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Training layout-parse

1. Installation

​ 1.1 Requirements

​ 1.2 Install PaddleDetection

2. Data preparation

3. Configuration

4. Training

5. Prediction

6. Deployment

​ 6.1 Export model

​ 6.2 Inference

1. Installation

1.1 Requirements

  • PaddlePaddle 2.1
  • OS 64 bit
  • Python 3(3.5.1+/3.6/3.7/3.8/3.9)，64 bit
  • pip/pip3(9.0.1+), 64 bit
  • CUDA >= 10.1
  • cuDNN >= 7.6

1.2 Install PaddleDetection

# Clone PaddleDetection repository
cd <path/to/clone/PaddleDetection>
git clone https://github.com/PaddlePaddle/PaddleDetection.git

cd PaddleDetection
# Install other dependencies
pip install -r requirements.txt

For more installation tutorials, please refer to： Install doc

2. Data preparation

Download the PubLayNet dataset

cd PaddleDetection/dataset/
mkdir publaynet
# execute the command，download PubLayNet
wget -O publaynet.tar.gz https://dax-cdn.cdn.appdomain.cloud/dax-publaynet/1.0.0/publaynet.tar.gz?_ga=2.104193024.1076900768.1622560733-649911202.1622560733
# unpack
tar -xvf publaynet.tar.gz

PubLayNet directory structure after decompressing ：

File or Folder Description num
train/ Images in the training subset 335,703
val/ Images in the validation subset 11,245
test/ Images in the testing subset 11,405
train.json Annotations for training images 1
val.json Annotations for validation images 1
LICENSE.txt Plaintext version of the CDLA-Permissive license 1
README.txt Text file with the file names and description 1

For other datasets，please refer to the PrepareDataSet

3. Configuration

We use the configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml configuration for training，the configuration file is as follows

_BASE_: [
  '../datasets/coco_detection.yml',
  '../runtime.yml',
  './_base_/ppyolov2_r50vd_dcn.yml',
  './_base_/optimizer_365e.yml',
  './_base_/ppyolov2_reader.yml',
]

snapshot_epoch: 8
weights: output/ppyolov2_r50vd_dcn_365e_coco/model_final

The ppyolov2_r50vd_dcn_365e_coco.yml configuration depends on other configuration files, in this case:

  • coco_detection.yml：mainly explains the path of training data and verification data

  • runtime.yml：mainly describes the common parameters, such as whether to use the GPU and how many epoch to save model etc.

  • optimizer_365e.yml：mainly explains the learning rate and optimizer configuration

  • ppyolov2_r50vd_dcn.yml：mainly describes the model and the network

  • ppyolov2_reader.yml：mainly describes the configuration of data readers, such as batch size and number of concurrent loading child processes, and also includes post preprocessing, such as resize and data augmention etc.

Modify the preceding files, such as the dataset path and batch size etc.

4. Training

PaddleDetection provides single-card/multi-card training mode to meet various training needs of users:

  • GPU single card training
export CUDA_VISIBLE_DEVICES=0 #Don't need to run this command on Windows and Mac
python tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml
  • GPU multi-card training
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval

--eval: training while verifying

  • Model recovery training

During the daily training, if training is interrupted due to some reasons, you can use the -r command to resume the training:

export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m paddle.distributed.launch --gpus 0,1,2,3 tools/train.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --eval -r output/ppyolov2_r50vd_dcn_365e_coco/10000

Note: If you encounter "Out of memory error" , try reducing batch_size in the ppyolov2_reader.yml file

prediction

5. Prediction

Set parameters and use PaddleDetection to predict：

export CUDA_VISIBLE_DEVICES=0
python tools/infer.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --infer_img=images/paper-image.jpg --output_dir=infer_output/ --draw_threshold=0.5 -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final --use_vdl=Ture

--draw_threshold is an optional parameter. According to the calculation of NMS, different threshold will produce different results, keep_top_k represent the maximum amount of output target, the default value is 10. You can set different value according to your own actual situation。

6. Deployment

Use your trained model in Layout Parser

6.1 Export model

n the process of model training, the model file saved contains the process of forward prediction and back propagation. In the actual industrial deployment, there is no need for back propagation. Therefore, the model should be translated into the model format required by the deployment. The tools/export_model.py script is provided in PaddleDetection to export the model.

The exported model name defaults to model.*, Layout Parser's code model is inference.*, So change [PaddleDetection/ppdet/engine/trainer. Py ](https://github.com/PaddlePaddle/PaddleDetection/blob/b87a1ea86fa18ce69e44a17ad1b49c1326f19ff9/ppdet/engine/trainer.py# L512) (click on the link to see the detailed line of code), change 'model' to 'inference'.

Execute the script to export model:

python tools/export_model.py -c configs/ppyolo/ppyolov2_r50vd_dcn_365e_coco.yml --output_dir=./inference -o weights=output/ppyolov2_r50vd_dcn_365e_coco/model_final.pdparams

The prediction model is exported to inference/ppyolov2_r50vd_dcn_365e_coco ,including:infer_cfg.yml(prediction not required), inference.pdiparams, inference.pdiparams.info,inference.pdmodel

More model export tutorials, please refer to：EXPORT_MODEL

6.2 Inference

model_path represent the trained model path, and layoutparser is used to predict:

import layoutparser as lp
model = lp.PaddleDetectionLayoutModel(model_path="inference/ppyolov2_r50vd_dcn_365e_coco", threshold=0.5,label_map={0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"},enforce_cpu=True,enable_mkldnn=True)

More PaddleDetection training tutorials，please reference：PaddleDetection Training