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前言

代码/home/tang/RL_exa/NCO_code-main/single_objective/LCH-Regret/Regret-POMO/CVRP/POMO/CVRPTrainer.py
学习。
该代码功能是训练模型。

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一、init(self, env_params, model_params, optimizer_params, trainer_params)

函数功能

该函数的功能主要是初始化训练环境及相关组件，为训练过程做好准备。

函数执行流程图链接

函数代码

   def __init__(self,env_params,model_params,optimizer_params,trainer_params):# save arguments        保存传入参数self.env_params = env_paramsself.model_params = model_paramsself.optimizer_params = optimizer_paramsself.trainer_params = trainer_params# result folder, logger 结果文件夹和日志初始化self.logger = getLogger(name='trainer')self.result_folder = get_result_folder()self.result_log = LogData()# cuda                  设置设备（CUDA/CPU）USE_CUDA = self.trainer_params['use_cuda']if USE_CUDA:cuda_device_num = self.trainer_params['cuda_device_num']torch.cuda.set_device(cuda_device_num)device = torch.device('cuda', cuda_device_num)torch.set_default_tensor_type('torch.cuda.FloatTensor')else:device = torch.device('cpu')torch.set_default_tensor_type('torch.FloatTensor')# Main Components        初始化主要组件self.model = Model(**self.model_params) #初始化模型对象，将模型参数传递给 Model 类。self.env = Env(**self.env_params)       #初始化环境对象，用于生成数据和定义优化问题。self.optimizer = Optimizer(self.model.parameters(), **self.optimizer_params['optimizer'])#使用模型的参数初始化优化器（如 Adam、SGD）。self.scheduler = Scheduler(self.optimizer, **self.optimizer_params['scheduler'])#初始化学习率调度器，用于动态调整优化器的学习率。# Restore   加载预训练模型self.start_epoch = 1model_load = trainer_params['model_load']if model_load['enable']:checkpoint_fullname = '{path}/checkpoint-{epoch}.pt'.format(**model_load)checkpoint = torch.load(checkpoint_fullname, map_location=device)self.model.load_state_dict(checkpoint['model_state_dict'])self.start_epoch = 1 + model_load['epoch']self.result_log.set_raw_data(checkpoint['result_log'])self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])self.scheduler.last_epoch = model_load['epoch']-1self.logger.info('Saved Model Loaded !!')# utility   初始化辅助工具self.time_estimator = TimeEstimator()

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二、run(self)

函数功能

run 函数是 CVRPTrainer 类的核心函数，负责执行整个训练过程。

run 函数是一个完整的训练过程控制器，负责以下操作：

• 循环执行每个训练周期，进行训练并记录日志。
• 动态调整学习率。
• 定期保存模型、图像日志和训练结果。
• 在训练结束时输出训练总结并显示最终日志。

函数执行流程图链接

函数代码

    def run(self):self.time_estimator.reset(self.start_epoch)for epoch in range(self.start_epoch, self.trainer_params['epochs']+1):self.logger.info('=================================================================')# LR Decayself.scheduler.step()# Traintrain_score, train_loss = self._train_one_epoch(epoch)self.result_log.append('train_score', epoch, train_score)self.result_log.append('train_loss', epoch, train_loss)############################# Logs & Checkpoint############################elapsed_time_str, remain_time_str = self.time_estimator.get_est_string(epoch, self.trainer_params['epochs'])self.logger.info("Epoch {:3d}/{:3d}: Time Est.: Elapsed[{}], Remain[{}]".format(epoch, self.trainer_params['epochs'], elapsed_time_str, remain_time_str))all_done = (epoch == self.trainer_params['epochs'])model_save_interval = self.trainer_params['logging']['model_save_interval']img_save_interval = self.trainer_params['logging']['img_save_interval']# Save latest images, every epochif epoch > 1:self.logger.info("Saving log_image")image_prefix = '{}/latest'.format(self.result_folder)util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'],self.result_log, labels=['train_score'])util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'],self.result_log, labels=['train_loss'])# Save Modelif all_done or (epoch % model_save_interval) == 0:self.logger.info("Saving trained_model")checkpoint_dict = {'epoch': epoch,'model_state_dict': self.model.state_dict(),'optimizer_state_dict': self.optimizer.state_dict(),'scheduler_state_dict': self.scheduler.state_dict(),'result_log': self.result_log.get_raw_data()}torch.save(checkpoint_dict, '{}/checkpoint-{}.pt'.format(self.result_folder, epoch))# Save Imageif all_done or (epoch % img_save_interval) == 0:image_prefix = '{}/img/checkpoint-{}'.format(self.result_folder, epoch)util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'],self.result_log, labels=['train_score'])util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'],self.result_log, labels=['train_loss'])# All-done announcementif all_done:self.logger.info(" *** Training Done *** ")self.logger.info("Now, printing log array...")util_print_log_array(self.logger, self.result_log)

