基于PaddleOCR2.4的天池街景字符编码识别Baseline
基于PaddleOCR2.4，使用CRNN算法，骨干MobileNetV3，加载预训练模型，对现实街景字符编码进行识别
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一、 天池街景字符编码识别比赛
比赛地址：https://tianchi.aliyun.com/competition/entrance/531795/information
1.数据来源
赛题来源自Google街景图像中的门牌号数据集（The Street View House Numbers Dataset, SVHN），并根据一定方式采样得到比赛数据集。
2.数据基本情况
该数据来自真实场景的门牌号。训练集数据包括3W张照片，验证集数据包括1W张照片，每张照片包括颜色图像和对应的编码类别和具体位置；为了保证比赛的公平性，测试集A包括4W张照片，测试集B包括4W张照片。
enter image description here
3.数据集样本展示
4.字段表
所有的数据（训练集、验证集和测试集）的标注使用JSON格式，并使用文件名进行索引。如果一个文件中包括多个字符，则使用列表将字段进行组合。
| Field | Description |
|---|---|
| top | 左上角坐标Y |
| height | 字符高度 |
| left | 左上角坐标X |
| width | 字符宽度 |
| label | 字符编码 |
注：数据集来源自SVHN，网页链接http://ufldl.stanford.edu/housenumbers/，并进行匿名处理和噪音处理，请各位选手使用比赛给定的数据集完成训练。
二、环境设置
PaddleOCR https://github.com/paddlepaddle/PaddleOCR 是一款全宇宙最强的用的OCR工具库，开箱即用，速度杠杠的。
# 从gitee上下载PaddleOCR代码，也可以从GitHub链接下载
!git clone https://gitee.com/paddlepaddle/PaddleOCR.git --depth=1
# 升级pip
!pip install -U pip
# 安装依赖
%cd ~/PaddleOCR
%pip install -r requirements.txt
%cd ~/PaddleOCR/
!tree -L 1
/home/aistudio/PaddleOCR
.
├── benchmark
├── configs
├── deploy
├── doc
├── __init__.py
├── LICENSE
├── MANIFEST.in
├── paddleocr.py
├── ppocr
├── PPOCRLabel
├── ppstructure
├── README_ch.md
├── README.md
├── requirements.txt
├── setup.py
├── StyleText
├── test_tipc
├── tools
└── train.sh
10 directories, 9 files
三、数据准备
据悉train数据集共10万张，解压，并划分出10000张作为测试集。
1.数据下载解压
# 解压缩数据集
%cd ~
!unzip -qoa data/data124095/street_code_rec_data.zip -d ~/data/
/home/aistudio
# 重命名文件夹
!mv data/街景编码识别 data/street_code_rec_data
# 解压test数据集
!unzip -qoa data/street_code_rec_data/mchar_test_a.zip -d data/street_code_rec_data/
# 解压eval据集
!unzip -qoa data/street_code_rec_data/mchar_val.zip -d data/street_code_rec_data/
# 解压train数据集
!unzip -qoa data/street_code_rec_data/mchar_train.zip -d data/street_code_rec_data/
# 使用命令查看训练数据文件夹下数据量是否是3张
!cd data/street_code_rec_data/mchar_train && ls -l | grep "^-" | wc -l
30000
# 使用命令查看test数据文件夹下数据量是否是4万张
!cd data/street_code_rec_data/mchar_test_a && ls -l | grep "^-" | wc -l
40000
# 使用命令查看test数据文件夹下数据量是否是1万张
!cd data/street_code_rec_data/mchar_val && ls -l | grep "^-" | wc -l
10000
%cd data/street_code_rec_data
!rm *.zip
%cd ~
/home/aistudio/data/street_code_rec_data
/home/aistudio
2. 数据标签处理
import json
def trans(path):
with open(path + '.json', 'r') as f:
json_data = json.load(f)
print(len(json_data))
with open(path + '.csv', 'w') as ff:
for item in json_data:
label = json_data[item]['label']
label = [str(x) for x in label]
label = ''.join(label)
ff.write(item + '\t' + label + '\n')
trans('data/street_code_rec_data/mchar_val')
trans('data/street_code_rec_data/mchar_train')
10000
30000
3. 数据查看
!head data/street_code_rec_data/mchar_val.csv
000000.png 5
000001.png 210
000002.png 6
000003.png 1
000004.png 9
000005.png 1
000006.png 183
000007.png 65
000008.png 144
000009.png 16
!head data/street_code_rec_data/mchar_train.csv
000000.png 19
000001.png 23
000002.png 25
000003.png 93
000004.png 31
000005.png 33
000006.png 28
000007.png 744
000008.png 128
000009.png 16
from PIL import Image
img=Image.open('data/street_code_rec_data/mchar_train/000000.png')
print(img.size)
img
(741, 350)
![[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-Q3MaAzxd-1641566255538)(output_21_1.png)]](https://i-blog.csdnimg.cn/blog_migrate/c2e86c42cad108a0eade157f790e25ee.png)
四、配置训练参数
以PaddleOCR/configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml为基准进行配置
1.配置模型网络
使用CRNN算法，backbone是MobileNetV3，损失函数是CTCLoss
Architecture:
model_type: rec
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 48
Head:
name: CTCHead
fc_decay: 0.00001
2.配置数据
对Train.data_dir, Train.label_file_list, Eval.data_dir, Eval.label_file_list进行配置
Train:
dataset:
name: SimpleDataSet
data_dir: /home/aistudio/data/street_code_rec_data/mchar_train
label_file_list: ["/home/aistudio/data/street_code_rec_data/mchar_train.csv"]
...
...
Eval:
dataset:
name: SimpleDataSet
data_dir: /home/aistudio/data/street_code_rec_data/mchar_val
label_file_list: ["/home/aistudio/data/street_code_rec_data/mchar_val.csv"]
3. 显卡、评估设置
use_gpu、cal_metric_during_train分别是GPU、评估开关
Global:
use_gpu: false # true 使用GPU
.....
cal_metric_during_train: False # true 打开评估
4. 多线程任务
Train.loader.num_workers:4
Eval.loader.num_workers: 4
5.完整配置
Global:
use_gpu: True
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec_en_number_lite
save_epoch_step: 3
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [1000, 100]
# if pretrained_model is saved in static mode, load_static_weights must set to True
cal_metric_during_train: True
pretrained_model: ./en_number_mobile_v2.0_rec_train/best_accuracy.pdparams
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img:
# for data or label process
character_dict_path: ppocr/utils/en_dict.txt
max_text_length: 25
infer_mode: False
use_space_char: True
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0.00001
Architecture:
model_type: rec
algorithm: CRNN
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
small_stride: [1, 2, 2, 2]
Neck:
name: SequenceEncoder
encoder_type: rnn
hidden_size: 48
Head:
name: CTCHead
fc_decay: 0.00001
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
Train:
dataset:
name: SimpleDataSet
data_dir: /home/aistudio/data/street_code_rec_data/mchar_train
label_file_list: ["/home/aistudio/data/street_code_rec_data/mchar_train.csv"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- RecAug:
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 320]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: True
batch_size_per_card: 256
drop_last: True
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: /home/aistudio/data/street_code_rec_data/mchar_val
label_file_list: ["/home/aistudio/data/street_code_rec_data/mchar_val.csv"]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [3, 32, 320]
- KeepKeys:
keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size_per_card: 256
num_workers: 8
# 已配置好的文件，直接覆盖替换（-f）
!cp -f ~/rec_en_number_lite_train.yml ~/PaddleOCR/configs/rec/multi_language/rec_en_number_lite_train.yml
6.使用预训练模型
据悉使用预训练模型，训练速度更快！！！
PaddleOCR提供的可下载模型包括推理模型、训练模型、预训练模型、slim模型，模型区别说明如下：
| 模型类型 | 模型格式 | 简介 |
|---|---|---|
| 推理模型 | inference.pdmodel、inference.pdiparams | 用于预测引擎推理，详情 |
| 训练模型、预训练模型 | *.pdparams、*.pdopt、*.states | 训练过程中保存的模型的参数、优化器状态和训练中间信息，多用于模型指标评估和恢复训练 |
| slim模型 | *.nb | 经过飞桨模型压缩工具PaddleSlim压缩后的模型，适用于移动端/IoT端等端侧部署场景（需使用飞桨Paddle Lite部署）。 |
各个模型的关系如下面的示意图所示。
文本检测模型
英文识别模型
| 模型名称 | 模型简介 | 配置文件 | 推理模型大小 | 下载地址 |
|---|---|---|---|---|
| en_number_mobile_slim_v2.0_rec | slim裁剪量化版超轻量模型，支持英文、数字识别 | rec_en_number_lite_train.yml | 2.7M | 推理模型 / 训练模型 |
| en_number_mobile_v2.0_rec | 原始超轻量模型，支持英文、数字识别 | rec_en_number_lite_train.yml | 2.6M | 推理模型 / 训练模型 |
%cd ~/PaddleOCR/
# mobile模型
!wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar
!tar -xf en_number_mobile_v2.0_rec_train.tar
/home/aistudio/PaddleOCR
--2022-01-02 00:10:41-- https://paddleocr.bj.bcebos.com/dygraph_v2.0/multilingual/en_number_mobile_v2.0_rec_train.tar
Resolving paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)... 182.61.200.229, 182.61.200.195, 2409:8c04:1001:1002:0:ff:b001:368a
Connecting to paddleocr.bj.bcebos.com (paddleocr.bj.bcebos.com)|182.61.200.229|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 9123840 (8.7M) [application/x-tar]
Saving to: ‘en_number_mobile_v2.0_rec_train.tar’
en_number_mobile_v2 100%[===================>] 8.70M 8.63MB/s in 1.0s
2022-01-02 00:10:42 (8.63 MB/s) - ‘en_number_mobile_v2.0_rec_train.tar’ saved [9123840/9123840]
五、训练
%cd ~/PaddleOCR/
# mobile模型
!python tools/train.py -c ./configs/rec/multi_language/rec_en_number_lite_train.yml -o Global.checkpoints=./output/rec_en_number_lite/latest
1.选择合适的batch size
2.训练日志
2022/01/02 01:28:23] root INFO: save model in ./output/rec_en_number_lite/latest
[2022/01/02 01:28:23] root INFO: Initialize indexs of datasets:['/home/aistudio/data/street_code_rec_data/mchar_train.csv']
[2022/01/02 01:28:54] root INFO: epoch: [27/500], iter: 180, lr: 0.000986, loss: 1.043328, acc: 0.765624, norm_edit_dis: 0.863509, reader_cost: 2.26051 s, batch_cost: 2.59590 s, samples: 7168, ips: 276.12724
[2022/01/02 01:29:18] root INFO: epoch: [27/500], iter: 190, lr: 0.000986, loss: 1.056450, acc: 0.765624, norm_edit_dis: 0.864510, reader_cost: 1.18228 s, batch_cost: 1.65932 s, samples: 10240, ips: 617.12064
[2022/01/02 01:29:34] root INFO: epoch: [27/500], iter: 200, lr: 0.000985, loss: 1.069025, acc: 0.759277, norm_edit_dis: 0.860254, reader_cost: 0.74316 s, batch_cost: 1.15521 s, samples: 10240, ips: 886.42030
eval model:: 100%|██████████████████████████████| 10/10 [00:07<00:00, 2.12it/s]
[2022/01/02 01:29:42] root INFO: cur metric, acc: 0.6261999373800062, norm_edit_dis: 0.7362716930394972, fps: 4054.7339744968563
[2022/01/02 01:29:42] root INFO: save best model is to ./output/rec_en_number_lite/best_accuracy
[2022/01/02 01:29:42] root INFO: best metric, acc: 0.6261999373800062, start_epoch: 21, norm_edit_dis: 0.7362716930394972, fps: 4054.7339744968563, best_epoch: 27
3. visualdl可视化
- 本地安装visualdl
pip install visualdl - 下载日志至本地
- 启动visualdl可视化
visualdl --logdir ./ - 打开浏览器查看
http://localhost:8040/
六、模型评估
# GPU 评估， Global.checkpoints 为待测权重
%cd ~/PaddleOCR/
# mobile模型
!python -m paddle.distributed.launch tools/eval.py -c ./configs/rec/multi_language/rec_en_number_lite_train.yml \
-o Global.checkpoints=./output/rec_en_number_lite/best_accuracy.pdparams
/home/aistudio/PaddleOCR
----------- Configuration Arguments -----------
backend: auto
elastic_server: None
force: False
gpus: None
heter_devices:
heter_worker_num: None
heter_workers:
host: None
http_port: None
ips: 127.0.0.1
job_id: None
log_dir: log
np: None
nproc_per_node: None
run_mode: None
scale: 0
server_num: None
servers:
training_script: tools/eval.py
training_script_args: ['-c', './configs/rec/multi_language/rec_en_number_lite_train.yml', '-o', 'Global.checkpoints=./output/rec_en_number_lite/best_accuracy.pdparams']
worker_num: None
workers:
------------------------------------------------
WARNING 2022-01-02 01:32:26,892 launch.py:423] Not found distinct arguments and compiled with cuda or xpu. Default use collective mode
launch train in GPU mode!
INFO 2022-01-02 01:32:26,894 launch_utils.py:528] Local start 1 processes. First process distributed environment info (Only For Debug):
+=======================================================================================+
| Distributed Envs Value |
+---------------------------------------------------------------------------------------+
| PADDLE_TRAINER_ID 0 |
| PADDLE_CURRENT_ENDPOINT 127.0.0.1:33420 |
| PADDLE_TRAINERS_NUM 1 |
| PADDLE_TRAINER_ENDPOINTS 127.0.0.1:33420 |
| PADDLE_RANK_IN_NODE 0 |
| PADDLE_LOCAL_DEVICE_IDS 0 |
| PADDLE_WORLD_DEVICE_IDS 0 |
| FLAGS_selected_gpus 0 |
| FLAGS_selected_accelerators 0 |
+=======================================================================================+
INFO 2022-01-02 01:32:26,894 launch_utils.py:532] details abouts PADDLE_TRAINER_ENDPOINTS can be found in log/endpoints.log, and detail running logs maybe found in log/workerlog.0
launch proc_id:1384 idx:0
[2022/01/02 01:32:28] root INFO: Architecture :
[2022/01/02 01:32:28] root INFO: Backbone :
[2022/01/02 01:32:28] root INFO: model_name : small
[2022/01/02 01:32:28] root INFO: name : MobileNetV3
[2022/01/02 01:32:28] root INFO: scale : 0.5
[2022/01/02 01:32:28] root INFO: small_stride : [1, 2, 2, 2]
[2022/01/02 01:32:28] root INFO: Head :
[2022/01/02 01:32:28] root INFO: fc_decay : 1e-05
[2022/01/02 01:32:28] root INFO: name : CTCHead
[2022/01/02 01:32:28] root INFO: Neck :
[2022/01/02 01:32:28] root INFO: encoder_type : rnn
[2022/01/02 01:32:28] root INFO: hidden_size : 48
[2022/01/02 01:32:28] root INFO: name : SequenceEncoder
[2022/01/02 01:32:28] root INFO: Transform : None
[2022/01/02 01:32:28] root INFO: algorithm : CRNN
[2022/01/02 01:32:28] root INFO: model_type : rec
[2022/01/02 01:32:28] root INFO: Eval :
[2022/01/02 01:32:28] root INFO: dataset :
[2022/01/02 01:32:28] root INFO: data_dir : /home/aistudio/data/street_code_rec_data/mchar_val
[2022/01/02 01:32:28] root INFO: label_file_list : ['/home/aistudio/data/street_code_rec_data/mchar_val.csv']
[2022/01/02 01:32:28] root INFO: name : SimpleDataSet
[2022/01/02 01:32:28] root INFO: transforms :
[2022/01/02 01:32:28] root INFO: DecodeImage :
[2022/01/02 01:32:28] root INFO: channel_first : False
[2022/01/02 01:32:28] root INFO: img_mode : BGR
[2022/01/02 01:32:28] root INFO: CTCLabelEncode : None
[2022/01/02 01:32:28] root INFO: RecResizeImg :
[2022/01/02 01:32:28] root INFO: image_shape : [3, 32, 320]
[2022/01/02 01:32:28] root INFO: KeepKeys :
[2022/01/02 01:32:28] root INFO: keep_keys : ['image', 'label', 'length']
[2022/01/02 01:32:28] root INFO: loader :
[2022/01/02 01:32:28] root INFO: batch_size_per_card : 1024
[2022/01/02 01:32:28] root INFO: drop_last : False
[2022/01/02 01:32:28] root INFO: num_workers : 8
[2022/01/02 01:32:28] root INFO: shuffle : False
[2022/01/02 01:32:28] root INFO: Global :
[2022/01/02 01:32:28] root INFO: cal_metric_during_train : True
[2022/01/02 01:32:28] root INFO: character_dict_path : ppocr/utils/en_dict.txt
[2022/01/02 01:32:28] root INFO: checkpoints : ./output/rec_en_number_lite/best_accuracy.pdparams
[2022/01/02 01:32:28] root INFO: debug : False
[2022/01/02 01:32:28] root INFO: distributed : False
[2022/01/02 01:32:28] root INFO: epoch_num : 500
[2022/01/02 01:32:28] root INFO: eval_batch_step : [100, 100]
[2022/01/02 01:32:28] root INFO: infer_img : None
[2022/01/02 01:32:28] root INFO: infer_mode : False
[2022/01/02 01:32:28] root INFO: log_smooth_window : 20
[2022/01/02 01:32:28] root INFO: max_text_length : 25
[2022/01/02 01:32:28] root INFO: pretrained_model : ./en_number_mobile_v2.0_rec_train/best_accuracy.pdparams
[2022/01/02 01:32:28] root INFO: print_batch_step : 10
[2022/01/02 01:32:28] root INFO: save_epoch_step : 3
[2022/01/02 01:32:28] root INFO: save_inference_dir : None
[2022/01/02 01:32:28] root INFO: save_model_dir : ./output/rec_en_number_lite
[2022/01/02 01:32:28] root INFO: use_gpu : True
[2022/01/02 01:32:28] root INFO: use_space_char : True
[2022/01/02 01:32:28] root INFO: use_visualdl : False
[2022/01/02 01:32:28] root INFO: Loss :
[2022/01/02 01:32:28] root INFO: name : CTCLoss
[2022/01/02 01:32:28] root INFO: Metric :
[2022/01/02 01:32:28] root INFO: main_indicator : acc
[2022/01/02 01:32:28] root INFO: name : RecMetric
[2022/01/02 01:32:28] root INFO: Optimizer :
[2022/01/02 01:32:28] root INFO: beta1 : 0.9
[2022/01/02 01:32:28] root INFO: beta2 : 0.999
[2022/01/02 01:32:28] root INFO: lr :
[2022/01/02 01:32:28] root INFO: learning_rate : 0.001
[2022/01/02 01:32:28] root INFO: name : Cosine
[2022/01/02 01:32:28] root INFO: name : Adam
[2022/01/02 01:32:28] root INFO: regularizer :
[2022/01/02 01:32:28] root INFO: factor : 1e-05
[2022/01/02 01:32:28] root INFO: name : L2
[2022/01/02 01:32:28] root INFO: PostProcess :
[2022/01/02 01:32:28] root INFO: name : CTCLabelDecode
[2022/01/02 01:32:28] root INFO: Train :
[2022/01/02 01:32:28] root INFO: dataset :
[2022/01/02 01:32:28] root INFO: data_dir : /home/aistudio/data/street_code_rec_data/mchar_train
[2022/01/02 01:32:28] root INFO: label_file_list : ['/home/aistudio/data/street_code_rec_data/mchar_train.csv']
[2022/01/02 01:32:28] root INFO: name : SimpleDataSet
[2022/01/02 01:32:28] root INFO: transforms :
[2022/01/02 01:32:28] root INFO: DecodeImage :
[2022/01/02 01:32:28] root INFO: channel_first : False
[2022/01/02 01:32:28] root INFO: img_mode : BGR
[2022/01/02 01:32:28] root INFO: RecAug : None
[2022/01/02 01:32:28] root INFO: CTCLabelEncode : None
[2022/01/02 01:32:28] root INFO: RecResizeImg :
[2022/01/02 01:32:28] root INFO: image_shape : [3, 32, 320]
[2022/01/02 01:32:28] root INFO: KeepKeys :
[2022/01/02 01:32:28] root INFO: keep_keys : ['image', 'label', 'length']
[2022/01/02 01:32:28] root INFO: loader :
[2022/01/02 01:32:28] root INFO: batch_size_per_card : 1024
[2022/01/02 01:32:28] root INFO: drop_last : True
[2022/01/02 01:32:28] root INFO: num_workers : 8
[2022/01/02 01:32:28] root INFO: shuffle : True
[2022/01/02 01:32:28] root INFO: profiler_options : None
[2022/01/02 01:32:28] root INFO: train with paddle 2.2.1 and device CUDAPlace(0)
[2022/01/02 01:32:28] root INFO: Initialize indexs of datasets:['/home/aistudio/data/street_code_rec_data/mchar_val.csv']
W0102 01:32:28.580307 1384 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0102 01:32:28.584791 1384 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[2022/01/02 01:32:33] root INFO: resume from ./output/rec_en_number_lite/best_accuracy
[2022/01/02 01:32:33] root INFO: metric in ckpt ***************
[2022/01/02 01:32:33] root INFO: acc:0.6261999373800062
[2022/01/02 01:32:33] root INFO: start_epoch:28
[2022/01/02 01:32:33] root INFO: norm_edit_dis:0.7362716930394972
[2022/01/02 01:32:33] root INFO: fps:4054.7339744968563
[2022/01/02 01:32:33] root INFO: best_epoch:27
eval model:: 0%| | 0/10 [00:00<?, ?it/s]
eval model:: 10%|█ | 1/10 [00:03<00:32, 3.65s/it]
eval model:: 20%|██ | 2/10 [00:04<00:21, 2.67s/it]
eval model:: 30%|███ | 3/10 [00:04<00:14, 2.01s/it]
eval model:: 40%|████ | 4/10 [00:04<00:09, 1.51s/it]
eval model:: 50%|█████ | 5/10 [00:05<00:05, 1.16s/it]
eval model:: 60%|██████ | 6/10 [00:05<00:03, 1.10it/s]
eval model:: 70%|███████ | 7/10 [00:05<00:02, 1.35it/s]
eval model:: 80%|████████ | 8/10 [00:06<00:01, 1.61it/s]
eval model:: 90%|█████████ | 9/10 [00:06<00:00, 1.87it/s]
eval model:: 100%|██████████| 10/10 [00:06<00:00, 2.20it/s]
[2022/01/02 01:32:40] root INFO: metric eval ***************
[2022/01/02 01:32:40] root INFO: acc:0.6261999373800062
[2022/01/02 01:32:40] root INFO: norm_edit_dis:0.7362716930394972
[2022/01/02 01:32:40] root INFO: fps:4274.752272925087
INFO 2022-01-02 01:32:41,951 launch.py:311] Local processes completed.
七、结果预测
预测脚本使用预测训练好的模型，并将结果保存成txt格式，可以直接送到比赛提交入口测评，文件默认保存在output/rec/predicts_chinese_lite_v2.0.txt
1.提交内容与格式
本次比赛要求参赛选手必须提交使用深度学习平台飞桨（PaddlePaddle）训练的模型。参赛者要求以.txt 文本格式提交结果，其中每一行是图片名称和文字预测的结果，中间以 “\t” 作为分割符，示例如下：
| new_name | value |
|---|---|
| 0.jpg | 文本0 |
2. infer_rec.py修改
with open(save_res_path, "w") as fout:
# 添加列头
fout.write('file_name' + "," + 'file_code' +'\n')
for file in get_image_file_list(config['Global']['infer_img']):
logger.info("infer_img: {}".format(file))
with open(file, 'rb') as f:
img = f.read()
data = {'image': img}
batch = transform(data, ops)
if config['Architecture']['algorithm'] == "SRN":
encoder_word_pos_list = np.expand_dims(batch[1], axis=0)
gsrm_word_pos_list = np.expand_dims(batch[2], axis=0)
gsrm_slf_attn_bias1_list = np.expand_dims(batch[3], axis=0)
gsrm_slf_attn_bias2_list = np.expand_dims(batch[4], axis=0)
others = [
paddle.to_tensor(encoder_word_pos_list),
paddle.to_tensor(gsrm_word_pos_list),
paddle.to_tensor(gsrm_slf_attn_bias1_list),
paddle.to_tensor(gsrm_slf_attn_bias2_list)
]
if config['Architecture']['algorithm'] == "SAR":
valid_ratio = np.expand_dims(batch[-1], axis=0)
img_metas = [paddle.to_tensor(valid_ratio)]
images = np.expand_dims(batch[0], axis=0)
images = paddle.to_tensor(images)
if config['Architecture']['algorithm'] == "SRN":
preds = model(images, others)
elif config['Architecture']['algorithm'] == "SAR":
preds = model(images, img_metas)
else:
preds = model(images)
post_result = post_process_class(preds)
info = None
if isinstance(post_result, dict):
rec_info = dict()
for key in post_result:
if len(post_result[key][0]) >= 2:
rec_info[key] = {
"label": post_result[key][0][0],
"score": float(post_result[key][0][1]),
}
info = json.dumps(rec_info)
else:
if len(post_result[0]) >= 2:
info = post_result[0][0] + "\t" + str(post_result[0][1])
if info is not None:
logger.info("\t result: {}".format(info))
fout.write(file + "," + post_result[0][0] +'\n')
logger.info("success!")
%cd ~/PaddleOCR/
# mobile模型
!python tools/infer_rec.py -c configs/rec/multi_language/rec_en_number_lite_train.yml \
-o Global.infer_img="/home/aistudio/data/street_code_rec_data/mchar_test_a" \
Global.checkpoints=./output/rec_en_number_lite/best_accuracy.pdparams
预测日志
[2022/01/02 02:01:08] root INFO: result: 2123 0.9544541
[2022/01/02 02:01:08] root INFO: infer_img: /home/aistudio/data/street_code_rec_data/mchar_test_a/039996.png
[2022/01/02 02:01:08] root INFO: result: 341 0.8990403
[2022/01/02 02:01:08] root INFO: infer_img: /home/aistudio/data/street_code_rec_data/mchar_test_a/039997.png
[2022/01/02 02:01:08] root INFO: result: 167 0.95185596
[2022/01/02 02:01:08] root INFO: infer_img: /home/aistudio/data/street_code_rec_data/mchar_test_a/039998.png
[2022/01/02 02:01:08] root INFO: result: 235 0.9978804
[2022/01/02 02:01:08] root INFO: infer_img: /home/aistudio/data/street_code_rec_data/mchar_test_a/039999.png
[2022/01/02 02:01:08] root INFO: result: 910 0.93325263
[2022/01/02 02:01:08] root INFO: success!
