--- a
+++ b/opengait/modeling/losses/bce.py
@@ -0,0 +1,41 @@
+import torch
+from .base import BaseLoss
+from evaluation import mean_iou
+
+
+class BinaryCrossEntropyLoss(BaseLoss):
+    def __init__(self, loss_term_weight=1.0, eps=1.0e-9):
+        super(BinaryCrossEntropyLoss, self).__init__(loss_term_weight)
+        self.eps = eps
+
+    def forward(self, logits, labels):
+        """
+            logits: [n, 1, h, w]
+            labels: [n, 1, h, w]
+        """
+        # predts = torch.sigmoid(logits.float())
+        labels = labels.float()
+        logits = logits.float()
+
+        loss = - (labels * torch.log(logits + self.eps) +
+                  (1 - labels) * torch.log(1. - logits + self.eps))
+
+        n = loss.size(0)
+        loss = loss.view(n, -1)
+        mean_loss = loss.mean()
+        hard_loss = loss.max()
+        miou = mean_iou((logits > 0.5).float(), labels)
+        self.info.update({
+            'loss': mean_loss.detach().clone(),
+            'hard_loss': hard_loss.detach().clone(),
+            'miou': miou.detach().clone()})
+
+        return mean_loss, self.info
+
+
+if __name__ == "__main__":
+    loss_func = BinaryCrossEntropyLoss()
+    ipts = torch.randn(1, 1, 128, 64)
+    tags = (torch.randn(1, 1, 128, 64) > 0.).float()
+    loss = loss_func(ipts, tags)
+    print(loss)