From 2c2e780700faf78488f2c6ec30c31397edd1ab88 Mon Sep 17 00:00:00 2001
From: alikia2x <alikia2x@outlook.com>
Date: Fri, 7 Mar 2025 22:25:37 +0800
Subject: [PATCH] update: idfk

---
 filter/RunningLogs.txt          |  3 +--
 filter/checkpoint_conversion.py | 26 --------------------------
 filter/train.py                 |  4 ++--
 3 files changed, 3 insertions(+), 30 deletions(-)
 delete mode 100644 filter/checkpoint_conversion.py

diff --git a/filter/RunningLogs.txt b/filter/RunningLogs.txt
index ba7b286..7b8cc7f 100644
--- a/filter/RunningLogs.txt
+++ b/filter/RunningLogs.txt
@@ -34,5 +34,4 @@ Note
 0219: V6.1  # MPS训练
 0242: V6.1  # 自定义loss
 0259: V6.1  # 调整学习率 (自定义loss)
-0314: V6.1  # 调整学习率（交叉熵损失）
-0349: V6.3  # 增加层数至2
\ No newline at end of file
+0314: V6.1  # 调整学习率（交叉熵损失）
\ No newline at end of file
diff --git a/filter/checkpoint_conversion.py b/filter/checkpoint_conversion.py
deleted file mode 100644
index 61a72ed..0000000
--- a/filter/checkpoint_conversion.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import torch
-
-from modelV3_10 import VideoClassifierV3_10
-from modelV3_9 import VideoClassifierV3_9
-
-
-def convert_checkpoint(original_model, new_model):
-    """转换原始checkpoint到新结构"""
-    state_dict = original_model.state_dict()
-    
-    # 直接复制所有参数（因为结构保持兼容）
-    new_model.load_state_dict(state_dict)
-    return new_model
-
-# 使用示例
-original_model = VideoClassifierV3_9()
-new_model = VideoClassifierV3_10()
-
-# 加载原始checkpoint
-original_model.load_state_dict(torch.load('./filter/checkpoints/best_model_V3.9.pt'))
-
-# 转换参数
-converted_model = convert_checkpoint(original_model, new_model)
-
-# 保存转换后的模型
-torch.save(converted_model.state_dict(), './filter/checkpoints/best_model_V3.10.pt')
\ No newline at end of file
diff --git a/filter/train.py b/filter/train.py
index f35e0d7..3a65346 100644
--- a/filter/train.py
+++ b/filter/train.py
@@ -76,9 +76,9 @@ eval_interval = 20
 num_epochs = 20
 total_steps = samples_count * num_epochs / batch_size
 warmup_rate = 0.1
-optimizer = optim.AdamW(model.parameters(), lr=5e-5, weight_decay=1e-5)
+optimizer = optim.AdamW(model.parameters(), lr=1e-5, weight_decay=1e-3)
 cosine_annealing_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps - int(total_steps * warmup_rate))
-warmup_scheduler = optim.lr_scheduler.LinearLR(optimizer, start_factor=0.1, end_factor=1.0, total_iters=int(total_steps * warmup_rate))
+warmup_scheduler = optim.lr_scheduler.LinearLR(optimizer, start_factor=0.4, end_factor=1.0, total_iters=int(total_steps * warmup_rate))
 scheduler = optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler, cosine_annealing_scheduler], milestones=[int(total_steps * warmup_rate)])
 criterion = nn.CrossEntropyLoss(weight=class_weights).to(device)