1180 lines
50 KiB
Python
Executable File
1180 lines
50 KiB
Python
Executable File
# train.py头部添加这些修改
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import os
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os.environ['GIT_PYTHON_REFRESH'] = 'quiet'
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os.environ['WANDB_MODE'] = 'disabled'
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import argparse
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import logging
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import math
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import os
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import random
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import time
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from copy import deepcopy
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from pathlib import Path
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from threading import Thread
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import numpy as np
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import torch.distributed as dist
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torch.optim.lr_scheduler as lr_scheduler
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import torch.utils.data
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import datetime
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import yaml
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from torch.cuda import amp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.utils.tensorboard import SummaryWriter
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from tqdm import tqdm
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from kafka import KafkaProducer
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# jcq: 增加web端数据传输 - 0508
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import requests
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from flask import Flask, request, jsonify
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from threading import Thread
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import uuid
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import json
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from flask_restful import Api
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app = Flask(__name__)
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api = Api(app)
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import test # import test.py to get mAP after each epoch
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from models.experimental import attempt_load
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from models.yolo import Model
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from utils.autoanchor import check_anchors
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from utils.datasets import create_dataloader
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from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
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fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
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check_requirements, print_mutation, set_logging, one_cycle, colorstr
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from utils.google_utils import attempt_download
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from utils.loss import ComputeLoss
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from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
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from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first, is_parallel
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from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
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# Kafka imports
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from kafka import KafkaConsumer
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import json
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import threading
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from ast import literal_eval
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import io
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import json
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import cv2
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import os
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import urllib3
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from minio import Minio
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# train.py头部添加这些修改
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import os
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os.environ['GIT_PYTHON_REFRESH'] = 'quiet'
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os.environ['WANDB_MODE'] = 'disabled'
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import numpy as np
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np.int = np.int64 # 全局替换已弃用的np.int
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# MinIO
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ENDPOINT = "minio-jndsj.t-aaron.com:2443" # MinIO服务器地址
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ACCESS_KEY = "PJM0c2qlauoXv5TMEHm2" # 访问密钥
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SECRET_KEY = "Wr69Dm3ZH39M3GCSeyB3eFLynLPuGCKYfphixZuI" # 密钥
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# 创建MinIO客户端
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minioClient = Minio(
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ENDPOINT,
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access_key=ACCESS_KEY,
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secret_key=SECRET_KEY
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)
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logger = logging.getLogger(__name__)
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topic_train ='training-tasks'
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# jcq: 增加web端数据传输 - 0508
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# 替换为实际的目标 URL
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url = "http://172.18.0.129:8084/api/admin/trainTask/updateTrainTask"
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# 自定义请求头，根据实际接口需求调整
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headers = {
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"Accept": "application/json",
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"Content-Type": "application/json"
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}
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class TrainingServer:
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def __init__(self):
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self.shutdown_flag = False
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self.current_training_thread = None
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self.running_tasks = {} # Dictionary to track running tasks {request_id: (thread, stop_event)}
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self.kafka_producer = None
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self.setup_logging()
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self.setup_producer()
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def setup_producer(self):
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"""Initialize and maintain Kafka producer connection"""
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producer_config = {
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'bootstrap_servers': '172.18.0.126:9094',
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'value_serializer': lambda v: json.dumps(v).encode('utf-8'),
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'retries': 5,
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'retry_backoff_ms': 1000,
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'request_timeout_ms': 30000,
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'max_block_ms': 60000,
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'acks': 'all' # Ensure message durability
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}
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try:
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self.kafka_producer = KafkaProducer(**producer_config)
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logger.info("Kafka producer initialized successfully")
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except Exception as e:
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logger.error(f"Failed to initialize Kafka producer: {e}")
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self.kafka_producer = None
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def get_producer(self):
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"""获取可用的生产者实例"""
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if self.kafka_producer is None:
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self.setup_producer()
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return self.kafka_producer
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def safe_send_metrics(self, topic, data):
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"""Thread-safe metric sending with error handling"""
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producer = self.get_producer()
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if producer:
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try:
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future = producer.send(
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topic,
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value=data,
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timestamp_ms=int(time.time()*1000))
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# 修正后的回调函数
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def delivery_callback(err, msg=None):
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if err:
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logger.error(f"Message delivery failed: {err}")
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self.kafka_producer = None
