分类任务实现模型集成代码模版

分类任务实现模型（投票式）集成代码模版

简介

本实验使用上一博客的深度学习分类模型训练代码模板-CSDN博客，自定义投票式集成，手动实现模型集成（投票法）的代码。最后通过tensorboard进行可视化，对每个基学习器的性能进行对比，直观的看出模型集成的作用。

代码

# -*- coding:utf-8 -*-
import os
import torch
import torchvision
import torchmetrics
import torch.nn as nn
import my_utils as utils
import torchvision.transforms as transforms
from torch.utils.tensorboard import SummaryWriter
from torch.utils.data import DataLoader
from torchensemble.utils import set_module
from torchensemble.voting import VotingClassifierclasses = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']def get_args_parser(add_help=True):import argparseparser = argparse.ArgumentParser(description="PyTorch Classification Training", add_help=add_help)parser.add_argument("--data-path", default=r"E:\Pytorch-Tutorial-2nd\data\datasets\cifar10-office", type=str,help="dataset path")parser.add_argument("--model", default="resnet8", type=str, help="model name")parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")parser.add_argument("-b", "--batch-size", default=128, type=int, help="images per gpu, the total batch size is $NGPU x batch_size")parser.add_argument("--epochs", default=200, type=int, metavar="N", help="number of total epochs to run")parser.add_argument("-j", "--workers", default=4, type=int, metavar="N", help="number of data loading workers (default: 16)")parser.add_argument("--opt", default="SGD", type=str, help="optimizer")parser.add_argument("--random-seed", default=42, type=int, help="random seed")parser.add_argument("--lr", default=0.1, type=float, help="initial learning rate")parser.add_argument("--momentum", default=0.9, type=float, metavar="M", help="momentum")parser.add_argument("--wd","--weight-decay",default=1e-4,type=float,metavar="W",help="weight decay (default: 1e-4)",dest="weight_decay",)parser.add_argument("--lr-step-size", default=80, type=int, help="decrease lr every step-size epochs")parser.add_argument("--lr-gamma", default=0.1, type=float, help="decrease lr by a factor of lr-gamma")parser.add_argument("--print-freq", default=80, type=int, help="print frequency")parser.add_argument("--output-dir", default="./Result", type=str, help="path to save outputs")parser.add_argument("--resume", default="", type=str, help="path of checkpoint")parser.add_argument("--start-epoch", default=0, type=int, metavar="N", help="start epoch")return parserdef main():args = get_args_parser().parse_args()utils.setup_seed(args.random_seed)args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")device = args.devicedata_dir = args.data_pathresult_dir = args.output_dir# ------------------------------------  log ------------------------------------logger, log_dir = utils.make_logger(result_dir)writer = SummaryWriter(log_dir=log_dir)# ------------------------------------ step1: dataset ------------------------------------normMean = [0.4948052, 0.48568845, 0.44682974]normStd = [0.24580306, 0.24236229, 0.2603115]normTransform = transforms.Normalize(normMean, normStd)train_transform = transforms.Compose([transforms.Resize(32),transforms.RandomCrop(32, padding=4),transforms.ToTensor(),normTransform])valid_transform = transforms.Compose([transforms.ToTensor(),normTransform])# root变量下需要存放cifar-10-python.tar.gz 文件# cifar-10-python.tar.gz可从 "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz" 下载train_set = torchvision.datasets.CIFAR10(root=data_dir, train=True, transform=train_transform, download=True)test_set = torchvision.datasets.CIFAR10(root=data_dir, train=False, transform=valid_transform, download=True)# 构建DataLodertrain_loader = DataLoader(dataset=train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.workers)valid_loader = DataLoader(dataset=test_set, batch_size=args.batch_size, num_workers=args.workers)# ------------------------------------ tep2: model ------------------------------------model_base = utils.resnet20()# model_base = utils.LeNet5()model = MyEnsemble(estimator=model_base, n_estimators=3, logger=logger, device=device, args=args,classes=classes, writer=writer, save_dir=log_dir)model.set_optimizer(args.opt, lr=args.lr, weight_decay=args.weight_decay)model.fit(train_loader, test_loader=valid_loader, epochs=args.epochs)class MyEnsemble(VotingClassifier):def __init__(self, **kwargs):# logger, device, args, classes, writersuper(VotingClassifier, self).