...
...
八、基于预测引擎的预测
1.模型大小限制
约束性条件1：模型总大小不超过10MB（以.pdmodel和.pdiparams文件非压缩状态磁盘占用空间之和为准）；
2.解决办法
训练过程中保存的模型是checkpoints模型，保存的只有模型的参数，多用于恢复训练等。实际上，此处的约束条件限制的是inference 模型的大小。inference 模型一般是模型训练，把模型结构和模型参数保存在文件中的固化模型，多用于预测部署场景。与checkpoints模型相比，inference 模型会额外保存模型的结构信息，在预测部署、加速推理上性能优越，灵活方便，适合于实际系统集成，模型大小也会小一些。
# 静态模型导出
%cd ~/PaddleOCR/
# mobile模型
!python tools/export_model.py -c configs/rec/multi_language/rec_en_number_lite_train.yml \
-o Global.checkpoints=./output/rec_en_number_lite/best_accuracy.pdparams \
Global.save_inference_dir=./inference/rec_inference/
/home/aistudio/PaddleOCR
W0102 02:06:39.026404 4766 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0102 02:06:39.030951 4766 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[2022/01/02 02:06:43] root INFO: resume from ./output/rec_en_number_lite/best_accuracy
[2022/01/02 02:06:45] root INFO: inference model is saved to ./inference/rec_inference/inference
%cd ~/PaddleOCR/
!du -sh ./inference/rec_inference/
/home/aistudio/PaddleOCR
2.8M ./inference/rec_inference/
- 可以看到，当前训练使用的CRNN算法导出inference后，仅有2.8M。
- 导出的inference模型也可以用来预测，预测逻辑如下代码所示。
# 使用导出静态模型预测
%cd ~/PaddleOCR/
!python3.7 tools/infer/predict_rec.py --rec_model_dir=./inference/rec_inference/ --image_dir="/home/aistudio/data/street_code_rec_data/mchar_test_a"
预测日志
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012500.png:('疗绚娇', 0.71012855)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012501.png:('绚诚', 0.9246478)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012502.png:('溜', 0.93994504)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012503.png:('诚溜', 0.95832443)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012504.png:('溜溜', 0.87103844)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012505.png:('贿', 0.34199885)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012506.png:('题', 0.9996681)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012507.png:('绚绚', 0.9908391)
[2022/01/02 02:08:37] root INFO: Predicts of /home/aistudio/data/street_code_rec_data/mchar_test_a/012508.png:('绚', 0.58176464)
...
...
九、提交
预测结果保存到配置文件指定的 output/rec/predicts_chinese_lite_v2.0.txt文件，可直接提交即可。
%cd ~
!head PaddleOCR/output/rec/predicts_rec.txt
/home/aistudio
file_name,file_code
/home/aistudio/data/street_code_rec_data/mchar_test_a/000000.png,59
/home/aistudio/data/street_code_rec_data/mchar_test_a/000001.png,290
/home/aistudio/data/street_code_rec_data/mchar_test_a/000002.png,113
/home/aistudio/data/street_code_rec_data/mchar_test_a/000003.png,97
/home/aistudio/data/street_code_rec_data/mchar_test_a/000004.png,63
/home/aistudio/data/street_code_rec_data/mchar_test_a/000005.png,39
/home/aistudio/data/street_code_rec_data/mchar_test_a/000006.png,126
/home/aistudio/data/street_code_rec_data/mchar_test_a/000007.png,1475
/home/aistudio/data/street_code_rec_data/mchar_test_a/000008.png,48
7.png,1475
/home/aistudio/data/street_code_rec_data/mchar_test_a/000008.png,48
随便跑跑82分，大家可以再处理处理，把检测数据也用上，优化优化，多跑几轮，一定可以取得更好的成绩。
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