提交 603e33df 编写于 作者: M mindspore-ci-bot 提交者: Gitee

!5542 warpctc and maskrcnn readme recitification

Merge pull request !5542 from gengdongjie/readme_recitification
# Warpctc Example # Contents
## Description - [WarpCTC Description](#warpctc-description)
- [Model Architecture](#model-architecture)
These is an example of training Warpctc with self-generated captcha image dataset in MindSpore. - [Dataset](#dataset)
- [Environment Requirements](#environment-requirements)
## Requirements - [Quick Start](#quick-start)
- [Script Description](#script-description)
- Install [MindSpore](https://www.mindspore.cn/install/en). - [Script and Sample Code](#script-and-sample-code)
- [Script Parameters](#script-parameters)
- Generate captcha images. - [Training Script Parameters](#training-script-parameters)
- [Parameters Configuration](#parameters-configuration)
> The [captcha](https://github.com/lepture/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. - [Dataset Preparation](#dataset-preparation)
> ``` - [Training Process](#training-process)
> $ cd scripts - [Training](#training)
> $ sh run_process_data.sh - [Distributed Training](#distributed-training)
> - [Evaluation Process](#evaluation-process)
> # after execution, you will find the dataset like the follows: - [Evaluation](#evaluation)
> . - [Model Description](#model-description)
> └─warpctc - [Performance](#performance)
> └─data - [Training Performance](#training-performance)
> ├─ train # train dataset - [Evaluation Performance](#evaluation-performance)
> └─ test # evaluate dataset - [Description of Random Situation](#description-of-random-situation)
> ... - [ModelZoo Homepage](#modelzoo-homepage)
# [WarpCTC Description](#contents)
## Structure
This is an example of training WarpCTC with self-generated captcha image dataset in MindSpore.
# [Model Architecture](#content)
WarpCTC is a two-layer stacked LSTM appending with one-layer FC neural network. See src/warpctc.py for details.
# [Dataset](#content)
The dataset is self-generated using a third-party library called [captcha](https://github.com/lepture/captcha), which can randomly generate digits from 0 to 9 in image. In this network, we set the length of digits varying from 1 to 4.
# [Environment Requirements](#contents)
- Hardware(Ascend/GPU)
- Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend, please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. You will be able to have access to related resources once approved.
- Framework
- [MindSpore](https://gitee.com/mindspore/mindspore)
- For more information, please check the resources below:
- [MindSpore tutorials](https://www.mindspore.cn/tutorial/en/master/index.html)
- [MindSpore API](https://www.mindspore.cn/api/en/master/index.html)
# [Quick Start](#contents)
- Generate dataset.
Run the script `scripts/run_process_data.sh` to generate a dataset. 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
```
- After the dataset is prepared, you may start running the training or the evaluation scripts as follows:
- Running on Ascend
```
# distribute training example in Ascend
$ bash run_distribute_train.sh rank_table.json ../data/train
# evaluation example in Ascend
$ bash run_eval.sh ../data/test warpctc-30-97.ckpt Ascend
# standalone training example in Ascend
$ bash run_standalone_train.sh ../data/train Ascend
```
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
Please follow the instructions in the link below:
https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools.
- Running on GPU
```
# distribute training example in GPU
$ bash run_distribute_train_for_gpu.sh 8 ../data/train
# standalone training example in GPU
$ bash run_standalone_train.sh ../data/train GPU
# evaluation example in GPU
$ bash run_eval.sh ../data/test warpctc-30-97.ckpt GPU
```
# [Script Description](#contents)
## [Script and Sample Code](#contents)
```shell ```shell
. .
...@@ -43,14 +117,27 @@ These is an example of training Warpctc with self-generated captcha image datase ...@@ -43,14 +117,27 @@ These is an example of training Warpctc with self-generated captcha image datase
├── lr_generator.py # generate learning rate for each step ├── lr_generator.py # generate learning rate for each step
├── metric.py # accuracy metric for warpctc network ├── metric.py # accuracy metric for warpctc network
├── warpctc.py # warpctc network definition ├── warpctc.py # warpctc network definition
└── warpctc_for_train.py # warp network with grad, loss and gradient clip └── warpctc_for_train.py # warpctc network with grad, loss and gradient clip
├── eval.py # eval net ├── eval.py # eval net
├── process_data.py # dataset generation script ├── process_data.py # dataset generation script
└── train.py # train net └── train.py # train net
``` ```
## [Script Parameters](#contents)
## Parameter configuration ### Training Script Parameters
```
# 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]
```
### Parameters Configuration
Parameters for both training and evaluation can be set in config.py. Parameters for both training and evaluation can be set in config.py.
