详细记录DBNet.pytorch训练 Win10

DBNet.pytorch: 添加链接描述
系统：windows 10
1、数据集预处理：
（1）把训练数据train和测试数据test的img和gt，放到datasets文件夹下

（2）将训练数据和测试数据生成如下图的格式：

生成train.txt和test.txt,保存到datasets文件夹下(必须也把test.txt也生成)。
生成文件的代码如下：

import os
def get_images(img_path):
    '''
    find image files in data path
    :return: list of files found
    '''
    img_path = os.path.abspath(img_path)
    files = []
    exts = ['jpg', 'png', 'jpeg', 'JPG', 'PNG']
    for parent, dirnames, filenames in os.walk(img_path):
        for filename in filenames:
            for ext in exts:
                if filename.endswith(ext):
                    files.append(os.path.join(parent, filename))
                    break
    print('Find {} images'.format(len(files)))
    return sorted(files)

def get_txts(txt_path):
    '''
    find gt files in data path
    :return: list of files found
    '''
    txt_path = os.path.abspath(txt_path)
    files = []
    exts = ['txt']
    for parent, dirnames, filenames in os.walk(txt_path):
        for filename in filenames:
            for ext in exts:
                if filename.endswith(ext):
                    files.append(os.path.join(parent, filename))
                    break
    print('Find {} txts'.format(len(files)))
    return sorted(files)

if __name__ == '__main__':
    import json
    #img_path = './data/ch4_training_images'
    #img_path = './train/img'
    img_path = './test/img'
    files = get_images(img_path)
    #txt_path = './data/ch4_training_localization_transcription_gt'
    #txt_path = './train/gt'
    txt_path = './test/gt'
    txts = get_txts(txt_path)
    n = len(files)
    assert len(files) == len(txts)
    with open('test.txt', 'w') as f:
        for i in range(n):
            line = files[i] + '\t' + txts[i] + '\n'
            #line = files[i] + ' ' + txts[i] + '\n'
            f.write(line)
    print('dataset generated ^_^ ')

参考：添加链接描述

2、配置文件的修改

（1）把data_path的路径改为：- E:\ZhuoZhuangOCR\Paper\Latest\DB-Resnet\DBNet.pytorch\datasets\train.txt （使用绝对路径）

dataset:
  train:
    dataset:
      args:
        data_path:
          - E:\ZhuoZhuangOCR\Paper\Latest\DB-Resnet\DBNet.pytorch\datasets\train.txt
        img_mode: RGB

（2）把base的路径由相对路径改为绝对路径，

base: ['E:/ZhuoZhuangOCR/Paper/Latest/DB-Resnet/DBNet.pytorch/config/icdar2015.yaml']