change_hair/train_hairstyles_parallel.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""批量并行训练多个发型

流程：
  阶段A: 串行 step1（准备训练数据，共用GPU模型，避免重复加载）
  阶段B: 并行 step2（LoRA训练，kohya独立进程，限制并发数）
  阶段C: 启动服务后串行 step3(回调生成ref材质) + step4(模板) + step5(预览)

用法:
  cd /home/xsl/change_hair/project/hair_service_sd
  python /home/xsl/change_hair/train_hairstyles_parallel.py
"""
import os
import sys
import time
import json
import subprocess
from concurrent.futures import ThreadPoolExecutor, as_completed

HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd"
os.chdir(HAIR_SERVICE_DIR)
sys.path.insert(0, HAIR_SERVICE_DIR)
os.environ.setdefault("HF_HUB_OFFLINE", "1")
os.environ.setdefault("TRANSFORMERS_OFFLINE", "1")
os.environ.setdefault("CRYPTOGRAPHY_OPENSSL_NO_LEGACY", "1")
os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0")

# ====== 配置：本次要训练的发型 ======
# (hair_id, input_dir, gender, template_img)
HAIRSTYLES = [
    ("chang_tuoyuan", "/home/xsl/change_hair/hair_type_images/chang_tuoyuan", "girl", "/home/xsl/change_hair/hair_type_images/chang_tuoyuan/chang_tuoyuan.jpg"),
    ("chang_bolang",  "/home/xsl/change_hair/hair_type_images/chang_bolang",  "girl", "/home/xsl/change_hair/hair_type_images/chang_bolang/chang_bolang.jpg"),
    ("chang_zhixian", "/home/xsl/change_hair/hair_type_images/chang_zhixian", "girl", "/home/xsl/change_hair/hair_type_images/chang_zhixian/chang_zhixian.jpg"),
    ("chang_huaban",  "/home/xsl/change_hair/hair_type_images/chang_huaban",  "girl", "/home/xsl/change_hair/hair_type_images/chang_huaban/chang_huaban.jpg"),
    ("chang_xinxing", "/home/xsl/change_hair/hair_type_images/chang_xinxing", "girl", "/home/xsl/change_hair/hair_type_images/chang_xinxing/chang_xinxing.jpg"),
]
PARALLEL = 2  # LoRA 训练并发数

# 导入训练模块（复用其 step 函数）
from train_hairstyle_full import (
    step1_prepare_data, step2_train, step3_wait_and_callback,
    step4_template_material, step5_preview, get_models
)
import train_hairstyle_full as TH
from common.logger import config


def log(msg):
    print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)


def main():
    t0 = time.time()
    ids = [h[0] for h in HAIRSTYLES]
    log(f"批量训练 {len(ids)} 个发型: {ids}  (并发={PARALLEL})")

    # ============ 阶段A: 串行 step1（准备数据）============
    log("=" * 60)
    log("阶段A: 串行准备训练数据 (step1)")
    log("=" * 60)
    log("  预加载模型...")
    get_models()  # 预加载，5个发型共用
    step1_ok, step1_fail = [], []
    for hair_id, input_dir, gender, _ in HAIRSTYLES:
        # 清理可能的历史训练数据，确保干净
        train_dir = config.get('default', 'train_dir')
        out_dir = os.path.join(train_dir, hair_id, "images", "1_hairstyle")
        os.makedirs(out_dir, exist_ok=True)
        os.makedirs(os.path.join(train_dir, hair_id, "model"), exist_ok=True)

        log(f"  --- step1: {hair_id} ---")
        try:
            ok = step1_prepare_data(hair_id, input_dir, gender)
            if ok:
                step1_ok.append(hair_id)
                log(f"  ✓ {hair_id} 数据准备完成")
            else:
                step1_fail.append(hair_id)
                log(f"  ✗ {hair_id} 数据准备失败")
        except Exception as e:
            step1_fail.append(hair_id)
            log(f"  ✗ {hair_id} 数据准备异常: {e}")
    log(f"阶段A完成: 成功 {len(step1_ok)}/{len(ids)}，失败 {step1_fail}")
    if not step1_ok:
        log("全部 step1 失败，退出"); return

    # 释放 step1 用的 GPU 模型，给 step2 训练腾显存
    log("释放 step1 模型显存...")
    import torch
    try:
        for m in [TH._detector, TH._aligner, TH._matte]:
            if m is not None and hasattr(m, 'model'):
                pass
        torch.cuda.empty_cache()
    except Exception:
        pass

    # ============ 阶段B: 并行 step2（LoRA 训练）============
    log("=" * 60)
    log(f"阶段B: 并行 LoRA 训练 (step2, 并发={PARALLEL})")
    log("=" * 60)
    step2_results = {}  # hair_id -> bool
    with ThreadPoolExecutor(max_workers=PARALLEL) as ex:
        futures = {ex.submit(step2_train, hid): hid for hid in step1_ok}
        for fut in as_completed(futures):
            hid = futures[fut]
            try:
                ok = fut.result()
                step2_results[hid] = ok
                log(f"  {'✓' if ok else '✗'} {hid} step2 (训练启动)={'成功' if ok else '失败'}")
            except Exception as e:
                step2_results[hid] = False
                log(f"  ✗ {hid} step2 异常: {e}")

    # 等待所有 LoRA 训练真正完成（step2 只是启动，训练在 photo_service 后台跑）
    log("等待所有 LoRA 训练完成（检查权重文件）...")
    train_dir = config.get('default', 'train_dir')
    pending = [hid for hid in step1_ok if step2_results.get(hid)]
    completed = set()
    deadline = time.time() + 3600  # 最多等1小时
    while pending and time.time() < deadline:
        still = []
        for hid in pending:
            lora = os.path.join(train_dir, hid, "model", "hairstyle_hd_lora.safetensors")
            if os.path.exists(lora) and os.path.getsize(lora) > 100000000:
                completed.add(hid)
                log(f"  ✓ {hid} LoRA 训练完成 ({os.path.getsize(lora)//1024//1024}MB)")
            else:
                still.append(hid)
        pending = still
        if pending:
            log(f"  ...等待中: {pending}")
            time.sleep(30)
    if pending:
        log(f"⚠️ 超时未完成: {pending}")
    log(f"阶段B完成: LoRA 训练完成 {len(completed)}/{len(step1_ok)}")

    # 写一个完成清单文件，供阶段C脚本读取
    done_file = "/home/xsl/change_hair/project/logs/train_batch_done.json"
    with open(done_file, "w") as f:
        json.dump({"completed": sorted(completed), "all": ids}, f)
    log(f"已写入完成清单: {done_file}")
    log(f"\n批量训练阶段A+B总耗时 {time.time()-t0:.0f}s")
    log(f"完成训练的发型: {sorted(completed)}")
    log("接下来需启动服务跑阶段C（step3/4/5 生成材质和预览）")


if __name__ == "__main__":
    main()