在全新ubuntu上用gpu训练paddleocr模型遇到的坑与解决办法

一. 我的ubuntu版本

二.首先拉取paddleocr源代码

下载地址：https://gitee.com/paddlepaddle/PaddleOCR

三.下载模型

• 我要训练一个中文模型，看到该预训练模型泛化性能最优，于是下载这个模型
  https://gitee.com/link?target=https%3A%2F%2Fpaddleocr.bj.bcebos.com%2FPP-OCRv3%2Fchinese%2Fch_PP-OCRv3_rec_train.tar

• 其他模型地址：https://gitee.com/paddlepaddle/PaddleOCR/blob/release/2.6/doc/doc_ch/models_list.md

四.训练前的准备

1.在源代码文件夹里创造一个自己放东西的文件

• config文件夹用来装yml配置文件
  pretrained_model用来装上一步下载的预训练模型
  split_rec_label用来放数据集
  output用来放训练出的模型

• 创建文件夹非强制，只是这样更方便管理自己文件，yml源文件地址就在
  PaddleOCR-release-2.6/configs/rec/PP-OCRv3这个路径下

2.准备数据

2.1数据标注

参考博客：https://blog.csdn.net/qq_49627063/article/details/119134847

2.2数据划分

在训练之前，所有图片都在一个文件夹中，所有label信息都在同一个txt文件中，因此需要编写脚本，将其按照8:1:1的比例进行分割。

import os
import re
import shutil
import random
import argparse

def split_label(all_label, train_label, val_label, test_label):
    f = open(all_label, 'r')
    f_train = open(train_label, 'w')
    f_val = open(val_label, 'w')
    f_test = open(test_label, 'w')
    raw_list = f.readlines()
    num_train = int(len(raw_list) * 0.8)
    num_val = int(len(raw_list) * 0.1)
    num_test = int(len(raw_list) * 0.1)
    random.shuffle(raw_list)
    for i in range(num_train):
        f_train.writelines(raw_list[i])
    for i in range(num_train, num_train + num_val):
        f_val.writelines(raw_list[i])
    for i in range(num_train + num_val, num_train + num_val + num_test):
        f_test.writelines(raw_list[i])
    f.close()
    f_train.close()
    f_val.close()
    f_test.close()


def split_img(all_imgs, train_label, train_imgs, val_label, val_imgs, test_label, test_imgs):
    f_train = open(train_label, 'r')
    f_val = open(val_label, 'r')
    f_test = open(test_label, 'r')
    train_list = f_train.readlines()
    val_list = f_val.readlines()
    test_list = f_test.readlines()
    for i in range(len(train_list)):
        img_path = os.path.join(all_imgs, re.split("[/\t]", train_list[i])[1])
        shutil.move(img_path, train_imgs)
    for i in range(len(val_list)):
        img_path = os.path.join(all_imgs, re.split("[/\t]", val_list[i])[1])
        shutil.move(img_path, val_imgs)
    for i in range(len(test_list)):
        img_path = os.path.join(all_imgs, re.split("[/\t]", test_list[i])[1])
        shutil.move(img_path, test_imgs)


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--all_label", default="../paddleocr/PaddleOCR/train_data/cls/cls_gt_train.txt")
    parser.add_argument("--all_imgs_dir", default="../paddleocr/PaddleOCR/train_data/cls/images/")
    parser.add_argument("--train_label", default="../paddleocr/PaddleOCR/train_data/cls/train.txt")
    parser.add_argument("--train_imgs_dir", default="../paddleocr/PaddleOCR/train_data/cls/train/")
    parser.add_argument("--val_label", default="../paddleocr/PaddleOCR/train_data/cls/val.txt")
    parser.add_argument("--val_imgs_dir", default="../paddleocr/PaddleOCR/train_data/cls/val/")
    parser.add_argument("--test_label", default="../paddleocr/PaddleOCR/train_data/cls/test.txt")
    parser.add_argument("--test_imgs_dir", default="../paddleocr/PaddleOCR/train_data/cls/test/")
    return parser.parse_args()


