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fix: use multiclass accuracy by default

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Alexander Engelsberger 2023-06-20 18:30:18 +02:00
parent 4cd6aee330
commit 2a665e220f
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2 changed files with 35 additions and 17 deletions
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30
prototorch/models/abstract.py
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@ -2,6 +2,7 @@
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
@ -186,21 +187,32 @@ class SupervisedPrototypeModel(PrototypeModel):
def log_acc(self, distances, targets, tag): def log_acc(self, distances, targets, tag):
preds = self.predict_from_distances(distances) preds = self.predict_from_distances(distances)
accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int()) accuracy = torchmetrics.functional.accuracy(
# `.int()` because FloatTensors are assumed to be class probabilities preds.int(),
targets.int(),
"multiclass",
num_classes=self.num_classes,
)
self.log(tag, self.log(
accuracy, tag,
on_step=False, accuracy,
on_epoch=True, on_step=False,
prog_bar=True, on_epoch=True,
logger=True) prog_bar=True,
logger=True,
)
def test_step(self, batch, batch_idx): def test_step(self, batch, batch_idx):
x, targets = batch x, targets = batch
preds = self.predict(x) preds = self.predict(x)
accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int()) accuracy = torchmetrics.functional.accuracy(
preds.int(),
targets.int(),
"multiclass",
num_classes=self.num_classes,
)
self.log("test_acc", accuracy) self.log("test_acc", accuracy)

22
prototorch/models/cbc.py
View File

@ -55,14 +55,20 @@ class CBC(SiameseGLVQ):
def training_step(self, batch, batch_idx, optimizer_idx=None): def training_step(self, batch, batch_idx, optimizer_idx=None):
y_pred, train_loss = self.shared_step(batch, batch_idx, optimizer_idx) y_pred, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
preds = torch.argmax(y_pred, dim=1) preds = torch.argmax(y_pred, dim=1)
accuracy = torchmetrics.functional.accuracy(preds.int(), accuracy = torchmetrics.functional.accuracy(
batch[1].int()) preds.int(),
self.log("train_acc", batch[1].int(),
accuracy, "multiclass",
on_step=False, num_classes=self.num_classes,
on_epoch=True, )
prog_bar=True, self.log(
logger=True) "train_acc",
accuracy,
on_step=False,
on_epoch=True,
prog_bar=True,
logger=True,
)
return train_loss return train_loss
def predict(self, x): def predict(self, x):