mindspore/model_zoo/official/cv/psenet

• Contents
• PSENet Description
• PSENet Example
• • Description
• Dataset
• Environment Requirements
• Quick Start
• Script Description
• • Script and Sample Code
  • Script Parameters
  • Training Process
  • • Distributed Training
  • Evaluation Process
  • • run test code
    • Eval Script for ICDAR2015
    • • Usage
      • Result
• Model Description
• • Performance
  • • Evaluation Performance
    • Inference Performance
  • How to use
  • • Inference

Contents

• PSENet Description
• Dataset
• Features
  • Mixed Precision
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
    • Training
    • Distributed Training
  • Evaluation Process
    • Evaluation
• Model Description
  • Performance
    • Evaluation Performance
    • Inference Performance
  • How to use
    • Inference
    • Continue Training on the Pretrained Model
    • Transfer Learning

PSENet Description

With the development of convolutional neural network, scene text detection technology has been developed rapidly. However, there are still two problems in this algorithm, which hinders its application in industry. On the one hand, most of the existing algorithms require quadrilateral bounding boxes to accurately locate arbitrary shape text. On the other hand, two adjacent instances of text can cause error detection overwriting both instances. Traditionally, a segmentation-based approach can solve the first problem, but usually not the second. To solve these two problems, a new PSENet (PSENet) is proposed, which can accurately detect arbitrary shape text instances. More specifically, PSENet generates different scale kernels for each text instance and gradually expands the minimum scale kernel to a text instance with full shape. Because of the large geometric margins between the minimum scale kernels, our method can effectively segment closed text instances, making it easier to detect arbitrary shape text instances. The effectiveness of PSENet has been verified by numerous experiments on CTW1500, full text, ICDAR 2015, and ICDAR 2017 MLT.

Paper: Wenhai Wang, Enze Xie, Xiang Li, Wenbo Hou, Tong Lu, Gang Yu, Shuai Shao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9336-9345

PSENet Example

Description

Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-shape text in natural scene.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

Dataset used: ICDAR2015
A training set of 1000 images containing about 4500 readable words
A testing set containing about 2000 readable words

Environment Requirements

• Hardware（Ascend）
  • Prepare hardware environment with Ascend processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API
• install Mindspore
• install pyblind11
• install Opencv3.4

Quick Start

After installing MindSpore via the official website, you can start training and evaluation as follows:

# run distributed training example
sh scripts/run_distribute_train.sh rank_table_file pretrained_model.ckpt

#download opencv library
download pyblind11, opencv3.4

#install pyblind11 opencv3.4
setup pyblind11(install the library by the pip command)
setup opencv3.4(compile source code install the library)

#enter the path ,run Makefile to product file
cd ./src/ETSNET/pse/;make

#run test.py
python test.py --ckpt=pretrained_model.ckpt

#download eval method from [here](https://rrc.cvc.uab.es/?ch=4&com=tasks#TextLocalization).
#click "My Methods" button,then download Evaluation Scripts
download script.py
# run evaluation example
sh scripts/run_eval_ascend.sh

Script Description

Script and Sample Code

└── PSENet  
 ├── export.py                           // export mindir file
 ├── __init__.py
 ├── mindspore_hub_conf.py               // hub config file
 ├── README_CN.md                        // descriptions about PSENet in Chinese
 ├── README.md                           // descriptions about PSENet in English
 ├── scripts  
  ├── run_distribute_train.sh    // shell script for distributed
  └── run_eval_ascend.sh     // shell script for evaluation
 ├── src  
  ├── config.py                       // parameter configuration
  ├── dataset.py                      // creating dataset
  ├── ETSNET  
   ├── base.py                     // convolution and BN operator
   ├── dice_loss.py                // calculate PSENet loss value
   ├── etsnet.py                   // Subnet in PSENet
   ├── fpn.py                      // Subnet in PSENet
   ├── __init__.py  
   ├── pse                         // Subnet in PSENet
                ├── __init__.py
                ├── adaptor.cpp
                ├── adaptor.h
                ├── Makefile
   ├── resnet50.py                 // Subnet in PSENet
  ├── __init__.py  
  ├── lr_schedule.py                 // define learning rate
  ├── network_define.py               // learning ratio generation
 ├── test.py                             //  test script
 ├── train.py                            // training script

Script Parameters

Major parameters in train.py and config.py are:

--pre_trained: Whether training from scratch or training based on the
               pre-trained model.Optional values are True, False.
--device_id: Device ID used to train or evaluate the dataset. Ignore it
             when you use train.sh for distributed training.
--device_num: devices used when you use train.sh for distributed training.