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else:
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logger.debug(f"Message delivered to {msg.topic}[{msg.partition}]")
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future.add_callback(delivery_callback)
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return True
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except Exception as e:
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logger.error(f"Error sending metrics: {e}")
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self.kafka_producer = None
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return False
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def start_training(self, opt, request_id,version,dspModelCode):
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# 确定保存文件夹路径
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"""Start training with given options"""
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try:
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# Set save_dir if not already set
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if not hasattr(opt, 'save_dir') or not opt.save_dir:
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opt.save_dir = str(Path(opt.project) / opt.name)
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opt.save_dir = increment_path(Path(opt.save_dir), exist_ok=opt.exist_ok)
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# Load hyperparameters
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with open(opt.hyp) as f:
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hyp = yaml.load(f, Loader=yaml.SafeLoader)
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# # Set device
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device = select_device(opt.device, batch_size=opt.batch_size)
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self.current_training_thread = threading.Thread(
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target=self.train_wrapper,
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args=(hyp, opt, device, request_id,version,dspModelCode),
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daemon=True
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)
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self.current_training_thread.start()
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except Exception as e:
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logger.error(f"Error starting training: {e}")
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def stop_training(self, request_id):
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"""Stop a specific training task by request_id"""
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if request_id in self.running_tasks:
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thread, stop_event = self.running_tasks[request_id]
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stop_event.set()
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thread.join(timeout=5)
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if thread.is_alive():
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logger.warning(f"Training thread for {request_id} did not stop gracefully")
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del self.running_tasks[request_id]
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logger.info(f"Stopped training task {request_id}")
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# Prepare and send the API request
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current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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payload = {
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"id": request_id, # Using the actual request_id instead of hardcoded "JCQ"
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"status": 5
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}
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try:
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response = requests.post(
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"http://172.18.0.129:8084/api/admin/trainTask/updateTrainTask",
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json=payload
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)
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response.raise_for_status() # Raise exception for bad status codes
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logger.info(f"Successfully updated task {request_id} via API")
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except requests.exceptions.RequestException as e:
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logger.error(f"Failed to update task {request_id} via API: {str(e)}")
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return True
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else:
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logger.warning(f"No running training task found with ID {request_id}")
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return False
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def train_wrapper(self, hyp, opt, device, request_id, version, dspModelCode):
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"""Wrapper for the train function to handle exceptions and stop events"""
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stop_event = threading.Event()
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self.running_tasks[request_id] = (threading.current_thread(), stop_event)
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try:
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logger.info(f"Starting training for request {request_id}")
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# Send training start notification
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self.safe_send_metrics('train_events', {
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'event_type': 'training_started',
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'request_id': request_id,
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'timestamp': time.time(),
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'params': vars(opt)
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})
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# 调用训练函数
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device = torch.device('cpu')
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train(hyp, opt, device, request_id, version, dspModelCode, None, stop_event)
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logger.info(f"Training completed successfully for request {request_id}")
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self.safe_send_metrics('train_events', {
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'event_type': 'training_completed',
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'request_id': request_id,
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'timestamp': time.time()
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})
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except Exception as e:
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logger.error(f"Training failed for request {request_id}: {str(e)}", exc_info=True)
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# 构建错误响应数据
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error_data = {
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"id": request_id,
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"status": 5, # 错误状态码
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"error": str(e)
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}
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# 发送错误通知到API
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try:
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response = requests.post(
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"http://172.18.0.129:8084/api/admin/trainTask/updateTrainTask",
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headers=headers,
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json=error_data
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)
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response.raise_for_status()
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logger.info(f"Successfully sent error notification for task {request_id}")
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except requests.exceptions.RequestException as api_err:
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logger.error(f"Failed to send error notification: {api_err}")
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# 发送错误事件到Kafka
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self.safe_send_metrics('train_events', {
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'event_type': 'training_failed',
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'request_id': request_id,
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'timestamp': time.time(),
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'error': str(e)
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})
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finally:
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if request_id in self.running_tasks:
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del self.running_tasks[request_id]
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def run_server(self):
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"""Main server loop to listen for training requests"""
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logger.info("Starting YOLOv5 Training Server")
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while not self.shutdown_flag:
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try:
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consumer = self.setup_kafka_consumer()
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logger.info("Kafka consumer initialized, waiting for training requests...")
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for message in consumer:
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if self.shutdown_flag:
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logger.info("Shutdown flag detected, stopping consumer...")
