release code

Dassl.ProGrad.pytorch/dassl/modeling/ops/cross_entropy.py

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+import torch
+from torch.nn import functional as F
+
+
+def cross_entropy(input, target, label_smooth=0, reduction="mean"):
+    """Cross entropy loss.
+
+    Args:
+        input (torch.Tensor): logit matrix with shape of (batch, num_classes).
+        target (torch.LongTensor): int label matrix.
+        label_smooth (float, optional): label smoothing hyper-parameter.
+            Default is 0.
+        reduction (str, optional): how the losses for a mini-batch
+            will be aggregated. Default is 'mean'.
+    """
+    num_classes = input.shape[1]
+    log_prob = F.log_softmax(input, dim=1)
+    zeros = torch.zeros(log_prob.size())
+    target = zeros.scatter_(1, target.unsqueeze(1).data.cpu(), 1)
+    target = target.type_as(input)
+    target = (1-label_smooth) * target + label_smooth/num_classes
+    loss = (-target * log_prob).sum(1)
+    if reduction == "mean":
+        return loss.mean()
+    elif reduction == "sum":
+        return loss.sum()
+    elif reduction == "none":
+        return loss
+    else:
+        raise ValueError