def segmentation_stats_gf1(pred_seg, target):
n_classes = pred_seg.size(1)
pred_lbls = pred_seg.data.max(1)[1].cpu().numpy()
gt = np.squeeze(target.data.cpu().numpy())
gts, preds = [], []
for gt_, pred_ in zip(gt, pred_lbls):
gts.append(gt_)
preds.append(pred_)
iou = segmentation_scores_gf1(gts, preds, n_class=n_classes)
dice = dice_score_list(gts, preds, n_class=n_classes)
return iou,dice
def segmentation_stats(pred_seg, target):
n_classes = pred_seg.size(1)
pred_lbls = pred_seg.data.max(1)[1].cpu().numpy()
gt = np.squeeze(target.data.cpu().numpy(), axis=1)
gts, preds = [], []
for gt_, pred_ in zip(gt, pred_lbls):
gts.append(gt_)
preds.append(pred_)
iou = segmentation_scores(gts, preds, n_class=n_classes)
dice = dice_score_list(gts, preds, n_class=n_classes)
precision, recall = precision_and_recall(gts,
preds,
n_class=int(n_classes))
return iou, dice, precision, recall
def segmentation_stats(pred_seg, target):
n_classes = pred_seg.size(1)
pred_lbls = pred_seg.data.max(1)[1].cpu().numpy()
#gt = np.squeeze(target.data.cpu().numpy(), axis=1)
if target.size()[0] > 1: # Batch of size 1
gt = np.squeeze(target.data.cpu().numpy()
) #OS: previously raised an error for multimodal
else:
gt = target.data.cpu().numpy()
gts, preds = [], []
for gt_, pred_ in zip(gt, pred_lbls):
gts.append(gt_)
preds.append(pred_)
iou = segmentation_scores(gts, preds, n_class=n_classes)
dice = dice_score_list(gts, preds, n_class=n_classes)
return iou, dice
def segmentation_stats(prediction, target):
n_classes = prediction.size(1)
if n_classes == 1:
pred_lbls = (torch.sigmoid(prediction) > 0.5)[0].int().cpu().numpy()
n_unique_classes = n_classes + 1
else:
pred_lbls = prediction.data.max(1)[1].cpu().numpy()
n_unique_classes = n_classes
gt = np.squeeze(target.data.cpu().numpy(), axis=1)
gts, preds = [], []
for gt_, pred_ in zip(gt, pred_lbls):
gts.append(gt_)
preds.append(pred_)
iou = segmentation_scores(gts, preds, n_class=n_unique_classes)
class_wise_dice = dice_score_list(gts, preds, n_class=n_unique_classes)
single_class_dice = single_class_dice_score(gts, preds)
roc_auc_score = roc_auc(gts, preds)
return iou, class_wise_dice, single_class_dice, roc_auc_score
