--- a
+++ b/opengait/modeling/losses/ce.py
@@ -0,0 +1,32 @@
+import torch.nn.functional as F
+
+from .base import BaseLoss
+
+
+class CrossEntropyLoss(BaseLoss):
+    def __init__(self, scale=2**4, label_smooth=True, eps=0.1, loss_term_weight=1.0, log_accuracy=False):
+        super(CrossEntropyLoss, self).__init__(loss_term_weight)
+        self.scale = scale
+        self.label_smooth = label_smooth
+        self.eps = eps
+        self.log_accuracy = log_accuracy
+
+    def forward(self, logits, labels):
+        """
+            logits: [n, c, p]
+            labels: [n]
+        """
+        n, c, p = logits.size()
+        logits = logits.float()
+        labels = labels.unsqueeze(1)
+        if self.label_smooth:
+            loss = F.cross_entropy(
+                logits*self.scale, labels.repeat(1, p), label_smoothing=self.eps)
+        else:
+            loss = F.cross_entropy(logits*self.scale, labels.repeat(1, p))
+        self.info.update({'loss': loss.detach().clone()})
+        if self.log_accuracy:
+            pred = logits.argmax(dim=1)  # [n, p]
+            accu = (pred == labels).float().mean()
+            self.info.update({'accuracy': accu})
+        return loss, self.info