architecture: SOLOv2
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
load_static_weights: True

SOLOv2:
  backbone: ResNet
  neck: FPN
  solov2_head: SOLOv2Head
  mask_head: SOLOv2MaskHead

ResNet:
  # index 0 stands for res2
  depth: 50
  norm_type: bn
  freeze_at: 0
  return_idx: [0,1,2,3]
  num_stages: 4

FPN:
  in_channels: [256, 512, 1024, 2048]
  out_channel: 256
  min_level: 0
  max_level: 4
  spatial_scale: [0.25, 0.125, 0.0625, 0.03125]

SOLOv2Head:
  seg_feat_channels: 512
  stacked_convs: 4
  num_grids: [40, 36, 24, 16, 12]
  kernel_out_channels: 256
  solov2_loss: SOLOv2Loss
  mask_nms: MaskMatrixNMS

SOLOv2MaskHead:
  in_channels: 256
  mid_channels: 128
  out_channels: 256
  start_level: 0
  end_level: 3

SOLOv2Loss:
  ins_loss_weight: 3.0
  focal_loss_gamma: 2.0
  focal_loss_alpha: 0.25

MaskMatrixNMS:
  pre_nms_top_n: 500
  post_nms_top_n: 100