HRNet源码阅读笔记（1），train的命令行

源代码网址：

https://github.com/leoxiaobin/deep-high-resolution-net.pytorch

命令行方式：

Training on COCO train2017 dataset

python tools/train.py \
    --cfg experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml \

命令行中已经看到了，找tools/train.py 文件。

def parse_args():
    parser = argparse.ArgumentParser(description='Train keypoints network')
    # general
    parser.add_argument('--cfg',
                        help='experiment configure file name',
                        required=True,
                        type=str)

    parser.add_argument('opts',
                        help="Modify config options using the command-line",
                        default=None,
                        nargs=argparse.REMAINDER)

    # philly
    parser.add_argument('--modelDir',
                        help='model directory',
                        type=str,
                        default='')
    parser.add_argument('--logDir',
                        help='log directory',
                        type=str,
                        default='')
    parser.add_argument('--dataDir',
                        help='data directory',
                        type=str,
                        default='')
    parser.add_argument('--prevModelDir',
                        help='prev Model directory',
                        type=str,
                        default='')

    args = parser.parse_args()

    return args

那么命令行说的是，变量cfg，对应的就是

experiments/coco/hrnet/w32_256x192_adam_lr1e-3.yaml

那么，打开这个文件看看吧：

AUTO_RESUME: true
CUDNN:
  BENCHMARK: true
  DETERMINISTIC: false
  ENABLED: true
DATA_DIR: ''
GPUS: (0,1,2,3)
OUTPUT_DIR: 'output'
LOG_DIR: 'log'
WORKERS: 24
PRINT_FREQ: 100

DATASET:
  COLOR_RGB: true
  DATASET: 'coco'
  DATA_FORMAT: jpg
  FLIP: true
  NUM_JOINTS_HALF_BODY: 8
  PROB_HALF_BODY: 0.3
  ROOT: 'data/coco/'
  ROT_FACTOR: 45
  SCALE_FACTOR: 0.35
  TEST_SET: 'val2017'
  TRAIN_SET: 'train2017'
MODEL:
  INIT_WEIGHTS: true
  NAME: pose_hrnet
  NUM_JOINTS: 17
  PRETRAINED: 'models/pytorch/imagenet/hrnet_w32-36af842e.pth'
  TARGET_TYPE: gaussian
  IMAGE_SIZE:
  - 192
  - 256
  HEATMAP_SIZE:
  - 48
  - 64
  SIGMA: 2
  EXTRA:
    PRETRAINED_LAYERS:
    - 'conv1'
    - 'bn1'
    - 'conv2'
    - 'bn2'
    - 'layer1'
    - 'transition1'
    - 'stage2'
    - 'transition2'
    - 'stage3'
    - 'transition3'
    - 'stage4'
    FINAL_CONV_KERNEL: 1
    STAGE2:
      NUM_MODULES: 1
      NUM_BRANCHES: 2
      BLOCK: BASIC
      NUM_BLOCKS:
      - 4
      - 4
      NUM_CHANNELS:
      - 32
      - 64
      FUSE_METHOD: SUM
    STAGE3:
      NUM_MODULES: 4
      NUM_BRANCHES: 3
      BLOCK: BASIC
      NUM_BLOCKS:
      - 4
      - 4
      - 4
      NUM_CHANNELS:
      - 32
      - 64
      - 128
      FUSE_METHOD: SUM
    STAGE4:
      NUM_MODULES: 3
      NUM_BRANCHES: 4
      BLOCK: BASIC
      NUM_BLOCKS:
      - 4
      - 4
      - 4
      - 4
      NUM_CHANNELS:
      - 32
      - 64
      - 128
      - 256
      FUSE_METHOD: SUM
LOSS:
  USE_TARGET_WEIGHT: true
TRAIN:
  BATCH_SIZE_PER_GPU: 32
  SHUFFLE: true
  BEGIN_EPOCH: 0
  END_EPOCH: 210
  OPTIMIZER: adam
  LR: 0.001
  LR_FACTOR: 0.1
  LR_STEP:
  - 170
  - 200
  WD: 0.0001
  GAMMA1: 0.99
  GAMMA2: 0.0
  MOMENTUM: 0.9
  NESTEROV: false
TEST:
  BATCH_SIZE_PER_GPU: 32
  COCO_BBOX_FILE: 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'
  BBOX_THRE: 1.0
  IMAGE_THRE: 0.0
  IN_VIS_THRE: 0.2
  MODEL_FILE: ''
  NMS_THRE: 1.0
  OKS_THRE: 0.9
  USE_GT_BBOX: true
  FLIP_TEST: true
  POST_PROCESS: true
  SHIFT_HEATMAP: true
DEBUG:
  DEBUG: true
  SAVE_BATCH_IMAGES_GT: true
  SAVE_BATCH_IMAGES_PRED: true
  SAVE_HEATMAPS_GT: true
  SAVE_HEATMAPS_PRED: true