def main(args):
    if not os.path.isdir(args.train_imgs_dir):
        os.makedirs(args.train_imgs_dir)
    if not os.path.isdir(args.val_imgs_dir):
        os.makedirs(args.val_imgs_dir)
    if not os.path.isdir(args.test_imgs_dir):
        os.makedirs(args.test_imgs_dir)
    split_label(args.all_label, args.train_label, args.val_label, args.test_label)
    split_img(args.all_imgs_dir, args.train_label, args.train_imgs_dir, args.val_label, args.val_imgs_dir, args.test_label, args.test_imgs_dir)


if __name__ == "__main__":
    main(get_args())

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73

3.改写yml配置文件

• 源地址：https://gitee.com/paddlepaddle/PaddleOCR/blob/release/2.6/configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml

Global:
  debug: false
  use_gpu: true
  epoch_num: 800
  log_smooth_window: 20
  print_batch_step: 10
  save_model_dir: wjp/output/rec_ppocr_v3_distillation
  save_epoch_step: 3
  eval_batch_step: [0, 2000]
  cal_metric_during_train: true
  pretrained_model:
  checkpoints:
  save_inference_dir:
  use_visualdl: false
  infer_img: doc/imgs_words/ch/word_1.jpg
  character_dict_path: ppocr/utils/ppocr_keys_v1.txt
  max_text_length: &max_text_length 25
  infer_mode: false
  use_space_char: true
  distributed: true
  save_res_path: wjp/output/rec/predicts_ppocrv3_distillation.txt


Optimizer:
  name: Adam
  beta1: 0.9
  beta2: 0.999
  lr:
    name: Piecewise
    decay_epochs : [700]
    values : [0.0005, 0.00005]
    warmup_epoch: 5
  regularizer:
    name: L2
    factor: 3.0e-05


Architecture:
  model_type: &model_type "rec"
  name: DistillationModel
  algorithm: Distillation
  Models:
    Teacher:
      pretrained:
      freeze_params: false
      return_all_feats: true
      model_type: *model_type
      algorithm: SVTR
      Transform:
      Backbone:
        name: MobileNetV1Enhance
        scale: 0.5
        last_conv_stride: [1, 2]
        last_pool_type: avg
      Head:
        name: MultiHead
        head_list:
          - CTCHead:
              Neck:
                name: svtr
                dims: 64
                depth: 2
                hidden_dims: 120
                use_guide: True
              Head:
                fc_decay: 0.00001
          - SARHead:
              enc_dim: 512
              max_text_length: *max_text_length
    Student:
      pretrained:
      freeze_params: false
      return_all_feats: true
      model_type: *model_type
      algorithm: SVTR
      Transform:
      Backbone:
        name: MobileNetV1Enhance
        scale: 0.5
        last_conv_stride: [1, 2]
        last_pool_type: avg
      Head:
        name: MultiHead
        head_list:
          - CTCHead:
              Neck:
                name: svtr
                dims: 64
                depth: 2
                hidden_dims: 120
                use_guide: True
              Head:
                fc_decay: 0.00001
          - SARHead:
              enc_dim: 512
              max_text_length: *max_text_length
Loss:
  name: CombinedLoss
  loss_config_list:
  - DistillationDMLLoss:
      weight: 1.0
      act: "softmax"
      use_log: true
      model_name_pairs:
      - ["Student", "Teacher"]
      key: head_out
      multi_head: True
      dis_head: ctc
      name: dml_ctc
  - DistillationDMLLoss:
      weight: 0.5
      act: "softmax"
      use_log: true
      model_name_pairs:
      - ["Student", "Teacher"]
      key: head_out
      multi_head: True
      dis_head: sar
      name: dml_sar
  - DistillationDistanceLoss:
      weight: 1.0
      mode: "l2"
      model_name_pairs:
      - ["Student", "Teacher"]
      key: backbone_out
  - DistillationCTCLoss:
      weight: 1.0
      model_name_list: ["Student", "Teacher"]
      key: head_out
      multi_head: True
  - DistillationSARLoss:
      weight: 1.0
      model_name_list: ["Student", "Teacher"]
      key: head_out
      multi_head: True

PostProcess:
  name: DistillationCTCLabelDecode
  model_name: ["Student", "Teacher"]
  key: head_out
  multi_head: True

Metric:
  name: DistillationMetric
  base_metric_name: RecMetric
  main_indicator: acc
  key: "Student"
  ignore_space: False

Train:
  dataset:
    name: SimpleDataSet
    data_dir: wjp/split_rec_label/train
    ext_op_transform_idx: 1
    label_file_list:
    - wjp/split_rec_label/train.txt
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - RecConAug:
        prob: 0.5
        ext_data_num: 2
        image_shape: [48, 320, 3]
        max_text_length: *max_text_length
    - RecAug:
    - MultiLabelEncode:
    - RecResizeImg:
        image_shape: [3, 48, 320]
    - KeepKeys:
        keep_keys:
        - image
        - label_ctc
        - label_sar
        - length
        - valid_ratio
  loader:
    shuffle: true
    batch_size_per_card: 32
    drop_last: true
    num_workers: 4
Eval:
  dataset:
    name: SimpleDataSet
    data_dir: wjp/split_rec_label/val
    label_file_list:
    - wjp/split_rec_label/val.txt
    transforms:
    - DecodeImage:
        img_mode: BGR
        channel_first: false
    - MultiLabelEncode:
    - RecResizeImg:
        image_shape: [3, 48, 320]
    - KeepKeys:
        keep_keys:
        - image
        - label_ctc
        - label_sar
        - length
        - valid_ratio
  loader:
    shuffle: false
    drop_last: false
    batch_size_per_card: 128
    num_workers: 4

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207

4.安装anaconda

参考博客：https://blog.csdn.net/wyf2017/article/details/118676765

• 创建python虚拟环境

conda create -n ppocr
1

• 切换虚拟环境

source activate ppocr
1

五.开始训练

python tools/train.py -c wjp/ch_PP-OCRv3_rec_distillation.yml -o Global.pretrained_model=wjp/ch_PP-OCRv3_rec_train/best_accuracy
//-c参数放配置文件地址，-o参数放预训练模型地址

1
2
3

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple

六.报错

（1） libGL.so.1

ImportError: libGL.so.1: cannot open shared object file: No such file or directory
1

• 解决办法：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple opencv-python-headless
1

（2）Polygon

ModuleNotFoundError: No module named 'Polygon'
1

• 解决办法：

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple Polygon3
1

(3) lanms

ModuleNotFoundError: No module named 'lanms'
1

源码下载地址：https://github.com/AndranikSargsyan/lanms-nova/tree/master

参考我这个教程编译：http://t.csdnimg.cn/BqOW6

• 将__init __.py文件替换

import numpy as np


def merge_quadrangle_n9(polys, thres=0.3, precision=10000):
    if len(polys) == 0:
        return np.array([], dtype='float32')
    p = polys.copy()
    p[:, :8] *= precision
    ret = np.array(merge_quadrangle_n9(p, thres), dtype='float32')
    ret[:, :8] /= precision
    return ret


1
2
3
4
5
6
7
8
9
10
11
12
13

• 找到linux种anaconda的包放在什么地方

pip show numpy
1

就知道该环境下的包安装地址

• 将编译好库的整个lanms文件夹移动到该地址去即可调用

（4）报错UnicodeDecodeError: ‘utf-8’ codec can’t decode byte 0xbc in position 2: invalid start byt

f = open('txt01.txt',encoding='utf-8')
1

将 encoding=’utf-8’ 改为GB2312、gbk、ISO-8859-1，随便尝试一个均可以

（5）Out of memory error on GPU 0. Cannot allocate xxxxMB memory on GPU 0, xxxxGB memory has been allocated and available memory is only 0.000000B.

将训练的配置yml文件中的batch_size_per_card参数不断改小（除以2），直到不再报这个错即可。