data_loader读取器

import random

import numpy as np

import pandas as pd

import cv2

def date_loader(image_dir, file_name, batch_size=1, mode='train'):

    train_dir_list = []

    train_label = []

    test_dir_list = []

    test_label = []

    val_dir_list = []

    val_label = []

    df = pd.read_csv(file_name)

    # 生成训练和测试数据集 0.8 /0.2

    df = df.sample(frac=1)

    for i in range(len(df)):

        if i <= (len(df)*0.8-1):

            dir =  image_dir+ '/' + df.iloc[i][0] + '.jpg'

            train_dir_list.append(dir)

            train_label.append(int(df.iloc[i][1]-1))

        else:

            dir =  image_dir+ '/' + df.iloc[i][0] + '.jpg'

            test_dir_list.append(dir)

            test_label.append(int(df.iloc[i][1]-1))

    # 生成随机验证集，比列0.2

    df1 = df.sample(frac=0.2)

    for i in range(len(df1)):

        dir =  image_dir+ '/' + df1.iloc[i][0] + '.jpg'

        val_dir_list.append(dir)

        val_label.append(int(df.iloc[i][1]-1))

    def reader():

        batch_img = []

        batch_label = []

        if mode == 'train':

            count = 0

            for i in range(len(train_dir_list)):

                img = cv2.imread(train_dir_list[i])

                img = cv2.resize(img, (224,224), interpolation=cv2.INTER_CUBIC)/255

                img = np.transpose(img, (2,0,1))

                batch_img.append(img)

                batch_label.append(train_label[i])

                count +=1

                if (count %batch_size==0):

                    # print(len(train_label))

                    yield np.array(batch_img).astype('float32'), np.asarray(batch_label).astype('int64').reshape(batch_size,1)

                    batch_img = []

                    batch_label = []

        elif mode == 'test':

            count = 0

            for i in range(len(test_dir_list)):

                img = cv2.imread(test_dir_list[i])

                img = cv2.resize(img, (224,224), interpolation=cv2.INTER_CUBIC)/255

                img = np.transpose(img, (2,0,1))

                batch_img.append(img)

                batch_label.append(test_label[i])

                count +=1

                if (count %batch_size==0):

                    # print(len(test_label))

                    yield np.array(batch_img).astype('float32'), np.asarray(batch_label).astype('int64').reshape(batch_size,1)

                    batch_img = []

                    batch_label = []

        elif mode == 'val':

            count = 0

            for i in range(len(val_dir_list)):

                img = cv2.imread(val_dir_list[i])

                img = cv2.resize(img, (224,224), interpolation=cv2.INTER_CUBIC)/255

                img = np.transpose(img, (2,0,1))

                batch_img.append(img)

                batch_label.append(val_label[i])

                count +=1

                if (count %batch_size==0):

                    # print(len(val_dir_list))

                    yield np.array(batch_img).astype('float32'), np.asarray(batch_label).astype('int64').reshape(batch_size,1)

                    batch_img = []

                    batch_label = []

    return reader

a = date_loader('image2_100','a_100_drop_p.csv',mode='test')

for n , data in enumerate(a()):

    images, label = data

    # print(label)

    break
train_reader = paddle.batch(date_loader('image2_100','a_100_drop_p.csv',mode='train'), batch_size=10)

test_reader = paddle.batch(date_loader('image2_100','a_100_drop_p.csv',mode='test'), batch_size=10)
巴特西

data_loader读取器

最新文章

热门文章
