protos, plabels = self.proto_layer() protos, plabels = self.proto_layer()
x, y = train_batch x, y = train_batch
@ -100,7 +100,7 @@ class MedianLVQ(NonGradientMixin, GLVQ):
lower_bound = (gamma * f.log()).sum() lower_bound = (gamma * f.log()).sum()
return lower_bound return lower_bound
def training_step(self, train_batch, batch_idx, optimizer_idx=None): def training_step(self, train_batch, batch_idx):
protos, plabels = self.proto_layer() protos, plabels = self.proto_layer()
x, y = train_batch x, y = train_batch

6
src/prototorch/models/probabilistic.py
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@ -21,7 +21,7 @@ class CELVQ(GLVQ):
# Loss # Loss
self.loss = torch.nn.CrossEntropyLoss() self.loss = torch.nn.CrossEntropyLoss()
def shared_step(self, batch, batch_idx, optimizer_idx=None): def shared_step(self, batch, batch_idx):
x, y = batch x, y = batch
out = self.compute_distances(x) # [None, num_protos] out = self.compute_distances(x) # [None, num_protos]
_, plabels = self.proto_layer() _, plabels = self.proto_layer()
@ -63,7 +63,7 @@ class ProbabilisticLVQ(GLVQ):
prediction[confidence < self.rejection_confidence] = -1 prediction[confidence < self.rejection_confidence] = -1
return prediction return prediction
def training_step(self, batch, batch_idx, optimizer_idx=None): def training_step(self, batch, batch_idx):
x, y = batch x, y = batch
out = self.forward(x) out = self.forward(x)
_, plabels = self.proto_layer() _, plabels = self.proto_layer()
@ -123,7 +123,7 @@ class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
self.loss = torch.nn.KLDivLoss() self.loss = torch.nn.KLDivLoss()
# FIXME # FIXME
# def training_step(self, batch, batch_idx, optimizer_idx=None): # def training_step(self, batch, batch_idx):
# x, y = batch # x, y = batch
# y_pred = self(x) # y_pred = self(x)
# batch_loss = self.loss(y_pred, y) # batch_loss = self.loss(y_pred, y)