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手把手教你部署一个蘑菇识别的小应用

本文介绍使用飞桨EasyEdge平台部署蘑菇分类模型的流程。先定义图像分类任务，解压并标注蘑菇数据集，划分训练集和验证集，定义数据集类并做图像增强。选用mobilenet_v2网络，配置优化器训练模型，最后保存为静态图，通过EasyEdge平台完成部署，操作简洁。

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项目背景

飞桨最近新上了EasyEdge端与边缘AI服务平台，这对于新手来说非常友好

在原来我们开发了模型之后，没有办法快速部署到手机上

现在有了EasyEdge这个平台，直接在EasyEdge端与边缘AI服务平台部署就可以了，操作十分简洁流畅！下面以一个示例来给大家展示：

       

① 问题定义

对于一个任务，当你想使用深度学习来解决时，一般流程如下：

①问题定义->②数据准备->③模型选择与开发->④模型训练和调优->⑤模型评估测试->⑥部署上线

本项目中的蘑菇的分类的本质是图像分类任务，采用轻量级卷积神经网络mobilenet_v2进行相关实践。

② 数据准备

2.1 解压缩数据集

我们将网上获取的数据集以压缩包的方式上传到aistudio数据集中，并加载到我们的项目内。

在使用之前我们进行数据集压缩包的一个解压。

In [ ] 
# !unzip -oq /home/aistudio/data/data81902/mushrooms_train.zip -d work/
    In [ ] 
import paddle
paddle.seed(8888)import numpy as npfrom typing import Callable#参数配置config_parameters = {    "class_dim": 9,  #分类数
    "target_path":"/home/aistudio/work/",                     
    'train_image_dir': '/home/aistudio/work/trainImages',    'eval_image_dir': '/home/aistudio/work/evalImages',    'epochs':100,    'batch_size': 128,    'lr': 0.01}
   

2.2 数据标注

我们先看一下解压缩后的数据集长成什么样子。

In [ ] 
import osimport randomfrom matplotlib import pyplot as pltfrom PIL import Image

imgs = []
paths = os.listdir('work/mushrooms_train')for path in paths:   
    img_path = os.path.join('work/mushrooms_train', path)    if os.path.isdir(img_path):
        img_paths = os.listdir(img_path)
        img = Image.open(os.path.join(img_path, random.choice(img_paths)))
        imgs.append((img, path))

f, ax = plt.subplots(3, 3, figsize=(12,12))for i, img in enumerate(imgs[:9]):
    ax[i//3, i%3].imshow(img[0])
    ax[i//3, i%3].axis('off')
    ax[i//3, i%3].set_title('label: %s' % img[1])
plt.show()
       
               

2.3 划分数据集与数据集的定义

接下来我们使用标注好的文件进行数据集类的定义，方便后续模型训练使用。

2.3.1 划分数据集

In [ ] 
# import os# import shutil# train_dir = config_parameters['train_image_dir']# eval_dir = config_parameters['eval_image_dir']# paths = os.listdir('work/mushrooms_train')# if not os.path.exists(train_dir):#     os.mkdir(train_dir)# if not os.path.exists(eval_dir):#     os.mkdir(eval_dir)# for path in paths:#     imgs_dir = os.listdir(os.path.join('work/mushrooms_train', path))#     target_train_dir = os.path.join(train_dir,path)#     target_eval_dir = os.path.join(eval_dir,path)#     if not os.path.exists(target_train_dir):#         os.mkdir(target_train_dir)#     if not os.path.exists(target_eval_dir):#         os.mkdir(target_eval_dir)#     for i in range(len(imgs_dir)):#         if ' ' in imgs_dir[i]:#             new_name = imgs_dir[i].replace(' ', '_')#         else:#             new_name = imgs_dir[i]#         target_train_path = os.path.join(target_train_dir, new_name)#         target_eval_path = os.path.join(target_eval_dir, new_name)     #         if i % 5 == 0:#             shutil.copyfile(os.path.join(os.path.join('work/mushrooms_train', path), imgs_dir[i]), target_eval_path)#         else:#             shutil.copyfile(os.path.join(os.path.join('work/mushrooms_train', path), imgs_dir[i]), target_train_path)# # print('finished train val split!')
   

