mmclassification training data preparation script

1. Randomly divide training, verification, and testing

import os
import glob
import random
import shutil

dataset_dir = './XXX_classification/'
train_dir = './datasets/train/'
valid_dir = './datasets/val/'
test_dir = './datasets/test/'

train_per = 0.8
valid_per = 0.1
test_per = 0.1


def makedir(new_dir):
    if not os.path.exists(new_dir):
        os.makedirs(new_dir)


if __name__ == '__main__':

    for root, dirs, files in os.walk(dataset_dir):
        for sDir in dirs:
            imgs_list = glob.glob(os.path.join(root, sDir)+'/*.jpg')
            random.seed(666)
            random.shuffle(imgs_list)
            imgs_num = len(imgs_list)

            train_point = int(imgs_num * train_per)
            valid_point = int(imgs_num * (train_per + valid_per))

            for i in range(imgs_num):
                if i < train_point:
                    out_dir = train_dir + sDir + '/'
                elif i < valid_point:
                    out_dir = valid_dir + sDir + '/'
                else:
                    out_dir = test_dir + sDir + '/'

                makedir(out_dir)
                out_path = out_dir + os.path.split(imgs_list[i])[-1]
                shutil.copy(imgs_list[i], out_path)

            print('Class:{}, train:{}, valid:{}, test:{}'.format(sDir, train_point, valid_point-train_point, imgs_num-valid_point))

2. Generate meta format for divided training, verification, and testing

import os
from glob import glob
from pathlib import Path


def generate_mmcls_ann(data_dir, img_type='.jpg'):
    data_dir = str(Path(data_dir)) + '/'
    classes = ['0000', '0001', '0002', '0003']
    class2id = dict(zip(classes, range(len(classes))))
    data_dir = str(Path(data_dir)) + '/'
    dir_types = ['train', 'val', 'test']

    sub_dirs = os.listdir(data_dir)
    ann_dir = data_dir + 'meta/'
    if not os.path.exists(ann_dir):
        os.makedirs(ann_dir)
    for sd in sub_dirs:
        if sd not in dir_types:
            continue
        annotations = []
        target_dir = data_dir + sd + '/'
        for d in os.listdir(target_dir):
            class_id = str(class2id[d])
            images = glob(target_dir + d + '/*' + img_type)
            for img in images:
                img = d + '/' + os.path.basename(img)
                annotations.append(img + ' ' + class_id + '\n')
        annotations[-1] = annotations[-1].strip()
        with open(ann_dir + sd + '.txt', 'w') as f:
            f.writelines(annotations)


if __name__ == '__main__':
    data_dir = './datasets/'
    generate_mmcls_ann(data_dir)