mindspore2022/model_zoo/official/cv/ssd

Contents

• Contents
  • SSD Description
  • Model Architecture
  • Dataset
  • Environment Requirements
  • Quick Start
    • Prepare the model
    • Run the scripts
  • Script Description
    • Script and Sample Code
    • Script Parameters
    • Training Process
      • Training on Ascend
      • Training on GPU
      • Evaluation while training
      • Transfer Training
    • Evaluation Process
      • Evaluation on Ascend
      • Evaluation on GPU
  • Inference Process
    • Export MindIR
    • Infer on Ascend310
    • result
    • Post Training Quantization
  • Model Description
    • Performance
      • Evaluation Performance
      • Inference Performance
  • Description of Random Situation
  • ModelZoo Homepage

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.

We present four different base architecture.

• ssd300, reference from the paper. Using mobilenetv2 as backbone and the same bbox predictor as the paper present.
• *ssd-mobilenet-v1-fpn, using mobilenet-v1 and FPN as feature extractor with weight-shared box predcitors.
• *ssd-resnet50-fpn, using resnet50 and FPN as feature extractor with weight-shared box predcitors.
• ssd-vgg16, reference from the paper. Using vgg16 as backbone and the same bbox predictor as the paper present.

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: 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

• 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. First, install Cython ,pycocotool and opencv to process data and to get evaluation result.

  pip install Cython
  
  pip install pycocotools
  
  pip install opencv-python
  

  1. If coco dataset is used. Select dataset to coco when run script.

     Change the coco_root and other settings you need in model_utils/ssd_xxx.yaml. The directory structure is as follows:

     .
     └─coco_dataset
       ├─annotations
         ├─instance_train2017.json
         └─instance_val2017.json
       ├─val2017
       └─train2017
     

  2. If VOC dataset is used. Select dataset to voc when run script. Change classes, num_classes, voc_json and voc_root in model_utils/ssd_xxx.yaml. voc_json is the path of json file with coco format for evaluation, voc_root is the path of VOC dataset, the directory structure is as follows:

     .
     └─voc_dataset
       └─train
         ├─0001.jpg
         └─0001.xml
         ...
         ├─xxxx.jpg
         └─xxxx.xml
       └─eval
         ├─0001.jpg
         └─0001.xml
         ...
         ├─xxxx.jpg
         └─xxxx.xml
     

  3. 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 model_utils/ssd_xxx.yaml.

Quick Start

Prepare the model

1. Chose the model by changing the using_model in model_utils/ssd_xxx.yaml. The optional models are: ssd300, ssd_mobilenet_v1_fpn, ssd_vgg16, ssd_resnet50_fpn.
2. Change the dataset config in the corresponding config. model_utils/ssd_xxx.yaml, xxx is the corresponding backbone network name
3. If you are running with ssd_mobilenet_v1_fpn or ssd_resnet50_fpn, you need a pretrained model for mobilenet_v1 or resnet50. Set the checkpoint path to feature_extractor_base_param in model_utils/ssd_xxx.yaml. For more detail about training pre-trained model, please refer to the corresponding backbone network.

Run the scripts

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

• running on Ascend

# distributed training on Ascend
bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH]

# run eval on Ascend
bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]

# run inference on Ascend310, MINDIR_PATH is the mindir model which you can export from checkpoint using export.py
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID] [CONFIG_PATH]

• running on GPU

# distributed training on GPU
bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [CONFIG_PATH]

# run eval on GPU
bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]

• running on CPU(support Windows and Ubuntu)

CPU is usually used for fine-tuning, which needs pre_trained checkpoint.

# training on CPU
python train.py --device_target=CPU --lr=[LR] --dataset=[DATASET] --epoch_size=[EPOCH_SIZE] --batch_size=[BATCH_SIZE] --config_path=[CONFIG_PATH] --pre_trained=[PRETRAINED_CKPT] --filter_weight=True --save_checkpoint_epochs=1

# run eval on GPU
python eval.py --device_target=CPU --dataset=[DATASET] --checkpoint_file_path=[PRETRAINED_CKPT] --config_path=[CONFIG_PATH]

• Run on docker

Build docker images(Change version to the one you actually used)

# build docker
docker build -t ssd:20.1.0 . --build-arg FROM_IMAGE_NAME=ascend-mindspore-arm:20.1.0

Create a container layer over the created image and start it

# start docker
bash scripts/docker_start.sh ssd:20.1.0 [DATA_DIR] [MODEL_DIR]

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 config directory to "config_path=/The path of config in S3/"
# (3) Set the code directory to "/path/ssd" 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 config directory to "config_path=/The path of config in S3/"
# (4) Set the code directory to "/path/ssd" 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.

Then you can run everything just like on ascend.

Script Description

Script and Sample Code

.
└─ cv
  └─ ssd
    ├─ README.md                      ## descriptions about SSD
    ├─ ascend310_infer                ## source code of 310 inference
    ├─ scripts
      ├─ docker start.sh              ## shell script for start docker container
      ├─ run_distribute_train.sh      ## shell script for distributed on ascend
      ├─ run_distribute_train_gpu.sh  ## shell script for distributed on gpu
      ├─ run_eval.sh                  ## shell script for eval on ascend
      ├─ run_eval_gpu.sh              ## shell script for eval on gpu
      └─ run_infer_310.sh             ## shell script for 310 inference
    ├─ src
      ├─ __init__.py                      ## init file
      ├─ anchor_generator.py              ## anchor generator
      ├─ box_util.py                      ## bbox utils
      ├─ config.py                        ## total config
      ├─ config_ssd_mobilenet_v1_fpn.py   ## configuration for feature extraction network uses mobilenet-v1 with fpn
      ├─ config_ssd_resnet50_fpn.py       ## configuration for feature extraction network uses resnet50 with fpn
      ├─ config_ssd_vgg16.py              ## configuration for backbone of vgg16
      ├─ config_ssd300.py                 ## configuration for backbone of mobilenet-v2
      ├─ dataset.py                       ## create dataset and process dataset
      ├─ eval_callback.py                 ## eval callback function definition
      ├─ eval_utils.py                    ## eval utils
      ├─ fpn.py                           ## feature pyramid network
      ├─ init_params.py                   ## parameters utils
      ├─ lr_schedule.py                   ## learning ratio generator
      ├─ mobilenet_v1.py                  ## network definition for mobilenet-v1
      ├─ resnet.py                        ## network definition for resnet
      ├─ ssd.py                           ## ssd architecture
      └─ vgg16.py                         ## network definition for vgg16
      ├── model_utils
      │   ├── config.py                   ## parameter configuration
      │   ├── device_adapter.py           ## device adapter
      │   ├── local_adapter.py            ## local adapter
      │   ├── moxing_adapter.py           ## moxing adapter
    ├─ config
        ├─ ssd_mobilenet_v1_fpn_config.yaml ## parameter configuration
        ├─ ssd_resnet50_fpn_config.yaml ## parameter configuration
        ├─ ssd_vgg16_config.yaml ## parameter configuration
        ├─ ssd300_config.yaml ## parameter configuration
        ├─ ssd_mobilenet_v1_fpn_config_gpu.yaml ## GPU parameter configuration
        ├─ ssd_resnet50_fpn_config_gpu.yaml ## GPU parameter configuration
        ├─ ssd_vgg16_config_gpu.yaml ## GPU parameter configuration
        ├─ ssd300_config_gpu.yaml ## GPU parameter configuration
    ├─ Dockerfile                         ## docker file
    ├─ eval.py                            ## eval scripts
    ├─ export.py                          ## export mindir script
    ├─ postprocess.py                     ## post-processing script for 310 inference
    ├─ train.py                           ## train scripts
    └─ mindspore_hub_conf.py              ## mindspore hub interface

Script Parameters

Major parameters in train.py and config.py 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
  "filter_weight": False                           # Load parameters in head layer or not. If the class numbers of train dataset is different from the class numbers in pre_trained checkpoint, please set True.
  "freeze_layer": "none"                           # Freeze the backbone parameters or not, support none and backbone.
  "run_eval": False                                # Run evaluation when training
  "save_best_ckpt": True                           # Save best checkpoint when run_eval is True
  "eval_start_epoch": 40                           # Evaluation start epoch when run_eval is True
  "eval_interval": 1                               # valuation interval when run_eval is True

  "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": "/data/voc_dataset"                  # VOC original dataset path
  "voc_json": "annotations/voc_instances_val.json" # is the path of json file with coco format for evaluation
  "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

To train the model, run train.py. If the mindrecord_dir is empty, it will generate mindrecord files by coco_root(coco dataset), voc_root(voc 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.

