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基于Pytorch框架的深度学习densenet121神经网络鸟类行为识别分类系统源码

news 来源：原创 2024/9/20 12:41:28

第一步：准备数据

5种鸟类行为数据：self.class_indict =

 ["bowing_status", "grooming", "headdown", "vigilance_status", "walking"]

，总共有23790张图片，每个文件夹单独放一种数据

第二步：搭建模型

简介：

DenseNet（Dense Convolutional Network）稠密卷积网络
CVPR2017的优秀文章
从feature入手，通过对feature的极致利用达到更好的效果和更少的参数。

优点：

减轻了vanishing-gradient（梯度消失）
加强了feature的传递
更有效地利用了feature
一定程度上较少了参数数量

在深度学习网络中，随着网络深度的加深，梯度消失问题会愈加明显，解决方法是创建浅层与深层之间的短路径。在DenseNet中，在保证网络中层与层之间最大程度的信息传输的前提下，直接将所有层连接起来。

在传统卷积神经网络中，如果你有L层，那么就会有L个连接，但是在DenseNet中，会有（L+1）/2个连接。简单来说，就是每一层的输入来自前面所有层的输出。如下图是dense block的结构图，x是数据，H是网络层。

第三步：训练代码

1）损失函数为：交叉熵损失函数

2）训练代码：

import os
import math
import argparseimport torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_schedulerfrom model import densenet121, load_state_dict
from my_dataset import MyDataSet
from utils import read_split_data, train_one_epoch, evaluatedef main(args):device = torch.device(args.device if torch.cuda.is_available() else "cpu")print(args)print('Start Tensorboard with "cd", view at http://localhost:6006/')tb_writer = SummaryWriter()if os.path.exists("./weights") is False:os.makedirs("./weights")train_images_path, train_images_label, val_images_path, val_images_label = read_split_data(args.data_path)data_transform = {"train": transforms.Compose([transforms.RandomResizedCrop(224),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),"val": transforms.Compose([transforms.Resize(256),transforms.CenterCrop(224),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}# 实例化训练数据集train_dataset = MyDataSet(images_path=train_images_path,images_class=train_images_label,transform=data_transform["train"])# 实例化验证数据集val_dataset = MyDataSet(images_path=val_images_path,images_class=val_images_label,transform=data_transform["val"])batch_size = args.batch_sizenw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workersprint('Using {} dataloader workers every process'.format(nw))train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=batch_size,shuffle=True,pin_memory=True,num_workers=nw,collate_fn=train_dataset.collate_fn)val_loader = torch.utils.data.DataLoader(val_dataset,batch_size=batch_size,shuffle=False,pin_memory=True,num_workers=nw,collate_fn=val_dataset.collate_fn)# 如果存在预训练权重则载入model = densenet121(num_classes=args.num_classes).to(device)if args.weights != "":if os.path.exists(args.weights):load_state_dict(model, args.weights)else:raise FileNotFoundError("not found weights file: {}".format(args.weights))# 是否冻结权重if args.freeze_layers:for name, para in model.named_parameters():# 除最后的全连接层外，其他权重全部冻结if "classifier" not in name:para.requires_grad_(False)pg = [p for p in model.parameters() if p.requires_grad]optimizer = optim.SGD(pg, lr=args.lr, momentum=0.9, weight_decay=1E-4, nesterov=True)# Scheduler https://arxiv.org/pdf/1812.01187.pdflf = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf  # cosinescheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)for epoch in range(args.epochs):# trainmean_loss = train_one_epoch(model=model,optimizer=optimizer,data_loader=train_loader,device=device,epoch=epoch)scheduler.step()# validateacc = evaluate(model=model,data_loader=val_loader,device=device)print("[epoch {}] accuracy: {}".format(epoch, round(acc, 3)))tags = ["loss", "accuracy", "learning_rate"]tb_writer.add_scalar(tags[0], mean_loss, epoch)tb_writer.add_scalar(tags[1], acc, epoch)tb_writer.add_scalar(tags[2], optimizer.param_groups[0]["lr"], epoch)torch.save(model.state_dict(), "./weights/model-{}.pth".format(epoch))if __name__ == '__main__':parser = argparse.ArgumentParser()parser.add_argument('--num_classes', type=int, default=5)parser.add_argument('--epochs', type=int, default=100)parser.add_argument('--batch-size', type=int, default=16)parser.add_argument('--lr', type=float, default=0.001)parser.add_argument('--lrf', type=float, default=0.1)# 数据集所在根目录# https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgzparser.add_argument('--data-path', type=str,default=r"E:\20240717\data")# densenet121 官方权重下载地址# https://download.pytorch.org/models/densenet121-a639ec97.pthparser.add_argument('--weights', type=str, default='densenet121.pth',help='initial weights path')parser.add_argument('--freeze-layers', type=bool, default=False)parser.add_argument('--device', default='cuda:0', help='device id (i.e. 0 or 0,1 or cpu)')opt = parser.parse_args()main(opt)

第四步：统计正确率