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c1596ac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | from torchmetrics.classification import (
MulticlassAccuracy
)
from transforms.mixup import mixup_data
from transforms.cutmix import cutmix_data
def train_one_epoch(
model,
loader,
criterion,
optimizer,
device,
num_classes,
augmentation=None
):
model.train()
metric = MulticlassAccuracy(
num_classes=num_classes
).to(device)
total_loss = 0
for images, labels in loader:
images = images.to(device)
labels = labels.to(device)
if augmentation == "mixup":
images, labels_a, labels_b, lam = mixup_data(
images,
labels
)
elif augmentation == "cutmix":
images, labels_a, labels_b, lam = cutmix_data(
images,
labels
)
outputs = model(images)
if augmentation in ["mixup", "cutmix"]:
loss = (
lam * criterion(outputs, labels_a)
+ (1 - lam) * criterion(outputs, labels_b)
)
else:
loss = criterion(
outputs,
labels
)
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
preds = outputs.argmax(dim=1)
metric.update(
preds,
labels
)
acc = metric.compute().item()
return total_loss / len(loader), acc |