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三、_train_one_epoch(self, epoch)

函数功能

_train_one_epoch 函数是训练过程中的核心部分，负责执行一个训练周期（epoch）的训练过程。其主要功能是：

• 迭代多个训练回合（episodes），每个回合训练一个批次。
• 计算并更新每个批次的训练得分（score）和损失（loss）。
• 记录并输出训练的进度和统计信息，包括每个周期的得分和损失。
• 在训练的第一个周期（epoch）中，输出前 10 个批次的训练信息，以便于监控训练初期的进展。

函数执行流程图链接

函数代码

    def _train_one_epoch(self, epoch):score_AM = AverageMeter()loss_AM = AverageMeter()train_num_episode = self.trainer_params['train_episodes']episode = 0loop_cnt = 0while episode < train_num_episode:remaining = train_num_episode - episodebatch_size = min(self.trainer_params['train_batch_size'], remaining)avg_score, avg_loss = self._train_one_batch(batch_size)score_AM.update(avg_score, batch_size)loss_AM.update(avg_loss, batch_size)episode += batch_size# Log First 10 Batch, only at the first epochif epoch == self.start_epoch:loop_cnt += 1if loop_cnt <= 10:self.logger.info('Epoch {:3d}: Train {:3d}/{:3d}({:1.1f}%)  Score: {:.4f},  Loss: {:.4f}'.format(epoch, episode, train_num_episode, 100. * episode / train_num_episode,score_AM.avg, loss_AM.avg))# Log Once, for each epochself.logger.info('Epoch {:3d}: Train ({:3.0f}%)  Score: {:.4f},  Loss: {:.4f}'.format(epoch, 100. * episode / train_num_episode,score_AM.avg, loss_AM.avg))return score_AM.avg, loss_AM.avg

问题

批次与回合

• 一个回合包含多个批次。如果数据集的大小为 N，而每个批次包含 B 个样本，那么每个回合会有 N / B 个批次。
• 训练过程中的批次更新：在一个回合中，模型会在每个批次上进行一次训练，逐步更新模型的权重。每完成一个批次，模型的权重就会更新一次。

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四、_train_one_batch(self, batch_size)

函数功能

该函数的目标是通过每个批次的训练来不断优化模型的表现，最终提高模型的得分并减少损失。

函数执行流程图链接

函数代码

    def _train_one_batch(self, batch_size):# Prep###############################################self.model.train()self.env.load_problems(batch_size)reset_state, _, _ = self.env.reset()self.model.pre_forward(reset_state)prob_list = torch.zeros(size=(batch_size, self.env.pomo_size, 0))# shape: (batch, pomo, 0~problem)# POMO Rollout###############################################state, reward, done = self.env.pre_step()while not done:selected, prob = self.model(state)# shape: (batch, pomo)state, reward, done = self.env.step(selected)prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2)# Loss###############################################advantage = reward - reward.float().mean(dim=1, keepdims=True)# shape: (batch, pomo)prob_list = prob_list.log()regret_weight = 1log_prob = torch.zeros((self.env.batch_size, self.env.pomo_size))for t in range(prob_list.shape[2]):regret_index=(self.env.regret_mask_matrix[:,:,t].unsqueeze(2)).nonzero()add_index=(self.env.add_mask_matrix[:,:,t].unsqueeze(2)).nonzero()log_prob[add_index[:, 0], add_index[:, 1]] = log_prob[add_index[:, 0], add_index[:, 1]].clone()+prob_list[add_index[:,0],add_index[:,1],t]log_prob[regret_index[:, 0], regret_index[:, 1]] = log_prob[regret_index[:, 0], regret_index[:, 1]].clone() + regret_weight*prob_list[regret_index[:, 0], regret_index[:, 1], t]loss = -advantage * log_prob  # Minus Sign: To Increase REWARD# shape: (batch, pomo)loss_mean = loss.mean()# Score###############################################max_pomo_reward, _ = reward.max(dim=1)  # get best results from pomoscore_mean = -max_pomo_reward.float().mean()  # negative sign to make positive value# Step & Return###############################################self.model.zero_grad()loss_mean.backward()self.optimizer.step()return score_mean.item(), loss_mean.item()