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break
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try:
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request_data = message.value
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logger.info(f"Received training request: {request_data}")
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command, data= self.process_training_request(request_data)
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request_id ,version,dspModelCode = request_data.get("Request_ID"),request_data.get("Version"),request_data.get("Model")
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print("###line318",request_id,version,dspModelCode)
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if command == 'start':
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opt, request_id = data
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if request_id in self.running_tasks:
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logger.warning(f"Training already in progress for ID {request_id}")
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continue
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logger.info(f"Starting training with params: {vars(opt)}")
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self.start_training(opt, request_id,version,dspModelCode)
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elif command == 'stop':
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request_id = data
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self.stop_training(request_id)
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except Exception as e:
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logger.error(f"Error processing Kafka message: {e}", exc_info=True)
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continue
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except Exception as e:
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logger.error(f"Error processing Kafka message: {e}", exc_info=True)
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continue
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def setup_logging(self):
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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def setup_kafka_consumer(self):
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"""Setup Kafka consumer with proper configuration"""
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kafka_config = {
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'bootstrap_servers': '172.18.0.126:9094', # Use your actual broker IP
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'group_id': 'yolov5_training_server',
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'auto_offset_reset': 'latest',
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'enable_auto_commit': True,
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'auto_commit_interval_ms': 5000,
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'session_timeout_ms': 10000,
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'heartbeat_interval_ms': 3000,
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'max_poll_interval_ms': 300000,
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'value_deserializer': lambda x: json.loads(x.decode('utf-8'))
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}
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return KafkaConsumer(topic_train, **kafka_config)
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def parse_training_parameters(self, train_params_str):
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"""Parse training parameters string"""
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try:
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params = literal_eval(train_params_str)
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if not isinstance(params, list):
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return None
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return {
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'batch_size': params[0] if len(params) > 0 else 8,
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'imgsz': params[1] if len(params) > 1 else 640,
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'epochs': params[2] if len(params) > 2 else 300,
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'data': params[3] if len(params) > 3 else 'data/ship2.yaml',
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'weights': params[4] if len(params) > 4 else 'yolov5s.pt'
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}
|
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except Exception as e:
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logger.error(f"Error parsing training parameters: {e}")
|
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return None
|
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|
||
|
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|
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def process_training_request(self, request_data):
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|
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|
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"""Process incoming training request with all required attributes"""
|
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if request_data.get('Scene') != 'Train':
|
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return None, None
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|
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if request_data.get('Command') == 'stop':
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return 'stop', request_data.get('Request_ID')
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|
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|
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|
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# Parse training parameters
|
||
params = self.parse_training_parameters(request_data.get('TrainParameter', '[]'))
|
||
if not params:
|
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params = {
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'batch_size': 8,
|
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'imgsz': 640,
|
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'epochs': 300,
|
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'data': 'data/ship2.yaml',
|
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'weights': 'yolov5s.pt'
|
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}
|
||
|
||
# Create training arguments with all required attributes
|
