/customize-training-with-detectron

Customize Training with Detectron

MIT LicenseMIT

Customize Training with Detectron

拉取镜像 Detectron

sudo docker pull gouchicao/detectron:latest

创建工程（COCO格式）

└── helmet          工程目录
    ├── images        样本图片目录
    ├── helmet_train.json 训练的样本标注信息
    ├── helmet_val.json  验证的样本标注信息
    ├── test               测试图片目录
    ├── predict            预测图片目录
    ├── model        模型目录
    └── e2e_mask_rcnn_R-101-FPN_2x.yaml  网络配置文件

运行容器 Detectron，挂载工程目录。

sudo docker run -it --runtime=nvidia --name detectron-helmet -v /helmet-project-realpath:/detectron/project gouchicao/detectron:latest

修复BBOX_XFORM_CLIP错误

nano /detectron/detectron/utils/env.py 

yaml_load = lambda x: yaml.load(x, Loader=yaml.Loader)

工程设置

  1. 配置数据集
nano /detectron/detectron/datasets/dataset_catalog.py

_DATASETS = {
    'coco_helmet_train': {
        _IM_DIR: '/detectron/project/images',
        _ANN_FN: '/detectron/project/helmet_train.json' 
    },
    'coco_helmet_val': {
        _IM_DIR: '/detectron/project/images',
        _ANN_FN: '/detectron/project/helmet_val.json' 
    },
    ......
}
  1. 修改网络配置文件
nano /detectron/project/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_2x.yaml

MODEL:
  TYPE: generalized_rcnn
  CONV_BODY: FPN.add_fpn_ResNet101_conv5_body
  NUM_CLASSES: 2
  FASTER_RCNN: True
  MASK_ON: True
NUM_GPUS: 1
SOLVER:
  WEIGHT_DECAY: 0.0001
  LR_POLICY: steps_with_decay
  BASE_LR: 0.002
  GAMMA: 0.1
  MAX_ITER: 4000
  STEPS: [0, 3000, 4000]
FPN:
  FPN_ON: True
  MULTILEVEL_ROIS: True
  MULTILEVEL_RPN: True
FAST_RCNN:
  ROI_BOX_HEAD: fast_rcnn_heads.add_roi_2mlp_head
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 7
  ROI_XFORM_SAMPLING_RATIO: 2
MRCNN:
  ROI_MASK_HEAD: mask_rcnn_heads.mask_rcnn_fcn_head_v1up4convs
  RESOLUTION: 28  # (output mask resolution) default 14
  ROI_XFORM_METHOD: RoIAlign
  ROI_XFORM_RESOLUTION: 14  # default 7
  ROI_XFORM_SAMPLING_RATIO: 2  # default 0
  DILATION: 1  # default 2
  CONV_INIT: MSRAFill  # default GaussianFill
TRAIN:
  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
  DATASETS: ('coco_helmet_train', 'coco_helmet_val')
  SCALES: (800,)
  MAX_SIZE: 1333
  BATCH_SIZE_PER_IM: 512
  RPN_PRE_NMS_TOP_N: 2000  # Per FPN level
TEST:
  DATASETS: ('coco_2014_minival',)
  SCALE: 800
  MAX_SIZE: 1333
  NMS: 0.5
  RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
  RPN_POST_NMS_TOP_N: 1000
OUTPUT_DIR: .

训练模型

python /detectron/tools/train_net.py --cfg /detectron/project/e2e_mask_rcnn_R-101-FPN_2x.yaml OUTPUT_DIR /detectron/project/model

测试模型

python /detectron/tools/infer_simple.py --cfg /detectron/project/e2e_mask_rcnn_R-101-FPN_2x.yaml --output-dir /detectron/project/predict --image-ext jpg --wts /detectron/project/model/train/coco_helmet_train\:coco_helmet_train/generalized_rcnn/model_final.pkl /detectron/project/test

参考资料