2.3.2 导入数据集的定义实现

In [ ] 
#数据集的定义class TowerDataset(paddle.io.Dataset):
    """
    步骤一：继承paddle.io.Dataset类
    """
    def __init__(self, transforms: Callable, mode: str ='train'):
        """
        步骤二：实现构造函数，定义数据读取方式
        """
        super(TowerDataset, self).__init__()
        
        self.mode = mode
        self.transforms = transforms

        train_image_dir = config_parameters['train_image_dir']
        eval_image_dir = config_parameters['eval_image_dir']

        train_data_folder = paddle.vision.DatasetFolder(train_image_dir)
        eval_data_folder = paddle.vision.DatasetFolder(eval_image_dir)        
        if self.mode  == 'train':
            self.data = train_data_folder        elif self.mode  == 'eval':
            self.data = eval_data_folder    def __getitem__(self, index):
        """
        步骤三：实现__getitem__方法，定义指定index时如何获取数据，并返回单条数据（训练数据，对应的标签）
        """
        data = np.array(self.data[index][0]).astype('float32')

        data = self.transforms(data)

        label = np.array([self.data[index][1]]).astype('int64')        
        return data, label        
    def __len__(self):
        """
        步骤四：实现__len__方法，返回数据集总数目
        """
        return len(self.data)
   

2.3.3 实例化数据集类及图像增强

根据所使用的数据集需求实例化数据集类，并查看总样本量。

In [ ] 
from paddle.vision import transforms as T#数据增强transform_train =T.Compose([T.Resize((256,256)),
                            T.RandomHorizontalFlip(10),
                            T.RandomRotation(10),
                            T.Transpose(),
                            T.Normalize(mean=[0, 0, 0],                           # 像素值归一化
                                        std =[255, 255, 255]),                    # transforms.ToTensor(), # transpose操作 + (img / 255),并且数据结构变为PaddleTensor
                            T.Normalize(mean=[0.50950350, 0.54632660, 0.57409690],# 减均值 除标准差    
                                        std= [0.26059777, 0.26041326, 0.29220656])# 计算过程：output[channel] = (input[channel] - mean[channel]) / std[channel]
                            ])
transform_eval =T.Compose([ T.Resize((256,256)),
                            T.Transpose(),
                            T.Normalize(mean=[0, 0, 0],                           # 像素值归一化
                                        std =[255, 255, 255]),                    # transforms.ToTensor(), # transpose操作 + (img / 255),并且数据结构变为PaddleTensor
                            T.Normalize(mean=[0.50950350, 0.54632660, 0.57409690],# 减均值 除标准差    
                                        std= [0.26059777, 0.26041326, 0.29220656])# 计算过程：output[channel] = (input[channel] - mean[channel]) / std[channel]
                            ])
    In [ ] 
train_dataset = TowerDataset(mode='train',transforms=transform_train)
eval_dataset  = TowerDataset(mode='eval', transforms=transform_eval )#数据异步加载train_loader = paddle.io.DataLoader(train_dataset, 
                                    places=paddle.CUDAPlace(0), 
                                    batch_size=128, 
                                    shuffle=True,                                    #num_workers=2,
                                    #use_shared_memory=True
                                    )
eval_loader = paddle.io.DataLoader (eval_dataset, 
                                    places=paddle.CUDAPlace(0), 
                                    batch_size=128,                                    #num_workers=2,
                                    #use_shared_memory=True
                                    )
    In [ ] 
print('训练集样本量: {}，验证集样本量: {}'.format(len(train_loader), len(eval_loader)))
       
