_BASE_: [
  'detector/ppyoloe_crn_l_36e_640x640_mot17half.yml',
  '_base_/mot17.yml',
  '_base_/deepsort_reader_1088x608.yml',
]
metric: MOT
num_classes: 1

EvalMOTDataset:
  !MOTImageFolder
    dataset_dir: dataset/mot
    data_root: MOT17/images/half
    keep_ori_im: True # set as True in DeepSORT

det_weights: https://paddledet.bj.bcebos.com/models/mot/deepsort/ppyoloe_crn_l_36e_640x640_mot17half.pdparams
reid_weights: https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams

# reader
EvalMOTReader:
  sample_transforms:
    - Decode: {}
    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
    - Permute: {}
  batch_size: 1

TestMOTReader:
  inputs_def:
    image_shape: [3, 640, 640]
  sample_transforms:
    - Decode: {}
    - Resize: {target_size: [640, 640], keep_ratio: False, interp: 2}
    - NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
    - Permute: {}
  batch_size: 1


# DeepSORT configuration
architecture: DeepSORT
pretrain_weights: None

DeepSORT:
  detector: YOLOv3 # PPYOLOe version
  reid: PPLCNetEmbedding
  tracker: DeepSORTTracker


# reid and tracker configuration
# see 'configs/mot/deepsort/reid/deepsort_pplcnet.yml'
PPLCNetEmbedding:
  input_ch: 1280
  output_ch: 512

DeepSORTTracker:
  input_size: [64, 192]
  min_box_area: 0
  vertical_ratio: -1
  budget: 100
  max_age: 70
  n_init: 3
  metric_type: cosine
  matching_threshold: 0.2
  max_iou_distance: 0.9
  motion: KalmanFilter


# detector configuration: PPYOLOe version
# see 'configs/mot/deepsort/detector/ppyoloe_crn_l_300e_640x640_mot17half.yml'
YOLOv3:
  backbone: CSPResNet
  neck: CustomCSPPAN
  yolo_head: PPYOLOEHead
  post_process: ~

CSPResNet:
  layers: [3, 6, 6, 3]
  channels: [64, 128, 256, 512, 1024]
  return_idx: [1, 2, 3]
  use_large_stem: True

CustomCSPPAN:
  out_channels: [768, 384, 192]
  stage_num: 1
  block_num: 3
  act: 'swish'
  spp: true

# Tracking requires higher quality boxes, so NMS score_threshold will be higher
PPYOLOEHead:
  fpn_strides: [32, 16, 8]
  grid_cell_scale: 5.0
  grid_cell_offset: 0.5
  static_assigner_epoch: -1 # 100
  use_varifocal_loss: True
  loss_weight: {class: 1.0, iou: 2.5, dfl: 0.5}
  static_assigner:
    name: ATSSAssigner
    topk: 9
  assigner:
    name: TaskAlignedAssigner
    topk: 13
    alpha: 1.0
    beta: 6.0
  nms:
    name: MultiClassNMS
    nms_top_k: 1000
    keep_top_k: 100
    score_threshold: 0.4 # 0.01 in original detector
    nms_threshold: 0.6