基于Pytorch框架的深度学习EfficientNetV2神经网络中草药识别分类系统源码

 第一步：准备数据

5种中草药数据：self.class_indict = ["百合", "党参", "山魈", "枸杞", "槐花", "金银花"]

，总共有900张图片，每个文件夹单独放一种数据

第二步：搭建模型

本文选择一个EfficientNetV2网络，其原理介绍如下：

        该网络主要使用训练感知神经结构搜索和缩放的组合；在EfficientNetV1的基础上，引入了Fused-MBConv到搜索空间中；引入渐进式学习策略、自适应正则强度调整机制使得训练更快；进一步关注模型的推理速度与训练速度

与EfficientV1相比，主要有以下不同：

1. V2中除了使用MBConv模块外，还使用了Fused-MBConv模块
2. V2中会使用较小的expansion ratio，在V1中基本都是6。这样的好处是能够减少内存访问开销
3. V2中更偏向使用更小的kernel_size(3 x 3)，在V1中很多5 x 5。优于3 x 3的感受野是比5 x 5小的，所以需要堆叠更多的层结构以增加感受野
4. 移除了V1中最优一个步距为1的stage

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 efficientnetv2_s as create_model
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 "tensorboard --logdir=runs", 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)
 
    img_size = {"s": [300, 384],  # train_size, val_size
                "m": [384, 480],
                "l": [384, 480]}
    num_model = "s"
 
    data_transform = {
        "train": transforms.Compose([transforms.RandomResizedCrop(img_size[num_model][0]),
                                     transforms.RandomHorizontalFlip(),
                                     transforms.ToTensor(),
                                     transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]),
        "val": transforms.Compose([transforms.Resize(img_size[num_model][1]),
                                   transforms.CenterCrop(img_size[num_model][1]),
                                   transforms.ToTensor(),
                                   transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])}
 
    # 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 = create_model(num_classes=args.num_classes).to(device)
    if args.weights != "":
        if os.path.exists(args.weights):
            weights_dict = torch.load(args.weights, map_location=device)
            load_weights_dict = {k: v for k, v in weights_dict.items()
                                 if model.state_dict()[k].numel() == v.numel()}
            print(model.load_state_dict(load_weights_dict, strict=False))
        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head外，其他权重全部冻结
            if "head" not in name:
                para.requires_grad_(False)
            else:
                print("training {}".format(name))
 
    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)
    # 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
        train_loss, train_acc = train_one_epoch(model=model,
                                                optimizer=optimizer,
                                                data_loader=train_loader,
                                                device=device,
                                                epoch=epoch)
 
        scheduler.step()
 
        # validate
        val_loss, val_acc = evaluate(model=model,
                                     data_loader=val_loader,
                                     device=device,
                                     epoch=epoch)
 
        tags = ["train_loss", "train_acc", "val_loss", "val_acc", "learning_rate"]
        tb_writer.add_scalar(tags[0], train_loss, epoch)
        tb_writer.add_scalar(tags[1], train_acc, epoch)
        tb_writer.add_scalar(tags[2], val_loss, epoch)
        tb_writer.add_scalar(tags[3], val_acc, epoch)
        tb_writer.add_scalar(tags[4], 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=4)
    parser.add_argument('--lr', type=float, default=0.01)
    parser.add_argument('--lrf', type=float, default=0.01)
 
    # 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"G:\demo\data\ChineseMedicine")
 
    # download model weights
    # 链接: https://pan.baidu.com/s/1uZX36rvrfEss-JGj4yfzbQ  密码: 5gu1
    parser.add_argument('--weights', type=str, default='./pre_efficientnetv2-s.pth',
                        help='initial weights path')
    parser.add_argument('--freeze-layers', type=bool, default=True)
    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

第五步：搭建GUI界面

第六步：整个工程的内容

有训练代码和训练好的模型以及训练过程，提供数据，提供GUI界面代码

代码的下载路径（新窗口打开链接）：基于Pytorch框架的深度学习EfficientNetV2神经网络中草药识别分类系统源码

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