diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index 03337985..60974fe2 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -59,26 +59,3 @@ def fuse_conv_and_bn(conv, bn):
         fusedconv.bias.copy_(b_conv + b_bn)
 
         return fusedconv
-
-
-class FocalLoss(nn.Module):
-    # Wraps focal loss around existing loss_fcn() https://arxiv.org/pdf/1708.02002.pdf
-    # i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=2.5)
-    def __init__(self, loss_fcn, alpha=1, gamma=2, reduction='mean'):
-        super(FocalLoss, self).__init__()
-        self.loss_fcn = loss_fcn
-        self.alpha = alpha
-        self.gamma = gamma
-        self.reduction = reduction
-
-    def forward(self, input, target):
-        loss = self.loss_fcn(input, target, reduction='none')
-        pt = torch.exp(-loss)
-        loss *= self.alpha * (1 - pt) ** self.gamma
-
-        if self.reduction == 'mean':
-            return loss.mean()
-        elif self.reduction == 'sum':
-            return loss.sum()
-        else:  # 'none'
-            return loss
diff --git a/utils/utils.py b/utils/utils.py
index e3621a50..0d2e2b39 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -288,6 +288,29 @@ def wh_iou(box1, box2):
     return inter_area / union_area  # iou
 
 
+class FocalLoss(nn.Module):
+    # Wraps focal loss around existing loss_fcn() https://arxiv.org/pdf/1708.02002.pdf
+    # i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=2.5)
+    def __init__(self, loss_fcn, alpha=1, gamma=2, reduction='mean'):
+        super(FocalLoss, self).__init__()
+        self.loss_fcn = loss_fcn
+        self.alpha = alpha
+        self.gamma = gamma
+        self.reduction = reduction
+
+    def forward(self, input, target):
+        loss = self.loss_fcn(input, target, reduction='none')
+        pt = torch.exp(-loss)
+        loss *= self.alpha * (1 - pt) ** self.gamma
+
+        if self.reduction == 'mean':
+            return loss.mean()
+        elif self.reduction == 'sum':
+            return loss.sum()
+        else:  # 'none'
+            return loss
+
+
 def compute_loss(p, targets, model, giou_loss=True):  # predictions, targets, model
     ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
     lcls, lbox, lobj = ft([0]), ft([0]), ft([0])