simonfranken/mmp_wise2526_franksim
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

adapts metrics

Browse Source
This commit is contained in:
franksim
2025-11-18 09:14:23 +01:00
parent f21fb57303
commit c50d9e83b8
1 changed files with 14 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
mmp/a5/main.py
View File

@@ -67,9 +67,9 @@ def get_random_sampling_mask(labels: torch.Tensor, neg_ratio: float) -> torch.Te
def get_detection_metrics( def get_detection_metrics(
output: Tensor, labels: torch.Tensor, threshold: float output: Tensor, labels: torch.Tensor, threshold: float
) -> tuple[float, float, float]: ) -> tuple[float, float, float, float]:
""" """
Returns precision, recall, f1 for the positive (human) class. Returns precision, recall, f1 for the positive (human) class, and overall accuracy.
""" """
with torch.no_grad(): with torch.no_grad():
probs = torch.softmax(output, dim=-1)[..., 1] probs = torch.softmax(output, dim=-1)[..., 1]
@@ -82,16 +82,20 @@ def get_detection_metrics(
TN = ((preds == 0) & (labels == 0)).sum().item() TN = ((preds == 0) & (labels == 0)).sum().item()
precision = TP / (TP + FP) if (TP + FP) > 0 else 0.0 precision = TP / (TP + FP) if (TP + FP) > 0 else 0.0
neg_precision = TN / (TN + FN) if (TN + FN) > 0 else 0.0
recall = TP / (TP + FN) if (TP + FN) > 0 else 0.0 recall = TP / (TP + FN) if (TP + FN) > 0 else 0.0
neg_recall = TN / (TN + FP) if (TN + FP) > 0 else 0.0
f1 = ( f1 = (
2 * precision * recall / (precision + recall) 2 * precision * recall / (precision + recall)
if (precision + recall) > 0 if (precision + recall) > 0
else 0.0 else 0.0
) )
accuracy = (TP + TN) / (TP + TN + FP + FN) if (TP + TN + FP + FN) > 0 else 0.0
return precision, recall, f1, neg_precision, neg_recall return (
precision,
recall,
f1,
accuracy,
)
def evaluate( def evaluate(
@@ -122,12 +126,12 @@ def evaluate(
if all_outputs and all_labels: if all_outputs and all_labels:
outputs_cat = torch.cat(all_outputs) outputs_cat = torch.cat(all_outputs)
labels_cat = torch.cat(all_labels) labels_cat = torch.cat(all_labels)
precision, recall, f1, neg_precision, neg_recall = get_detection_metrics( precision, recall, f1, acc = get_detection_metrics(
outputs_cat, labels_cat, threshold=0.5 outputs_cat, labels_cat, threshold=0.5
) )
else: else:
precision = recall = f1 = 0.0 precision = recall = f1 = 0.0
return avg_loss, precision, recall, f1, neg_precision, neg_recall return avg_loss, precision, recall, f1, acc
def train( def train(
@@ -239,7 +243,7 @@ def main():
_, optimizer = get_criterion_optimizer(model=model) _, optimizer = get_criterion_optimizer(model=model)
criterion = NegativeMiningCriterion(enable_negative_mining=True) criterion = NegativeMiningCriterion(enable_negative_mining=True)
criterion_eval = NegativeMiningCriterion(enable_negative_mining=False) criterion_eval = NegativeMiningCriterion(enable_negative_mining=False)
num_epochs = 7 num_epochs = 10
for epoch in range(num_epochs): for epoch in range(num_epochs):
train_loss = train( train_loss = train(
@@ -248,7 +252,7 @@ def main():
criterion=criterion, criterion=criterion,
optimizer=optimizer, optimizer=optimizer,
) )
avg_loss, precision, recall, f1, neg_precision, neg_recall = evaluate( avg_loss, precision, recall, f1, acc = evaluate(
model=model, criterion=criterion_eval, dataloader=dataloader_val model=model, criterion=criterion_eval, dataloader=dataloader_val
) )
@@ -257,8 +261,7 @@ def main():
writer.add_scalar("Loss/eval_epoch", avg_loss, epoch) writer.add_scalar("Loss/eval_epoch", avg_loss, epoch)
writer.add_scalar("Acc/precision", precision, epoch) writer.add_scalar("Acc/precision", precision, epoch)
writer.add_scalar("Acc/recall", recall, epoch) writer.add_scalar("Acc/recall", recall, epoch)
writer.add_scalar("Acc/neg_precision", neg_precision, epoch) writer.add_scalar("Acc/acc", acc, epoch)
writer.add_scalar("Acc/neg_recall", neg_recall, epoch)
writer.add_scalar("Acc/f1", f1, epoch) writer.add_scalar("Acc/f1", f1, epoch)
if writer is not None: if writer is not None: