基于Paddle2.0的猫12分类

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发布时间：2025-07-21 11:30:13

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来源于php中文网

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本项目用paddle2.0rc解决12类猫的分类问题，旨在掌握其图像分类使用方法。数据集含2160张训练图、240张测试图，先解压并加载库，配置参数后划分训练和验证集，定义数据集类，初始化mobilenet_v1模型，配置优化器等，训练10轮，最后评估、测试并保存模型与结果。

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基于paddle2.0的猫12分类 - php中文网

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项目描述

本项目采用Paddle2.0rc版本解决猫的分类问题。

主要目的是跑通流程，掌握Paddle2.0rc版本在图像分类问题上的使用方法。

数据集介绍

训练集共有2160张图片，测试集共有240张图片。共有12个猫的分类。

解压缩原始数据集

In [ ]

!unzip -q data/data63045/cat_12_train.zip -d work/
!unzip -q data/data63045/cat_12_test.zip -d work/
!cp data/data63045/train_list.txt work/

加载需要的库

In [ ]

import osimport shutilimport numpy as npimport paddlefrom paddle.io import Datasetfrom paddle.vision.datasets import DatasetFolder, ImageFolderfrom paddle.vision.transforms import Compose, Resize, Transpose

全局参数配置

In [ ]

'''
参数配置
'''train_parameters = {    "class_dim": 12,  #分类数
    "target_path":"/home/aistudio/work/",                     
    'train_image_dir': '/home/aistudio/work/trainImages',    'eval_image_dir': '/home/aistudio/work/evalImages',    'test_image_dir': '/home/aistudio/work/cat_12_test',    'train_list_path': '/home/aistudio/work/train_list.txt',
}

划分训练集和验证集

In [ ]

def create_train_eval():
    '''
    划分训练集和验证集
    '''
    train_dir = train_parameters['train_image_dir']
    eval_dir = train_parameters['eval_image_dir']
    train_list_path = train_parameters['train_list_path']
    target_path = train_parameters['target_path']    print('creating training and eval images')    if not os.path.exists(train_dir):
        os.mkdir(train_dir)    if not os.path.exists(eval_dir):
        os.mkdir(eval_dir) 

    with open(train_list_path, 'r') as f:
        data = f.readlines()        for i in range(len(data)):
            img_path = data[i].split('\t')[0]
            class_label = data[i].split('\t')[1][:-1]            if i % 8 == 0:                                  # 每8张图片取一个做验证数据
                eval_target_dir = os.path.join(eval_dir, str(class_label)) 
                eval_img_path = os.path.join(target_path, img_path)                if not os.path.exists(eval_target_dir):
                    os.mkdir(eval_target_dir)  
                shutil.copy(eval_img_path, eval_target_dir)                         
            else:
                train_target_dir = os.path.join(train_dir, str(class_label)) 
                train_img_path = os.path.join(target_path, img_path)                     
                if not os.path.exists(train_target_dir):
                    os.mkdir(train_target_dir)
                shutil.copy(train_img_path, train_target_dir) 

    print ('划分训练集和验证集完成！')

In [ ]

create_train_eval()

creating training and eval images
划分训练集和验证集完成！

定义数据集

In [ ]

class CatDataset(Dataset):
    """
    步骤一：继承paddle.io.Dataset类
    """
    def __init__(self, mode='train'):
        """
        步骤二：实现构造函数，定义数据读取方式，划分训练和测试数据集
        """
        super(CatDataset, self).__init__()
        train_image_dir = train_parameters['train_image_dir']
        eval_image_dir = train_parameters['eval_image_dir']
        test_image_dir = train_parameters['test_image_dir']

        transform_train = Compose([Resize(size=(112,112)), Transpose()])
        transform_eval = Compose([Resize(size=(112,112)), Transpose()])
        train_data_folder = DatasetFolder(train_image_dir, transform=transform_train)
        eval_data_folder = DatasetFolder(eval_image_dir, transform=transform_eval)
        test_data_folder = ImageFolder(test_image_dir, transform=transform_eval)
        self.mode = mode        if self.mode  == 'train':
            self.data = train_data_folder        elif self.mode  == 'eval':
            self.data = eval_data_folder        elif self.mode  == 'test':
            self.data = test_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')        if self.mode  == 'test':            return data        else:
            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)

In [ ]

train_dataset = CatDataset(mode='train')
val_dataset = CatDataset(mode='eval')
test_dataset = CatDataset(mode='test')

初始化模型

In [ ]

# 使用内置的模型,这边可以选择多种不同网络，这里选了mobilenet_v1网络model = paddle.vision.models.mobilenet_v1(pretrained=True, num_classes=train_parameters["class_dim"])

model = paddle.Model(model)

100%|██████████| 25072/25072 [00:00<00:00, 28070.41it/s]
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1175: UserWarning: Skip loading for fc.weight. fc.weight receives a shape [1024, 1000], but the expected shape is [1024, 12].
  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:1175: UserWarning: Skip loading for fc.bias. fc.bias receives a shape [1000], but the expected shape is [12].
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))

In [ ]

## 查看模型结构model.summary((-1, 3, 224, 224))

---------------------------------------------------------------------------------
    Layer (type)          Input Shape          Output Shape         Param #    
=================================================================================
     Conv2D-160        [[1, 3, 224, 224]]   [1, 32, 112, 112]         864      
   BatchNorm2D-107    [[1, 32, 112, 112]]   [1, 32, 112, 112]         128      
       ReLU-88        [[1, 32, 112, 112]]   [1, 32, 112, 112]          0       
    ConvBNLayer-1      [[1, 3, 224, 224]]   [1, 32, 112, 112]          0       
     Conv2D-161       [[1, 32, 112, 112]]   [1, 32, 112, 112]         288      
   BatchNorm2D-108    [[1, 32, 112, 112]]   [1, 32, 112, 112]         128      
       ReLU-89        [[1, 32, 112, 112]]   [1, 32, 112, 112]          0       
    ConvBNLayer-2     [[1, 32, 112, 112]]   [1, 32, 112, 112]          0       
     Conv2D-162       [[1, 32, 112, 112]]   [1, 64, 112, 112]        2,048     
   BatchNorm2D-109    [[1, 64, 112, 112]]   [1, 64, 112, 112]         256      
       ReLU-90        [[1, 64, 112, 112]]   [1, 64, 112, 112]          0       
    ConvBNLayer-3     [[1, 32, 112, 112]]   [1, 64, 112, 112]          0       
DepthwiseSeparable-1  [[1, 32, 112, 112]]   [1, 64, 112, 112]          0       
     Conv2D-163       [[1, 64, 112, 112]]    [1, 64, 56, 56]          576      
   BatchNorm2D-110     [[1, 64, 56, 56]]     [1, 64, 56, 56]          256      
       ReLU-91         [[1, 64, 56, 56]]     [1, 64, 56, 56]           0       
    ConvBNLayer-4     [[1, 64, 112, 112]]    [1, 64, 56, 56]           0       
     Conv2D-164        [[1, 64, 56, 56]]     [1, 128, 56, 56]        8,192     
   BatchNorm2D-111     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      
       ReLU-92         [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       
    ConvBNLayer-5      [[1, 64, 56, 56]]     [1, 128, 56, 56]          0       
DepthwiseSeparable-2  [[1, 64, 112, 112]]    [1, 128, 56, 56]          0       
     Conv2D-165        [[1, 128, 56, 56]]    [1, 128, 56, 56]        1,152     
   BatchNorm2D-112     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      
       ReLU-93         [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       
    ConvBNLayer-6      [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       
     Conv2D-166        [[1, 128, 56, 56]]    [1, 128, 56, 56]       16,384     
   BatchNorm2D-113     [[1, 128, 56, 56]]    [1, 128, 56, 56]         512      
       ReLU-94         [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       
    ConvBNLayer-7      [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       
DepthwiseSeparable-3   [[1, 128, 56, 56]]    [1, 128, 56, 56]          0       
     Conv2D-167        [[1, 128, 56, 56]]    [1, 128, 28, 28]        1,152     
   BatchNorm2D-114     [[1, 128, 28, 28]]    [1, 128, 28, 28]         512      
       ReLU-95         [[1, 128, 28, 28]]    [1, 128, 28, 28]          0       
    ConvBNLayer-8      [[1, 128, 56, 56]]    [1, 128, 28, 28]          0       
     Conv2D-168        [[1, 128, 28, 28]]    [1, 256, 28, 28]       32,768     
   BatchNorm2D-115     [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024     
       ReLU-96         [[1, 256, 28, 28]]    [1, 256, 28, 28]          0       
    ConvBNLayer-9      [[1, 128, 28, 28]]    [1, 256, 28, 28]          0       
DepthwiseSeparable-4   [[1, 128, 56, 56]]    [1, 256, 28, 28]          0       
     Conv2D-169        [[1, 256, 28, 28]]    [1, 256, 28, 28]        2,304     
   BatchNorm2D-116     [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024     
       ReLU-97         [[1, 256, 28, 28]]    [1, 256, 28, 28]          0       
   ConvBNLayer-10      [[1, 256, 28, 28]]    [1, 256, 28, 28]          0       
     Conv2D-170        [[1, 256, 28, 28]]    [1, 256, 28, 28]       65,536     
   BatchNorm2D-117     [[1, 256, 28, 28]]    [1, 256, 28, 28]        1,024     
       ReLU-98         [[1, 256, 28, 28]]    [1, 256, 28, 28]          0       
   ConvBNLayer-11      [[1, 256, 28, 28]]    [1, 256, 28, 28]          0       
DepthwiseSeparable-5   [[1, 256, 28, 28]]    [1, 256, 28, 28]          0       
     Conv2D-171        [[1, 256, 28, 28]]    [1, 256, 14, 14]        2,304     
   BatchNorm2D-118     [[1, 256, 14, 14]]    [1, 256, 14, 14]        1,024     
       ReLU-99         [[1, 256, 14, 14]]    [1, 256, 14, 14]          0       
   ConvBNLayer-12      [[1, 256, 28, 28]]    [1, 256, 14, 14]          0       
     Conv2D-172        [[1, 256, 14, 14]]    [1, 512, 14, 14]       131,072    
   BatchNorm2D-119     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-100         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-13      [[1, 256, 14, 14]]    [1, 512, 14, 14]          0       
DepthwiseSeparable-6   [[1, 256, 28, 28]]    [1, 512, 14, 14]          0       
     Conv2D-173        [[1, 512, 14, 14]]    [1, 512, 14, 14]        4,608     
   BatchNorm2D-120     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-101         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-14      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-174        [[1, 512, 14, 14]]    [1, 512, 14, 14]       262,144    
   BatchNorm2D-121     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-102         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-15      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
DepthwiseSeparable-7   [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-175        [[1, 512, 14, 14]]    [1, 512, 14, 14]        4,608     
   BatchNorm2D-122     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-103         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-16      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-176        [[1, 512, 14, 14]]    [1, 512, 14, 14]       262,144    
   BatchNorm2D-123     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-104         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-17      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
DepthwiseSeparable-8   [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-177        [[1, 512, 14, 14]]    [1, 512, 14, 14]        4,608     
   BatchNorm2D-124124     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-105         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-18      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-178        [[1, 512, 14, 14]]    [1, 512, 14, 14]       262,144    
   BatchNorm2D-125     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-106         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-19      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
DepthwiseSeparable-9   [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-179        [[1, 512, 14, 14]]    [1, 512, 14, 14]        4,608     
   BatchNorm2D-126     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-107         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-20      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-180        [[1, 512, 14, 14]]    [1, 512, 14, 14]       262,144    
   BatchNorm2D-127     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-108         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-21      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
DepthwiseSeparable-10  [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-181        [[1, 512, 14, 14]]    [1, 512, 14, 14]        4,608     
   BatchNorm2D-128     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-109         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-22      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-182        [[1, 512, 14, 14]]    [1, 512, 14, 14]       262,144    
   BatchNorm2D-129     [[1, 512, 14, 14]]    [1, 512, 14, 14]        2,048     
      ReLU-110         [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
   ConvBNLayer-23      [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
DepthwiseSeparable-11  [[1, 512, 14, 14]]    [1, 512, 14, 14]          0       
     Conv2D-183        [[1, 512, 14, 14]]     [1, 512, 7, 7]         4,608     
   BatchNorm2D-130      [[1, 512, 7, 7]]      [1, 512, 7, 7]         2,048     
      ReLU-111          [[1, 512, 7, 7]]      [1, 512, 7, 7]           0       
   ConvBNLayer-24      [[1, 512, 14, 14]]     [1, 512, 7, 7]           0       
     Conv2D-184         [[1, 512, 7, 7]]     [1, 1024, 7, 7]        524,288    
   BatchNorm2D-131     [[1, 1024, 7, 7]]     [1, 1024, 7, 7]         4,096     
      ReLU-112         [[1, 1024, 7, 7]]     [1, 1024, 7, 7]           0       
   ConvBNLayer-25       [[1, 512, 7, 7]]     [1, 1024, 7, 7]           0       
DepthwiseSeparable-12  [[1, 512, 14, 14]]    [1, 1024, 7, 7]           0       
     Conv2D-185        [[1, 1024, 7, 7]]     [1, 1024, 7, 7]         9,216     
   BatchNorm2D-132     [[1, 1024, 7, 7]]     [1, 1024, 7, 7]         4,096     
      ReLU-113         [[1, 1024, 7, 7]]     [1, 1024, 7, 7]           0       
   ConvBNLayer-26      [[1, 1024, 7, 7]]     [1, 1024, 7, 7]           0       
     Conv2D-186        [[1, 1024, 7, 7]]     [1, 1024, 7, 7]       1,048,576   
   BatchNorm2D-133     [[1, 1024, 7, 7]]     [1, 1024, 7, 7]         4,096     
      ReLU-114         [[1, 1024, 7, 7]]     [1, 1024, 7, 7]           0       
   ConvBNLayer-27      [[1, 1024, 7, 7]]     [1, 1024, 7, 7]           0       
DepthwiseSeparable-13  [[1, 1024, 7, 7]]     [1, 1024, 7, 7]           0       
 AdaptiveAvgPool2D-3   [[1, 1024, 7, 7]]     [1, 1024, 1, 1]           0       
      Linear-3            [[1, 1024]]            [1, 12]            12,300     
=================================================================================
Total params: 3,241,164
Trainable params: 3,197,388
Non-trainable params: 43,776
---------------------------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 174.57
Params size (MB): 12.36
Estimated Total Size (MB): 187.51
---------------------------------------------------------------------------------

{'total_params': 3241164, 'trainable_params': 3197388}

模型配置：为模型训练做准备，设置优化器，损失函数和精度计算方式

In [ ]

# 调用飞桨框架的VisualDL模块，保存信息到目录中。callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')

In [ ]

model.prepare(optimizer=paddle.optimizer.Adam(
              learning_rate=0.001,
              parameters=model.parameters()),
              loss=paddle.nn.CrossEntropyLoss(),
              metrics=paddle.metric.Accuracy())

模型训练

In [ ]

model.fit(train_dataset,
          val_dataset,
          epochs=10,
          batch_size=32,
          callbacks=callback,
          verbose=1)

Epoch 1/10

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:637: UserWarning: When training, we now always track global mean and variance.
  "When training, we now always track global mean and variance.")

step 60/60 [==============================] - loss: 2.2463 - acc: 0.6090 - 294ms/step        
Eval begin...
step 9/9 [==============================] - loss: 0.4193 - acc: 0.6704 - 285ms/step
Eval samples: 270
Epoch 2/10
step 60/60 [==============================] - loss: 1.6789 - acc: 0.8571 - 292ms/step        
Eval begin...
step 9/9 [==============================] - loss: 1.3652 - acc: 0.7630 - 280ms/step
Eval samples: 270
Epoch 3/10
step 60/60 [==============================] - loss: 2.7546 - acc: 0.9069 - 301ms/step        
Eval begin...
step 9/9 [==============================] - loss: 0.5788 - acc: 0.7704 - 277ms/step
Eval samples: 270
Epoch 4/10
step 60/60 [==============================] - loss: 2.8266 - acc: 0.9212 - 294ms/step        
Eval begin...
step 9/9 [==============================] - loss: 1.0253 - acc: 0.7630 - 289ms/step
Eval samples: 270
Epoch 5/10
step 60/60 [==============================] - loss: 2.9948 - acc: 0.9111 - 298ms/step        
Eval begin...
step 9/9 [==============================] - loss: 1.0942 - acc: 0.6963 - 279ms/step
Eval samples: 270
Epoch 6/10
step 60/60 [==============================] - loss: 0.8608 - acc: 0.9101 - 293ms/step        
Eval begin...
step 9/9 [==============================] - loss: 1.5086 - acc: 0.7444 - 282ms/step
Eval samples: 270
Epoch 7/10
step 60/60 [==============================] - loss: 2.5132 - acc: 0.9392 - 296ms/step        
Eval begin...
step 9/9 [==============================] - loss: 1.0570 - acc: 0.7593 - 276ms/step
Eval samples: 270
Epoch 8/10
step 60/60 [==============================] - loss: 0.8880 - acc: 0.9508 - 298ms/step        
Eval begin...
step 9/9 [==============================] - loss: 1.3956 - acc: 0.7111 - 285ms/step
Eval samples: 270
Epoch 9/10
step 60/60 [==============================] - loss: 0.0301 - acc: 0.9370 - 291ms/step        
Eval begin...
step 9/9 [==============================] - loss: 1.6584 - acc: 0.7333 - 280ms/step
Eval samples: 270
Epoch 10/10
step 60/60 [==============================] - loss: 8.0418 - acc: 0.9757 - 294ms/step        
Eval begin...
step 9/9 [==============================] - loss: 0.2750 - acc: 0.7630 - 279ms/step
Eval samples: 270

可视化结果

启动模型评估，指定数据集，设置日志格式

In [ ]

model.evaluate(val_dataset, verbose=1)

Eval begin...
step 270/270 [==============================] - loss: 1.6444 - acc: 0.7630 - 16ms/step        
Eval samples: 270

{'loss': [1.6443943], 'acc': 0.762962962962963}

启动模型测试，指定测试集

In [ ]

results = model.predict(test_dataset)

Predict begin...
step 240/240 [==============================] - 14ms/step         
Predict samples: 240

预测结果写入csv文件

In [ ]

labels = []for result in results[0]:
    lab = np.argmax(result)
    labels.append(lab)

In [ ]

test_paths = os.listdir('work/cat_12_test')

In [ ]

final_result=[]for i in range(len(labels)):
    final_result.append(test_paths[i] + ',' + str(labels[i]) + '\n')

In [ ]

with open("work/result.csv","w") as f: 
    f.writelines(final_result)

保存模型

In [ ]

model.save('work/model')  # save for training

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