init

utilities/learningrate_scheduler.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+#############################################################
+# File: learningrate_scheduler.py
+# Created Date: Tuesday January 5th 2021
+# Author: Chen Xuanhong
+# Email: chenxuanhongzju@outlook.com
+# Last Modified:  Tuesday, 5th January 2021 2:04:00 pm
+# Modified By: Chen Xuanhong
+# Copyright (c) 2021 Shanghai Jiao Tong University
+#############################################################
+
+# Refer to basicSR https://github.com/xinntao/BasicSR
+
+
+import math
+from collections import Counter
+from torch.optim.lr_scheduler import _LRScheduler
+
+
+class MultiStepRestartLR(_LRScheduler):
+    """ MultiStep with restarts learning rate scheme.
+
+    Args:
+        optimizer (torch.nn.optimizer): Torch optimizer.
+        milestones (list): Iterations that will decrease learning rate.
+        gamma (float): Decrease ratio. Default: 0.1.
+        restarts (list): Restart iterations. Default: [0].
+        restart_weights (list): Restart weights at each restart iteration.
+            Default: [1].
+        last_epoch (int): Used in _LRScheduler. Default: -1.
+    """
+
+    def __init__(self,
+                 optimizer,
+                 milestones,
+                 gamma=0.1,
+                 restarts=(0,),
+                 restart_weights=(1,),
+                 last_epoch=-1):
+        self.milestones = Counter(milestones)
+        self.gamma = gamma
+        self.restarts = restarts
+        self.restart_weights = restart_weights
+        print(type(self.restarts),self.restarts)
+        print(type(self.restart_weights),self.restart_weights)
+        assert len(self.restarts) == len(
+            self.restart_weights), 'restarts and their weights do not match.'
+        super(MultiStepRestartLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        if self.last_epoch in self.restarts:
+            weight = self.restart_weights[self.restarts.index(self.last_epoch)]
+            return [
+                group['initial_lr'] * weight
+                for group in self.optimizer.param_groups
+            ]
+        if self.last_epoch not in self.milestones:
+            return [group['lr'] for group in self.optimizer.param_groups]
+        return [
+            group['lr'] * self.gamma**self.milestones[self.last_epoch]
+            for group in self.optimizer.param_groups
+        ]
+
+
+def get_position_from_periods(iteration, cumulative_period):
+    """Get the position from a period list.
+
+    It will return the index of the right-closest number in the period list.
+    For example, the cumulative_period = [100, 200, 300, 400],
+    if iteration == 50, return 0;
+    if iteration == 210, return 2;
+    if iteration == 300, return 2.
+
+    Args:
+        iteration (int): Current iteration.
+        cumulative_period (list[int]): Cumulative period list.
+
+    Returns:
+        int: The position of the right-closest number in the period list.
+    """
+    for i, period in enumerate(cumulative_period):
+        if iteration <= period:
+            return i
+
+
+class CosineAnnealingRestartLR(_LRScheduler):
+    """ Cosine annealing with restarts learning rate scheme.
+
+    An example of config:
+    periods = [10, 10, 10, 10]
+    restart_weights = [1, 0.5, 0.5, 0.5]
+    eta_min=1e-7
+
+    It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the
+    scheduler will restart with the weights in restart_weights.
+
+    Args:
+        optimizer (torch.nn.optimizer): Torch optimizer.
+        periods (list): Period for each cosine anneling cycle.
+        restart_weights (list): Restart weights at each restart iteration.
+            Default: [1].
+        eta_min (float): The mimimum lr. Default: 0.
+        last_epoch (int): Used in _LRScheduler. Default: -1.
+    """
+
+    def __init__(self,
+                 optimizer,
+                 periods,
+                 restart_weights=(1),
+                 eta_min=0,
+                 last_epoch=-1):
+        self.periods = periods
+        self.restart_weights = restart_weights
+        self.eta_min = eta_min
+        assert (len(self.periods) == len(self.restart_weights)
+                ), 'periods and restart_weights should have the same length.'
+        self.cumulative_period = [
+            sum(self.periods[0:i + 1]) for i in range(0, len(self.periods))
+        ]
+        super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch)
+
+    def get_lr(self):
+        idx = get_position_from_periods(self.last_epoch,
+                                        self.cumulative_period)
+        current_weight = self.restart_weights[idx]
+        nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1]
+        current_period = self.periods[idx]
+
+        return [
+            self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) *
+            (1 + math.cos(math.pi * (
+                (self.last_epoch - nearest_restart) / current_period)))
+            for base_lr in self.base_lrs
+        ]