Training on Ascend

• Distribute mode

    bash run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [CONFIG_PATH] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)

• Standalone training

    bash run_standalone_train.sh [DEVICE_ID] [EPOCH_SIZE] [LR] [DATASET] [CONFIG_PATH] [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.
• CONFIG_PATH: parameter configuration.
• 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 log

epoch: 1 step: 458, loss is 3.1681802
epoch time: 228752.4654865265, per step time: 499.4595316299705
epoch: 2 step: 458, loss is 2.8847265
epoch time: 38912.93382644653, per step time: 84.96273761232868
epoch: 3 step: 458, loss is 2.8398118
epoch time: 38769.184827804565, per step time: 84.64887516987896
...

epoch: 498 step: 458, loss is 0.70908034
epoch time: 38771.079778671265, per step time: 84.65301261718616
epoch: 499 step: 458, loss is 0.7974688
epoch time: 38787.413120269775, per step time: 84.68867493508685
epoch: 500 step: 458, loss is 0.5548882
epoch time: 39064.8467540741, per step time: 85.29442522723602

Training on GPU

• Distribute mode

    bash run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [CONFIG_PATH] [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.
• CONFIG_PATH: parameter configuration.
• 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 is "LOG". Under this, you can find checkpoint files together with result like the followings in log

epoch: 1 step: 1, loss is 420.11783
epoch: 1 step: 2, loss is 434.11032
epoch: 1 step: 3, loss is 476.802
...
epoch: 1 step: 458, loss is 3.1283689
epoch time: 150753.701, per step time: 329.157
...

Evaluation while training

You can add run_eval to start shell and set it True, if you want evaluation while training. And you can set argument option: save_best_ckpt, eval_start_epoch, eval_interval when run_eval is True.

Transfer Training

You can train your own model based on either pretrained classification model or pretrained detection model. You can perform transfer training by following steps.

1. Convert your own dataset to COCO or VOC style. Otherwise you have to add your own data preprocess code.
2. Change config_xxx.py according to your own dataset, especially the num_classes.
3. Prepare a pretrained checkpoint. You can load the pretrained checkpoint by pre_trained argument. Transfer training means a new training job, so just keep pre_trained_epoch_size same as default value 0.
4. Set argument filter_weight to True while calling train.py, this will filter the final detection box weight from the pretrained model.
5. Build your own bash scripts using new config and arguments for further convenient.

Evaluation Process

Evaluation on Ascend

bash run_eval.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]

We need four parameters for this scripts.

• DATASET：the dataset mode of evaluation dataset.
• CHECKPOINT_PATH: the absolute path for checkpoint file.
• DEVICE_ID: the device id for eval.
• CONFIG_PATH: parameter configuration.

checkpoint can be produced in training process.

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

Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.238
Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.400
Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.240
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.039
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.198
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.438
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.250
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.389
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 ] = 0.434
Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.697

========================================

mAP: 0.23808886505483504

Evaluation on GPU

bash run_eval_gpu.sh [DATASET] [CHECKPOINT_PATH] [DEVICE_ID] [CONFIG_PATH]

We need four parameters for this scripts.

• DATASET：the dataset mode of evaluation dataset.
• CHECKPOINT_PATH: the absolute path for checkpoint file.
• DEVICE_ID: the device id for eval.
• CONFIG_PATH: parameter configuration.

checkpoint can be produced in training process.

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

Average Precision (AP) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.224
Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.375
Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.228
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.034
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.189
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.407
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.243
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.382
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.417
Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.120
Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.425
Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.686

========================================

mAP: 0.2244936111705981

Inference Process

Export MindIR

Export MindIR on local

python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --config_path [CONFIG_PATH]

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

Export on ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start as follows)

# Export on ModelArts
# (1) Perform a or b.
#       a. Set "enable_modelarts=True" on default_config.yaml file.
#          Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
#          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
#          Set "file_name='./ssd'" on default_config.yaml file.
#          Set "file_format='AIR'" on default_config.yaml file.
#          Set other parameters on default_config.yaml file you need.
#       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='s3://dir_to_trained_ckpt/'" on the website UI interface.
#          Add "file_name='./ssd'" on the website UI interface.
#          Add "file_format='AIR'" on the website UI interface.
#          Add other parameters on the website UI interface.
# (2) Set the config_path="/path/yaml file" on the website UI interface.
# (3) Set the code directory to "/path/ssd" on the website UI interface.
# (4) Set the startup file to "export.py" on the website UI interface.
# (5) Set the "Output file path" and "Job log path" to your path on the website UI interface.
# (6) Create your job.

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. The precision calculation process needs about 70G+ memory space, otherwise the process will be killed for execeeding memory limits.

