детали миссии:
Эта практика представляет собой задачу множественной классификации, и необходимо идентифицировать драгоценные камни на фотографии отдельно и выполнитьИдентификация драгоценных камней
Практическая платформа: обучающая платформа Baidu AI-AI Studio, динамическая карта PaddlePaddle2.0.0
Требование: Используйте метод CNN для распознавания драгоценных камней!
Сверточная нейронная сеть
Сверточная нейронная сеть — это классическая сеть для извлечения признаков изображения, и ее структура обычно включает в себя альтернативную комбинацию нескольких сверточных слоев и объединенных слоев.
Введение в набор данных
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Файлы набора данных называются archive_train.zip, archive_test.zip.
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Набор данных содержит изображения 25 различных категорий драгоценных камней.
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Эти категории были разделены на данные обучения и тестирования.
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Изображения различаются по размеру и имеют формат .jpeg.
# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原
# View dataset directory. This directory will be recovered automatically after resetting environment.
!ls /home/aistudio/data
data55032 dataset
#导入需要的包
import os
import zipfile
import random
import json
import cv2
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import paddle
from paddle.io import Dataset
import paddle.nn as nn
1. Подготовка данных
'''
参数配置
'''
train_parameters = {
"input_size": [3, 224, 224], # 输入图片的shape
"class_dim": 25, # 分类数
"src_path":"data/data55032/archive_train.zip", # 原始数据集路径
"target_path":"/home/aistudio/data/dataset", # 要解压的路径
"train_list_path": "./train.txt", # train_data.txt路径
"eval_list_path": "./eval.txt", # eval_data.txt路径
"label_dict":{}, # 标签字典
"readme_path": "/home/aistudio/data/readme.json",# readme.json路径
"num_epochs":10, # 训练轮数
"train_batch_size": 32, # 批次的大小
"learning_strategy": { # 优化函数相关的配置
"lr": 0.0005 # 超参数学习率
}
}
def unzip_data(src_path,target_path):
'''
解压原始数据集，将src_path路径下的zip包解压至data/dataset目录下
'''
if(not os.path.isdir(target_path)):
z = zipfile.ZipFile(src_path, 'r')
z.extractall(path=target_path)
z.close()
else:
print("文件已解压")
def get_data_list(target_path,train_list_path,eval_list_path):
'''
生成数据列表
'''
# 获取所有类别保存的文件夹名称
data_list_path=target_path
class_dirs = os.listdir(data_list_path)
if '__MACOSX' in class_dirs:
class_dirs.remove('__MACOSX')
# 存储要写进eval.txt和train.txt中的内容
trainer_list=[]
eval_list=[]
class_label=0
i = 0
for class_dir in class_dirs:
path = os.path.join(data_list_path,class_dir)
# 获取所有图片
img_paths = os.listdir(path)
for img_path in img_paths: # 遍历文件夹下的每个图片
i += 1
name_path = os.path.join(path,img_path) # 每张图片的路径
if i % 10 == 0:
eval_list.append(name_path + "\t%d" % class_label + "\n")
else:
trainer_list.append(name_path + "\t%d" % class_label + "\n")
train_parameters['label_dict'][str(class_label)]=class_dir
class_label += 1
#乱序
random.shuffle(eval_list)
with open(eval_list_path, 'a') as f:
for eval_image in eval_list:
f.write(eval_image)
#乱序
random.shuffle(trainer_list)
with open(train_list_path, 'a') as f2:
for train_image in trainer_list:
f2.write(train_image)
print ('生成数据列表完成！')
# 参数初始化
src_path=train_parameters['src_path']
target_path=train_parameters['target_path']
train_list_path=train_parameters['train_list_path']
eval_list_path=train_parameters['eval_list_path']
batch_size=train_parameters['train_batch_size']
# 解压原始数据到指定路径
unzip_data(src_path,target_path)
#每次生成数据列表前，首先清空train.txt和eval.txt
with open(train_list_path, 'w') as f:
f.seek(0)
f.truncate()
with open(eval_list_path, 'w') as f:
f.seek(0)
f.truncate()
#生成数据列表
get_data_list(target_path,train_list_path,eval_list_path)
文件已解压
生成数据列表完成！
class Reader(Dataset):
def __init__(self, data_path, mode='train'):
"""
数据读取器
:param data_path: 数据集所在路径
:param mode: train or eval
"""
super().__init__()
self.data_path = data_path
self.img_paths = []
self.labels = []
if mode == 'train':