||
train_args = {
|
||
'weights': params['weights'],
|
||
'data': params['data'],
|
||
'epochs': params['epochs'],
|
||
'batch_size': params['batch_size'],
|
||
'img_size': [params['imgsz'], params['imgsz']],
|
||
'project': f"runs/train/{request_data.get('Request_ID', 'default')}",
|
||
# 'name': request_data.get('Version', 'v1.0'),
|
||
'name': request_data.get('Version'),
|
||
'exist_ok': True,
|
||
'from_kafka': True,
|
||
|
||
# Add all required default attributes
|
||
'save_dir': '', # This will be set in train() function
|
||
'global_rank': -1,
|
||
'local_rank': -1,
|
||
'world_size': 1,
|
||
'total_batch_size': params['batch_size'],
|
||
'hyp': 'data/hyp.scratch.yaml',
|
||
'cfg': '',
|
||
'rect': False,
|
||
'resume': False,
|
||
'nosave': False,
|
||
'notest': False,
|
||
'noautoanchor': False,
|
||
'cache_images': False,
|
||
'image_weights': False,
|
||
'device': '',
|
||
'multi_scale': False,
|
||
'single_cls': False,
|
||
'adam': False,
|
||
'sync_bn': False,
|
||
'workers': 8,
|
||
'entity': None,
|
||
'upload_dataset': False,
|
||
'bbox_interval': -1,
|
||
'quad': False,
|
||
'linear_lr': False,
|
||
'label_smoothing': 0.0,
|
||
'save_period': -1,
|
||
'artifact_alias': "latest",
|
||
'bucket': '',
|
||
'evolve': False
|
||
}
|
||
|
||
opt = argparse.Namespace(**train_args)
|
||
|
||
# 增加返回的参数
|
||
|
||
return 'start', (opt, request_data.get('Request_ID'))
|
||
|
||
|
||
def train(hyp, opt, device,task_id ,version,dspModelCode,tb_writer=None, stop_event=None):
|
||
|
||
try:
|
||
|
||
print("###line473",task_id,version,dspModelCode)
|
||
|
||
# 移除局部生产者初始化，使用server实例的生产者
|
||
producer = server.get_producer() # 通过全局server实例获取
|
||
# 初始化 Kafka 生产者
|
||
kafka_producer = KafkaProducer(
|
||
bootstrap_servers='172.18.0.126:9094',
|
||
value_serializer=lambda v: json.dumps(v).encode('utf-8')
|
||
)
|
||
|
||
|
||
"""主函数修改，支持从Kafka启动"""
|
||
# 如果是通过Kafka启动的训练，不检查git状态
|
||
if not getattr(opt, 'from_kafka', False):
|
||
if opt.global_rank in [-1, 0]:
|
||
print("##line513")
|
||
# check_git_status()
|
||
# check_requirements()
|
||
|
||
|
||
logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
|
||
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
|
||
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
|
||
|
||
# Directories
|
||
wdir = save_dir / 'weights'
|
||
wdir.mkdir(parents=True, exist_ok=True) # make dir
|
||
last = wdir / 'last.pt'
|
||
best = wdir / 'best.pt'
|
||
|
||
#jcq:
|
||
yolov5 = wdir / 'yolov5.pt'
|
||
|
||
results_file = save_dir / 'results.txt'
|
||
|
||
# Save run settings
|
||
with open(save_dir / 'hyp.yaml', 'w') as f:
|
||
yaml.dump(hyp, f, sort_keys=False)
|
||
with open(save_dir / 'opt.yaml', 'w') as f:
|
||
yaml.dump(vars(opt), f, sort_keys=False)
|
||
|
||
# Configure
|
||
plots = not opt.evolve # create plots
|
||
cuda = device.type != 'cpu'
|
||
init_seeds(2 + rank)
|
||
with open(opt.data) as f:
|
||
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
|
||
is_coco = opt.data.endswith('coco.yaml')
|
||
|
||
# Logging- Doing this before checking the dataset. Might update data_dict
|
||
loggers = {'wandb': None} # loggers dict
|
||
if rank in [-1, 0]:
|
||
opt.hyp = hyp # add hyperparameters
|
||
run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
|
||
# run_id = torch.load(weights, weights_only=False).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
|
||
wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
|
||
loggers['wandb'] = wandb_logger.wandb
|
||
data_dict = wandb_logger.data_dict
|
||
if wandb_logger.wandb:
|
||
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
|
||
|
||
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
|
||
names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
|
||
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
|
||
|
||
# Model
|
||
pretrained = weights.endswith('.pt')
|
||
if pretrained:
|
||
with torch_distributed_zero_first(rank):
|
||
print("###567")
|
||
|
||
|
||
|
||
|
||
# attempt_download(weights) # download if not found locally
|
||
|
||
|
||
ckpt = torch.load(weights, map_location=device) # load checkpoint
|
||
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||
exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
|
||
state_dict = ckpt['model'].float().state_dict() # to FP32
|
||
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
|
||
model.load_state_dict(state_dict, strict=False) # load
|
||
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
|
||
else:
|
||
model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||
with torch_distributed_zero_first(rank):
|
||
check_dataset(data_dict) # check
|
||
train_path = data_dict['train']
|
||
test_path = data_dict['val']
|
||
|
||
# Freeze
|
||
freeze = [] # parameter names to freeze (full or partial)
|
||
for k, v in model.named_parameters():
|
||
v.requires_grad = True # train all layers
|
||
if any(x in k for x in freeze):
|
||
print('freezing %s' % k)
|
||
v.requires_grad = False
|
||
|
||
# Optimizer
|
||
nbs = 64 # nominal batch size
|
||
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
|
||
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
|
||
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
|
||
|
||
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
|
||
for k, v in model.named_modules():
|
||
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
|
||
pg2.append(v.bias) # biases
|
||
if isinstance(v, nn.BatchNorm2d):
|
||
pg0.append(v.weight) # no decay
|
||
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
|
||
pg1.append(v.weight) # apply decay
|
||
|
||
if opt.adam:
|
||
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
|
||
else:
|
||
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
|
||
|
||
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
|
||
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
|
||
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
|
||
del pg0, pg1, pg2
|
||
|
||
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
|
||
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
|
||
if opt.linear_lr:
|
||
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
|
||
else:
|
||
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
|
||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
|
||
# plot_lr_scheduler(optimizer, scheduler, epochs)
|
||
|
||
# EMA
|
||
ema = ModelEMA(model) if rank in [-1, 0] else None
|
||
|
||
# Resume
|
||
start_epoch, best_fitness = 0, 0.0
|
||
if pretrained:
|
||
# Optimizer
|
||
if ckpt['optimizer'] is not None:
|
||
optimizer.load_state_dict(ckpt['optimizer'])
|
||
best_fitness = ckpt['best_fitness']
|
||
|
||
# EMA
|
||