# 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. Note that the image shape of ssd_vgg16 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 ] = 0.339
Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.521
Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.370
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.168
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.386
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.461
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.310
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.481
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.515
Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.293
Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.659
mAP: 0.33880018942412393

Post Training Quantization

Relative executing script files reside in the directory "ascend310_quant_infer". Please implement following steps sequentially to complete post quantization. Current quantization project bases on COCO2017 dataset.

1. Generate data of .bin format required for AIR model inference at Ascend310 platform.

python export_bin.py --config_path [YMAL CONFIG PATH] --coco_root [COCO DATA DIR] --mindrecord_dir [MINDRECORD PATH]

2. Export quantized AIR model.

Post quantization of model requires special toolkits for exporting quantized AIR model. Please refer to official website.

python post_quant.py --config_path [YMAL CONFIG PATH] --checkpoint_path [CKPT_PATH] --coco_root [COCO DATA DIR] --mindrecord_dir [MINDRECORD PATH]

The quantized AIR file will be stored as "./results/ssd_quant.air".

3. Implement inference at Ascend310 platform.

# Ascend310 quant inference
bash run_quant_infer.sh [AIR_PATH] [IMAGE_DATA] [IMAGE_ID] [IMAGE_SHAPE] [ANN_FILE]

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 ] = 0.237
Average Precision (AP) @[ IoU=0.50      | area= all   | maxDets=100 ] = 0.386
Average Precision (AP) @[ IoU=0.75      | area= all   | maxDets=100 ] = 0.240
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.042
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.200
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.425
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=  1 ] = 0.255
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets= 10 ] = 0.404
Average Recall    (AR) @[ IoU=0.50:0.95 | area= all   | maxDets=100 ] = 0.441
Average Recall    (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.136
Average Recall    (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.455
Average Recall    (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.707
mAP: 0.23657619676441116

Model Description

Performance

Evaluation Performance

Parameters	Ascend	GPU	Ascend	GPU
Model Version	SSD V1	SSD V1	SSD-Mobilenet-V1-Fpn	SSD-Mobilenet-V1-Fpn
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8	NV SMX2 V100-16G	Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8	NV SMX2 V100-32G
uploaded Date	07/05/2021 (month/day/year)	09/24/2020 (month/day/year)	01/13/2021 (month/day/year)	07/20/2021 (month/day/year)
MindSpore Version	1.3.0	1.0.0	1.1.0	1.3.0
Dataset	COCO2017	COCO2017	COCO2017	COCO2017
Training Parameters	epoch = 500, batch_size = 32	epoch = 800, batch_size = 32	epoch = 60, batch_size = 32	epoch = 60, batch_size = 16
Optimizer	Momentum	Momentum	Momentum	Momentum
Loss Function	Sigmoid Cross Entropy,SmoothL1Loss	Sigmoid Cross Entropy,SmoothL1Loss	Sigmoid Cross Entropy,SmoothL1Loss	Sigmoid Cross Entropy,SmoothL1Loss
Speed	8pcs: 90ms/step	8pcs: 121ms/step	8pcs: 547ms/step	1pcs: 547ms/step
Total time	8pcs: 4.81hours	8pcs: 12.31hours	8pcs: 4.22hours	1pcs: 4.22hours
Parameters (M)	34	34	48M	97M
Scripts	https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd	https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd	https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd	https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/cv/ssd

Inference Performance

Parameters	Ascend	GPU	Ascend	GPU
Model Version	SSD V1	SSD V1	SSD-Mobilenet-V1-Fpn	SSD-Mobilenet-V1-Fpn
Resource	Ascend 910; OS Euler2.8	GPU	Ascend 910; OS Euler2.8	NV SMX2 V100-32G
Uploaded Date	07/05/2020 (month/day/year)	09/24/2020 (month/day/year)	09/24/2020 (month/day/year)	07/20/2021 (month/day/year)
MindSpore Version	1.3.0	1.0.0	1.1.0	1.3.0
Dataset	COCO2017	COCO2017	COCO2017	COCO2017
batch_size	1	1	1	1
outputs	mAP	mAP	mAP	mAP
Accuracy	IoU=0.50: 23.8%	IoU=0.50: 22.4%	Iout=0.50: 30%	Iout=0.50: 30%
Model for inference	34M(.ckpt file)	34M(.ckpt file)	48M(.ckpt file)	97M(.ckpt file)

Description of Random Situation

In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.

ModelZoo Homepage

Please check the official homepage.