Shaun ZhouandClaude Opus 5
ef64e5bf77
- trainer: AMP、梯度裁剪、cosine/step 调度、last/best 检查点，按 monitor 指标挑选 best - losses: penalty-reduced focal loss（heatmap）+ 掩码 L1（wh/offset）+ 分类损失 （多类 BCE / 单类 CE，随 label_mode 切换） - metrics: 定位 IoU 匹配下的 P/R/F1 与 mean IoU；分类多标签 micro-F1 与单标签 accuracy， 两种模式都输出 cls_f1，故 train.monitor 无需跟着改 - optim: BatchNorm 与 bias 不做 weight decay - visualizer: SwanLab 记录损失/学习率/验证指标/显存峰值，未安装或离线时降级为警告 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
18 lines
427 B
Python
18 lines
427 B
Python
"""训练 / 评估流程。"""
|
|
|
|
from engine.checkpoint import load_checkpoint, save_checkpoint
|
|
from engine.evaluator import Evaluator
|
|
from engine.losses import DetectionLoss
|
|
from engine.optim import build_optimizer, build_scheduler
|
|
from engine.trainer import Trainer
|
|
|
|
__all__ = [
|
|
"Trainer",
|
|
"Evaluator",
|
|
"DetectionLoss",
|
|
"build_optimizer",
|
|
"build_scheduler",
|
|
"save_checkpoint",
|
|
"load_checkpoint",
|
|
]
|