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benchmark/PaddleOCR_DBNet/utils/schedulers.py

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+from paddle.optimizer import lr
+import logging
+
+__all__ = ["Polynomial"]
+
+
+class Polynomial(object):
+    """
+    Polynomial learning rate decay
+    Args:
+        learning_rate (float): The initial learning rate. It is a python float number.
+        epochs(int): The decay epoch size. It determines the decay cycle, when by_epoch is set to true, it will change to epochs=epochs*step_each_epoch.
+        step_each_epoch: all steps in each epoch.
+        end_lr(float, optional): The minimum final learning rate. Default: 0.0001.
+        power(float, optional): Power of polynomial. Default: 1.0.
+        warmup_epoch(int): The epoch numbers for LinearWarmup. Default: 0, , when by_epoch is set to true, it will change to warmup_epoch=warmup_epoch*step_each_epoch.
+        warmup_start_lr(float): Initial learning rate of warm up. Default: 0.0.
+        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
+        by_epoch: Whether the set parameter is based on epoch or iter, when set to true,, epochs and warmup_epoch will be automatically multiplied by step_each_epoch. Default: True
+    """
+
+    def __init__(
+        self,
+        learning_rate,
+        epochs,
+        step_each_epoch,
+        end_lr=0.0,
+        power=1.0,
+        warmup_epoch=0,
+        warmup_start_lr=0.0,
+        last_epoch=-1,
+        by_epoch=True,
+        **kwargs,
+    ):
+        super().__init__()
+        if warmup_epoch >= epochs:
+            msg = f'When using warm up, the value of "epochs" must be greater than value of "Optimizer.lr.warmup_epoch". The value of "Optimizer.lr.warmup_epoch" has been set to {epochs}.'
+            logging.warning(msg)
+            warmup_epoch = epochs
+        self.learning_rate = learning_rate
+        self.epochs = epochs
+        self.end_lr = end_lr
+        self.power = power
+        self.last_epoch = last_epoch
+        self.warmup_epoch = warmup_epoch
+        self.warmup_start_lr = warmup_start_lr
+
+        if by_epoch:
+            self.epochs *= step_each_epoch
+            self.warmup_epoch = int(self.warmup_epoch * step_each_epoch)
+
+    def __call__(self):
+        learning_rate = (
+            lr.PolynomialDecay(
+                learning_rate=self.learning_rate,
+                decay_steps=self.epochs,
+                end_lr=self.end_lr,
+                power=self.power,
+                last_epoch=self.last_epoch,
+            )
+            if self.epochs > 0
+            else self.learning_rate
+        )
+        if self.warmup_epoch > 0:
+            learning_rate = lr.LinearWarmup(
+                learning_rate=learning_rate,
+                warmup_steps=self.warmup_epoch,
+                start_lr=self.warmup_start_lr,
+                end_lr=self.learning_rate,
+                last_epoch=self.last_epoch,
+            )
+        return learning_rate