with open(os.path.join(self.data_path, "train.txt"), "r", encoding="utf-8") as f:
self.info = f.readlines()
for img_info in self.info:
img_path, label = img_info.strip().split('\t')
self.img_paths.append(img_path)
self.labels.append(int(label))
else:
with open(os.path.join(self.data_path, "eval.txt"), "r", encoding="utf-8") as f:
self.info = f.readlines()
for img_info in self.info:
img_path, label = img_info.strip().split('\t')
self.img_paths.append(img_path)
self.labels.append(int(label))
def __getitem__(self, index):
"""
获取一组数据
:param index: 文件索引号
:return:
"""
# 第一步打开图像文件并获取label值
img_path = self.img_paths[index]
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img = img.resize((224, 224), Image.BILINEAR)
img = np.array(img).astype('float32')
img = img.transpose((2, 0, 1)) / 255
label = self.labels[index]
label = np.array([label], dtype="int64")
return img, label
def print_sample(self, index: int = 0):
print("文件名", self.img_paths[index], "\t标签值", self.labels[index])
def __len__(self):
return len(self.img_paths)
#训练数据加载
train_dataset = Reader('/home/aistudio/',mode='train')
train_loader = paddle.io.DataLoader(train_dataset, batch_size=16, shuffle=True)
#测试数据加载
eval_dataset = Reader('/home/aistudio/',mode='eval')
eval_loader = paddle.io.DataLoader(eval_dataset, batch_size = 8, shuffle=False)
train_dataset.print_sample(200)
print(train_dataset.__len__())
eval_dataset.print_sample(0)
print(eval_dataset.__len__())
print(eval_dataset.__getitem__(10)[0].shape)
print(eval_dataset.__getitem__(10)[1].shape)
文件名 /home/aistudio/data/dataset/Fluorite/fluorite_35.jpg 标签值 14
730
文件名 /home/aistudio/data/dataset/Danburite/danburite_15.jpg 标签值 18
81
(3, 224, 224)
(1,)
Batch=0
Batchs=[]
all_train_accs=[]
def draw_train_acc(Batchs, train_accs):
title="training accs"
plt.title(title, fontsize=24)
plt.xlabel("batch", fontsize=14)
plt.ylabel("acc", fontsize=14)
plt.plot(Batchs, train_accs, color='green', label='training accs')
plt.legend()
plt.grid()
plt.show()
all_train_loss=[]
def draw_train_loss(Batchs, train_loss):
title="training loss"
plt.title(title, fontsize=24)
plt.xlabel("batch", fontsize=14)
plt.ylabel("loss", fontsize=14)
plt.plot(Batchs, train_loss, color='red', label='training loss')
plt.legend()
plt.grid()
plt.show()
2. Определите модель
# 定义卷积神经网络实现宝石识别
class MyCNN(nn.Layer):
def __init__(self):
super(MyCNN,self).__init__()
self.conv0=nn.Conv2D(in_channels=3, out_channels=64, kernel_size=3, stride=1)
self.pool0=nn.MaxPool2D(kernel_size=2, stride=2)
self.conv1=nn.Conv2D(in_channels=64, out_channels=128, kernel_size=4, stride=1)
self.pool1=nn.MaxPool2D(kernel_size=2, stride=2)
self.conv2=nn.Conv2D(in_channels=128, out_channels=50, kernel_size=5)
self.pool2=nn.MaxPool2D(kernel_size=2, stride=2)
self.conv3=nn.Conv2D(in_channels=50, out_channels=50, kernel_size=5)
self.pool3=nn.MaxPool2D(kernel_size=2, stride=2)
self.conv4=nn.Conv2D(in_channels=50, out_channels=50, kernel_size=5)
self.pool4=nn.MaxPool2D(kernel_size=2, stride=2)
self.fc1=nn.Linear(in_features=50*3*3, out_features=25)
def forward(self,input):
print("input.shape:",input.shape)
x=self.conv0(input)
print("x.shape:",x.shape)
x=self.pool0(x)
print('x0.shape:',x.shape)
x=self.conv1(x)
print(x.shape)
x=self.pool1(x)
print('x1.shape:',x.shape)
x=self.conv2(x)
print(x.shape)
x=self.pool2(x)
print('x2.shape:',x.shape)
x=self.conv3(x)
print(x.shape)
x=self.pool3(x)
print('x3.shape:',x.shape)
x=self.conv4(x)
print(x.shape)
x=self.pool4(x)
print('x4.shape:',x.shape)
x=paddle.reshape(x, shape=[-1, 50*3*3])
print('x3.shape:',x.shape)
y=self.fc1(x)
print('y.shape:', y.shape)
# input.shape: [16, 3, 224, 224]
# x.shape: [16, 64, 222, 222]
# x0.shape: [16, 64, 111, 111]
# [16, 128, 108, 108]
# x1.shape: [16, 128, 54, 54]
# [16, 50, 50, 50]
# x2.shape: [16, 50, 25, 25]
# [16, 50, 21, 21]
# x3.shape: [16, 50, 10, 10]
# [16, 50, 6, 6]
# x4.shape: [16, 50, 3, 3]
return y
# 定义卷积神经网络实现宝石识别
class MyCNN2(nn.Layer):
def __init__(self):
super(MyCNN2,self).__init__()
