asymmetric_loss

timm/loss/__init__.py

@@ -1,2 +1,3 @@
 from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
 from .jsd import JsdCrossEntropy
+from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel

timm/loss/asymmetric_loss.py

@@ -0,0 +1,97 @@
+import torch
+import torch.nn as nn
+
+
+class AsymmetricLossMultiLabel(nn.Module):
+    def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
+        super(AsymmetricLossMultiLabel, self).__init__()
+
+        self.gamma_neg = gamma_neg
+        self.gamma_pos = gamma_pos
+        self.clip = clip
+        self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss
+        self.eps = eps
+
+    def forward(self, x, y):
+        """"
+        Parameters
+        ----------
+        x: input logits
+        y: targets (multi-label binarized vector)
+        """
+
+        # Calculating Probabilities
+        x_sigmoid = torch.sigmoid(x)
+        xs_pos = x_sigmoid
+        xs_neg = 1 - x_sigmoid
+
+        # Asymmetric Clipping
+        if self.clip is not None and self.clip > 0:
+            xs_neg = (xs_neg + self.clip).clamp(max=1)
+
+        # Basic CE calculation
+        los_pos = y * torch.log(xs_pos.clamp(min=self.eps))
+        los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))
+        loss = los_pos + los_neg
+
+        # Asymmetric Focusing
+        if self.gamma_neg > 0 or self.gamma_pos > 0:
+            if self.disable_torch_grad_focal_loss:
+                torch._C.set_grad_enabled(False)
+            pt0 = xs_pos * y
+            pt1 = xs_neg * (1 - y)  # pt = p if t > 0 else 1-p
+            pt = pt0 + pt1
+            one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)
+            one_sided_w = torch.pow(1 - pt, one_sided_gamma)
+            if self.disable_torch_grad_focal_loss:
+                torch._C.set_grad_enabled(True)
+            loss *= one_sided_w
+
+        return -loss.sum()
+
+
+class AsymmetricLossSingleLabel(nn.Module):
+    def __init__(self, gamma_pos=1, gamma_neg=4, eps: float = 0.1, reduction='mean'):
+        super(AsymmetricLossSingleLabel, self).__init__()
+
+        self.eps = eps
+        self.logsoftmax = nn.LogSoftmax(dim=-1)
+        self.targets_classes = []  # prevent gpu repeated memory allocation
+        self.gamma_pos = gamma_pos
+        self.gamma_neg = gamma_neg
+        self.reduction = reduction
+
+    def forward(self, inputs, target, reduction=None):
+        """"
+        Parameters
+        ----------
+        x: input logits
+        y: targets (1-hot vector)
+        """
+
+        num_classes = inputs.size()[-1]
+        log_preds = self.logsoftmax(inputs)
+        self.targets_classes = torch.zeros_like(inputs).scatter_(1, target.long().unsqueeze(1), 1)
+
+        # ASL weights
+        targets = self.targets_classes
+        anti_targets = 1 - targets
+        xs_pos = torch.exp(log_preds)
+        xs_neg = 1 - xs_pos
+        xs_pos = xs_pos * targets
+        xs_neg = xs_neg * anti_targets
+        asymmetric_w = torch.pow(1 - xs_pos - xs_neg,
+                                 self.gamma_pos * targets + self.gamma_neg * anti_targets)
+        log_preds = log_preds * asymmetric_w
+
+        if self.eps > 0:  # label smoothing
+            self.targets_classes.mul_(1 - self.eps).add_(self.eps / num_classes)
+
+        # loss calculation
+        loss = - self.targets_classes.mul(log_preds)
+
+        loss = loss.sum(dim=-1)
+        if self.reduction == 'mean':
+            loss = loss.mean()
+
+        return loss