solov2_r101_vd_fpn_3x.yml

architecture: SOLOv2
use_gpu: true
max_iters: 270000
snapshot_iter: 30000
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar
metric: COCO
weights: output/solov2_r101_vd_fpn_3x/model_final
num_classes: 81
use_ema: true
ema_decay: 0.9998

SOLOv2:
  backbone: ResNet
  fpn: FPN
  bbox_head: SOLOv2Head
  mask_head: SOLOv2MaskHead

ResNet:
  depth: 101
  feature_maps: [2, 3, 4, 5]
  freeze_at: 2
  norm_type: bn
  dcn_v2_stages: [3, 4, 5]
  variant: d

FPN:
  max_level: 6
  min_level: 2
  num_chan: 256
  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
  reverse_out: True

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
  dcn_v2_stages: [0, 1, 2, 3]

SOLOv2MaskHead:
  in_channels: 128
  out_channels: 256
  start_level: 0
  end_level: 3
  use_dcn_in_tower: True

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

LearningRate:
  base_lr: 0.01
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [180000, 240000]
  - !LinearWarmup
    start_factor: 0.
    steps: 1000

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0001
    type: L2

_READER_: 'solov2_reader.yml'
TrainReader:
  batch_size: 2
  sample_transforms:
  - !DecodeImage
    to_rgb: true
  - !Poly2Mask {}
  - !ResizeImage
    target_size: [640, 672, 704, 736, 768, 800]
    max_size: 1333
    interp: 1
    use_cv2: true
    resize_box: true
  - !RandomFlipImage
    prob: 0.5
  - !NormalizeImage
    is_channel_first: false
    is_scale: true
    mean: [0.485,0.456,0.406]
    std: [0.229, 0.224,0.225]
  - !Permute
    to_bgr: false
    channel_first: true
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
  - !Gt2Solov2Target
    num_grids: [40, 36, 24, 16, 12]
    scale_ranges: [[1, 96], [48, 192], [96, 384], [192, 768], [384, 2048]]
    coord_sigma: 0.2