if ema and ckpt.get('ema'):
|
||
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
|
||
ema.updates = ckpt['updates']
|
||
|
||
# Results
|
||
if ckpt.get('training_results') is not None:
|
||
results_file.write_text(ckpt['training_results']) # write results.txt
|
||
|
||
# Epochs
|
||
start_epoch = ckpt['epoch'] + 1
|
||
if opt.resume:
|
||
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
|
||
if epochs < start_epoch:
|
||
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
|
||
(weights, ckpt['epoch'], epochs))
|
||
epochs += ckpt['epoch'] # finetune additional epochs
|
||
|
||
del ckpt, state_dict
|
||
|
||
# Image sizes
|
||
gs = max(int(model.stride.max()), 32) # grid size (max stride)
|
||
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
|
||
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
|
||
|
||
# DP mode
|
||
if cuda and rank == -1 and torch.cuda.device_count() > 1:
|
||
model = torch.nn.DataParallel(model)
|
||
|
||
# SyncBatchNorm
|
||
if opt.sync_bn and cuda and rank != -1:
|
||
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
|
||
logger.info('Using SyncBatchNorm()')
|
||
|
||
# Trainloader
|
||
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
|
||
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
|
||
world_size=opt.world_size, workers=opt.workers,
|
||
image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
|
||
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
|
||
nb = len(dataloader) # number of batches
|
||
logger.info('#####bs:%d steps:%d ' % (batch_size, nb))
|
||
|
||
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
|
||
|
||
# Process 0
|
||
if rank in [-1, 0]:
|
||
testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader
|
||
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
|
||
world_size=opt.world_size, workers=opt.workers,
|
||
pad=0.5, prefix=colorstr('val: '))[0]
|
||
|
||
if not opt.resume:
|
||
labels = np.concatenate(dataset.labels, 0)
|
||
c = torch.tensor(labels[:, 0]) # classes
|
||
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
|
||
# model._initialize_biases(cf.to(device))
|
||
if plots:
|
||
plot_labels(labels, names, save_dir, loggers)
|
||
# if tb_writer:
|
||
# tb_writer.add_histogram('classes', c, 0)
|
||
|
||
# Anchors
|
||
if not opt.noautoanchor:
|
||
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
|
||
model.half().float() # pre-reduce anchor precision
|
||
|
||
# DDP mode
|
||
if cuda and rank != -1:
|
||
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank,
|
||
# nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698
|
||
find_unused_parameters=any(isinstance(layer, nn.MultiheadAttention) for layer in model.modules()))
|
||
|
||
# Model parameters
|
||
hyp['box'] *= 3. / nl # scale to layers
|
||
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
|
||
hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
|
||
hyp['label_smoothing'] = opt.label_smoothing
|
||
model.nc = nc # attach number of classes to model
|
||
model.hyp = hyp # attach hyperparameters to model
|
||
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
|
||
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
|
||
model.names = names
|
||
|
||
# Start training
|
||
t0 = time.time()
|
||
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
|
||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||
maps = np.zeros(nc) # mAP per class
|
||
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||
scheduler.last_epoch = start_epoch - 1 # do not move
|
||
scaler = amp.GradScaler(enabled=cuda)
|
||
compute_loss = ComputeLoss(model) # init loss class
|
||
logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
|
||
f'Using {dataloader.num_workers} dataloader workers\n'
|
||
f'Logging results to {save_dir}\n'
|
||
f'Starting training for {epochs} epochs...')
|
||
|
||
# 在循环外部记录整个训练开始的时间
|
||
total_train_start_time = time.time()
|
||
print("line691",total_train_start_time)
|
||
|
||
|
||
|
||
# 转换时间戳为可读时间格式
|
||
start_time_str = datetime.datetime.fromtimestamp(total_train_start_time).strftime("%Y-%m-%d %H:%M:%S.%f")
|
||
print("line691 训练开始时间（正常格式）:", start_time_str)
|
||
|
||
|
||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||
|
||
|
||
trainStartTime = time.time()
|
||
|
||
# Check if we should stop
|
||
if stop_event and stop_event.is_set():
|
||
logger.info(f"Training stopped by request at epoch {epoch}")
|
||
break
|
||
|
||
model.train()
|
||
|
||
# Update image weights (optional)
|
||
if opt.image_weights:
|
||
# Generate indices
|
||
if rank in [-1, 0]:
|
||
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
|
||
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
|
||
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
|
||
# Broadcast if DDP
|
||
if rank != -1:
|
||
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
|
||
dist.broadcast(indices, 0)
|
||
if rank != 0:
|
||
dataset.indices = indices.cpu().numpy()
|
||
|
||
# Update mosaic border
|
||
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
|
||
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
|
||
|
||
mloss = torch.zeros(4, device=device) # mean losses
|
||
if rank != -1:
|
||
dataloader.sampler.set_epoch(epoch)
|
||
pbar = enumerate(dataloader)
|
||
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
|
||
if rank in [-1, 0]:
|
||
pbar = tqdm(pbar, total=nb) # progress bar
|
||
optimizer.zero_grad()
|
||
# print('+++++++++++++++++++++++++++++%s'% paths)
|
||
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
|
||
ni = i + nb * epoch # number integrated batches (since train start)
|
||
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
|
||
|
||
# Warmup
|
||
if ni <= nw:
|
||
xi = [0, nw] # x interp
|
||
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
|
||
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
|
||
for j, x in enumerate(optimizer.param_groups):
|
||
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
|
||
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
|
||
if 'momentum' in x:
|
||
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
|
||
|
||
# Multi-scale
|
||
if opt.multi_scale:
|
||
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
|
||
sf = sz / max(imgs.shape[2:]) # scale factor
|
||
if sf != 1:
|
||
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
|
||
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
|
||
|
||
# Forward
|
||
with amp.autocast(enabled=cuda):
|
||
pred = model(imgs) # forward
|
||
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
|
||
if rank != -1:
|
||
loss *= opt.world_size # gradient averaged between devices in DDP mode
|
||
if opt.quad:
|
||
loss *= 4.
