mindspore/model_zoo/official/cv/warpctc

• Warpctc Example
• • Description
  • Requirements
  • Structure
  • Parameter configuration
  • Running the example
  • • Train
    • • Usage
      • Launch
      • Result
    • Evaluation
    • • Usage
      • Launch
      • Result

Warpctc Example

Description

These is an example of training Warpctc with self-generated captcha image dataset in MindSpore.

Requirements

• Install MindSpore.

• Generate captcha images.

The captcha library can be used to generate captcha images. You can generate the train and test dataset by yourself or just run the script scripts/run_process_data.sh. By default, the shell script will generate 10000 test images and 50000 train images separately.

$ cd scripts
$ sh run_process_data.sh

# after execution, you will find the dataset like the follows:
.  
└─warpctc
  └─data
    ├─ train  # train dataset
    └─ test   # evaluate dataset
  ...

Structure

.
└──warpctc
  ├── README.md
  ├── script
    ├── run_distribute_train.sh         # launch distributed training in Ascend(8 pcs)
    ├── run_distribute_train_for_gpu.sh # launch distributed training in GPU
    ├── run_eval.sh                     # launch evaluation
    ├── run_process_data.sh             # launch dataset generation
    └── run_standalone_train.sh         # launch standalone training(1 pcs)
  ├── src
    ├── config.py                       # parameter configuration
    ├── dataset.py                      # data preprocessing
    ├── loss.py                         # ctcloss definition
    ├── lr_generator.py                 # generate learning rate for each step
    ├── metric.py                       # accuracy metric for warpctc network
    ├── warpctc.py                      # warpctc network definition
    └── warpctc_for_train.py            # warp network with grad, loss and gradient clip
  ├── eval.py                           # eval net
  ├── process_data.py                   # dataset generation script
  └── train.py                          # train net

Parameter configuration

Parameters for both training and evaluation can be set in config.py.

"max_captcha_digits": 4,                    # max number of digits in each 
"captcha_width": 160,                       # width of captcha images
"captcha_height": 64,                       # height of capthca images
"batch_size": 64,                           # batch size of input tensor
"epoch_size": 30,                           # only valid for taining, which is always 1 for inference
"hidden_size": 512,                         # hidden size in LSTM layers
"learning_rate": 0.01,                      # initial learning rate
"momentum": 0.9                             # momentum of SGD optimizer
"save_checkpoint": True,                    # whether save checkpoint or not
"save_checkpoint_steps": 97,                # the step interval between two checkpoints. By default, the last checkpoint will be saved after the last step
"keep_checkpoint_max": 30,                  # only keep the last keep_checkpoint_max checkpoint
"save_checkpoint_path": "./checkpoint",     # path to save checkpoint

Running the example

Train

Usage

# distributed training in Ascend
Usage: bash run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH]

# distributed training in GPU
Usage: bash run_distribute_train_for_gpu.sh [RANK_SIZE] [DATASET_PATH]

# standalone training
Usage: bash run_standalone_train.sh [DATASET_PATH] [PLATFORM]

Launch

# distribute training example in Ascend
bash run_distribute_train.sh rank_table.json ../data/train

# distribute training example in GPU
bash run_distribute_train_for_gpu.sh 8 ../data/train

# standalone training example in Ascend
bash run_standalone_train.sh ../data/train Ascend

# standalone training example in GPU
bash run_standalone_train.sh ../data/train GPU

About rank_table.json, you can refer to the distributed training tutorial.

Result

Training result will be stored in folder scripts, whose name begins with "train" or "train_parallel". Under this, you can find checkpoint file together with result like the followings in log.

# distribute training result(8 pcs)
Epoch: [  1/ 30], step: [   97/   97], loss: [0.5853/0.5853], time: [376813.7944]
Epoch: [  2/ 30], step: [   97/   97], loss: [0.4007/0.4007], time: [75882.0951]
Epoch: [  3/ 30], step: [   97/   97], loss: [0.0921/0.0921], time: [75150.9385]
Epoch: [  4/ 30], step: [   97/   97], loss: [0.1472/0.1472], time: [75135.0193]
Epoch: [  5/ 30], step: [   97/   97], loss: [0.0186/0.0186], time: [75199.5809]
...

Evaluation

Usage

# evaluation
Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [PLATFORM]

Launch

# evaluation example in Ascend
bash run_eval.sh ../data/test warpctc-30-97.ckpt Ascend

# evaluation example in GPU
bash run_eval.sh ../data/test warpctc-30-97.ckpt GPU

checkpoint can be produced in training process.

Result

Evaluation result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.

result: {'WarpCTCAccuracy': 0.9901472929936306}