delete binary_iou

Author: likyoo

change_detection_pytorch/utils/functional.py

@@ -126,26 +126,3 @@ def recall(pr, gt, eps=1e-7, threshold=None, ignore_channels=None):
     score = (tp + eps) / (tp + fn + eps)
 
     return score
-
-def binary_miou(pr, gt, eps=1e-7, threshold=None, ignore_channels=None):
-    """Calculate binary_miou score between ground truth and prediction
-    Args:
-        pr (torch.Tensor): predicted tensor
-        gt (torch.Tensor):  ground truth tensor
-        eps (float): epsilon to avoid zero division
-        threshold: threshold for outputs binarization
-    Returns:
-        float: precision score
-    """
-
-    pr = _threshold(pr, threshold=threshold)
-    pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels)
-
-    tp = torch.sum(gt * pr)
-    fp = torch.sum(pr) - tp
-    fn = torch.sum(gt) - tp
-    tn = torch.sum(gt == pr, dtype=pr.dtype) - tp
-
-    score = ((tp + eps) / (tp + fp + fn + eps) + (tn + eps) / (tn + fp + fn + eps)) / 2
-
-    return score

change_detection_pytorch/utils/metrics.py

@@ -98,24 +98,5 @@ def forward(self, y_pr, y_gt):
             ignore_channels=self.ignore_channels,
         )
 
-# customized and unofficial
 
-class Binary_mIOU(base.Metric):
-    __name__ = 'binary_miou'
-
-    def __init__(self, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs):
-        super().__init__(**kwargs)
-        self.eps = eps
-        self.threshold = threshold
-        self.activation = Activation(activation)
-        self.ignore_channels = ignore_channels
-
-    def forward(self, y_pr, y_gt):
-        y_pr = self.activation(y_pr)
-        return F.binary_miou(
-            y_pr, y_gt,
-            eps=self.eps,
-            threshold=self.threshold,
-            ignore_channels=self.ignore_channels,
-        )