Training Process

Distributed Training

sh scripts/run_distribute_train.sh rank_table_file pretrained_model.ckpt

rank_table_file which is specified by RANK_TABLE_FILE is needed when you are running a distribute task. You can generate it by using the hccl_tools.
The above shell script will run distribute training in the background. You can view the results through the file
device[X]/test_*.log. The loss value will be achieved as follows:

# grep "epoch: " device_*/loss.log
device_0/log:epoch: 1, step: 20, loss is 0.80383
device_0/log:epcoh: 2, step: 40, loss is 0.77951
...
device_1/log:epoch: 1, step: 20, loss is 0.78026
device_1/log:epcoh: 2, step: 40, loss is 0.76629

Evaluation Process

run test code

python test.py --ckpt=./device*/ckpt*/ETSNet-*.ckpt

Eval Script for ICDAR2015

Usage

step 1: download eval method from here.

step 2: click "My Methods" button,then download Evaluation Scripts.

step 3: it is recommended to symlink the eval method root to $MINDSPORE/model_zoo/psenet/eval_ic15/. if your folder structure is different,you may need to change the corresponding paths in eval script files.

sh ./script/run_eval_ascend.sh.sh  

Result

Calculated!{"precision": 0.814796668299853, "recall": 0.8006740491092923, "hmean": 0.8076736279747451, "AP": 0}

Model Description

Performance

Evaluation Performance

Parameters	Ascend
Model Version	PSENet
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8
uploaded Date	09/30/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	ICDAR2015
Training Parameters	start_lr=0.1; lr_scale=0.1
Optimizer	SGD
Loss Function	LossCallBack
outputs	probability
Loss	0.35
Speed	1pc: 444 ms/step; 8pcs: 446 ms/step
Total time	1pc: 75.48 h; 8pcs: 7.11 h
Parameters (M)	27.36
Checkpoint for Fine tuning	109.44M (.ckpt file)
Scripts	https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/psenet

Inference Performance

Parameters	Ascend
Model Version	PSENet
Resource	Ascend 910
Uploaded Date	09/30/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	ICDAR2015
outputs	probability
Accuracy	1pc: 81%; 8pcs: 81%

How to use

Inference

If you need to use the trained model to perform inference on multiple hardware platforms, such as GPU, Ascend 910 or Ascend 310, you can refer to this Link. Following the steps below, this is a simple example:

# Load unseen dataset for inference
dataset = dataset.create_dataset(cfg.data_path, 1, False)

# Define model
config.INFERENCE = False
net = ETSNet(config)
net = net.set_train()
param_dict = load_checkpoint(args.pre_trained)
load_param_into_net(net, param_dict)
print('Load Pretrained parameters done!')

criterion = DiceLoss(batch_size=config.TRAIN_BATCH_SIZE)

lrs = lr_generator(start_lr=1e-3, lr_scale=0.1, total_iters=config.TRAIN_TOTAL_ITER)
opt = nn.SGD(params=net.trainable_params(), learning_rate=lrs, momentum=0.99, weight_decay=5e-4)

# warp model
net = WithLossCell(net, criterion)
net = TrainOneStepCell(net, opt)

time_cb = TimeMonitor(data_size=step_size)
loss_cb = LossCallBack(per_print_times=20)
# set and apply parameters of check point
ckpoint_cf = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=2)
ckpoint_cb = ModelCheckpoint(prefix="ETSNet", config=ckpoint_cf, directory=config.TRAIN_MODEL_SAVE_PATH)

model = Model(net)
model.train(config.TRAIN_REPEAT_NUM, ds, dataset_sink_mode=False, callbacks=[time_cb, loss_cb, ckpoint_cb])

# Load pre-trained model
param_dict = load_checkpoint(cfg.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)

# Make predictions on the unseen dataset
acc = model.eval(dataset)
print("accuracy: ", acc)