...@@ -69,82 +156,82 @@ Parameters for both training and evaluation can be set in config.py. ...@@ -69,82 +156,82 @@ Parameters for both training and evaluation can be set in config.py.
"save_checkpoint_path": "./checkpoint", # path to save checkpoint "save_checkpoint_path": "./checkpoint", # path to save checkpoint
``` ```
## Running the example ## [Dataset Preparation](#contents)
- You may refer to "Generate dataset" in [Quick Start](#quick-start) to automatically generate a dataset, or you may choose to generate a captcha dataset by yourself.
### 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 ## [Training Process](#contents)
bash run_distribute_train_for_gpu.sh 8 ../data/train
# standalone training example in Ascend - Set options in `config.py`, including learning rate and other network hyperparameters. Click [MindSpore dataset preparation tutorial](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html#mindspore) for more information about dataset.
bash run_standalone_train.sh ../data/train Ascend
### [Training](#contents)
- Run `run_standalone_train.sh` for non-distributed training of WarpCTC model, either on Ascend or on GPU.
# standalone training example in GPU ``` bash
bash run_standalone_train.sh ../data/train GPU bash run_standalone_train.sh [DATASET_PATH] [PLATFORM]
``` ```
### [Distributed Training](#contents)
- Run `run_distribute_train.sh` for distributed training of WarpCTC model on Ascend.
> About rank_table.json, you can refer to the [distributed training tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html). ``` bash
bash run_distribute_train.sh [RANK_TABLE_FILE] [DATASET_PATH]
#### 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 - Run `run_distribute_train_gpu.sh` for distributed training of WarpCTC model on GPU.
``` bash
#### Usage bash run_distribute_train_gpu.sh [RANK_SIZE] [DATASET_PATH]
```
# evaluation
Usage: bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [PLATFORM]
``` ```
#### Launch ## [Evaluation Process](#contents)
### [Evaluation](#contents)
```
# evaluation example in Ascend
bash run_eval.sh ../data/test warpctc-30-97.ckpt Ascend
# evaluation example in GPU - Run `run_eval.sh` for evaluation.
bash run_eval.sh ../data/test warpctc-30-97.ckpt GPU ``` bash
bash run_eval.sh [DATASET_PATH] [CHECKPOINT_PATH] [PLATFORM]
``` ```
> checkpoint can be produced in training process. # [Model Description](#contents)
#### Result ## [Performance](#contents)
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. ### [Training Performance](#contents)
``` | Parameters | Ascend 910 | GPU |
result: {'WarpCTCAccuracy': 0.9901472929936306} | -------------------------- | --------------------------------------------- |---------------------------------- |
``` | Model Version | v1.0 | v1.0 |
| Resource | Ascend 910,CPU 2.60GHz 56cores,Memory 314G | GPU(Tesla V100 SXM2),CPU 2.1GHz 24cores,Memory 128G /
| uploaded Date | 07/01/2020 (month/day/year) | 08/01/2020 (month/day/year) |
| MindSpore Version | 0.5.0-alpha | 0.6.0-alpha |
| Dataset | Captcha | Captcha |
| Training Parameters | epoch=30, steps per epoch=98, batch_size = 64 | epoch=30, steps per epoch=98, batch_size = 64 |
| Optimizer | SGD | SGD |
| Loss Function | CTCLoss | CTCLoss |
| outputs | probability | probability |
| Loss | 0.0000157 | 0.0000246 |
| Speed | 980ms/step(8pcs) | 150ms/step(8pcs)|
| Total time | 30 mins | 5 mins|
| Parameters (M) | 2.75 | 2.75 |
| Checkpoint for Fine tuning | 20.3M (.ckpt file) | 20.3M (.ckpt file) |
| Scripts | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/warpctc) | [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/warpctc) |
### [Evaluation Performance](#contents)
| Parameters | WarpCTC |
| ------------------- | --------------------------- |
| Model Version | V1.0 |
| Resource | Ascend 910 |
| Uploaded Date | 08/01/2020 (month/day/year) |
| MindSpore Version | 0.6.0-alpha |
| Dataset | Captcha |
| batch_size | 64 |
| outputs | ACC |
| Accuracy | 99.0% |
| Model for inference | 20.3M (.ckpt file) |
# [Description of Random Situation](#contents)
In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py for weight initialization.
# [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
\ No newline at end of file
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册