训练集样本量: 42，验证集样本量: 11
       

③ 模型选择和开发

3.1 网络构建

本次我们使用mobilenet_v2网络来完成我们的案例实践。

In [ ] 
network=paddle.vision.models.mobilenet_v2(pretrained=True,num_classes=9)
model=paddle.Model(network)
model.summary((-1, 3, 256, 256))
       
2025-06-03 21:51:44,710 - INFO - unique_endpoints {''}
2025-06-03 21:51:44,711 - INFO - Downloading mobilenet_v2_x1.0.pdparams from https://paddle-hapi.bj.bcebos.com/models/mobilenet_v2_x1.0.pdparams
100%|██████████| 20795/20795 [00:00<00:00, 22406.24it/s]
2021-06-03 21:51:46,068 - INFO - File /home/aistudio/.cache/paddle/hapi/weights/mobilenet_v2_x1.0.pdparams md5 checking...
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1303: UserWarning: Skip loading for classifier.1.weight. classifier.1.weight receives a shape [1280, 1000], but the expected shape is [1280, 9].
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1303: UserWarning: Skip loading for classifier.1.bias. classifier.1.bias receives a shape [1000], but the expected shape is [9].
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
        
-------------------------------------------------------------------------------
   Layer (type)         Input Shape          Output Shape         Param #    
===============================================================================
     Conv2D-1        [[1, 3, 256, 256]]   [1, 32, 128, 128]         864      
   BatchNorm2D-1    [[1, 32, 128, 128]]   [1, 32, 128, 128]         128      
      ReLU6-1       [[1, 32, 128, 128]]   [1, 32, 128, 128]          0       
     Conv2D-2       [[1, 32, 128, 128]]   [1, 32, 128, 128]         288      
   BatchNorm2D-2    [[1, 32, 128, 128]]   [1, 32, 128, 128]         128      
      ReLU6-2       [[1, 32, 128, 128]]   [1, 32, 128, 128]          0       
     Conv2D-3       [[1, 32, 128, 128]]   [1, 16, 128, 128]         512      
   BatchNorm2D-3    [[1, 16, 128, 128]]   [1, 16, 128, 128]         64       
InvertedResidual-1  [[1, 32, 128, 128]]   [1, 16, 128, 128]          0       
     Conv2D-4       [[1, 16, 128, 128]]   [1, 96, 128, 128]        1,536     
   BatchNorm2D-4    [[1, 96, 128, 128]]   [1, 96, 128, 128]         384      
      ReLU6-3       [[1, 96, 128, 128]]   [1, 96, 128, 128]          0       
     Conv2D-5       [[1, 96, 128, 128]]    [1, 96, 64, 64]          864      
   BatchNorm2D-5     [[1, 96, 64, 64]]     [1, 96, 64, 64]          384      
      ReLU6-4        [[1, 96, 64, 64]]     [1, 96, 64, 64]           0       
     Conv2D-6        [[1, 96, 64, 64]]     [1, 24, 64, 64]         2,304     
   BatchNorm2D-6     [[1, 24, 64, 64]]     [1, 24, 64, 64]          96       
InvertedResidual-2  [[1, 16, 128, 128]]    [1, 24, 64, 64]           0       
     Conv2D-7        [[1, 24, 64, 64]]     [1, 144, 64, 64]        3,456     
   BatchNorm2D-7     [[1, 144, 64, 64]]    [1, 144, 64, 64]         576      
      ReLU6-5        [[1, 144, 64, 64]]    [1, 144, 64, 64]          0       
     Conv2D-8        [[1, 144, 64, 64]]    [1, 144, 64, 64]        1,296     
   BatchNorm2D-8     [[1, 144, 64, 64]]    [1, 144, 64, 64]         576      
      ReLU6-6        [[1, 144, 64, 64]]    [1, 144, 64, 64]          0       
     Conv2D-9        [[1, 144, 64, 64]]    [1, 24, 64, 64]         3,456     
   BatchNorm2D-9     [[1, 24, 64, 64]]     [1, 24, 64, 64]          96       
InvertedResidual-3   [[1, 24, 64, 64]]     [1, 24, 64, 64]           0       
     Conv2D-10       [[1, 24, 64, 64]]     [1, 144, 64, 64]        3,456     