self.conv0 = nn.Conv2D(in_channels= 3,out_channels=64, kernel_size=3,stride=1)
self.pool0 = nn.MaxPool2D(kernel_size=2,stride=2)
self.conv1 = nn.Conv2D(in_channels = 64,out_channels=128,kernel_size=4,stride = 1)
self.pool1 = nn.MaxPool2D(kernel_size=2,stride=2)
self.conv2 = nn.Conv2D(in_channels= 128,out_channels=50,kernel_size=5)
self.pool2 = nn.MaxPool2D(kernel_size=2,stride=2)
self.fc1 = nn.Linear(in_features=50*25*25,out_features=25)
def forward(self,input):
x = self.conv0(input)
x = self.pool0(x)
print("x:", x.shape)
x = self.conv1(x)
x = self.pool1(x)
print("x:", x.shape)
x = self.conv2(x)
x = self.pool2(x)
print("x:", x.shape)
x = paddle.reshape(x,shape=[-1,50*25*25])
y = self.fc1(x)
return y
3. Модель обучения — CNN
model=MyCNN() # 模型实例化
model.train() # 训练模式
cross_entropy = paddle.nn.CrossEntropyLoss()
opt=paddle.optimizer.SGD(learning_rate=train_parameters['learning_strategy']['lr'],\
parameters=model.parameters())
epochs_num=train_parameters['num_epochs'] #迭代次数
for pass_num in range(train_parameters['num_epochs']):
for batch_id,data in enumerate(train_loader()):
image = data[0]
label = data[1]
predict=model(image) #数据传入model
loss=cross_entropy(predict,label)
acc=paddle.metric.accuracy(predict,label)#计算精度
if batch_id!=0 and batch_id%5==0:
Batch = Batch+5
Batchs.append(Batch)
all_train_loss.append(loss.numpy()[0])
all_train_accs.append(acc.numpy()[0])
print("epoch:{},step:{},train_loss:{},train_acc:{}".format(pass_num,batch_id,loss.numpy(),acc.numpy()))
loss.backward()
opt.step()
opt.clear_grad() #opt.clear_grad()来重置梯度
paddle.save(model.state_dict(),'MyCNN')#保存模型
draw_train_acc(Batchs,all_train_accs)
draw_train_loss(Batchs,all_train_loss)
y.shape: [16, 25]
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
/tmp/ipykernel_3036/2664449083.py in <module>
7 epochs_num=train_parameters['num_epochs'] #迭代次数
8 for pass_num in range(train_parameters['num_epochs']):
----> 9 for batch_id,data in enumerate(train_loader()):
10 image = data[0]
11 label = data[1]
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dataloader/dataloader_iter.py in __next__(self)
349 try:
350 if in_dygraph_mode():
--> 351 return self._reader.read_next_var_list()
352 else:
353 if self._return_list:
KeyboardInterrupt:
4. Оценка модели — CNN
#模型评估
para_state_dict = paddle.load("MyCNN")
model = MyCNN()
model.set_state_dict(para_state_dict) #加载模型参数
model.eval() #验证模式
accs = []
for batch_id,data in enumerate(eval_loader()):#测试集
image=data[0]
label=data[1]
predict=model(image)
acc=paddle.metric.accuracy(predict,label)
accs.append(acc.numpy()[0])
avg_acc = np.mean(accs)
print("当前模型在验证集上的准确率为:",avg_acc)
5. Предсказание модели — CNN
def unzip_infer_data(src_path,target_path):
'''
解压预测数据集
'''
if(not os.path.isdir(target_path)):
z = zipfile.ZipFile(src_path, 'r')
z.extractall(path=target_path)
z.close()
def load_image(img_path):
'''
预测图片预处理
'''
img = Image.open(img_path)
if img.mode != 'RGB':
img = img.convert('RGB')
img = img.resize((224, 224), Image.BILINEAR)
img = np.array(img).astype('float32')
img = img.transpose((2, 0, 1)) # HWC to CHW
img = img/255 # 像素值归一化
return img
infer_src_path = '/home/aistudio/data/data55032/archive_test.zip'
infer_dst_path = '/home/aistudio/data/archive_test'
unzip_infer_data(infer_src_path,infer_dst_path)
para_state_dict = paddle.load("MyCNN")
model = MyCNN()
model.set_state_dict(para_state_dict) #加载模型参数
model.eval() #验证模式
#展示预测图片
infer_path='data/archive_test/alexandrite_3.jpg'
img = Image.open(infer_path)
plt.imshow(img) #根据数组绘制图像
plt.show() #显示图像
#对预测图片进行预处理
infer_imgs = []
infer_imgs.append(load_image(infer_path))
infer_imgs = np.array(infer_imgs)
label_dic = train_parameters['label_dict']
for i in range(len(infer_imgs)):
data = infer_imgs[i]
dy_x_data = np.array(data).astype('float32')
dy_x_data=dy_x_data[np.newaxis,:, : ,:]
img = paddle.to_tensor (dy_x_data)
out = model(img)
lab = np.argmax(out.numpy()) #argmax():返回最大数的索引
print("第{}个样本,被预测为：{},真实标签为：{}".format(i+1,label_dic[str(lab)],infer_path.split('/')[-1].split("_")[0]))
print("结束")