|
||
|
||
# Backward
|
||
scaler.scale(loss).backward()
|
||
|
||
# Optimize
|
||
if ni % accumulate == 0:
|
||
scaler.step(optimizer) # optimizer.step
|
||
scaler.update()
|
||
optimizer.zero_grad()
|
||
if ema:
|
||
ema.update(model)
|
||
|
||
# Print
|
||
if rank in [-1, 0]:
|
||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
|
||
s = ('%10s' * 2 + '%10.4g' * 6) % (
|
||
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
|
||
pbar.set_description(s)
|
||
|
||
# Plot
|
||
if plots and ni < 3:
|
||
f = save_dir / f'train_batch{ni}.jpg' # filename
|
||
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
|
||
# if tb_writer:
|
||
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
|
||
# tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph
|
||
elif plots and ni == 10 and wandb_logger.wandb:
|
||
wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
|
||
save_dir.glob('train*.jpg') if x.exists()]})
|
||
|
||
# end batch ------------------------------------------------------------------------------------------------
|
||
# end epoch ----------------------------------------------------------------------------------------------------
|
||
|
||
# Scheduler
|
||
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
|
||
scheduler.step()
|
||
|
||
# DDP process 0 or single-GPU
|
||
if rank in [-1, 0]:
|
||
# mAP
|
||
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
|
||
final_epoch = epoch + 1 == epochs
|
||
if not opt.notest or final_epoch: # Calculate mAP
|
||
wandb_logger.current_epoch = epoch + 1
|
||
results, maps, times = test.test(data_dict,
|
||
batch_size=batch_size * 2,
|
||
imgsz=imgsz_test,
|
||
model=ema.ema,
|
||
single_cls=opt.single_cls,
|
||
dataloader=testloader,
|
||
save_dir=save_dir,
|
||
verbose=nc < 50 and final_epoch,
|
||
plots=plots and final_epoch,
|
||
wandb_logger=wandb_logger,
|
||
compute_loss=compute_loss,
|
||
is_coco=is_coco)
|
||
|
||
# Write
|
||
with open(results_file, 'a') as f:
|
||
f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss
|
||
if len(opt.name) and opt.bucket:
|
||
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
|
||
|
||
|
||
|
||
# 计算进度百分比（保留两位小数）
|
||
progress_percentage = f"{(epoch + 1) / epochs * 100:.2f}%"
|
||
|
||
|
||
|
||
# 修改消息发送部分
|
||
if producer:
|
||
try:
|
||
|
||
# 示例：在训练中显示总耗时（带正常时间格式）
|
||
current_time = time.time()
|
||
elapsed_seconds = current_time - total_train_start_time
|
||
elapsed_time = datetime.timedelta(seconds=elapsed_seconds)
|
||
|
||
|
||
elapsed_minutes = int(1 + elapsed_seconds // 60) # 转换为分钟（取整）
|
||
print(f"###########line 876 Epoch {epoch+1}/{epochs} - 已耗时: {elapsed_minutes} 分钟")
|
||
|
||
|
||
|
||
# 转换时间戳为可读时间格式
|
||
current_time_str = datetime.datetime.fromtimestamp(current_time).strftime("%Y-%m-%d %H:%M:%S.%f")
|
||
print("line879 训练开始时间（正常格式）:", start_time_str)
|
||
|
||
|
||
|
||
value = 0.6789
|
||
|
||
|
||
Accuracy_percent = f"{results[0]:.2%}"
|
||
Recall_percent = f"{results[1]:.2%}"
|
||
map_percent = f"{results[2]:.2%}"
|
||
|
||
|
||
percentage = f"{value:.2%}" # 自动乘以100并保留两位小数，添加百分号
|
||
print(percentage) # 输出: 67.89%
|
||
|
||
monitor_data = {
|
||
"id": task_id,
|
||
"dspModelCode": dspModelCode,
|
||
"algorithmMap": map_percent,
|
||
"algorithmAccuracy":Accuracy_percent,
|
||
"algorithmRecall": Recall_percent,
|
||
"aiVersion": version,
|
||
"progress": progress_percentage,
|
||
"trainStartTime": start_time_str,
|
||
"trainEndTime": current_time_str,
|
||
"trainDuration": str(elapsed_minutes), #持续时间
|
||
"status": 4 if progress_percentage == "100.00%" else 2
|
||
}
|
||
|
||