  BatchNorm2D-10     [[1, 144, 64, 64]]    [1, 144, 64, 64]         576      
      ReLU6-7        [[1, 144, 64, 64]]    [1, 144, 64, 64]          0       
     Conv2D-11       [[1, 144, 64, 64]]    [1, 144, 32, 32]        1,296     
  BatchNorm2D-11     [[1, 144, 32, 32]]    [1, 144, 32, 32]         576      
      ReLU6-8        [[1, 144, 32, 32]]    [1, 144, 32, 32]          0       
     Conv2D-12       [[1, 144, 32, 32]]    [1, 32, 32, 32]         4,608     
  BatchNorm2D-12     [[1, 32, 32, 32]]     [1, 32, 32, 32]          128      
InvertedResidual-4   [[1, 24, 64, 64]]     [1, 32, 32, 32]           0       
     Conv2D-13       [[1, 32, 32, 32]]     [1, 192, 32, 32]        6,144     
  BatchNorm2D-13     [[1, 192, 32, 32]]    [1, 192, 32, 32]         768      
      ReLU6-9        [[1, 192, 32, 32]]    [1, 192, 32, 32]          0       
     Conv2D-14       [[1, 192, 32, 32]]    [1, 192, 32, 32]        1,728     
  BatchNorm2D-14     [[1, 192, 32, 32]]    [1, 192, 32, 32]         768      
     ReLU6-10        [[1, 192, 32, 32]]    [1, 192, 32, 32]          0       
     Conv2D-15       [[1, 192, 32, 32]]    [1, 32, 32, 32]         6,144     
  BatchNorm2D-15     [[1, 32, 32, 32]]     [1, 32, 32, 32]          128      
InvertedResidual-5   [[1, 32, 32, 32]]     [1, 32, 32, 32]           0       
     Conv2D-16       [[1, 32, 32, 32]]     [1, 192, 32, 32]        6,144     
  BatchNorm2D-16     [[1, 192, 32, 32]]    [1, 192, 32, 32]         768      
     ReLU6-11        [[1, 192, 32, 32]]    [1, 192, 32, 32]          0       
     Conv2D-17       [[1, 192, 32, 32]]    [1, 192, 32, 32]        1,728     
  BatchNorm2D-17     [[1, 192, 32, 32]]    [1, 192, 32, 32]         768      
     ReLU6-12        [[1, 192, 32, 32]]    [1, 192, 32, 32]          0       
     Conv2D-18       [[1, 192, 32, 32]]    [1, 32, 32, 32]         6,144     
  BatchNorm2D-18     [[1, 32, 32, 32]]     [1, 32, 32, 32]          128      
InvertedResidual-6   [[1, 32, 32, 32]]     [1, 32, 32, 32]           0       
     Conv2D-19       [[1, 32, 32, 32]]     [1, 192, 32, 32]        6,144     
  BatchNorm2D-19     [[1, 192, 32, 32]]    [1, 192, 32, 32]         768      
     ReLU6-13        [[1, 192, 32, 32]]    [1, 192, 32, 32]          0       
     Conv2D-20       [[1, 192, 32, 32]]    [1, 192, 16, 16]        1,728     
  BatchNorm2D-20     [[1, 192, 16, 16]]    [1, 192, 16, 16]         768      
     ReLU6-14        [[1, 192, 16, 16]]    [1, 192, 16, 16]          0       
     Conv2D-21       [[1, 192, 16, 16]]    [1, 64, 16, 16]        12,288     
  BatchNorm2D-21     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256      
InvertedResidual-7   [[1, 32, 32, 32]]     [1, 64, 16, 16]           0       
     Conv2D-22       [[1, 64, 16, 16]]     [1, 384, 16, 16]       24,576     
  BatchNorm2D-22     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-15        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-23       [[1, 384, 16, 16]]    [1, 384, 16, 16]        3,456     
  BatchNorm2D-23     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-16        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-24       [[1, 384, 16, 16]]    [1, 64, 16, 16]        24,576     
  BatchNorm2D-24     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256      
InvertedResidual-8   [[1, 64, 16, 16]]     [1, 64, 16, 16]           0       
     Conv2D-25       [[1, 64, 16, 16]]     [1, 384, 16, 16]       24,576     
