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rtdetr_paddle/ppdet/modeling/losses/smooth_l1_loss.py

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+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from ppdet.core.workspace import register
+
+__all__ = ['SmoothL1Loss']
+
+@register
+class SmoothL1Loss(nn.Layer):
+    """Smooth L1 Loss.
+    Args:
+        beta (float): controls smooth region, it becomes L1 Loss when beta=0.0
+        loss_weight (float): the final loss will be multiplied by this 
+    """
+    def __init__(self,
+                 beta=1.0,
+                 loss_weight=1.0):
+        super(SmoothL1Loss, self).__init__()
+        assert beta >= 0
+        self.beta = beta
+        self.loss_weight = loss_weight
+
+    def forward(self, pred, target, reduction='none'):
+        """forward function, based on fvcore.
+        Args:
+            pred (Tensor): prediction tensor
+            target (Tensor): target tensor, pred.shape must be the same as target.shape
+            reduction (str): the way to reduce loss, one of (none, sum, mean)
+        """
+        assert reduction in ('none', 'sum', 'mean')
+        target = target.detach()
+        if self.beta < 1e-5:
+            loss = paddle.abs(pred - target)
+        else:
+            n = paddle.abs(pred - target)
+            cond = n < self.beta
+            loss = paddle.where(cond, 0.5 * n ** 2 / self.beta, n - 0.5 * self.beta)
+        if reduction == 'mean':
+            loss = loss.mean() if loss.size > 0 else 0.0 * loss.sum()
+        elif reduction == 'sum':
+            loss = loss.sum()
+        return loss * self.loss_weight