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SSD Description

SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape.Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.

Paper: Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg.European Conference on Computer Vision (ECCV), 2016 (In press).

Model Architecture

The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum suppression step to produce the final detections. The early network layers are based on a standard architecture used for high quality image classification, which is called the base network. Then add auxiliary structure to the network to produce detections.

Dataset

Dataset used: COCO2017

Dataset size：19G
- Train：18G，118000 images
- Val：1G，5000 images
- Annotations：241M，instances，captions，person_keypoints etc
Data format：image and json files
- Note：Data will be processed in dataset.py

Environment Requirements

Hardware（Ascend/GPU）
- Prepare hardware environment with Ascend or GPU processor.
Framework
- MindSpore
For more information, please check the resources below：
- MindSpore Tutorials
- MindSpore Python API
Install MindSpore.
Download the dataset COCO2017.
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, and you can also install mmcv to process data.
```
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:
```
.
└─cocodataset
  ├─annotations
    ├─instance_train2017.json
    └─instance_val2017.json
  ├─val2017
  └─train2017
```
2. If your own dataset is used. Select dataset to other when run script. Organize the dataset information 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.

Quick Start

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

# single npu training on Ascend
python train.py

# distributed training on Ascend
sh run_distribute_train_ghostnet.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE]

# run eval on Ascend
python eval.py --device_id 0 --dataset coco --checkpoint_file_path LOG4/ssd-500_458.ckpt

If you want to run in modelarts, please check the official documentation of modelarts, and you can start training and evaluation as follows:

# run distributed training on modelarts example
# (1) First, Perform a or b.
#       a. Set "enable_modelarts=True" on yaml file.
#          Set other parameters on yaml file you need.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Set the Dataset directory in config file.
# (3) Set the code directory to "/path/ssd_ghostne" on the website UI interface.
# (4) Set the startup file to "train.py" on the website UI interface.
# (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (6) Create your job.

# run evaluation on modelarts example
# (1) Copy or upload your trained model to S3 bucket.
# (2) Perform a or b.
#       a. Set "enable_modelarts=True" on yaml file.
#          Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
#          Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
#       b. Add "enable_modelarts=True" on the website UI interface.
#          Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
#          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
# (3) Set the Dataset directory in config file.
# (4) Set the code directory to "/path/ssd_ghostnet" on the website UI interface.
# (5) Set the startup file to "eval.py" on the website UI interface.
# (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
# (7) Create your job.

Script Description

Script and Sample Code


  ├── ssd_ghostnet
    ├── README.md                 ## readme file of ssd_ghostnet
    ├── ascend310_infer           ## application for 310 inference
    ├── scripts
      ├─ run_distribute_train_ghostnet.sh  ## shell script for distributed on ascend
      └─ run_infer_310.sh                  ## shell script for 310inference on ascend
    ├── src
      ├─ box_util.py              ## bbox utils
      ├─ coco_eval.py             ## coco metrics utils
      ├─ dataset.py               ## create dataset and process dataset
      ├─ init_params.py           ## parameters utils
      ├─ lr_schedule.py           ## learning ratio generator
      └─ ssd_ghostnet.py          ## ssd architecture
      ├── model_utils
      │   ├── config.py           ## parameter configuration
      │   ├── device_adapter.py   ## device adapter
      │   ├── local_adapter.py    ## local adapter
      │   ├── moxing_adapter.py   ## moxing adapter
    ├── default_config.yaml       ## parameter configuration
    ├── eval.py                   ## eval scripts
    ├── train.py                  ## train scripts
    ├── export.py                 ## export mindir script
    ├── postprocess.py            ## postprocess scripts
    ├── mindspore_hub_conf.py     ## export model for hub

Script Parameters

Major parameters in train.py and default_config.yaml as follows:

  "device_num": 1                            # Use device nums
  "lr": 0.05                                 # Learning rate init value
  "dataset": coco                            # Dataset name
  "epoch_size": 500                          # Epoch size
  "batch_size": 32                           # Batch size of input tensor
  "pre_trained": None                        # Pretrained checkpoint file path
  "pre_trained_epoch_size": 0                # Pretrained epoch size
  "save_checkpoint_epochs": 10               # The epoch interval between two checkpoints. By default, the checkpoint will be saved per 10 epochs
  "loss_scale": 1024                         # Loss scale

  "class_num": 81                            # Dataset class number
  "image_shape": [300, 300]                  # Image height and width used as input to the model
  "mindrecord_dir": "/data/MindRecord_COCO"  # MindRecord path
  "coco_root": "/data/coco2017"              # COCO2017 dataset path
  "voc_root": ""                             # VOC original dataset path
  "image_dir": ""                            # Other dataset image path, if coco or voc used, it will be useless
  "anno_path": ""                            # Other dataset annotation path, if coco or voc used, it will be useless

Training Process

Training on Ascend

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

Distribute mode

    sh run_distribute_train_ghostnet.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)

We need five or seven parameters for this scripts.

DEVICE_NUM: the device number for distributed train.
EPOCH_NUM: epoch num for distributed train.
LR: learning rate init value for distributed train.
DATASET：the dataset mode for distributed train.
RANK_TABLE_FILE : the path of rank_table.json, it is better to use absolute path.
PRE_TRAINED : the path of pretrained checkpoint file, it is better to use absolute path.
PRE_TRAINED_EPOCH_SIZE : the epoch num of pretrained.

Training result will be stored in the current path, whose folder name begins with "LOG". Under this, you can find checkpoint file together with result like the followings in LOG4/log.txt.

Evaluation Process

Evaluation on Ascend

python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.ckpt

Inference Process

Export MindIR

python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]

The ckpt_file parameter is required, FILE_FORMAT should be in ["AIR", "MINDIR"]

Infer on Ascend310

Before performing inference, the mindir file must be exported by export.py script. We only provide an example of inference using MINDIR model. Current batch_size can only be set to 1.

# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]

DVPP is mandatory, and must choose from ["DVPP", "CPU"], it's case-insensitive. SSD_ghostnet only support CPU mode. Note that the image shape of ssd_ghostnet inference is [300, 300], The DVPP hardware restricts width 16-alignment and height even-alignment. Therefore, the network needs to use the CPU operator to process images.
DEVICE_ID is optional, default value is 0.

result

Inference result is saved in current path, you can find result like this in acc.log file.

Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.243
Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.411
Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.244
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.205
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.450
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.252
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.391
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.424
Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.122
Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.699
mAP: 0.24270569394180577

Model Description

Performance

Evaluation Performance

Parameters	Ascend
Model Version	SSD ghostnet
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8
MindSpore Version	0.7.0
Dataset	COCO2017
Training Parameters	epoch = 500, batch_size = 32
Optimizer	Momentum
Loss Function	Sigmoid Cross Entropy,SmoothL1Loss
Total time	8pcs: 12hours

Inference Performance

Parameters	Ascend
Model Version	SSD ghostnet
Resource	Ascend 910; OS Euler2.8
Uploaded Date	09/08/2020 (month/day/year)
MindSpore Version	0.7.0
Dataset	COCO2017
batch_size	1
outputs	mAP
Accuracy	IoU=0.50: 24.1%
Model for inference	55M(.ckpt file)

310Inference Performance

Parameters	Ascend
Model Version	SSD ghostnet
Resource	Ascend 310; OS Euler2.8
Uploaded Date	06/01/2021 (month/day/year)
MindSpore Version	1.2.0
Dataset	COCO2017
batch_size	1
outputs	mAP
Accuracy	IoU=0.50: 24.2%
Model for inference	52.5M(.ckpt file)

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