  BatchNorm2D-25     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-17        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-26       [[1, 384, 16, 16]]    [1, 384, 16, 16]        3,456     
  BatchNorm2D-26     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-18        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-27       [[1, 384, 16, 16]]    [1, 64, 16, 16]        24,576     
  BatchNorm2D-27     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256      
InvertedResidual-9   [[1, 64, 16, 16]]     [1, 64, 16, 16]           0       
     Conv2D-28       [[1, 64, 16, 16]]     [1, 384, 16, 16]       24,576     
  BatchNorm2D-28     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-19        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-29       [[1, 384, 16, 16]]    [1, 384, 16, 16]        3,456     
  BatchNorm2D-29     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-20        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-30       [[1, 384, 16, 16]]    [1, 64, 16, 16]        24,576     
  BatchNorm2D-30     [[1, 64, 16, 16]]     [1, 64, 16, 16]          256      
InvertedResidual-10  [[1, 64, 16, 16]]     [1, 64, 16, 16]           0       
     Conv2D-31       [[1, 64, 16, 16]]     [1, 384, 16, 16]       24,576     
  BatchNorm2D-31     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-21        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-32       [[1, 384, 16, 16]]    [1, 384, 16, 16]        3,456     
  BatchNorm2D-32     [[1, 384, 16, 16]]    [1, 384, 16, 16]        1,536     
     ReLU6-22        [[1, 384, 16, 16]]    [1, 384, 16, 16]          0       
     Conv2D-33       [[1, 384, 16, 16]]    [1, 96, 16, 16]        36,864     
  BatchNorm2D-33     [[1, 96, 16, 16]]     [1, 96, 16, 16]          384      
InvertedResidual-11  [[1, 64, 16, 16]]     [1, 96, 16, 16]           0       
     Conv2D-34       [[1, 96, 16, 16]]     [1, 576, 16, 16]       55,296     
  BatchNorm2D-34     [[1, 576, 16, 16]]    [1, 576, 16, 16]        2,304     
     ReLU6-23        [[1, 576, 16, 16]]    [1, 576, 16, 16]          0       
     Conv2D-35       [[1, 576, 16, 16]]    [1, 576, 16, 16]        5,184     
  BatchNorm2D-35     [[1, 576, 16, 16]]    [1, 576, 16, 16]        2,304     
     ReLU6-24        [[1, 576, 16, 16]]    [1, 576, 16, 16]          0       
     Conv2D-36       [[1, 576, 16, 16]]    [1, 96, 16, 16]        55,296     
  BatchNorm2D-36     [[1, 96, 16, 16]]     [1, 96, 16, 16]          384      
InvertedResidual-12  [[1, 96, 16, 16]]     [1, 96, 16, 16]           0       
     Conv2D-37       [[1, 96, 16, 16]]     [1, 576, 16, 16]       55,296     
  BatchNorm2D-37     [[1, 576, 16, 16]]    [1, 576, 16, 16]        2,304     
     ReLU6-25        [[1, 576, 16, 16]]    [1, 576, 16, 16]          0       
     Conv2D-38       [[1, 576, 16, 16]]    [1, 576, 16, 16]        5,184     
  BatchNorm2D-38     [[1, 576, 16, 16]]    [1, 576, 16, 16]        2,304     
     ReLU6-26        [[1, 576, 16, 16]]    [1, 576, 16, 16]          0       
     Conv2D-39       [[1, 576, 16, 16]]    [1, 96, 16, 16]        55,296     
  BatchNorm2D-39     [[1, 96, 16, 16]]     [1, 96, 16, 16]          384      
InvertedResidual-13  [[1, 96, 16, 16]]     [1, 96, 16, 16]           0       
     Conv2D-40       [[1, 96, 16, 16]]     [1, 576, 16, 16]       55,296     
  BatchNorm2D-40     [[1, 576, 16, 16]]    [1, 576, 16, 16]        2,304     
     ReLU6-27        [[1, 576, 16, 16]]    [1, 576, 16, 16]          0       