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附录

代码（全）

import torch
from logging import getLoggerfrom CVRPEnv import CVRPEnv as Env
from CVRPModel import CVRPModel as Modelfrom torch.optim import Adam as Optimizer
from torch.optim.lr_scheduler import MultiStepLR as Schedulerfrom utils.utils import *class CVRPTrainer:def __init__(self,env_params,model_params,optimizer_params,trainer_params):# save arguments        保存传入参数self.env_params = env_paramsself.model_params = model_paramsself.optimizer_params = optimizer_paramsself.trainer_params = trainer_params# result folder, logger 结果文件夹和日志初始化self.logger = getLogger(name='trainer')self.result_folder = get_result_folder()self.result_log = LogData()# cuda                  设置设备（CUDA/CPU）USE_CUDA = self.trainer_params['use_cuda']if USE_CUDA:cuda_device_num = self.trainer_params['cuda_device_num']torch.cuda.set_device(cuda_device_num)device = torch.device('cuda', cuda_device_num)torch.set_default_tensor_type('torch.cuda.FloatTensor')else:device = torch.device('cpu')torch.set_default_tensor_type('torch.FloatTensor')# Main Components        初始化主要组件self.model = Model(**self.model_params) #初始化模型对象，将模型参数传递给 Model 类。self.env = Env(**self.env_params)       #初始化环境对象，用于生成数据和定义优化问题。self.optimizer = Optimizer(self.model.parameters(), **self.optimizer_params['optimizer'])#使用模型的参数初始化优化器（如 Adam、SGD）。self.scheduler = Scheduler(self.optimizer, **self.optimizer_params['scheduler'])#初始化学习率调度器，用于动态调整优化器的学习率。# Restore   加载预训练模型self.start_epoch = 1model_load = trainer_params['model_load']if model_load['enable']:checkpoint_fullname = '{path}/checkpoint-{epoch}.pt'.format(**model_load)checkpoint = torch.load(checkpoint_fullname, map_location=device)self.model.load_state_dict(checkpoint['model_state_dict'])self.start_epoch = 1 + model_load['epoch']self.result_log.set_raw_data(checkpoint['result_log'])self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])self.scheduler.last_epoch = model_load['epoch']-1self.logger.info('Saved Model Loaded !!')# utility   初始化辅助工具self.time_estimator = TimeEstimator()def run(self):self.time_estimator.reset(self.start_epoch)for epoch in range(self.start_epoch, self.trainer_params['epochs']+1):self.logger.info('=================================================================')# LR Decayself.scheduler.step()# Traintrain_score, train_loss = self._train_one_epoch(epoch)self.result_log.append('train_score', epoch, train_score)self.result_log.append('train_loss', epoch, train_loss)############################# Logs & Checkpoint############################elapsed_time_str, remain_time_str = self.time_estimator.get_est_string(epoch, self.trainer_params['epochs'])self.logger.info("Epoch {:3d}/{:3d}: Time Est.: Elapsed[{}], Remain[{}]".format(epoch, self.trainer_params['epochs'], elapsed_time_str, remain_time_str))all_done = (epoch == self.trainer_params['epochs'])model_save_interval = self.trainer_params['logging']['model_save_interval']img_save_interval = self.trainer_params['logging']['img_save_interval']# Save latest images, every epochif epoch > 1:self.logger.info("Saving log_image")image_prefix = '{}/latest'.format(self.result_folder)util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'],self.result_log, labels=['train_score'])util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'],self.result_log, labels=['train_loss'])# Save Modelif all_done or (epoch % model_save_interval) == 0:self.logger.info("Saving trained_model")checkpoint_dict = {'epoch': epoch,'model_state_dict': self.model.state_dict(),'optimizer_state_dict': self.optimizer.state_dict(),'scheduler_state_dict': self.scheduler.state_dict(),'result_log': self.result_log.get_raw_data()}torch.save(checkpoint_dict, '{}/checkpoint-{}.pt'.format(self.result_folder, epoch))# Save Imageif all_done or (epoch % img_save_interval) == 0:image_prefix = '{}/img/checkpoint-{}'.format(self.result_folder, epoch)util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_1'],self.result_log, labels=['train_score'])util_save_log_image_with_label(image_prefix, self.trainer_params['logging']['log_image_params_2'],self.result_log, labels=['train_loss'])# All-done announcementif all_done:self.logger.info(" *** Training Done *** ")self.logger.info("Now, printing log array...")util_print_log_array(self.logger, self.result_log)def _train_one_epoch(self, epoch):score_AM = AverageMeter()loss_AM = AverageMeter()train_num_episode = self.trainer_params['train_episodes']episode = 0loop_cnt = 0while episode < train_num_episode:remaining = train_num_episode - episodebatch_size = min(self.trainer_params['train_batch_size'], remaining)avg_score, avg_loss = self._train_one_batch(batch_size)score_AM.update(avg_score, batch_size)loss_AM.update(avg_loss, batch_size)episode += batch_size# Log First 10 Batch, only at the first epochif epoch == self.start_epoch:loop_cnt += 1if loop_cnt <= 10:self.logger.info('Epoch {:3d}: Train {:3d}/{:3d}({:1.1f}%)  Score: {:.4f},  Loss: {:.4f}'.format(epoch, episode, train_num_episode, 100. * episode / train_num_episode,score_AM.avg, loss_AM.avg))# Log Once, for each epochself.logger.info('Epoch {:3d}: Train ({:3.0f}%)  Score: {:.4f},  Loss: {:.4f}'.format(epoch, 100. * episode / train_num_episode,score_AM.avg, loss_AM.avg))return score_AM.avg, loss_AM.avgdef _train_one_batch(self, batch_size):# Prep###############################################self.model.train()self.env.load_problems(batch_size)reset_state, _, _ = self.env.reset()self.model.pre_forward(reset_state)prob_list = torch.zeros(size=(batch_size, self.env.pomo_size, 0))# shape: (batch, pomo, 0~problem)# POMO Rollout###############################################state, reward, done = self.env.pre_step()while not done:selected, prob = self.model(state)# shape: (batch, pomo)state, reward, done = self.env.step(selected)prob_list = torch.cat((prob_list, prob[:, :, None]), dim=2)# Loss###############################################advantage = reward - reward.float().mean(dim=1, keepdims=True)# shape: (batch, pomo)prob_list = prob_list.log()regret_weight = 1log_prob = torch.zeros((self.env.batch_size, self.env.pomo_size))for t in range(prob_list.shape[2]):regret_index=(self.env.regret_mask_matrix[:,:,t].unsqueeze(2)).nonzero()add_index=(self.env.add_mask_matrix[:,:,t].unsqueeze(2)).nonzero()log_prob[add_index[:, 0], add_index[:, 1]] = log_prob[add_index[:, 0], add_index[:, 1]].clone()+prob_list[add_index[:,0],add_index[:,1],t]log_prob[regret_index[:, 0], regret_index[:, 1]] = log_prob[regret_index[:, 0], regret_index[:, 1]].clone() + regret_weight*prob_list[regret_index[:, 0], regret_index[:, 1], t]loss = -advantage * log_prob  # Minus Sign: To Increase REWARD# shape: (batch, pomo)loss_mean = loss.mean()# Score###############################################max_pomo_reward, _ = reward.max(dim=1)  # get best results from pomoscore_mean = -max_pomo_reward.float().mean()  # negative sign to make positive value# Step & Return###############################################self.model.zero_grad()loss_mean.backward()self.optimizer.step()return score_mean.item(), loss_mean.item()