__init__(kwargs["estimator"], kwargs["n_estimators"])self.logger = kwargs["logger"]self.writer = kwargs["writer"]self.device = kwargs["device"]self.args = kwargs["args"]self.classes = kwargs["classes"]self.save_dir = kwargs["save_dir"]@staticmethoddef save(model, save_dir, logger):"""Implement model serialization to the specified directory."""if save_dir is None:save_dir = "./"if not os.path.isdir(save_dir):os.mkdir(save_dir)# Decide the base estimator nameif isinstance(model.base_estimator_, type):base_estimator_name = model.base_estimator_.__name__else:base_estimator_name = model.base_estimator_.__class__.__name__# {Ensemble_Model_Name}_{Base_Estimator_Name}_{n_estimators}filename = "{}_{}_{}_ckpt.pth".format(type(model).__name__,base_estimator_name,model.n_estimators,)# The real number of base estimators in some ensembles is not same as# `n_estimators`.state = {"n_estimators": len(model.estimators_),"model": model.state_dict(),"_criterion": model._criterion,}save_dir = os.path.join(save_dir, filename)logger.info("Saving the model to `{}`".format(save_dir))# Savetorch.save(state, save_dir)returndef fit(self, train_loader, epochs=100, log_interval=100, test_loader=None, save_model=True, save_dir=None, ):# 模型、优化器、学习率调整器、评估器 列表创建estimators = []for _ in range(self.n_estimators):estimators.append(self._make_estimator())optimizers = []schedulers = []for i in range(self.n_estimators):optimizers.append(set_module.set_optimizer(estimators[i],self.optimizer_name, **self.optimizer_args))scheduler_ = torch.optim.lr_scheduler.MultiStepLR(optimizers[i], milestones=[100, 150],gamma=self.args.lr_gamma)  # 设置学习率下降策略# scheduler_ = torch.optim.lr_scheduler.StepLR(optimizers[i], step_size=self.args.lr_step_size,#                                             gamma=self.args.lr_gamma)  # 设置学习率下降策略schedulers.append(scheduler_)acc_metrics = []for i in range(self.n_estimators):# task类型与任务一致# num_classes与分类任务的类别数一致acc_metrics.append(torchmetrics.Accuracy(task="multiclass", num_classes=len(self.classes)))self._criterion = nn.CrossEntropyLoss()# epoch循环迭代best_acc = 0.for epoch in range(epochs):# trainingfor model_idx, (estimator, optimizer, scheduler) in enumerate(zip(estimators, optimizers, schedulers)):loss_m_train, acc_m_train, mat_train = \utils.ModelTrainerEnsemble.train_one_epoch(train_loader, estimator, self._criterion, optimizer, scheduler, epoch,self.device, self.args, self.logger, self.classes)# 学习率更新scheduler.step()# 记录self.writer.add_scalars('Loss_group', {'train_loss_{}'.format(model_idx):loss_m_train.avg}, epoch)self.writer.add_scalars('Accuracy_group', {'train_acc_{}'.format(model_idx):acc_m_train.avg}, epoch)self.writer.add_scalar('learning rate', scheduler.get_last_lr()[0], epoch)# 训练混淆矩阵图conf_mat_figure_train = utils.show_conf_mat(mat_train, classes, "train", save_dir, epoch=epoch,verbose=epoch == epochs - 1, save=False)self.writer.add_figure('confusion_matrix_train', conf_mat_figure_train, global_step=epoch)# validateloss_valid_meter, acc_valid, top1_group, mat_valid = \utils.ModelTrainerEnsemble.evaluate(test_loader, estimators, self._criterion, self.device, self.classes)# 日志self.writer.add_scalars('Loss_group', {'valid_loss':loss_valid_meter.avg}, epoch)self.writer.add_scalars('Accuracy_group', {'valid_acc':acc_valid * 100}, epoch)# 验证混淆矩阵图conf_mat_figure_valid = utils.show_conf_mat(mat_valid, classes, "valid", save_dir, epoch=epoch,verbose=epoch == epochs - 1, save=False)self.writer.add_figure('confusion_matrix_valid', conf_mat_figure_valid, global_step=epoch)self.logger.info('Epoch: [{:0>3}/{:0>3}]  ''Train Loss avg: {loss_train:>6.4f}  ''Valid Loss avg: {loss_valid:>6.4f}  ''Train Acc@1 avg:  {top1_train:>7.2f}%   ''Valid Acc@1 avg: {top1_valid:>7.2%}    ''LR: {lr}'.format(epoch, self.args.epochs, loss_train=loss_m_train.avg, loss_valid=loss_valid_meter.avg,top1_train=acc_m_train.avg, top1_valid=acc_valid, lr=schedulers[0].get_last_lr()[0]))for model_idx, top1_meter in enumerate(top1_group):self.writer.add_scalars('Accuracy_group',{'valid_acc_{}'.format(model_idx): top1_meter.compute() * 100}, epoch)if acc_valid > best_acc:best_acc = acc_validself.estimators_ = nn.ModuleList()self.estimators_.extend(estimators)if save_model:self.save(self, self.save_dir, self.logger)if __name__ == "__main__":main()

效果图

本实验采用3个学习器进行投票式集成，因此绘制了7条曲线，其中各学习器在训练和验证各有2条曲线，集成模型的结果通过 valid_acc输出（蓝色），通过下图可发现，集成模型与三个基学习器相比，分类准确率都能提高3-4百分点左右，是非常高的提升了。

参考

7.7 TorchEnsemble 模型集成库 · PyTorch实用教程（第二版） (tingsongyu.github.io)