     Conv2D-41       [[1, 576, 16, 16]]     [1, 576, 8, 8]         5,184     
  BatchNorm2D-41      [[1, 576, 8, 8]]      [1, 576, 8, 8]         2,304     
     ReLU6-28         [[1, 576, 8, 8]]      [1, 576, 8, 8]           0       
     Conv2D-42        [[1, 576, 8, 8]]      [1, 160, 8, 8]        92,160     
  BatchNorm2D-42      [[1, 160, 8, 8]]      [1, 160, 8, 8]          640      
InvertedResidual-14  [[1, 96, 16, 16]]      [1, 160, 8, 8]           0       
     Conv2D-43        [[1, 160, 8, 8]]      [1, 960, 8, 8]        153,600    
  BatchNorm2D-43      [[1, 960, 8, 8]]      [1, 960, 8, 8]         3,840     
     ReLU6-29         [[1, 960, 8, 8]]      [1, 960, 8, 8]           0       
     Conv2D-44        [[1, 960, 8, 8]]      [1, 960, 8, 8]         8,640     
  BatchNorm2D-44      [[1, 960, 8, 8]]      [1, 960, 8, 8]         3,840     
     ReLU6-30         [[1, 960, 8, 8]]      [1, 960, 8, 8]           0       
     Conv2D-45        [[1, 960, 8, 8]]      [1, 160, 8, 8]        153,600    
  BatchNorm2D-45      [[1, 160, 8, 8]]      [1, 160, 8, 8]          640      
InvertedResidual-15   [[1, 160, 8, 8]]      [1, 160, 8, 8]           0       
     Conv2D-46        [[1, 160, 8, 8]]      [1, 960, 8, 8]        153,600    
  BatchNorm2D-46      [[1, 960, 8, 8]]      [1, 960, 8, 8]         3,840     
     ReLU6-31         [[1, 960, 8, 8]]      [1, 960, 8, 8]           0       
     Conv2D-47        [[1, 960, 8, 8]]      [1, 960, 8, 8]         8,640     
  BatchNorm2D-47      [[1, 960, 8, 8]]      [1, 960, 8, 8]         3,840     
     ReLU6-32         [[1, 960, 8, 8]]      [1, 960, 8, 8]           0       
     Conv2D-48        [[1, 960, 8, 8]]      [1, 160, 8, 8]        153,600    
  BatchNorm2D-48      [[1, 160, 8, 8]]      [1, 160, 8, 8]          640      
InvertedResidual-16   [[1, 160, 8, 8]]      [1, 160, 8, 8]           0       
     Conv2D-49        [[1, 160, 8, 8]]      [1, 960, 8, 8]        153,600    
  BatchNorm2D-49      [[1, 960, 8, 8]]      [1, 960, 8, 8]         3,840     
     ReLU6-33         [[1, 960, 8, 8]]      [1, 960, 8, 8]           0       
     Conv2D-50        [[1, 960, 8, 8]]      [1, 960, 8, 8]         8,640     
  BatchNorm2D-50      [[1, 960, 8, 8]]      [1, 960, 8, 8]         3,840     
     ReLU6-34         [[1, 960, 8, 8]]      [1, 960, 8, 8]           0       
     Conv2D-51        [[1, 960, 8, 8]]      [1, 320, 8, 8]        307,200    
  BatchNorm2D-51      [[1, 320, 8, 8]]      [1, 320, 8, 8]         1,280     
InvertedResidual-17   [[1, 160, 8, 8]]      [1, 320, 8, 8]           0       
     Conv2D-52        [[1, 320, 8, 8]]     [1, 1280, 8, 8]        409,600    
  BatchNorm2D-52     [[1, 1280, 8, 8]]     [1, 1280, 8, 8]         5,120     
     ReLU6-35        [[1, 1280, 8, 8]]     [1, 1280, 8, 8]           0       
AdaptiveAvgPool2D-1  [[1, 1280, 8, 8]]     [1, 1280, 1, 1]           0       
     Dropout-1          [[1, 1280]]           [1, 1280]              0       
     Linear-1           [[1, 1280]]             [1, 9]            11,529     
===============================================================================
Total params: 2,269,513
Trainable params: 2,201,289
Non-trainable params: 68,224
-------------------------------------------------------------------------------
Input size (MB): 0.75
Forward/backward pass size (MB): 199.66
Params size (MB): 8.66
Estimated Total Size (MB): 209.07
-------------------------------------------------------------------------------
        
{'total_params': 2269513, 'trainable_params': 2201289}
               

④ 模型训练和优化器的选择

In [ ] 
#优化器选择class SaveBestModel(paddle.callbacks.Callback):
    def __init__(self, target=0.5, path='work/best_model', verbose=0):
        self.target = target
        self.epoch = None
        self.path = path    def on_epoch_end(self, epoch, logs=None):
        self.epoch = epoch    def on_eval_end(self, logs=None):
        if logs.get('acc') > self.target:
            self.target = logs.get('acc')
            self.model.save(self.path)            print('best acc is {} at epoch {}'.format(self.target, self.epoch))

callback_visualdl = paddle.callbacks.VisualDL(log_dir='work/mushroom')
callback_savebestmodel = SaveBestModel(target=0.5, path='work/best_model')
callbacks = [callback_visualdl, callback_savebestmodel]

base_lr = config_parameters['lr']
epochs = config_parameters['epochs']def make_optimizer(parameters=None):
    momentum = 0.9

    learning_rate= paddle.optimizer.lr.CosineAnnealingDecay(learning_rate=base_lr, T_max=epochs, verbose=False)
    weight_decay=paddle.regularizer.L2Decay(0.0001)
    optimizer = paddle.optimizer.Momentum(
        learning_rate=learning_rate,
        momentum=momentum,
        weight_decay=weight_decay,
        parameters=parameters)    return optimizer

optimizer = make_optimizer(model.parameters())
    In [ ] 
model.prepare(optimizer,
              paddle.nn.CrossEntropyLoss(),
              paddle.metric.Accuracy())
    In [14] 
model.fit(train_loader,
          eval_loader,
          epochs=100,
          batch_size=128,           # 是否打乱样本集     
          callbacks=callbacks, 
          verbose=1)   # 日志展示格式
   

⑤模型评估测试

       

⑥部署上线

将我们训练得到的模型进行保存为静态图，得到mushroom.pdmodel和mushroom.pdiparams两个文件，准备一个label_list.txt文件

In [15] 
model.save('mushroom',training=False)
   

6.1上传原模型

根据图示进行上传文件验证，生成Demo

       

6.2生成端模型

       

       

6.3Demo下载体验

         


# python  # ai  # 异步加载  # cos  # red  # igs  # 数据结构  # 压缩包  # 解压缩  # 服务平台  # 保存为  # 均值  # 边缘  # 加载  # 标准差  # 上了 


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