release code

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

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+import torch
+
+
+def mixup(x1, x2, y1, y2, beta, preserve_order=False):
+    """Mixup.
+
+    Args:
+        x1 (torch.Tensor): data with shape of (b, c, h, w).
+        x2 (torch.Tensor): data with shape of (b, c, h, w).
+        y1 (torch.Tensor): label with shape of (b, n).
+        y2 (torch.Tensor): label with shape of (b, n).
+        beta (float): hyper-parameter for Beta sampling.
+        preserve_order (bool): apply lmda=max(lmda, 1-lmda).
+            Default is False.
+    """
+    lmda = torch.distributions.Beta(beta, beta).sample([x1.shape[0], 1, 1, 1])
+    if preserve_order:
+        lmda = torch.max(lmda, 1 - lmda)
+    lmda = lmda.to(x1.device)
+    xmix = x1*lmda + x2 * (1-lmda)
+    lmda = lmda[:, :, 0, 0]
+    ymix = y1*lmda + y2 * (1-lmda)
+    return xmix, ymix