From f43502cc214e318ac8ed332572235258443199c7 Mon Sep 17 00:00:00 2001
From: HengZhang <cz189@qq.com>
Date: Thu, 28 Nov 2024 11:51:45 +0800
Subject: [PATCH] Delete  LR_Scheduler.py

---
 .../cl/lr_scheduler/ LR_Scheduler.py          | 68 -------------------
 1 file changed, 68 deletions(-)
 delete mode 100644 federatedscope/cl/lr_scheduler/ LR_Scheduler.py

diff --git a/federatedscope/cl/lr_scheduler/ LR_Scheduler.py b/federatedscope/cl/lr_scheduler/ LR_Scheduler.py
deleted file mode 100644
index 90b82a5..0000000
--- a/federatedscope/cl/lr_scheduler/ LR_Scheduler.py	
+++ /dev/null
@@ -1,68 +0,0 @@
-import numpy as np
-from federatedscope.register import register_scheduler
-
-
-# LR Scheduler
-class LR_Scheduler(object):
-    def __init__(self,
-                 optimizer,
-                 warmup_epochs,
-                 warmup_lr,
-                 num_epochs,
-                 base_lr,
-                 final_lr,
-                 iter_per_epoch,
-                 constant_predictor_lr=False):
-        self.base_lr = base_lr
-        self.constant_predictor_lr = constant_predictor_lr
-        warmup_iter = iter_per_epoch * warmup_epochs
-        warmup_lr_schedule = np.linspace(warmup_lr, base_lr, warmup_iter)
-        decay_iter = iter_per_epoch * (num_epochs - warmup_epochs)
-        cosine_lr_schedule = final_lr + 0.5 * (base_lr - final_lr) * (
-            1 + np.cos(np.pi * np.arange(decay_iter) / decay_iter))
-
-        self.lr_schedule = np.concatenate(
-            (warmup_lr_schedule, cosine_lr_schedule))
-        self.optimizer = optimizer
-        self.iter = 0
-        self.current_lr = 0
-
-    def step(self):
-        for param_group in self.optimizer.param_groups:
-
-            if self.constant_predictor_lr and param_group[
-                    'name'] == 'predictor':
-                param_group['lr'] = self.base_lr
-            else:
-                lr = param_group['lr'] = self.lr_schedule[self.iter]
-
-        self.iter += 1
-        self.current_lr = lr
-        return lr
-
-    def get_lr(self):
-        return self.current_lr
-
-
-def get_scheduler(optimizer, type):
-    try:
-        import torch.optim as optim
-    except ImportError:
-        optim = None
-        scheduler = None
-
-    if type == 'cos_lr_scheduler':
-        if optim is not None:
-            lr_lambda = [lambda epoch: epoch // 30]
-            scheduler = optim.lr_scheduler.LambdaLR(optimizer,
-                                                    warmup_epochs=0,
-                                                    warmup_lr=0,
-                                                    num_epochs=50,
-                                                    base_lr=30,
-                                                    final_lr=0,
-                                                    iter_per_epoch=int(50000 /
-                                                                       512))
-        return scheduler
-
-
-register_scheduler('cos_lr_scheduler', get_scheduler)