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jinyaohui 391a060f21 remove two context param		2020-05-12 22:11:50 +08:00
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README.md	eliminate external lins to dataset	2020-05-08 17:15:25 +08:00
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eval.py	remove two context param	2020-05-12 22:11:50 +08:00
run_distribute_train.sh	add ssd scipt	2020-05-05 18:14:37 +08:00
train.py	remove two context param	2020-05-12 22:11:50 +08:00
util.py	clear pylint warning	2020-05-08 09:59:59 +08:00

README.md

SSD Example

Description

SSD network based on MobileNetV2, with support for training and evaluation.

Requirements

Install MindSpore.
Dataset

We use coco2017 as training dataset in this example by default, and you can also use your own datasets.
1. If coco dataset is used. Select dataset to coco when run script. Install Cython and pycocotool.
```
pip install Cython

pip install pycocotools
```
  And change the COCO_ROOT and other settings you need in config.py. The directory structure is as follows:
```
└─coco2017
    ├── annotations  # annotation jsons
    ├── train2017    # train dataset
    └── val2017      # infer dataset
```
2. If your own dataset is used. Select dataset to other when run script. Organize the dataset infomation into a TXT file, each row in the file is as follows:
```
train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
```
  Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. We read image from an image path joined by the IMAGE_DIR(dataset directory) and the relative path in ANNO_PATH(the TXT file path), IMAGE_DIR and ANNO_PATH are setting in config.py.

Running the example

Training

To train the model, run train.py. If the MINDRECORD_DIR is empty, it will generate mindrecord files by COCO_ROOT(coco dataset) or IMAGE_DIR and ANNO_PATH(own dataset). Note if MINDRECORD_DIR isn't empty, it will use MINDRECORD_DIR instead of raw images.

Stand alone mode
```
python train.py --dataset coco
```
You can run python train.py -h to get more information.
Distribute mode
```
sh run_distribute_train.sh 8 150 coco /data/hccl.json
```
The input parameters are device numbers, epoch size, dataset mode and hccl json configuration file. It is better to use absolute path.

You will get the loss value of each step as following:

epoch: 1 step: 455, loss is 5.8653416
epoch: 2 step: 455, loss is 5.4292373
epoch: 3 step: 455, loss is 5.458992
...
epoch: 148 step: 455, loss is 1.8340507
epoch: 149 step: 455, loss is 2.0876894
epoch: 150 step: 455, loss is 2.239692

Evaluation

for evaluation , run eval.py with ckpt_path. ckpt_path is the path of checkpoint file.

python eval.py --ckpt_path ssd.ckpt --dataset coco

You can run python eval.py -h to get more information.