chore: import upstream snapshot with attribution

ppocr/optimizer/regularizer.py

@@ -0,0 +1,78 @@
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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
+from __future__ import unicode_literals
+
+import paddle
+
+
+class L1Decay(object):
+    """
+    L1 Weight Decay Regularization, which encourages the weights to be sparse.
+    Args:
+        factor(float): regularization coeff. Default:0.0.
+    """
+
+    def __init__(self, factor=0.0):
+        super(L1Decay, self).__init__()
+        self.coeff = factor
+
+    def __call__(self):
+        reg = paddle.regularizer.L1Decay(self.coeff)
+        return reg
+
+
+class L2Decay(object):
+    """
+    L2 Weight Decay Regularization, which helps to prevent the model over-fitting.
+    Args:
+        factor(float): regularization coeff. Default:0.0.
+    """
+
+    def __init__(self, factor=0.0):
+        super(L2Decay, self).__init__()
+        self.coeff = float(factor)
+
+    def __call__(self):
+        return self.coeff
+
+
+class CosineL2Decay(object):
+    """
+    L2 Weight Decay with cosine annealing schedule.
+
+    Anneals the weight decay coefficient from `factor` to `end_factor`
+    following a cosine curve over total training steps, with optional
+    linear warmup. Avoids over-regularizing small-capacity models.
+
+    Reference: EfficientNetV2 (Tan & Le, 2021) - "annealing the loss
+    incurred by weight decay regularization over the course of training".
+
+    Args:
+        factor(float): initial weight decay coefficient.
+        end_factor(float): final weight decay coefficient. Default: 0.0.
+        warmup_epoch(int|float): warmup epochs (same as lr warmup). Default: 0.
+    """
+
+    def __init__(self, factor=5e-5, end_factor=0.0, warmup_epoch=0):
+        super(CosineL2Decay, self).__init__()
+        self.start_factor = float(factor)
+        self.end_factor = float(end_factor)
+        self.warmup_epoch = warmup_epoch
+
+    def __call__(self):
+        return self.start_factor