Add a BCE loss impl that converts dense targets to sparse /w smoothing as an alternate to CE w/ smoothing. For training experiments.

timm/loss/binary_cross_entropy.py

@@ -0,0 +1,23 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class DenseBinaryCrossEntropy(nn.Module):
+    """ BCE using one-hot from dense targets w/ label smoothing
+    NOTE for experiments comparing CE to BCE /w label smoothing, may remove
+    """
+    def __init__(self, smoothing=0.1):
+        super(DenseBinaryCrossEntropy, self).__init__()
+        assert 0. <= smoothing < 1.0
+        self.smoothing = smoothing
+        self.bce = nn.BCEWithLogitsLoss()
+
+    def forward(self, x, target):
+        num_classes = x.shape[-1]
+        off_value = self.smoothing / num_classes
+        on_value = 1. - self.smoothing + off_value
+        target = target.long().view(-1, 1)
+        target = torch.full(
+            (target.size()[0], num_classes), off_value, device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
+        return self.bce(x, target)