JMU-P01/train_model.py

import numpy as np
import os
import paddle as paddle
import paddle.fluid as fluid
from PIL import Image
import cv2
import matplotlib.pyplot as plt
from multiprocessing import  cpu_count
from paddle.fluid.dygraph import Pool2D,Conv2D,Linear
data_path ='./data_set'
train_data = "./train_data.list"
test_data = './test_data.list'
character_folders = os.listdir(data_path)
label = 0
label_temp = {}
if(os.path.exists(train_data)):
    os.remove(train_data)
if(os.path.exists(test_data)):
    os.remove(test_data)
for character_folder in character_folders:
    with open(train_data,'a') as file_train:
        with open(test_data,'a') as file_test:
            if character_folder == '.DS_Store' or character_folder == '.ipynb_checkpoints' or character_folder == 'data23617':
                continue
           # print(character_folder+str(label))
            label_temp[str(label)] =character_folder
            character_imgs = os.listdir(os.path.join(data_path,character_folder))
            for i in range(len(character_imgs)):
                filePath = data_path + "/" + character_folder+"/"+character_imgs[i]
                if i % 10 == 0:
                    file_test.write(filePath+ "\t" + str(label) + '\n')
                else:
                    file_train.write(filePath + "\t" + str(label) + '\n')
    label = label + 1
print('图像列表已生成')

#定义图像列表定义字符训练集和测试集的reader

def data_mapper(sample):
    imgpath, label = sample
    img = paddle.dataset.image.load_image(file=imgpath, is_color=False)
    img = np.array(img).astype('float32')/ 255.0
    print(imgpath,img.shape)
    img =img.reshape(1,20,20)
    return img, label
def data_reader(data_list_path):
    def reader():
        with open(data_list_path,'r') as f:
            lines= f.readlines()
            for line in lines:
                img,label = line.split('\t')
                yield img,int(label)
    return paddle.reader.xmap_readers(data_mapper,reader,cpu_count(),1024)

# 用于训练的数据提供器
train_reader = paddle.batch(reader=paddle.reader.shuffle(reader=data_reader('./train_data.list'), buf_size=512), batch_size=64)
# 用于测试的数据提供器
test_reader = paddle.batch(reader=data_reader('./test_data.list'), batch_size=10)

class LeNet_Model(fluid.dygraph.Layer):
    def __init__(self):
        super(LeNet_Model,self).__init__()
        self.hidden1_1 = Conv2D(num_channels=1,num_filters=28,filter_size=5,stride=1)
        self.hidden1_2 = Pool2D(pool_size=2,pool_type='max',pool_stride=1)
        self.hidden2_1 = Conv2D(num_channels=28,num_filters=32,filter_size=3,stride=1)
        self.hidden2_2 = Pool2D(pool_size=2,pool_type='max',pool_stride=1)
        self.hidden3=Conv2D(num_channels=32,num_filters=32,filter_size=3,stride=1)
        self.hidden4=Linear(32*10*10,64,act='softmax')
    def forward(self, input):
        x= self.hidden1_1(input)
        x =self.hidden1_2(x)
        x = self.hidden2_1(x)
        x=self.hidden2_2(x)
        x =self.hidden3(x)
        x= fluid.layers.reshape(x,[-1,32*10*10])
        y= self.hidden4(x)
        return y
#开启训练模式
with fluid.dygraph.gard():
    model = LeNet_Model() #实例化模型
    model.train() #开启训练模式
    opt = fluid.optimizer.SGDOptimizer(learning_rate=0.001,parameter_list=model.parameters())
    eporchs_num = 2
    for pass_num in range(eporchs_num):
        for batch_id,data in enumerate(train_reader()):
            images = np.array([x[0] for x in data],np.float32)
            labels = np.array([x[1] for x in data]).astype('int64')
            labels = labels[:, np.newaxis]
            image = fluid.dygraph.to_variable(images)
            label = fluid.dygraph.to_variable(labels)

            predict = model(image)
            loss = fluid.layers.cross_entropy(predict,label)
            avg_loss= fluid.layers.mean(loss) #获取精度的平均值

            acc = fluid.layers.accuracy(predict,label)
            if batch_id!=0 and batch_id%50==0:
                print("train_pass:{},batch_id:{},train_loss:{},train_acc:{}".format(pass_num, batch_id, avg_loss.numpy(),
                                                                                  acc.numpy()))
            avg_loss.backward()
            opt.minimize(avg_loss)
            model.clear_gradients()
    fluid.save_dygraph(model.state_dict(),"letnet_model")