solov2_r101_vd_fpn_3x_coco.yml

_BASE_: [
  '../datasets/coco_instance.yml',
  '../runtime.yml',
  '_base_/solov2_r50_fpn.yml',
  '_base_/optimizer_1x.yml',
  '_base_/solov2_reader.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
weights: output/solov2_r101_vd_fpn_3x_coco/model_final
epoch: 36
use_ema: true
ema_decay: 0.9998

ResNet:
  depth: 101
  variant: d
  freeze_at: 0
  return_idx: [0,1,2,3]
  dcn_v2_stages: [1,2,3]
  num_stages: 4

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:
  mid_channels: 128
  out_channels: 256
  start_level: 0
  end_level: 3
  use_dcn_in_tower: True


LearningRate:
  base_lr: 0.01
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [24, 33]
  - !LinearWarmup
    start_factor: 0.
    steps: 2000

TrainReader:
  sample_transforms:
  - Decode: {}
  - Poly2Mask: {}
  - RandomResize: {interp: 1,
                     target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]],
                     keep_ratio: True}
  - RandomFlip: {}
  - NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
  - Permute: {}
  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}
  batch_size: 2
  shuffle: true
  drop_last: true