print("#####864",monitor_data)
|
||
|
||
#最终上传路径：
|
||
# http://172.18.0.129:8084/algorithm/027/V1.1/best.pt
|
||
|
||
|
||
# jcq: 将数据发送到web 端 - 0508
|
||
try:
|
||
# 发送包含自定义请求头和 JSON 数据的 POST 请求
|
||
response = requests.post(url, headers=headers, json=monitor_data)
|
||
# 检查响应状态码
|
||
response.raise_for_status()
|
||
print("状态码:", response.status_code)
|
||
print("响应内容:", response.text)
|
||
except requests.exceptions.HTTPError as http_err:
|
||
print(f"HTTP 错误发生: {http_err}")
|
||
except requests.exceptions.RequestException as req_err:
|
||
print(f"请求错误发生: {req_err}")
|
||
|
||
future = producer.send(
|
||
'train_monitor',
|
||
value=monitor_data,
|
||
timestamp_ms=int(time.time()*1000))
|
||
|
||
def callback(err, msg=None):
|
||
if err:
|
||
logger.error(f"Delivery failed: {err}")
|
||
else:
|
||
logger.debug(f"Message delivered to {msg.topic}[{msg.partition}]")
|
||
|
||
future.add_callback(callback)
|
||
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error sending metrics: {e}")
|
||
# 标记生产者不可用，下次会自动重建
|
||
server.kafka_producer = None
|
||
|
||
|
||
|
||
# 原有日志逻辑保持不变
|
||
if len(opt.name) and opt.bucket:
|
||
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
|
||
|
||
tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', ...] # 保持原有
|
||
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
|
||
if tb_writer:
|
||
tb_writer.add_scalar(tag, x, epoch)
|
||
if wandb_logger.wandb:
|
||
wandb_logger.log({tag: x})
|
||
|
||
# 在函数结束时关闭生产者
|
||
if 'kafka_producer' in locals():
|
||
kafka_producer.close()
|
||
|
||
# Log
|
||
tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
|
||
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
|
||
'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
|
||
'x/lr0', 'x/lr1', 'x/lr2'] # params
|
||
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
|
||
if tb_writer:
|
||
tb_writer.add_scalar(tag, x, epoch) # tensorboard
|
||
if wandb_logger.wandb:
|
||
wandb_logger.log({tag: x}) # W&B
|
||
|
||
# Update best mAP
|
||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||
if fi > best_fitness:
|
||
best_fitness = fi
|
||
wandb_logger.end_epoch(best_result=best_fitness == fi)
|
||
|
||
|
||
|
||
# Save model
|
||
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
|
||
ckpt = {'epoch': epoch,
|
||
'best_fitness': best_fitness,
|
||
'training_results': results_file.read_text(),
|
||
'model': deepcopy(model.module if is_parallel(model) else model).half(),
|
||
'ema': deepcopy(ema.ema).half(),
|
||
'updates': ema.updates,
|
||
'optimizer': optimizer.state_dict(),
|
||
'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
|
||
|
||
# Save last, best and delete
|
||
torch.save(ckpt, last)
|
||
if best_fitness == fi:
|
||
torch.save(ckpt, best)
|
||
|
||
torch.save(ckpt,yolov5)
|
||
|
||
|
||
monitor_data = {
|
||
"id": task_id,
|
||
"dspModelCode": dspModelCode,
|
||
"algorithmMap": map_percent,
|
||
"algorithmAccuracy":Accuracy_percent,
|
||
"algorithmRecall": Recall_percent,
|
||
"aiVersion": version,
|
||
"progress": progress_percentage,
|
||
"trainStartTime": start_time_str,
|
||
"trainEndTime": current_time_str,
|
||
"trainDuration": str(elapsed_minutes), #持续时间
|
||
"status": 2 if progress_percentage == "100.00%" else 4
|
||
}
|
||
|
||
|
||
aa = best # 本地文件名
|
||
|
||
|
||
# 构建 bucket 名称和对象路径
|
||
bucket_name = "algorithm" # 根据实际情况可能需要调整
|
||
weights = "weights"
|
||
object_path = f"{weights}/{dspModelCode}/{version}/yolov5.pt" # 在 MinIO 中的存储路径
|
||
|
||
try:
|
||
# 上传文件到 MinIO
|
||
result = minioClient.fput_object(
|
||
bucket_name=bucket_name,
|
||
