From 12cb7652cfc56846adf4be9dacf6d67a09bafbbe Mon Sep 17 00:00:00 2001
From: gameloader <ggwqqo@163.com>
Date: Wed, 3 Sep 2025 07:13:25 +0000
Subject: [PATCH] feat: add sigmoid learning rate scheduler

Add new 'sigmoid' learning rate adjustment method to utils/tools.py
with logistic growth rate and warm-up smoothing for better training
convergence.
---
 utils/tools.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/utils/tools.py b/utils/tools.py
index 6efa712..701d0b6 100644
--- a/utils/tools.py
+++ b/utils/tools.py
@@ -22,6 +22,11 @@ def adjust_learning_rate(optimizer, epoch, args):
         lr_adjust = {epoch: args.learning_rate if epoch < 3 else args.learning_rate * (0.9 ** ((epoch - 3) // 1))}
     elif args.lradj == "cosine":
         lr_adjust = {epoch: args.learning_rate /2 * (1 + math.cos(epoch / args.train_epochs * math.pi))}
+    elif args.lradj == 'sigmoid':
+        k = 0.5 # logistic growth rate
+        s = 10  # decreasing curve smoothing rate
+        w = 10  # warm-up coefficient
+        lr_adjust = {epoch: args.learning_rate / (1 + np.exp(-k * (epoch - w))) - args.learning_rate / (1 + np.exp(-k/s * (epoch - w*s)))}
     if epoch in lr_adjust.keys():
         lr = lr_adjust[epoch]
         for param_group in optimizer.param_groups: