基于飞桨2.0的食品图片分类实战应用

!unzip -d work data/data76472/food-11.zip # 解压缩food-11数据集

!rm -rf work/__MACOSX

第一步 必要的库引入，数据读取

1. 读取数据，新建路径+标签的txt的三个文件
2. 核实数据量与题目所给是否相同

import osimport paddleimport paddle.vision.transforms as Timport numpy as npfrom PIL import Imageimport paddleimport paddle.nn.functional as Ffrom sklearn.utils import shuffle#在python中运行代码经常会遇到的情况是——代码可以正常运行但是会提示警告，有时特别讨厌。#那么如何来控制警告输出呢？其实很简单，python通过调用warnings模块中定义的warn()函数来发出警告。我们可以通过警告过滤器进行控制是否发出警告消息。import warnings
warnings.filterwarnings("ignore")

data_path = 'work/food-11/'  # 设置初始文件地址character_folders = os.listdir(data_path)  # 查看地址下文件夹# 每次运行前删除txt，重新新建标签列表if(os.path.exists('./training_set.txt')):  # 判断有误文件
    os.remove('./training_set.txt')  # 删除文件if(os.path.exists('./validation_set.txt')):
    os.remove('./validation_set.txt')if(os.path.exists('./testing_set.txt')):
    os.remove('./testing_set.txt')for character_folder in character_folders:  #循环文件夹列表
    with open(f'./{character_folder}_set.txt', 'a') as f_train:  # 新建文档以追加的形式写入
        character_imgs = os.listdir(os.path.join(data_path,character_folder))  # 读取文件夹下面的内容
        count = 0
        if character_folder in 'testing':  # 检查是否是测试集
            for img in character_imgs:  # 循环列表
                f_train.write(os.path.join(data_path,character_folder,img) + '\n')  # 把地址写入文档
                count += 1
            print(character_folder,count)        else:            for img in character_imgs:  # 检查是否是训练集和测试集
                f_train.write(os.path.join(data_path,character_folder,img) + '\t' + img[0:img.rfind('_', 1)] + '\n')  # 写入地址及标签
                count += 1
            print(character_folder,count)

testing 3347
training 9866
validation 3430

第二步 数据预处理

下面使用paddle.vision.transforms.Compose做数据预处理，主要是这几个部分：

1. 以RGB格式加载图片
2. 将图片resize，从224x224变成100x100
3. 进行transpose操作，从HWC格式转变成CHW格式
4. 将图片的所有像素值进行除以255进行归一化
5. 对各通道进行减均值、除标准差

# 只有第一次需要执行 一次需要一分钟多import numpy as npimport cv2import os
 
img_h, img_w = 100, 100   #适当调整，影响不大means, stdevs = [], []
img_list = []
 
imgs_path = 'work/food-11/training'imgs_path_list = os.listdir(imgs_path)
 
len_ = len(imgs_path_list)
i = 0for item in imgs_path_list:
    img = cv2.imread(os.path.join(imgs_path,item))
    img = cv2.resize(img,(img_w,img_h))
    img = img[:, :, :, np.newaxis]
    img_list.append(img)
    i += 1
    # print(i,'/',len_)imgs_path = 'work/food-11/testing'imgs_path_list = os.listdir(imgs_path)
 
len_ = len(imgs_path_list)
i = 0for item in imgs_path_list:
    img = cv2.imread(os.path.join(imgs_path,item))
    img = cv2.resize(img,(img_w,img_h))
    img = img[:, :, :, np.newaxis]
    img_list.append(img)
    i += 1imgs = np.concatenate(img_list, axis=3)
imgs = imgs.astype(np.float32) / 255.
 for i in range(3):
    pixels = imgs[:, :, i, :].ravel()  # 拉成一行
    means.append(np.mean(pixels))
    stdevs.append(np.std(pixels)) 
# BGR --> RGB ， CV读取的需要转换，PIL读取的不用转换means.reverse()
stdevs.reverse() 
print("normMean = {}".format(means))print("normStd = {}".format(stdevs))# 只需要执行一次代码记录住数据即可# normMean = [0.5560434, 0.4515875, 0.34473255]# normStd = [0.27080873, 0.2738704, 0.280732]

normMean = [0.5560434, 0.4515875, 0.34473255]
normStd = [0.27080873, 0.2738704, 0.280732]

# 定义数据预处理data_transforms = T.Compose([
    T.Resize(size=(100, 100)),
    T.RandomHorizontalFlip(100),
    T.RandomVerticalFlip(100),
    T.RandomRotation(90),
    T.CenterCrop(100),
    T.Transpose(),    # HWC -> CHW
    T.Normalize(
        mean=[0.5560434, 0.4515875, 0.34473255],      #归一化 上个模块所求的均值与标准差 
        std=[0.27080873, 0.2738704, 0.280732],
        to_rgb=True)    
        #计算过程：output[channel] = (input[channel] - mean[channel]) / std[channel]])

第三步 继承paddle.io.Dataset对数据集做处理

对数据集进行处理

class FoodDataset(paddle.io.Dataset):
    """
    数据集类的定义
    """
    def __init__(self, mode='training_set'):
        """
        初始化函数
        """
        self.data = []        with open(f'{mode}_set.txt') as f:            for line in f.readlines():
                info = line.strip().split('\t')                if len(info) > 0:
                    self.data.append([info[0].strip(), info[1].strip()])  
                      
    def __getitem__(self, index):
        """
        读取图片，对图片进行归一化处理，返回图片和标签
        """
        image_file, label = self.data[index]  # 获取数据
        img = Image.open(image_file).convert('RGB')  # 读取图片
        
        return data_transforms(img).astype('float32'), np.array(label, dtype='int64')    def __len__(self):
        """
        获取样本总数
        """
        return len(self.data)

train_dataset = FoodDataset(mode='training')
train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64, shuffle=True, num_workers=0)
eval_dataset = FoodDataset(mode='validation')
val_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64, shuffle=True, num_workers=0)# 查看训练和验证集数据的大小print('train size：', train_dataset.__len__())print('eval size：', eval_dataset.__len__())

train size： 9866
eval size： 3430

第四步 自行搭建CNN神经网络

# 继承paddle.nn.Layer类，用于搭建模型class MyCNN(paddle.nn.Layer):
    def __init__(self):
        super(MyCNN,self).__init__()
        self.conv0 = paddle.nn.Conv2D(in_channels=3, out_channels=20, kernel_size=5, padding=0)  # 二维卷积层
        self.pool0 = paddle.nn.MaxPool2D(kernel_size =2, stride =2)  # 最大池化层
        self._batch_norm_0 = paddle.nn.BatchNorm2D(num_features = 20)  # 归一层

        self.conv1 = paddle.nn.Conv2D(in_channels=20, out_channels=50, kernel_size=5, padding=0)
        self.pool1 = paddle.nn.MaxPool2D(kernel_size =2, stride =2)
        self._batch_norm_1 = paddle.nn.BatchNorm2D(num_features = 50)

        self.conv2 = paddle.nn.Conv2D(in_channels=50, out_channels=50, kernel_size=5, padding=0)
        self.pool2 = paddle.nn.MaxPool2D(kernel_size =2, stride =2)
        self.fc1 = paddle.nn.Linear(in_features=4050, out_features=218)  # 线性层
        self.fc2 = paddle.nn.Linear(in_features=218, out_features=100)
        self.fc3 = paddle.nn.Linear(in_features=100, out_features=11)    
    def forward(self,input):
        # 将输入数据的样子该变成[1,3,100,100]
        input = paddle.reshape(input,shape=[-1,3,100,100])  # 转换维读
        # print(input.shape)
        x = self.conv0(input)  #数据输入卷积层
        x = F.relu(x)  # 激活层
        x = self.pool0(x)  # 池化层
        x = self._batch_norm_0(x)  # 归一层

        x = self.conv1(x)
        x = F.relu(x)
        x = self.pool1(x)
        x = self._batch_norm_1(x)

        x = self.conv2(x)
        x = F.relu(x)
        x = self.pool2(x)
        x = paddle.reshape(x, [x.shape[0], -1])        # print(x.shape)

        x = self.fc1(x)  # 线性层
        x = F.relu(x)
        x = self.fc2(x)
        x = F.relu(x)
        x = self.fc3(x)        #y = F.softmax(x)  # 分类器
        return x

network = MyCNN()  # 模型实例化paddle.summary(network, (1,3,100,100))  # 模型结构查看

---------------------------------------------------------------------------
 Layer (type)       Input Shape          Output Shape         Param #    
===========================================================================
   Conv2D-1      [[1, 3, 100, 100]]    [1, 20, 96, 96]         1,520     
  MaxPool2D-1    [[1, 20, 96, 96]]     [1, 20, 48, 48]           0       
 BatchNorm2D-1   [[1, 20, 48, 48]]     [1, 20, 48, 48]          80       
   Conv2D-2      [[1, 20, 48, 48]]     [1, 50, 44, 44]        25,050     
  MaxPool2D-2    [[1, 50, 44, 44]]     [1, 50, 22, 22]           0       
 BatchNorm2D-2   [[1, 50, 22, 22]]     [1, 50, 22, 22]          200      
   Conv2D-3      [[1, 50, 22, 22]]     [1, 50, 18, 18]        62,550     
  MaxPool2D-3    [[1, 50, 18, 18]]      [1, 50, 9, 9]            0       
   Linear-1         [[1, 4050]]            [1, 218]           883,118    
   Linear-2          [[1, 218]]            [1, 100]           21,900     
   Linear-3          [[1, 100]]            [1, 11]             1,111     
===========================================================================
Total params: 995,529
Trainable params: 995,249
Non-trainable params: 280
---------------------------------------------------------------------------
Input size (MB): 0.11
Forward/backward pass size (MB): 3.37
Params size (MB): 3.80
Estimated Total Size (MB): 7.29
---------------------------------------------------------------------------

{'total_params': 995529, 'trainable_params': 995249}

第五步 模型配置以及训练

运行时长: 3小时22分钟19秒5毫秒

# 实例化模型inputs = paddle.static.InputSpec(shape=[None, 3, 100, 100], name='inputs')
labels = paddle.static.InputSpec(shape=[None, 11], name='labels')
model = paddle.Model(network,inputs,labels)# 模型训练相关配置，准备损失计算方法，优化器和精度计算方法# 定义优化器scheduler = paddle.optimizer.lr.LinearWarmup(
        learning_rate=0.001, warmup_steps=100, start_lr=0, end_lr=0.001, verbose=True)
optim = paddle.optimizer.SGD(learning_rate=scheduler, parameters=model.parameters())# 配置模型model.prepare(
    optim,
    paddle.nn.CrossEntropyLoss(),
    paddle.metric.Accuracy()
    )

visualdl = paddle.callbacks.VisualDL(log_dir='visualdl_log')# 模型训练与评估model.fit(
        train_loader,  # 训练数据集
        val_loader,   # 评估数据集
        epochs=100,       # 训练的总轮次
        batch_size=128,  # 训练使用的批大小
        verbose=1,      # 日志展示形式
        callbacks=[visualdl])  # 设置可视化# 模型评估model.evaluate(eval_dataset, batch_size=128, verbose=1)#已运行，结果太长，已经删除，数据在下图显示

多次运行，在验证集正确率达到0.50-0.55之间

第六步 保存模型

model.save('finetuning/mnist')  # 保存模型

第七步 测试集案例展示

def opening():  # 读取图片函数
    with open(f'testing_set.txt') as f:  #读取文件夹
        test_img = []
        txt =  []        for line in f.readlines():  # 循环读取每一行
            img = Image.open(line[:-1])  # 打开图片
            img = data_transforms(img).astype('float32')
            txt.append(line[:-1])  # 生成列表
            test_img.append(img)  
        return txt,test_img
img_path, img = opening()  # 读取列表from PIL import Image
model_state_dict = paddle.load('finetuning/mnist.pdparams')  # 读取模型model = MyCNN()  # 实例化模型model.set_state_dict(model_state_dict) 
model.eval()

site = 10  # 读取图片位置ceshi = model(paddle.to_tensor(img[site]))  # 测试print('预测的结果为:', np.argmax(ceshi.numpy()))  # 获取值value = ["面包","乳制品","甜点","鸡蛋","油炸食品","肉类","面条/意大利面","米饭","海鲜","汤","蔬菜/水果"]print('           ', value[np.argmax(ceshi.numpy())])

Image.open(img_path[site])  # 显示图片

预测的结果为: 9
            汤