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

news2024/9/17 4:08:03

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 argparse

import torch
import torch.optim as optim
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import torch.optim.lr_scheduler as lr_scheduler

from model import densenet121, load_state_dict
from my_dataset import MyDataSet
from utils import read_split_data, train_one_epoch, evaluate


def 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_size
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('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.pdf
    lf = lambda x: ((1 + math.cos(x * math.pi / args.epochs)) / 2) * (1 - args.lrf) + args.lrf  # cosine
    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)

    for epoch in range(args.epochs):
        # train
        mean_loss = train_one_epoch(model=model,
                                    optimizer=optimizer,
                                    data_loader=train_loader,
                                    device=device,
                                    epoch=epoch)

        scheduler.step()

        # validate
        acc = 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.tgz
    parser.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.pth
    parser.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