object_name=object_path, # 目标路径
|
||
file_path=aa, # 本地文件路径
|
||
)
|
||
print("文件上传成功:")
|
||
print(f"Bucket: {result.bucket_name}")
|
||
print(f"Object: {result.object_name}")
|
||
print(f"ETag: {result.etag}")
|
||
|
||
# 检查 bucket 是否存在
|
||
if minioClient.bucket_exists("th-dsp"):
|
||
print("th-dsp exists")
|
||
else:
|
||
print("th-dsp does not exist")
|
||
|
||
except Exception as e:
|
||
print(f"上传失败: {e}")
|
||
|
||
|
||
if wandb_logger.wandb:
|
||
if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
|
||
wandb_logger.log_model(
|
||
last.parent, opt, epoch, fi, best_model=best_fitness == fi)
|
||
|
||
del ckpt
|
||
|
||
# end epoch ----------------------------------------------------------------------------------------------------
|
||
# end training
|
||
if rank in [-1, 0]:
|
||
# Plots
|
||
if plots:
|
||
plot_results(save_dir=save_dir) # save as results.png
|
||
if wandb_logger.wandb:
|
||
files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
|
||
wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
|
||
if (save_dir / f).exists()]})
|
||
# Test best.pt
|
||
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
|
||
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
|
||
for m in (last, best) if best.exists() else (last): # speed, mAP tests
|
||
results, _, _ = test.test(opt.data,
|
||
batch_size=batch_size * 2,
|
||
imgsz=imgsz_test,
|
||
conf_thres=0.001,
|
||
iou_thres=0.7,
|
||
model=attempt_load(m, device).half(),
|
||
single_cls=opt.single_cls,
|
||
dataloader=testloader,
|
||
save_dir=save_dir,
|
||
save_json=True,
|
||
plots=False,
|
||
is_coco=is_coco)
|
||
|
||
# Strip optimizers
|
||
final = best if best.exists() else last # final model
|
||
for f in last, best:
|
||
if f.exists():
|
||
strip_optimizer(f) # strip optimizers
|
||
if opt.bucket:
|
||
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
|
||
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
|
||
wandb_logger.wandb.log_artifact(str(final), type='model',
|
||
name='run_' + wandb_logger.wandb_run.id + '_model',
|
||
aliases=['last', 'best', 'stripped'])
|
||
wandb_logger.finish_run()
|
||
else:
|
||
dist.destroy_process_group()
|
||
torch.cuda.empty_cache()
|
||
return results
|
||
|
||
|
||
except Exception as e:
|
||
logger.error(f"Training error occurred: {str(e)}", exc_info=True)
|
||
|
||
# 构建错误响应数据
|
||
error_data = {
|
||
"id": task_id,
|
||
"status": 3, # 错误状态码
|
||
"error": str(e)
|
||
}
|
||
|
||
# 发送错误通知到API
|
||
try:
|
||
response = requests.post(
|
||
"http://172.18.0.129:8084/api/admin/trainTask/updateTrainTask",
|
||
headers=headers,
|
||
json=error_data
|
||
)
|
||
response.raise_for_status()
|
||
logger.info(f"Successfully sent error notification for task {task_id}")
|
||
except requests.exceptions.RequestException as api_err:
|
||
logger.error(f"Failed to send error notification: {api_err}")
|
||
|
||
# 重新抛出异常以便外层捕获
|
||
raise
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# Set up logging
|
||
logging.basicConfig(
|
||
level=logging.INFO,
|
||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
|
||
handlers=[
|
||
logging.StreamHandler(),
|
||
logging.FileHandler('yolov5_training_server.log')
|
||
]
|
||
)
|
||
|
||
# Initialize server
|
||
global server # Make server instance available to train()
|
||
server = TrainingServer()
|
||
|
||
try:
|
||
server.run_server()
|
||
except KeyboardInterrupt:
|
||
logger.info("Received keyboard interrupt, shutting down...")
|
||
except Exception as e:
|
||
logger.error(f"Server error: {e}", exc_info=True)
|
||
finally:
|
||
server.shutdown_flag = True |