architecture: YOLOv4
use_gpu: true
max_iters: 140000
log_smooth_window: 20
save_dir: output
snapshot_iter: 1000
metric: VOC
pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams
weights: output/yolov4_cspdarknet_voc/model_final
num_classes: 20
use_fine_grained_loss: true

YOLOv4:
  backbone: CSPDarkNet
  yolo_head: YOLOv4Head

CSPDarkNet:
  norm_type: sync_bn
  norm_decay: 0.
  depth: 53

YOLOv4Head:
  anchors: [[12, 16], [19, 36], [40, 28], [36, 75], [76, 55],
            [72, 146], [142, 110], [192, 243], [459, 401]]
  anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
  nms:
    background_label: -1
    keep_top_k: -1
    nms_threshold: 0.45
    nms_top_k: -1
    normalized: true
    score_threshold: 0.001
  downsample: [8,16,32]

YOLOv3Loss:
  # batch_size here is only used for fine grained loss, not used
  # for training batch_size setting, training batch_size setting
  # is in configs/yolov3_reader.yml TrainReader.batch_size, batch
  # size here should be set as same value as TrainReader.batch_size
  batch_size: 4
  ignore_thresh: 0.7
  label_smooth: true
  downsample: [8,16,32]
  #scale_x_y: [1.2, 1.1, 1.05]
  iou_loss: IouLoss
  match_score: true

IouLoss:
  loss_weight: 0.07
  max_height: 608
  max_width: 608
  ciou_term: true
  loss_square: true

LearningRate:
  base_lr: 0.0001
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 110000
    - 130000
  - !LinearWarmup
    start_factor: 0.
    steps: 1000

OptimizerBuilder:
  clip_grad_by_norm: 10.
  optimizer:
    momentum: 0.949
    type: Momentum
  regularizer:
    factor: 0.0005
    type: L2

_READER_: '../yolov3_reader.yml'
TrainReader:
  inputs_def:
    fields: ['image', 'gt_bbox', 'gt_class', 'gt_score', 'im_id']
    num_max_boxes: 50
  dataset:
    !VOCDataSet
      anno_path: trainval.txt
      dataset_dir: dataset/voc
      with_background: false
  sample_transforms:
    - !DecodeImage
      to_rgb: True
    - !ColorDistort {}
    - !RandomExpand
      fill_value: [123.675, 116.28, 103.53]
    - !RandomCrop {}
    - !RandomFlipImage
      is_normalized: false
    - !NormalizeBox {}
    - !PadBox
      num_max_boxes: 50
    - !BboxXYXY2XYWH {}
  batch_transforms:
  - !RandomShape
    sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
    random_inter: True
  - !NormalizeImage
    mean: [0.,0.,0.]
    std: [1.,1.,1.]
    is_scale: True
    is_channel_first: false
  - !Permute
    to_bgr: false
    channel_first: True
  # Gt2YoloTarget is only used when use_fine_grained_loss set as true,
  # this operator will be deleted automatically if use_fine_grained_loss
  # is set as false
  - !Gt2YoloTarget
    anchor_masks: [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    anchors: [[12, 16], [19, 36], [40, 28],
              [36, 75], [76, 55], [72, 146],
              [142, 110], [192, 243], [459, 401]]
    downsample_ratios: [8, 16, 32]
  batch_size: 4
  shuffle: true
  drop_last: true
  worker_num: 8
  bufsize: 16
  use_process: true
  drop_empty: false

EvalReader:
  inputs_def:
    fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult']
    num_max_boxes: 90
  dataset:
    !VOCDataSet
      anno_path: test.txt
      dataset_dir: dataset/voc
      use_default_label: true
      with_background: false
  sample_transforms:
    - !DecodeImage
      to_rgb: True
    - !ResizeImage
      target_size: 608
      interp: 1
    - !NormalizeImage
      mean: [0., 0., 0.]
      std: [1., 1., 1.]
      is_scale: True
      is_channel_first: false
    - !PadBox
      num_max_boxes: 90
    - !Permute
      to_bgr: false
      channel_first: True
  batch_size: 4
  drop_empty: false
  worker_num: 8
  bufsize: 16

TestReader:
  dataset:
    !ImageFolder
    use_default_label: true
    with_background: false
  sample_transforms:
    - !DecodeImage
      to_rgb: True
    - !ResizeImage
      target_size: 608
      interp: 1
    - !NormalizeImage
      mean: [0., 0., 0.]
      std: [1., 1., 1.]
      is_scale: True
      is_channel_first: false
    - !Permute
      to_bgr: false
      channel_first: True