From 6ba70e5b5a2dc51f453b287193d647f38f2cfc9a Mon Sep 17 00:00:00 2001 From: ManoleAlexandru99 Date: Thu, 23 Mar 2023 11:00:20 +0200 Subject: [PATCH] Map prediction to class label for IoU computation --- utils/loss.py | 4 +--- val.py | 2 ++ 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/utils/loss.py b/utils/loss.py index 3912dbef5e37..02f945a767c3 100644 --- a/utils/loss.py +++ b/utils/loss.py @@ -161,9 +161,7 @@ def __call__(self, preds, targets, seg_masks): # predictions, targets lcls += self.BCEcls(pcls, t) # BCE # Mask Loss - # print('\n-----PRED MASK', pred_mask.shape, '-------\n') - # print('\n-----REAL MASK', seg_masks.shape, '-------\n') - print('\n----------- PRED VALID: ', torch.all(pred_mask >= 0), '-----------------\n') + # print('\n----------- PRED VALID: ', torch.all(pred_mask >= 0), '-----------------\n') print('\n----------- SEG MASK VALID: ', torch.all(seg_masks >= 0), '-----------------\n') seg_loss = nn.functional.binary_cross_entropy_with_logits(pred_mask, seg_masks, reduction='none').mean() lseg += seg_loss diff --git a/val.py b/val.py index ac22f4b55f75..b73a3f79c276 100644 --- a/val.py +++ b/val.py @@ -72,6 +72,8 @@ def save_one_json(predn, jdict, path, class_map): def compute_seg_iou(pred, target, n_classes=2): ious = [] pred = torch.sigmoid(pred) + pred[pred < 0.5] = 0 + pred[pred >= 0.5] = 1 pred = pred.view(-1) target = target.view(-1) print(target)